A joint panel binary logit and fractional split model for converting route-level transit ridership data to stop-level boarding and alighting data

Detailed ridership analytics requires refined data on transit ridership to understand factors af-fecting 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. We 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 sourced from route-level ridership. The model is developed for the Greater Orlando region with ridership data for 8 quadrimesters (four-month time periods) from May 2014 through December 2016. In the presence of repeated data mea-sures, panel version of the joint econometric models for boarding and alighting 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 our hypothesis that it is feasible to generate stop-level ridership with route-level ridership data. For transit agencies with ridership data at the stop-level, the proposed model can also be employed to understand how various stops along a route interact with one another to-ward affecting route-level ridership contributions.