Benefits and Costs of Full Operations and ITS Deployment · Benefits and Costs of Full Operations...
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A 2025 Forecast for Tucson Benefits and Costs of Full Operations and ITS Deployment
Transcript of Benefits and Costs of Full Operations and ITS Deployment · Benefits and Costs of Full Operations...
A 2025 Forecast for Tucson
Benefits andCosts of FullOperationsand ITSDeployment
Notice
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Benefits and Costs
of Full Operations
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Tucson
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2
Evaluation Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4
Tucson’s Full Operations and ITS Deployment Scenario . . . . . . . . . . . . . . . . . . . . . . .6
Benefits of Full Operations and ITS Deployment . . . . . . . . . . . . . . . . . . . . . . . . . . .9
Costs of Full Operations and ITS Deployment . . . . . . . . . . . . . . . . . . . . . . . . . . . .12
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .14
Technical Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15
FiguresFigure 1. Reduction in Hours of Delay Due to Operations and ITS Deployments . . . . .10
Figure 2. Proportion of Benefits Value in Tucson . . . . . . . . . . . . . . . . . . . . . . . . . .11
Figure A-1. IDAS Analysis Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .19
TablesTable 1. Strategies Included in the Tucson Full Operations and ITS Deployment Scenario . . .7
Table 2. Annual Benefits of Operations and ITS Deployment in Tucson . . . . . . . . . . .11
Table 3. Annual Cost of Operations and ITS Deployments in Tucson . . . . . . . . . . . .12
Table 4. Comparison of Annual Benefits and Costs in Tucson . . . . . . . . . . . . . . . .13
Table A-1. Cincinnati Analysis Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .24
Table A-2. Capacity Reductions Used to Represent Inclement Weather Conditions . . .24
Table A-3. Impacts of Weather and Work Zone Mitigation Strategies . . . . . . . . . . .26
Table A-4. Annualization Weights for Cincinnati . . . . . . . . . . . . . . . . . . . . . . . . . .27
Table of Contents
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Benefits and Costs
of Full Operations
and ITS Deployment:
Tucson
People who live in urban areas nationwidereport that traffic congestion is one of theirgreatest quality-of-life concerns. When thedemand for travel in a region exceeds theavailable capacity of the transportationsystem, residents suffer from excessive traveltimes, increased crash risks, diminished airquality, and other negative impacts. Stateand local transportation agencies havefound it difficult to increase the transportationsystem supply rapidly enough to keep pacewith the growing demand. Traditionalapproaches such as adding highway lanes,building new roads, or providing new transitlines are often too costly to be consideredas reasonable solutions, particularly in themore densely populated areas of majorcities. Transportation agencies are furtherchallenged by the time required to designand construct these traditional infrastructureimprovements.
In response to this dilemma, transportationagencies have increasingly turned toimproved operational strategies andIntelligent Transportation Systems (ITS) inorder to squeeze more operational efficiencyout of the existing transportation system.Examples of these operations and ITSstrategies include synchronizing thetiming of traffic signals to smooth trafficflow, providing incident response vehiclessuch as freeway service patrols to quicklyclear traffic incidents and breakdowns,automatically tracking and dispatchingtransit buses to improve their on-timeperformance, and providing meaningfultraveler information to the public to allowtravelers to better plan their trips. ITSAmerica, the professional organizationfounded to facilitate the successful
deployment of such systems, defines ITSas follows:
“Intelligent transportation systems encompassa broad range of wireless and wirelinecommunications-based information, controland electronics technologies. Whenintegrated into the transportation systeminfrastructure, and in vehicles themselves,these technologies help monitor andmanage traffic flow, reduce congestion,provide alternate routes to travelers,enhance productivity, and save lives,time and money.”1
Information technology has contributed to efficiency gains in a wide range ofindustries, and ITS produces similar resultsfor transportation. ITS solutions can beimplemented more quickly and lessexpensively in comparison to traditionalinfrastructure improvements, and nationwidedeployments of ITS have been shown toproduce significant benefits. By themselves,these operations and ITS strategies will noteradicate congestion; however, they areessential components to a well-balanced,well-operating transportation network.
Furthermore, these individual operationsand ITS improvements can be tied togetherto achieve even greater benefit than theycan alone. Recognizing that the whole isoften greater than the sum of its parts, theUnited States Department of Transportation(U.S. DOT) and numerous local agencieshave launched initiatives to encouragedeployment and integration of these systemsin order to maximize their potential benefits.
1 ITS America website: www.itsa.org.
Preface
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Benefits and Costs
of Full Operations
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Tucson
The goal of full deployment and completeintegration of ITS, however, has yet to berealized in any metropolitan area. Financialconstraints, along with technical andinstitutional barriers, have held up achieve-ment of this goal. Operations and ITSimplementation have typically advancedincrementally within communities, targetingthe highest priorities first while deferringenhancements until additional resourcesare available. This piecemeal approachhas hindered or sometimes prevented theintegration of the individual deployments,preventing the region from experiencingthe full potential of benefits from acoordinated and complementary system.
To date, the analysis and evaluation ofoperations and ITS deployments havefollowed a similar path—often focusingon the benefits of a single ITS technologyused in a single location. As a result ofthis narrow focus, very little informationexists that exemplifies the benefits ofincreased deployment and integration of operations and ITS strategies.
As a result, the Federal HighwayAdministration (FHWA) initiated a studyto explore the benefits and costs of fullydeploying operational strategies andintegrating ITS in metropolitan areas. Thegoal of this effort is to provide transportationprofessionals and decision makers with abetter understanding of the potentialbenefits of implementing the full suite ofavailable operations and ITS strategies ina metropolitan area.
The U.S. DOT selected Tucson, Cincinnati,and Seattle for case studies representingsmall, medium, and large metropolitanareas, respectively. Scenarios wereidentified comprising complete operationsand ITS deployment at an appropriate,logical scale for each area. These scenarioswere then evaluated to estimate theregionwide benefits and costs.
Beyond the difference in the size of thethree metropolitan areas, some additionalvariations in the analysis approach affectedthe relative benefits estimated in eachcase study area. Benefits were estimatedin the Tucson example based on forecastsof traffic in the year 2025, while thebenefits for Cincinnati and Seattle werebased on more current (2003) trafficconditions. The Cincinnati study alsoincludes the additional analysis of impactsduring inclement weather conditions andconstruction activity, as well as the addedbenefits of weather and work zonemitigation strategies—strategies that arenot included in the deployments forTucson or Seattle.
This report presents the findings of theTucson, Arizona scenario. The findings of the Seattle scenario are presented inBenefits and Costs of Full Operations andITS Deployment: A 2003 Simulation forSeattle (FHWA-JPO-04-033, EDL#13977). The findings of the Cincinnatiscenario are presented in Benefits andCosts of Full Operations and ITSDeployment: A 2003 Simulation forCincinnati (FHWA-JPO-04-031, EDL#13979).
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Benefits and Costs
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The analysis compares theFull Operations and ITSDeployment Scenario to one that contains nooperations and ITSdeployment whatsoever.
How Were Operations and ITS Strategies Selected?
The first step in evaluating the benefits of full operations and ITS deployment inTucson was to identify the suite of ITSimprovements that would constitute “fulldeployment” in the year 2025.2 Tucson’scurrent and planned operations and ITSimprovements were inventoried to serveas the building blocks for the full deploy-ment scenario. These elements were thenenhanced and expanded by identifyingadditional improvements to fill gaps,upgrade the existing systems with moreadvanced technologies, and then integratethe diverse systems. The result was theestablishment of the Full Operations andITS Deployment Scenario, defined as themaximum amount of locally desirableoperations and ITS—at the highest rangeof technical and institutional sophistication—that can be deployed without regard tofunding constraints.
