Adaptive passenger-finding recommendation system for taxi drivers with load balancing problem. (July 2022)

• Record Type: Journal Article
• Authors: Tran, Duy Hoang; Leyman, Pieter; De Causmaecker, Patrick
• Is Part Of: Computers & industrial engineering. Volume 169(2022)
• Publication Date: 2022-07
• Subjects: Intelligent transportation system -- Adaptive recommendation -- Passenger-finding strategy -- Hotspot detection -- Load balancing -- Trajectory data mining; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2022.108187
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: An adaptive recommendation system for taxi drivers is developed. A hybrid passenger-finding strategy is proposed. A new measure dealing with load balance is introduced. Two issues are integrated into a multi-objective optimisation problem. Abstract: In the literature, much effort has been devoted to providing taxi drivers with recommendations on how to find passengers efficiently. However, the load balancing problem is not taken seriously, leading to a situation where taxis gather and compete at some points while passengers wait a long time in other places due to low taxi supply. Besides, there are multiple passenger-finding strategies, and recommending appropriate strategies according to time and location helps to improve drivers' performance. This paper proposes a global load balancing approach that can be applied to different passenger-finding strategies; specifically, a new measure dealing with load balance between taxis and passengers is introduced, and an adaptive passenger-finding recommendation method is proposed. These two problems are integrated into a single coherent framework using a multi-objective optimisation approach to maximise drivers' potential benefits while minimising supply-demand imbalance. Extensive experiments on real-world taxi data from New York City and San Francisco verify that the proposed method improves upon the performance of existing methods in terms of both drivers' net profit and passengers' waiting time.