Technical Report Documentation Page 1. Report No.

SWUTC/96/465100-1 2. Government Accession No. 3. Recipient's Catalog No.

4. Title and Subtitle

Using Acceleration Characteristics in Air Quality and EnergyConsumption Analyses

5. Report Date

August 1996 6. Performing Organization Code

7. Author(s)

William L. Eisele, Shawn M. Turner, and Robert J. Benz 8. Performing Organization Report No.

465100-1 9. Performing Organization Name and Address

Texas Transportation InstituteThe Texas A&M University SystemCollege Station, Texas 77843-3135

10. Work Unit No.

11. Contract or Grant No.

0079 12. Sponsoring Agency Name and Address

Southwest Region University Transportation CenterTexas Transportation InstituteThe Texas A&M University SystemCollege Station, Texas 77843-3135

13. Type of Report and Period Covered

14. Sponsoring Agency Code

15. Supplementary Notes

Supported by a grant from the Office of the Governor of the state of Texas, Energy Office16. Abstract

This research investigated the effects of detailed speed and acceleration characteristics on energy consumption utilizingseveral fuel consumption models. The relationships between speed and acceleration characteristics, geometric characteristics(e.g., number of lanes, signal density, driveway density), and traffic flow variability for various roadways were alsoinvestigated. Finally, distributions were produced that summarize the operating characteristics of freeway and arterial streetfacilities in the Houston, Texas area. Data for the study were collected on a second-by-second basis on selected freeways andarterial streets in Houston, Texas using an electronic distance-measuring instrument (DMI) and the floating car technique.

The study found that fuel consumption models incorporating detailed speed and acceleration characteristics providestatistically different results. Similar results were obtained for both arterial and freeway roadways. Low coefficients ofdetermination (i.e., R2 less than 0.35) were found when regressing geometric characteristics with the speed and accelerationcharacteristics such as average speed or acceleration noise. Relationships between the coefficient of variation of speed oracceleration noise with average speed provided much higher R2 values when investigating the traffic flow variability of thetravel time runs. These results were similar for peak and off-peak conditions and the different roadway classifications (e.g.,arterials and freeways).

The distributions of operating characteristics for Houston, Texas summarize the percent of time vehicles are operatingwithin a given speed and acceleration range. This data is expected to be invaluable for individuals desiring the operationalcharacteristics of the Houston roadway system, or similar large urban area, as well as those individuals who can apply thisinformation to future or current mobile source emissions and energy consumption modeling applications.

17. Key Words

Fuel consumption models, emissions models,acceleration characteristics, travel time variability

18. Distribution Statement

No restrictions. This document is available to thepublic through NTIS:National Technical Information Service5285 Port Royal RoadSpringfield, Virginia 22161

19. Security Classif.(of this report)

Unclassified20. Security Classif.(of this page)

Unclassified21. No. of Pages

9622. Price

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized

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USING ACCELERATION CHARACTERISTICS IN AIR QUALITY AND ENERGY CONSUMPTION ANALYSES

by

William L. EiseleAssistant Research Scientist

Shawn M. TurnerAssistant Research Scientist

and

Robert J. BenzAssistant Research Scientist

Technical Report 465100-1

Sponsored by

The Office of the Governor of the State of Texas, Energy OfficeSouthwest Region University Transportation Center

Texas Transportation InstituteThe Texas A&M University System

College Station, TX 77843-3135

August 1996

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v

ABSTRACT

This research investigated the effects of detailed speed and acceleration characteristics onenergy consumption utilizing several fuel consumption models. The relationships between speed andacceleration characteristics, geometric characteristics (e.g., number of lanes, signal density, drivewaydensity), and traffic flow variability for various roadways were also investigated. Finally, distributionswere produced that summarize the operating characteristics of freeways and arterial streets in theHouston, Texas area. Data for the study were collected on a second-by-second basis on selectedfreeways and arterial streets in Houston, Texas using an electronic distance-measuring instrument(DMI) and the floating car technique.

The study found that fuel consumption models incorporating detailed speed and accelerationcharacteristics provide statistically different results. Similar results were obtained for both arterialand freeway roadways. Low coefficients of determination (i.e., R2 less than 0.35) were found whenregressing geometric characteristics with the speed and acceleration characteristics such as averagespeed or acceleration noise. Relationships between the coefficient of variation of speed oracceleration noise with average speed provided much higher R2 values when investigating the trafficflow variability of the travel time runs. These results were similar for peak and off-peak conditionsand the different roadway classifications (e.g., arterials and freeways).

The distributions of operating characteristics for Houston, Texas summarize the percent oftime vehicles are operating within a given speed and acceleration range. This data is expected to beinvaluable for individuals desiring the operational characteristics of the Houston roadway system, orsimilar large urban area, as well as those individuals who can apply this information to future orcurrent mobile source emissions and energy consumption modeling applications.

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DISCLAIMER

The contents of this report reflect the views of the authors who are responsible for theopinions, findings, and conclusions presented herein. The contents do not necessarily reflect theofficial views or policies of the Southwest Region University Transportation Center (SWUTC). Thisreport does not constitute a standard, specification, or regulation. Any reference to commercialsoftware packages or hardware is for explanatory purposes only and does not constitute anendorsement.

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ACKNOWLEDGMENTS

This publication was developed as a part of the University Transportation Centers Programwhich is funded 50 percent in oil overcharge funds from the Stripper Well Settlement as provided bythe State of Texas Governor’s Energy Office and approved by the US Department of Energy.Mention of trade names or commercial products does not constitute endorsement or recommendationfor use.

The authors would like to thank Dr. George Dresser for his insight at critical points in theproject. In addition, the authors would like to acknowledge the following individuals for theirassistance:

Luke Albert - data reduction;Brett Baker - travel time runs;Pat Beck - graphics;David Berry - data analyses and computer programming;Monye Brookover - travel time runs;Ryan Christianson - data analyses and computer programming;Ken Clark - travel time runs;Mark Coscio - travel time runs;Jim Cullison - travel time runs and quality control of data collection;Kim Duren - data reduction;David Fenno - travel time runs;Chris Hallin - travel time runs;Monty Poppe - data analyses and computer programming;Jordan Richard - graphics;Troy Rother - data reduction and final report editing;Woodraylyn Smith - travel time runs;John Vaughn - travel time runs;Tony Voight - travel time runs;Kathy Williams - final report editing; andSteve Wohlschlaeger - travel time runs.

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ix

EXECUTIVE SUMMARY

Introduction

Current mobile source emissions and energy consumption analyses are based on averagevehicular speeds over roadway sections that are typically greater than 1 mile (0.6 km) in length.Recent research has indicated that the fluctuation in speed (i.e., acceleration and deceleration) is moreimportant than the average speed in determining mobile source emissions and energy consumption.This fluctuation in speed, known as acceleration noise, has not yet been effectively utilized in vehicleemissions and fuel consumption analyses because of 1) the difficulty of collecting or estimating speeddata for very short time or distance intervals, and 2) the absence of appropriate computer models toconduct such analyses.

Study Objectives and Scope

The primary objective of this study is to characterize the speed and acceleration characteristicsof a wide range of traffic flow. Data were collected with a DMI using short increments of time.Researchers made a preliminary investigation of the effects of detailed speed and acceleration dataon existing fuel consumption models. Comparisons of fuel consumption estimates were made usingspeed and acceleration calculations based upon a segment-wide method (average method) and asecond-by-second method (instantaneous method). Detailed acceleration characteristics could beincorporated into the next generation of mobile source emissions and energy consumption models.Models that incorporate acceleration characteristics are expected to provide more accurate estimatesof mobile source emissions and energy consumption and of the changes in the emissions and energyconsumption associated with various transportation projects and programs.

The second objective of this study is to examine relationships between the geometriccharacteristics, speed and acceleration characteristics, and traffic flow variability for the differentroadway functional classes. These regression equations will be based upon data that aredisaggregated by functional roadway type (e.g., arterial Class I or II, freeways).

The final objective is to compile a reliable data set that describes the speed and accelerationcharacteristics of various roadways and operating conditions. The data set produced from this projectcan be supplied to interested individuals or organizations for use in development and/or validationof fuel consumption and/or emissions modeling.

Overview of the Study Design

To accomplish these objectives, data were collected with a distance measuring instrument(DMI) on a total of 233 centerline-miles (375 km) of freeway routes and 198 centerline-miles (319km) of arterial routes. From the speed information provided by the DMI, further speed andacceleration characteristics were calculated. In addition, geometric characteristics were collectedalong the corridors.

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Once the data were collected with the DMI, three distinct modules of information werecreated. The first data set includes the speed and acceleration characteristics computed from the DMIfiles for each corridor. The second data set includes the results of utilizing both an instantaneous andaverage calculation of several fuel consumption model estimates based upon speed and accelerationrate. Such analyses are imperative to show any differences in such models when collection of vehiclespeeds is allowed as often as every second. The last data set that is merged with the others is thegeometric characteristics that were collected along the travel time routes. The data were thencombined, summarized appropriately, and statistical analyses were performed in order to evaluate theobjectives of the study.

Findings

Fuel Consumption Model Analyses

Since the difference between the average and instantaneous methods of fuel consumptionestimation for several models was desired, a paired t-test was utilized. T-tests were performed onthe different arterial classes (e.g., Class I, Class II, and freeways) at the aggregated level (i.e., notdisaggregated by average speed, for example). The null hypothesis for the tests is that there is nodifference between the two methods of calculating fuel consumption. Therefore, if significance isfound, the null hypothesis can be rejected and there is a difference between the two methods of fuelconsumption estimation. A critical level of significance of 5 percent was used in the analyses todetermine significance. Results of the analyses are shown in Table 3. Some of the findings from thefuel consumption analyses are discussed below.

Raus’ model did not yield significant differences in fuel consumption estimation for any of thefunctional classes. Although the model is not intended for freeways, and indeed normality was notfound for that condition, the arterial classes yielded insignificant results as well. The FREQ10 modelsfor freeways and arterials were both found to be insignificant for the Class I arterials. The final modelthat demonstrates insignificant results is McGill for the Class II arterials. It should be noted thatinsignificance was only found for a few situations. Furthermore, some models demonstratesignificance for operating conditions for which the model does not have speed data.

It is important to note that the analyses presented here compare only the average andinstantaneous methods of fuel consumption estimation for any one model. It does not compare themodels to one another, nor is it possible from this analyses to determine that one model is better thananother model. However, one can determine which models demonstrate a significant difference thatcan be attributed to the detailed data set produced by performing travel time runs with the DMI.

Regression and Correlation Analyses

Models relating geometric characteristics and acceleration characteristics were developedbased upon peak and off-peak conditions, different functional classifications, and stratifications onsome variables (e.g., for driveway densities greater than 20 per mile). The linear regression models

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generally produced low coefficients of determination (R2). The highest R2 values achieved were lessthan 0.35 for any of the conditions studied. This model was often between the dependent variableof average speed and the independent variables of signal density and/or driveway density for thearterial sections in either the peak or off-peak conditions. The addition of independent variables aftersignal density and driveway density (i.e., producing graphs with greater than three independentvariables) often resulted in increasing the R2 value only a few hundredths. Analysis of variance(ANOVA) procedures were performed as part of the linear regression using a critical level ofsignificance of 5 percent to determine the significance of the independent variables in the models. Theresearch team hypothesized that the driveway density and signal density variables would have themost explanatory power in such relationships and provide higher R2 results than those observed.

For nearly all of the models developed, the signal density and driveway density variables werefound to be significant in the ANOVA procedure. Therefore, these variables were contributing tothe explanation of the variance within the model. It is interesting to note that the 24 hour volume andthe length of the section, the only independent variables used in the freeway analysis, were not alwayssignificant. The 24 hour volume produced significant results more often, however, than the variablerepresenting the length of the section. This would indicate that the 24 hour volume was critical inmany cases in explaining the variance in the freeway segments with the variables available.

Another observation that was made from evaluating the resulting models was the signs on thecoefficients of the independent variables. Often times, these signs did not make intuitive sense. Forexample, as the signal density went down, the coefficient of variation of the speed (CV) would goup. In this example, it does not make sense that the variation of the traffic speeds, represented by theCV, should go up when there is less interruption in the traffic stream (i.e., a lower signal density).However, it is possible that this indicates along these arterial corridors that the signal timing has beenoptimized to provide sufficient green time and increased average speeds.

Many observations can be made with regard to the traffic flow variability linear regressionresults. The relationship between average speed and the coefficient of variation provided relativelyhigh R2 values for all functional roadway classes, peak, and off-peak conditions. Further,relationships utilizing average speed to predict the acceleration noise produced relatively lower R2

results. Although acceleration noise is a better measure of the traffic variability over a travel time runthan average speed, the lower R2 values determined for this relationship indicate a significant amountof traffic operation that is unexplained by aggregating the instantaneous readings from the DMI.

The next portion of the analysis focused on investigating the value of CV, average speed,relevant geometric characteristics, and the speed profile at a given speed to realize any possible trendsthat may exist. The most interesting characteristic of these analyses was realizing the importance ofthe location of the travel time run section that is being investigated. Theoretically, one could havesections placed such that the CV could be just about any value (i.e., located anywhere along the speedprofile). Due to the inherent variability in these relationships, and the geometric versus speed andacceleration characteristics, developing estimating regression equations is difficult. Similar resultswere found for other roadway classes and conditions (e.g., peak and off-peak).

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Since the geometric characteristics (e.g., number of lanes, signal density) do not change fora given roadway section, it is possible to aggregate the resulting speed and acceleration characteristicstogether for these travel time runs. This was performed and regression equations were produced andthe results are shown in Table 5. The R2 values in Table 5 are very similar, or slightly higher, thanthose produced when each travel time run was plotted. This was expected since it produces a graphwith fewer points that are aggregated closer to the regression line.

Roadway Operating Characteristics: Speed and Acceleration

The travel time and speed data collected for this study were summarized to obtain speed andacceleration distributions. These speed and acceleration distributions provide quantitative informationabout the operating characteristics of the freeways and arterial streets under study. Thesedistributions are also very important in designing and validating the next generation of emissionsmodels that are based upon acceleration patterns, not average speeds.

The speed distributions for different functional classes were markedly different, with freewaysexhibiting higher speeds and arterial streets exhibiting lower speeds and more idle time. The data foroff-peak period conditions (mid-day) were also examined, and found to be similar to peak periodconditions. Although the researchers had hypothesized that a significant difference would existbetween peak and off-peak period operating characteristics, the examination of speed distributionswas unable to confirm the hypothesis.

The acceleration distributions for different functional classes where different but notnecessarily distinctive. The floating car method of data collection may have affected the trueacceleration characteristics of different roadway types, thereby smoothing the potentialacceleration/deceleration differences between freeways and arterial streets. The similarity of thedistributions for different functional classes may also indicate that, indeed, only small difference existbetween acceleration characteristics for different functional roadway classes.

The three-dimensional speed-acceleration distribution for all freeway and arterial street routesshows a large “peak” of the data at 60 mph (97 kph), with another smaller “peak” at 0 mph (steady-state). The acceleration and deceleration ranges close to 0 mph/sec can also be seen on the figure assmall “ridges.”

The three-dimensional speed-acceleration distribution for freeway routes only show a largeproportion of travel that occurs in the 55 to 60 mph (89 to 97 kph) range with a small range inaccelerations. Figures 31 and 32 show the speed-acceleration distributions for Class I and II arterials,respectively. Like the speed distributions discussed earlier, there is a marked difference betweenfunctional classes. Class I and II arterial streets show smaller but comparable speed “peaks” at 0mph, or idle time.

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Conclusions and Recommendations

Fuel Consumption Model Comparisons

Significance was determined for many of the functional classes when comparing fuelconsumption estimation based upon both the average and instantaneous methods. From these resultsit can be concluded that, in general, significant differences can be expected when applying a detaileddata set such as that produced by a DMI in a travel time run to the estimation of fuel consumption.It is important to note that when reviewing the results of Table 3, it is imperative to study Table 2 toverify the conditions (e.g., speed range, functional classification) for which a model is valid.

Regression and Correlation Analyses

Development of regression equations between speed and acceleration characteristics,geometric characteristics, and traffic flow variability was performed in the study. The regressionequations did not yield an R2 higher than 0.35 when comparing any combination of the geometriccharacteristics with the speed and acceleration characteristics. Signal density and/or driveway densitywere found to be significant for most of the conditions evaluated with the aid of ANOVA proceduresusing a critical level of significance of 5 percent.

Several factors that could account for the findings were considered. The true affect of thedriveway density may not be reflected in the travel time data since the floating car method wasutilized. It is possible that the influence of driveways on the right-most lane may not be included intoa travel time run that includes a driver passing as many vehicles as pass the driver. In addition, travelvariability induced by traffic signals is difficult to quantify. Peak and off-peak conditions often havedifferent signal timings to optimize traffic flow. Average speeds, and motorist delay, will varydepending upon when motorists arrive at the traffic signal. The location of the travel time run sectionwas also found to be of importance when measuring the coefficient of variation of the speed. If atravel time run is performed immediately prior to a traffic signal or lane-drop on a freeway, the resultswill differ compared to a run performed in an uninterrupted flow section. Unfortunately, the database did not contain a variable relating to the section definition (e.g., before or after a traffic signal)of the travel time run, but this would be an interesting element for further study. Finally, it wasfound that, although acceleration noise is a better measure to determine the operating characteristicsof a section than average speed, there is still a significant portion of the instantaneous travelcharacteristics (e.g., speed, acceleration) that are lost when aggregating over an entire section. Roadway Operating Characteristics: Speed and Acceleration

The travel time/speed data collected for this study showed a significant difference in the speeddistributions for different functional classes (e.g., freeways, Class I arterials, Class II arterials). Theacceleration distributions for different roadway functional classes were less distinctive betweenfunctional classes, indicating that acceleration characteristics were similar between freeways andarterial streets. The floating car data collection technique used in this study may have “smoothed”

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some of the acceleration differences between freeways and arterials streets, so a definitive statementcannot be made. A data collection that obtains a more representative sample of the range ofoperating characteristics of motorists (e.g., instrumenting random vehicles) would likely provide amore distinct difference between functional classes.

The study also produced three-dimensional speed-acceleration distributions that were typicalof the freeway and arterial street system in Houston, Texas. The speed-acceleration distributions doexhibit significant differences between freeways and arterial streets, mainly with respect to speeddifferences. The speed and acceleration data set used to produce these summary distributions isexpected to be useful in validating the next generation of emissions models that are currently in thedevelopmental stages.

