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SHRP 2 Project L35 - Local Methods for Modeling, Economic Evaluation, Justification and

Use of the Value of Travel Time Reliability in Transportation Decision Making

Amplified Research Plan 5-1

Section 5.Amplified Research Plan

Introduction The University of Maryland and Maryland State Highway Administration Team’s (UMD-SHA Team) research plan for project SHRP 2 L35 is designed to introduce the economic value of travel time reliability (VTTR) into practice in the State of Maryland, test it, and provide other public agencies with an example step-by-step procedure on how to incorporate and justify the VTTR into decision making. The UMD-SHA Team has assembled a unique group of experienced individuals led by the Maryland State Highway Administration (SHA) that, collectively, will ensure the successful delivery of each L35 objective. Highlights of the unique characteristics and capabilities of the UMD-SHA Team include:

An SHA project management team led by SHA representatives of planning, operations, and traffic and safety that will ensure agency involvement in all technical and decision making aspects of this research with respect to putting a VTTR performance measure into practice.

Continuous involvement of SHA’s Mobility Council which will ensure decision maker input throughout this project. The Mobility Council is chaired by SHA’s Director of Planning and Preliminary Engineering and includes representation of planners in the Maryland Department of Transportation, the Washington and Baltimore Metropolitan Planning Organizations, as well as other SHA Departments including operations, safety, traffic engineering, and maintenance.

A research project supporting cast led by the University of Maryland’s Center for Advanced Transportation Technology (CATT) together with the National Center for Smart Growth (NCSG) as well as the private sector companies Cambridge Systematics and Dunbar Transportation Consulting (a certified DBE).

SHA progress towards including reliability in transportation decision making as demonstrated by development of its first ever Maryland Statewide Mobility report which includes reporting of the reliability metric Planning Time Index on the entire Maryland State highway system.

SHA development, in partnership with the NCSG, the Maryland Statewide

Transportation Model that includes the entire state of Maryland to aid transportation planning and travel demand decision-making.

Access to large data sets that can be used to develop a VTTR performance measure based decision making process and model development including third party probe data through INRIX, HPMS data, side-fired radar and acoustic based traffic monitoring device volume and speed data (public and privately owned), Bluetooth probe data, and traffic incident data. The majority of this data is easily




accessible for analysis purposes through the Regional Integrated Transportation Information System (RITIS) developed by CATT in close partnership with SHA.

Following is a summary of how the UMD-SHA Team will use its collective abilities to achieve each L35 project objective.

Select and defend a value or range of values for travel time reliability for the Maryland State Highway network. Local stated/revealed preference data already collected in Maryland will be used to estimate utility functions taking into account the impact of travel time variability. In the context of mode/route choice utility, the effect of adopted travel time variability measure compared with the effect of travel cost will result in an estimate of the value for travel time reliability. Different values for different trip purposes and travelers with different socio-economic characteristics will be estimated. The range of estimated local VTTR will be verified against VOT and VTTR obtained locally and in other jurisdictions using best practices.

Use the VTTR in the Maryland SHA project development process to prioritize operational and capital improvements and determine if (and how) the ranking of projects changes due to the addition of VTTR. Savings associated with travel time variability reduction will be incorporated into SHA ‘s life-cycle benefit-cost analysis procedure. VTTR will serve as an input parameter to transform anticipated savings in travel time variability into equivalent monetary values. Savings in terms of travel time variability, in turn, will be estimated using appropriate data, models, and procedures that are currently available or will become available to Maryland SHA.

Report for the benefit of others the step-by-step process used to develop, justify, apply, and assess the use of VTTR in the Maryland SHA project evaluation and decision process. The process used to develop, justify, apply, and assess the use of local VTTR in the Maryland SHA project evaluation and decision process will be documented and shared with other interested agencies/individuals. Reactions of senior SHA officials, with both technical and policy interests, to the revised selection process and inclusion of VTTR will be noted. Concerns regarding any potential impacts of the selected VTTR on ranking of certain types of projects will be highlighted. The project deliverables will serve as a comprehensive guideline for others to learn from our lessons and to be better prepared for the challenges /opportunities adopting a new local VTTR and revising their evaluation process to account for that will bring about.




Research Approach

Task 1: Describe how your established processes to prioritize operational and capital improvements (baseline approach) meet the special evaluation criteria for this project. This includes analytical methods used to obtain performance metrics and a prioritization/evaluation process.

Through the adoption of various measurement and reporting methodologies and tools, Maryland SHA has been able to quantify current mobility and reliability conditions and trends on its highways. This provides a basis for examining how those variables change with the evolving transportation environment, and to assess how the agency’s actions can efficiently impact the users of the State’s transportation system. This also gives Maryland SHA the ability to develop better informed decisions regarding the use of its limited resources, identify critical transportation issues before they develop into more serious problems, and provide measurement of its success.

Task 1.1: Describe SHA’s established process to prioritize operational and capital improvements

Maryland SHA has a benefit cost analysis (BCA) process in place to identify and prioritize its operational and capital improvements. Under this task a comprehensive review of SHA’s baseline process will be conducted. The baseline process will be documented in the context of recent project evaluations performed in the agency. These projects will include both construction and operational types of investments.

The following is a brief overview of the baseline SHA process asUMD researches have come to understand in the course of several projects as well as based on their discussions with senior decision makers at SHA.

Figure 1 illustrates different stages of SHA’s BCA process. In the BCA process project benefits and costs over the anticipated life time of the project are estimated. Typically SHA considers a 20 year horizon for most construction projects. The considered life time of operational projects vary significantly and is dependent on the type of project. The established BCA process results in a benefit cost ratio (BCR) and internal rate of return (IRR) associated with the project. Then projects can be ranked based on these results.It should be noted that although user benefit analysis is an important criteria, local priorities and often qualitative measures are also accounted for in making project related decisions at SHA. This is done to ensure that SHA, as a public agency, is investing its scarce resources in projects which bring about the maximum societal benefits.

Figure 1 shows that benefits associated with transportation projects are primarily measured in the form of savings. The SHA process already recognizes travel time savings (both savings in average and reliability) as part of the benefits. Average travel time savings are calculated using traffic volume affected, average travel time improvement, and value of time (VOT). The first two factors are either measured or estimated using traffic models, but VOT is traditionally estimated based on a utility function calibrated in the context of discrete choice models.