Several additional guidelines were used in identifying the appropriate amount ofoperations and ITS to include in the fulldeployment scenario in order to avoidmaking overly optimistic assumptions aboutfuture benefits. First, the Full Operationsand ITS Deployment Scenario includesonly those strategies that are funded orsignificantly subsidized by the public sector.Private sector strategies, such as in-vehiclenavigation or safety systems in personalautomobiles, or freight management systemsused by commercial trucking firms, werenot considered.
Although the benefits of these systemsare not included in this analysis, they areexpected to offer significant benefits.
In addition, another limitation of the FullOperations and ITS Deployment Scenariowas that technologies and approacheswhich have not currently progressed pastdevelopment and testing were not includedbecause of a lack of industry consensuson their future costs, benefits, and marketpenetration. New operations and ITSstrategies are emerging constantly due tothe ever-changing nature of technology;however, those evaluated in this studyinclude only well-established systems thatare currently in use throughout the nation.These assumptions lead to a conservativeestimation of benefits in 2025, becausetechnical innovation will likely result insystems that can provide even greaterbenefit for the same or less cost thancurrent technologies.
How Were the BenefitsEstimated?
The analysis of the benefits and costs forthe Full Operations and ITS DeploymentScenario was conducted using the ITSDeployment Analysis System (IDAS). TheIDAS tool is specifically designed toestimate the benefits and costs of ITSdeployments based on observed, real-world costs and benefits. This analysistool was used to estimate benefits,including changes in travel time, traveltime reliability, number and severity ofcrashes, vehicle emissions, fuel use, andother important measures.
Evaluation Approach
2 The year 2025 was selected as the future year due to the availability of detailed traffic forecastsand models for the Tucson region for that year.
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The analysis compares the Full Operationsand ITS Deployment Scenario to one thatcontains no operations and ITS deploymentwhatsoever. This “all-or-nothing” approachwas used to compare the complete costsand benefits of operations and ITS in Tucson.This approach further allows the findingsto be applicable to other regions that maybe at different stages of operations andITS deployment.
A Technical Appendix accompanying thisreport provides additional detail on themethodology used in estimating thebenefits and costs.
The strategies included in Tucson’s FullOperations and ITS Deployment Scenariowere identified by first consulting withlocal agencies to identify the overall ITSprogram planned through 2025. To date,the Tucson metropolitan area has successfullydeployed a variety of operations and ITSstrategies and has adopted relativelyaggressive plans for geographic expansionand enhancement in the future. The regionis particularly advanced in the deploymentof traffic signal coordination along its manyarterial streets. The low-density developmentof many of Tucson’s neighborhoods thatare not conveniently served by thefreeway required Tucson to aggressivelypursue the deployment of advanced trafficsignal control and coordinationtechnologies.
Tucson’s existing and planned operationsand ITS improvements provided a solidbasis for identifying additional strategies,geographic coverage, and technologyupgrades that could naturally be built uponto create the Full Operations and ITSDeployment Scenario. Table 1 presents thestrategies selected and the number ofdeployment locations for Tucson in theyear 2025. The proportional coverage onthe system is also presented to portray theITS deployment density level relative to theentire system.
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Tucson’s Full Operations and ITS Deployment Scenario
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Table 1. Strategies Included in the Tucson Full Operations and ITS Deployment Scenario
Strategy Deployment Coverage
Arterial Traffic Management Systems
Central Control Signal 600 intersections 100% of major intersections; 96% ofCoordination major arterial and parkway miles
Emergency Vehicle Signal 600 intersections 100% of major intersections; 96% ofPreemption major arterial and parkway miles
1,340 emergency vehicles 100% of emergency vehicles
Transit Vehicle Signal 115 intersections 9 of 37 routes served (24% routes)Priority
80 transit vehicles 29% fixed-route transit vehicles
Highway Advisory Radio 1 transceiver 100% of arterial miles covered
Dynamic Message Signs 40 locations 100% of arterial incident managementcorridors and roadways subject tomajor flood
Freeway Management Systems
Central Control Ramping 70 on-ramps 73% of on-rampsMetering
Highway Advisory Radio 1 transceiver 100% of freeway miles
Dynamic Message Signs 9 locations 100% of freeway miles
Transit Management Systems
Fixed-Route Automated 275 transit vehicles 100% fixed-route transit vehiclesScheduling and AutomaticVehicle Location 5 transit stations 100% of transit transfer stations
Transit Management Systems
Incident Detection, Verifica- 20 freeway service 100% of freeway miles: 15% of parkwaytion, Response, and patrol vehicles and major arterial miles (110 of 721 miles)Management
Transit Management Systems
Emergency Vehicle Control 1,340 emergency vehicles 100% of emergency vehiclesService
Emergency Vehicle AVL 1,340 emergency vehicles 100% of emergency vehicles
Telemedicine 130 ambulances 100% ambulances
Transit Management Systems
Electronic Transit Fare 275 transit vehicles 100% of fixed-route transit vehiclesPayment
Traveler Information
Telephone- and Web- Regionwide 40% of market penetrationbased Traveler InformationSystem
Kiosk-based Traveler 5 kiosks 100% of transit transfer stationsInformation
Crash Prevention and Safety
Railroad Crossing 4 rail crossings 5% of major grade crossingsMonitoring Systems
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Table 1. Strategies Included in the Tucson Full Operations and ITS Deployment Scenario (Cont’d from page 7)
Strategy Deployment CoverageCommercial Vehicle Operations
Weigh-in-motion and Safety 1 check station N/AInformation Exchange
Combination Screening 1 check station N/Aand Clearance–Credentialsand Safety 53,000 equipped 40% market penetration
commercial vehicles
Supporting Deployments
Traffic Management Center One 100% of region
Transit Management Center One 100% of region
Emergency Management One 100% of region
Information Service One 100% of regionProvider Center
Traffic Surveillance– 950 locations 100% of freeway, parkway, and major Closed Circuit Television arterial miles
Traffic Surveillance– 1,200 locations 100% of freeway miles; 96% of parkwayLoop Detectors and major arterial miles; 100%
controlled signals
What Would Be the Benefits?
The performance of the Tucson transportationsystem in the year 2025 was analyzedfor two cases—No Operations and ITSDeployment and the Full Operations andITS Deployment Scenario. The results fromthe two scenarios were then compared toidentify the incremental change due to theinclusion of operational improvements andITS deployment.
The analysis of the Full Operations and ITSDeployment Scenario showed positiveimpacts on all performance measuresstudied, including:
■ Decreased travel times■ Increased vehicle speeds■ Decreased delay■ Decreased number and severity
of crashes■ Decreased environmental impacts
(reduced emissions and fuel use).
The following sections discuss theseimpacts in greater detail.
Decreased Travel TimesPersonal travel time in the region decreasedsignificantly as a result of full operationsimplementation and ITS deployment. Onaverage, more than 36,000 hours oftravel time were saved on a daily basis inthe Tucson region. This amounts to morethan 9 million person-hours saved annually,or a reduction of more than 7 hours perresident annually in the Tucson region. Alltravel modes (automobiles, commercialtrucks, and transit) were predicted toexperience reduced travel time under thefull ITS deployment scenario. In particular,transit riders were expected to experiencesignificant travel time improvements. Transit
riders saved an average of about 5 minutesevery trip, approximately 30 percent oftheir average trip time.
