A number of tools have been developed to support the evaluation of intelligent transportation system (ITS) alternatives. However, the methodologies and some associated parameters in these tools were developed and selected before widespread deployments of ITSs. Significant experience gained with ITS in recent years warrants a new assessment of the evaluation methodologies and parameters of these systems. There are also advantages to incorporating the evaluation of ITS deployments as part of existing regional demand forecasting modeling environments rather than using external tools to perform this functionality. This paper discusses the development and implementation of a tool and methodologies to estimate the benefits and costs of these systems as part of a travel demand forecasting modeling environment. It also presents an application of the developed tool to evaluate two of the most widely deployed types of ITS: incident management and advanced traveler information systems. Case study results indicate that the methodologies developed in this study can be used to assess ITS deployments.
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References
1.
Science Applications International Corporation. User's Manual for SCRITS, SCReening Analysis for ITS.Prepared for Office of Traffic Management and ITS Applications, FHWA, 1999.
HeitherC., and ThomasM.Testing of IDAS Capabilities Using Northeastern Illinois ITS Deployments.Working paper 03–06. Chicago Area Transportation Study, Chicago, Ill., 2003.
4.
MayA. D.Traffic Flow Fundamentals.Prentice Hall Inc., Englewood Cliffs, N.J., 1990.
5.
RakhaH. A., and ZhangW.Consistency of Shock-Wave and Queuing Theory Procedures for Analysis of Roadway Bottlenecks.Presented at 84th Annual Meeting of the Transportation Research Board, Washington, D.C., 2005.
6.
Case Study 4: Mid-America Regional Council's (Kansas City) Enhanced Congestion Management System. Cambridge Systematics Inc., Cambridge, Mass. http://idas.camsys.com/caseStudiesFrame.htm. Accessed June 4, 2009.
7.
Case Study 1: Ohio–Kentucky–Indiana Regional Council of Governments' Evaluation of ARTIMIS and ITS Program Plan. Cambridge Systematics Inc., Cambridge, Mass. http://idas.camsys.com/caseStudiesFrame.htm. Accessed June 4, 2009.
8.
Case Study 2: Michigan Department of Transportation Evaluation of the Temporary ITS for the Reconstruction of I-496 in Lansing, Michigan. Cambridge Systematics Inc., Cambridge, Mass. http://idas.camsys.com/caseStudiesFrame.htm. Accessed June 4, 2009.
9.
KristofT., LowryM., and RutherfordG. S.Assessing the Benefits of Traveler and Transportation Information Systems.Project report. Washington State Department of Transportation, Olympia, 2005.
10.
YimY., and MillerM.Evaluation of TravInfo Field Operational Test.California PATH Program, University of California, Berkeley, 2000.
11.
YimY., KhattakA. J., and RawJ.Traveler Response to New Dynamic Information Sources: Analyzing Corridor and Areawide Behavioral Surveys. In Transportation Research Record: Journal of the Transportation Research Board, No. 1803, Transportation Research Board of the National Academies, Washington, D.C., 2002, pp. 66–75.
12.
PeirceS., and LappinJ. E.Why Don't More People Use Advanced Traveler Information? Evidence from Seattle Area.Presented at 83rd Annual Meeting of the Transportation Research Board, Washington, D.C., 2004.
13.
511 America's Traveler Information Number Deployment Assistance Report 1: Business Models and Cost Considerations. 511 Deployment Coalition, FHWA, U.S. Department of Transportation, 2002.
14.
BarfieldW., ConquestL., SpyridakisJ., and HaselkornM.Information Requirements for Real-Time Motorist Information Systems. In Proc. Vehicle Navigation and Information Systems Conference (VNIS), New York, IEEE, 1989, pp. 101–112.
15.
MadanatS. M., YangC. Y. D., and YenY.-M.Analysis of Stated Route Diversion Intentions under Advanced Traveler Information Systems Using Latent Variable Modeling. In Transportation Research Record 1485, TRB, National Research Council, Washington, D.C., 1995, pp. 10–17.