Figure 1. SHA’s existing benefit cost analysis process

The other items accounted for in the benefits category include fuel cost and safety related savings. In SHA’s process, fuel cost savings are estimated based on the average fuel economy of the vehicles operating in pre and post project conditions. Equations 1 and 2 are the regression equations used by SHA to estimate average fuel economy of vehicles. These equations arebased on fuel efficiency data from the EPA/FHWA’s MOVES model.

Passenger Car Fuel Economy . speed . (speed) .1 (1)

Truck Fuel Economy 1. ln speed - . (2)

Benefits of projects from a congestion relief standpoint were developed using already established user cost savings procedures. SHA Business Plan objectives require at least a 5% reduction in delay due to the implementation of its mobility driven projects. This reduction in delay leads to savings in automobile and truck travel time, fuel and emission costs, etc. Following some of the recent trends of other transportation agency

Costs

Initial costs Continuing costs End of project costs

Analysis

Life cycle (20 years) Annual inflation rate Annual discount rate

Benefits Travel time savings

Travel time reliability savings

Fuel cost savings

Safety related savings

Environmental impacts savings

Economic/community development

Outcome

Benefit cost ratio (BCR)

Internal rate of return (IRR)

Priority/ Ranking




practices, particularly in European nations (where reliability benefits are accounted for as a percent of congestion reduction related savings), SHA added 75% of the congestion related savings as reliability related savings to overall project benefits. Crash and incident data collected for the last few years were factored in to develop safety benefits from potential project implementation.

Table 1 summarizes the basic parameters used in SHA’s BCA process to estimate monetary value of travel time savings, travel time reliability savings, and fuel cost savings.

Table 1. Parameters used by SHA in project benefit estimation (2011 values)

Saving Type Parameter Unit Categories SHA Value

Travel time VOT $/hr

Passenger 28.82

Truck driver 20.86

Cargo 45.40

Travel time reliability VTTR $/hr

Passenger 21.62

Truck driver 15.65

Cargo 34.05

Fuel cost $/gal Gasoline 3.58

Diesel 3.85

On the cost side, concepts were laid out to develop planning level cost estimates. Preventive maintenance costs and major rehabilitation costs were incorporated to perform a 20-year life cycle cost analysis. Table 2 illustrates the sub-items under three major cost items considered in SHA’s baseline procedure. Initial cost includes acquisition, planning, design, engineering, and construction costs. Continuing cost takes into account operational, maintenance, and rehabilitation costs. Lastly, end of project cost is comprised of its residual value, and its salvage value (considered as negative cost), and any other close-out costs incurred by the agency.

SHA conducts various “Before and After” studies for construction projects using field measured data collected specifically for the projects, INRIX data, and simulation models to demonstrate to what extent the projected improvements in performance (Level of Service, Delay, etc.) actually occurred.

Table 2.Items considered by SHA in project cost estimation

Cost Item Categories

Initial cost Acquisition




Planning

Design

Engineering

Construction/Implementation

Continuing cost

Operations

Maintenance

Rehabilitation

End of project cost

Residual value

Salvage value

Close-out

Task 1.2: Describe how a value of travel time is already established in the baseline approach

For the purposes of project evaluation, Maryland SHA has established values of travel time in place. Under this task a comprehensive review of the methodology by which these values are adopted and updated on an annual basis will be provided. Also, to put existing values into context, value of times used in the existing Maryland baseline approach will be compared with the value of times used in neighboring states and other states nationwide. Sensitivity of the baseline approach to the adopted VOT will be examined and case studies will be provided.

It is presumed that the value of time (VOT) varies with the trip purpose and socio-economic attributes of the traveler. In the baseline approach, thevalue of travel time for automobile passengers, truck drivers, and freight cargo are declared. These values are based on a series of studies1 that are primarily sponsored under SHA’s CHART program to evaluate economic value of its incident management initiatives.

In the most recent study of CHART incident management program benefits which reports on 2011 values, the passenger unit value of time is based on U.S. Census Bureau data. A truck driver’s value of time is based on information from the Bureau of Labor Statistics, the US DOT, and FHWA’s highway economic requirements system HERS2). Similarly, the cargo value of time is based on a study by the Texas transportation institute (TTI), a study by Levinson3, and a study by De Jong4.Table 1 showsthe current values of travel time used by Maryland SHA in its baseline approach.

1 Chang, G.-L., Performance Evaluation of CHART: An Incident Management Program, Report

prepared for Maryland State Highway Administration, University of Maryland, 2011. 2http://www.fhwa.dot.gov/infrastructure/asstmgmt/hersindex.cfm

3 Levinson, D., and B. Smalkoski, Value of Time for Commercial Vehicle Operators in Minnesota.

TRB International Symposium on Road Pricing, 2003. 4 De Jong, G., Value of Freight Travel Time Savings, in Hensher, D.A. and K.J. Button (eds.):

Handbook of Transport Modeling, Elsevier, 2000.

http://www.fhwa.dot.gov/infrastructure/asstmgmt/hersindex.cfm




Task 1.3: Describe how a life-cycle benefit/cost analysis framework is in place in the baseline approach

Maryland SHA has a life-cycle benefit/cost analysis framework in place that takes into account both estimated benefits and costs associated with a project throughout its life cycle. The life cycle of a typical project in the SHA baseline approach is considered to be 20 years. Under this task specifics of SHA’s baseline approach in terms of the project life cycle and its associated benefit and cost items will be documented.

Task 1.4: Describe data sources available to SHA to be used in calculating a reliability performance measure

Under this task speed and volume data sources available to SHA that are currently used in calculating congestion and reliability performance measures will be identified, described and documented. SHA has identified “Reliability” as a key measure of effectiveness in the current business plan (FY 2012-2015). Over the past few years, SHA has procured real-time vehicle probe based speed information to facilitate incident management, traveler information systems and 511 services.

SHA has procured the 2011 data sets for the entire state which provides speed information at 15 and 5 minutes intervals. This data set will augment the real time freeway data SHA already receives from INRIX through RITIS. Arterial reliability measurement and reporting is a key task identified under SHA FY 2013 work program. SHA uses this archived data along with other data for performance measurement, congestion and reliability analysis of its transportation infrastructure.