Increased Vehicle SpeedsAverage vehicle speeds throughout Tucson’sroadway network increased slightly as aresult of the operations and ITS deployments.However, the majority of the speed increasewas observed on major facilities, includingfreeways, parkways, and major arterialswhich served as a focus of a number ofthe ITS and operations improvements. Forexample, speeds increased by as much as10 percent on some congested segmentsof Interstate 10, which serves as the primaryfreeway in the region. The minor arterialsand collectors, which received fewerimprovements, experienced positive butless significant speed increases. Conse-quently, the overall network speed impactwas moderated by the smaller increase onthe local street system. Freeway rampfacilities were the only roadway type toexperience a small decrease in speeds,which likely occurred as a result of theaddition of ramp metering strategies. (Rampmetering briefly stops vehicles on the on-ramp to manage the traffic flow onto thefreeway and smooth the merge.)
Decreased DelayThe ITS and operations strategies wereshown to significantly reduce delay in theregion. This reduction included a decreasein the delay due to recurring congestion aswell as a decrease in the delay caused byunexpected traffic incidents. The amount ofdelay due to everyday, recurring congestionwas reduced by 5.6 percent for roadwayusers in the Tucson region. Consistentwith the observed speed changes, thereductions in delay were greatest on
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Benefits of Full Operations and ITS Deployment
Consistent with the observedspeed changes, the reductionsin delay were greatest onprimary roadways thatreceived the greatestconcentration of ITS andoperations deployments.
primary roadways that received the greatestconcentration of ITS and operations deploy-ments. For example, delay was reducedon freeways in the region by 10.6 percent,while delay on major arterial roadwaysand parkways was reduced by 6.4 percent.Less significant reductions in delay wereobserved on more minor, local streets inthe region.
In addition to the impacts on delay relatedto everyday congestion, many operationsand ITS strategies are intended to reduceincident-related delay by either decreasingthe number of crashes occurring on thenetwork or minimizing the time required torespond and clear incidents once they dooccur. Full deployment in the Tucson regionresulted in a dramatic reduction of incident-related delay, an average reduction ofmore than 11,500 hours per day. Incidentdelay, which was only estimated forfreeways, was reduced by more than 70percent by the use of operations and ITS.Figure 2 shows the reduction in hours ofeveryday recurring delay along with thereduction in hours of incident related delayon the freeways.
Decreased Number and Severity of CrashesOperations and ITS improvements werepredicted to significantly reduce the numberof traffic crashes occurring in the Tucsonregion. Additionally, through the deploymentof incident and emergency managementsystems, which minimize the response timeto crashes, the severity of crashes was alsoreduced. Overall, fatal crashes decreasedby more than 7 percent, while injury andproperty damage crashes were reducedby approximately 3 percent. Full operationsand ITS deployment resulted in the avoid-ance of approximately 11 fatal, 400 injury,and 540 property damage crashes peryear in the Tucson region.
Decreased Environmental ImpactsOperational improvements and ITS resultedin a reduction for all emissions analyzed.Carbon monoxide and hydrocarbonemissions were reduced by approximately10 and 12 percent, respectively, whilenitrous oxides were reduced by morethan 16 percent. Fuel use was cut bymore than 11 percent, representing asavings of more than 56 million gallonsof fuel per year, or nearly 60 gallonsper resident in the Tucson region.
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Note: Incident-related delay represents only the hours of delay on the regional freeways.
Figure 1. Reduction in Hours of Delay Due to Operations and ITS Deployments
0
60,000
40,000
20,000
80,000
100,000
120,000
Incident-Related Delay
No Operations and ITS
Hou
rs o
f Del
ay
Full Operations and ITS
Recurring Delay
What Would Be the Value of the Benefits?
The monetary value of the benefits of FullOperations and ITS Deployment in Tucsonwas estimated by applying a dollar valueto the impacts. For example, the estimatednumber of gallons of fuel saved per daywas multiplied by the number of days peryear and by the cost of a gallon of fuel.
When the impacts of the operations andITS deployments are annualized anddollar values are applied, the benefits totalmore than $455 million per year for theTucson region. The values of the studiedbenefits are presented in Table 2. Thepercentage of the value of each benefitcompared to the total is presented inFigure 3.
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Table 2. Annual Benefits of Operations and ITS Deployment in Tucson (in $ Millions in 2003 dollars)
Travel Time25%
Incident Delay 24%
Crashes13%
Emissions11%
FuelConsumption
19%
Public AgencyEfficiency
1%
Other7%
Figure 2. Proportion of Benefits Value in Tucson
Benefit Value
Reduction in travel time (Mobility) $113
Reduction in incident delay (Reliability) $110
Reduction in crashes (Safety) $57
Reduction in emissions (Environment) $50
Reduction in Fuel Consumption (Energy) $86
Increase in public agency efficiency (Productivity) $7
Other3 $33
Total Benefits $481
3 Other estimated benefits include reduced noise, decreased non- fuel operating costs, andadditional safety benefits associated with decreased emergency vehicle response time.
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The average costs of implementingoperations and ITS in Tucson wereestimated to be $72 million annually.These costs represent an annualized life-cycle cost of the resources necessary toimplement, operate, and maintain them.Although the strategies included in thisanalysis were primarily funded by thepublic sector, a portion of the costs wasexpected to be paid for by the privatesector. These private sector costs includethe equipment needed on commercialtrucks to enable the use of automatedscreening and clearance deployments atcheck stations. The considerable numberof commercial trucks assumed to beequipped in the full deployment scenario(53,000) influenced the substantial
estimated cost for this equipment. Table 3presents the costs for the Full Operationsand ITS Deployment Scenario in Tucson.
Supporting deployments presented inTable 3 represent the backbone infra-structure necessary to operate andmanage the deployed strategies. Theseinclude items such as traffic managementcenters, traffic surveillance cameras, andcommunication systems.
How Do the Costs and Benefits Compare?
When the annual estimated benefits arecompared to the costs, the results show theinvestment in operations and ITS in Tucson
Costs of Full Operations and ITS Deployment
Table 3. Annual Cost of Operations and ITS Deployments in Tucson (In $ Millions in 2003 Dollars)
Deployment Cost Percentage of Total Costs
Arterial management systems $3.9 5.5%
Freeway management systems $2.6 3.6%
Transit management systems $1.8 2.6%
Incident management systems $4.5 6.2%
Emergency management systems $2.1 2.9%
Electronic payment systems $1.1 1.0%
Traveler information $2.1 2.9%
Crash prevention and safety $0.2 0.3%
Commercial vehicle operations $20.3 28.1%
Supporting deployments $33.5 46.4%
Total costs $72.1 100.0%
Private sector costs $19.8 27.4%
Public sector costs $52.3 72.6%
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to be quite favorable and cost efficient.The benefits of the deployment outweighthe costs by a ratio of 6.3 to 1, as shownin Table 4. This ratio indicates that eachdollar spent on operations and ITS in theTucson region would return $6.30 inbenefits from decreased travel time,improved safety, and a reduction invehicle emissions and fuel consumption.
