Automated taxi queue management at high-demand venues

• Main Authors: JI, Mengyu, CHENG, Shih-Fen
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: airport management; taxi; demand prediction; Artificial Intelligence and Robotics; Computer Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/6209; https://ink.library.smu.edu.sg/context/sis_research/article/7212/viewcontent/case21_atm.pdf
• Institution: Singapore Management University

In this paper, we seek to identify an effective management policy that could reduce supply-demand gaps at taxi queues serving high-density locations where demand surges frequently happen. Unlike current industry practice, which relies on broadcasting to attract taxis to come and serve the queue, we propose more proactive and adaptive approaches to handle demand surges. Our design objective is to reduce the cumulative supply-demand gaps as much as we could by sending notifications to individual taxis. To address this problem, we first propose a highly effective passenger demand prediction system that is based on the real-time flight arrival information. By monitoring cumulative passenger arrivals, and control for factors such as the flight's departure cities, we demonstrate that a simple linear regression model can accurately predict the number of passengers joining taxi queues. We then propose an optimal control strategy based on a Markov Decision Process to model the decisions of notifying individual taxis that are at different distances away from the airport. By using a real-world dataset, we demonstrate that an accurate passenger demand prediction system is crucial to the effectiveness of taxi queue management. In our numerical studies based on the real-world data, we observe that our proposed approach of optimal control with demand predictions outperforms the same control strategy, yet with Poisson demand assumption, by 43%. Against the status quo, which has no external control, we could reduce the gap by 23%. These results demonstrate that our proposed methodology has strong real-world potential.