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미시적 교통 시뮬레이션을 활용한 실시간 수요대응형 자율주행 버스 영향 평가

Impact Assessment of an Autonomous Demand Responsive Bus in a Microscopic Traffic Simulation

  • 박상웅 (퍼듀대학교 토목공학과 ) ;
  • 김주영 (한국교통대학교 교통정책학과)
  • Sang ung, Park (Lyles School of Civil Engineering, Purdue University) ;
  • Joo young, Kim (Dept. of Transportation Planning & Management, Korea National University of Transportation)
  • 투고 : 2022.10.31
  • 심사 : 2022.11.29
  • 발행 : 2022.12.31

초록

실시간 수요대응형 자율주행 버스는 자율주행 버스와 실시간 수요대응형 버스의 단점을 상쇄시킨 미래교통수단이다. 하지만 버스 기능의 고도화로 실시간 수요대응형 자율주행 버스 도입 시 영향에 관한 정량화된 연구는 활발하지 않은 실정이다. 본 연구에서는 강화학습 기반 실시간 수요대응형 자율주행 버스를 미시적 교통 시뮬레이션에 적용하여 실시간 수요대응형 자율주행 버스의 정량화된 효과평가를 실시하였다. 구체적으로 수요 변화에 따라 실시간 수요대응형 자율주행 버스가 도로 네트워크에 끼치는 영향과 이용자 대기시간을 미시적 시뮬레이션 안에서 구현하였다. 시뮬레이션 대상지로는 한국교통대학교 인근을 선정하였다. 시뮬레이션 결과, 실시간 수요대응형 자율주행 버스는 기존 노선 고정형 버스 대비 이용자 대기시간과 평균제어지체는 감소하였고, 평균통행속도는 증가하였다. 본 연구를 통해 실시간 수요대응형 버스의 도입을 정량적으로 평가하는 것이 기대된다.

An autonomous demand-responsive bus with mobility-on-demand service is an innovative transport compensating for the disadvantages of an autonomous bus and a demand-responsive bus with mobility-on-demand service. However, less attention has been paid to the quantitative impact assessment of the autonomous demand-responsive bus due to the technological complexity of the autonomous demand-responsive bus. This study simulates autonomous demand-responsive bus trips by reinforcement learning on a microscopic traffic simulation to quantify the impact of the autonomous demand-responsive bus. The Chungju campus of the Korea National University of Transportation is selected as a testbed. Simulation results show that the introduction of the autonomous demand-responsive bus can reduce the wait time of passengers, average control delay, and increase the traffic speed compared to the results with fixed route bus service. This study contributes to the quantitative evaluation of the autonomous demand-responsive bus.

키워드

과제정보

본 연구는 국토교통과학기술진흥원의 지원(Grant 22AMDP-C161962-02)으로 수행하였습니다.

