01697667

Component

Detailed ridership analytics platform requires refined data on transit ridership to understand factors affecting ridership (at the stop and/or route-level). However, detailed data for stop-based boarding and alighting information are not readily available for the entire bus system. Transit agencies usually resort to compiling ridership data on a sample of buses operating on the various routes. The authors propose an approach to infer stop-level ridership for transit systems that only compile route-level ridership information. A joint model structure of binary logit and fractional split model is proposed to estimate stop-level ridership data from route-level ridership. The model is developed for the Greater Orlando region with ridership data for 8 four-month time periods from May 2014 through December 2016. In the presence of repeated data measures, panel version of the joint econometric models for boarding and ridership dimensions are estimated. The development of such an analytical framework will allow bus systems with only route-level ridership data to generate stop-level ridership data. The model results offer intuitive results and clearly supports the authors' hypothesis that it is feasible to generate stop-level ridership with route-level ridership data. The proposed model can be employed by transit agencies without stop-level data to estimate stop-level ridership metrics.

This paper was sponsored by TRB committee AP050 Standing Committee on Bus Transit Systems.

19-03818

English

10p

2019

Transportation Research Board 98th Annual Meeting

Digital/other

Figures; References; Tables

Boarding and alighting; Bus routes; Bus stops; Bus transit; Data analysis; Logits; Ridership; Transit riders

Fractional split model

Orlando (Florida)

Data and Information Technology; Planning and Forecasting; Public Transportation

Transportation Research Board Annual Meeting 2019 Paper #19-03818

TRIS, TRB, ATRI

Dec 7 2018 9:34AM