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, 1-14. 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. 53-61.
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
Data warehouse DATETIMEBUSROUTEDIRTRIPSTOPFARE TYPEPASS ID FAREBOX: ROUTEDIRPATTERNLENGTH STOP IDRTEDIRPATTLATLONG ROUTES: STOPS: GIS Data:
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