A system using farecard data at the Chicago Transit Authority in Chicago, Illinois, to estimate rail passenger flows within the rail rapid transit network has been validated and used for several planning and analysis purposes. The system, which derives destinations for entry-only rail farecard users, is an enhanced version of the New York City Transit farecard origin–destination model. Enhancements include algorithmic changes to improve destination inference, greater time period granularity, enhanced sampling techniques, and path assignment to individual scheduled trips. Applications of the model outputs, including its use in lieu of passenger counts, are illustrated.
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References
1.
RahbeeA. and CzerwinskiD.. Using Entry-Only Automatic Fare Collection Data to Estimate Rail Transit Passenger Flows at CTA. Proc., 2002 Transport Chicago Conference, Chicago, Ill., 2002.
2.
RahbeeA.Path Assignment for the CTA Farecard-Based Rail Passenger Flow Model.Chicago Transit Authority, Chicago, Ill., 2003.
3.
BarryJ. J.NewhouserR.RahbeeA. and SayedaS.. Origin and Destination Estimation in New York City Using Automated Fare System Data. In Transportation Research Record: Journal of the Transportation Research Board, No. 1817, Transportation Research Board of the National Academies, Washington, D.C., 2002, pp. 183–187.
4.
HofmannM.Hybrid Rule Based Architecture for Origin/Destination Extraction from Electronic Fare Collection Data. Proc., Annual Universities’ Transport Study Group, Leeds, U.K., 2005.
5.
ZhaoJ.The Planning and Analysis Implications of Automated Data Collection Systems: Rail Transit OD Matrix Inference and Path Choice Modeling Examples.MS thesis. Massachusetts Institute of Technology, Cambridge, 2004.
6.
FarzinJ.Constructing an Automated Bus Origin–Destination Matrix Using Farecard and Global Positioning System Data in São Paulo, Brazil. In Transportation Research Record: Journal of the Transportation Research Board, No. 2072, Transportation Research Board of the National Academies, Washington, D.C. 2008, pp. 30–37.
7.
BunemanK.Automated and Passenger-Based Transit Performance Measures. In Transportation Research Record 992, TRB, National Research Council, Washington, D.C., 1984, pp. 23–28.
8.
GordillaF.The Value of Automated Fare Collection Data for Transit Planning: An Example of Rail Transit OD Matrix Estimation.MS thesis. Massachusetts Institute of Technology, Cambridge, 2006.
9.
UtsunomiyaM.Possible Benefits of Increased Data Availability After Implementing Smart Card in Public Transportation Systems.MS thesis. Massachusetts Institute of Technology, Cambridge, 2004.
10.
CuiA.Bus Passenger Origin–Destination Matrix Estimation Using Automated Data Collection Systems.MS thesis. Massachusetts Institute of Technology, Cambridge, 2006.
