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http://dx.doi.org/10.12815/kits.2021.20.5.157

A Study on the Mitigation of Taxi Supply and Demand Discrepancy by Adjusting Expected Revenues of Platform Taxi Calls  

Song, Jaein (Research Institute of Science and Technology, Univ. of Hongik)
Kang, Min Hee (Dept. of Smart-city, Univ. of Hongik)
Hwang, Kee yeon (Dept. of Urban Planning, Univ. of Hongik)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.20, no.5, 2021 , pp. 157-171 More about this Journal
Abstract
As smartphones spread and ICT technologies develop, taxi services have changed from hovering to platform-based calls and reservations. This has improved the mobility and accessibility of taxi users but caused problems, such as digital observing (no-responses to calls) for either short-distance services or services during the peak-demand periods. Digital Observing means ignoring and not accepting calls when they occur, which require improvement. Therefore, this study aims to derive measures to mitigate discrepancies in taxi supply and demand by adjusting the expected revenue of each taxi service using reinforcement learning based on the Taxi operation data. The results confirmed that the average complete response rate to calls would increase from 50.29% to 54.24% when incentives are applied, and an improvement of 5.86% can be achieved in short-distance sections of less than 5,000 won incentives. It is expected that the improvement will increase profitability for drivers, reduce the waiting time for passengers, and improve satisfaction with taxi services overall.
Keywords
Taxi; Platform; Reinforcement Learning; Call Incentive;
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