Powerpoint - Transit GIS Clearinghouse

Report
PASSENGER O-D TRIP TABLE
FROM FAREBOX RECEIPTS
Kelly Chan
[email protected]
2013 GIS in Transit Conference, October 16, 2013
 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 82nd 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
Farebox Data
Data warehouse
FAREBOX:
DATE
TIME
BUS
ROUTE
DIR
TRIP
GIS Data:
ROUTES:
ROUTE
DIR
PATTERN
LENGTH
STOPS:
STOP ID
RTE
DIR
PATT
LAT
LONG
STOP
FARE TYPE
PASS ID
How to build a trip table
On-Board Survey
Expensive
Infrequent
Time consuming
Small sample size
Farebox Records
–Origins only
–Time of boardings
–Location of boardings
–Linkages of other data
APC (Automated Passenger Counts)
–Thousands of records per day
Trip ends not connected
Origins (PASS) from farebox
Destinations – process farebox boarding data
Distance
Opportunities
Elapsed Time
Unlinked Origins & destinations
200,000
90%
180,000
80%
160,000
70%
140,000
60%
120,000
100,000
50%
80,000
40%
60,000
30%
40,000
20%
20,000
0
10%
1
2
3
4
5
6
7
Total Boardings
8
9
10
11
12
Boardings with Bus Pass
13
14
15
Identified O-D Trips
16
17
18
Success Rate
19
20
21
22
23
OCTA Advantages:
 Data availability
•365 days, 24 hr/day, Free data
(collected anyway)
 Operational practicality
Demographics and socio-economic data
Automobile Ownership
Activities
Trip purposes
Mode Choices
 Multi-modal
 “park-and-ride”
 Inter-system transfers
Jim Sterling, [email protected]
Kelly Chan, [email protected]
[email protected]

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