DMI Technology for the Data Collection Effort

The distance measuring instrument was found to be an invaluable tool for performing thisstudy. The instantaneous data points provided at every 0.5 second yielded a data set that allows fordetailed speed and acceleration information. From this data, the significance of the instantaneous dataset on estimating fuel consumption estimates could be evaluated, regression equations wereevaluated, and traffic operating distributions could be prepared. The ASCII format of the output waseasily manipulated for analyses and evaluation. Data collection methods that produce theseinstantaneous speed and acceleration data will continue to prove to be useful in the transportationcommunity for application to many transportation concerns (e.g., air quality, traffic operations).

Future Research Needs

The study identified some areas where additional research is needed. The first is the need forthe development of mobile source emissions models that can incorporate acceleration characteristics.Research of this kind is currently in progress.

There is a need for better characterization of acceleration characteristics for different roadwayfacilities. Characterizing acceleration characteristics by percent of time in a particular drivingcondition (e.g., idle, cruise, acceleration, or deceleration) is useful for the development of appropriatedriving cycles that replicate these conditions.

There is much variability both along a travel time run and between travel time runs alongsections. Additional research is needed that focuses on determining appropriate methods to quantifythis variability in a consistent and meaningful manner (e.g., separate the driver and traffic influences).

In general, the DMI and similar technologies for data collection, allow for larger amounts ofdescriptive data that has not been possible in the past. Research must now begin to focus onperformance measures that are best utilized (e.g., coefficient of variation) for quantifying theaggregation of this data for transportation-related concerns.

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TABLE OF CONTENTS

PageABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

DISCLAIMER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi

ACKNOWLEDGMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii

EXECUTIVE SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

TABLE OF CONTENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv

LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii

LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xix

CHAPTER I. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2Study Objectives and Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2Organization of Report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

CHAPTER II. BACKGROUND . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5Acceleration Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Acceleration Noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5Quality of Flow Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6Total Absolute Second-to-Second Differences in Speed Per Mile (TAD) . . . . . . 7Other Acceleration Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7Graphical Representation of Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8Driving Cycles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Current Driving Cycles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12Concerns About the FTP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Fuel Consumption Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13Emissions Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15Graphical Representation of Fuel Consumption and Emissions Data . . . . . . . . . . . . . . . 16Summary of Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

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TABLE OF CONTENTS (continued)

PageCHAPTER III. STUDY DESIGN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Overview of Study Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

The Houston Metropolitan Area and the Data Collected . . . . . . . . . . . . . . . . . . 21Roadway Geometric Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21DMI Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Development of Data Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28Speed and Acceleration Characteristics Module . . . . . . . . . . . . . . . . . . . . . . . . 28Fuel Consumption Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29Geometric Characteristics Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Levels of Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32Quality Control Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33Initial Examination of the Data Set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33Fuel Consumption Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34Regression and Correlation Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34Data Base of Useful Emissions Modeling Information . . . . . . . . . . . . . . . . . . . . 35

CHAPTER IV. FINDINGS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37Fuel Consumption Model Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37Regression and Correlation Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39Roadway Operation Characteristics: Speed and Acceleration . . . . . . . . . . . . . . . . . . . . 49

Speed Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49Acceleration Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 543-Dimensional Speed-Acceleration Distributions . . . . . . . . . . . . . . . . . . . . . . . . 59

CHAPTER V. CONCLUSIONS AND RECOMMENDATIONS . . . . . . . . . . . . . . . . . . . . . . 69Fuel Consumption Model Comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69Regression and Correlation Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69Roadway Operation Characteristics: Speed and Acceleration . . . . . . . . . . . . . . . . . . . . 70DMI Technology for the Data Collection Effort . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71Future Research Needs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

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LIST OF FIGURES

PageFIGURE 1. Example Speed Profile for Katy Freeway (I-10) in Houston, Texas . . . . . . . . . . . 9FIGURE 2. Vehicle Speed Distributions for Three Data Sets . . . . . . . . . . . . . . . . . . . . . . . . . 9FIGURE 3. Cumulative Distribution of Acceleration Values . . . . . . . . . . . . . . . . . . . . . . . . 10FIGURE 4. Frequency Bar Graph of Acceleration Values . . . . . . . . . . . . . . . . . . . . . . . . . . 10FIGURE 5. Graph of Speed and Acceleration Noise about the Mean . . . . . . . . . . . . . . . . . . 11FIGURE 6. 3-Dimensional Graph Comparing Speed, Acceleration, and Frequency . . . . . . . 11FIGURE 7. Graphical Representation of Speed Profile and Emission Rates . . . . . . . . . . . . . 16FIGURE 8. Overall Approach for Using Acceleration Characteristics in Air

Quality and Energy Consumption Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20FIGURE 9. Sample Roadway Inventory Field Data Collection Sheet . . . . . . . . . . . . . . . . . . 22FIGURE 10. Travel Time Routes (Houston, Texas) Used for the Study . . . . . . . . . . . . . . . . 23FIGURE 11. Example of DMIREAD Input Screen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25FIGURE 12. Example of Output from DMIREAD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26FIGURE 13. Relationship Between Average Speed and CV for Freeway Sections

During Peak Periods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43FIGURE 14. Relationship Between Average Speed and Acceleration Noise for

Freeway Sections During Peak Periods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43FIGURE 15. Relationship Between Average Speed and Acceleration Noise for

Class I Arterials During Off-Peak Periods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44FIGURE 16. Relationship Between Average Speed and the Standard Deviation of

Speed for Class I Arterials During Off-Peak Periods . . . . . . . . . . . . . . . . . . . . . 44FIGURE 17. Relationship Between Average Speed and CV for Class II Arterials

During Peak Periods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45FIGURE 18. Relationship Between Average Speed and Acceleration Noise for

Class II Arterials During Peak Periods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45FIGURE 19. Relationship Between Average Speed and CV for Freeway Segments

During Peak Periods (Aggregated by Travel Time Section) . . . . . . . . . . . . . . . . 48FIGURE 20. Relationship Between Average Speed and Acceleration Noise for Freeway

Segments During Peak Periods (Aggregated by Travel Time Section) . . . . . . . . 48FIGURE 21. Speed Distribution for Freeways and Arterial Streets . . . . . . . . . . . . . . . . . . . . 50FIGURE 22. Speed Distribution for Freeways . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51FIGURE 23. Speed Distribution for Class I Arterial Streets . . . . . . . . . . . . . . . . . . . . . . . . . . 52FIGURE 24. Speed Distribution for Class II Arterial Streets . . . . . . . . . . . . . . . . . . . . . . . . . 53FIGURE 25. Acceleration Distribution for Freeways and Arterial Streets . . . . . . . . . . . . . . . 55FIGURE 26. Acceleration Distribution for Freeways . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56FIGURE 27. Acceleration Distribution for Class I Arterial Streets . . . . . . . . . . . . . . . . . . . . . 57FIGURE 28. Acceleration Distribution for Class II Arterial Streets . . . . . . . . . . . . . . . . . . . . 58FIGURE 29. 3-Dimensional Speed-Acceleration Distribution for

Freeways and Arterial Streets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60FIGURE 30. 3-Dimensional Speed-Acceleration Distribution for Freeways . . . . . . . . . . . . . . 62

xviii

LIST OF FIGURES (continued)

PageFIGURE 31. 3-Dimensional Speed-Acceleration Distribution for Class I Arterial Streets . . . . 64FIGURE 32. 3-Dimensional Speed-Acceleration Distribution for Class II Arterial Streets . . . . 66

xix

LIST OF TABLES

PageTABLE 1. Speed and Acceleration Distribution by Percentage of Time . . . . . . . . . . . . . . . 18TABLE 2. Characteristics of Fuel Consumption Models Utilized in the Analyses . . . . . . . . 31TABLE 3. Probabilities Resulting From Comparing the Average and Instantaneous

Methods of Fuel Consumption Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38TABLE 4. Traffic Operating Characteristics for Roadway Classes During Peak

and Off-Peak Periods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40TABLE 5. R2 Values Observed Between Average Speed and

Operating Characteristics for Different Roadway Classes . . . . . . . . . . . . . . . . . 47TABLE 6. Speed-Acceleration Matrix for Freeways and Arterial Streets . . . . . . . . . . . . . . 61TABLE 7. Speed-Acceleration Matrix for Freeways . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63TABLE 8. Speed-Acceleration Matrix for Class I Arterial Streets . . . . . . . . . . . . . . . . . . 65TABLE 9. Speed-Acceleration Matrix for Class II Arterial Streets . . . . . . . . . . . . . . . . . . 67TABLE 10. Percent of Time Spent in Each Operating Mode by

Roadway Functional Class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

1

1

CHAPTER 1. INTRODUCTION

Since the oil embargo of 1973, there has been an increased concern for energy efficiency andreduction of mobile source emissions from vehicles operating on the transportation system. Thisconcern for energy efficiency and reduced emissions has fostered the development of a new disciplinewithin the transportation field. Models for both energy consumption and mobile source emissionshave made up a large facet of this new discipline. As such models become more rigorous, there hasalso been an improvement in the ability to collect more detailed operations data. This additional data,in turn, can be applied to improve the accuracy and level of detail of the energy consumption andmobile source emissions modeling.

Mechanical distance measuring instruments (DMIs) that attach to a vehicles’s transmissionwere used in the late 1950s to collect speed and delay data. Reducing the large quantities of datacollected with the mechanical equipment to a usable form proved to be difficult and time-consuming.Electronic DMIs have replaced the mechanical versions, and the advent of portable computers hassimplified the collection and reduction of detailed speed data. Several transportation agencies acrossthe United States use DMIs and portable computers for travel time studies. Most of these agencies,however, have few uses for speed profiles other than the identification of geometric bottlenecks andproblem areas. These speed profiles are commonly aggregated to provide average speeds betweenmajor cross streets [½ to 1 mile intervals (0.8 to 1.6 km)] of the study corridor.

Acceleration noise, or the fluctuation of speed along a roadway, is a concept that was firststudied in the early 1960s. Acceleration noise is defined as the standard deviation of changes invehicular speed and has units of miles per hour per second. Detailed speed data at small intervals(speeds every second) are required to accurately calculate acceleration noise. This type of detailedspeed data is readily available using electronic DMIs and portable computers.

Two computer models, EMFAC and MOBILE, are used to estimate mobile source emissionrates for California and the remainder of the United States, respectively. Other computer models thatuse emission rates from EMFAC or MOBILE have been developed to estimate the potential emissionreduction benefits of transportation control measures (TCMs). TCMs are required by the Clean AirAct Amendments (CAAA) of 1990 for areas designated as severe or extreme non-attainment areas.The TCM computer models primarily rely on changes in the number of trips, vehicle miles of travel,and average speed to estimate the emission reductions of proposed TCMs. The EnvironmentalProtection Agency (EPA), the agency responsible for enforcing the CAAA of 1990, has not issuedany standard procedures or methodologies for calculating the potential emission reduction benefitsof TCMs.

Extensive research continues on new methods of evaluating drive cycle changes on vehicularemissions and fuel economy. Drive cycle changes are based on the four vehicle operating modes:acceleration, deceleration, cruise, and idle. The changes in speed are more important to estimatingemissions and fuel consumption than average speed. These operating modes can be easilycharacterized with the detailed speed data available through the use of electronic DMIs. Models in

2

development would presumably place an emphasis on the fluctuation of speeds instead of the averagespeed in computing emission rates and energy consumption. The concept of such techniquesrepresents a new approach to calculating emission rates and could become a standard for the nextgeneration of emission rate computer models.

Problem Statement

Current mobile source emissions and energy consumption analyses are based on averagevehicular speeds over roadway sections that are typically greater than 1 mile (0.6 km) in length.Recent research has indicated that the fluctuation in speed (i.e., acceleration and deceleration) is moreimportant than the average speed in determining mobile source emissions and energy consumption.This fluctuation in speed, known as acceleration noise, has not yet been effectively utilized in vehicleemissions and fuel consumption analyses because of 1) the difficulty of collecting or estimating speeddata for very short time or distance intervals, and 2) the absence of appropriate computer models toconduct such analyses.

Study Objectives and Scope

The primary objective of this study was to characterize the speed and accelerationcharacteristics of a wide range of traffic flow. Data were collected with a DMI using shortincrements of time. Researchers made a preliminary investigation of the effects of detailed speed andacceleration data on existing fuel consumption models. Comparisons of fuel consumption estimateswere made using speed and acceleration calculations based upon a segment-wide (average method)and a second-by-second (instantaneous method). Detailed acceleration characteristics could beincorporated into the next generation of mobile source emissions and energy consumption models.Models that incorporate acceleration characteristics are expected to provide more accurate estimatesof mobile source emissions and energy consumption and of the changes in the emissions and energyconsumption associated with various transportation projects and programs.

The second objective of this study is to examine relationships between the geometriccharacteristics, speed and acceleration characteristics, and traffic flow variability for the differentroadway functional classes. These regression equations will be based upon data that aredisaggregated by functional roadway type (e.g., arterial Class I or II, freeways).

The final objective is to compile a reliable data set that describes the speed and accelerationcharacteristics of various roadways and operating conditions. The data set produced from this projectcan be supplied to interested individuals or organizations for use in development and/or validationof fuel consumption and/or emissions modeling.

3

Organization of Report

This report is organized into five chapters:

Chapter One, Introduction, provides an introduction to the research topic and presents theresearch objectives and scope.

Chapter Two, Background, provides general information about previous studies ofacceleration characteristics and driving cycles. In addition, a summary is provided of previous fuelconsumption and emissions modeling.

Chapter Three, Study Design, contains a summary of the procedures used to collect the dataand develop the appropriate data bases (e.g., speed and acceleration characteristics, geometriccharacteristics, and fuel consumption model estimates). The analysis techniques are also describedin this section of the report.

Chapter Four, Findings, presents the major findings for the research study. The findingsinclude the results of statistical tests to evaluate the significance of utilizing a detailed speed andacceleration data set on fuel consumption estimation. The relationships between the speed andacceleration characteristics, geometric characteristics, and fuel consumption model estimates are alsodiscussed in this section. Trends in the data base containing operational characteristics of roadwaysin the Houston, Texas area and its application in emissions and fuel consumption modeling is alsoaddressed. This chapter concludes with remarks about the success of using DMI technology for datacollection.

Chapter Five, Conclusions and Recommendations, presents the conclusions andrecommendations based upon the findings described in Chapter Four. These conclusions begin witha discussion of the use of the detailed data set for evaluation of the differences for fuel consumptionmodel estimation. The useful regression relationships and the application to transportation planningconcerns are addressed. The content and usefulness of the operating characteristics data base for theHouston, Texas area is also reviewed. The advantages of DMI technology in data collection, and theneed for future research in several areas encountered in this research study are addressed at the endof this chapter.

4

5

CHAPTER II. BACKGROUND

This chapter provides general information about other studies of acceleration characteristics,related data collection techniques, driving cycles, and fuel consumption and emissions modeling. Inaddition, sections that discuss the graphical representation of fuel consumption and emissions dataare included. The literature search identified over forty references that are summarized topically inthe paragraphs that follow.

Acceleration Characteristics

Several acceleration characteristics will be evaluated to make comparisons between theFederal Test Procedure (FTP) and the various functional classes upon which data are obtained. Theseacceleration characteristics found in the literature are discussed in the following section. Acceleration Noise

Early studies demonstrate that acceleration noise is a useful traffic parameter for evaluatingtraffic flow by investigating it under different conditions (e.g., hilly, heavy volume) ( 1). Accelerationnoise, or standard deviation of the acceleration, is defined as the root mean square of theaccelerations, and is also described by Montroll and Potts in their car following study ( 2). The Jonesand Potts approximation to acceleration noise, which utilizes three variables of a constant speedchange [2 mph (3 kph) is frequently used], the running time of the vehicle for each speed change, andthe total running time of the vehicle, is found in the literature in various reports ( 1,3,4). Therelationship for acceleration noise, or standard deviation of acceleration, for good level roads rangesfrom about 0.01 times the acceleration due to gravity + 0.002 times the acceleration due to gravityfor speeds between 20 mph (32 kph) and 60 mph (97 kph). Furthermore, for speeds greater than 60mph (97 kph) or less than 20 mph (32 kph), these values increase ( 2,5,6). One study suggests thatacceleration noise generally has been observed to decrease with increasing speed, though that maynot hold true for very high speeds [perhaps greater than 60 mph (97 kph)] ( 7).

Furthermore, several studies have indicated that some acceleration noise is “natural” due toa driver’s inability to maintain a constant speed through all the changes in geometry and otherinformation processing tasks that consume attention time ( 2,7,8). The additional acceleration noiseis due to vehicle interactions. Equation 1 is the form of the acceleration noise as presented by Drewand Dudek (3).

Where:

( ]

1n a= (av)2 E _I

T i=O at;

a = acceleration noise in feet/second2;

T = total running time of vehicle in seconds; av = change in speed in miles/hour and; at =change in time in seconds.

(1)

Drew and Dudek discuss that travel time may be misrepresentative by itself as a measure of the true conditions on a stretch of roadway since two vehicles with identical travel times could have significantly different speed profiles over the course of their respective trips. Acceleration noise appears to offer the best combination of true representation of the system as well as the ability to calculate meaningful values over relatively short stretches of roadway. The authors also discuss the feasibility of combining acceleration noise data from several adjacent study sections [e.g., 500 feet (152 meters) to 1,000 feet (304 meters)] into one acceleration noise value (,l).

Quality of Flaw Index

Several sources make reference to the Quality of Flow Index developed by Greenshields (,l,~2,1.Q). The Q Index is a function of the average speed, absolute sum of speed changes per mile, and the number of speed changes per mile (Equation 2).

Where:

Q = the Quality of Flow Index; K = a constant of 1,000; S = the average speed in miles per hour; ..1.s = the speed changes per mile in miles per hour; f = the frequency of speed changes per mile.

(2)

The index is written such that driver satisfaction or level of service is directly proportional to the average speed of trave~ but inversely proportional to the sum and frequency of speed changes (2).

6

7

Total Absolute Second-to-Second Differences in Speed Per Mile (TAD)

Larsen and Effa introduce the use of a characteristic termed the Total Absolute Second-to-Second Differences in Speed Per Mile (TAD) in research performed in developing real-world drivecycles (11). This topic, as well as this variable, will be discussed in later sections of this report. Thisvariable is calculated in the same manner as “? s”, or speed changes per mile, in Greenshields’ Qualityof Flow Index.

