Automated inference of linked transit journeys in london using fare-transaction and vehicle location data

Transportation Research Record

Volumn 2343, Issue , 2013, Pages 17-24

  Gordon, Jason B ^a   Koutsopoulos, Harilaos N ^c   Wilson, Nigel H M ^b   Attanucci, John P ^b  

^a Massachusetts Institute of Technology ^*  (Canada)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^c ROYAL INSTITUTE OF TECHNOLOGY   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

LOCATION; MASS TRANSPORTATION; MATRIX ALGEBRA; URBAN TRANSPORTATION; VEHICLES;

AUTOMATED FARE COLLECTION; LOCATION SYSTEMS; ORIGIN DESTINATION MATRICES; PASSENGER BEHAVIOR; PUBLIC TRANSIT; PUBLIC TRANSIT NETWORKS; SOFTWARE IMPLEMENTATION; TRAVEL PATTERNS; VEHICLE LOCATION; VEHICLE LOCATION DATA;

SURVEYS;

EID: 84911426041     PISSN: 03611981     EISSN: 21694052     Source Type: Journal    
DOI: 10.3141/2343-03     Document Type: Article

References (30)

1. Chu, K. K. A., and R. Chapleau. Enriching Archived Smart Card Transaction Data for Transit Demand Modeling. In Transportation Research Record: Journal of the Transportation Research Board, No. 2063, Transportation Research Board of the National Academies, Washington, D.C., 2008, pp. 63–72.
2. Chu, K. K. A., and R. Chapleau. Augmenting Transit Trip Characterization and Travel Behavior Comprehension: Multiday Location-Stamped Smart Card Transactions. In Transportation Research Record: Journal of the Transportation Research Board, No. 2183, Transportation Research Board of the National Academies, Washington, D.C., 2010, pp. 29–40.
3. Farzin, J. M. 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.
4. Munizaga, M., and C. Palma. Estimation of a Disaggregate Multimodal Public Transport Origin-Destination Matrix from Passive Smartcard Data from Santiago, Chile. Transport Research Part C: Emerging Technologies, Vol. 24, 2012, pp. 9–18.
5. Zhao, J., A. Rahbee, and N. H. M. Wilson. Estimating a Rail Passenger Trip Origin-Destination Matrix Using Automatic Data Collection Systems. Computer-Aided Civil and Infrastructure Engineering, Vol. 22, No. 5, 2007, pp. 376–387.
6. Cui, A. Bus Passenger Origin–Destination Matrix Estimation Using Automated Data Collection Systems. MS thesis. Massachusetts Institute of Technology, Cambridge, 2006.
7. Wang, W., J. P. Attanucci, and N. H. M. Wilson. Bus Passenger Origin–Destination Estimation and Related Analyses Using Automated Data Collection Systems. Journal of Public Transportation, Vol. 14, No. 4, 2011, pp. 131–150.
8. Gordon, J. B. Intermodal Passenger Flows on London’s Public Transport Network: Automated Inference of Full Passenger Journeys Using Fare-Transaction and Vehicle-Location Data. MS thesis. Massachusetts Institute of Technology, Cambridge, 2012.
9. Barry, J. J., R. Newhouser, A. Rahbee, and S. Sayeda. Origin and Destination Estimation in New York City with 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.
10. Barry, J. J., R. Freimer, and H. L. Slavin. Use of Entry-Only Automatic Fare Collection Data to Estimate Linked Transit Trips in New York City. In Transportation Research Record: Journal of the Transportation Research Board, No. 2112, Transportation Research Board of the National Academies, Washington, D.C., 2009, pp. 53–61.
11. Navick, D. S., and P. G. Furth. Estimating Passenger Miles, Origin-Destination Patterns, and Loads with Location-Stamped Farebox Data. In Transportation Research Record: Journal of the Transportation Research Board, No. 1799, Transportation Research Board of the National Academies, Washington, D.C., 2002, pp. 107–113.
12. Trépanier, M., N. Tranchant, and R. Chapleau. 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.
