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시뮬레이션 기반 자율주행자동차 혼입률과 교통량 변화에 따른 도로 네트워크의 성능 분석

Performance of the Road Network with Market Penetration Rates and Traffic Volumes of Autonomous Vehicle using Traffic Simulation

  • 도명식 (국립한밭대학교 도시공학과) ;
  • 정유미 (국립한밭대학교 도시공학과)
  • 투고 : 2023.10.30
  • 심사 : 2024.01.09
  • 발행 : 2024.06.01

초록

본 연구에서는 레벨4 이상의 완전자율주행자동차(autonomous vehicle)의 혼입률과 교통량의 변화에 따른 도로 네트워크의 성능 분석을 목적으로 하였다. 먼저, 자율주행자동차의 차량제어변수 관련 선행연구 검토와 전문가 설문 조사를 통해 자율주행 시장점유율 50 %로 예측되는 시점인 2040년의 장래 교통 수요를 예측해 이를 시뮬레이션 분석에 반영하였다. 또한, 승용차, 화물차, 버스의 자율주행 혼입률 및 교통량을 0~100 %까지 단계별 25 %씩 증가시켜가면서 연속류와 단속류의 교통흐름의 변화를 분석하였다. 분석 결과 교통량이 많아짐에 따라 통행시간이 증가함을 확인하였으며, 자율주행자동차 점유율이 증가 즉, 기술의 발전에 따른 통행시간 감소 패턴도 확인할 수 있다. 나아가, 자율주행자동차 점유율이 증가함에 따라 통행속도는 증가하는 추세를 보임도 확인할 수 있었다. 본 연구에서는 자율주행자동차 혼입률을 증가시키면서 교통량과 속도의 조합에 따른 한계대체율 산정을 통해 한계대체율 체감(law of diminishing MRS)의 법칙이 성립함을 확인하였다. 나아가 무차별 곡선의 볼록성도 단속류와 연속류 환경에서 모두 성립함을 확인하였다.

The purpose of this study is to analyze the performance of the road network according to the penetration rate of autonomous vehicles (AV) of Level 4 or higher and the change in traffic volume. First, prior studies related to vehicle control variables of AV were reviewed, and future traffic demand in 2040, which is predicted to have a 50 % market share of AVs, was reflected in the simulation analysis. In addition, the change in traffic flow of continuous and intermittent flows was analyzed by increasing the AV market penetration rate and traffic volume of passenger cars, trucks, and buses by 25 % step by step from 0 to 100 %. As a result of the analysis, it was confirmed that the travel time increased as the traffic increased, and the pattern of decreasing the travel time due to the increase in the share of AVs, that is, the development of technology, can also be confirmed. Furthermore, it was also confirmed that the traffic speed showed a trend of increasing as the share of AVs increased. In this study, it was confirmed that the law of diminishing marginal rate of substitution (MRS) was satisfied by calculating the MRS according to the combination of traffic volume and speed while increasing the market penetration rate of AVs. Furthermore, it was confirmed that the convexity of the indifference curve was also satisfied in both intermittent and continuous traffic flow environments.