Other Acceleration Characteristics

A study attempting to predict accident risk proposed using the following characteristics: meanvelocity gradient (about the mean and the origin), velocity noise, average velocity, averageacceleration, and acceleration noise ( 7). The mean velocity gradient, defined as the acceleration noisedivided by the average velocity, was introduced by Helly and Baker ( 9). The authors point out thefact that acceleration noise is not a good measure when traffic is flowing slowly (e.g., signalizedsegments) and that the “mean velocity gradient” is a better measure since it is a relative measure thatcan accommodate for the congestion at slower speeds.

Data Collection

The method of data collection is one of the most critical aspects to be considered in anyresearch project. For studies of this type, test vehicle techniques are often utilized. This is themethod used for data collection in Houston, Texas for the Houston-Galveston RegionalTransportation Study to determine the travel times on different roadways in the area ( 12). Thismethod operates on the premise that the driver doing the data collection will pass roughly the samenumber of vehicles that pass him/her.

A distance measuring instrument (DMI) can be used to collect speed information at a giventime or distance interval. The instrument, which is accurate to + 1 foot (0.3 meter) in 1000 feet (305meters), is secured to the vehicle’s transmission and sends a pulse to an on-board lap-top computer.The computer then records the appropriate time, speed, and incremental distance. From thisinformation, a speed profile is easily constructed. A similar method of data collection has been usedin the past to determine a freeway congestion index (FCI) ( 13).

Another method of data collection is the “chase car” technique in which a vehicle is randomlyselected in the traffic stream (“target” vehicle) and is followed by an equipped data collection vehicle.This was the method utilized in a study to develop “real-world” driving cycles ( 11). Data wascollected with the aid of a laser system that can determine the “target” vehicle’s speed from theknown change in speed and distance of the vehicle. Larsen has indicated that since emissions are alarger problem at the higher speeds and higher accelerations and decelerations (i.e., non-averagedriving conditions), test vehicle methods may not be obtaining the appropriate type of data. It wassuggested that perhaps the outliers are the individuals of most importance and a method to select andobtain data for these individuals is necessary ( 14). Larsen expressed how data collection can also be

8

supplemented with simultaneous video taping. Effa and Larsen used such video as an additionalquality control measure in their study to ensure that the facility types for each section of the routewere characterized and grouped accurately. These route sections were aggregated together basedupon average speed only for analysis purposes ( 11).

Pela expressed a similar concern in regard to the use of test vehicle data collection ( 15). Pela’sconcern was that random vehicle sampling will not be achieved if instrumented vans are used, and hediscussed the fact that individuals were actually solicited for vehicle instrumentation in a study inwhich he was involved (16).

Graphical Representation of Data

After the data are collected, there are several different ways in which they can be presented tomake eventual comparisons between, for example, the FTP, other drive cycles, or field data. A verycommon graphical representation is speed versus time, or a speed profile (Figure 1) . Such a graphenables the reader to see the number and location of starts, stops, and the respective slopes in thegraph. The proportion of time throughout the trip that a vehicle is operating within a given speedrange is a valuable way of representing speed data (Figure 2). This information can also be presentedwith a cumulative distribution for either speeds or accelerations of vehicles (Figure 3). A frequencybar graph with the acceleration rate in mph/sec is also helpful (Figure 4). This graph could beestablished with metric units also (e.g., kph/sec). It allows the reader to see the distribution of theacceleration rates easily. Such a graph was found in many studies while reviewing the literature. Asimilar frequency bar graph can be constructed with speed ranges of 5 mph (8.0 kph). Such a graphmay not always demonstrate a normal distribution since the distribution may be based upon a limitednumber of observations.

There are several additional graphical representations in the literature as well. One studydemonstrated the use of graphical representations that show speed on the x-axis and the accelerationnoise about the mean along the y-axis (Figure 5). A similar graph was shown for acceleration noiseabout the origin, and this graph was also created for speed ( 7). These graphs tend to show acurvilinear relationship with relatively higher standard deviations of acceleration or speed at lowerspeeds [7 to 10 ft/sec (2 to 3 m/s)] and relatively smaller standard deviations of acceleration or speedat higher speeds [49 ft/sec (15 m/s)]. Winzer presents some interesting graphs relating accelerationnoise and combinations of two of the three primary macroscopic flow parameters ( 19). One suchnomograph shows the increase in acceleration noise as the traffic volume increases. Other graphsfeaturing two curves were required to account for the discontinuity in flow parameters betweencongested and non-congested flow.

70

60

so -.. ..c: S" 40 .......

"C G> 30 G> p..

t'/)

20

10

0 0 2 3 4 5 6 7 8 9 10 11 12

Distance (miles)

Figure 1. Example Speed Profile for Katy Freeway (1-:10) in Houston, Texas

1 cu 0.25

c:IJ c:: "ii .9 0.2 0 "ii F"" "'"' 8. 0.15 oo c::"'"' .g 0 0.1

"" 0.05 0

8 '1.

0 -' .,., .,., 0 "7 ~ 0 .,., -

0 .,., ' .,., ~

Vehicle Speed (mph)

-Atlanta - Baltimore - Spokane

Figure 2. Vehicle Speed Distributions for Three Data Sets (Adopted from Ref. !1)

9

JOO

90

~ 80

p f;I;, so

' 40 l : JO

0

70

60

~ so ......

1: 10

0

Cumulative Distribution or Acx:elendon

- - - - -,/

. . . . . . /

ti' I I I I

1 / --- - - -

2 3 4 s 6 7 8 9

Acx:elendon (mph/MC)

Figure 3. Cumulative Distribution of Acceleration Values

Distribution or .Aec*ntion

°" ., ~ "? ":' "

., ~ - C> C> - ... ... • .... IO "'" IO OI ~ ' s s s s s s s s s s v s s s s s s s s - C> - ... ... • .... IO "'" DO

°" ., I";' "? ":' "

., ~ ' Acx:elendon Range (mph/MC)

Figure 4. Frequency Bar Graph for Acceleration Values

10

0.45.,...-----------......

-N

0.40

! 0.35

0.30 0 I'll ..... z 0.25

s 0.20 ..... tU ... 0.15 ~ 8 0.10 ()'

< 0.05

+ + +

+ ++ +

"' :. +

+ ... + + + + +

+ +•+ + +t+ ++++

+ •• ,,., \+ ....

+ - + +;.+\ ! 0.00 r-,---.--.....-..-....,.... ........ ..--.......... ,_.....__,...__.-1

2 3 4 5 6 7 B 9 10 11 12 13 14 15

Speed (mis)

Figure 5. Graph of Speed and Acceleration Noise about the Mean (Adopted from Ref.1)

Some studies present figures illustrating the three-dimensional relationship between speed, acceleration, and frequency W,22). Figure 6 is an example of this type of three-dimensional graph. These graphs were used to compare different sectiOns of the FfP with data obtained in the field. This method of graphical presentation allows the reader to see where spikes or valleys occur (i.e., high or low frequencies, respectively) of speed and/or acceleration. Referring to Figure 6, more detail can be illustrated in such a figure by showing only the frequency of speeds and accelerations less than 4 percent.

20

18

16 -#. 14 -t" 12

l 10

8 e

llrll Speed (mph)

6 8 10

Acceleration (mph/1ee)

Figure 6. 3-Dimensional Graph Comparing Speed, Acceleration, and Frequency

11

12

Driving Cycles

The FTP is a driving cycle that was developed over twenty years ago to provide emissioninformation for light-duty vehicles. However, vehicles and driving characteristics have changed sincethe development of this cycle, and this area of study is quickly expanding as more representativedriving cycles are studied.

Current Driving Cycles

The US Environmental Protection Agency (EPA) supplied information about how to obtainthe current driving cycles being considered along with reports explaining their development. Belowis a summary of these driving cycles ( 21):

ARB02: This cycle was developed by the California Air Resources Board (CARB) based ondata from their Los Angeles chase car study. The purpose of the cycle is to test vehicles over in-useoperation outside of the FTP, including extreme in-use driving events.

HL07: This engineered cycle was developed by EPA in coordination with the automanufacturers. The purpose of this cycle is to test vehicles on a series of acceleration events overa range of speeds. The severity of the accelerations are such that most vehicles will go into wideopen throttle. This cycle has constant power (therefore, constant slopes in the time vs. speed profile)with high engine load for the engine wide-open.

REP05: This cycle was developed to represent in-use driving that is outside the boundary ofthe current FTP driving cycle. The cycle was generated from a composite data set which equallyrepresented Los Angeles chase car data and Baltimore 3-parameter instrumented vehicle data. Theprimary purpose of the cycle is assessing in-use emissions.

REM01: This cycle was developed to represent start driving behavior as well as that portionof in-use driving which is not represented by REP05. Start driving is represented by the first 258seconds of the cycle. The remainder of the cycle represents in-use driving which was not capturedby the start or REP05 cycles. When combined, the REP05 and REM01 are intended to characterizethe full range of in-use driving. The primary purpose of this cycle is assessing in-use emissions. Thecycle was generated from a composite data set which equally represented Los Angeles chase car dataand Baltimore 3-parameter instrumented vehicle data.

UNIF01: This cycle was developed to represent the full-range of in-use driving in a singlecycle. The methodology used in generating the cycle is largely consistent with previous efforts byCARB to develop a unified cycle. The cycle was generated from a composite data set which equallyrepresented Los Angeles chase car data and Baltimore 3-parameter instrumented vehicle data.

13

US06: This cycle is being proposed to make FTP more “realistic.” However, the proposalis not to replace the FTP but to make this an additional test to the FTP. Therefore, there would betwo (2) tests for the federal procedure. US06 is based on high speed accelerations which are notpresent in the FTP and is an “aggregation” to some extent of the REP05 and ARB02 cycles (whichare non-FTP conditions).

AC866: This cycle is part of the UDDS (Urban Dynamometer Driving Schedule--FTP). Itis the cycle which represents the second stage (i.e., the bag II stage) of the FTP.

SC01: This cycle is a start cycle which is the same as the first part of REM01. However, theremainder of it is different.

ST02: This is another start cycle which is similar to SC01.

Other cycles are being developed in research by individuals and organizations ( 22). Inaddition, the Coordinating Research Council has annual workshops that address these and manyrelated issues (23).

Concerns About the FTP

A study by Denis, et al, demonstrates the discrepancies between the “real-world” conditionsand what the FTP actually considers ( 20). This report explains, for example, that the FTP does nottake into account higher acceleration and deceleration rates that their test vehicle could exhibit whiledriving. Furthermore, the study explains that, “the FTP has more cruise, percent time stopped, andhard decelerations than the on-road data. The FTP under-represents coasting, and hard and mediumaccelerations” (20).

A study done by the California Air Resources Board (CARB) addressed these problems withthe FTP by using “chase” cars in the Los Angeles area to construct seven cycles that are morerepresentative of the on-road performance of motorists ( 11). These cycles are for both freeway andarterial sections. Perhaps the most significant work in re-evaluating the FTP was performed by theEnvironmental Protection Agency ( 18). This work concludes by explaining that current drivingcycles are still being developed with the data obtained from this study.

Fuel Consumption Modeling

There is extensive literature available on the subject of fuel consumption modeling, andconsiderable work is being continued in this area. Two documents explain the different types of fuelconsumption models available and the various independent variables that are necessary for theiroperation (24,25). The models are ranked according to their simplicity. For example, a simple linearregression model utilizes input such as the section distance and the number of stop/starts to determinefuel consumption per trip. On the other hand, an instantaneous fuel consumption model uses second-by-second speed and acceleration to obtain fuel consumption.

14

One study of a fuel economy model looks closely at driving cycle considerations as well ( 26).The authors note that their equation for fuel economy depends on five principal summary variables,and with certain simplifying assumptions, three or four variables may be sufficient: average speed,free-flow velocity, fraction of time vehicle stopped, and perhaps stops per mile. They also discusscold start as a factor that they have not yet incorporated into the equation. They also discuss that inthe early 1980s the FTP was estimated to have an error of about 15 percent as compared to actualdriving. Furthermore, the authors indicate it has been estimated to be as high as 30 percent by 2010,and that it is currently estimated at about 20 to 25 percent error. An and Ross mention that “certaindriving characteristics are critical for emissions but not for fuel use” ( 26). The authors cite velocitytimes acceleration (a variable closely related to engine power output) as an example. With thatdistinction made, they suggest that driving cycles for regulation of emissions should be defineddifferently from driving cycles for fuel economy. Emissions modeling will be further discussed in alater section.

One fuel consumption model that is available was developed for FREFLO (the freewaysimulation module of the FHWA CORFLO package for macroscopic modeling of freeway and arterialnetworks) (27). The model includes five terms which address rolling, air, and effective inertialresistances, idle fuel consumption, and effective acceleration. The acceleration was approximatedfrom FREFLO’s density output. Density was used as a surrogate for acceleration noise. It was foundthat the model accounted for “99.5 percent of the variation in the constant speed fuel consumptiondata and 86.1 percent of the variation in fuel consumption due to acceleration” ( 27). The model wasincorporated into the logical structure of FREFLO, and comparison with results from an INTRASsimulation (microscopic model) showed good correlation. Winzer reinforces Rao and Krammes’ useof density as a surrogate for acceleration noise ( 19). Winzer’s study shows that there is indeed a highcorrelation between the two, in all types of traffic conditions (e.g., low to heavy).

Another fuel consumption model called ARFCOM, developed by the Australian Road ResearchBoard (ARRB), was also found in the literature ( 28). The model is a detailed, incremental powermodel for estimating the fuel consumption of fully warmed-up vehicles. The inputs to the modelinclude power consumption due to tractive forces, drive-train inefficiencies, accessories and internalengine friction. The author asserts that even though individual components of total power demand(such as internal engine friction) change dramatically over a range of engine speeds, the fuel-to-powerefficiency of an engine is fairly constant over a range of power and engine speeds. This simplificationmakes the model much more practical for use in traffic management studies. The output of the modelis fuel consumption for the duration of a trip and estimates are within about 5 percent for tripdurations of 30 to 60 minutes. The model appears to be well-suited to estimating the fuel-consumption impacts of geometric and other improvements in roadways.

Another model that seems to be suitable for use in estimating the incremental effects ofchanges in traffic management schemes was also found in the literature (29). The author describesthis model as an instantaneous, basic (detailed and microscopic) model, and the inputs for this modelare instantaneous speed, acceleration, and grade. The output is fuel consumption for the duration ofa trip and the accuracy is about three percent. The three terms of this model require further

15

explanation. The first term allows for fuel consumption required to maintain engine operation. Thesecond term allows for fuel consumption required to provide tractive force to the vehicle inovercoming drag, inertia and gradient forces. The third term uses a product of energy and effectiveacceleration (acceleration including effects of gravity on a grade) to account for increased fuelconsumption during hard accelerations. A model developed by Bester in 1981 was also discoveredin the literature search. Although it is also a function of acceleration, speed, and gradient, it is of amuch simpler form (30).

Several models that are only a function of speed were also examined. Five of these models,from Raus (31), Lindley (32), FREQ10 (33), McGill (34), and NETFLO (35,36) utilize equations todetermine fuel consumption given only speed. However, two of these models are presented in a tableformat, as opposed to equations, that define fuel consumption as a function of the speed. One suchmodel was developed by McGill in 1985 (34) and the other is FREQ10 (33). In addition, FREQ10contains values for both arterials and freeways, allowing separate evaluation of both facility types(37). Conversely, NETSIM utilizes a table to determine the fuel consumption as a function of bothacceleration and speed ( 35,36,38).

Emissions Modeling

There is considerable literature in the area of emissions modeling as well. Much of theliterature supports the suspicions that the FTP is not representative of real world driving. One suchreport focuses on the high accelerations that are outside of the envelope of the FTP and are highemitters (39). The authors report that, “a single hard acceleration event could produce emissionsequivalent to 50 percent to 64 percent of the total FTP emissions for hydrocarbons, and 236 percentto 262 percent of the total for carbon monoxide” ( 39).

Further studies investigate doubts about the accuracy of the inputs and, hence, the output ofsome emissions models. In her report on carbon monoxide modeling, Chapin expresses the concernthat microscale dispersion models depend heavily on input variables that have substantial uncertainties(40). She suggests that traffic volumes, both overall and on local arterials, may be underestimatedby as much as ten to twenty percent. This, in turn, can result in uncertainties inherent in determiningthe composite emissions factor that may result in underestimating emissions by as much as 50 percent.Furthermore, the author indicates that with all these uncertainties built into dispersion models, themodels are better used in relative comparisons (e.g., comparing “do project” to “don’t do project”alternatives) than in attempting to determine whether absolute CO concentrations at critical points(e.g., hospitals and parks) exceed the National Ambient Air Quality Standards (NAAQS). A studysimilar to this was performed by Vaughn to describe the impact of Intelligent Transportation Systems(ITS) on projects with respect to emissions in arterial systems and networks ( 41).

A current study is also looking at the effects of grade and other loads on emission rates ( 42). Randall Guensler is involved with much ofthis research at the Georgia Institute of Technology (43). One such project involves utilizing a geographic information system (GIS) to incorporate the effects of grade into an emissions model. The ongoing research in the area of emissions modeling is immense. This research is focused on finding answers to the many concerns that are cited in the articles and reports referenced above as well as many others.

Graphical Representation of Fuel Consumption and Emissions Data

Several graphical methods for fuel consumption and emission rates were also discovered in the literature. Several of these were presented by Cicero-Fernandez and Long (39). One useful method places the different cycles across the x-axis with the respective emission rate in gm/sec along they­axis. This graph shows confidence intervals around the points and a different graph is presented for each type of emission. Similar graphs are created for each emission (e.g., HC and CO) with the time in seconds of each cycle on the x-axis and the speed in miles per hour on the y-axis on the right side (Figure 7). The graph has the driving cycle placed within the graph with dotted lines. A darker line is used within the graph to represent the amount of emissions. This allows the reader to find the outline of the driving cycle and the corresponding amount of the emission being shown. Another method shown in the same report is the cumulative amount of each emission in grams over the time of the cycle.

0.07 • 70 NOx - • •• • • • - • - 0.06 • ••• .. 60 t.) • ••• • •• • Q) • • • • •• • • • • • <ll • • •• •

l 0.05 • • • • • •• • • so • • • • • • • • • -• • • • •• • • •• • - • • • ,.s:: '-' • l Q) 0.04 • • .. .. . • • 40 • • • • ii • • • • • ~ • • • • • 0.03 •• • • • • 30 "O • • Q) = • • • • Q) 0 • Q. • • • • ·- 0.02 • • • 20 t:t.:l <ll <ll • • • • • ·- ~ . • !3 • ll:i • 10

0.00 0 1 51 101 151 201 251 301 351 401 451 501

Time (s)

Figure 7. Graphical Representation of Speed Proide and Emission Rates (Adopted from Ref. W

16

17

Another report found in the literature describes graphs which are presented in a similar manner(44). However, this report provides various percentile ranges within the box plots. For comparisonof different driving cycles and their respective emission rates for different conditions (e.g., running,cold start, or hot start), bar charts can be utilized ( 45).