The benefits estimated for the fulldeployment of operations and ITS inTucson were overwhelmingly positivewhen compared with the costs. Theconservative treatment of several factorsrelated to the analysis suggests that futurebenefits of operations and ITS may beeven greater. These assumptions include:
■ The analysis only considered thoseoperations and ITS deployments that are
funded or significantly subsidized by thepublic sector. Many additional privatesector ITS initiatives are currently deployedor planned, but were not considered inthis analysis. These private sector deploy-ments, such as in-vehicle navigation andsafety systems, and commercial vehicletracking and dispatching, would likelyprovide benefits beyond those presentedin this report.
■ The analysis did not attempt to predictthe capabilities or costs of future techno-logy. Instead, the analysis consideredthe impact of deploying currently availabletechnologies at a greater rate than pre-sently deployed. Benefits and costs ofthe deployments were based on currentlyobserved impacts and equipment costs.Presumably, future advances will providegreater capabilities at lower costs.
The benefits of thedeployment outweigh thecosts by a ratio of 6.3 to 1.
Table 4. Comparison of Annual Benefits and Costs in Tucson (In $ Millions in 2003 Dollars)
Average annual benefit $455
Average annual costs $72
Benefit-cost ratio 6.3
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This analysis examined the potentialbenefits and costs of fully deploying andintegrating operations and ITS strategies inthe Tucson region. A Full Operations andITS Deployment Scenario was identifiedfor the year 2025 and compared to ascenario without any operations and ITSdeployments in order to identify the changesin impacts. The results showed the investmentin ITS to be cost-efficient—returning $6.30in benefits for every dollar invested.
Operations and ITS strategies were shownto have a positive impact in reducingtravel time delays caused by incidents,as well as reducing travel times duringnormal conditions. By reducing theamount of time people spend stuck incongestion, operations and ITS wouldalso reduce the frustration of travelersand likely have a positive influence onregional productivity. Reductions in crashes,vehicle emissions, and fuel use wouldfurther contribute to improvements in thequality of life in the Tucson region.
The benefits estimated for the fulldeployment of operations and ITS inTucson were overwhelmingly positivewhen compared with the costs. Theconservative treatment of several factorsrelated to the analysis suggests that futurebenefits of operations and ITS may beeven greater.
For More Information
Additional information on the operationsand ITS strategies discussed in this reportcan be obtained through the FHWA’sOffice of Operations www.ops.fhwa.govand through the U.S. Department ofTransportation’s ITS Joint Program Officewww.its.dot.gov. For additional informationon the individual benefits and costs ofthe ITS deployments presented in thisreport, please visit the ITS Joint ProgramOffice’s ITS Benefits and Costs Databaseat www.benefitcost.its.dot.gov. Moreinformation on the IDAS analysis tool used in this evaluation may be found atidas.camsys.com. Please visit the ITSDeployment Tracking websitewww.itsdeployment.its.dot.gov for moreinformation on the current and historicallevels of operations and ITS deployment in U.S. metropolitan areas.
Summary
Background
This Technical Appendix provides a generaloverview of the methodology used in thestudy of the potential benefits of fullydeploying operations and ITS strategies.This study was initiated by the U.S. DOTto explore the benefits and costs offully deploying and integrating ITS andoperations strategies in metropolitan areas.Three test sites—Tucson, Arizona; Cincinnati,Ohio; and Seattle, Washington—wereselected to represent small, medium,and large metropolitan areas, respectively.Hypothetical deployment scenarios weredeveloped to represent the full logicaldeployment of operations and ITS strategiesin each area. These scenarios were thenevaluated to identify the likely benefits andcosts of the deployments. The goal ofthis study was to provide transportationprofessionals and decision makers with anincreased understanding of the potentialbenefits possible through the full deploymentof ITS and operations strategies.
The findings from these three case studiesare summarized in individual reports. Thisappendix provides additional detail on thesimilar approach used in all three regionsto estimate the likely benefits and costs offull operations and ITS deployment.
Methodology Overview
The goal of this analysis was to estimate thelikely benefits and costs resulting from thefull deployment and integration of ITS andoperations strategies in a region. For thepurpose of this study, ”full deployment” isdefined as the maximum amount of locallydesirable ITS and transportation operations
strategies—at the highest range of technicaland institutional sophistication—that can bedeployed without regard to fundingconstraints. Consistent with this goal anddefinition, full operations and ITS deploymentscenarios were identified for the threecase study regions.
The analysis methodology used in this studywas developed to identify the incrementalbenefits and costs of the strategies containedin the full operations and ITS deploymentscenario. To identify these incrementalimpacts, it was necessary to estimatewhat travel conditions would be in the fulloperations and ITS deployment scenario,as compared with a scenario that did notcontain any operations and ITS deploy-ments. This ”all-or-nothing” approach wasused to isolate the full costs and benefitsof the operations and ITS deployments.
The FHWA’s ITS Deployment AnalysisSystem software was used in conjunctionwith the locally validated travel demandmodels for the three case study regionsto predict the traffic conditions that wouldbe likely in the two deployment scenarios—the No Operations and ITS DeploymentScenario and the Full Operations andITS Deployment Scenario. An overview ofthe IDAS tool analysis process is providedin a subsequent section.
This analysis approach resulted in numerousregional performance measures beingestimated for the two scenarios, such asthe person hours of travel, roadway speeds,the number of crashes, and the gallonsof fuel used, among others. To identifythe incremental impact resulting from thedeployment of ITS, the performance
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Technical Appendix
measures from the Full Operations andITS Deployment Scenario were subtractedfrom the identical performance measures forthe No Operations and ITS DeploymentScenario. The difference between theperformance measures of the twoscenarios represented the incremental impactcaused by ITS during the day or time periodrepresented by the model data. The annualimpact was determined by multiplying thedaily incremental impact by the number ofdays per year.
For example, the Tucson case study useda single daily model in the analysis. Toestimate the impact on any particularperformance measure, such as the numberof fatality crashes, the following approachwas used:
■ Annual Benefit = (Number of FatalityCrashes Occurring in the No Operationsand ITS Deployment Scenario – Numberof Fatality Crashes Occurring in the FullOperations and ITS DeploymentScenario) * Number of Days Per Year
For those models having multiple timeperiods represented within a day, separateNo Operations and ITS Deployment andFull Operations and ITS DeploymentScenarios were developed for each timeperiod. The performance measure for theNo Operations and ITS Deployment andthe Full Operations and ITS DeploymentScenarios were then compared within eachtime period to identify the incrementalimpact. The incremental impacts from allthe available time periods summed up
the daily impact.A-1 This summed figurewas then multiplied by the number of daysper year to annualize the benefit. Anexample of this approach for annualizingthe results for models with multiple time-of-day analysis is shown below:
Where:■ AMNo = performance measure from
the AM Peak Period – No Operationsand ITS Deployment Scenario
■ AMFull = performance measure fromthe AM Peak Period – Full Operationsand ITS Deployment Scenario
■ MDNo = performance measure fromthe Mid-day Period – No Operationsand ITS Deployment Scenario
■ MDFull = performance measure fromthe Mid-day Period – Full Operationsand ITS Deployment Scenario
■ PMNo = performance measure fromthe PM Peak Period – No Operationsand ITS Deployment Scenario
■ PMFull = performance measure fromthe PM Peak Period – Full Operationsand ITS Deployment Scenario
■ OPNo = performance measure fromthe Off-Peak Period – No Operationsand ITS Deployment Scenario
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A-1 The summing of the performance measures across all time periods was performed for all cumulative impacts. Non-cumulativeperformance measures, such as vehicle speeds, were not summed. Instead, these performance measures were calculatedfrom the cumulative performance measures. For example, the estimate of daily speed was determined by summing the vehiclemiles traveled (VMT) for all periods and dividing by summed vehicle hours traveled (VHT) for all periods.
