A Cost-Effective Sequential Route Recommender System for Taxi Drivers

Junming Liu (), Mingfei Teng (), Weiwei Chen () and Hui Xiong ()
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Junming Liu: Department of Information Systems, City University of Hong Kong Hong Kong SAR, China
Mingfei Teng: Department of Management Science and Information Systems, Rutgers University, Newark, New Jersey 07102
Weiwei Chen: Department of Supply Chain Management, Rutgers University, Newark, New Jersey 07102
Hui Xiong: Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou), Nansha, Guangdong 511458, China

INFORMS Journal on Computing, 2023, vol. 35, issue 5, 1098-1119

Abstract: This paper develops a cost-effective sequential route recommender system to provide real-time routing recommendations for vacant taxis searching for the next passenger. We propose a prediction-and-optimization framework to recommend the searching route that maximizes the expected profit of the next successful passenger pickup based on the dynamic taxi demand-supply distribution. Specifically, this system features a deep learning-based predictor that dynamically predicts the passenger pickup probability on a road segment and a recursive searching algorithm that recommends the optimal searching route. The predictor integrates a graph convolution network (GCN) to capture the spatial distribution and a long short-term memory (LSTM) to capture the temporal dynamics of taxi demand and supply. The GCN-LSTM model can accurately predict the pickup probability on a road segment with the consideration of potential taxi oversupply. Then, the dynamic distribution of pickup probability is fed into the route optimization algorithm to recommend the optimal searching routes sequentially as route inquiries emerge in the system. The recursion tree-based route optimization algorithm can significantly reduce the computational time and provide the optimal routes within seconds for real-time implementation. Finally, extensive experiments using Beijing Taxi GPS data demonstrate the effectiveness and efficiency of the proposed recommender system.

Keywords: recommender system; deep learning; business effective strategy; route recommendation (search for similar items in EconPapers)
Date: 2023
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