A useful method to provide speed and acceleration data in table form is shown in Table 1 ( 18).Such a table shows the frequency at each speed and acceleration in two dimensions rather than witha three-dimensional graph as shown in Figure 6. With this method of presentation, it is easier toprecisely read percent distribution values than on the three-dimensional graph. This aids individualswho desire these results for entry into emissions models.

Summary of Literature Review

The preceding literature review has discussed numerous acceleration characteristics, drivingcycles, and related fuel consumption and emissions modeling issues. Acceleration noise is founddiscussed with relation to car following stability as early as 1959 ( 5). The report discusses theinteractions of vehicles and its effect upon motorists’ speeds. These alternating speeds, and ensuingacceleration noise, are formulated and special note is made that future research will be performed toattempt to correlate acceleration noise to parameters such as mean speed, number of lanes of traffic,and traffic density. As evidenced in the literature review, acceleration noise has been studied further,along with additional acceleration characteristics. Additional characteristics include the quality oftraffic flow index (Q Index) introduced by Greenshields ( 10).

In addition to the study of acceleration characteristics, there is continued study on the issuesof fuel consumption and emissions modeling. Shortcomings in regard to the errors of these modelsare apparent throughout the literature. A large concern of such models is the error inherent withsome of the input variables (e.g., traffic volumes), and the subsequent effect on the results ( 40).Several other concerns stem from the fact that the current driving cycle for testing emissions is notrepresentative of current driving conditions. Hence, much work has been done, and is beingcontinued, to develop additional cycles that will consider the current driving situations that are notrepresented in the FTP.

> <=

Speed (""*') > <=

0 0 5 5 10

10 1S 1S 20 20 25 25 30 30 35 3S 40 40 45 45 50 50 SS 5S 60 60 65 6S 70 70 75 75 80 80 85 85 90 90 95

ALL

·10

" 0.000 o.ooo 0.000 0.001 0.000 0.000 0.000 0.000 0.000

0.000

0.003

-00

-10 ·9

" 0.000 o.ooo 0.001 0.001 0.001 0.001 0.000 0.000 0.000 0.000

0.004

·9 ·8 ·8 ·7

" " 0.000 0.001 0.001 0.002 0.003 0.012 0.004 0.014 0.004 0.011 0.002 0.007 0.001 0.003 0.001 0.002 0.000 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000

0.016 0.054

Table 1. Speed and Acceleration Distribution by Percentage of Time (Adopted from Ref. !§)

Acceleration ·7 ·6 ·5 ·4 ·3 ·2 ·1 0 0 1 2 3 4

·6 ·5 ·4 ·3 -2 ·1 0 0 1 2 3 4 5

" x x x x x x x x x x " x

0.004 0.015 0.042 0.109 0.235 0.414 0.275 19.920 . . 0.008 0.034 0.107 0.258 0.543 0.902 1.257 0.030 1.912 0.597 0.288 0.156 0.077

0.040 0.117 0.2S3 0.400 0.526 0.619 0.896 0.020 0.811 0.538 0.402 0.296 0.196

0.044 0.126 0.270 0.423 0.509 0.578 0.874 0.020 0.934 0.769 0.634 0.418 0.233

0.035 0.102 0.239 0.390 0.479 0.574 0.910 0.030 1.048 0.971 0.820 0.413 0.149

0.022 0.062 0.163 0.302 0.446 0.649 1.327 0.040 1.573 1.114 0.756 0.317 0.095

0.010 0.031 0.089 0.191 0.353 0.733 2.208 0.080 2.652 1.248 0.523 0.154 0.042

0.005 0.014 0.042 0.101 0.226 0.666 2.990 0.120 3.600 1.045 0.288 0.062 0.015

0.002 0.006 0.019 0.049 0.121 0.450 2.677 0.120 3.263 0.676 0.134 0.024 0.006

0.001 0.003 0.008 0.021 0.064 0.295 1.897 0.080 2.323 0.385 0.062 0.010 0.002

0.000 0.001 0.004 0.010 0.034 0.200 1.441 0.070 1.706 0.225 0.033 0.004 0.000

0.000 0.000 0.001 0.005 0.019 0.159 1.487 0.080 1.678 0.152 0.017 0.002 0.000

0.000 o.ooo 0.001 0.002 0.011 0.129 1.899 0.120 2.103 0.111 0.010 0.001 o.ooo 0.000 0.000 0.000 0.001 0.006 0.088 1.633 0.100 1.841 o.on 0.005 0.000

o.ooo 0.000 0.000 0.002 0.043 0.840 0.060 1.004 0.036 0.003 0.000

0.000 0.000 0.001 0.015 0.205 0.010 0.276 0.012 0.001 0.000 0.000 0.002 0.031 0.046 0.003 0.000

. 0.000 0.001 0.002 0.004 0.001 o.ooo 0.000 0.000 0.001 0.001 0.000

. . . 0.000 0.000 o.ooo 0.000 . 0.171 O.S12 1.237 2.262 3.575 6.518 22.853 20.900 26.775 7.956 3.975 1.857 0.815

5 6 7 8 9 6 7 8 9 10 10

" " " " x " 0.139 0:066 0.038 0.020 0.006 0.098 0.029 0.008 0.002 0.003 0.002 0.041 0.011 0.002 0.001 0.000 0.000 0.022 0.003 0.000 o.ooo o.ooo 0.000 0.012 0.002 o.ooo o.ooo o.ooo 0.004 o.ooo 0.000 0.001 0.000 0.000 0.000 0.000 . 0.000

. 0.317 0.110 0.049 0.023 0.010 0.003

19

CHAPTER III. STUDY DESIGN

This chapter contains a summary of the procedures used to collect the data and develop theappropriate data bases (e.g., geometric and acceleration characteristics). Also contained within thischapter are the methodology and analyses techniques that were used to quantify the fuel consumptionmodel estimates, investigate correlations and relationships, and develop the data base of usefulemissions modeling information. The overall study approach is described first and is followed witha discussion of the data collection effort. Subsequent sections discuss the pertinent data bases thatwere created for analyses purposes. The final sections of this chapter discuss the statistical analysesutilized to study correlations and relationships within the data set and to evaluate several fuelconsumption model estimates.

Overview of Study Design

Figure 8 illustrates the procedure that was followed to accomplish the objectives of the study.The top of the figure begins with the objectives the study has targeted. These objectives are asfollows:

• Determine the effects of detailed speed and acceleration characteristics on fuelconsumption;

• Investigate relationships between speed and acceleration characteristics, geometriccharacteristics, and traffic flow variability; and,

• Establish a data base for emissions modeling that can be utilized by others.

To accomplish these tasks, data were collected with a distance measuring instrument (DMI)on a total of 233 centerline-miles (375 km) of freeway routes and 198 centerline-miles (319 km) ofarterial routes. Summary speed and acceleration characteristics were calculated from the speedinformation provided by the DMI. Geometric characteristics were also collected along the corridors.The following section entitled, “Data Collection” discusses the data collection procedures used forthe study.

Once the data were collected with the DMI, three distinct modules of information werecreated. These are shown in Figure 8. The first data set includes the speed and accelerationcharacteristics computed from the DMI files for each corridor. The second data set includes theresults of utilizing both an instantaneous and average calculation of several fuel consumption modelestimates based upon speeds and acceleration rates. Such analyses are imperative to show anydifferences in such models when collection of vehicle speeds is allowed as often as every second. Thelast data set that is merged with the others is the geometric characteristics that were collected alongthe travel time routes.

The data were then combined, summarized appropriately, and statistical analyses wereperformed in order to complete the objectives of the study. The results and conclusions are containedin Chapters IV and V.

OBJECTIVE: Determine effects of detailed speed and acceleration

characteristics on fuel consumption estimation.

DATA MODULE 1

Determine speed and acceleration characteristics for each section.

Determine significant differences in fuel consumption model estimates between

instantaneous and average calculations of both speed and acceleration characteristics.

OBJECTIVE: Investigate relationships between speed and acceleration

characteristics, geometric characteristics, and traffic flow variability.

of arterials and 233 centerline miles of freeways in Houston, Texas

DATA MODULE 2

Determine instantaneous and average values of fuel consumption given

speed and acceleration characteristics.

Combine data sets, summarize, and perform further

analyses.

~

I L I \

I \ ~ -· i-"l o 11

" "

I t3 ~EY:H:!Efm!:f:B ITEJ I ~~g

Determine correlations between speed and acceleration characteristics,

geometric characteristics, and traffic flow variability.

OBJECTIVE: Establish data set for emissions modeling that can be utilized by others in the field.

DATA MODULE 3

Incorporate geometric characteristics into data set.

Create data set for future emissions modeling based upon percent time at

each speed and acceleration.

Figure 8. Overall Approach for Using Acceleration Characteristics in Air Quality and Energy Consumption Analyses

20

21

Data Collection

The Houston Metropolitan Area and the Data Collected

Houston is the fourth largest city in the United States, and the metropolitan area ranks as thetenth largest. The population is estimated at nearly 1.8 million within the city limits, and 4 millionwithin the Greater Houston metropolitan area. Geographically, the Houston urbanized area coversapproximately 3,000 square miles (7,770 square kilometers). Due to the large population andgeographic area, Houston has heavy traffic during peak periods that occur from approximately 6:00to 9:30 a.m. and 3:30 to 7:00 p.m.

Several different types of data were collected for the study. Speed and accelerationcharacteristics were obtained from base travel time data. Roadway geometrics were collected inconjunction with another study being conducted by TTI ( 12). Driveway information was alsocollected by recording the number of “curb cuts” along sections. The speed data were collected usingthe DMI technology discussed in the next section of this chapter to obtain speed information everyhalf second.

Roadway Geometric Characteristics

The Houston TxDOT District’s Planning Department is unique since they have been collectingdetailed roadway geometric information since the mid 1960s for long-term transportation planningpurposes. Roadway inventory, as the geometric information data collection is called, is one of thetypes of data that is collected for planning purposes. This information is necessary since Houston hasno zoning, with the exception of deed restrictions. Geometric roadway information is often used todevelop growth trends, estimate existing capacity, and determine projected facility needs.

Data for the Roadway Inventory were collected within the Houston Galveston RegionalTransportation Study (HGRTS) (12). The study known as Roadway Inventory consists of thecollection of roadway information shown in Figure 9 (field data collection sheet). The data collectionincludes a survey of every FHWA functionally classed roadway segment as well as other selectedroadway segments. Data are collected on all segments and compiled in a data base. Roadways weredivided into segments with limits set where minor arterials or higher class roadways cross thesurveyed roadway, or when the roadway cross section has a geometric change (e.g., number of lanes,median type). Segments typically ranged from 0.10 miles (0.2 km) to 1.5 miles (2.4 km) in urbanareas and up to 6.0 miles (9.7 km) in rural areas. With the roadways divided into segments, physicalinventories were conducted in the field. Field inventories involved measurements with a measuringwheel, observations, and recording all other pertinent information.

From the roadway segments provided in the Roadway Inventory, the research team determinedwhich travel time routes would be utilized for the data collection effort. The team considered thelocation and convenience of the roadways with their data collection cost, while providing a randomsampling of routes. Figure 10 illustrates the location of the freeways, Class I arterials, and Class II

22

Key Map # Sheet #

HOUSTON-GALVESTON REGIONAL TRANSPORTATION STUDYHARRIS COUNTY STREET INVENTORY

AS OF / /

STREET LIMITS SECTION NO. TYPE FUNCTIONAL CLASS STATE SYSTEM FEDERAL SYSTEM TEXAS TRUNK SYSTEM LOCATION URBAN AREA MAINTENANCE

LENGTH MI. R.O.W. WIDTH ROAD WIDTH NO. OF LANES MEDIAN WIDTH MEDIAN DESIGN SIDEWALKS CURBS SHOULDERS FT. SHOULDER TYPE SURFACE TYPE SURFACE CONDITION ILLUMINATION PARKING MARKING TRAFFIC SIGNALS STOP SIGNS YIELD SIGNS CAUTION LIGHTS CHANNELIZED INTERSECTIONS SPEED LIMIT RAILROAD CROSSINGS:UNPROTECTED CROSSBUCKS FLASHING LIGHTS FLASHING LIGHTS & GATES

24 - HOUR VOLUME COUNT DATE ADT YEAR

REMARKS:

RECORD #: 1PAGE #: 1 (OVER) FIELD INITIALS

arterials used for the travel time routes in the study. The terminology of Class I and Class II arterialsegments, defined in the Highway Capacity Manual (HCM), was used by the research team to classifythese segments based upon the posted speed limit and geometric characteristics of the arterial ( 46)

Figure 9. Sample Roadway Inventory Field Data Collection Sheet

SCALE 012345 --Miles

LEGEND

VJ VJ w a a. >­u I

a w >l a <[

"' IH 10 KATY FWY

Class I Arterials Class II Arterials Freeways

~Airport

z a> 0

I­>- VJ <[ ;:)

=- 0 I- :r _J w 2: Ill <[

FM 1093

BEECHNUT

BISSDNNET

I-­VJ w :a

FM 1960

BAYTOWN EAST

PROPOSED BELTWAY 8

Figure 10. Travel Time Routes (Houston, Texas) Used for the Study

N

24

DMI Data Collection

Travel time data collection in the Houston area is currently performed with a DMI for theHouston-Galveston Regional Transportation Study (HGRTS) ( 12). The following discussionexplains the details of DMI data collection and how it was utilized for this study.

About the Software: Computer Aided Transportation Software (CATS), an integrated computerprogram, was developed by the Texas Transportation Institute (TTI) to improve the collection oftravel time and speed data for traffic studies ( 47). The CATS software currently consists of threemodules: DMIREAD, DMISTAT, and DMIPLOT.

The data collection module, DMIREAD, which is used with a laptop computer connectedthrough the serial port to a DMI, collects detailed speed, distance, and clock time data while travelingthrough a corridor. The operator enters information about the run from a menu system that includes:Roadway Name, Roadway Type, Travel Direction, User Name, Odometer Reading, WeatherConditions, etc. (Figure 11). DMIREAD opens the serial port, sets the DMI to the correct mode,places the operator’s input (run data) in the header, obtains speed and distance information from theDMI, time stamps each data record, formats the data, and writes it to an ASCII file. The informationthat is provided in the ASCII file are the event number, cumulative and interval distance, speed, aclock time stamp for each reading, and the header (run data) information (Figure 12). The programtypically collects data on a half-second interval, but has the capability of collecting the data asfrequently as 0.10 of a second.

Reliability in the data collection effort results when the travel time run is performed on apredetermined route. Prior to beginning the data collection, a driver and an observer will use theDMI to accurately determine the distances between the predetermined checkpoints. The checkpointdistances are collected and entered into a “yard-stick” file that will be used by the DMISTAT analysismodule to perform statistics on each section. The travel time run is then conducted, taking care tomark the first checkpoint and all subsequent checkpoints very accurately by pressing any key on thelaptop computer which will write a “!!! MARK !!!” to the ASCII file (see Figure 12). Redundancyis introduced by the operator concurrently marking the checkpoints as the travel time run is beingmade and comparing this information with the data obtained by using the “yard-stick” to determinethe checkpoints. If a conflict is found, typically the problem is that the operator is marking the wrongcheckpoint location (i.e., the wrong cross street). Of course, each problem requires a case-by-caseinvestigation. Extensive quality control was performed on the travel time data files prior to analysesto ensure the data were acceptable prior to analyses. Quality control measures are discussed in a latersection of this report.

