All Issue

2024 Vol.42, Issue 4 Preview Page

Article

Evaluating Road Infrastructure Safety in Mixed Traffic Conditions of Autonomous Mobility and Manual Vehicles : A Comprehensive Simulation Approach

자율주행 모빌리티와 일반 차량 혼재상황에서 교통류 및 주행 시뮬레이션 연계를 통한 도로 인프라 안전성 평가

Yeseo GU1
,
Young JO2*
,
Cheol OH3
,
Jeahong PARK2
,
Dukgeun YUN4
구 예서1
,
조 영2*
,
오 철3
,
박 재홍2
,
윤 덕근4
1Ph.D. Student, Department of Transportation and Logistics Engineering, Hanyang University, Gyeonggi 15588, Korea
2Senior Researcher, Department of Highway and Transportation Research, Korea Institute of Civil Engineering and Building Technology, Gyeonggi 10223, Korea
3Professor, Department of Transportation and Logistics Engineering, Hanyang University, Gyeonggi 15588, Korea
4Senior Research Fellow, Department of Highway and Transportation Research, Korea Institute of Civil Engineering and Building Technology, Gyeonggi 10223, Korea
1한양대학교 교통·물류공학과 박사과정
2한국건설기술연구원 도로교통연구본부 수석연구원
3한양대학교 교통·물류공학과 교수
4한국건설기술연구원 도로교통연구본부 선임연구위원
*Corresponding Author
31 August 2024. pp. 456-471
PDF XML Citation
Abstract

The expected persistence of mixed traffic environments, consisting of both autonomous vehicles and manually-driven vehicles, necessitates the development of methodologies to evaluate the driving safety of uninterrupted flow road infrastructure under mixed conditions. This study develops an integrated risk score (IRS) to evaluate the relative safety of various road alignments and facilities under different market penetration rates (MPRs) of autonomous vehicles. The research utilizes driving and traffic simulations to analyze the safety of 41 road alignments and facilities across various autonomous vehicle market penetration rate scenarios. The analysis results show that the modified Intelligent Driver Model (IDM) parameters for manually-driven vehicles following autonomous vehicles show increased safe time headway and reduced maximum acceleration and desired deceleration due to the consistent driving behavior of the leading autonomous vehicles. The application of these modified parameters in VISSIM facilitated the analysis of the relative safety of different road design elements. The findings revealed that regardless of the MPR of autonomous vehicles, parallel acceleration lanes were the most vulnerable. Four distinct driving safety patterns were observed: low variability, downward trend, upward trend, and convex. This study presents a comprehensive methodology for assessing road infrastructure safety in mixed traffic environments, considering different autonomous vehicle market penetration rates, and aims to contribute to the enhancement of driving safety in future road environments with the increasing adoption of autonomous vehicles.

Keywords
autonomous driving
integrated safety indicator
mixed flow
multi agent driving simulation
road safety evaluation method

자율주행 차량의 주행행태는 운전자 조작에 의한 비자율주행 차량의 주행과 근본적인 차이가 존재한다. 혼합교통류 환경이 향후 수십 년간 지속될 것으로 전망됨에 따라, 자율-비자율 혼합교통류 환경에서 도로 인프라 주행안전성을 평가하기 위한 방법론을 개발할 필요성이 있다. 본 연구에서는 연속류 도로선형 조건 및 시설에 따른 상대적 안전성을 평가하기 위한 통합 평가지표 integrated risk score(IRS)를 개발하였다. 주행-교통 시뮬레이션을 연계하여 41개의 평가 대상 선형 및 시설에 대한 자율주행 차량의 시장 점유율에 따른 주행안전성 취약 구간을 분석하였다. Multi agent driving simulator(MADS) 실험을 통해 자율주행 차량을 추종하는 비자율주행 차량의 거동을 현실적으로 모사하였고, 자율차의 시장 점유율 변화에 따른 교통류 안전성을 분석하기 위해 교통 시뮬레이션 분석을 수행하였다. 분석 결과, 자율주행 차량을 추종하는 비자율주행 차량의 수정된 intelligent driver model 파라미터는 더 큰 안전 거리를 유지하고 선행 차량의 일관성 있는 주행행태로 인해 가속 및 감속이 감소한 것으로 도출되었다. 수정된 파라미터를 VISSIM에 적용하여 도로 설계 요소별 상대적 안전성 분석한 결과, 자율주행 차량의 시장 점유율에 관계 없이 평행식 가속차로가 가장 취약한 것으로 도출되었다. 각 평가지표와 IRS 순위는 자율주행 MPR 변화에 따라 변동 미미, 우하향, 우상향, 볼록형 4가지 주행안전성 패턴이 관찰되었다. 본 연구는 향후 자율주행 차량 도입이 확대되는 미래도로 환경의 주행안정성 증진에 기여할 것으로 기대된다.