AnnualBenefit
*Number ofDays PerYear
AMNo – AMFullMDNo – MDFullPMNo – PMFullOPNo – OPFull
= ∑
■ OPFull = performance measure fromthe Off-Peak Period – Full Operationsand ITS Deployment Scenario
The value of the annual benefit was thendetermined by applying the appropriatebenefit values from the IDAS tool to theincremental change in the performancemeasures. The values from all the variousperformance measures were summed todetermine the total annual benefit of alloperations and ITS strategies included inthe Full Operations and ITS DeploymentScenario. This benefit value was comparedwith the annual cost of the strategies topresent the benefit/cost ratio for theincluded strategies.
IDAS Overview
What is IDAS?The IDAS software was developed by theFHWA as a tool focused on analyzing thespecific impacts of ITS. IDAS operates asa post-processor to travel demand modelsused by metropolitan planning organizationsand by state departments of transportationfor transportation-planning purposes. IDAS isintended to mimic and build upon theresults of these tools, and shares many ofthe same analysis techniques and processes.Although a sketch-planning tool, IDASimplements the modal split and trafficassignment steps associated with atraditional planning model. These stepsare key to estimating the changes in modal,route, and temporal decisions of travelersresulting from ITS technologies.
IDAS was developed as a tool specificallyfocused on analyzing the specific impactsof ITS. IDAS was also designed to serve asa repository of information on the impacts of
various types of ITS deployments and of thecosts associated with various types of ITSequipment. The default ITS impacts and costsused in the IDAS tool are based on theobserved experiences of deployingagencies, as maintained in the U.S.DOT’s ITS Benefits and Costs Database,www.benefitcost.its.dot.gov. By offeringthese capabilities, IDAS provides the abilityto critically analyze and compare differentITS deployment strategies, prioritize thedeployments, and compare the benefits ofthe ITS deployments with other improvementsto better integrate ITS with traditionalplanning processes.
How Does IDAS Work?The IDAS tool works by importing theresults from travel demand models in orderto recreate the validated regional networkstructure and travel demand within IDAS.The data are imported into IDAS using aspecial internal input/output interface,which is capable of reading and interpretingASCII text data files. These input data filesare created from data generated by theregional travel demand models. The dataexchanged between the travel demandmodel and IDAS include network dataregarding the characteristics of transportationfacilities in the region and travel demanddata, including the number of trips andmode share of travel between differentzone pairs in the model. Depending onthe needs of the analysis and the formatof the available data, the user is typicallyrequired to perform some data conversionprior to import into IDAS. Once the dataare imported, they are stored in a databaseaccessible by the IDAS software and maybe viewed in a graphical output by the user.
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Once the data input is complete, IDAS iscapable of operating independently of thetravel demand model. The IDAS user is thenable to create analysis alternatives byselecting ITS components from a menuof more than 60 ITS improvements, andplacing these on the desired location onthe network. The user then providesadditional information regarding theirdeployment, such as the implementationdate and proposed operational strategies.
Once the analysis alternative has beencreated, the IDAS software then modifiesthe network or travel characteristics torepresent the likely impacts of the ITSdeployments placed on the network bythe user. These modifications are based onreal-world impacts observed in otherregions following their deployment ofsimilar ITS components, and may includechanges in link capacities or speeds,zone-to-zone travel times, crash or emissionsrates, or other impacts specific to the ITScomponent. The specific default impactsassociated with each of the various ITSdeployments are described in Appendix B– IDAS Default Values of the IDAS User’sManual available on the IDAS softwarewebsite idas.camsys.com.
The IDAS model then uses analysistechniques similar to the travel demandmodels to analyze the impacts created bythe modifications to the alternative networkand travel characteristics. A traffic assign-ment routine is used to estimate the changesin travel patterns caused by the modifi-cations, and a mode shift routine is usedto estimate any travel mode changes. Theresults of this analysis are revised link
volumes and speeds and mode shares.IDAS conducts the same analysis procedureson the unmodified, baseline network(without ITS deployments) as well as themodified alternative network (with ITSdeployments). These two scenarios are thencompared to identify the incrementalimpact resulting from the ITS deployment.
The changes in link volumes and speedsand mode shares are then used by IDASin another series of analysis to calculatechanges in the travel time, the number ofcrashes, the amount of emissions andother impacts. Dollar values are thenapplied by IDAS to these impacts toprovide an estimate of the benefits ofthe ITS components deployed in thealternative.
In a separate process, the costs of the ITSdeployments are also estimated by IDAS.The costs of the ITS deployments arecalculated by identifying the inventoryof equipment necessary to deploy andoperate each improvement, based onthe suggested equipment packages inthe ITS National Architecture. IDAS thenapplies unit costs (capital and operationsand maintenance [O&M] costs) to eachpiece of equipment in the inventory andannualizes the capital costs based on theanticipated useful life of the equipment.A-2
The costs of all equipment included inthe inventory for a particular deploymentalternative are summed and comparedwith the benefits in the form of a benefit/cost ratio. These outputs are summarizedand displayed to the user in severalformats. The complete IDAS analysisprocess is summarized in Figure A-1.18
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Additional information regarding thestructure of IDAS and its processes ispresented in the IDAS User’s Manual,which is distributed electronically with theIDAS software, or is available on theIDAS website at idas.camsys.com.
Use of IDAS in Analyzing the Impactsof Full Operations and ITS DeploymentExcept where noted, the analysis of theimpacts of full operations and ITS deploy-ment used the default IDAS procedures,parameters, and impacts. These parametersand impact values were held constant inthe three case study regions in order toproduce comparable results.
The following exceptions to the standardIDAS methodology were made in theanalysis:
■ Estimation of Costs – A separate costestimation spreadsheet tool wasdeveloped outside the IDAS software tocalculate the cost of the operations andITS deployments. This spreadsheet toolapplied the same methodology andused the identical equipment unit costsas the IDAS software. This externalspreadsheet method was used toimprove the ease of use for the analysts,and better account for particular ITSequipment not currently represented inthe IDAS software.
■ Estimation of the Impacts of AdvancedTraveler Information Systems (ATIS) – A blanket assumption of the overalleffectiveness of all ATIS deploymentswas made, rather than make individualassumptions regarding the likely marketpenetration and effectiveness of eachindividual component. It was assumed thatthe various deployed ATIS components(pre-trip and in-route systems) weresuccessful in reaching 40 percent oftravelers. Of those travelers receivingthe information, 25 percent were ableto save 6.3 percent of their travel time.This impact assumption was based ona comparison of the various IDAS impactassumption values for the individualATIS components.
■ Estimation of Benefit/Cost – An externalspreadsheet tool was developed tocompare the benefits and costs for thefull deployment scenario. This separatespreadsheet was needed in order toaggregate the results from multiple IDASruns representing different time periods
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1. Appropriate regional data are obtained from travel model
2. User formats output from travel model for input into IDAS
3. IDAS reads and interprets input data
4. User creates alternatives by deploying ITS on the network
5. IDAS modifies network/travel characteristics to represent ITS impacts
6. IDAS reassigns traffic and estimates mode shift caused by modifications to
network/travel characteristics
7. IDAS uses new link volumes and speeds, andmode shares to calculate changes in travel time,
accidents, emissions, and other impacts
8. IDAS assigns dollar values to the impacts, andestimates the costs of the ITS deployments
9. User is able to view (benefit/cost comparison,performance measures) and compare alternatives
Figure A-1. IDAS Analysis Process
(A.M., P.M., etc.). IDAS currently onlyhas the ability to compare benefits andcosts for a single time period. Thisspreadsheet compiled the results frommultiple time-period scenarios intocombined daily and annual results.