TRAVEL TIME RUN USING A DMI MAIN MENU

ITEMS

ROADWAY NAME ROADWAY TYPE TRAVEL DIRECTION SCHEDULED TIME DRIVER NAME ODOMETER WEATHER LIGHT PAVEMENT START DMI

CANCEL

SELECTIONS

NORTH FREEWAY MAIN LANES NORTH BOUND 17:00 (hh:mm) ••.••..... Robert J. Benz ............................. 43367 ........... CLEAR NORMAL DAYLIGHT DRY

Figure 11. Example of DMIREAD Input Screen

25

26

FREEWAY NAME : KATY FREEWAYFREEWAY TYPE : MAIN LANESFREEWAY DIRECTION : IN BOUNDDATE TODAY : 3/9/1995WEATHER CONDITION : CLEARLIGHT CONDITION : NORMAL DAYLIGHTPAVEMENT CONDITION : DRYSCHDEULED TIME : 06:30DRIVER : atmMILE START : 15171START TIME : Thu Mar 09 06:33:11 1995

!!! MARK !!! 1. 0.001 0.001 46 @ Thu Mar 09 06:33:11 1995 2. 0.003 0.002 46 @ Thu Mar 09 06:33:11 1995 3. 0.004 0.001 46 @ Thu Mar 09 06:33:12 1995 4. 0.007 0.004 46 @ Thu Mar 09 06:33:12 1995 5. 0.016 0.009 47 @ Thu Mar 09 06:33:13 1995 6. 0.021 0.005 47 @ Thu Mar 09 06:33:13 1995 7. 0.026 0.006 47 @ Thu Mar 09 06:33:14 1995 8. 0.031 0.005 47 @ Thu Mar 09 06:33:14 1995 9. 0.037 0.006 47 @ Thu Mar 09 06:33:14 1995 10. 0.042 0.006 47 @ Thu Mar 09 06:33:15 1995

11. 0.048 0.006 47 @ Thu Mar 09 06:33:15 1995 12. 0.054 0.006 47 @ Thu Mar 09 06:33:16 1995 13. 0.059 0.006 47 @ Thu Mar 09 06:33:16 1995 14. 0.065 0.006 46 @ Thu Mar 09 06:33:17 1995 15. 0.070 0.006 46 @ Thu Mar 09 06:33:17 1995 16. 0.076 0.006 47 @ Thu Mar 09 06:33:17 1995 17. 0.081 0.006 47 @ Thu Mar 09 06:33:18 1995 18. 0.086 0.005 46 @ Thu Mar 09 06:33:18 1995 19. 0.091 0.006 46 @ Thu Mar 09 06:33:19 1995 20. 0.097 0.006 46 @ Thu Mar 09 06:33:19 1995 21. 0.102 0.006 45 @ Thu Mar 09 06:33:20 1995 22. 0.107 0.005 45 @ Thu Mar 09 06:33:20 1995 23. 0.113 0.006 44 @ Thu Mar 09 06:33:20 1995 24. 0.118 0.005 44 @ Thu Mar 09 06:33:21 1995 25. 0.123 0.006 44 @ Thu Mar 09 06:33:21 1995 26. 0.128 0.005 45 @ Thu Mar 09 06:33:22 1995 27. 0.133 0.005 45 @ Thu Mar 09 06:33:22 1995 28. 0.138 0.006 44 @ Thu Mar 09 06:33:22 1995 29. 0.143 0.005 44 @ Thu Mar 09 06:33:23 1995 30. 0.148 0.006 42 @ Thu Mar 09 06:33:23 1995 31. 0.153 0.005 42 @ Thu Mar 09 06:33:24 1995 32. 0.157 0.005 42 @ Thu Mar 09 06:33:24 1995 33. 0.162 0.005 40 @ Thu Mar 09 06:33:25 1995 !!! MARK !!! 34. 0.167 0.005 40 @ Thu Mar 09 06:33:25 1995 !!! MARK !!! 35. 0.171 0.005 39 @ Thu Mar 09 06:33:25 1995 !!! MARK !!! 36. 0.176 0.005 39 @ Thu Mar 09 06:33:26 1995 37. 0.180 0.005 37 @ Thu Mar 09 06:33:26 1995 38. 0.184 0.005 37 @ Thu Mar 09 06:33:27 1995 39. 0.188 0.004 37 @ Thu Mar 09 06:33:27 1995 40. 0.193 0.005 37 @ Thu Mar 09 06:33:28 1995 41. 0.197 0.004 37 @ Thu Mar 09 06:33:28 1995 42. 0.201 0.005 36 @ Thu Mar 09 06:33:28 1995 43. 0.206 0.005 36 @ Thu Mar 09 06:33:29 1995 44. 0.210 0.005 36 @ Thu Mar 09 06:33:29 1995 45. 0.214 0.005 37 @ Thu Mar 09 06:33:30 1995 46. 0.219 0.005 37 @ Thu Mar 09 06:33:30 1995 47. 0.223 0.005 37 @ Thu Mar 09 06:33:30 1995 48. 0.227 0.005 37 @ Thu Mar 09 06:33:31 1995 49. 0.232 0.005 37 @ Thu Mar 09 06:33:31 1995 50. 0.236 0.005 37 @ Thu Mar 09 06:33:32 1995

Figure 12. Example of Output From DMIREAD

27

Either method enables the user to post-analyze the data via the ASCII format as shown inFigure 12. Header information (run data) provided by the operator from a menu format is used touniquely name each ASCII output data file so that it indicates a roadway name, roadway type,direction of travel, date, and time. This feature eliminates the problem of overwriting previouslycollected data files.

The other two software modules are Excel macros that are written in Visual Basic. TheDMIPLOT module opens the ASCII file, formats the data, plots a speed profile, and uses the headerinformation for title and labeling purposes. The DMISTAT module opens the ASCII file, formatsthe data, provides interval and cumulative distance and time, calculates average speed, standarddeviation, percent time between different speed ranges, and estimates a level of service based onspeed.

Increase in Quantity of Data: The software system provides a vast amount of data for each traveltime run, as shown in Figure 12. The operator enters information about the travel time run such asroute name, direction, type, and weather (see Figure 11), before the program starts, and writes theheader data to the ASCII file. The software system (program and DMI) provides header informationwhich includes:

C Route NameC Route DirectionC Route TypeC Driver NameC Run Date

C Start Time (computer generated) C Weather ConditionsC Light Conditions (daylight, night, fog)C Pavement Status (wet, dry, etc.)C Scheduled Start Time

In addition, the program provides an event number, cumulative distance, speed, and computertime stamp at a rate of up to once every 0.1 seconds. This results in much more data than is collectedusing the manual method. This level of detail provides some distinct advantages. First, checkpointscan be determined from the travel time data log even if the observer does not mark the location whilemaking the travel time run. Quality control checks can be made to determine if the location of thecheckpoints are being marked accurately. The greatest benefit from this type of data collectiontechnique is the increased volume of data. Instead of an average speed every quarter mile to twomiles (0.4 to 3.2 kilometers) the data can be recorded every 0.10 second. From these data files a farmore detailed analysis can be performed. In addition, the program uses the header data for theautomatic file naming system to ensure that no files are overwritten.

Ease and Consistency of Data Collection: The software system provides ease and efficiency ofoperation, as well as consistency of the data collection process. Once the travel time runs arecompleted, files can be downloaded and the analysis software, DMIPLOT and DMISTAT, can beused to process the data within minutes per output file. With computer generated files, there are noproblems with interpretation of the data or data entry errors. More accurate and consistent recordingof time that each run was started and when checkpoints are crossed are provided. The consistent dataformat allows for automated data reduction, which will be discussed later.

28

Another distinct advantage is that the data is in ASCII format, allowing the output files to beviewed or analyzed in almost any software package. Other programs are in various data base formatswhich are not easily viewed or manipulated.

Potential Limitations: As with any type of data collection, there are some drawbacks. One of thelimitations associated with computer aided data collection is that the number of runs are limited tothe number of laptops and the number of DMI units that are installed in available vehicles. Additionaldrawbacks are that the size of each data file can get overwhelming and disk storage space is apotential problem. However, the increased size of and decreased cost of hard disk space, along withthe use of compression utilities for long-term storage, alleviates this problem. A minor problem isgetting power to the laptop computers. Battery life can be as long as 4 hours, but if batteries are notcharged fully or if they are altered by constant charging and discharging, problems can develop. A/Cadapters are available at a modest cost.

The human factor (e.g., missing checkpoints and inaccurate calibration) will always be aproblem. However, these problems can be solved or isolated with proper education and training.These problems can be easily overcome with modest precautions, and the benefits far outweigh anydisadvantages. Quality control was a very important aspect of the study. Several checks and cross-checks, were performed in order to discover and correct travel time run files that were incorrect fordifferent reasons (e.g., incorrect units, unreasonable speeds).

Development of Data Matrix

The final data set analyzed for this study was quite extensive and was comprised of data fromthree sources: 1) speed and acceleration characteristics, 2) fuel consumption calculations, and 3)geometric characteristics. This section will further explain the development, content, and usefulnessof these three modules.

Speed and Acceleration Characteristics Module

The speed and acceleration characteristics portion of the data set is a direct result of the DMItravel time runs. The travel time run output (see Figure 12), contains the speed of the vehicle at aspecified distance. A total of 382 travel time runs are incorporated into the final data set. There are:100 Class I arterials, 81 Class II arterials, 71 with both Class I and Class II arterial segments together,and 130 freeway segments. These travel time runs are further disaggregated by section becauseseveral of the travel time runs cover sections of roadway that are not geometrically similar and,therefore, should not be analyzed together. For example, a roadway can change from divided toundivided or from a two-lane section to a four-lane section. Travel time runs that contain portionsof arterial Class I and arterial Class II sections were separated.

29

After disaggregating the DMI travel time runs for the different functional classes of roadway,there were 843 travel time runs for Class I arterials, 1,018 runs for Class II arterials, and 1,087 runson freeways. As discussed in the preceding section “Data Collection,” the travel time runs werecollected at different times of the day. The reduction of these DMI travel time runs into similargroups for analyses is discussed in the “Levels of Analyses” section in a later section of this report.The following speed and acceleration characteristics were calculated with the DMI data:

• Average Speed• Average Acceleration• Standard Deviation of Speed • Standard Deviation of Acceleration (Acceleration Noise)• Mean Velocity Gradient as Defined by Helly and Baker (9)• Greenshields’ Q Index (10)• Total Absolute Second-to-Second Differences in Speed Per Mile (TAD) ( 11)

While calculations were created to perform the analyses of the acceleration characteristics,variables were also utilized that calculate and save the speed and acceleration distributions bypercentage of time (see Table 1). This information is valuable for individuals who require data setsbroken down by percent of time at a specified speed and acceleration for different conditions (e.g.,facility type). Such information is critical in emissions modeling and related fields (e.g., determinationof drive cycles).

Fuel Consumption Module

The literature search identified several fuel consumption models that would be beneficial in acomparative analysis. The instantaneous speed and acceleration, and average speed and accelerationmodel results were compared using these models. The instantaneous method involved calculatingthe speed and acceleration for every other observation (i.e., every 1.0 second) in the data set andcalculating the instantaneous fuel consumption estimate for the different models. The instantaneousfuel consumption estimate for every other observation was then averaged over the entire travel timerun to determine the average fuel consumption. Hence, the final result is a fuel consumption estimatebased upon instantaneous calculations of fuel consumption.

The fuel consumption estimate based upon average speed values was obtained as follows. Thespeed for the travel time run was computed as the total distance over the total time. The accelerationwas computed the same as for the instantaneous method. This computation remained the same sincethe true acceleration is required and can be obtained in this fashion. In addition, by keeping theaccelerations consistent, the respective differences in fuel consumption will be produced in aconsistent manner. After computing speed and acceleration in this manner, the fuel consumptionestimate for the average method was performed for each travel time run. Analyses in the “Levels ofAnalyses” section of this report discuss the methods used to test for significant differences betweenthe instantaneous and average methods of fuel consumption estimation.

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Models Used for Analyses . Several of the models identified in the literature search were used in thefuel consumption analyses portion of the study. Table 2 displays the models used in the analyses.In addition to the model name and date, this table also shows the independent variables used in themodel to calculate fuel consumption, the applicable speed range, and the functional class orientationof the model (e.g., arterial and/or freeway).

There are some important notes that should be made about the information provided in Table2. First, most of the models are based upon equations, but some utilize look-up tables to estimatefuel consumption. The models that estimate fuel consumption with the look-up tables are FREQ10,McGill, and NETSIM. Another important point is that throughout this study, grade, which isincorporated into two of the models, was assumed to be zero. This is a reasonable estimate for theHouston, Texas area where the data were collected. The “Speed Range” and “Functional ClassOrientation” values are based upon what is in the literature, or in the absence, professional judgement.

For the purposes of this study it was necessary to convert the output units of the fuelconsumption models to a common unit. The unit 1,000 multiplied by gallons/second, was utilized.A time-based unit for fuel consumption (e.g., seconds) was selected over a distance-based unit (e.g.,miles) since it provides reasonable values during idle conditions (i.e., stopped at signals or trafficqueues).

In order to perform analyses on all the models in the same manner, it was necessary to ensurethat all the models were extrapolated from 0 to 75 miles per hour (121 kph) (i.e., the range ofpossible values encountered in the data set). Linear extrapolation provided a reasonable assessmentof fuel consumption estimates for undefined ranges. Interpolation was used to estimate fuelconsumption rates in the models that use look-up tables since the values are often recorded inincrements of 5 miles per hour (8.0 kph) in the tables.

31

Table 2. Characteristics of Fuel Consumption Models Utilized in the Analyses

MODEL No. NAME / DATE INDEPENDENTVARIABLE(S)

SPEED RANGE(MPH)

FUNCTIONAL CLASSORIENTATION

1 Bowyer, Akcelik,and Biggs / 1985 Speed and Acceleration 9 to 76 Arterial and Freeway

2 Biggs and Akcelik /1986

Speed, Acceleration, andGrade1 9 to 76 Arterial and Freeway

3 Bester / 1980 Speed, Acceleration, andGrade1 25 to 81 Arterial and Freeway

4 Raus / 1981 Speed 1 to 35 Arterial

5 Lindley / 1986 Speed 1 to 55 Arterial and Freeway

6 FREQ10 / 1990 Speed 0 to 70 Freeway

7 FREQ10 / 1990 Speed 5 to 40 Arterial

8 McGill / 1984 Speed 15 to 70 Freeway

9 FREFLO / 1994 Speed and Acceleration 7 to 75 Freeway

10 NETFLO/pre-1980data Speed 0 to 25 Arterial

11 NETSIM / 1986data Speed and Acceleration 0 to 75 Arterial

1 Grade assumed to be zero.

32

Geometric Characteristics Module

The final component of the data set is the geometric characteristics. Many of thesecharacteristics are part of the roadway inventory that is collected for the Houston-Galveston RegionalTransportation Study (HGRTS) (12). In addition to the variables available in HGRTS, the numberof driveways along the study routes were recorded during the data collection effort. The list belowdisplays the variables contained in the geometric characteristics module along with any necessarydescription.

• Study route• 1st cross street• 2nd cross street• Roadway section number (numbered consecutively)• Facility type (e.g., under construction, divided, undivided, one-way) • Functional classification (e.g., interstate, freeway, principal arterial, minor arterial)• Length of the section• Width of the roadway surface• Number of lanes• Width of median• Design of median (e.g., no median, curbs, guardrail and/or fence, open and/or drainage ditch,

painted)• Parking restrictions (e.g., none, no restrictions both sides, no parking anytime both sides, a.m.

and/or p.m. restrictions both sides)• Number of signals• Number of stop signs• Posted speed limit• 24-hour tube volume• Number of channelizations per section (e.g., turn bays)• Number of driveways in traveling direction

From the list above, informative density variables such as signals per mile, stops per mile, anddriveways per mile can be computed. Additional variables that were calculated include average lanewidth and the hourly volume per lane. These density variables will be used for analyses of geometriccharacteristics and the speed and acceleration characteristics. Such analyses are further described inthe sections that follow.

Levels of Analyses

The three data modules described above were merged together prior to beginning the statisticalanalyses. In addition, several quality control measures were used to ensure the data were readproperly and contain appropriate information.

33

Quality Control Measures

Quality control of the data prior to statistical analyses was an important element of this study.Since the data were subject to many conversions and disaggregations it was imperative that the databe closely scrutinized throughout the data reduction process.

The original DMI travel time run files were checked for several items that would createproblems in the computer analyses and subsequent results. These checks include searching for blanklines or missing variables, formatting problems (i.e., information not located in the correct place),incorrect units, nonconsecutive observation numbers, lack of consistency, and unreasonable values.

Similar checks were also made after the original DMI travel time run files were disaggregatedinto sections. A check was made to ensure that observations (i.e., lines of the data files) were notcopied twice or left out while being disaggregated. Several of the DMI travel time run files wereselected for individual scrutiny to ensure that no areas of concern could be found. The most effectivemethod to search for errors was running the files through the statistical package utilized for analyses.The program alerts the operator to suspect values and syntax problems that may be present in theDMI travel time run files.

Initial Examination of the Data Set

Prior to analyses of the data set, it is important to examine the data to determine exactly whattrends and patterns may be present. At this preliminary point of the analyses, it is important to obtaina basic understanding of the data set and its contents. This information is generally provided byobtaining descriptive statistics for the data set. The following list contains the analyses that wereperformed at this initial level.

• Produce frequency tables that illustrate the number of observations in different groups ofdata (e.g., at average speeds in a given speed range for freeways).

• Ensure that the number of observations in the different groups is sufficient for statisticalanalyses.

• Produce histograms and normality tests to ensure the data obtained for each of the groupsoriginates from a normal distribution.

In addition to allowing the researcher to get an understanding for the distributions of the data,such analyses allow for checks that ensure the data originate from a normal distribution and whetherthe sample sizes are adequate. Since many statistical analyses (e.g., t-tests, analysis of variance) areonly applicable when these assumptions are true, it is important to investigate these descriptivestatistics.

34

After looking at the data, it was necessary to decide how the roadway functional classes wouldbe separated into groups. The Highway Capacity Manual (HCM) shows the level of service fordifferent arterial classes based upon the average travel speed ( 46). The research team used theterminology of Class I and Class II from the HCM to classify the arterial segments based upon theposted speed limit and geometric characteristics of the arterial. A typical free-flow speed of 40 mph(64 kph) is reported for Class I arterials in the HCM, 33 mph (53 kph) for Class II arterials, and 27mph (43 kph) for Class III arterials according to the HCM ( 46). Class III arterials tend to be in, ornear, the downtown and since travel time data were not being collected in these areas, Class IIIarterials were not included in the study.

Fuel Consumption Analyses

The objective of the fuel consumption analyses is to investigate any significant differencesbetween model results for the instantaneous and average methods of estimating fuel consumption.The calculation of the instantaneous and average fuel consumption estimates was explained in detailin a previous section entitled, “Fuel Consumption Module ”. The tests for significance wereperformed utilizing t-tests within each roadway functional class. If significance is found, analyses ofvariance (ANOVA) tests were performed to discover what variables explain where the variance isbeing introduced. Example variables include signal density, number of lanes, or hourly volume perlane.

It is important to note that these analyses will not conclude which model is the “best” modelfor fuel consumption estimation. These analyses only identify significant differences between the twomethods of fuel consumption estimation. However, the analyses can provide a relative measure ofthe estimation trends of the models (i.e., a group of models give similar results while one or two seemto give “outlier” estimations relative to the group).

Regression and Correlation Analyses

The next step of the analyses was to bring the three modules of data together to investigateany correlations among the variables involved for a given functional roadway class. Regressionequations were evaluated between the speed and acceleration characteristics, geometriccharacteristics, and traffic flow variability (e.g., average speed compared to the coefficient ofvariation of speed). Coefficient of determination (R2 value) was evaluated to determine the degreeto which the independent variables explain the effects on the dependent variable. There were severalrelationships that were expected to make intuitive sense to be highly related (e.g., signal density andspeed for a given arterial class). These analyses allowed such intuitions to be tested as well as todiscover other relationships.

35

Data Base of Useful Emissions Modeling Information

The final objective of the study is to establish distributions of the operating characteristics ofthe Houston, Texas area. This distribution will stratify the operating characteristics (e.g., speed andacceleration) by peak and off-peak conditions for different roadway classifications. This informationcan be utilized by individuals or organizations for use in development and/or validation of fuelconsumption and/or emissions modeling. The tables that were produced with this analysis step aresimilar to Table 1. The graphs show acceleration rates along the x-axis and speed bins along the y-axis. Each cell contains the percent of operating time at the given acceleration and speed rangesalong the travel time run.

36

37

CHAPTER IV. FINDINGS

This chapter presents the major findings for the research study. Presented first are the findingsthat discuss the significant differences found in comparing the fuel consumption model estimates fromboth the instantaneous and average calculations of both speed and acceleration characteristics. Thenext section discusses the correlations and regression equations that were found between the speedand acceleration characteristics and geometric characteristics. The findings with regard to thedevelopment of a speed and acceleration data base are then presented. Finally, the success of usingDMI technology for data collection is discussed.