13. Kuhnimhof, T., and V. Wassmuth. Do You Go to the Movies on your Lunch Break? Trip-Context Data-Based Modeling of Activities. In Transportation Research Record: Journal of the Transportation Research Board, No. 1807, Transportation Research Board of the National Academies, Washington, D.C., 2002, pp. 34–42.
14. Bagchi, M., and P. White. What Role for Smart Card Data from Bus Systems. Proceedings of the Institution of Civil Engineers Municipal Engineer, Vol. 157 (March), 2004, pp. 39–46.
15. Trip Linking Procedures: Working Paper 2: Bay Area Travel Survey 2000. Metropolitan Transportation Commission, Oakland, Calif., 2003.
16. Okamura, T., J. Zhang, and F. Akimasa. Finding Behavioral Rules of Urban Public Transport Passengers by Using Boarding Records of Integrated Stored Fare Card System. Presented at 10th World Conference on Transport Research, Istanbul, Turkey, 2004.
17. McCaig, E., and M. Yip. Technical Note: A-BODS Stage 2. Transport for London, 2010.
18. Hofmann, M., and M. O’Mahony. Transfer Journey Identification and Analyses from Electronic Fare Collection Data. Proc., 8th International IEEE Conference on Intelligent Transportation Systems, Vienna, Austria, 2005.
19. Seaborn, C., J. Attanucci, and N. H. M. Wilson. Analyzing Multimodal Public Transport Journeys in London with Smart Card Fare Payment Data. In Transportation Research Record: Journal of the Transportation Research Board, No. 2121, Transportation Research Board of the National Academies, Washington, D.C., 2009, pp. 55–62.
20. Deming, W. E., and F. F. Stephan. On a Least Squares Adjustment of a Sampled Frequency Table When the Expected Marginal Totals Are Known. Annals of Mathematical Statistics, Vol. 11, No. 4, 1940, pp. 427–444.
21. Ben Akiva, M., P. P. Macke, and P. S. Hsu. Alternative Methods to Estimate Route-Level Trip Tables and Expand On-Board Surveys. In Transportation Research Record 1037, TRB, National Research Council, Washington, D.C., 1985, pp. 1–11.
22. Wilson, N. H. M., J. P. Attanucci, J. Chan, and A. Cui. The Use of Automated Data Collection Systems to Improve Public Transport Performance. Proc., 10th International Conference on Application of Advanced Technologies in Transportation, Athens, Greece, 2008.
23. McCord, M. R., R. G. Mishalani, P. Goel, and B. Strohl. Iterative Proportional Fitting Procedure to Determine Bus Route Passenger Origin-Destination Flows. In Transportation Research Record: Journal of the Transportation Research Board, No. 2145, Transportation Research Board of the National Academies, Washington, D.C., 2010, pp. 59–65.
24. Gordillo, F. 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.
25. Chan, J. Rail Transit OD Matrix Estimation and Journey Time Reliability Metrics Using Automated Fare Data. MS thesis. Massachusetts Institute of Technology, Cambridge, 2007.
26. Schil, M. Measuring Travel Time Reliability in London Using Automated Data Collection Systems. MS thesis. Massachusetts Institute of Technology, Cambridge, 2012.
27. Gordon, J. B. London in Motion. 2011. http://jaygordon.net/londonviz.html.
28. Ng, A. Use of Automatically Collected Data for the Preliminary Impact Analysis of the East London Line Extension. MS thesis. Massachusetts Institute of Technology, Cambridge, 2011.
29. Muhs, K. Using Automatically Collected Data to Infer Travel Behavior: A Case Study of the East London Line Extension. MS thesis. Massachusetts Institute of Technology, Cambridge, 2012.
30. Brakewood, C., and G. Kocur. Modeling Transit Rider Preferences for Contactless Bank Cards as Fare Media: Transport for London and the Chicago, Illinois, Transit Authority. In Transportation Research Record: Journal of the Transportation Research Board, No. 2216, Transportation Research Board of the National Academies, Washington, D.C., 2011, pp. 100–107.