Using INRIX based vehicle probe data, SHA has developed congestion and reliability related measures [(Travel Time Index (TTI) and Planning Time Index (PTI)] on all the freeways and expressways in Maryland. From a congestion standpoint, percent system congested during peak hours and percent of VMT traveling in congested conditions during peak hours are two major measures of highway performance. Vehicles travelling at seventy percent of free flow speed (equivalent to TTI of 1.3) on a freeway are considered to be experiencing congestion. Level of congestion varies between light, moderate and severe. Similarly, depending on the PTI, segments of freeways are considered as highly reliable, moderately reliable and unreliable. Findings of these analyses have been summarized in a recent report on status of mobility in the state of Maryland5. A sample reliability mapas well as corridor mobility analysis produced as part of this report is presented in appendix A.

Based on and analysis of INRIX data since October 2009, SHA has been able to identify the most congested segments and most unreliable segments on its freeway/ expressway systems. SHA has also identified the list of bottlenecks based on an impact factor that considers the number of occurrences, duration and length of queue of a bottleneck. This allows SHA to report on changes in performance of the system over time and allows for

5 Maryland State Highway Administration, 2012 Maryland State Highway Mobility Report, June

2012.




an “apples-to-apples” comparison of performance between routes, regions, and scenarios. Using the congestion and reliability metrics, SHA evaluates the underlying causes of congestion: recurring or non-recurring (incident related or work zones etc.).

Task 1.5: Describe how you plan to forecast future travel time reliability measures

Under this task methods that can be used to forecast future travel time reliability measures will be identified and their performance in the context of SHA’s mobility improvement program will be evaluated.

Depending on the cause of congestion, SHA evaluates congestion relief solutions that range from higher deployment of service patrol vehicles and ITS technology to small-scale geometric improvements to major construction. SHA’s recently completed Baltimore Beltway Geometric Improvement Study is a great example of how SHA is utilizing the power of advanced datasets from INRIX, RITIS and CHART for congestion management and planning for operations related activities. SHA utilized the TTI and PTI metrics using the CATT Lab Vehicle Probe Project Suite of applications to understand the spatial and temporal extent of congestion and reliability. A measure called the Stability Index was developed to systemically identify project needs. Once the project segments were identified, VISSIM models were developed and calibrated to reflect existing peak operations and INRIX reported TTI. These models served as the basis to evaluate the local and system level impacts of low cost geometric improvements such as extending acceleration lanes, eliminating loop ramps, providing auxiliary lanes etc.

Using a performance based approach that uses vehicle probe data based congestion and reliability measures along with advanced micro-simulation models, SHA develops congestion relief solutions in a strategic manner. The process works seamlessly as opposed to a traditional hierarchical process where a project moves from planning to design to construction; these fast track projects are developed concurrently with planning, design, construction and operations folks working together from problem identification to project implementation.

The Maryland Statewide Transportation Model (MSTM) is a long-term travel demand model developed by the National Center for Smart Growth (NCSG) at UMD6. This model covers transportation and land use activities at three distinct layers: national, statewide, and MPO. Figure 2 illustrates the four step modeling approach undertaken by MSTM to model person and truck travels. In the MSTM framework it is possible to incorporate travel time variability measures into the utility of mode and route choice alternatives between each origin-destination pair. MSTM is a functional travel demand model currently used for a number of practices at SHA.

Using MSTM capabilities NCSG researchers will develop methods to evaluate the interaction between travelers’ perception of travel time un-reliability and supply side’s performance under various scenarios leading to variations in users travel time.

6 The National Center for Smart Growth, and Parsons Brinckerhoff, MSTM User’s Guide: Draft

Final Report, Report for Maryland State Highway Administration, June 2011.




Figure 2. Overview of MSTM-Phase III

It is commonly accepted that there are a limited number of basic factors that cause congestion to form. They are commonly referred to as “the seven sources of congestion”7:

1. Traffic incidents,

2. Weather,

3. Work zones,

4. Fluctuations in demand,

5. Special events,

6. Traffic control devices, and

7. Bottleneck/inadequate base capacity.

UMD CATT is charged by Maryland SHA with the task to look into root causes of congestion and lack of reliability on state facilities as is indicated by vehicle probe based speed data. One of the main outcomes of this task would be models/tools to describe the impact of traffic incidents, weather, and work zones, among other contributing factors, on travel time reliability.

7 Cambridge Systematics, Analytical Procedures for Determining the Impacts of Reliability

Mitigation Strategies, SHRP 2 Project L03, Draft Final Report, September 2011.




Currently, methodologies to forecast future travel time reliability can be divided into two groups. Prior to the SHRP 2 Reliability Program, no research existed into estimating reliability metrics based on the travel time distribution. The work that does exist from the pre-SHRP 2 period distinguishes between recurring and nonrecurring delay (typically defined as just incident delay), and then uses nonrecurring delay as an indicator of reliability.

Pre SHRPII Research

Probability Tree

Dowling developed a method for estimating recurring and nonrecurring delay for Caltrans based on a probability tree to predict the expected number and duration of incidents.8 The method is designed to be applied to a few selected facilities in a district and the results extrapolated to obtain district totals. The recurrent and non-recurrent delays for each sample facility are computed for three prototypical days (weekday, weekend, holiday) in each of the four seasons of the year (winter, spring, summer, fall). The delays computed for each prototypical day are factored to seasonal totals according to the number of days that each day represents of each season. The seasonal totals are then summed to obtain annual totals. The method requires geometric data, demand data, a collision history, frequency of maintenance and construction activities, frequency of inclement weather days, and frequency of special events. Default parameters and distributions are provided for use when local data is not available.

Binary Combinations of Conditions

The University of Florida developed a series of simple predictive equations for total travel time based on binary combinations of conditions (present/not present) for: congestion, incidents, weather, and work zones.9 The analyst then estimates the probability of each combination occurring, and a weighted total travel time is computed. This method is currently being adapted for statewide use by FDOT.

Microscopic Simulation

The University of Maryland, as part of the ongoing CHART evaluations, developed a predictive equation model based on running experiments with microscopic simulation:

Excess Delay Due to Incidents = e(-10.19 * (V )2.8 * (NLB /TNL)1.4 *(ID)1.78)

where:

TNL = Total number of lanes

NLB = Number of lanes blocked

V = Traffic volume

8 Dowling, Richard et al., Methodology for Measuring Recurrent and Non-Recurrent Traffic

Congestion, Paper 04-4473, presented at the 83th Annual Transportation Research Board Meeting, January 2004. 9 University of Florida, Travel Time Reliability and Truck Level of Service on the Strategic

Intermodal System, FDOT Contract BD-545, RPWO #48 (UF Project 00054045), April 23, 2007.