Fuel Consumption Model Analyses

The first step of the fuel consumption analyses was to ensure that the data are from a normaldistribution. The normality test was satisfied for all the variables representing a difference betweenthe average method and instantaneous method of fuel consumption estimation except for Raus’ modelon freeways. Since Raus’ model is only good for velocities ranging from 1 to 35 mph (1.6 to 56 kph)it makes sense that normality may not be found for freeway conditions. Once normality wasdiscovered for the other models, statistical analyses were performed.

Since the difference between the average and instantaneous methods of fuel consumptionestimation is of concern, a paired t-test is the appropriate analysis tool. T-tests were performed onthe different arterial classes (e.g., Class I, Class II, and freeways) at the aggregated level (i.e., notdisaggregated by average speed, for example). The null hypothesis for the tests is that there is nodifference between the two methods of calculating fuel consumption. Therefore, if significance isfound, the null hypothesis can be rejected and there is a difference between the two methods of fuelconsumption estimation. A critical level of significance of 5 percent was used in the analyses todetermine significance. Results of the analyses are shown in Table 3.

A cursory review of Table 3 indicates that Raus’ model did not yield significant differences infuel consumption estimation for any of the functional classes. The FREQ10 models for freeways andarterials were both found to be insignificant for the Class I arterials. The final model thatdemonstrates insignificant results is McGill for the Class II arterials.

It is interesting to note that, although insignificance was only found for a few situations shownin Table 3, some models demonstrate significance for which the model does not have speed data. Forexample, FREFLO is utilized for modeling freeway conditions, but it is based upon speed data in therange from 7 to 75 mph (11 to 121 kph) (i.e., including arterial speeds) and was found to demonstratea significant difference for both arterial and freeway conditions. NETFLO is also for modelingarterials and is based upon speeds from 0 to 25 mph (0 to 40 kph), but was found to be significantfor freeways. Finally, NETSIM, utilized for simulating arterials, is based upon speed data from 0 to75 mph (0 to 121 kph) and yields significant results for both arterial classes as well as freeways. Theauthors would like to alert the reader that it is important to look at Table 2 in union with Table 3when interpreting the results to realize what conditions the fuel consumption model is based upon

38

(e.g., speed range) and the functional class orientation for which the model was developed. Thisensures an accurate understanding and interpretation of the results.

It is important to note that the analyses presented here compare only the average andinstantaneous methods of fuel consumption estimation for any one model. It does not compare themodels to one another, nor is it possible from this analyses to determine that any one model is betterthan another model. However, by interpreting Tables 2 and 3 together, one can determine whichmodels demonstrate a significant difference that can be attributed to the detailed data set producedby performing travel time runs with the DMI.

Table 3. Probabilities Resulting From Comparing the Average and Instantaneous Methodsof Fuel Consumption Estimation

MODELProbability for Different Functional Classes1

Class I Arterials(n=841)

Class II Arterials(n=1020)

Freeways(n=1087)

Bowyer, Akcelik, andBiggs (1985) 0.0001 0.0001 0.0001

Biggs and Akcelik(1986) 0.0001 0.0001 0.0001

Bester 0.0001 0.0001 0.0001

Raus 0.1000 0.0641 N/A2

Lindley 0.0001 0.0001 0.0001

FREQ10 (Freeways) 0.1257 0.0001 0.0001

FREQ10 (Arterials) 0.2349 0.0001 0.0001

McGill 0.0001 0.0592 0.0001

FREFLO 0.0001 0.0001 0.0001

NET FLO 0.0001 0.0001 0.0001

NETSIM 0.0001 0.0001 0.00011Shaded cells indicate insignificance compared to a critical level of significance of 2.5 percent (two- tailed test).2The data for this cell were not found to be from a normal distribution.

39

Regression and Correlation Analyses

The next objective of the study was to investigate regression equations between speed andacceleration characteristics, geometric characteristics, and traffic flow variability. A concern priorto beginning this evaluation was the time periods of the travel time runs that included the peak periodconditions (i.e., relatively lower average speeds). The data were studied, including investigatingnormal distributions of speed by the time of day of the travel time run, to notice reductions in averagespeeds due to the peak period conditions. From such analysis, the peak periods for the travel timeruns in Houston, Texas were taken as 6 to 9 a.m. and 4 to 7 p.m.

Table 4 provides a summary of the operating characteristics that were discovered in the dataset once these peak and off-peak times were determined. Summary statistics are shown for speed,acceleration, and the coefficient of variation (CV) for speed. The summary statistics include thefollowing values: average, maximum, minimum, standard deviation, average minimum, and averagemaximum. The average maximum and minimum values are the averages for the maximum andminimum that were recorded for each travel time run section. The average value is the averagerecorded for each travel time run on a particular roadway segment.

A cursory review of Table 4 indicates that there is not a significant difference between thevalues obtained for peak and off-peak conditions. The research team believes that the peak and off-peak conditions are similar for freeways since there were numerous miles of freeways included in thepeak period that were not congested. These freeways were not congested since they are further awayfrom the urban area, and some experience nearly free-flow conditions during the peak period. Thesame argument can be made for the high off-peak speeds for arterial Class I and Class II segmentsas well. Signal timing can also affect the average speeds of Class I and Class II arterial segmentsalong the corridor (e.g., optimized signal timing can increase average speeds while poor signal timingcan reduce the average speeds). In addition, the acceleration and CV variables are lower and thespeeds are higher for freeway sections than for arterials. This would imply that the arterial segmentsare generally experiencing more stop-and-go conditions with hard breaking (i.e., deceleration) and/orrelatively high accelerations. The stop-and-go conditions are generally due to the signal timing alongthe corridor.

Once the peak period conditions were determined, geometric characteristics were identifiedto use in the linear regression models. The following variables were utilized as independent variables:signal density expressed as the number of signals per mile, driveway density expressed as the numberof driveways per mile, stop density expressed as the number of stops per mile, length of the sectionin miles, and the 24 hour volume per lane. Signal density, driveway density, and stop density werenot applicable for freeway sections. The dependent variables (i.e., speed and accelerationcharacteristics) that were used for model development were the standard deviation of the acceleration(i.e., acceleration noise) in mph/sec, standard deviation of the speed in mph, average speed in mph,and the coefficient of variation expressed as a percentage (standard deviation of the speed/averagespeed).

40

Table 4. Traffic Operating Characteristics for Roadway Classes During Peak and Off-Peak Periods

TrafficStream

Characteristics

All Freeways and Arterials Freeways

Arterials

Class I1 Class II1

Peak2 Off-Peak3 Peak2 Off-

Peak3 Peak2 Off-Peak3 Peak2 Off-

Peak3

Speed (mph)

Average 42.0 39.6 54.7 57.2 38.1 38.5 28.8 28.8

Avg. Maximum 54.0 51.3 61.1 61.6 53.0 51.9 45.7 43.9

Avg. Minimum 22.6 17.8 46.4 51.2 8.7 8.8 2.9 3.7

StandardDeviation 9.7 10.2 3.9 2.8 13.8 12.9 13.9 12.8

Acceleration (mph/sec)

Avg. Maximum 1.96 2.06 1.09 0.91 2.36 2.33 2.75 2.57

Avg. Minimum -2.30 -2.44 -1.32 -1.10 -2.81 -2.68 -3.16 -3.11

StandardDeviation

(AccelerationNoise)

0.64 0.66 0.42 0.37 0.74 0.72 0.85 0.80

Coefficient of Variation of Speed4

Average 32.5 34.2 10.2 5.9 42.3 38.2 53.5 49.3

Maximum 235.6 125.5 111.9 86.2 132.8 125.5 235.6 120.0

Minimum 0.5 0.7 0.5 0.7 1.1 2.4 6.6 2.6 1As defined by the 1994 Highway Capacity Manual (HCM). 2Peak Conditions are from 6 to 9 a.m. and 4 to 7 p.m. 3Off-Peak Condition is from 10 a.m. to 3 p.m. 4Coefficient of Variation = Standard Deviation of Speed/Average Speed.

41

Normal distribution graphs of these variables were then evaluated to look for useful stratifications(e.g., for driveway densities greater than 20 per mile) for model development. These stratificationswere used when evaluating linear regression model development between the accelerationcharacteristics and geometric characteristics. Models were developed based upon peak and off-peakconditions, different functional classifications, and stratifications on some variables (e.g., for drivewaydensities greater than 20 per mile). The linear regression models generally produced low R 2

relationships. In fact, the highest relationships achieved were less than 0.35 for any of the conditionsstudied. This model was often between the dependent variable of average speed and the independentvariables of signal density and/or driveway density for the arterial sections in either the peak or off-peak conditions. The addition of independent variables after signal density and driveway density (i.e.,producing graphs with greater than three independent variables) often resulted in increasing the R 2

value only a few hundredths. Analysis of variance (ANOVA) procedures were performed as part ofthe linear regression using a critical level of significance of 5 percent for the independent variablesin the relationships. The research team hypothesized that the driveway density and signal densityvariables would have the most explanatory power in such relationships and provide higher R 2 results.

For nearly all of the models developed, the signal density and driveway density variables werefound to be significant in the ANOVA procedure. Therefore, these variables were contributing tothe explanation of the variance within the model. It is interesting to note that the 24 hour volume andthe length of the section, the only independent variables used in the freeway analysis, were not alwayssignificant. The 24 hour volume produced significant results more often, however, than the variablerepresenting the length of the section. This would indicate that the 24 hour volume was critical inmany cases in explaining the variance in the freeway segments with the variables available.

The research team suspected that floating car method did not measure the true affects of thedriveway density on travel times. Since the floating car technique is based upon the premise that thedriver will pass as many vehicles as pass the driver, it is possible that any significant affects of thedriveway density in the right-most lane are not reflected in the results of the travel time runs. Thiscould influence the results of the model relationships. It was also important to note that there werenot distinct differences between the peak and off-peak periods with respect to the R 2 values andsignificance of variables. This will be discussed further in later sections when further analysis aredescribed.

Another observation that was made from evaluating the resulting models was the signs on thecoefficients of the independent variables. Often times, these signs did not make intuitive sense. Forexample, as the signal density went down, the coefficient of variation of speed (CV) would go up.In this example, it does not make sense that the variation of the traffic speeds, represented by the CV,should go up when there is less interruption in the traffic stream (i.e., a lower signal density).However, this could indicate that along these arterial corridors the signal timing has been optimizedto provide sufficient green time and increased average speeds.

42

One point that should be discussed is the use of the variable representing the length of thesegment. This variable was included into the analysis since it was hypothesized that the length of asegment could explain some of the relationship with the acceleration and speed characteristics.Intuition would suggest that it is possible, given a longer or shorter section, the degree of aggregationincurred would produce significant results for the variable. However, this variable was found to besignificant in only one or two cases. This result, along with further analysis below that will bedescribed, led the research team to believe that a variable explaining the location of the travel timerun would possibly be more explanatory. Unfortunately, such a variable did not exist in the data setfor the study. Closer investigation of the speed profiles indicated that depending upon where a traveltime section was located there could be anticipated results. For example, lower speeds may beincurred prior to a signal location on arterials or upstream of a lane drop on a freeway section.

One final point that should be discussed is the relationship between average speed and the 24 hourvolume. This relationship also produced an R 2 value less than 0.35 which is counter-intuitive.However, this low correlation results since the volume and speed data were not collectedsimultaneously. Project resources did not allow for the collection of simultaneous speed and volumedata collection. Therefore, speed data were collected with the DMI while 24 hour volumeinformation was obtained from the Roadway Inventory.

After the geometric characteristics were evaluated, attention was focused upon the traffic flowvariability. This was studied with the aid of graphs producing R 2 values between average speed(independent variable) and the dependent variables of acceleration noise, standard deviation of thespeed, and the coefficient of variation of the speed. One point on the figure represents the averagespeed and average operating characteristic (e.g., CV) for a travel time run through a section. Figures13 through 18 show these relationships for selected conditions. Figures 13 and 14 are for freewaysections (peak conditions), Figures 15 and 16 are for Class I arterials (off-peak conditions), andFigures 17 and 18 are for Class II arterials (peak conditions).

120

• CV= -1.29(Average Speed)+ 80.78

> IOO R2 =0.84 u • -= c 80 ·-- • • • ·- • .. ~ 60 ... '""' . ~."··. c -= . .._. . ~ 40 ... _ .... ·-u e •• ~· # ~ • • ·~4:: c 20 u •

0

0 IO 20 30 40 50 60 70

Average Speed (mph)

Figure 13. Relationship Between Average Speed and CV for Freeway Sections During Peak Periods

1.20

'Y' 1.00 ~

~ .c:: =- 0.80 e -~ "" ·- 0.60 c z = c ·- 0.40 -• .. ~ -~ u u 0.20 <

0.00

0 IO

•

Acceleration Noise= -0.0l(Average Speed)+ I. IO R2 = 0.70

20 30 40 50 60

Average Speed (mph)

70

Figure 14. Relationship Between Average Speed and Acceleration Noise for Freeway Sections During Peak Periods

43

~ ~

~ -= c. E -~ fl) ·-0 z = 0 ·-..... f ~ al u u <

1.40

1.20

1.00

0.80

0.60

0.40

0.20

0.00

• • ••• . ' . •••••• ••• •

Acceleration Noise= -0.0l(Average Speed)+ 1.30 R2 = 0.33

0 10 20 30 40 50 60

Average Speed (mph)

Figure 15. Relationship Between Average Speed and Acceleration Noise for Class I Arterials During Off-Peak Periods

35.00

--= c. 30.00 E -'2 25.00 ~ c.

rl.l 'c; 20.00

= 0 ·-..... • 15.00 ·-t = ? 10.00

• .,, = • 5.00 .....

rl.l

0.00

0

Standard Deviation of Speed= -0.51(Average Speed)+ 32.62 R2 = 0.53

•

10 20 30 40 50

Average Speed (mph)

60

Figure 16. Relationship Between Average Speed and the Standard Deviation of Speed for Class I Arterials During Off-Peak Periods

44

140

> 120 u -= 100 c ·--• 80 ·c ~ """ 60 c -= ~ ·-u 40 e ~ c u 20

0

0 10

CV= -2.53(Average Speed)+ 126.25

•

• • ... , 20

• •

R2 = 0.70

30 40

Average Speed (mph)

50 60 .

Figure 17. Relationship Between Average Speed and CV for Class Il Arterials During Peak Periods

1.60

1.40 ~ ~

1.20 ~ .c::: =-e 1.00 -~ ,,, ·- 0.80 c z = 0.60 c ·--• ... ~ 0.40 -~ u u < 0.20

0.00

0 10 20 30 40 50 60

Average Speed (mph)

Figure 18. Relationship Between Average Speed and Acceleration Noise for Class Il Arterials During Peak Periods

45

46

The relationship between average speed and the coefficient of variation provided relatively highR2 values for all functional roadway classes, peak, and off-peak conditions. Since CV is really anormalization with the average speed for a travel time run, the relationships are more explanatory.This normalization is evidently critical since the R2 values decrease significantly when investigatingthe relationship between average speed and the standard deviation of the speed. Further, relationshipsutilizing average speed to predict the acceleration noise produced relatively lower R 2 results. Thisis probably due to the inherent averaging that occurs to determine the acceleration noise over aparticular travel time run.

Investigation of Figure 13 indicates that there is a cluster of points at higher speeds with lowercoefficient of variations while at lower speeds the coefficients of variation indicate a much largerrange of values. The next portion of the analysis focused on investigating the value of CV, averagespeed, relevant geometric characteristics, and the speed profile at a given speed to realize anypossible trends that may exist. This evaluation was performed by selecting an average speed for agiven condition (e.g., peak period freeway sections) and evaluating a point below, near, and abovethe regression line. After the three points were identified, other runs on the same section werestudied to ensure that the points selected were “typical” for the particular travel time run section.

As the CV increased for the off-peak Class I arterials, the driveway density and volume increased,making intuitive sense. However, signal density did not increase throughout as the CV increased.In fact, the signal density went down to 0.6 signals/mile for the point with the highest CV of 56percent. In addition to some of the previous findings, this also alludes to the fact that there maysimply not be any clear relationships between the variables considered. For the peak period Class Iarterials, it is interesting to note that the point with the highest CV was from a travel time run thatconcluded just prior to a traffic signal. Therefore, the section had no signal density and produced arelatively high CV since the vehicle was slowing as it approached the intersection. This findingreinforces that the location of the section should be evaluated since theoretically one could havesections placed such that the CV could be just about any value (i.e., located anywhere along the speedprofile). Due to the inherent variability in these relationships, and the geometric versus speed andacceleration characteristics, developing estimating regression equations is difficult. Similar resultswere found for other roadway classes and conditions.

After the evaluations above, consideration was again given to the relationships shown in Figures13 through 18. Since the geometric characteristics (e.g., number of lanes, signal density) do notchange for a given section on which travel time runs are being performed, it is possible to aggregatethe resulting speed and acceleration characteristics together for these runs. This was performed andregression equations were produced and the results are shown in Table 5. The R 2 values in Table 5are very similar, or slightly higher, than those produced when each travel time run was plotted. Thiswas expected since it produces a graph with fewer points that are aggregated closer to the regressionline. For illustrative purposes, Figures 19 and 20 show the resulting graphs for average speed versusCV and acceleration noise, respectively, for peak period freeway conditions.

47

Table 5. R2 Values Observed Between Average Speed and Operating Characteristics forDifferent Roadway Classes

RoadwayFunctional

Class

Dependent VariableRegressed withAverage Speed

R2 Value of Linear Regression

Peak PeriodConditions

Off-Peak PeriodConditions

All RoadwayClasses

Coefficient ofVariation 0.87 0.84

Acceleration Noise 0.79 0.72

Standard Deviationof Speed 0.67 0.66

Freeways

Coefficient ofVariation 0.85 0.73

Acceleration Noise 0.75 0.55

Standard Deviationof Speed 0.61 0.70

ArterialsClass I

Coefficient ofVariation 0.84 0.78

Acceleration Noise 0.54 0.43

Standard Deviationof Speed 0.58 0.46

ArterialsClass II

Coefficient ofVariation 0.68 0.68

Acceleration Noise 0.21 0.13

Standard Deviationof Speed 0.03 0.13

60

$:' 50 u -= c 40 ·-..... • ·-"" ~ 30 ....

c ..... = 20 ~ ·-Cool e ~ 10 c u

0

•

35 40

•

45

CV= -1.32(Average Speed)+ 82.25 R2 = 0.85

•• 50 55 60

Average Speed (mph)

65

Figure 19. Relationship Between Average Speed and CV for Freeway Sections During Peak Periods (Aggregated by Travel Time Section)

1.00

-Cool 0.90 ~

~ -= 0.80 s=. E

• -~ 0.70

W.I ·-c 0.60 z

= c 0.50 ·-.....

