The Journal of The Korea Institute of Intelligent Transport Systems (한국ITS학회 논문지)

The Korea Institute of Intelligent Transport Systems (한국ITS학회)
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Driving Behaivor Optimization Using Genetic Algorithm and Analysis of Traffic Safety for Non-Autonomous Vehicles by Autonomous Vehicle Penetration Rate

유전알고리즘을 이용한 주행행태 최적화 및 자율주행차 도입률별 일반자동차 교통류 안전성 분석

  • Somyoung Shin (Dept. of Transportation Eng., Univ. of Seoul) ;
  • Shinhyoung Park (Dept. of Transportation Eng., Univ. of Seoul) ;
  • Jiho Kim (Dept. of Transportation Eng., Univ. of Seoul)
  • 신소명 (서울시립대학교 교통공학과) ;
  • 박신형 (서울시립대학교 교통공학과) ;
  • 김지호 (서울시립대학교 교통공학과)
  • Received : 2023.07.28
  • Accepted : 2023.08.23
  • Published : 2023.10.31
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Abstract

Various studies have been conducted using microtraffic simulation (VISSIM) to analyze the safety of traffic flow when introducing autonomous vehicles. However, no studies have analyzed traffic safety in mixed traffic while considering the driving behavior of general vehicles as a parameter in VISSIM. Therefore, the aim of this study was to optimize the input variables of VISSIM for non-autonomous vehicles through genetic algorithms to obtain realistic behavior. A traffic safety analysis was then performed according to the penetration rate of autonomous vehicles. In a 640 meter section of US highway I-101, the number of conflicts was analyzed when the trailing vehicle was a non-autonomous vehicle. The total number of conflicts increased until the proportion of autonomous vehicles exceeded 20%, and the number of conflicts decreased continuously after exceeding 20%. The number of conflicts between non-autonomous vehicles and autonomous vehicles increased with proportions of autonomous vehicles of up to 60%. However, there was a limitation in that the driving behavior of autonomous vehicles was based on the results of the literature and did not represent actual driving behavior. Therefore, for a more accurate analysis, future studies should reflect the actual driving behavior of autonomous vehicles.

자율주행시대에 맞춰 다양한 연구에서 미시교통시뮬레이션(VISSIM)을 활용하여 자율주행차 도입시 교통류 안전성 분석을 수행중이다. 그러나, 일반자동차의 주행행태를 VISSIM 내 파라미터로 반영하여 혼재시의 교통류 안전성을 분석한 연구는 미비하였다. 따라서 본 연구에서는 실제 주행행태와 유사한 주행행태를 구현하기 위하여 일반자동차의 VISSIM 입력변수를 유전알고리즘을 통해 최적화 한 후, 자율주행차 도입률에 따른 교통류 안전성 분석을 수행하는 것을 목적으로 한다. US I-101 고속도로의 640m 구간을 대상으로 후행차량이 일반자동차인 경우에 대해서 상충횟수 분석을 수행한 결과, 전체 상충횟수는 자율주행차 도입률 20%까지 증가하였으며 20%를 초과한 이후부터는 지속적으로 감소하였다. 일반자동차와 자율주행차 사이의 상충횟수는 자율주행차 도입률 60%까지 증가하는 것으로 분석되었다. 그러나, 자율주행차의 주행행태를 기존 문헌결과를 바탕으로 하여 실제 주행행태를 표현하지 못했다는 한계가 존재한다. 그러므로 보다 정확한 분석을 위해 향후 연구에서는 자율주행차의 실제 주행행태를 반영하여 연구를 수행할 필요가 있다.

Keywords

Acknowledgement

본 연구는 국토교통부/국토교통과학기술진흥원(과제번호 23AMDP-C162353-03)의 지원으로 수행하였습니다.

