01661384

Component

Development of an origin-destination demand matrix is crucial for transit planning. The development process is eased with automated data collection, making it possible to mine boarding and alighting patterns on an individual basis. This research proposes a probabilistic method for trip chaining which uses Automatic Fare Collection (AFC) and General Feed Transit Specification (GTFS) data. The proposed method avoids problems resulting from errors in AFC transaction locations or selection of incorrect subroutes from GTFS data which may result in incorrect inference of the origin or destination. The method has been applied to the Twin Cities AFC data as a case study. The transit system is an open system where passengers tap smart cards once while boarding or sometimes alighting (on outbound pay-exit buses). Based on the consecutive tags of the passenger, an algorithm with different capabilities is developed for different cases based on the pay-exit property. The method is compared with a baseline algorithm which shows improvements in the quantity and quality of inferred trips. Finally, the inferred origin-destination demand matrices as well as route ridership are presented visually for planning purposes.

This paper was sponsored by TRB committee ADB30 Standing Committee on Transportation Network Modeling.

18-05342

English

7p

2018

Transportation Research Board 97th Annual Meeting

Digital/other

References

Algorithms; Automatic fare collection; Data collection; Origin and destination; Public transit; Smart cards; Traffic estimation; Transportation planning; Trip chaining

General Transit Feed Specification

Twin Cities Metropolitan Area (Minnesota)

Data and Information Technology; Planning and Forecasting; Public Transportation

Transportation Research Board Annual Meeting 2018 Paper #18-05342

TRIS, TRB, ATRI

Jan 8 2018 11:21AM