Safety benefits of arterials' crash risk under connected and automated vehicles

被引:107
|
作者
Rahman, Md Sharikur [1 ]
Abdel-Aty, Mohamed [1 ]
Lee, Jaeyoung [1 ]
Rahman, Md Hasibur [1 ]
机构
[1] Univ Cent Florida, Dept Civil Environm & Construct Engn, Orlando, FL 32816 USA
关键词
Connected vehicles; Lower level of automation; Driving behavior; Segment crash risk; Intersection crash risk; Surrogate safety assessment; Market penetration rates; ADAPTIVE CRUISE CONTROL; END COLLISION RISKS; AUTONOMOUS VEHICLES; MODEL; SIMULATION; IMPACT; CONGESTION; STABILITY; FRAMEWORK; CONFLICTS;
D O I
10.1016/j.trc.2019.01.029
中图分类号
U [交通运输];
学科分类号
08 ; 0823 ;
摘要
This paper aims to investigate the safety impact of connected vehicles and connected vehicles with the lower level of automation features under vehicle-to-vehicle (V2V) and infrastructure-to vehicle (I2V) communication technologies. Examining the lower level of automation is more realistic in the foreseeable future. This study considered two automated features such as automated braking and lane keeping assistance which are widely available in the market with low penetration rates. Driving behavior of connected vehicles (CV) and connected vehicles lower level automation (CVLLA) were modeled in the C++ programming language with considering realistic car following models in VISSIM. To this end, safety impact on both segment and intersection crash risks were explored through surrogate safety assessment techniques under various market penetration rates (MPRs). Segment crash risk was analyzed based on both time proximity-based and evasive action-based surrogate measures of safety: time exposed time-to-collision (fm), time integrated time-to-collision (TIT), time exposed rear-end crash risk index (TERCRI), lane changing conflicts (LCC), and number of critical jerks (NCJ). However, the intersection crash risk was evaluated through the number of conflicts extracted from micro simulation (VISSIM) using the Surrogate Safety Assessment Model (SSAM). A logistic regression model was also developed to quantify the crash risk in terms of observed conflicts obtained in the intersection influence areas. The results suggest that both CV and CVLLA reduce segment crash risk significantly in terms of the five surrogate measures of safety. Furthermore, the logistic regression results clearly showed that both CV and CVLLA have lower intersection crash risks compared to the base scenario. In terms of both segment and intersection crash risks, CVLLA significantly outperforms CV when MPRs are 60% or higher. Thus, the results indicate a significant safety improvement resulting from implementing CV and CVLLA technologies at both segments and intersections on arterials.
引用
收藏
页码:354 / 371
页数:18
相关论文
共 50 条
  • [41] Can Relocation Influence Human Acceptance of Connected and Automated Vehicles?
    Zhang, Ying
    Zhang, Chu
    Chen, Jun
    Yang, Guang
    Wang, Wei
    SYSTEMS, 2024, 12 (08):
  • [42] Review on eco-driving control for connected and automated vehicles
    Li, Jie
    Fotouhi, Abbas
    Liu, Yonggang
    Zhang, Yuanjian
    Chen, Zheng
    RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2024, 189
  • [43] Experimental validation of connected automated vehicle design among human-driven vehicles
    Ge, Jin I.
    Avedisov, Sergei S.
    He, Chaozhe R.
    Qin, Wubing B.
    Sadeghpour, Mehdi
    Orosz, Gabor
    TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2018, 91 : 335 - 352
  • [44] Connected and automated road vehicles: state of the art and future challenges
    Ersal, Tulga
    Kolmanovsky, Ilya
    Masoud, Neda
    Ozay, Necmiye
    Scruggs, Jeffrey
    Vasudevan, Ram
    Orosz, Gabor
    VEHICLE SYSTEM DYNAMICS, 2020, 58 (05) : 672 - 704
  • [45] Don't Worry: Connected Automated Vehicles Are Better Drivers Than We Are and They Will Not Break the Internet! [Connected and Automated Vehicles]
    Uhlemann, Elisabeth
    IEEE VEHICULAR TECHNOLOGY MAGAZINE, 2023, 18 (02): : 109 - 112
  • [46] Data Security Risk Assessment Method for Connected and Automated Vehicles
    Zhou, Shiying
    Yang, Xuezhu
    Li, Muxi
    Yang, Huawei
    Ji, Haojie
    2022 IEEE 7TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION ENGINEERING, ICITE, 2022, : 379 - 387
  • [47] Assessing the Impact of Automated and Connected Automated Vehicles on Virginia Freeways
    Kim, Bumsik
    Heaslip, Kevin P.
    Aad, Mirla Abi
    Fuentes, Antonio
    Goodall, Noah
    TRANSPORTATION RESEARCH RECORD, 2021, 2675 (09) : 870 - 884
  • [48] Public concerns and connected and automated vehicles: safety, privacy, and data security
    Lee, Dasom
    Hess, David J.
    HUMANITIES & SOCIAL SCIENCES COMMUNICATIONS, 2022, 9 (01):
  • [49] Human Interaction Safety Analysis Method for Agreements with Connected Automated Vehicles
    Warg, Fredrik
    Skoglund, Martin
    Sassman, Matthew
    2021 IEEE 94TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2021-FALL), 2021,
  • [50] Benefits of V2V Communication for Autonomous and Connected Vehicles
    Darbha, Swaroop
    Konduri, Shyamprasad
    Pagilla, Prabhakar R.
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2019, 20 (05) : 1954 - 1963