Understanding the Dynamics of Drivers' Locations for Passengers Pickup Performance: A Case Study

TL;DR

This study analyzes drivers' and passengers' locations at booking requests to understand pickup performance, using a novel clustering method and predictive scoring to improve driver-passenger matching in ride-hailing services.

Contribution

It introduces a modified co-clustering technique and scoring mechanisms to evaluate and predict drivers' pickup performance based on spatial-temporal data.

Findings

Clusters of driver and passenger locations reveal patterns affecting pickup success.

Predictive models can estimate timely pickups without full trajectory data.

Scoring mechanisms effectively prioritize drivers for passenger requests.

Abstract

With the emergence of e-hailing taxi services, a growing number of scholars have attempted to analyze the taxi trips data to gain insights from drivers' and passengers' flow patterns and understand different dynamics of urban public transportation. Existing studies are limited to passengers' location analysis e.g., pick-up and drop-off points, in the context of maximizing the profits or better managing the resources for service providers. Moreover, taxi drivers' locations at the time of pick-up requests and their pickup performance in the spatial-temporal domain have not been explored. In this paper, we analyze drivers' and passengers' locations at the time of booking request in the context of drivers' pick-up performances. To facilitate our analysis, we implement a modified and extended version of a co-clustering technique, called sco-iVAT, to obtain useful clusters and co-clusters from big relational data, derived from booking records of Grab ride-hailing service in Singapore. We also explored the possibility of predicting timely pickup for a given booking request, without using entire trajectories data. Finally, we devised two scoring mechanisms to compute pickup performance score for all driver candidates for a booking request. These scores could be integrated into a booking assignment model to prioritize top-performing drivers for passenger pickups.