키워드

참고문헌

  1. Al-Turki, M., Ratrout, N. T. and Al-Sghan, I. (2023). "Impact of Autonomous vehicles on the performance of a signalized intersection under different mixed traffic conditions: a simulation-based investigation." Journal of Applied Engineering Science, Institute for research and design in industry, Vol. 21, No. 1, pp. 224-240, https://doi.org/10.5937/jaes0-39994. 
  2. Al-Turki, M., Ratrout, N. T., Rahman, S. M. and Reza, I. (2021). "Impacts of autonomous vehicles on traffic flow characteristics under mixed traffic environment: Future perspectives." Sustainability, MDPI, Vol. 13, No. 19, 11052, https://doi.org/10.3390/su131911052. 
  3. Austroads (2022). Minimum Physical Infrastructure Standard for the Operation of Automated Driving Part B: Scenarios for Potential Availability and Usage of Different Levels and Types of Automated Driving, AP-R665B-22, Sydney, New South Wales.
  4. Calvert, S. C., Schakel, W. J. and van Lint, J. W. C. (2017). "Will automated vehicles negatively impact traffic flow?." Journal of Advanced Transportation, Hindawi, Vol. 2017, 3082781, https://doi.org/10.1155/2017/3082781. 
  5. Daejeon Traffic Information Center (2023). Available at: http://traffic.daejeon.go.kr (Accessed: November 20, 2023) (in Korean).
  6. Fernandes, P. and Nunes, U. (2012). "Platooning with IVC-enabled autonomous vehicles: Strategies to mitigate communication delays, improve safety and traffic flow." IEEE Transactions on Intelligent Transportation Systems, IEEE, Vol. 13, No. 1, pp. 91-106, https://doi.org/10.1109/TITS.2011.2179936.
  7. Jones, S. R. and Philips, B. H. (2013). "Cooperative adaptive cruise control: critical human factors issues and research questions." Proceedings of the 7th International Driving Symposium on Human Factors in Diver Assessment, Tranining, and Vehicle Design, University of Iowa, Iowa, https://pubs.lib.uiowa.edu/driving/article/id/28532/.
  8. Kim, S. H., Lee, J. H., Kim, Y. J. and Lee, C. W. (2018). "Simulation-based analysis on dynamic merge control at freeway work zones in automated vehicle environment." KSCE Journal of Civil and Environmental Engineering Research, KSCE, Vol. 38, No. 6, pp. 867-878, https://doi.org/10.12652/Ksce.2018.38.6.0867 (in Korean). 
  9. Korea Development Institute (2008). A study on standard guidelines for pre-feasibility study on road and railway projects, 5th Edition (in Korean).
  10. Land and Housing Institute (2022). A Final Report on the Simulation of Self-Driving Shuttle and DRT Operation in Geumto District, Seongnam City (in Korean).
  11. Lee, J. G. (1994). Microeconomics, 2nd Edition, Beopmunsa, (in Korean).
  12. Lu, Z., Fu, T., Fu, L., Shiravi, S. and Jiang, C. (2016). "A video-based approach to calibrating car-following parameters in VISSIM for urban traffic." International Journal of Transportation Science and Technology, Tongji University, Vol. 5, No. 1, pp. 1-9, https://doi.org/10.1016/j.ijtst.2016.06.001. 
  13. Lu, Q., Tettamanti, T., Horcher, D. and Varga, I. (2020). "The impact of autonomous vehicles on urban traffic network capacity: an experimental analysis by microscopic traffic simulation." Transportation Letters, Informa UK Limited, Vol. 12, No. 8, pp. 540-549, https://doi.org/10.1080/19427867.2019.1662561. 
  14. Mankiw, N. G. (2005). Principles of Economics, 3rd edition, Kim, K. H. and Kim, J. S., Kyobomungo (in Korean).
  15. Mehr, N. and Horowitz, R. (2020). "How will the presence of autonomous vehicles affect the equilibrium state of traffic networks?." IEEE Transactions on Control of Network Systems, IEEE, Vol. 7, No. 1, pp. 96-105, https://doi.org/10.1109/TCNS.2019.2918682. 
  16. Ministry of Land, Infrastructure and Transport (MOLIT) (2022a). Non-stop innovation toward the future Mobility innovation roadmap (in Korean).
  17. Ministry of Land, Infrastructure and Transport (MOLIT) (2022b). Appraisal Guidelines for Transport Facilities Investment (7th Edition) (in Korean).
  18. Motamedidehkordi, N., Margreiter, M. and Benz, T. (2016). "Shockwave suppression by vehicle-to-vehicle communication." Transportation Research Procedia, Elsevier, Vol. 15, pp. 471-482, https://doi.org/10.1016/j.trpro.2016.06.040. 
  19. Muhammad, T., Kashmiri, F. A., Naeem, H., Qi, X., Chia-Chun, H. and Lu, H. (2020). "Simulation study of autonomous vehicles' effect on traffic flow characteristics including autonomous buses." Journal of Advanced Transportation, Vol. 2020, Hindawi, 4318652, https://doi.org/10.1155/2020/4318652. 
  20. Pakusch, C., Stevens, G., Boden, A. and Bossauer, P. (2018). "Unintended effects of autonomous driving: A study on mobility preferences in the future." Sustainability, MDPI, Vol. 10, No. 7, 2404, https://doi.org/10.3390/su10072404. 
  21. Park, J. E., Byun, W. H., Kim, Y. C., Ahn, H. J. and Shin, D. K. (2021). "The impact of automated vehicles on traffic flow and road capacity on urban road networks." Journal of Advanced Transportation, Hindawi, Vol. 2021, 8404951, https://doi.org/10.1155/2021/8404951. 
  22. Pinjari, A. R., Augustin, B. and Menon, N. (2013). "Highway capacity impacts of autonomous vehicles: An assessment." Center for Urban Transportation Research, University of South Florida, Tampa, https://abdulpinjari.weebly.com/uploads/9/6/7/8/9678119/abdul_pinjari_autonomous_vehicles_whitepaper_ recent.pdf.
  23. PTV GROUP (2020). CoExist Automation-Ready Modeling with PTV VISSIM, http://www.ptvgroup.com/ (Accessed: December 15, 2023).
  24. PwC (2019). The 2019 Strategy & Digital Auto Report.
  25. Stern, R. E., Cuib, S., Monachec, M. L. D., Bhadanid, R., Buntingd, M., Churchilla, M., Hamilton, N., Haulcy, R., Pohlmann, H., Wu, F., Piccolih, B., Seiboldb, B., Sprinkled, J. and Work, D. B. (2018). "Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments." Transportation Research Part C: Emerging Technologies, Elsevier, Vol. 89, pp. 205-221, https://doi.org/10.1016/j.trc.2018.02.005. 
  26. Sukennik, P., Lohmiller, J. and Schlaich, J. (2018). "Simulation- based forecasting the impacts of autonomous driving." Proceedings of the International Symposium of Transport Simulation (ISTS'18) and the International Workshop on Traffic Data Collection and its Standardization (IWTDCS'18), Elsevier, Matsuyama, Japan.
  27. 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, Elsevier, Vol. 71, pp. 143-163, https://doi.org/10.1016/j.trc.2016.07.007. 
  28. Wang, Q., Li, B., Li, Z. and Li, L. (2017). "Effect of connected automated driving on traffic capacity." Proceedings of 2017 Chinese Automation Congress, IEEE, Jinhan, China, pp. 633-637, https://doi.org/10.1109/CAC.2017.8242845.
  29. Wang, Y. and Wang, L. (2017). "Autonomous vehicles' performance on single lane road: A simulation under VISSIM environment." Proceedings of 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), IEEE, Shanghai, China, pp. 1-5, https://doi.org/10.1109/CISP-BMEI.2017.8302162.
  30. Wiedemann, R. (1974). Simulation des Strassenverkehrsflusses, Schriftenreihe des Institut fur Verkehrswesen der Universitat Karlsruhe, Heft 8 (in German).