IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i16p9351-d618143.html
   My bibliography  Save this article

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Yunji Cho & Jaein Song & Minhee Kang & Keeyeon Hwang, 2021. "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," Sustainability, MDPI, vol. 13(16), pages 1-17, August.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:16:p:9351-:d:618143
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/16/9351/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/16/9351/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pueboobpaphan, Suthatip & Indra-Payoong, Nakorn & Opasanon, Sathaporn, 2019. "Experimental analysis of variable surcharge policy of taxi service auction," Transport Policy, Elsevier, vol. 76(C), pages 134-148.
    2. Guillermo Gallego & Garrett van Ryzin, 1994. "Optimal Dynamic Pricing of Inventories with Stochastic Demand over Finite Horizons," Management Science, INFORMS, vol. 40(8), pages 999-1020, August.
    3. Adnan, Muhammad & Nahmias Biran, Bat-hen & Baburajan, Vishnu & Basak, Kakali & Ben-Akiva, Moshe, 2020. "Examining impacts of time-based pricing strategies in public transportation: A study of Singapore," Transportation Research Part A: Policy and Practice, Elsevier, vol. 140(C), pages 127-141.
    4. Gabriel R. Bitran & Susana V. Mondschein, 1997. "Periodic Pricing of Seasonal Products in Retailing," Management Science, INFORMS, vol. 43(1), pages 64-79, January.
    5. Rambha, Tarun & Boyles, Stephen D., 2016. "Dynamic pricing in discrete time stochastic day-to-day route choice models," Transportation Research Part B: Methodological, Elsevier, vol. 92(PA), pages 104-118.
    6. Jörg Firnkorn & Martin Müller, 2012. "Selling Mobility instead of Cars: New Business Strategies of Automakers and the Impact on Private Vehicle Holding," Business Strategy and the Environment, Wiley Blackwell, vol. 21(4), pages 264-280, May.
    7. Wedad Elmaghraby & P{i}nar Keskinocak, 2003. "Dynamic Pricing in the Presence of Inventory Considerations: Research Overview, Current Practices, and Future Directions," Management Science, INFORMS, vol. 49(10), pages 1287-1309, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Adam J. Mersereau & Dan Zhang, 2012. "Markdown Pricing with Unknown Fraction of Strategic Customers," Manufacturing & Service Operations Management, INFORMS, vol. 14(3), pages 355-370, July.
    2. Sabri Çelik & Alp Muharremoglu & Sergei Savin, 2009. "Revenue Management with Costly Price Adjustments," Operations Research, INFORMS, vol. 57(5), pages 1206-1219, October.
    3. Gonca P. Soysal & Lakshman Krishnamurthi, 2012. "Demand Dynamics in the Seasonal Goods Industry: An Empirical Analysis," Marketing Science, INFORMS, vol. 31(2), pages 293-316, March.
    4. Wedad Elmaghraby & Altan Gülcü & P{i}nar Keskinocak, 2008. "Designing Optimal Preannounced Markdowns in the Presence of Rational Customers with Multiunit Demands," Manufacturing & Service Operations Management, INFORMS, vol. 10(1), pages 126-148, June.
    5. Goker Aydin & Serhan Ziya, 2008. "Pricing Promotional Products Under Upselling," Manufacturing & Service Operations Management, INFORMS, vol. 10(3), pages 360-376, June.
    6. Pavithra Harsha & Shivaram Subramanian & Joline Uichanco, 2019. "Dynamic Pricing of Omnichannel Inventories," Service Science, INFORMS, vol. 21(1), pages 47-65, January.
    7. Bernardo Bertoldi & Chiara Giachino & Alberto Pastore, 2016. "Strategic pricing management in the omnichannel era," MERCATI & COMPETITIVIT?, FrancoAngeli Editore, vol. 2016(4), pages 131-152.
    8. Ibrahim, Michael Nawar & Atiya, Amir F., 2016. "Analytical solutions to the dynamic pricing problem for time-normalized revenue," European Journal of Operational Research, Elsevier, vol. 254(2), pages 632-643.
    9. Dasu, Sriram & Tong, Chunyang, 2010. "Dynamic pricing when consumers are strategic: Analysis of posted and contingent pricing schemes," European Journal of Operational Research, Elsevier, vol. 204(3), pages 662-671, August.
    10. , & , & ,, 2011. "Revenue maximization in the dynamic knapsack problem," Theoretical Economics, Econometric Society, vol. 6(2), May.
    11. Alderighi, Marco & Gaggero, Alberto A. & Piga, Claudio A., 2022. "Hidden prices with fixed inventory: Evidence from the airline industry," Transportation Research Part B: Methodological, Elsevier, vol. 157(C), pages 42-61.
    12. moldovanu, benny & Gershkov, Alex, 2007. "The Dynamic Assignment of Heterogenous Objects: A Mechanism Design Approach," CEPR Discussion Papers 6439, C.E.P.R. Discussion Papers.
    13. Peter Seele & Claus Dierksmeier & Reto Hofstetter & Mario D. Schultz, 2021. "Mapping the Ethicality of Algorithmic Pricing: A Review of Dynamic and Personalized Pricing," Journal of Business Ethics, Springer, vol. 170(4), pages 697-719, May.
    14. C S M Currie & R C H Cheng & H K Smith, 2008. "Dynamic pricing of airline tickets with competition," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(8), pages 1026-1037, August.
    15. Netessine, Serguei, 2006. "Dynamic pricing of inventory/capacity with infrequent price changes," European Journal of Operational Research, Elsevier, vol. 174(1), pages 553-580, October.
    16. Chia-Wei Kuo & Hyun-Soo Ahn & Göker Aydın, 2011. "Dynamic Pricing of Limited Inventories When Customers Negotiate," Operations Research, INFORMS, vol. 59(4), pages 882-897, August.
    17. Tamer Boyacı & Özalp Özer, 2010. "Information Acquisition for Capacity Planning via Pricing and Advance Selling: When to Stop and Act?," Operations Research, INFORMS, vol. 58(5), pages 1328-1349, October.
    18. Kyle Y. Lin, 2004. "A sequential dynamic pricing model and its applications," Naval Research Logistics (NRL), John Wiley & Sons, vol. 51(4), pages 501-521, June.
    19. Michael Dziecichowicz & Daniela Caro & Aurélie Thiele, 2015. "Robust timing of markdowns," Annals of Operations Research, Springer, vol. 235(1), pages 203-231, December.
    20. Shanshan Hu & Xing Hu & Qing Ye, 2017. "Optimal Rebate Strategies Under Dynamic Pricing," Operations Research, INFORMS, vol. 65(6), pages 1546-1561, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:16:p:9351-:d:618143. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.