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

A demonstrative diagram showing how on-damand ridesharing trips may serve areas away from backbone public transportation options. Gray lines are hypothetical streets. Blue line with blue squares show hypothetical transit options with stops. Green square with a white telephone inside and surrounding by peach show hotspots in locations demanding ridesharing. Blue areas highlight trips that ridesharing options are making between the hotspots.
Figure 1. Diagram of hotspots in on-demand services and critical corridors of connections between two hotspots on a hypothetical set of streets.

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

Jiahua Qiua, Yue Jing, Wang Peng, Lili Du, Yujie Hu

Article first published online: 21 November 2022

DOI: https://doi.org/10.1080/15472450.2022.2132389

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.

A grid of square cells to demonstrate how ridesharing origin, transit, and destination areas are identified on a grid. Peach areas show zones with common ridesharing origin and destination points. Red shows area where ridesharing rides are typically in transit to the destination. Arrows show the direction of travel for an example ride.
Figure 7. Schematic representation of the “sandwich” pattern. Peach areas show zones with common ridesharing origin and destination points. Red shows area where ridesharing rides are typically in transit to the destination.

Read the full publication at the Journal of Intelligent Transportation Systems.