16.
BensonB. G.Motorist Attitudes about Content of Variable-Message Signs. In Transportation Research Record 1550, TRB, National Research Council, Washington, D.C., 1996, pp. 48–57.
17.
ChatterjeeK., HounsellN. B., FirminP. E., and BonsallP. W.Driver Response to Variable Message Sign Information in London. Transportation Research C, Vol. 10, No. 2, 2002, pp. 149–169.
18.
TarryS., and GrahamA.The Role of Evaluation in ATT Development. Traffic Engineering and Control. Vol. 36, No. 12, 1995, pp. 688–693.
19.
MahmassaniH. S.Dynamic Models of Commuter Behavior: Experimental Investigation and Application to the Analysis of Planned Traffic Disruptions. Transportation Research A, Vol. 24, No. 6, 1990, pp. 465–484.
20.
LiuY. H., and MahmassaniH. S.Dynamic Aspects of Commuter Decisions under Advanced Traveler Information Systems: Modeling Framework and Experimental Results. In Transportation Research Record 1645, TRB, National Research Council, Washington, D.C., 1998, pp. 111–119.
21.
PeetaS., RamosJ. L., and PasupathyR.Content of Variable Message Signs and On-Line Driver Behavior. In Transportation Research Record: Journal of the Transportation Research Board, No. 1725, TRB, National Research Council, Washington, D.C., 2000, pp. 102–108.
22.
KhattakA. J., KoppelmanF. C., and SchoferJ. L.Stated Preferences for Investigating Commuters' Diversion Propensity. Transportation, Vol. 20, 1993, pp. 107–127.
23.
HuchingsonR. D., and DudekC. L.Delay, Time Saved, and Travel Time Information for Freeway Traffic Management. In Transportation Research Record 722, TRB, National Research Council, Washington, D.C., 1979, pp. 36–40.
24.
YangS., MeiF., and HadiM. A.Estimation of Incident Delays on Arterial Streets.Presented at 87th Annual Meeting of the Transportation Research Board, Washington, D.C., 2008.
25.
HadiM., and ZhanC.Benefit–Cost Analysis of FDOT District 4 SMART SunGuide ITS Operations.Final report. Prepared for Florida Department of Transportation, Tallahassee, 2006.
26.
ZhuX.Design Strategies for an Artificial Neural Network Based Algorithm for Automatic Incident Detection on Major Arterial Streets. Submitted to Institute of Transportation Engineers for the Daniel B. Fambro Student Paper Award Competition, 2008.
27.
Intelligent Transportation Systems 2005 Update Benefits, Costs, and Lessons Learned. FHWA, U.S. Department of Transportation, 2005.
28.
HadiM., SinhaP., QuigleyE., and HokeP.Intelligent Transportation Systems Deployment Analysis System Customization—Florida Specific Intelligent Transportation System Deployment Benefits.Technical Memorandum 5. Prepared for Florida Department of Transportation, Tallahassee, 2004.
29.
ShererE.Preliminary Detail and Budget Estimate for ATIS and Telecommunications Components.Prepared for Florida Department of Transportation, Tallahassee, 2003.
30.
RamanM., and ZimmermanC.What Does It Cost to Deploy 511? A Case Study. Presented at 511 Deployment Coalition Meeting, Chicago, Ill., 2005.
31.
SandersD., SlussJ. J., HavlicekJ. P., and RadhakrishnanS.Deployment Plan—511 Traveler Information Services.OTC Report 2005-1. Prepared for the Oklahoma Department of Transportation by the Oklahoma Transportation Center, Norman, 2005.
32.
511 Deployment Costs: A Case Study. 511 Deployment Coalition, FHWA, 2006.
33.
BermanM.Southeast Florida ATIS Management Plan.Prepared for Florida Department of Transportation, Tallahassee, 2006.
34.
SantanaR.The 511 South Florida Status Report. Miami, 2007.