참고문헌

  1. Azevedo, C. L., Marczuk, K., Raveau, S., Soh, H., Adnan, M., Basak, K., Loganathan, H., Deshmunkh, N., Lee, D., Frazzoli, E. and Ben-Akiva, M.(2016), "Microsimulation of demand and supply of autonomous mobility on demand", Transportation Research Record, vol. 2564, no. 1, pp.21-30. https://doi.org/10.3141/2564-03
  2. Basu, R., Araldo, A., Akkinepally, A. P., Nahmias Biran, B. H., Basak, K., Seshadri, R., Deshmukh, N., Kumar, N., Azevedo, C. L. and Ben-Akiva, M.(2018), "Automated mobility-on-demand vs. mass transit: a multi-modal activity-driven agent-based simulation approach", Transportation Research Record, vol. 2672, no. 8, pp.608-618. https://doi.org/10.1177/0361198118758630
  3. Butler, L., Yigitcanlar, T. and Paz, A.(2020), "Smart urban mobility innovations: A comprehensive review and evaluation", IEEE Access, vol. 8, pp.196034-196049. https://doi.org/10.1109/access.2020.3034596
  4. Cao, Z. and Ceder, A. A.(2019), "Autonomous shuttle bus service timetabling and vehicle scheduling using skip-stop tactic", Transportation Research Part C: Emerging Technologies, vol. 102, pp.370-395. https://doi.org/10.1016/j.trc.2019.03.018
  5. Fagnant, D. J. and Kockelman, K. M.(2014), "The travel and environmental implications of shared autonomous vehicles, using agent-based model scenarios", Transportation Research Part C: Emerging Technologies, vol. 40, pp.1-13. https://doi.org/10.1016/j.trc.2013.12.001
  6. Golbabaei, F., Yigitcanlar, T. and Bunker, J.(2021), "The role of shared autonomous vehicle systems in delivering smart urban mobility: A systematic review of the literature", International Journal of Sustainable Transportation, vol. 15, no. 10, pp.731-748. https://doi.org/10.1080/15568318.2020.1798571
  7. Hatzenbuhler, J., Cats, O. and Jenelius, E.(2020), "Transitioning towards the deployment of line-based autonomous buses: Consequences for service frequency and vehicle capacity", Transportation Research Part A: Policy and Practice, vol. 138, pp.491-507. https://doi.org/10.1016/j.tra.2020.06.019
  8. Jeon, S., Chung, S. and Kim, S.(2012), "A Study on Analysis of Operating Cost Properties to Demand Responsive Transport System in Rural Areas", Journal of the Korean Society of Civil Engineers D, vol. 32, no. 6, pp.571-577. https://doi.org/10.12652/Ksce.2012.32.6D.571
  9. Jin, H., Kim, S. and Kim, T.(2022), "A Study on Safety of Urban Road Traffic Flow of Autonomous Vehicles in Adverse Conditions", Journal of Korean Society of Transportation, vol. 40, no. 2, pp.161-177. https://doi.org/10.7470/jkst.2022.40.2.161
  10. Kim, H., Yoo, S., Lee, J., Baek, B. and Shin, J.(2022), "Real-Time Dynamic Route Generation Algorithm for Demand-Responsive Driverless Transit Operation (DRDTO) Applied to Corridors to Consider U-Turns", Journal of Korean Society of Transportation, vol. 40, no. 2, pp.260-276. https://doi.org/10.7470/jkst.2022.40.2.260
  11. Kim, J. and Bang, S.(2022), "Development of a Model for Dynamic Station Assignment to Optimize Demand Responsive Transit Operation", The Journal of The Korea Institute of Intelligent Transport Systems, vol. 21, no. 1, pp.17-34.
  12. Kim, J.(2020), "Assessment of the DRT system based on an Woptimal routing strategy", Sustainability, vol. 12, no. 2, p.714.
  13. Kim, W., Lim, S. H. and Hong, S. H.(2022), "An Influence of Demand Responsive Transport Service on User's Activities: An Empirical Analysis of the Differences between Regions", Journal of Korean Society of Transportation, vol. 40, no. 3, pp.335-343. https://doi.org/10.7470/jkst.2022.40.3.335
  14. Kopelias, P., Demiridi, E., Vogiatzis, K., Skabardonis, A. and Zafiropoulou, V.(2020), "Connected & autonomous vehicles-Environmental impacts-A review", Science of the Total Environment, vol. 712, p.135237.
  15. Korea National University for Transportation(KoNuT)(2022), Smart Campus Challenge Project: A Demonstration of Multi-purpose Mobility Service Using OHMIO Shuttle, https://smartcity.go.kr/en/%ED%94%84%EB%A1%9C%EC%A0%9D%ED%8A%B8/%EC%8A%A4%EB%A7%88%ED%8A%B8-%EC%B1%8C%EB%A6%B0%EC%A7%80/%EC%8A%A4%EB%A7%88%ED%8A%B8%EC%BA%A0%ED%8D%BC%EC%8A%A4-%EC%B1%8C%EB%A6%B0%EC%A7%80/, 2022.09.03.