키워드
자율주행
통합평가지표
혼합교통류
시뮬레이션 연계
도로 인프라 안전성 평가 방법론
References
1

Arvin R., Khattak A. J., Kamrani M., Rio-Torres J. (2020), Safety Evaluation of connected and automated vehicles in mixed traffic with conventional vehicles at intersections, Journal of Intelligent Transportation Systems, 25(2), 170-187.

10.1080/15472450.2020.1834392
2

Aryal P. (2020), Optimization of Geometric Road Design for Autonomous Vehicle, Digitala Vetenskapliga Arkivet.

3

Cai M., Mao C., Zhou W., Yu B. (2024), Neural Network-Driven Reliability Analysis in Safety Evaluation of LiDAR-Based Automated Vehicles: Considering Highway Vertical Alignments and Adverse Weather Conditions, Electronics, 13(5), 881.

10.3390/electronics13050881
4

Calvi A., D'Amico F., Ferrante C., Calcaterra G. (2022), A Driving Simulator Study on the Effects of Autonomous Vehicles on Drivers Behaviour Under Car-Following Conditions, Human Factors in Transportation, 60(60), 10-54941.

10.54941/ahfe1002434
5

Chen P., Ni H., Wang L., Yu G., Sun J. (2024), Safety Performance Evaluation of Freeway Merging Areas under Autonomous Vehicles Environment Using a Co-simulation Platform, Accident Analysis & Prevention, 199, 107530.

10.1016/j.aap.2024.10753038437756
6

Chen S., Zong S., Chen T., Huang Z., Chen Y., Labi S. (2023), A Taxonomy for Autonomous Vehicles Considering Ambient Road Infrastructure, Sustainability, 15(14), 11258.

10.3390/su151411258
7

Faria M. V., Baptista P. C., Farias T. L., Pereira J. M. (2020), Assessing the Impacts of Driving Environment on Driving Behavior Patterns, Transportation, 47(3), 1311-1337.

10.1007/s11116-018-9965-5
8

García A., Camacho-Torregrosa F. J. (2020), Influence of Lane Width on Semi-autonomous Vehicle Performance, Transportation Research Record, 2674(9), 279-286.

10.1177/0361198120928351
9

García A., Camacho-Torregrosa F. J., Baez P. V. P. (2020), Examining the Effect of Road Horizontal Alignment on the Speed of Semi-automated Vehicles, Accident Analysis & Prevention, 146, 105732.

10.1016/j.aap.2020.10573232853991
10

Higgs B., Abbas M., Medina A. (2011), Analysis of the Wiedemann Car Following Model over Different Speeds Using Naturalistic Data, In Procedia of RSS Conference, 1-22.

10.1109/WSC.2012.6465329
11

Hu G., Lu W., Whalin R. W., Wang F., Kwembe T. A. (2021), Analytical Approximation for Macroscopic Fundamental Diagram of Urban Corridor with Mixed Human and Connected and Autonomous Traffic, IET Intelligent Transport Systems, 15(2), 261-272.

10.1049/itr2.1202038023822PMC10658801
12

Jin J., Huang H., Li Y., Zhang G., Dong Y., Zhou B., Xue H. (2023), Variable Speed Limit Modelling to Improve Traffic Safety and Efficiency of Mixed Traffic Flow by a Two-stage Framework, Transportmetrica A: Transport Science, 1-25.

10.1080/23249935.2023.2253476
13

Jo Y., Jung A., Park H., Park J., Oh C. (2022), Prioritizing driving safety indicators using real-world C-ITS data to identify hazardous freeway sections, J. Korean Soc. Transp., 40(6), Korean Society of Transportation, 863-878.

10.7470/jkst.2022.40.6.863
14

Jo Y., Oh C. (2023a), Development of Proactive Lane-change Assistant Strategy based on Infrastructure Cooperation for Managing Mixed Traffic in Connected Environments, J. Korean Soc. Transp., 41(5), Korean Society of Transportation, 607-623.

10.7470/jkst.2023.41.5.607
15

Jo Y., Oh C. (2023b), Mixed Traffic Management Strategy for Proactive Safety in Cooperative Driving Automation Systems, Doctoral Dissertation, Hanyang University.

16

Jung A., Jo Y., Oh C. (2023), A Methodology of Identifying Hazardous Freeway Segment Based on Multi-agent Driving Simulations for the Mixed Situation of Autonomous and Manual Vehicles, J. Korean Soc. Transp., 41(4), Korean Society of Transportation, 495-508.

