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PASSENGER O-D TRIP TABLE FROM FAREBOX RECEIPTS Kelly Chan 2013 GIS in Transit Conference, October 16, 2013.

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Presentation on theme: "PASSENGER O-D TRIP TABLE FROM FAREBOX RECEIPTS Kelly Chan 2013 GIS in Transit Conference, October 16, 2013."— Presentation transcript:

1 PASSENGER O-D TRIP TABLE FROM FAREBOX RECEIPTS Kelly Chan 2013 GIS in Transit Conference, October 16, 2013

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5  Up-Down method for trip length  < 0.5% adult passengers, ~ 3% student passengers  Richardson, AJ (2003). “Estimating Average Distance Travelled from Bus Boarding Counts.” Paper presented at the 82 nd Annual Meeting of the Transportation Research Board, Washington, DC. The Urban Transport Institute.  Société de transport de l’Outaouais (STO, Gatineau, Québec)  66% success  Trépanier, M, Tranchant, N, and Chapleau, R (2007). “Individual Trip Destination Estimation in a Transit Smart Card Automated Fare Collection System,” Journal of Intelligent Transportation System, 11: 1,  Barry JJ, Freimer R, and Slavin H (2009). “Use of Entry-Only Automatic Fare Collection Data to Estimate Linked Transit Trips in New York City,” Transportation Research Record: Journal of the Transportation Research Board, No 2112, pp

6 Data sources for trip table On-Board Survey Intercept Interviews Passenger Count Manual Counting Automated Counting Approximately 2,500 trips per day (6 am ― 3 pm ) Approximately 500 buses Approximately 2,000 – 3,000 staff-hours to have 70% coverage

7 Farebox Data

8 Data warehouse DATETIMEBUSROUTEDIRTRIPSTOPFARE TYPEPASS ID FAREBOX: ROUTEDIRPATTERNLENGTH STOP IDRTEDIRPATTLATLONG ROUTES: STOPS: GIS Data:

9 How to build a trip table On-Board Survey Expensive Infrequent Time consuming Small sample size APC (Automated Passenger Counts) Trip ends not connected Farebox Records – Origins only – Time of boardings – Location of boardings – Linkages of other data – Thousands of records per day

10 Origins (PASS) from farebox

11 Destinations – process farebox boarding data

12  Distance  Opportunities  Elapsed Time

13 Unlinked Origins & destinations

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20  OCTA Advantages:  Data availability 365 days, 24 hr/day, Free data (collected anyway)  Operational practicality

21  Demographics and socio-economic data  Automobile Ownership  Activities  Trip purposes  Mode Choices  Multi-modal  “park-and-ride”  Inter-system transfers

22 Jim Sterling, Kelly Chan,


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