ID = Incident duration

Cambridge Systematics also developed a set of predictive equations for predicting recurring- and incident-related delay using a stochastic approach that varied both incident characteristics and demand levels. This procedure was adopted for use by FHWA’s Highway Economic Requirements HERS model. An example is:

Incident Delay for 3 directional freeway lanes = IncRate * DurFac2 * ( 1 + 3.77 * (1-SF)1.04) * 3.39E-11 * X9.04E+00 * e-3.04E-01 * X

where:

X = AADT/C ratio (0 to 18);

IncRate = incident rate factor; = (target incident rate)/(default incident rate);

DurFac = duration factor; = (target mean incident duration)/38.0;

SFac= shoulder factor;

= 1.0 for usable shoulders both sides;

= 0.5 for usable shoulders one side only;

= 0 for no usable shoulders;

Work by Cambridge Systematics

This same approach was also used by Cambridge Systematics to develop incident delay relationships for the Intelligent Transportation Systems Deployment Analysis System (IDAS) model.10 In both the HERS and IDAS models, recurring and incident delay are assigned monetary values. Recurring delay is valued at the rate established by USDOT11. Incident delay is valued at twice that rate, but the basis for the valuation is older studies from prior to 1999.12

For the Integrated Corridor Management program, Cambridge Systematics developed a scenario-based approach for use with microscopic simulation models, for a corridor-level analysis.13 The scenarios are primarily based on combinations of demand level and incident characteristics. Empirical data are used to estimate the probability of each scenario occurring, and the results of each simulation are combined via weighting.

SHRP 2

The Puget Sound Regional Council (PSRC) incorporated reliability directly in their travel demand model, using principles established in SHRP2 Project C11. His essentially amounts to a shifting of the speed-flow curves to the left, to account for the extra “impedance” caused by unreliable travel i.e., nonrecurring congestion sources .

10

http://www.fhwa.dot.gov/research/deployment/idas.cfm 11

http://bca.transportationeconomics.org/benefits/travel-time/categories-of-travel-time 12

Cohen, Harry, and Southworth, Frank, On the Measurement and Valuation of Travel Time Variability Due to Incidents on Freeways, 1999, http://ntl.bts.gov/lib/7000/7500/7598/2cohen.pdf 13

Cambridge Systematics, Integrated Corridor Management Analysis, Modeling, and Simulation Experimental Plan for the Test Corridor, FHWA-JPO-08-035EDL 14415, March 2008.

http://www.fhwa.dot.gov/research/deployment/idas.cfm

http://bca.transportationeconomics.org/benefits/travel-time/categories-of-travel-time

http://ntl.bts.gov/lib/7000/7500/7598/2cohen.pdf




Because the adjustment is internalized, only the value of typical travel time needs to be applied.

Second group of studies in this area are comprised of SHRP2 Reliability Program studies. Several SHRP 2 projects deal with the prediction of reliability, from a variety of resolution scales, from sketch planning to microscopic simulation:

SHRP 2 L03 – developed statistically-derived reliability equations based on empirical data. Two types of models were developed: “data poor” which required only an estimate of recurring delay and “data rich” which requires information on demand, capacity, incident characteristics, and weather conditions. The “data poor” equations have been adapted for use in Projects L , C10B, and C11.

SHRP 2 L04 – is developing a simulation-based approach to reliability estimation, using a combination of mesoscopic and microscopic models. Reliability is modeled using the scenario approach discussed above.

SHRP 2 L07 – is developing a hybrid approach for predicting reliability based on combining microsimulation experiments with the data rich equations from L03.

SHRP 2 L08 – is developing a scenario-based approach combined with macroscopic modeling methods for inclusion of reliability into the Highway Capacity Manual.

SHRP 2 L11 – did not develop reliability prediction methods, but did develop an original approach to valuing reliability based on options theory.

In particular, Data Poor and Data Rich Models proposed by L03 Project will be further considered. This is partly due to the fact that SHRP 2 program intends to promote the use of these models in post processing planning results, and also the fact that Maryland SHA seeks to be an early adopter in this area. Currently, SHRP 2 Project L33 is following this objective to validate these models. This task is going to serve as a validation of these models for Maryland SHA. The following is a brief overview of the Project L03 models and is aimed at putting the presentation of the validation effort into perspective. Data Poor Models In L03 Project, Data Poor Models for link level urban freeways are calibrated based on Atlanta and Seattle datasets. These models have a simple power form incorporating a single explanatory variable and a single parameter. (1) where, parameter for 95 and 80 percentiles of the travel time distribution is given in the following table

X RMSE

95 1.6954 16.3%

80 1.3162 7.4%




The same simple form is adopted for models aimed at capturing relationship between average travel time index and the standard deviation

( ) ( ) (2) And, the correction for recurring only mean travel times is given by ( ) (3) Although these model types are very easy to use and have shown a fairly good performance on their respective calibration datasets, their theoretical and numerical validity have to be further investigated. These validation steps will be taken using the acquired datasets to prove that the proposed models are performing with enough accuracy on different datasets other than those they have been calibrated on. This is also the case with section level urban freeway model presented as a revision in Appendix H of the L03 Draft Final Report. These models are calibrated on a larger dataset, including Atlanta, Minneapolis, Jacksonville, Los Angeles, Houston, and San Diego. ( ) (4) where, parameter for 95, 90, and 80 percentiles of the travel time distribution is given in the following table. It should be noted that no RMSE measures are reported for this set of models in L03 Draft Final Report.

X RMSE

95 3.6700 -

90 2.7809 -

80 2.1406 -

This is also the case for the standard deviation model

( ) (5) and, for the models expressing percent trips arriving on time at a given speed measure. It is notable that two of these models are based on a negative exponential relationship, while the one belonging to the lower speed travels follows a sigmoid relationship.

( ( )) (6)

( ( )) (7)

[ ( ( ( )))⁄ ] (8)

Data Rich Models These models follow the causal relationship between the impacting factors and the travel time reliability measures. Data Rich Models are presented in two stages. First Stage models express the relationship between the mean travel time and the contributing factors representing demand to capacity ratio, number of hours lost and number of rainy hours during a year in an exponential form. A similar relationship is given for percentiles of travel time distribution ( ) (9) ( ) (10) Following table summarizes all the parameters used in models (9) and (10) at different time periods.