10.7470/jkst.2023.41.4.495
17

Kamrani M., Arvin R., Khattak A. J. (2018), Extracting Useful Information from Basic Safety Message Data: An Empirical Study of Driving Volatility Measures and Crash Frequency at Intersections, Transportation Research Record, 2672(38), 290-301.

10.1177/0361198118773869
18

Kang J. G., Lee S. (2002), Traffic Accident Prediction Model by Freeway Geometric Types, J. Korean Soc. Transp, 20(4), 163-175.

19

Korea Expressway Corporation (2022), Korea's Freeway Crash Database.

20

Lee S., Jo Y., Kim H., Park J., Oh C. (2024), Driving Safety Evaluation of Mixed Car-Following Situations by Autonomous and Manual Vehicles at Urban Interrupted Road Facilities, Transportation Research Record, 03611981231222237.

10.1177/03611981231222237
21

Li X., Liu Z., Li M., Liu Y., Wang C., Ma X., Liang Y. (2023), Research on the Weaving Area Capacity of Freeways under man-machine mixed traffic flow, Physica A: Statistical Mechanics and its Applications, 625, 129040.

10.1016/j.physa.2023.129040
22

Li Y., Pan B., Xing L., Yang M., Dai J. (2022), Developing Dynamic Speed Limit Strategies for Mixed Traffic Flow to Reduce Collision Risks at Freeway Bottlenecks, Accident Analysis & Prevention, 175, 106781.

10.1016/j.aap.2022.10678135926373
23

Litman T. (2023), Autonomous Vehicle Implementation Predictions: Implications for Transport Planning, Victoria Transport Policy Institute.

24

Mahdinia I., Mohammadnazar A., Arvin R., Khattak A. J. (2021), Integration of Automated Vehicles in Mixed Traffic: Evaluating Changes in Performance of Following Human-Driven Vehicles, Accident Analysis & Prevention, 152, 106006.

10.1016/j.aap.2021.10600633556655
25

Ministry of Land, Infrastructure and Transport (2020), Explanation of the Rules on the Standards of Structural Facilities in Roads.

26

Shin C. H., Kim, N. (2019), A Study on Road Safety Evaluation Method for Iovement Project of Two-Lane Road, KSCE Journal of Civil and Environmental Engineering Research, 39(1), 105-114.

27

Sukennik P. (2020), Micro-simulation guide for automated vehicles-Final, Deliverable 2.11 of the CoEXist project.

28

Thorn E., Kimmel S. C., Chaka M., Hamilton B. A. (2018). A framework for automated driving system testable cases and scenarios (No. DOT HS 812 623). United States. Department of Transportation. National Highway Traffic Safety Administration.

29

Treiber M., Hennecke A., Helbing, D. (2000), Congested Traffic States in Empirical Observations and Microscopic Simulations, Physical Review E, 62(2), 1805.

10.1103/PhysRevE.62.180511088643
30

Tu H., Wang M., Li H., Sun L. (2023), Safety Risk Assessment for Autonomous Vehicle Road Testing, Traffic Injury Prevention, 24(7), 652-661.

10.1080/15389588.2023.223545437486240
31

Xu Z., Fang Y., Zheng N., Vu H. L. (2022), Analyzing the Inconsistency in Driving Patterns between Manual and Autonomous Modes under Complex Driving Scenarios with a VR-enabled Simulation Platform, Journal of Intelligent and Connected Vehicles, 5(3), 215-234.

10.1108/JICV-05-2022-0017
32

Zheng Y., Yao Z., Xu Y., Qu X., Ran B. (2024), Lane Management for Mixed Traffic Flow on Roadways Considering the Car-following Behaviors of Human-driven Vehicles to Follow Connected and Automated Vehicles, Physica A: Statistical Mechanics and its Applications, 635, 129503.

10.1016/j.physa.2024.129503
33

Zhu M., Wang X., Tarko A. (2018), Modeling Car-following Behavior on Urban Expressways in Shanghai A Naturalistic Driving Study, Transportation Research Part C Emerging Technologies, 93, 425-445.

10.1016/j.trc.2018.06.009
Information
  • Publisher :Korean Society of Transportation
  • Publisher(Ko) :대한교통학회
  • Journal Title :Journal of Korean Society of Transportation
  • Journal Title(Ko) :대한교통학회지
  • Volume : 42
  • No :4
  • Pages :456-471
  • Received Date : 2024-05-27
  • Revised Date : 2024-07-08
  • Accepted Date : 2024-07-29
  • DOI :https://doi.org/10.7470/jkst.2024.42.4.456
Journal Informaiton Journal of Korean Society of Transportation Journal of Korean Society of Transportation
Archives
: 2015 Vol.34~
  • NRF
  • KOFST
  • crossref cited-by
  • crosscheck
  • orcid
  • open access
  • ccl
Journal Informaiton Journal Informaiton - close