한국융합학회논문지 (Journal of the Korea Convergence Society)

  • 제9권5호
  • /
  • Pages.7-14
  • /
  • 2018
  • /
  • 2233-4890(pISSN)
  • /
  • 2713-6353(eISSN)
한국융합학회 (Korea Convergence Society)
권호 표지
DOI QR코드

DOI QR Code

자율주행차 운영 환경하에서 통근자 출발시간 선택의 영향에 관한 연구

Exploring the influence of commuter's variable departure time in autonomous driving car operation

  • 김찬성 (국교통연구원 스마트교통본부) ;
  • 진영근 (충남도립대학교 컴퓨터정보과) ;
  • 박지영 (국교통연구원 스마트교통본부)
  • Kim, Chansung (Smart transport center, The Korea Transport Institute) ;
  • Jin, Young-Goun (Department of Computer & Information, Chungnam Provincial University) ;
  • Park, Jiyoung (Smart transport center, The Korea Transport Institute)
  • 투고 : 2018.03.12
  • 심사 : 2018.05.20
  • 발행 : 2018.05.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

자율주행택시, 자율주행셔틀과 같은 새로운 교통서비스들에 대한 연구들이 전 세계적으로 여러 도시들을 대상으로 진행 중이지만 대부분 현재 통행 수요는 출발시간이 고정적이라고 가정하고 기존 교통수단과 새로운 교통수단의 도입 효과를 분석한다. 본 연구는 자율주행기반 교통서비스 운영에서 통근자의 출발시간 조정에 따른 교통체계의 영향을 행위자기반 모형으로 분석하였다. 통행시간 선택에 대해 다양한 시나리오를 설정하였고 자율차를 수용할 수 있는 도로용량의 영향도 분석하였다. 분석결과 통근자가 원하는 출발시간에서 집에서의 활동종료시간과 출발시간이 상당히 조정된 후 시스템적으로 안정적인 통근통행이 완료되었으며, 또한 도로용량의 감소는 과도한 스케줄 조정에도 불구하고, 많은 통행자들이 9시 이전에 통근하기 어려운 것으로 나타났다. 이와 같은 결과를 통해 현재와 다른 교통운영과 교통가격정책이 필요성을 정책적 제언으로 제시하였다.

The purpose of this study is to analyze the effect of commuter's departure time on transportation system in future traffic system operated autonomous vehicle using agent based model. Various scenarios have been set up, such as when all passenger choose a similar departure time, or if the passenger chooses a different departure time. Also, this study tried to analyze the effect of road capacity. It was found that although many of the scenarios had been completed in a stable manner, many commuters were significantly coordinated at the desired departure time. In particular, in the case of a reduction in road capacity or in certain scenarios, it has been shown that, despite excessive schedule adjustments, many passengers are unable to commute before 9 o'clock. As a result, it is suggested that traffic management and pricing policies are different from current ones in the era of autonomous car operation.

키워드

참고문헌

  1. M, Maciejewsk & J. Bischoff, (2016). Congestion effects of autonomous taxi fleets, Transport, 1-10.
  2. J. Bischoff & M., Maciejewski, (2016), Simulation of city-wide replacement of private cars with autonomous taxis in Berlin, Procedia Computer Science, 83, 237- 244. https://doi.org/10.1016/j.procs.2016.04.121
  3. D. Freitas, L. Meyer, O. Schuemperlin, & M. Balac, (2016), Road pricing: An analysis of equity effects with MATSim, Conference paper STRC.
  4. D. J. Fagnant & K. M., Kockelman, (2014). The travel and environmental implications of shared autonomous vehicles, using agent-based model scenarios, Transportation Research Part C, 40. 1-13. https://doi.org/10.1016/j.trc.2013.12.001
  5. D. J Fagnant, K. M., Kockelman & P. Bansal, (2015), Operations of a shared autonomous vehicle fleet for austin, texas, market. Transportation Research Record: Journal of the Transportation Research Board,. 2536. 98-106. https://doi.org/10.3141/2536-12
  6. H. Sebastian, A. Erath, K. W. Axhausen (2017). Simulation of autonomous taxis in a multi-modal trac scenario with dynamic demand, TRB annual meeting.
  7. A. Horni, K. Nagel & K. W. Axhausen (2015), The Multi-Agent Transport Simulation MATSim, Ubiquity Press, London.
  8. A. Horni & K. W. Axhausen (2014). Gridlock Modeling with MATSim, 14th Swiss Transport Research Conference.
  9. Y. H Kim & S. C Kan. (2011), Innovative Traffic Demand Management Strategy : Expressway Reservation System, Transportation Research Record, 3245, 27-35.
  10. L. Patryk. (2015). Multi-Agent Traffic Assignment of a Synthetic Stockholm Population, Royal Institute of Technology (KTH) Stockholm, Sweden, Master Thesis.
  11. L. Martinez & P. Crist. (2015). Urban Mobility System Upgrade-How shared self-driving cars could change city traffic, International Transport Forum. OECD.
  12. L. A. Merlin. (2017). Comparing Automated Shared Taxis and Conventional Bus Transit for a Small City. Journal of Public Transportation, 20. 2, 19-39. https://doi.org/10.5038/2375-0901.20.2.2
  13. N., Andreas, I. Kaddoura & K. Nagel (2016). Mind the Gap-Passenger Arrival Patterns in Multi-agent Simulations, International Journal of Transportation .4, 1, 27-40. http://dx.doi.org/10.14257/ijt.2016.4.1.02
  14. K. Spieser, et al. (2017). Toward a systematic approach to the design and evaluation of automated mobility-on-demand systems: A case study in Singapore. MIT Open Access Articles.
  15. X., Yuliang, D. Qingyun, H. Biao, R. Fu, Y. Zhang & Y. Xinyue. (2017). The Dynamic Optimization of the Departure Times of Metro Users during Rush Hour in an Agent-Based Simulation: A Case Study in Shenzhen, China, Appl. Sci. 7, 1102; doi:10.3390/app7111102
  16. S. Zhu, & A. L. Kornhauser. (2017). The Interplay Between Fleet Size, Level-of-Service and Empty Vehicle Repositioning Strategies in Large-Scale, Shared-Ride Autonomous Taxi Mobility-on-Demand Scenarios, Transportation Research Board 96th Annual Meeting, 17-059.
  17. H. M. Abdul Aziz, B. H. Park, A. Morton, N. Robert, M. Stewart, Hilliard & M. Maness, (2018). A high resolution agent-based model to support walk-bicycle infrastructure investment decisions: A case study with New York City, Transportation Research Part C 86, 280-299. https://doi.org/10.1016/j.trc.2017.11.008
  18. S. Y Oh. (2015). The Implementation of an Advanced Taxi Movement Model in the ONE Simulator, Journal of Digital Convergence 13, 237-241.
  19. S. K Park. (2018). A Study of the Autonomous Vehicle Technology and its Future Trend : Focusing on Current Industry and Technology Convergence of Trend, Journal of the Korea Convergence Society, 9, 253-259.