Time Period X a b c RMSE

Peak Period MeanTTI 0.09677 0.00862 0.00904 18.8%

99%-ile TTI 0.33477 0.01235 0.025315 39.8%

95%-ile TTI 0.23233 0.01222 0.01777 32.3%

80%-ile TTI 0.13992 0.01118 0.01271 25.8%

50%-ile TTI 0.09335 0.00932 - 20.5%

10%-ile TTI 0.01180 0.00145 - 6.7%

Peak Hour MeanTTI 0.27886 0.01089 0.02935 26.4%

99%-ile TTI 1.13062 0.01242 - 41.3%

95%-ile TTI 0.63071 0.01219 0.04744 38.3%

80%-ile TTI 0.52013 0.01544 - 34.1%

50%-ile TTI 0.29097 0.01380 - 28.3%

10%-ile TTI 0.07643 0.00405 - 15.2%

Mid-day (11am-2pm, weekdays)

MeanTTI 0.02599 - - 7.5%

99%-ile TTI 0.19167 - - 33.4%

95%-ile TTI 0.07812 - - 21.8%

80%-ile TTI 0.02612 - - 9.2%

50%-ile TTI 0.01134 - - 21.8%

10%-ile TTI 0.00389 - - 5.1%

Weekday(*) MeanTTI 0.00949 0.00067 - 29.3%

99%-ile TTI 0.07028 0.00222 - 38.9%

95%-ile TTI 0.03632 0.00282 - 31.8%

80%-ile TTI 0.00842 0.00117 - 14.7%

50%-ile TTI 0.0021 - - 4.7%

10%-ile TTI 0.00047 - - 2.0%

(*) In weekday models is used instead of .

Second Stage models are designed to enable evaluation of any of the three main contributing factors to be used in the First Stage models. Specifically, two models are given for d/c ratio estimation. One based on available datasets to L03 researchers

( ) {( ) } (11)

which holds for the case peak period is less than 200 minute long. And, a second relation based on empirical data Peak hour: ⁄ ( ) ⁄ (12) where, Weekdays: (13) Mid-days (11am-2pm): (14) Number of Lane-Hours Lost (ILHL) will be computed using the following equation (15) where, (16)




(17) and,

{

(18)

(19) Task 1.6: Describe data sources available to SHA to be used in calculating a local value of travel time reliability

Under this task a comprehensive review of data sources available to SHA that can be used to estimate a local value of travel time reliability will be conducted. The identified data sources will be documented and a value of travel time reliability obtained from them will be reported.

Experimental Approaches and Further Development of Maryland Models

The following are experimental approaches to improving Maryland modeling capabilities. They have promise for improving reliability estimates, but as yet have not been implemented and will not be completed in time to be included in the methods developed. However, they are presented to point out future directions for reliability research.

NCSG researchers are currently developing a prototype time dependent MSTM model. The project titled “Inclusion of Time Dependent Networks in the Maryland Statewide Transportation Model” is on-going and is co-sponsored by Maryland SHA and FHWA. This provides a future possibility for proposers to forecast travel time variations under different scenarios.

In a separate study sponsored by the exploratory advanced research program (EARP) of FHWA, a group of UMD researchers14are developing a coherent agent-based model that simulates transportation system dynamics as an evolutionary process with an explicit clock for time tracking. Different agents rely on behavioral rule sets that can be empirically estimated to make driving and travel decisions as decision-situations emerge or are triggered by external stimuli (e.g. information, recurrent or non-recurrent congestion, toll, new travel option). Each person is tracked in the agent-based model, and his/her spatial knowledge and experiences accumulate over time as he/she makes decisions as a driver, an individual traveler, or as part of a household. Building on this vision, the UMD researchers will develop an innovative agent-based approach for integrated driver and traveler behavior modeling with applications for transportation systems management, capital investment evaluation, transportation planning, and beyond. The proposed scope of work focuses on agent decision types including en-route diversions, pre-trip route choice, departure time choice, and mode choice, which

14

Zhang, L., Chang, L., Zhu, S., Xiong, C., Du, L., Mollanejad, M., Hopper, N., and Mahapatra, S.,

Integrating an agent-based travel behavior model with large-scale microscopic traffic simulation for corridor-level and sub-area transportation operations and planning applications. ASCE Journal of Urban Planning and Development (Accepted for Publication).




provide the crucial linkages between traditional traffic simulation, travel demand and innovative agent-based models. Data required for building driver and traveler agents will be collected with techniques proven in the previous research, including interactive laboratory experiments, driving simulators and traditional/web-based/GPS-based surveys. Agent behavior rules will be empirically estimated with rule-based artificial intelligence methods and possibly utility-based methods when detailed agent behavior data is not available. In terms of ABMS implementation, the research team has extensive experience in developing well-known ABMS tools (e.g. NetLogo), integrating ABMS with existing network traffic simulation and travel demand models, and developing original computer code for specific ABMS applications.

Both the agent based research and the incorporation of a DTA into the MSMT have great potential, neither project will be complete in time to be incorporated into the reliability assessment. This project will address how these additional capabilities would improve the reliability estimates.

Recently, University of Maryland researchers15 have conducted a stated preference (SP) survey to estimate value of travel time and value of travel time reliability in the presence of managed lanes on the Maryland sections of Capital Beltway (I-495). This dedicated stated preference survey is designed to capture response of the potential regional drivers on toll; congestion, travel time reliability and shifts in departure time. The questionnaire was designed as a web-based survey; respondents were recruited by distributing flyers at several exit locations of I-495. Information on the flyer contains the website address and the questionnaire instructions to guide respondents in answering the questionnaire. The sample population consisted of car drivers traveling on I-495 during weekday extended peak periods (8:00 AM - 11:00 AM and 3:30 PM. - 6:00 PM) on March 21-25, and May 23-27, 2011. Table 3 summarizes the characteristics and methodology of the survey. A total of 200 respondents from a sample of 4,000 who received the flyers responded to the questionnaire which results in the overall response rate of 5%. Within the 200 responded surveys, 173 of the respondents completed the survey, which results in the effective sample size of 173 observations for the model estimation.

Table 3.Survey characteristics and methodology.

Time Frame March 21-25, and May 23-27, 2011

Target Population Potential HOT users

Sampling Frame Current I-495 users with internet

Sample Design Flyers distributed at randomly selected exits of I-495

Mode of Administration Self-administered

15

Cirillo, C., Mannes, M., and N.U. Serulle, Measuring Value Of Travel Time in the Presence of Managed Lanes: Preliminary Results from a Stated Preference Survey on the Capital Beltway, Paper submitted for presentation to the 92

nd TRB annual meeting, Washington, D.C., January

2013.




