Smart card automated fare collection systems have been effective for the collection of data about the travel behavior of users on public transit networks. Because some systems record only the boarding (origin) locations, a method is needed for estimating the alighting (destination) locations. Existing algorithms can estimate the destination for most trips. However, unlinked trips, which are not part of a trip chain during the day, are more difficult to analyze. The proposed improvement to the existing model for destination estimation, especially for unlinked trips, is based on kernel density estimation of the spatial and temporal probabilities of each destination. The Société de Transport de l'Outaouais, a medium-sized bus service near Ottawa, Ontario, Canada, provided data for a 1-month period in 2009 (908,303 total transactions). Existing algorithms can handle only 80.64% of the trips; the proposed method handles an additional 10.9%. These results are analyzed, and future research directions are discussed.
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References
1.
BagchiM., and WhiteP.R.What Role for Smart-Card Data from Bus Systems?Municipal Engineer, Vol. 157, 2004, pp. 39–46.
2.
TrépanierM., BarjS., DufourC., and PoilpréR.Examen des Potentialités d'Analyse des Données d'un Système de Paiement par Carte à Puce en Transport Urbain. Presented at Congrès Annuel de l'Association des Transports du Canada, Quebec, 2004.
3.
BagchiM., and WhiteP.R.The Potential of Public Transport Smart Card Data. Transport Policy, Vol. 12, No. 5, 2005, pp. 464–474.
4.
UtsunomiyaM., AttanucciJ., and WilsonN.Potential Uses of Transit Smart Card Registration and Transaction Data to Improve Transit Planning. In Transportation Research Record: Journal of the Transportation Research Board, No. 1971, Transportation Research Board of the National Academies, Washington, D.C., 2006, pp. 119–126.
5.
TrépanierM., TranchantN., and ChapleauR.Individual Trip Destination Estimation in a Transit Smart Card Automated Fare Collection System. Journal of Intelligent Transportation Systems, Vol. 11, No. 1, 2007, pp. 1–14.
6.
MunizagaM.A., and PalmaC.Estimation of a Disaggregate Multimodal Public Transport Origin–Destination Matrix from Passive Smartcard Data from Santiago, Chile. Transportation Research Part C, Vol. 24, 2012, pp. 9–18.
7.
ChuK. K. A., ChapleauR., and TrépanierM.Driver-Assisted Bus Interview: Passive Transit Travel Survey with Smart Card Automatic Fare Collection System and Applications. In Transportation Research Record: Journal of the Transportation Research Board, No. 2105, Transportation Research Board of the National Academies, Washington, D.C., 2009, pp. 1–10.
8.
PelletierM.P., TrépanierM., and MorencyC.Smart Card Data Use in Public Transit: A Literature Review. Transportation Research Part C, Vol. 19, 2011, pp. 557–568.
9.
MorencyC., TrépanierM., and AgardB.Measuring Transit Use Variability with Smart-Card Data. Transport Policy, Vol. 14, No. 3, 2007, pp. 193–203.
10.
DevillaineF., MunizagaM., and TrépanierM.Detection of Activities of Public Transport Users by Analyzing Smart Card Data. In Transportation Research Record: Journal of the Transportation Research Board, No. 2276, Transportation Research Board of the National Academies, Washington, D.C., 2012, pp. 48–55.
11.
SpurrT., ChapleauR., and PichéD.Use of Subway Smart Card Transactions for the Discovery and Partial Correction of Travel Survey Bias. In Transportation Research Record: Journal of the Transportation Research Board, No. 2405, Transportation Research Board of the National Academies, Washington, D.C., 2014, pp. 57–67.
12.
ShimamotoH.Generation and Calibration of Transit Hyperpath Using Smart Card Data. Presented at 1st International Workshop on Utilizing Transit Smart Card Data for Service Planning, Gifu, Japan, 2014.
13.
NassirN., KhaniA., LeeS. G., NohH., and HickmanM.Transit Stop-Level Origin–Destination Estimation Through Use of Transit Schedule and Automated Data Collection System. In Transportation Research Record: Journal of the Transportation Research Board, No. 2263, Transportation Research Board of the National Academies, Washington, D.C., 2011, pp. 140–150.
14.
van OortN.Short Term Public Transport Prediction Model, Based on Smartcard Data. Presented at 1st International Workshop on Utilizing Transit Smart Card Data for Service Planning, Gifu, Japan, 2014.
15.
LeeS., and AliA.Analyzing Subway Origin–Destination Flows by Utilizing Smart Transit Card Data: Case of Seoul Metropolitan Area. Presented at 1st International Workshop on Utilizing Transit Smart Card Data for Service Planning, Gifu, Japan, 2014.
16.
ShiX., and HangfeiL.The Analysis of Bus Commuters’ Travel Characteristics Using Smart Card Data: The Case of Shenzhen, China. Presented at 93rd Annual Meeting of the Transportation Research Board, Washington, D.C., 2014.
17.
TrépanierM., and MorencyC.Assessing Transit Loyalty with Smart Card Data. Presented at 12th World Conference on Transport Research, Lisbon, Portugal, 2010.
18.
TrépanierM., and VassivièreF.Democratized Smartcard Data for Transit Operators. 15th World Congress on Intelligent Transport Systems, New York, 2008.
19.
TrépanierM., MorencyC., and AgardB.Calculation of Transit Performance Measures Using Smartcard Data. Journal of Public Transportation, Vol. 12, No. 1, 2009, pp. 79–96.
20.
NakamuraT.Travel and Shopping Behavior Analysis Using Smart Card Data. Presented at 1st International Workshop on Utilizing Transit Smart Card Data for Service Planning, Gifu, Japan, 2014.
21.
TrépanierM., MorencyC., AgardB., DescoimpsE., and MarcotteJ.-S.Using Smart Card Data to Assess the Impacts of Weather on Public Transport User Behavior. Presented at CASPT12 Conference on Advanced Systems for Public Transport, Santiago, Chile, 2012.
22.
WilsonN.Using Smart Card Data and Complementary Data to Improve the Performance of Public Transport Systems: A Framework and Applications. Presented at 1st International Workshop on Utilizing Transit Smart Card Data for Service Planning, Gifu, Japan, 2014.
23.
SiB., FuL., LiuJ., and ShiraviS.A Multi-Class Traffic Assignment Model for Predicting Transit Passenger Flows—A Case Study of Beijing Subway Network. Presented at 93rd Annual Meeting of the Transportation Research Board, Washington, D.C., 2014.
24.
SunY., and SchonfeldP.M.Schedule-Based Route Choice Estimation with Automatic Fare Collection Data for Rail Transit Passengers. Presented at 93rd Annual Meeting of the Transportation Research Board, Washington, D.C., 2014.
