DOI QR코드

DOI QR Code

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

택시호출 간 기대수익 조정을 통한 택시 수급불일치 완화방안 연구

  • 송재인 (홍익대학교 과학기술연구소) ;
  • 강민희 (홍익대학교 일반대학원 산업융합협동과정 스마트도시전공) ;
  • 황기연 (홍익대학교 도시공학과)
  • Received : 2021.08.02
  • Accepted : 2021.10.05
  • Published : 2021.10.31

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.

스마트폰 보급과 ICT 기술을 발전에 따라 택시영업의 형태는 배회영업에서 플랫폼 기반 영업으로 변화해왔다. 이는 이용자의 이동성 및 접근성을 향상시키는 장점을 갖고 있지만 반대로 단거리 및 첨두수요 시간대의 간접 승차거부 등의 문제를 지속적으로 발생시키고 있다. 간접승차거부는 호출이 발생했을 때 이를 무시하고 수락하지 않는 경우를 의미하며 이를 개선할 필요가 있다. 이에 본 연구에서는 택시 운행 데이터를 통해 강화 학습 기반 호출 간 기대수익 조정 시뮬레이션을 수행하여 택시 수급의 불일치 완화 방안을 도출하고자 한다. 분석 결과 운행 완료율에 따라 인센티브 지급을 할 경우 평균 운행 완료율이 50.29%에서 54.24% 수준까지 증가함을 확인하였으며 5,000원 미만 단거리 구간에서 5.86%의 개선 효과를 도출하였다. 운행 완료율의 개선으로 운전자에게는 수익성 개선, 승객에게는 대기시간 감소의 편익을 줄 수 있을 것으로 기대되며, 택시 서비스 전반의 만족도 향상이 나타날 것으로 사료된다.

