A Method for Public Transit OD Estimation Using Transit Smart Card Data

Abstract

With the utilization of Intelligent Transportation Systems (ITS) in public transportation (PT), trip data of passengers are recorded continuously by transit agencies. Researchers developed various algorithms to infer the origin and destination (OD) points of the PT users by using their transit smart card data. The origin and destination matrix is a very vital metric that can help the authorities to analyze public transportation. This study suggests a comprehensive method for OD inference of trips taken on public transportation. The OD pairs of the trips that cannot be inferred using chaining trips are found using the trip histories of the passengers. For the trips that have no further information, we used the overall OD distribution of the studied route.

Keywords

• DC Systems
• Intelligent Transportation Systems
• Origin and Destination Matrix
• Public Transit OD Estimation

References

1. Barry, J.J., R. Newhouser, A. Rahbee, and S. Sayeda, 2002, “Origin and Destination Estimation in New York City with Automated Fare System Data.” Transportation Research Record 1817 (1): 183–87.
2. Cui, A., 2006, Bus Passenger Origin-Destination Matrix Estimation Using Automated Data Collection Systems. M.Sc. Thesis, Massachusetts Institute of Technology.
3. Fidanoglu, A., 2015, Origin and Destination Inference of Bus Passengers: Istanbul Case Study. M.Sc. Thesis, Boğaziçi University.
4. Gordon, J.B., H.N. Koutsopoulos, and N.H.M. Wilson, 2018, “Estimation of Population Origin–Interchange– Destination Flows on Multimodal Transit Networks.” Transportation Research Part C: Emerging Technologies 90 (May): 350–65.
5. Jung, J., and K. Sohn, 2017, “Deep‐learning Architecture to Forecast Destinations of Bus Passengers from Entry‐only Smart‐card Data.” IET Intelligent Transport Systems 11 (6): 334–39.
6. Munizaga, M.A., and C. Palma, 2012, “Estimation of a Disaggregate Multimodal Public Transport Origin- Destination Matrix from Passive Smartcard Data from Santiago, Chile.” Transportation Research Part C: Emerging Technologies 24 (October): 9–18.
7. Sánchez-Martínez, G.E., 2017, “Inference of Public Transportation Trip Destinations by Using Fare Transaction and Vehicle Location Data: Dynamic Programming Approach.” Transportation Research Record 2652 (January): 1–7.
8. Trépanier, M., N. Tranchant, and R. Chapleau, 2007, “Individual Trip Destination Estimation in a Transit Smart Card Automated Fare Collection System.” Journal of Intelligent Transportation Systems: Technology, Planning, and Operations 11 (1): 1–14.

9. Wang, W., J.P. Attanucci, and N.H.M. Wilson, 2011, “Bus Passenger Origin-Destination Estimation and Related Analyses Bus Passenger Origin-Destination Estimation and Related Analyses Using Automated Data Collection Systems Background and Purpose.” Journal of Public Transportation 14 (4).
10. Zhao, J., A. Rahbee, and N.H.M. Wilson, 2007, “Estimating a Rail Passenger Trip Origin-Destination Matrix Using Automatic Data Collection Systems.” Computer-Aided Civil and Infrastructure Engineering 22(5): 376–87.