  16. Kucharska, E.(2019), "Dynamic vehicle routing problem-Predictive and unexpected customer availability", Symmetry, vol. 11, no. 4, p.546.
  17. Leich, G. and Bischoff, J.(2019), "Should autonomous shared taxis replace buses? A simulation study", Transportation Research Procedia, vol. 41, pp.450-460. https://doi.org/10.1016/j.trpro.2019.09.076
  18. Mahmassani, H. S.(2016), "50th anniversary invited article-Autonomous vehicles and connected vehicle systems: Flow and operations considerations", Transportation Science, vol. 50, no. 4, pp.1140-1162. https://doi.org/10.1287/trsc.2016.0712
  19. Melis, L. and Sorensen, K.(2022), "The real-time on-demand bus routing problem: The cost of dynamic requests", Computers & Operations Research, vol. 147, p.105941.
  20. Ministry of Land, Transport and Maritime Affairs(2013), Korean Highway Capacity Manual, Ministry of Land, Transport and Maritime Affairs, p.7.
  21. Nguyen, J., Powers, S. T., Urquhart, N., Farrenkopf, T. and Guckert, M.(2021), "An overview of agent-based traffic simulators", Transportation Research Interdisciplinary Perspectives, vol. 12, p.100486.
  22. Papanikolaou, A. and Basbas, S.(2021), "Analytical models for comparing Demand Responsive Transport with bus services in low demand interurban areas", Transportation Letters, vol. 13, no. 4, pp.255-262. https://doi.org/10.1080/19427867.2020.1716474
  23. Rau, A., Tian, L., Jain, M., Xie, M., Liu, T. and Zhou, Y.(2019), "Dynamic autonomous road transit (DART) for use-case capacity more than bus", Transportation Research Procedia, vol. 41, pp.812-823. https://doi.org/10.1016/j.trpro.2019.09.131
  24. Shen, Y., Zhang, H. and Zhao, J.(2018), "Integrating shared autonomous vehicle in public transportation system: A supply-side simulation of the first-mile service in Singapore", Transportation Research Part A: Policy and Practice, vol. 113, pp.125-136. https://doi.org/10.1016/j.tra.2018.04.004
  25. Son, H. and Lee, J.(2022), "Transit Bus Network Design using Multi-Agent Reinforcement Learning", The 86th Conference of Korean Society of Transportation, vol. 86, pp.330-331.
  26. Steiner, K. and Irnich, S.(2020), "Strategic planning for integrated mobility-on-demand and urban public bus networks", Transportation Science, vol. 54, no. 6, pp.1616-1639. https://doi.org/10.1287/trsc.2020.0987
  27. Sutton, R. S. and Barto, A. G.(2018), Reinforcement learning: An introduction, MIT Press, p.93.
  28. Talebpour, A. and Mahmassani, H. S.(2016), "Influence of connected and autonomous vehicles on traffic flow stability and throughput", Transportation Research Part C: Emerging Technologies, vol. 71, pp.143-163. https://doi.org/10.1016/j.trc.2016.07.007
  29. Viergutz, K. and Schmidt, C.(2019), "Demand responsive-vs. conventional public transportation: A MATSim study about the rural town of Colditz, Germany", Procedia Computer Science, vol. 151, pp.69-76. https://doi.org/10.1016/j.procs.2019.04.013
  30. Wang, J. and Sun, L.(2020), "Dynamic holding control to avoid bus bunching: A multi-agent deep reinforcement learning framework", Transportation Research Part C: Emerging Technologies, vol. 116, p.102661.
  31. Wang, S. J. and Chang, S. K.(2021), "Autonomous Bus Fleet Control Using Multiagent Reinforcement Learning", Journal of Advanced Transportation, vol. 2021, 6654254.
  32. Zhang, R. and Pavone, M.(2016), "Control of robotic mobility-on-demand systems: A queueing-theoretical perspective", The International Journal of Robotics Research, vol. 35, no. 1-3, pp.186-203. https://doi.org/10.1177/0278364915581863
  33. Zhang, R., Rossi, F. and Pavone, M.(2016), "Model predictive control of autonomous mobility-on-demand systems", In 2016 IEEE International Conference on Robotics and Automation(ICRA), vol. 2016, pp.1382-1389.