Identifying critical transfer zones to coordinate transit with on-demand services using crowdsourced trajectory data

Abstract

This study develops a data-driven approach for identifying critical transfer zones in the city to facilitate the coordination of transit and emerging on-demand services. First, the methods convert the trajectories into a 3 D grid with an optimal cube size. Built upon that, we zoom in and study the trajectory density of each mode in a cube and present the results by heatmaps. After that, we zoom out and aggregate those cube information fragments through the clustering algorithms to explore two critical patterns: the ridesharing swarm (RS) zones where many ridesharing trips go through, and the “sandwich pattern” zones where a transit trajectory dominant zone is sandwiched by two ridesharing trajectory dominant zones. Our numerical analysis confirms that these RS zones are well correlated to the promising areas/corridors for integrating transit and on-demand services; the “sandwich patterns” help discover first/last mile (FLM) zones. Last, we further develop a two-channel deep learning network to predict the variation of the FLM gaps so that adaptive services can be planned. A case study based on the field data of the second ring region of Chengdu, China confirms the effectiveness and capability of our analysis approach.