An Application of a Deep Q-Network Based Dynamic Fare Bidding System to Improve the Use of Taxi Services during Off-Peak Hours in Seoul

Yunji Cho, Jaein Song, Minhee Kang and Keeyeon Hwang
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Yunji Cho: National Transport Safety and Disaster Prevention Research Center, Korea Transport Institute, Sejong 30147, Korea
Jaein Song: Research Institute of Science and Technology, Hongik University, Seoul 04066, Korea
Minhee Kang: Department of Smart City, Hongik University, Seoul 04066, Korea
Keeyeon Hwang: Department of Urban Planning, Hongik University, Seoul 04066, Korea

Sustainability, 2021, vol. 13, issue 16, 1-17

Abstract: The problem of structural imbalance in terms of supply and demand due to changes in traffic patterns by time zone has been continuously raised in the mobility market. In Korea, unlike large overseas cities, the waiting time tolerance increases during the daytime when supply far exceeds demand, resulting in a large loss of operating profit. The purpose of this study is to increase taxi demand and further improve driver’s profits through real-time fare discounts during off-peak daytime hours in Seoul, Korea. To this end, we propose a real-time fare bidding system among taxi drivers based on a dynamic pricing scheme and simulate the appropriate fare discount level for each regional time zone. The driver-to-driver fare competition system consists of simulating fare competition based on the multi-agent Deep Q-Network method after developing a fare discount index that reflects the supply and demand level of each region in 25 districts in Seoul. According to the optimal fare discount level analysis in the off-peak hours, the lower the OI Index, which means the level of demand relative to supply, the higher the fare discount rate. In addition, an analysis of drivers’ profits and matching rates according to the distance between the origin and destination of each region showed up to 89% and 65% of drivers who actively offered discounts on fares. The results of this study in the future can serve as the foundation of a fare adjustment system for varying demand and supply situations in the Korean mobility market.

Keywords: dynamic pricing; deep Q-network; reinforcement learning; Mobility as a Service (MaaS); platform-based taxi (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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