Computer Assistance Computer-assisted self interview (CASI) and web-based survey

Reporting Unit One person age 18 or older per household reports for the entire household

Time Dimension Cross-sectional survey with hypothetical stated preference experiments

Frequency Two four-days phases of flyers distribution

Levels of Observation Household, vehicle, person

The questionnaire consists of three parts: Socioeconomic and Vehicle Ownership, Recent Trip, and Stated Preference questions. The description for each part of the survey is described next.

Socioeconomic and Vehicle Ownership

This section gathers socioeconomic data of the potential managed lane users on the I-495. The respondent is asked to describe his/her characteristics via the following constructs: gender, age, household income range, education, occupation, number of worker in household, number of vehicle in the household, vehicle type most used by the respondent, number of years the vehicle owned, and ZIP code of work place.

Recent Trip

This section focuses on the respondent’s most recent trip on I-495. The purpose of this section is to use respondent’s experienced trip condition as the pivot point when designing the stated preference question. This ensures that the stated scenario in the SP part is realistic for each respondent. The respondent is asked to describe his/her most recent trip information on I-495 via the following constructs: mode choice, number of passenger, trip purpose, departure time, arrival time, preferred departure time, preferred arrival time, total travel time in minutes, total trip distance in miles, fuel cost, parking cost, toll cost, entry and exit ramp locations, shortest and longest travel time experienced on the whole trip in minutes, shortest and longest travel time experienced on the beltway in minutes, number of departure time alternatives respondents have considered, corresponded departure and arrival time for the alternative departure time, work starting/ending time, and work schedule flexibility (whether can start work 30 minutes later).

Stated Preference Questionnaire

Two stated preference experiments were including in the survey. The first experiment presents respondents with various lane choice options under different travel time conditions (normal, congested, uncertainty). The second experiment adds departure time as an attribute and presents different lane and time options to respondents. The following sections describe the experimental design of these experiments.




Experiment 1: Toll lane use and travel time variability

The first experiment (SP1) presents respondents with different travel conditions on lane alternatives to investigate the acceptability of toll lanes and the willingness to pay for reduced travel time subjected to congestion and uncertainty. We assume that on toll lane the total travel time will be significantly reduced due to less congestion and uncertainty. The multiple scenarios proposed to each respondent have different levels of variation and are constructed on the actual trip characteristics to ensure that alternatives are realistic. This experiment consists of three alternatives (Normal lane, HOT lane, and HOV lane) and five variables. In addition to the three alternatives displayed, the respondent can choose to take an alternative route that does not include the Beltway. Each variable has up to three levels of variation per alternative. The survey is designed with an orthogonal design approach. The description of the variables used in the experiment is as follows:

• Average Travel Time: This is the travel time the respondent can generally expect for their trip. This time is given for both the entire trip and the Beltway portion.

• Travel Time due to Congestion: This is the additional travel time the respondent can expect when congestion occurs. This time is given for both the entire trip and the Beltway portion.

• Travel Time due to Uncertainty: This is the additional travel time the respondent could experience for unlikely events such as accidents or sporting events. This time is given for both the entire trip and the Beltway portion.

• Fuel cost: This variable is designed to reflect varying fuel costs due to fuel economy difference and change in fuel price per unit. The fuel cost is pivoted from the reported fuel cost in the RP part.

• Toll cost: The toll cost is designed as a flat fee. This is one of the options under analysis at Maryland SHA.

Figure 3 illustrates an example of the presentation of the experiment to the respondents on the website.




Figure 3. SP Experiment 1 – web interface .

Experiment 2: Departure time and uncertainty in travel time

This experiment consists of three alternatives (Normal lane, HOT lane, and HOV lane) and five variables. These variables have the maximum of five levels of variation per alternative. The variables included in the stated preference are: departure time, travel time range, arrival time range, fuel cost, and toll. These variables are designed to account for traffic conditions by time of day taking into account observed respondents’ departure time where the peak period is defined as 8:00 AM. to10:00 AM., and 3:00 PM. to 7:00 PM. (Crunkleton, 2008). The description of the variables used in the survey is as follows: • Departure time: departure time is pivoted from respondent’s reported departure

time. • Total travel time range: this variable is designed to account for both time-of-day

conditions based on the respondent reported departure time and travel condition on toll lane. It is aimed at capturing travel time uncertainty.

• Fuel cost: the fuel cost is designed to reflect higher expenses in the peak period and on the normal lane. The fuel cost is pivoted from the reported fuel cost in the RP part.

• Toll cost: the toll cost is designed as a mileage based using the Inter-county Connector toll rates (MDTA, 2010). The toll rate for the HOT lane accounts varies depending on whether the respondents’ reported departure time is in the peak or non-peak period.

Figure 4 provides an example of one of the scenarios proposed to the respondents on the website.




Figure 4.SP Experiment 2 – web interface.

Task 1 is expected to take one month to finish.

Task 2: Develop and apply a methodology to select a travel time reliability performance measure and a value or range of values for travel time reliability.

Task 2.1: Develop and apply a methodology to select a travel time reliability performance measure

Currently, Maryland SHA is using PTI to measure travel time reliability on highway facilities falling in its jurisdiction. Under this task, other metrics will be investigated for the purpose of project evaluation. Results of this task will be documented in a progress report and will be shared with project technical expert task group.

Travel time reliability measures essentially reflect variability in travel times experience by users of the facility under consideration. While variability of travel time can be conveniently depicted as a frequency distribution, expressing it with a unique numerical measure is much more difficult. Nevertheless, it is necessary to use travel time reliability performance measures to communicate either the observed or anticipated level of variability.

Figure 5shows a sample travel time frequency distributionover a freeway segment used




for illustration in SHRP 2 Project L03 report16. As shown in Figure 5, the distribution is skewed to the right indicating infrequent (non-recurrent) but extreme travel times are experienced on this segment.

Figure 5. Sample travel time frequency distribution (12)

Table 4 summarizes definitions of reliability performance measures that are typically used in practice. Among these measures, Planning Time Index (PTI) is the most popular metric for travel time reliability. However, other metrics such as Buffer Index and Skew Statistic are also useful in communicating long-term level of variability in travel times.

Table 4 Recommended Reliability Metrics(13)

Reliability Performance Metric

Definition Units

Buffer Index (BI) The difference between the 95th percentile travel time and the average travel time, normalized by the average travel time.