I!! ~

0.40 -~ Cool Cool

< 0.30

0.20

35 40

Acceleration Noise= -0.0l(Average Speed)+ 1.14 R2 = 0.75

•

45 50 55 60

Average Speed (mph)

65

Figure 20. Relationship Between Average Speed and Acceleration Noise for Freeway Sections During Peak Periods (Aggregated by Travel Time Section)

48

49

Roadway Operating Characteristics: Speed and Acceleration

The travel time and speed data collected for this study were summarized to obtain speed andacceleration distributions. These speed and acceleration distributions provide quantitative informationabout the operating characteristics of the freeways and arterial streets under study. Thesedistributions are also very important in designing and validating the next generation of emissionsmodels that are based upon acceleration patterns, not average speeds. The objective of this study wasto quantify the speed and acceleration characteristics on a large sample of freeways and arterial streetsin Houston, Texas, ideally representing conditions for other urban areas with similar roadway anddevelopment patterns. The following sections contain a description of the speed, acceleration, andspeed-acceleration distributions found on these freeways and arterial streets.

Speed Distributions

The speed distributions are a quantitative summary of the percentage of time that was spent indifferent 5 mph (8 kph) speed ranges. Figure 21 illustrates the speed distribution for all freeway andarterial street routes that were surveyed in this study. The distribution shows that 15 percent of thetotal trip time was spent traveling above 55 mph (89 kph), while 7 percent was spent in idleconditions (0 mph). Because this distribution combines freeway and arterial street routes, the higherspeeds of freeway travel are mixed with lower speeds typical of arterial streets. By disaggregatingthe data by functional class, the speed distributions for different functional classes become moredistinctive. This study disaggregated the study routes by freeways, Class I arterials, and Class IIarterials.

The speed distribution for the freeway routes is shown in Figure 22. The speed distribution showsa marked increase in travel speeds above 55 mph (89 kph). For freeways, approximately 72 percentof the total freeway trip time is relatively free-flow [i.e., above 55 mph (89 kph)]. Another 14 percentof the trip time is between 45 mph (72 kph) and 55 mph (89 kph) (i.e., slightly congested conditions),and the remaining 14 percent of the trip time is spent in moderate to severe congestion [i.e., below45 mph (72 kph)].

The freeway speed distributions contrast sharply with the arterial street speed distributions shownin Figures 23 and 24. The arterial street distributions show speeds patterns more typical ofinterrupted flow. The speeds are lower and more distributed over the entire speed range. Forinstance, 32 percent of the total trip time on Class I arterials was above 55 mph (89 kph), while only1 percent of the total trip time on Class II arterials was above 55 mph (89 kph). The arterial streetdistribution also illustrate the additional idle time experienced at signalized intersections: 7 percentof total trip time for Class I arterials, and 13 percent for Class II arterials.

In summary, the speed distributions for different functional classes were markedly different, withfreeways exhibiting higher speeds and arterial streets exhibiting lower speeds and more idle time. Thedata shown in Figures 21 through 24 are for peak period conditions, or those times when congestionwas prevalent on the study routes. The data for off-peak period conditions (mid-day)

VI 0

FREEWAYS AND ARTERIAL STREETS Morning and Eveniq Peak Period

(6 to 9 am, 4 to 7 pm)

Speed Frequency Distribution

Speed Raqe Cumulative Ranae (mDh) FreauencY (9/e) Freouencv c•/e)

0 6.8 6.8 OtoS 2.S 9.2 s to 10 1.7 10.9

10 to IS 2.0 12.9 lS to20 2.S lS.S 20to2S 3.4 18.8 2S to30 4.3 23.1 30 to 3S 6.3 29.4 3S to40 8.9 38.3 40to4S 9.9 48.2 4S to so 9.8 S8.l SO to SS 9.9 68.0 SS to60 17.1 8S.l 60to6S 13.7 98.8 6S to70 1.2 100.0 70to7S 0.0 100.0 1S to 80 0.0 100.0

>80 0.0 100.0

40

35

.... 30 ~ ._, 25

1~ IS

fl;c 10

s 0

0 "' s 0

.... 100

~ 90

t' 80

I 70

r 60

fl;c so

i 40

30

~ 20

10

0 ,......--

0

Distribution of Speed

0 "' ~ ~ Ii! "' $ ~ ~ "' $ ... ... .... "' s s s s s s s s s s s "' 0 "' ~ ~ Ii! "' $ "' lit "' ... ... .... • "'

Speed Raqe (mph)

Cumulative Distribution of Speed

/ ,,,,,. /

/ _/'

~ ~-

10 20 30 40

Speed (mph)

Figure 21. Speed Distribution for Freeways and Arterial Streets

:9 ~ IC i i s s s s /\

$ "' ~ "' IO ""'

~ ~

r - -I

60 70 80

FREEWAYS Morning and Evening Peak Period

(6 to 9 am, 4 to 7 pm)

Speed Frequency Distribution

Speed Range Cumulative Ran•e (moh) Freaueney (•/.) Freaueney (•/.)

0 0.4 0.4 Oto5 0.6 1.1 5to10 0.8 1.9 10 to IS 1.1 3.0 15to20 1.4 4.4 20to2S 2.0 6.4 25 to 30 1.9 8.3 30to 35 2.0 10.3 35 to40 2.0 12.3 40to4S 2.2 14.S 45toSO 3.7 18.2 S0to5S 9.8 28.0 S5to60 36.7 64.7 60to65 32.5 97.1 6Sto10 2.9 100.0 70to75 0.0 100.0 75to80 0.0 100.0

>80 0.0 100.0

40

35

~30 • """ t- 25

1: f;r;t 10

5

0 Cl

..-. 100

"' 90 """ t- 80

I 70

l 60

50

1 40

30

20 10

0

0

Distribution of Speed

"' Cl "' Iii ~ ~ "' $ ; ~ "' i :s ~ s ... ... ... "' Cl s s s s s s s s s s s s s

"' Cl :!l Iii ~ ~ "' $ ~ ~ "' i :s ... ... "' Speed Range (mph)

Cumulative Distribution of Speed

r I

I

' I I

I / -----

10 20 30 50 60

Speed (mph)

Figure 22. Speed Distribution for Freeways

;c i i s s A

~ "' ...

- --

70 80

CLASS I ARTERIAL STREETS Morning and Evening Peak Period

(6 to 9 am, 4 to 7 pm)

Speed Frequency Distribution

Speed Range Cumulative Ranie (mph) Frequency (%) Frequency (%)

0 9.7 9.7 0 to 5 2.9 12.6 5 to IO 1.6 14.2 10 to 15 1.9 16.l 15 to 20 2.2 18.3 20 to25 2.7 21.0 25 to 30 3.5 24.4 30 to 35 4.8 29.3 35 to40 8.0 37.2 40 to45 16.1 53.3 45 to 50 22.9 76.2 50 to 55 17.7 93.9 55 to 60 5.6 99.6 60 to 65 0.4 100.0 65 to 70 0.0 100.0 70 to 75 0.0 100.0 75 to 80 0.0 100.0

> 80 0.0 100.0

,-. ~ • _..

~ fi ::I Cl" f ~

,-. ~ • _..

~ r:I

~ Cl" f ~

.~ .... • -::I El ::I tJ

Distribution of Speed

40

35

30

25

20

15

10

5

0 0 .,.. 0 .,..

~ .,., 0 .,.,

~ .,.,

:;: .,..

I! s .... .... .... .... .... • .,., 0 s s s s s s s s s s s .,.. 0 :!) ~

.,.. ~

.,.. ~ .,., :;: .,.. .... .... .... • .,..

Speed Range (mph)

Cumulative Distribution of Speed

100

90

80

70

60

so 40

30

20

~ / ,

/

" / ~·

~_.....--

- --10

0

0 10 20 30 40 so

Speed (mph)

Figure 23. Speed Distribution for Class I Arterial Streets

:g i:: .,., 0 2 ... .. s s s s A

I! .,.,

i:: .,..

\0 ...

-

60 70 80

CLASS Il ARTERIAL STREETS Morning and Evening Peak Period

(6 to 9 am, 4 to 7 pm)

Speed Frequency Distribution

Speed Range Cumulative RaDfle (mph) Frequencv (•/.) Freauencv (•/e)

0 12.7 12.7 OtoS 4.S 17.2 s to 10 2.8 20.0 10 to lS 3.4 23.4 lS to20 4.2 27.7 20to2S S.8 33.4 2S to 30 8.0 41.4 30to 3S 13.3 54.7 3S to40 18.6 73.3 40to4S lS.l 88.4 4S to so 7.3 9S.1 SO to SS 3.S 99.1 SS to60 0.8 100.0 60to6S 0.0 100.0 6Sto10 0.0 100.0 10to1S 0.0 100.0 1S to80 0.0 100.0

>80 0.0 100.0

40

35 .... 30 "fF. ._. t- 2'

1~ 10

' 0 0 "' a

0

.~

Distribution of Speed

0 "' !iS ~ ~ "' * ~ &! ~ i ... ... "' a a a a a a a a a a a "' 0 "' ~ ~ ~ "' * "' &! "' ... ... "' • "'

Speed Range (mph)

Cumulative Distribution of Speed

~-

,/'

II' /

~

/ ~

_ _..,,.-

10 20 30 40

Speed (mph)

Figure 24. Speed Distribution for Class II Arterial Streets

:a ~ I!? i i a a a a " i "' ~ I!? IO

- - - -- - -

60 70 80

54

were also examined, and found to be similar to peak period conditions. Although the researchers hadhypothesized that a significant difference would exist between peak and off-peak period operatingcharacteristics, the examination of speed distributions was unable to confirm the hypothesis.

Acceleration Distributions

Similar to the speed distribution, the acceleration distribution examined in this study is aquantitative summary of the acceleration rates [in 1 mph/sec (1.6 kph/sec) ranges] experienced duringa trip. The acceleration distributions were calculated for all freeway and arterial street routes, thendisaggregated by functional class.

The acceleration distribution for all freeway and arterial street study routes is shown in Figure 25.The figure shows that 60 percent of the total trip time on freeways and arterial streets was at 0mph/sec, or steady-state conditions. These steady-state conditions could have occurred while thevehicle was idling at a traffic signal or while the vehicle was traveling at a constant speed on afreeway or arterial route. Accelerations account for another 21 percent of the trip time, whereasdecelerations are 19 percent of the total trip. The maximum acceleration range was 2 to 3 mph/sec(3 to 5 kph/sec), and the maximum deceleration range was -4 to -5 mph/sec (-6 to -8 kph/sec). Aswith the speed distributions, the researchers hypothesized that acceleration characteristics would varyby functional class, with arterial streets exhibiting a broader range of accelerations and decelerations.

Figure 26 shows the acceleration distribution for freeway routes only. The steady-state conditionsnow account for 66 percent of the total trip time, with the maximum acceleration range at 1 to 2mph/sec (2 to 3 kph/sec) and the maximum deceleration range from -2 to -3 mph/sec (-3 to -5kph/sec). The freeway distribution shows more time spent in steady-state conditions (traveling atconstant speed) with less severe acceleration and deceleration rates.

Figures 27 and 28 illustrate the acceleration distributions for Class I and II arterial streets,respectively. About 60 percent of total trip time for Class I arterials is steady-state, whereas only 54percent is steady-state for Class II arterials. The maximum acceleration and deceleration rates arealso comparable as well.

In summary, the acceleration distributions for different functional classes where different but notnecessarily distinctive. The floating car method of data collection may have affected the trueacceleration characteristics of different roadway types, thereby smoothing the potentialacceleration/deceleration differences between freeways and arterial streets. The similarity of thedistributions for different functional classes may also indicate that, indeed, only small difference existbetween acceleration characteristics for different functional roadway classes.

Vl Vl

FREEWAYS AND ARTERIAL STREETS Morning and Evening Peak Perlocl

(Uo 9 am, 4 to 7 pm)

Acceleration Frequency Distribution

Speed Ranp Cumulative Rarure (mnh\ - (%) - (%) ...

<-9 0.0 0.0 -9 to-8 0.0 0.0 -8 to-7 0.0 0.0 -7 to-6 0.0 0.0 -6to-S 0.0 0.0 -S to-4 0.1 0.1 -4 to-3 0.1 0.2 -3 to-2 1.4 1.7 -2 to-I 4.3 S.9 -I toO 12.7 18.7

0 60.0 78.7 Oto I IS.3 94.0 I to2 s.o 99.0 2to3 0.9 100.0 3to4 0.0 100.0 4toS 0.0 100.0 Sto6 0.0 100.0 6to7 0.0 100.0 7to8 0.0 100.0 8to9 0.0 100.0

>9 0.0 100.0

Distribution or Acceleration

70-r-~~~~~~~~~~~~~~~~~~~~~~~~--.

60+-~~~~~~~~---

i so+------------._,

J :~+------------20+------------

10+-~--------~

O+---+--+-----+--+---+--f--. ... ~ ~ ~ ~ ~ ~ ~ ~ ~ 0 0 - N ~ • ~ ~ ~ M ~ ~ v s s s s s s s s s s s s s s s s s s

100

90

i 80 ._,

1: .J 40

~: 10

0

~ ~ ~ ~ ~ ~ ~ ~ ~ 0 - N ~ • ~ ~ ~ M

Acceleration Range (mphlaec)

Cumulative Distribution or Acceleration . . ~ . . . y- - - - - - -

/ T I I I I I

/' . . . - ~ - - -

~ ~ ~ ~ ~ 4 ~ ~ ~ 0 1 2 3 4 5 6 7 8 9 Acceleration (mph/aec)

Figure 25. Acceleration Distribution for Freeways and Arterial Streets

Vl 0\

FREEWAYS Mornlq and Evenlni Peak Period

(6 to 9 am, 4 to 7 pm)

Acceleration Frequency Distribution

Speed Raap Cumulative Rane (mnh\ - (%) (%) ...

<-9 0.0 0.0 -9 to-8 0.0 0.0 -8 to-7 0.0 0.0 -7 to-6 0.0 0.0 -6 to-S 0.0 0.0 -s to-4 0.0 0.0 -4 to-3 0.0 0.0 -3 to-2 0.3 0.3 -2 to-I 2.0 2.3 -1 too 14.l 16.4

0 66.0 82.S Oto l lS.S 97.9 l to2 2.0 99.9 2to3 0.1 100.0 3to4 0.0 100.0 4to5 0.0 100.0 5to6 0.0 100.0 6to7 0.0 100.0 7to8 0.0 100.0 8to9 0.0 100.0

>9 0.0 100.0

70

60

i so ......

r 30

20

10

0

100

90

i ...... 80

J: j :

10

0

°" ., "':'

v s s °"

.,

- -- -

Distribution of Acceleration

'9 '? " '1 '1 '7 0 0 - "' ... " "' IO "'" 00 0\ ~ s s s s s s s s s s s s s s s s "':' '9 '? " '1 '1 '7 0 - "' ... .... "' IO "'" 00

Acceleration Range (mph/sec)

Cumulative Distribution of Acceleration

- - - - - - -/' - - - - - -

,/

I I I I I I ~

/ --2 3 4 s 6 7 8 9

Acceleration (mph/sec)

Figure 26. Acceleration Distribution for Freeways

CLASS I ARTERIAL STREETS Morning IUld Evenln& Peak Period

(6 to 9 am, 4 to 7 pm)

Acceleration Frequency Dlatrlbudon

Speed Ranae Cumulative Ran.. (mnh\

-,.~\ - (%) JI -·

<-9 0.0 0.0 -9to-8 0.0 0.0 -8 to-7 0.0 0.0 -7 to-6 0.0 0.0 -6to-S 0.0 0.0 -S to-4 0.1 0.1 -4 to-3 0.2 0.3 -3 to-2 2.0 2.3 -2 to-1 4.9 7.3 -1 too 11.8 19.1

0 58.3 77.4 Oto 1 15.1 92.4 1 to2 6.3 98.7 2to3 1.2 100.0 3to4 0.0 100.0 4toS 0.0 100.0 Sto6 0.0 100.0 6to7 0.0 100.0 7to8 0.0 100.0 8to9 0.0 100.0

>9 0.0 100.0

60

so ,......

e 40

1~ 20

10

0

°" ., I";' "i

v s s s °"

., I";'

JOO

,... 90 e 80

J: I :

JO

0 --

Dlatrlbudon of Acc:elentlon

":' "t '? ~ '7 0 0 - N "' • .,.. IO .... 00 0\ ~ s s s s s s s s s s s s s s s "i ":' "t '? ~ '7 0 - N "' • .,.. IO .... 00

Acceleration Raap (mph/sec)

Cumulative Distribution of Acc:elentlon

- - - - - -,,.,.- - - - - -/ ,

I I I I I

/ ~ -..... -

2 3 4 s 6 7 8 9

Acceleration (mph/sec)

Figure 27. Acceleration Distribution for Class I Arterial Streets

VI 00

CLASS II ARTERIAL STREETS Morning and Evening Peak Period

(6 to hm, 4 to 7 pm)

Acceleration Frequency Distribution

Speed Raap Cumulative a.- lmnhl - 19/el

- (%\ ... <·9 0.0 0.0

-9to -8 0.0 0.0 -8 to -7 0.0 0.0 -7to-6 0.0 0.0 -6to -S 0.0 0.0 -s to-4 0.1 O.l -4 to-3 0.3 0.4 -3 to-2 2.6 2.9 -2 to -l 6.7 9.6 -1 toO 11.7 21.3

0 S3.6 74.9 Oto l lS.3 90.2 l to2 8.0 98.2 2to3 1.7 99.9 3to4 0.1 100.0 4toS 0.0 100.0 Sto6 0.0 100.0 6to7 0.0 100.0 7to8 0.0 100.0 8to9 0.0 100.0

>9 0.0 100.0

70

60

i so ...... r 30

20

10

0

100

90 i ...... 80

J: j :

10

0

Distribution of Acceleration

~ ., ":' ~ "';' .... '? <1 "T 0 0 - N "' .... "' IO "" 00 0\ ~ v a a a a a a a a a a a a a a a a a a

~ ., ":' ~ "';' .... '? <1 "T 0 - N "' .... "' IO "" 00

Acceleration Range (mph/sec)

Cumulative Distribution of Acceleration

. . . . . . ~ - - - - -

/ ~ I I I I

1 /

~ - - -~ ~ ~ ~ ~ 4 ~ 4 ~ 0 2 3 4 s 6 7 8 9

Acceleration (mph/sec)

Figure 28. Acceleration Distribution for Class Il Arterial Streets

59

3-Dimensional Speed-Acceleration Distributions

The most useful distribution in the development of emissions and related modeling tools is thespeed-accelerati on distribution, which shows the typical acceleration rates for various speed ranges.The speed-acceleration distribution combines the speed distributions (Figures 21 through 24) withthe acceleration distributions (Figures 25 through 28) to form a 3-dimensional distribution. Thesedistributions provide a quantitative summary of the speeds and accelerations on a second-by-secondbasis.