■ Estimation of the Impacts of Weatherand Work Zone Mitigation Strategies –Weather and work zone mitigationstrategies are not currently available asdeployments within the IDAS software.Special analysis techniques weredeveloped, using capabilities withinthe IDAS software, to analyze theimpacts of these specific strategies.These techniques are described in asubsequent section.
■ Estimation of the Incident-Related Delay on Freeway Facilities – TheIDAS software contains a defaultmethodology and parameters forestimating the incident related delay forfreeway facilities. Although previousIDAS studies conducted by numerousagencies have served to vet theseimpacts as reasonable representationsfor individual deployments, this studyincludes combinations and intensities ofdeployment that exceed any that havebeen tested using this methodology. Itwas the opinion of an expert panel thatreviewed the preliminary results that theinitial estimates of the cumulative impactto incident related delay overstated thepotential reduction. In order to ensure aconservative estimation of benefits, asensitivity analysis was performed toidentify the default impact parametersused in IDAS that were most likely toresult in an overestimation of benefits.These parameters were modified andthe model analysis was re-run toproduce the more reserved final results.
Model Networks and Adjustments
Network and travel demand data from theregional travel demand models formed thebasis of the analysis. These models variedfrom region to region in their size andcomplexity. Additionally, some adjustmentswere necessary to modify the availabletravel demand model data to match thespecific needs of the desired analysis.This section summarizes the models usedin the three regions and describes thenecessary modifications to generate thebaseline data needed for the analysis.
TucsonThe model data available for the Tucsonregion represented daily travel conditions inthe year 2025. This model was developedand maintained by the Pima Association ofGovernments (PAG). The Tucson modelwas the smallest of the models used inthe analysis, representing a daily total ofapproximately 5.4 million person tripstraveling between 870 possible originsand destinations. Three vehicle modeswere represented in the model, including:Auto, Light Truck, and Heavy Truck. Twopublic transit modes were represented;however, both represented bus travel. Thetransit modes were differentiated by theform of access to the transit stop: TransitWalk Access and Transit Drive Access.
No significant modifications were requiredto prepare the Tucson model data for usein the analysis. Minor reformatting of thedata was performed to prepare the datafor input into the IDAS software tool.
CincinnatiThe Cincinnati region model, obtainedfrom the Ohio-Kentucky-Indiana RegionalCouncil of Governments (OKI), was themost complex of the three regional modelsused in the analysis. The model had
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recently undergone a significant update,which resulted in the merging of theregional travel demand modelsrepresenting the Cincinnati and Dayton,Ohio, regions. Models were specificallydeveloped for this analysis representingtravel demand for the year 2003. Thesemodels were developed to represent fourseparate time periods: A.M. Peak Period(2.5 hours), Mid-day Peak Period (6.5hours), P.M. Peak Period (3.5 hours), andOff-Peak Period (11.5 hours). Thecombined travel demand in these fourperiods represented approximately 9.3million daily person trips traveling between2,999 possible origins and destinations.Approximately 69 percent of this traveloccurs in the Cincinnati region.
Adding to the complexity of the Cincinnatimodel was the disaggregation of travelinto 11 possible modes, including fivevehicle modes: Single Occupancy Vehicle,High Occupancy Vehicle (two people),High Occupancy Vehicle (three or morepeople), Single-Unit Truck, and Multiple-Unit Truck. Six separate bus transit modeswere also available, segmented by thetype of bus service and access mode,including: Local Bus Walk Access, LocalBus Park & Ride, Local Bus Kiss & Ride,Express Bus Walk Access, Express BusPark & Ride, and Express Bus Kiss & Ride.
Several significant modifications weremade to the existing Cincinnati models toprepare the data for use in this analysis.The first modification was the developmentof models representing travel in the year2003. No specific existing models wereavailable representing this year. Traveldemand from models representing theyear 2000 and 2010 were interpolatedto develop travel demand trip tables for
each of the analysis periods representingthe year 2003. The model networks fromthe 2000 models were used since thesemodels already contained roadwayimprovements that were expected to becompleted by 2003.
A second modification was required toallow the analysis to focus only on theimpacts in the Cincinnati region. Therecent model update had merged theprevious models from the Cincinnati andDayton regions into a single model;however, the focus of this analysis wasonly on the Cincinnati region. A specialdata flag was added to the network linkdata to identify in which region eachroadway was located. This enhancementallowed performance measures to beextracted from only those portions of thenetwork located in the Cincinnati (OKI)region.
Other minor modifications were requiredto reformat the data for input into the IDASsoftware. Additional modifications werealso required to perform a separateanalysis of the impacts of weather andwork zone mitigation strategies in theCincinnati region. These specificmodifications are discussed in asubsequent section.
SeattleThe Seattle regional models used in theanalysis represented travel demand in theyear 2003 for three separate timeperiods: A.M. Peak Period, P.M. PeakPeriod, and the Off-Peak Period. Thesemodels were based on the Puget SoundRegional Council (PSRC) travel demandmodels. These models represented acombined daily travel demand ofapproximately 10.8 million person trips
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traveling between 850 possible originsand destinations. Five separate travelmodes were used in the analysisincluding: Single Occupancy Vehicle,High Occupancy Vehicle, Truck, Transit(bus and rail), and Ferry.
Several modifications were made to theexisting PSRC models to generate datasuitable to the analysis of full operationsand ITS deployment. The first modificationwas the development of specific modelsrepresenting travel conditions in the year2003. Travel demand data from existingyear 2000 and 2005 models wereinterpolated to develop these interim yearmodels.
A second modification to the Seattlemodel networks was required to allow theanalysis of ramp metering strategies. On-ramp facilities are not represented in thecurrent Seattle models. Instead, theseinterchanges are coded similar to surfacestreet intersections and allow traffic tomove directly from arterial roadways tofreeway facilities. The IDAS softwaretypically requires that ramp facilities becoded in the network to allow the analysisof ramp metering strategies. When rampmeters are deployed, additionalimpedance is added to the ramp facilitiesto simulate the impact of the ramp signalon traffic entering the freeway. Since theramp facilities were not available in theSeattle model network, modifications wererequired to properly represent this impact.Turning movement restrictions, available foruse in the IDAS software, were speciallymodified to represent the additionalimpedance caused by ramp meteringstrategies in the absence of ramp facilities.
A final modification to the Seattle modelswas required to properly representautomobile carrying ferries in the IDAS
analysis. Some reformatting of the modeldata was necessary to properly accountfor this specific travel mode that isprevalent in the Puget Sound region.
Additional Analysis for Estimating the Impacts of Weather and Work Zones
Analysis ScenariosAdditional analysis was conducted inCincinnati to identify the impacts, benefits,and costs that could be expected withthe addition of specialized operationsand ITS strategies intended to counterthe effects of inclement weather andhelp mitigate the negative impactsoccurring as a result of road constructionand maintenance.
