Heuristics for electric taxi fleet management at Teo Taxi

Abstract

The advent of new technologies in the area of transportation has led to the creation of new business models that benefit from their capabilities. Among them, Teo Taxi, a Taxelco project, uses a unique business model: taxi drivers are employees instead of being self-employed. In addition, the company uses only electric vehicles. This shift entails new challenges regarding fleet management and process optimization. This article tackles these issues and proposes solutions to three operational problems: the dispatch, relocation and charging of electric taxis. To evaluate the performance of our algorithms, we designed a discrete-event simulator. The application models a taxi fleet and computes relative performance according to average waiting time and hourly income per taxi. We show that dispatching taxis based on the solution to an assignment problem can provide a 20% increase in income relative to the simplistic approach where requests are assigned to the nearest taxi. Furthermore, we adapt to the case of taxi fleet management a relocation strategy designed for ambulances. Finally, in order to efficiently manage battery charges, we use a strategy that involves terminal calls. This strategy stabilizes the average state of charge and eases operations overall.

Keywords:

• Optimization
• heuristics
• simulation
• taxi service
• dispatch
• location problem
• charging problem
• electric vehicles
• routing problem

Acknowledgments

The authors thank two anonymous referees for their valuable comments which have helped improve the paper.

Additional information

Funding

This research was supported by MITACS and by the Natural Sciences and Engineering Research Council of Canada; CRSNG. This support is gratefully acknowledged.