Dual-objective optimization of taxi dispatching in simulated road network based on reinforcement learning

Mingbo Yang; Kai Zhang; Yuhong Yuan; Yu Liang; Yuhan Dong

doi:10.1117/12.3039914

28 August 2024 Dual-objective optimization of taxi dispatching in simulated road network based on reinforcement learning

Mingbo Yang, Kai Zhang, Yuhong Yuan, Yu Liang, Yuhan Dong

Author Affiliations +

Proceedings Volume 13251, Ninth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2024); 132515J (2024) https://doi.org/10.1117/12.3039914
Event: 9th International Conference on Electromechanical Control Technology and Transportation (ICECTT 2024), 2024, Guilin, China

Abstract

The surge in urban population has led to an imbalance between the demand of residents for travelling and available taxi resources in some specific spatial-temporal contexts. This paper delves into the utilization of reinforcement learning technology to enhance taxi dispatching, with a particular emphasis on optimizing passenger and driver satisfaction. The optimization objective is to maximize revenue while simultaneously minimizing waiting times. We introduce a novel dual-objective optimization system for taxi dispatching, employing reinforcement learning techniques. This system comprises three core modules of the traffic environment simulation module, the mathematical modeling module, and the RL-based dispatching optimization module. Employing a comprehensive approach, we specifically design reward models in reinforcement learning to ensure thorough optimization of taxi scheduling. Stability plays a pivotal role in addressing the intricacies of urban taxi scheduling, given the extensive variations in state and action spaces amidst dynamic environmental conditions. Our reinforcement learning model, based on A3C, streamlines strategy adaptation by learning a unified approach, thus bolstering algorithmic stability through gradient averaging across all agents.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Mingbo Yang, Kai Zhang, Yuhong Yuan, Yu Liang, and Yuhan Dong "Dual-objective optimization of taxi dispatching in simulated road network based on reinforcement learning", Proc. SPIE 13251, Ninth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2024), 132515J (28 August 2024); https://doi.org/10.1117/12.3039914

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KEYWORDS

Mathematical optimization

Mathematical modeling

Data modeling

Roads

Computer simulations

Data acquisition

Decision making

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