Additional scenarios were needed toanalyze these strategies because thebaseline networks obtained from the traveldemand model assume no inclementweather or road construction activity. Theanalysis scenarios that were developeddiffered by four separate variables: thepresence of roadwork, weather conditions,deployment intensity, and time of day.These variables were defined as follows:
■ Presence of Roadwork –- Two separateroadwork scenarios were evaluated,including a network with a representativesample of construction activity and anetwork without road construction/reconstruction activity. The impact ofroadwork activity was represented byreducing facility capacities through theconstruction zones, as described in asubsequent section.
■ Weather Conditions –- Three separateweather conditions were evaluated:clear, rain, and ice/snow. The networkrepresenting clear conditions was identicalto the baseline network obtained from
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the travel demand model. The impactsof the rain and ice/snow conditionswere represented by decreasingcapacities throughout the network, asdescribed in a subsequent section.
■ Deployment Intensity –- Several differentdeployment intensities were evaluated.These include a No Operations and ITSDeployment Scenario, which did notcontain any ITS or operationalimprovements, and a Full Operationsand ITS Deployment Scenario, whichcontained the full compliment ofoperations and ITS deployments. Notethat for those scenarios that contained thenegative impacts of inclement weatheror construction activity conditions, thedeployment scenario was enhancedby adding either weather or workzone mitigation strategies, or both, asappropriate to the conditions includedin the scenario. These specific mitigationstrategies were not included in thescenarios that did not contain eitherthe inclement weather or constructionactivity. For example, the impacts ofwork zone mitigation strategies wereonly analyzed in those scenarios withroadwork conditions.
■ Time of Day –- Models representingfour separate time periods wereavailable for the Cincinnati region,including A.M. Peak Period, P.M. PeakPeriod, Mid-day Period, and Off-Peak.
An analysis approach was developedby creating a matrix of all the potentialcombinations of these variables and thendiscarding illogical combinations. Forexample, no scenarios analyzing conditions
representing roadwork activity during ice/snow conditions were evaluated sincelittle construction activity is anticipated inthe winter months. To accommodate thesevariables in the analysis, 40 separatescenarios were developed and analyzed.Table A-1 presents these scenarios.
The following sections describe how thevarious impacts of weather and constructionactivity were simulated on the network tocreate these scenarios.
Simulation of Weather ImpactsThree different weather situations wereconsidered in this analysis—clear, rain,and snow. Clear weather scenarioswere represented using the baselineroadway network from the TDM. Scenariosrepresenting rain and snow weatherconditions were represented by reducingthe capacity of network roadways tosimulate the negative impact of theinclement weather. Weather impactson capacity represented a weightedaverage of suggested capacity reductionsfrom the Highway Capacity Manual 2000 A-3
and the FHWA’s Operations websitewww.ops.fhwa.dot.gov. The capacityreductions are shown in Table A-2.
Simulation of Construction Activity ImpactsThe negative impacts of construction activitywere simulated on the model networks byfirst identifying a set of construction projectsthat would be representative of a typicalconstruction season. These were identifiedby reviewing major regional constructionprojects from the previous three years andselecting a set of projects representative ofa typical construction season. Eight projectswere selected: four lane-addition projects,
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two reconstruction projects, and tworesurfacing projects. The constructionschedules for these projects were alsoevaluated to estimate the typical number ofdays within a year in which constructionactivity was estimated to occur.
The construction projects were then codedinto those scenarios meant to analyze workzone projects. Since the representativeconstruction activities represent real projects,they were coded in the actual networklocations they occurred. The negative
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Weather Conditions Freeway Reduction Arterial Reduction
Clear None None
Rain -6% -6%
Ice/Snow -10% -12%
Table A-2. Capacity Reductions Used to Represent Inclement Weather Conditions
A-3 Transportation Research Board (2000). Highway Capacity Manual, Washington D.C.
Weather Construction Activity? Scenarios with No Operation and ITS
Scenarios with Full Operation and ITS
Clear
No A.M. PeakMid-day
P.M. PeakOff-Peak
A.M. PeakMid-day
P.M. PeakOff-Peak
Yes A.M. PeakMid-day
P.M. PeakOff-Peak
A.M. PeakMid-day
P.M. PeakOff-Peak
Rain
No A.M. PeakMid-day
P.M. PeakOff-Peak
A.M. PeakMid-day
P.M. PeakOff-Peak
Yes A.M. PeakMid-day
P.M. PeakOff-Peak
A.M. PeakMid-day
P.M. PeakOff-Peak
Ice/Snow No A.M. PeakMid-day
P.M. PeakOff-Peak
A.M. PeakMid-day
P.M. PeakOff-Peak
Table A-1. Cincinnati Analysis Scenarios
impacts of the construction activities weresimulated by reducing the baselinecapacities for those roadway links identifiedas being within the construction zone. Thisreduction was conducted on an individuallink-by-link basis, based on the initial numberof roadway lanes, the number of lanesclosed during construction, and the type ofconstruction activity. The capacity reductionfor each individual link included in the workzone was calculated by first subtracting outthe number of lanes anticipated to be closedas a result of the construction activity. Thecapacities of the remaining lanes were thenreduced based on the recommendedcapacity reduction factor from the highwaycapacity manual (based on the number oflanes in normal conditions and the type ofconstruction activity). These capacityadjustments, for the lanes remaining openfor the various projects, ranged from 75percent of the original capacity for a two-lane facility undergoing resurfacing to 93percent of the original capacity for a 3+ lanefacility undergoing the addition of new lanes.
Additional Weather and Work ZoneMitigation StrategiesAdditional weather and work zonemitigation strategies were deployed andanalyzed in the appropriate Full Operationsand ITS Deployment Scenarios containing thenegative impacts of inclement weather and/or construction activity. These operationsand ITS strategies are not currently includedas available components for analysiswithin the IDAS tool. The software doeshave the capability, however, to deployand analyze ”generic,” user-definedcomponents. For these generic deployments,the user is provided the opportunity tospecify the impacts of the components.The components are then analyzedidentically to all other existing deployments
in the scenario, providing the opportunity toanalyze the impacts of the user-definedcomponents side-by-side with existing IDAScomponents to capture the full synergisticimpacts of all components. This capabilitywas used to simulate the weather and workzone improvements on the network.
The impacts used in the analysis torepresent weather and work zonemitigation strategies were based on theobserved impacts from these types ofdeployments, where available, or theimpact of similar operations and ITScomponents already available withinIDAS. The impacts associated with thevarious weather and work zonemitigation strategies are presented inTable A-3.
Estimating the Annual Impact of the Full ITS Deployment Scenario in CincinnatiEach of the 40 individual scenarios wereanalyzed separately to estimate the likelytraffic conditions that would occur for eachgiven time-of-day period with similarweather, construction activity andoperations, and ITS deployment intensity.The results of the individual scenarioswere then annualized by applying aweight to each scenario representinghow many days a year that scenariowould be anticipated to occur in atypical year.
The applied weights were developed byreviewing historical weather patterns andconstruction schedules. Historical weatherdata from the National Weather Servicerevealed that rain would be expected tooccur on 17 percent of days annually,and measurable ice/snow precipitationoccurs on an average of 18 days peryear. A similar review of the construction
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schedules of the representative projectsincluded in the typical construction seasonindicated that construction activity wouldbe expected to occur on 53 percent ofthe days annually. The analysis furtherassumed that 45 percent of the rain dayswould occur during the construction season.
