Understanding charging dynamics of fully-electrified taxi services using large-scale trajectory data

TL;DR

This study analyzes the charging behaviors of a fully electrified taxi fleet in Shenzhen using large-scale trajectory data, revealing regular patterns and preferences that inform better planning of E-shared mobility services.

Contribution

It provides one of the first comprehensive analyses of charging dynamics in a fully electrified shared taxi system from both system and individual perspectives.

Findings

Charging activities show strong within-day and daily regularities.

Drivers prefer a small subset of charging stations, following a power-law distribution.

Individual drivers exhibit stable daily charging patterns influenced by shift schedules.

Abstract

An accurate understanding of "when, where and why" of charging activities is crucial for the optimal planning and operation of E-shared mobility services. In this study, we leverage a unique trajectory of a city-wide fully electrified taxi fleet in Shenzhen, China, and we present one of the first studies to investigate charging behavioral dynamics of a fully electrified shared mobility system from both system-level and individual driver perspectives. The electric taxi (ET) trajectory data contain detailed travel information of over 20,000 ETs over one month period. By combing the trajectory and charging infrastructure data, we reveal remarkable regularities in infrastructure utilization, temporal and spatial charging dynamics as well as individual driver level charging preferences. Specifically, we report that both temporal and spatial distributions of system-level charging activities present strong within-day and daily regularities, and most charging activities are induced from drivers' shift schedules. Further, with 425 charging stations, we observe that the drivers show strong preferences over a small subset of charging stations, and the power-law distribution can well characterize the charging frequency at each charging station. Finally, we show that drivers' shift schedules also dominate the individual charging behavior, and there are strikingly stable daily charging patterns at the individual level. The results and findings of our study represent lessons and insights that may be carried over to the planning and operation of E-shared mobility in other cities and deliver important justifications for future studies on the modeling of E-shared mobility services.