Keywords

References

  1. An G.(2015), Seoul's Taxi Usage and Operation Status and Improvement Plan, Policy Report 186, Seoul Institute: Seocho-gu, Seoul, Korea.
  2. Arnott R.(1996), "Taxi Travel Should Be Subsidized," Journal of Urban Economics, vol. 40, no. 3, pp.316-333. https://doi.org/10.1006/juec.1996.0035
  3. Billhardt H., Fernandez A., Ossowski S., Palanca J. and Bajo J.(2019), "Taxi dispatching strategies with compensations," Expert Systems with Applications, vol. 122, pp.173-182. https://doi.org/10.1016/j.eswa.2019.01.001
  4. Chen M., Shen W., Tang P. and Zuo S.(2017), Optimal vehicle dispatching schemes via dynamic pricing, arXiv preprint arXiv: 1707.01625.
  5. Choi J., Cho Y. and Jeong I.(2014), "Multiple-Intersection Traffic Signal Control based on Traffic Pattern Learning," Journal of the Society of Information and Sciences: Practical and Letters in Computing, vol. 20, no. 3, pp.171-179.
  6. Fang Z., Huang L. and Wierman A.(2019), "Prices and subsidies in the sharing economy," Performance Evaluation, vol. 136, 102037. https://doi.org/10.1016/j.peva.2019.102037
  7. Fang Z., Su R. and Huang L.(2018), "Understanding the effect of an E-hailing app subsidy war on taxicab operation zones," Journal of Advanced Transportation, vol. 2018, 7687852.
  8. Guan Y., Annaswamy A. M. and Tseng H. E.(2019), Towards Dynamic Pricing for Shared Mobility on Demand using Markov Decision Processes and Dynamic Programming, arXiv preprint arXiv: 1910.01993.
  9. Hang C., Liu Z., Wang Y., Hu C., Su Y. and Dong Z.(2019), "Sharing diseconomy: Impact of the subsidy war of ride-sharing companies on urban congestion," International Journal of Logistics Research and Applications, vol. 22, no. 5, pp.491-500. https://doi.org/10.1080/13675567.2019.1619677
  10. He F., Wang X., Lin X. and Tang X.(2018), "Pricing and penalty/compensation strategies of a taxi-hailing platform," Transportation Research Part C: Emerging Technologies, vol. 86, pp.263-279. https://doi.org/10.1016/j.trc.2017.11.003
  11. Jittrapirom P., Marchau V., Van der Heijden R. and Meurs H.(2018), "Future implementation of Mobility as a Service (MaaS): Results of an international Delphi study," Travel Behaviour and Society, vol. 21, pp.281-294.
  12. Joo H. and Lim Y.(2020), "Distributed Traffic Signal Control at Multiple Intersections Based on Reinforcement Learning," Journal of the Korea Communications Association, vol. 45, no. 2, pp.303-310.
  13. Kamatani T., Nakata Y. and Arai S.(2019), "Dynamic pricing meth to maximize utilization of one-way car sharing service," In 2019 IEEE International Conference on Agents (ICA), October, IEEE, pp.65-68.
  14. Kim D. H. and Jung O.(2019), "A Study on Cooperative Traffic Signal Control at multi-intersection," Journal of Electrical and Electronic Society, vol. 23, no. 4, pp.266-271.
  15. Kim J. H. and Kim S. I.(2019), "A study on User Experience of Mobility Platform Service-Focused on kakao Taxi and Tada-," Journal of Digital Convergence, vol. 17, no. 7, pp.351-357. https://doi.org/10.14400/JDC.2019.17.7.351
  16. Lei C., Jiang Z. and Ouyang Y.(2019), "Path-based dynamic pricing for vehicle allocation in ridesharing systems with fully compliant drivers," Transportation Research Part B: Methological, vol. 38, pp.77-97.
  17. Leng B., Du H., Wang J., Li L. and Xiong Z.(2015), "Analysis of taxi drivers' behaviors within a battle between two taxi apps," IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 1, pp.296-300. https://doi.org/10.1109/TITS.2015.2461000
  18. Minister of Land, Infrastructure and Transport, https://www.molit.go.kr, 2021.07.15.
  19. Rambha T. and Boyles S. D.(2016), "Dynamic pricing in discrete time stochastic day-to-day route choice models," Transportation Research Part B: Methodological, vol. 92, pp.104-118. https://doi.org/10.1016/j.trb.2016.01.008
  20. Song J., Cho Y. J., Kang M. H. and Hwang K. Y.(2020), "An Application of Reinforced Learning-Based Dynamic Pricing for Improvement of Ridesharing Platform Service in Seoul," Electronics, vol. 9, no. 11, p.1818. https://doi.org/10.3390/electronics9111818
  21. Su R., Fang Z., Xu H. and Huang L.(2018), "Uncovering spatial inequality in taxi services in the context of a subsidy war among E-hailing apps," ISPRS International Journal of Geo-Information, vol. 7, no. 6, p.230. https://doi.org/10.3390/ijgi7060230
  22. Sutton R. S. and Barto A. G.(2018), Reinforcement learning: An introduction, MIT Press.
  23. Suzuki Y. and Hino S.(2016), "Study of Price Sensitivity of Taxi Fare and Feeling of Satisfaction on Using a Taxi," Journal of the City Planning Institute of Japan, vol. 51, no. 3.
  24. Tong Y., Chen Y., Zhou Z., Chen L., Wang J., Yang Q. and Lv W.(2017), "The simpler the better: A unified approach to predicting original taxi demands based on large-scale online platforms," In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August, pp.1653-1662.
  25. Wei C., Wang Y., Yan X. and Shao C.(2017), "Look-ahead insertion policy for a shared-taxi system based on reinforcement learning," IEEE Access, vol. 6, pp.5716-5726. https://doi.org/10.1109/access.2017.2769666
  26. Wen J., Zou M., Ma Y. and Luo H.(2017), "Evaluating the influence of taxi subsidy programs on mitigating difficulty getting a taxi in basis of taxi empty-loaded rate," International Journal of Statistics and Probability, vol. 6, no. 2, pp.9-20. https://doi.org/10.5539/ijsp.v6n2p9
  27. Wu T., Joseph A. D. and Russell S. J.(2016), Automated pricing agents in the on-demand economy, Electrical Engineering and Computer Sciences University of California at Berkeley.
  28. Xu T., Zhu H., Zhao X., Liu Q., Zhong H., Chen E. and Xiong H.(2016), "Taxi driving behavior analysis in latent vehicle-to-vehicle networks: A social influence perspective," In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August, pp.1285-1294.
  29. Xu Z., Li Z., Guan Q., Zhang D., Li Q., Nan J. and Ye J.(2018), "Large-scale order dispatch in on-demand ride-hailing platforms: A learning and planning approach," In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, July, pp.905-913.
  30. Yazici M. A., Kamga C. and Singhal A.(2013), "A big data driven model for taxi drivers' airport pick-up decisions in new york city," In 2013 IEEE International Conference on Big Data, October, IEEE, pp.37-44.