The difference between the 95th percentile travel time and the median travel time, normalized by the median travel time.

Percent

16

Cambridge Systematics, Analytical Procedures for Determining the Impacts of Reliability Mitigation Strategies, SHRP 2 Project L03, Draft Final Report, September 2011.




Failure/On-Time Measures Percent of trips with travel times <:

• 1.1 * Median Travel Time ; and

• 1. * Median Travel Time .

Percent of trips with space mean speed <:

• mph, mph, mph .

Percent

Planning Time Index 95th percentile Travel Time Index. None

80th Percentile Travel Time Index

Self-explanatory. None

Skew Statistic The ratio of (90th percentile travel time minus the median) divided by (the median minus the 10th percentile).

None

Misery Index (Modified) The average of the highest five percent of travel times divided by the free-flow travel time.

None

Task 2.2: Develop and apply a methodology to select a value or range of values for travel time reliability

In past studies, two distinct approaches have been used to define reliability for valuation purposes17. First, mean-variance approach which uses statistical measures to separate out the value of typical/usual travel time (VOT) and the value of travel time reliability (VTTR). The second approach is based on the schedule delay concept which focuses on the magnitude of the time embodied by both early and late arrivals in relation to a pre-determined schedule.

The mean-variance approach is easy to implement but has some theoretical drawbacks since there is concern for double counting of benefits using this approach. The scheduling delay approach is conceptually more appealing but it is more difficult to implement. Carrion and Levinson 18 give a comprehensive overview of the two approaches and evidence gathered over the years regarding the value of travel time reliability.

Stated preference (SP) surveys, revealed preference (RP) surveys, and options theoretic approach have been used to determine VOT and VTTR analytically. The reliability ratio (RR), the ratio of VTTR divided by VOT, is a convenient way of estimating VTTR for project evaluation purposes. An established RR along with the knowledge of VOT simplifies the task of VTTR estimation. However, previous studies in the U.S. and

17

Cambridge Systematics, and ICF International, Value of Travel Time Reliability Synthesis Report & Workshop Working Paper, SHRP 2 Workshop on the Value of Travel Time Reliability, April 26, 2012. 18

Carrion, C., and D. Levinson, Value of Travel Time Reliability: A Review of Current Evidence, Transportation Research Part A 46, pp. 720-741, 2012




elsewhere have resulted in very different RR values.

Therefore, it is crucial for Maryland SHA to either validate the current RR value of 0.75 it is using as a default value in its baseline approach or to estimate updated RR values based on its local data. As is the case with more frequently studied VOT, VTTR is also influenced by following factors

Trip purpose – RR for trip to work is higher than the trip from work or non-work

trips

Income –for the work trip, lower income groups have a higher RR

due to their less flexible work schedules

Trip length – for the work trip, RR decreases with trip distance

SHRP 2 Projects C04, and L04 propose a set of RR for combinations of trip purpose, income level, and trip length. These studies will be used in performing this task.

The UMD-SHA Team will research past approaches to the development of a value or range of values for travel time reliability. These will include behavioral modeling approaches(based on stated/revealed preference), and Options Theoretic approach based on speed data variability, and selection based on a detailed search of the literature including a to-be release report in May 2013 from the Netherlands and their methodology for developing a value of reliability.

Task 2.3: Develop and submit a draft Task 1 & 2 report for review

A draft Task 1 & 2 report will be developed and submitted to the technical ETG for their review and comments.

The Task 1& 2 report will include a draft outline of the final report for this project.

Task 2.4: Incorporate comments received from reviewing committee

Comments received from the review process will be addressed and incorporated into the final deliverable.

Task 2.5: Deliver a final Task 1 & 2 report

At the end, a final Task 1 & 2 report will be prepared and will be submitted.

Task 3: Incorporate this performance and the value of travel time reliability into your project evaluation process and use that information to inform policy decisions about transportation alternatives.

The baseline Maryland SHA approach already incorporates a reliability performance measure (PTI) and values travel time reliability at 75 percent of the value of average congestion savings for all projects/alternatives.

Task 3.1: Incorporate Task 1 & 2 results into SHA project evaluation process




Under this task the reliability performance metric and VTTR selected in Task 2 will replace the current values in the baseline approach. This may necessitate revision of some parts of the evaluation process as we may come up with different metrics for different facility types and may also decide to use different metrics for different project types and life cycles. The VTTR may also vary with trip purpose, user income levels, and their relevant trip lengths.

Task 3.2: Present the revised process to SHA and regional stakeholders

The new approach has to comply with general guidelines and policies of MarylandSHA. Therefore, decisions on updating the evaluation approach will be taken in close collaboration with Maryland SHA officials through the Mobility Council. The Mobility Council is chaired by SHA’s Director of Planning and Preliminary Engineering and includes representation of planners in the Maryland Department of Transportation, the Washington and Baltimore Metropolitan Planning Organizations, as well as other SHA Departments including operations, safety, traffic engineering, and maintenance.Their input and review of the process will be key in the successful implementation of this task.

Task 3.3: Develop and submit a draft Task 3 report for review

A draft Task 3 report will be developed and submitted to the technical ETG for their review and comments.



Task 3.5: Deliver a final Task 3 report

At the end, a final Task 3 report will be prepared and will be submitted.

Task 4: Analyze and compare alternatives using the baseline approach and the revised method that includes VTTR.

Task 4.1: Identify current pool of projects considered by Maryland SHA

Under this task current projects considered by Maryland SHA for funding will be identified. Different aspects of the benefits and costs associated with these projects will be identified and documented.

The set of projects will include operational investment options as well as infrastructure investments and both types will be set to compete for the same funding resources.

Task 4.2: Evaluate identified projects using baseline approach




Under this task current projects considered by Maryland SHA for funding will be evaluated for economic viability using baseline (Task 1).

Task 4.3: Evaluate identified projects using revised approach

Under this task current projects considered by Maryland SHA for funding will be evaluated for economic viability using updated approaches (Task 3) when it makes use of proposed reliability metric and VTTR (Task 2).

Task 4.4: Note any changes in the evaluations as a result of inclusion of reliability

Any change in evaluation results will be noted particularly if inclusion of benefits associated with reliability savings will lead to different set of projects being picked for funding.

Task 5: Explore the sensitivity of priorities to changes/ranges of the VTTR.