References

  1. Ahmed, H. U., Huang, Y. and Lu, P.(2021), "A Review of Car-Following Models and Modeling Tools for Human and Autonomous-Ready Driving Behaviors in Micro-Simulation", Smart Cities, vol. 4, no. 1, pp.314-335. https://doi.org/10.3390/smartcities4010019
  2. Albeaik, S., Bayen, A., Chiri, M. T., Gong, X., Hayat, A., Kardous, N., Keimer, A., McQuade, S. T., Piccoli, B. and You, Y.(2022), "Limitations and Improvements of the Intelligent Driver Model (IDM)", Society for Industrial and Applied Mathematics Journal on Applied Dynamical Systems, vol. 21, no. 3, pp.1-32. https://doi.org/10.1137/21M1406477
  3. Alkis, P., Mohammed, Q. and Marianna, I.(2019), "Evaluating the safety impact of connected and autonomous vehicles on motorways", Accident Analysis and Prevention, vol. 124, pp.12-22. https://doi.org/10.1016/j.aap.2018.12.019
  4. Amolika, S., Sai, C., Kasun, P. W., Navreet, V. and Vinayak, D.(2020), "Crash Severity and Rate Evaluation of Conventional Vehicles in Mixed Fleets with Connected and Automated Vehicles", Procedia Computer Science, vol. 170, pp.688-695. https://doi.org/10.1016/j.procs.2020.03.170
  5. Arvin, R., Kamrani, M., Khattak, A. J. and Rios-Torres, J.(2018), "Safety Impacts of Automated Vehicles in Mixed Traffic", Transportation Research Board 97th Annual Meeting.
  6. Atkins(2016), Research on the Impacts of Connected and Autonomous Vehicles (CAVs) on Traffic Flow, Stage 2: Traffic Modelling and Analysis Technical Report, Department for Transport.
  7. Bando, M., Hasebe, K., Nakayama, A., Shibata, A. and Sugiyama, Y.(1995), "Dynamical model of traffic congestion and numerical simultation", PHYSICAL REVIEW E, vol. 51, no. 2, pp.1035-1042. https://doi.org/10.1103/PhysRevE.51.1035
  8. Choi, H., Cho, S. H., Cho, Y. J. and Kim, W. K.(2017), "A Study of Driver Cognitive Reaction Time for Traffic Crash Interpretation", 77th Presentation of the Korean Society of Transportation, pp.196-201.
  9. Garg, M. and Bouroche, M.(2023), "Can Connected Autonomous Vehicles Improve Mixed Traffic Safety Without Compromising Efficiency in Realistic Scenarios?", IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 6, pp.6674-6689. https://doi.org/10.1109/TITS.2023.3238889
  10. Jin, H. R., Kim, S. H. and Kim, T. H.(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
  11. Jung, E. B.(2015), Methodology for Designing Traffic Information and Control Systems based on Vehicle Cooperations, Doctoral Dissertation, Dept. Transportation Eng., Univ. Hanyang, Korea.
  12. Jung, J. H., Park, J. Y. and Oh, C.(2020), "Assessing the Safety Effects of Expressway Managed Lane for Autonomous Bus Using Traffic Simulation", Journal of Korean Society of Transportation, vol. 38, no. 1, pp.58-67. https://doi.org/10.7470/jkst.2020.38.1.058
  13. Kim, B., Pourrahmani, E. and Fagnant, D. J.(2017), "The Potential Benefits and Cost of Connected and Automated Vehicles: A Texas Case Study", Transportation Research Board 96th Annual Meeting Transportation Research Board.
  14. Ko, W. R., Park, S. M., So, J. H. and Yun, I. S.(2021), "Analysis of Effects of Autonomous Vehicle Market Share Changes on Expressway Traffic Flow Using IDM", The Journal of The Korea Institute of Intelligent Transport Systems, vol. 20, no. 4, pp.13-27. https://doi.org/10.12815/kits.2021.20.4.13
  15. Lazar, H., Rhoulami, K. and Rahmani, D.(2016), "A Review Analysis of Optimal Velocity Models", Periodica Polytechnica Transportation Engineering, vol. 44, no. 2, pp.123-131. https://doi.org/10.3311/PPtr.8753
  16. Lee, J. Y., Lee, E. T., Lee, S. J., Kim, T. W. and Son, B. S.(2023), "Estimating the Effects of Freeway Weaving Section Length on Level of Service Based on Microsimulation", Transportation Research Record: Journal of the Transportation Research Board, pp.1-14.
  17. Lee, S. Y., Jeong, E. B., Oh, M. S. and Oh, C.(2019), "Driving aggressiveness management policy to enhance the performance of mixed traffic conditions in automated driving environments", Transportation Research Part A: Policy and Practice, vol. 121, pp.136-146. https://doi.org/10.1016/j.tra.2019.01.010
  18. M. Sc. Qinglong, L.(2022), Calibration of Cal Following Model with Genetic Algorithm and Particle Swarm Optimiztion methods, Department of Mobility Systems Engineering TUM School of Engineering and Design Technical University of Munich.
  19. Morando, M. M., Tian, Q., Truong, L. T. and Vu, H. L.(2018), "Studying the Safety Impact of Autonomous Vehicles Using Simulation-Based Surrogate Safety Measures", Journal of Advanced Transportation, vol. 2018, pp.1-11. https://doi.org/10.1155/2018/6135183
  20. Navreet, V., Hanna, G. S., Travis, W. and Vinayak, D.(2019), "A safety assessment of mixed fleets with Connected and Autonomous Vehicles using the Surrogate Safety Assessment Module", Accident Analysis and Prevention, vol. 131, pp.95-111. https://doi.org/10.1016/j.aap.2019.06.001
  21. Oh, M. S., Kown, S. M. and Oh, C.(2018), "Analyzing the Impact of Weigh-in-Motion(WIM)-based Overloading Enforcement Systems on Freeway Traffic Stream", International Journal of Highway Engineering, vol. 20, no. 6, pp.129-140. https://doi.org/10.7855/IJHE.2018.20.5.129
  22. Park, B. K. and Qi, H.(2006), "Microscopic Simulation Model Calibration and Validation for Freeway Work Zone Network-A Case Study of VISSIM", IEEE Intelligent Transportation Systems Conference, pp.1471-1476.
  23. Seelam, S., Arpan, M. and Asish, P.(2017), "Calibration of Vissim Model For Multilane Highways Using Speed Flow Crurves", The Civil Engineering Journal, vol. 26, no. 3, pp.303-314.
  24. Zeidelr, V., Buck, S. H., Kautzsch, L. and Vortisch, P.(2019), "Simulation of Autonomous Vehicles Based on Wiedemann's Car Following Model in PTV Vissim", 98th Annual Meeting of the Transportation Research Board(TRB), pp.1-12.
  25. Zhu, J. and Tasic, I.(2021), "Safety analysis of freeway on-ramp merging with the presence of autonomous vehicles", Accident Analysis and Prevention, vol. 152, no. 105966, pp.1-15. https://doi.org/10.1016/j.aap.2020.105966