Figure 29 illustrates the 3-dimensional speed-acceleration distribution for all freeway and arterialstreet routes in this study. The figure shows a large “peak” of the data at 60 mph (97 kph), withanother smaller “peak” at 0 mph (steady-state). The acceleration and deceleration ranges close to0 mph/sec can also be seen on the figure as small “ridges.” Table 6, shown on the facing page ofFigure 29, shows the matrix of the percent of total trip time in various speed and acceleration rangesused to create Figure 29.

The 3-dimensional speed-acceleration distribution for freeway routes only is shown in Figure 30.The figure clearly show the large proportion of travel that occurs in the 55 to 60 mph (89 to 97 kph)range with a small range in accelerations. Table 7 shows the matrix of total trip time in various speedand acceleration ranges used to create Figure 30. Figures 31 and 32 show the speed-accelerationdistributions for Class I and II arterials, respectively. Like the speed distributions discussed earlier,there is a marked difference between functional classes. Class I and II arterial streets show smallerbut comparable speed “peaks” at 0 mph, or idle time. Tables 8 and 9 provide the percent of total triptime in various speed and acceleration ranges for Figures 31 and 32, respectively.

25

-'#. -i = l

Speed (mph)

Acceleration (mph/sec) 8 10

Figure 29. 3-Dimensional Speed-Acceleration Distribution for Freeways and Arterial Streets

ACCELERATION RANGE (MPH/SEC)

SPEED LESS -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 MORE RANGE THAN TO TO TO TO TO TO TO TO TO TO ZERO TO TO TO TO TO TO TO TO TO TO THAN (MPH) -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 ACCEL 1 2 3 4 5 6 7 8 9 10 10

0 MPH . . 0.004 0.067 0.273 6.426 . . 0- 5 0.001 0.009 0.017 0.135 0.419 0.361 0.816 0.437 0.251 0.030 . 5-10 0.001 0.008 0.020 0.183 0.329 0.135 0.386 0.087 0.211 0.272 0.015 0.002

10-15 . 0.002 0.015 0.023 0.210 0.347 0.163 0.517 0.144 0.388 0.220 0.006 0.003 15-20 0.001 0.002 0.013 0.026 0.238 0.339 0.214 0.702 0.266 0.542 0.178 0.002 0.001 20-25 0.001 0.011 0.024 0.212 0.422 0.306 1.065 0.501 0.694 0.127 0.003 . 25-30 0.001 0.008 0.016 0.190 0.485 0.463 1.488 0.782 0.784 0.040 0.001 0.001 30-35 0.006 0.008 0.135 0.536 0.840 2.758 1.282 0.744 0.021 . 35-40 0.003 0.005 0.078 0.464 1.315 4.607 1.824 0.570 0.014 0.002 0.001 . 40-45 0.001 0.004 0.037 0.350 1.417 5.870 1.916 0.338 0.009 0.002 0.002 0.001 45-50 0.002 0.014 0.186 1.467 6.253 1.726 0.187 0.003 0.001 50-55 0.005 0.125 1.481 6.517 1.622 0.117 0.002 55-60 0.001 0.122 2 .605 11. 974 2.301 0.105 60-65 0.079 1.586 9.783 2.200 0.079 65-70 0.116 0.857 0.210 0.012 70-75 0.008 0.002 75-80 80-85 85-90

> 90 . . . . . . . . . . . ALL 0.001 0.008 0.074 0.145 1.442 4.270 12.742 60.037 15.300 5.022 0.916 0.031 0.011 0.001

0\ -Table 6. Speed-Acceleration Matrix for Freeways and Arterial Streets

25

-~ -~ to

5 90

:I

!" l°i'c

Speed (mph)

Acceleration (mph/sec)

Figure 30. 3-Dimensional Speed-Acceleration Distribution for Freeways

ACCELERATION RANGE (MPH/SEC)

SPEED LESS -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 MORE

RANGE THAN TO TO TO TO TO TO TO TO TO TO ZERO TO TO TO TO TO TO TO TO TO TO THAN

(MPH) -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 ACCEL 1 2 3 4 5 6 7 8 9 10 10

0 MPH 0.009 0.039 0.382 0- 5 0.002 0.002 0.023 0.092 0.106 0.241 0.120 0.047 0.002 5-10 . 0.001 0.003 0.030 0.107 0.110 0.321 0.104 0.107 0.029 0.002 0.001

10-15 0.001 0.005 0.006 0.032 0.120 0.152 0.414 0.174 0.145 0.024 0.001 15-20 0.003 0.003 0.036 0.137 0.211 0.595 0.249 0.175 0.017 0.001 0.001 20-25 0.002 0.001 0.028 0.166 0.305 0.897 0.372 0.191 0.018 25-30 0.002 0.003 0.029 0.151 0.275 0.912 0.369 0.193 0.008 30-35 0.001 0.001 0.018 0.151 0.294 0.966 0.420 0.160 0.003 35-40 0.001 0.001 0.011 0.123 0.313 0.998 0.449 0.120 0.002 40-45 0.002 0.002 0.016 0.141 0.316 1.146 0.465 0.110 0.001 45-50 0.001 0.001 0.001 0.019 0.148 0.617 2.041 0.703 0.121 0.001 50-55 0.001 0.011 0.198 1.658 6.212 1.572 0.155 0.004 55-60 0.002 0.278 5.681 25.713 4.763 0.218 0.001 60-65 0.185 3. 766 23.151 5.190 0.180 65-70 0.001 0.276 2.043 0.502 0.028 70-75 0.019 0.004 75-80 80-85 85-90

> 90 . ALL 0.002 0.020 0.024 0.255 2.007 14.119 66.051 15.456 1.950 0.110 0.003 0.003

Table 7. Speed-Acceleration Matrix for Freeways

25

-'I. -

Speed (mph)

Acceleration (mph/sec) 10

Figure 31. 3-Dimensional Speed-Acceleration Distribution for Class I Arterial Streets

ACCELERATION RANGE (MPH/SEC)

SPEED LESS -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 MORE RANGE THAN TO TO TO TO TO TO TO TO TO TO ZERO TO TO TO TO TO TO TO TO TO TO THAN (MPH) -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 ACCEL 1 2 3 4 5 6 7 8 9 10 10

0 MPH 0.006 0.079 0.345 9.248 . . o- 5 . 0.001 0.004 0.009 0.015 0.168 0.473 0.431 1.006 0.501 0.294 0.034 . . 5-10 0.001 0.001 0.007 0.019 0.218 0.376 0.095 0.330 0.028 0.174 0.359 0.017 0.002

10-15 0.002 . 0.004 0.017 0.027 0.242 0.384 0.106 0.383 0.067 0.372 0.297 0.003 0.004 15-20 . 0.001 0.002 0.016 0.040 0.294 0.328 0.111 0.464 o. 140 0.571 0.240 0.002 20-25 0.001 0.002 0.017 0.038 0.278 0.404 0.106 0.586 0.255 0.771 0.189 0.006 25-30 . 0.002 0.019 0.020 0.276 0.494 0.239 0.894 0.465 0.999 0.052 0.002 30-35 0.001 0.001 0.013 0.019 0.234 0.584 0.454 1.561 0.910 1.003 0.027 35-40 0.001 0.009 0.011 0.172 0.674 1 .031 3.394 1.788 0.888 0.020 0.001 . . 40-45 0.002 0.011 0.081 0.664 2.340 9.195 3.131 0.630 0.017 0.002 0.001 0.001 45-50 0.005 0.016 0.336 3.387 14.975 3.792 0.377 0.004 0.001 0.001 50-55 0.001 0.128 2.428 12.088 2.951 0.141 0.001 0.001 0.001 55-60 0.018 0.716 3.887 0.969 0.041 60-65 0.004 0.029 0.284 0.088 0.011 65-70 0.002 70-75 75-80 80-85 85-90

> 90 . . . . . . . . . . . . ALL 0.004 0.003 0.017 0. 109 0.205 1.986 4.946 11.818 58.297 15.085 6.245 1.240 0.034 0.008 0.003

Table 8. Speed-Acceleration Matrix for Class I Arterial Streets

25

-'I--90

Speed (mph)

Acceleration (mph/sec) 8 10

Figure 32. 3-Dimensional Speed-Acceleration Distribution for Oass Il Arterial Streets

ACCELERATION RANGE (MPH/SEC)

SPEED LESS -10 -9 -8 -7 -6 -5· -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 MORE RANGE THAN TO TO TO TO TO TO TO TO TO TO ZERO TO TO TO TO TO TO TO TO TO TO THAN (MPH) -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 ACCEL 1 2 3 4 5 6 7 8 9 10 10

0 MPH 0.001 0.008 0.132 0.516 11.982 . . 0- 5 0.001 0.017 0.037 0.254 0.800 0.634 2.014 0.796 0.479 0.063 . . . 5-10 . 0.018 0.043 0.354 0.580 0.199 0.546 0.113 0.376 0.517 0.032 0.002 0.001

10-15 . 0.002 0.025 0.043 0.414 0.611 0.225 0.757 0.166 0.715 0.414 0.016 0.004 0.001 15-20 0.001 0.001 0.004 0.025 0.044 0.456 0.609 0.301 1.031 0.390 0.995 0.336 0.004 20-25 . 0.003 0.016 0.040 0.397 0.771 0.470 1.669 0.868 1.284 0.218 0.003 25-30 0.001 0.001 0.008 0.028 0.329 0.911 0.888 2.716 1.575 1.375 0.071 0.001 0.002 30-35 0.008 0.008 0.206 0.998 1.863 6.060 2.704 1.291 0.040 . . 35-40 0.001 0.007 0.087 0.734 2.849 10.285 3.639 0.895 0.025 0.004 0.003 . . 40-45 0.002 0.029 0.365 2.094 9.296 2.808 0.393 0.014 0.004 0.004 0.001 0.001 45-50 0.001 0.005 0.113 1.001 4.597 1.366 0.118 0.004 0.001 0.003 50-55 0.001 0.027 0.468 2.330 0.591 0.049 55-60 0.002 0.113 0.553 0.160 0.008 60-65 0.001 0.015 0.004 0.001 65-70 70-75 75-80 80-85 85-90 > 90 . . . . . . . . . . . . . ALL 0.001 0.001 0.001 0.011 0.118 0.254 2.540 6.653 11.622 53.851 15.180 7.979 1.702 0.065 0.018 0.003 0.001

Table 9. Speed-Acceleration Matrix for Class II Arterial Streets

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Table 10 summarizes the percent of time that the data collection vehicles were operating in themodes of idle, steady-state (cruise), acceleration, and deceleration for each roadway functional classbased upon the information contained in Tables 6 through 9. A review of Table 10 illustrates that thearterial streets experienced more time spent in idle, acceleration, and deceleration modes and less timein steady-state conditions than observed on the freeway segments. The research team believes thatthese characteristics are explained by the stop-and-go traffic flow due to signalized corridors.

Table 10. Percent of Time Spent in Each Operating Mode by Roadway Functional Class

VehicleOperating

Mode

Roadway Functional Class

Freeways andArterial Streets Freeways Class I

ArterialsClass II

Arterials

Idle 6.4 0.4 9.2 12.0

Steady-state 53.6 65.7 49.1 41.9

Acceleration 21.3 17.5 22.6 24.9

Deceleration 18.7 16.4 19.1 21.2

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CHAPTER V. CONCLUSIONS & RECOMMENDATIONS

This chapter presents the conclusions and recommendations based upon the findings describedin Chapter Four. The discussion begins with conclusions drawn from findings of statistical analysesthat were applied to the fuel consumption estimation models. The discussion continues with therelationships that were discovered when comparing the speed and acceleration characteristics,geometric characteristics, and fuel consumption estimations. The potential uses for theserelationships and prediction equations in planning applications is explained. The content andusefulness of the data base of operating conditions in the Houston, Texas area is then evaluated. Therecommendations conclude with discussion of the usefulness of the DMI for data collection and theneed for future research in the areas encountered throughout this research study.

Fuel Consumption Model Comparisons

The findings presented in Table 3 show the significance that the detailed data set has when appliedto fuel consumption analyses. Significance was determined for many of the functional classes whencomparing fuel consumption estimation based upon both the average and instantaneous methods.From these results it can be concluded that, in general, significant differences can be expected whenapplying a detailed data set such as that produced by a DMI in a travel time run to the estimation offuel consumption. It is important to note that when reviewing the results of Table 3, it is imperativeto study Table 2 to verify the conditions (e.g., speed range, functional classification) for which amodel is valid.

Regression and Correlation Analyses

Development of regression equations between speed and acceleration characteristics, geometriccharacteristics, and traffic flow variability was performed in the study. The regression equations didnot yield an R2 higher than 0.35 when comparing any combination of the geometric characteristicswith the speed and acceleration characteristics. Signal density and/or driveway density were foundto be significant for most of the conditions evaluated with the aid of ANOVA procedure using acritical level of significance of 5 percent.

The evaluation of traffic flow variability performed by regressing average speed with theindependent variables of speed and acceleration characteristics resulted in the R 2 values shown inTable 5. These values clearly indicate that the CV of speed was a more explanatory indicator of thetrip variability when regressed with average speed (i.e., yielded higher R 2 values) than the standarddeviation of the speed or the acceleration noise.

Several factors that could account for the findings were considered. The true effect of thedriveway density may not be reflected in the travel time data since the floating car method wasutilized. It is possible that the influence of driveways on the right-most lane may not be included intoa travel time run that includes a driver passing as many vehicles as pass the driver. In addition, travelvariability induced by traffic signals is difficult to quantify. Peak and off-peak conditions often have

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different signal timings to optimize traffic flow. Average speeds, and motorist delay, will varydepending upon when motorists arrive at the traffic signal. The location of the travel time run wasalso found to be of importance when measuring the coefficient of variation of the speed. If a traveltime run is performed immediately prior to a traffic signal or lane-drop on a freeway, the results willdiffer compared to a run performed in an uninterrupted flow section. Unfortunately, the data basedid not contain a variable relating to the section definition (e.g., before or after a traffic signal) of thetravel time run, but this would be an interesting element for further study. Finally, it was found that,although acceleration noise is a better measure to determine the operating characteristics of a sectionthan average speed, there is still a significant portion of the instantaneous travel characteristics (e.g.,speed, acceleration) that are lost when aggregating over an entire section.

It was also found that peak and off-peak conditions seemed to operate very similarly (see Table4). Although the peak and off-peak conditions were carefully selected to define these ranges, trafficoperation did not appear significantly different for the two conditions. This apparent discrepancy islikely due to the inclusion of freeway routes into the peak-period analyses that are operating nearfree-flow conditions during the peak-period. These freeways are those that are relatively far from theurban area. A similar concern exists for the arterial segments as well. In addition, the arterialsections could have similar operating characteristics due to the signal timings in the peak or off-peakperiods. Roadway Operating Characteristics: Speed and Acceleration

This study examined the operating characteristics of freeway and arterial street routes in Houston,with a specific emphasis on the speed and acceleration distributions. The travel time/speed datacollected for this study showed a significant difference in the speed distributions for differentfunctional classes (e.g., freeways, Class I arterials, Class II arterials). The result confirmed theobvious but also provided specific quantitative evidence of the differences between classes.

The acceleration distributions for different roadway functional classes were less distinctivebetween functional classes, indicating that acceleration characteristics were similar between freewaysand arterial streets. The floating car data collection technique used in this study may have“smoothed” some of the acceleration differences between freeways and arterials streets, so adefinitive statement cannot be made. A data collection method that obtains a representative sampleof the range of operating characteristics of motorists (e.g., instrumenting random vehicles) wouldlikely provide a more distinct difference between functional classes.

The study also produced three-dimensional speed-acceleration distributions that were typical ofthe freeway and arterial street system in Houston, Texas. The speed-acceleration distributions doexhibit significant differences between freeways and arterial streets, mainly with respect to speeddifferences. The speed and acceleration data set used to produce these summary distributions isexpected to be useful in validating the next generation of emissions models that are currently in thedevelopmental stages.

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The distribution data contained in this report was collected during the peak traffic period (6 to9 a.m. and 4 to 7 p.m.). Data during the off-peak traffic period at mid-day (10 a.m. to 3 p.m.) werealso collected and analyzed, with the analyses indicating no significant difference between the peakand off-peak data. Although it was expected that the peak and off-peak data would be distinctive,the study could not determine why these distinctions were not apparent.

DMI Technology for the Data Collection Effort

The distance measuring instrument was found to be an invaluable tool for performing this study.The instantaneous data points provided at every ½-second yield a data set that allows for detailedspeed and acceleration information. From this data, the significance of the instantaneous data set onestimating fuel consumption could be evaluated, regression equations were studied, and trafficoperating distributions could be prepared. The ASCII format of the output was easily manipulatedfor analyses and evaluation. Data collection methods, such as a DMI or global positioning systems,that produce these instantaneous speed and acceleration data will continue to prove to be useful inthe transportation community for application to many transportation concerns such as air quality andtraffic operations.

Future Research Needs

The study identified some areas where additional research is needed. The first is the need for thedevelopment of mobile source emissions models that can incorporate acceleration characteristics.Research of this kind is currently in progress.

There is a need for better characterization of acceleration characteristics for different roadwayfacilities. Characterizing acceleration characteristics by percent of time in a particular drivingcondition (e.g., idle, cruise, acceleration, or deceleration) is useful for the development of appropriatedriving cycles that replicate these conditions.

There is much variability both along a travel time run and between travel time runs along sections.Additional research is needed that focuses on determining appropriate methods to quantify thisvariability in a consistent and meaningful manner (e.g., separate the driver and traffic influences).

In general, the DMI and similar technologies for data collection, allow for larger amounts ofdescriptive data that has not been possible in the past. Research must now begin to focus onperformacne measures that are best utilized (e.g., coefficient of variation) for quantifying theaggregation of this data for transortation-related concerns.

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