Under this task, sensitivity of evaluation results obtained in Task 4 to changes in VTTR will be investigated. Particularly, the critical VTTR that makes pairs of projects comparable will be identified. Then, this critical VTTR will be compared against selected VTTR to investigate the possibility whether a practical but different VTTR would have led to a different ranking among set of competing projects.

Task 5.1: Evaluate identified projects using baseline approach with varying VTTR

Task 5.2: Evaluate identified projects using revised approach with varying VTTR

Task 5.3: Develop and submit a draft Task 4 & 5 report for review

A draft Task 4 & 5 report will be developed and submitted to the technical ETG for their review and comments.



Task 5.5: Deliver a final Task 4 & 5 report

At the end, a final Task 4 & 5 report will be prepared and will be submitted.

Task 6: Brief management and/or policy board on the results of Task 4 and 5 and assess the reaction of members.




In order to gauge the validity and usefulness of the methods evaluated under Tasks 4 and 5, the Maryland SHA officials will be briefed. This briefing will include other relevant stakeholders from the region, including MPOs and other transportation providers involved in regional transportation investment decisions. Once the appropriate stakeholders are identified, a briefing (or briefings) will be scheduled. At a minimum, the results of Task and will be presented to SHA’s Mobility Council. Members of the I-95 Corridor Coalition’s Partners Using Archived Operations Data and the Vehicle Probe Project Suite User Group will also be invited to participate.

Task 6.1: Develop a series of planning scenarios using both baseline and revised approaches

The usefulness of reliability and VTTR in investment decision-making will be best understood by developing a series of planning scenarios to share with the stakeholders. These planning scenarios will be prepared to show how the use of reliability and VTTR change the resulting investment decision.

Task 6.2: Identify relevant state and regional stakeholders

Relevant members of the management and/or policy board of the Maryland SHA and other regional stakeholders will be identified. They will be contacted in advance to be briefed about the project. SHA-UMD researchers will then schedule a meeting with the identified stakeholders to go over the findings and to obtain their point of view in justification and use of value of travel time reliability in transportation decision making.

Task 6.3: Meeting with relevant stakeholders

During the meeting, each scenario will be shown with and without the new criteria and presented to the stakeholders in a planning charrette format to encourage discussion and interaction among the participants. The project team will facilitate this discussion and lead the stakeholders through each scenario, making special note of their:

reactions with regard to the credibility, usefulness, and understandability of the

proposed approach;

change in perception of the importance of reliability performance measures and

VTTR;

opinions regarding the ease with which the approach might be applied within

their planning/decision-making contexts;

outlook on the likelihood the public would/could embrace the use of the

outlined concepts;

comments as to how the approach might impact their agency’s specific project

decisions; and

overall support for the recommendations and change in planning strategy.




Task 6.4: Measure the likelihood Maryland SHA will adopt the revised approach

The goal of the planning charrette will be to conclude with a group consensus on the validity of the approach, the effect its use might have on regional decisions and the likelihood the various agencies represented will modify their current evaluation processes to include reliability and VTTR.

Proposers will make every attempt to carry these measures through the project selection process.

Task 6.5: Develop and submit a draft Task 6 report for review

A draft Task 6 report will be developed and submitted to the technical ETG for their review and comments.



Task 6.7: Deliver a final Task 6 report

At the end, a final Task 6 report will be prepared and will be submitted.

Task 7: Prepare a Draft Final Report including lessons learned from tasks 1-6.

This task is expected to take two months to finish. At the end of Task 7 a Draft Final Report on the methodology and finding of Project L35 will be prepared and submitted within 90 days of the contract end date. Comments from the ETG on previous progress reports will be obtained and addressed in this Draft Report.

Task 7.1: Develop and submit a draft final report for review

A draft final report will be developed and submitted to the technical ETG for their review and comments.



Task 7.3: Deliver a draft final report

At the end, an updateddraft final report will be prepared and will be submitted.




Task 8: Prepare Final Report.

Task 8.1: Develop final report The comments received on the Draft Final Report will be taken into account in preparation of the Final Report. Communication with the ETG will be ongoing during this period to receive their comments and to address any outstanding issues in the Final Report.

Anticipated Research Results In addition to all the previously identified deliverables, the UMD-SHA Team anticipates the following will be achieved as a direct result of performing the research plan outlined here:

1. Maryland SHA will demonstrate reliability benefits to its customers, stakeholders, and elected officials with a sound VTTR methodology.

2. SHA will refine its processes to more accurately account for reliability.

3. The research will identify and prioritize corridors, segments, and design policies to advance its mission to provide its customers with a well maintained, safe, and reliable highway system that offers mobility choices.

4. It will attempt to quantitatively report the mobility and reliability benefits

without under-counting, or double-counting.

5. MPOs, and local jurisdictions will benefit from this process to streamline their congestion management system activities and prioritize programs and projects.

6. Since private sector probe data has become mainstream, Maryland’s

experience will enhance the state of practice with real implementation of SHRP 2 reliability products.

7. Researchers will document the process used to incorporate VTTR into the

existing decision making process so that it can be shared with others.

Applicability of Results to SHRP 2 Objectives The following briefly describes how L35 project results will meet SHRP 2 L35 Objectives




Select and defend a value or range of values for travel time reliability for the Maryland State Highway network. Task 1 & 2 Reports

Maryland SHA will demonstrate reliability benefits to its customers,

stakeholders, and elected officials with a sound VTTR methodology.

SHA will refine its processes to more accurately account for reliability.

Use the VTTR in the Maryland SHA project development process to prioritize operational and capital improvements and determine if (and how) the ranking of projects changes due to the addition of VTTR.

Tasks 3 Report, Task 4&5 Report, Task 6 Report

The research will identify and prioritize corridors, segments, and design

policies to advance its mission to provide its customers with a well maintained, safe, and reliable highway system that offers mobility choices.

It will attempt to quantitatively report the mobility and reliability benefits

without under-counting, or double-counting.

Report for the benefit of others the step-by-step process used to develop, justify, apply, and assess the use of VTTR in the Maryland SHA project evaluation and decision process. Task 8 Final Report

MPOs, and local jurisdictions will benefit from this process to streamline

their congestion management system activities and prioritize programs and projects.

Since private sector probe data has become mainstream, Maryland’s

experience will enhance the state of practice with real implementation of SHRP 2 reliability products.

Researchers will document the process used to incorporate VTTR into the

existing decision making process so that it can be shared with others.

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