The number of days in a year was assumedto be 250, representing the number ofweekdays in a year, not includingsignificant holidays. The historical ratesof occurrence for the various weatherand construction activities were thenapplied to identify weights (in numberof days per year) for the No Operationsand ITS Deployment Scenarios. The
weights for the Full Operations and ITSDeployment Scenarios were determinedsimilarly, with the following exception.The weight representing number of dayswith construction activity in the peakperiods was reduced to reflect theimpact of alternative work schedulingstrategies. The construction season forthe off-peak scenarios was thenextended to reflect the additional workshifted to the nighttime periods.
These identified weights were appliedto each scenario and the resultingperformance measures were summed forthe No Operations and ITS Deploymentand the Full Operations and ITS
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Strategy Analysis Impact
Weather
Weather ATIS/Road Weather Information Systems (RWIS)
• ATIS information reaches 40 percent of regional travelers. Of those travelers receiving the information, 25 percent were able to save 6.3 percent of their travel time. (Based on existing IDASATIS methodology)
Work Zones
Work Zone ATIS • ATIS information reaches an additional 10 percent of travelers using the work zone corridors. Of those travelers receiving theinformation, 25 percent were able to save 6.3 percent of theirtravel time. (Based on existing IDAS ATIS methodology)
Work Zone Incident Detection • 15 percent reduction in incident duration in work zones. 15 percent reduction in fuel use rate and emissions rates in work zone. (Based on existing IDAS methodology and information from similar work zone deployment in Albuquerque, NM)
Lane Merging Applications • 5 percent restoration of facility capacity in work zone. (Based on information from Midwest Smart Work Zone Initiative)
Alternative Route Management • 10 percent increase in facility capacity for selected parallel arterial corridors serving as diversion routes. (Based on existing IDAS methodology for traffic signal coordination)
Alternative Work Hours • Reduction in the number of days (annually) with construction activityoccuring in the peak hours, offset by lesser increase in the numberof days with construction occurring in the nighttime period. (Basedon information from Midwest Smart Work Zone Initiative)
Table A-3. Impacts of Weather and Work Zone Mitigation Strategies
Deployment Scenario. The summedresults were then compared to identifythe annual incremental benefits of theOperations and ITS strategies. Table A-4presents the annualization rates thatwere applied in the analysis for eachpossible scenario, and shows how theproportion of days included in theannualization changes between the NoOperations and ITS Deployment andFull Operations and ITS DeploymentScenarios. For the peak periods (AM,Mid-day, and PM) the proportion ofdays with road construction is reducedbetween the No Operations and ITSDeployment and Full Operations andITS Deployment Scenarios to representthe impacts of alternative work hours.This table also shows the impact ofshifting some of these roadworkactivities to the off-peak periods.
Study Caveats
As documented in this appendix, theanalysis of the three case study regionswere conducted using similar, but not
identical approaches and assumptions.Therefore, comparisons of major trendsacross the three regions are generallyvalid. Caution should be applied in anydetailed cross-cutting analysis of specificimpacts, however, due to model andapproach differences that may haveskewed results. The differences in theanalysis approaches may make it difficultto discern if variations observed betweenthe three regions are valid, or are a productof the analysis methodology. Some of thesignificant variations in the models andapproaches which have the potential toimpact results are documented below.
Tucson The analysis of impacts in the Tucsonregion employed model data representingaverage daily travel in the year 2025.This region was the only one to use afuture forecast of travel demand. The useof this future demand may result in theinflation of benefits, relative to otherregions, since travel demand and relatedcongestion is presumably greater than inthe current year. The Tucson region was
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Table A-4. Annualization Weights for Cincinnati
Peak Periods(Includes AM, Mid-Day, and PM Peak Periods)
Off-Peak Periods
No Ops and ITS Full Ops and ITS No Ops and ITS Full Ops and ITS
Scenario Days % Days % Days % Days %
Clear 49 20% 66 26% 49 20% 32 13%
Rain 21 8% 24 10% 21 8% 18 7%
Ice/Snow 46 18% 46 18% 46 18% 46 18%
Clear withRoadwork
113 45% 96 38% 113 45% 130 52%
Rain withRoadwork
21 8% 18 7% 21 8% 24 10%
TOTAL 250 100% 250 100% 250 100% 250 100%
also the only region where a single dailyforecast was used in the analysis. Thisunique characteristic may have the impactof decreased benefits relative to the otherareas because the daily traffic model doesnot capture the impacts of increasedcongestion during the peak hours. TheTucson model was also not adjusted tospecifically analyze variations in weatherconditions or construction activity, aswas performed in Cincinnati.
CincinnatiThe analysis of impacts in the Cincinnatiregion used model data representingtravel conditions in 2003 for four separateperiods—A.M. Peak Period, Mid-dayPeak Period, P.M. Peak Period, and Off-Peak Period—with the sum of these periodsequal to a single day. Further, additionalmodels were constructed from these basemodels to represent traffic conditions duringdifferent combinations of weather conditionsand road maintenance activity typifying anormal construction season. These additionalmodels resulted in the analysis of ITSimpacts during 20 unique traffic conditions,greatly adding sensitivity to the analysiscompared to the other regions. Because theanalysis produced increased benefitestimates for those alternatives representinginclement weather or construction activity,it is likely that the overall benefits estimatedfor Cincinnati are greater relative to theother areas. The analysis in Tucson andSeattle were not conducted with this sensitivityto weather conditions or constructionactivity, and would not have capturedthese additional benefits.
SeattleThe Seattle regional models used in theanalysis represented travel demand in theyear 2003 for three separate time periods:A.M. Peak Period, P.M. Peak Period,and the Off-Peak Period. The results from
the Seattle analysis are, therefore, sensitiveto the variations in impacts caused bypeak period congestion. The Seattlemodels were not adjusted, however, tospecifically analyze variations in weatherconditions or construction activity, as wasperformed in Cincinnati.
In addition to the model differences notedabove, other factors and parametersinternal to the individual region’s modelsmay also affect the estimated impacts.Model characteristics such as the lengthof peak periods, volume-delay functions,and mode choice sensitivity may alsopromote differences in the analysis results.
Additional CaveatsImpacts of the operations and ITSdeployments on incident-related delaywere estimated in all three case studyregions. The use of incident-related delay,non-recurring congestion, or travel timereliability as a measure of systemperformance is an emerging practice.As yet, there is often little consensus onthe specific definitions of the performancemeasures used or the analysis method-ologies applied in different studies. Inthis study, ”incident-related delay” isestimated only for freeway facilities andrepresents the expected amount of delayoccurring as a result of traffic incidents(crashes, stalls, and breakdowns). Thisperformance measure is synonymous withthe ”travel time reliability” impact within theIDAS analysis methodology. Currentincident data availability limits the appli-cation of this analysis methodology to onlyfreeway facilities and does not currentlyallow for the estimation of incident-relateddelay for other surface roadways.
Other caveats, specific to the individualcase study regions, are documentedwithin the individual reports.
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U.S. Department of Transportation400 7th Street, SW
Washington, DC 20590
FHWA-JPO-04-032 May 2005 EDL #13978
Intelligent Transportation
Systems
ITS Web ResourcesITS Joint Program Office:http://www.its.dot.gov
ITS Cooperative Deployment Network:http://www.nawgits.com/icdn
ITS Electronic Document Library (EDL):http://www.its.dot.gov/itsweb/welcome.htm
ITS Professional Capacity Building Program:http://www.pcb.its.dot.gov
