IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i23p9888-d451524.html
   My bibliography  Save this article

Crash Risk Assessment for Heterogeneity Traffic and Different Vehicle-Following Patterns Using Microscopic Traffic Flow Data

Author

Listed:
  • Jiajun Shen

    (School of Civil Science and Engineering, Yangzhou University, Yangzhou 225127, China)

  • Guangchuan Yang

    (Institute for Transportation Research and Education, North Carolina State University, Raleigh, NC 27606, USA)

Abstract

This paper investigates the impacts of heavy vehicles (HV) on speed variation and assesses the rear-end crash risk for four vehicle-following patterns in a heterogeneous traffic flow condition using three surrogate safety measures: speed variation, time-to-collision (TTC), and deceleration rate to avoid a crash (DRAC). A video-based data collection approach was employed to collect the speed of each individual vehicle and vehicle-following headway; a total of 3859 vehicle-following pairs were identified. Binary logistic regression modeling was employed to assess the impacts of HV percentage on crash risk. TTCs and DRACs were calculated based on the collected traffic flow data. Analytical models were developed to estimate the minimum safe vehicle-following headways for the four vehicle-following patterns. Field data revealed that the variation of speed first increased with HV percentage and reached the maximum when HV percentage was at around 0.35; then, it displayed a decreasing trend with HV percentage. Binary logistic regression modeling results suggest that a high risk of rear-end collision is expected when HV percentage is between 0.19 and 0.5; while, when HV percentage is either below 0.19 or exceed 0.5, a low risk of rear-end collision is anticipated. Analytical modeling results show that the passenger car (PC)-HV vehicle-following pattern requires the largest minimum safe space headway, followed by HV-HV, PC-PC, and HV-PC vehicle-following patterns. Findings from this research present insights to transportation engineers regarding the development of crash mitigation strategies and have the potential to advance the design of real-time in-vehicle forward collision warnings to minimize the risk of rear-end crash.

Suggested Citation

  • Jiajun Shen & Guangchuan Yang, 2020. "Crash Risk Assessment for Heterogeneity Traffic and Different Vehicle-Following Patterns Using Microscopic Traffic Flow Data," Sustainability, MDPI, vol. 12(23), pages 1-18, November.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:23:p:9888-:d:451524
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/23/9888/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/23/9888/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Denos C. Gazis & Robert Herman & Richard W. Rothery, 1961. "Nonlinear Follow-the-Leader Models of Traffic Flow," Operations Research, INFORMS, vol. 9(4), pages 545-567, August.
    2. Xu, Chengcheng & Wang, Yong & Liu, Pan & Wang, Wei & Bao, Jie, 2018. "Quantitative risk assessment of freeway crash casualty using high-resolution traffic data," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 299-311.
    3. Weichuan Yin & Yingqun Zhang, 2020. "Identification Method for Optimal Urban Bus Corridor Location," Sustainability, MDPI, vol. 12(17), pages 1-22, September.
    4. Olumide F. Abioye & Maxim A. Dulebenets & Junayed Pasha & Masoud Kavoosi, 2019. "A Vessel Schedule Recovery Problem at the Liner Shipping Route with Emission Control Areas," Energies, MDPI, vol. 12(12), pages 1-28, June.
    5. Ciyun Lin & Kang Wang & Dayong Wu & Bowen Gong, 2020. "Passenger Flow Prediction Based on Land Use around Metro Stations: A Case Study," Sustainability, MDPI, vol. 12(17), pages 1-22, August.
    6. Rodrigo V. Ventura & Manoela Cabo & Rafael Caixeta & Elton Fernandes & Vicente Aprigliano Fernandes, 2020. "Air Transportation Income and Price Elasticities in Remote Areas: The Case of the Brazilian Amazon Region," Sustainability, MDPI, vol. 12(15), pages 1-18, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhaoshi Geng & Xiaofeng Ji & Rui Cao & Mengyuan Lu & Wenwen Qin, 2022. "A Conflict Measures-Based Extreme Value Theory Approach to Predicting Truck Collisions and Identifying High-Risk Scenes on Two-Lane Rural Highways," Sustainability, MDPI, vol. 14(18), pages 1-24, September.
    2. Zhao, Xiaohua & Yang, Haiyi & Yao, Ying & Qi, Hang & Guo, Miao & Su, Yuelong, 2022. "Factors affecting traffic risks on bridge sections of freeways based on partial dependence plots," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marek Drliciak & Jan Celko & Michal Cingel & Dusan Jandacka, 2020. "Traffic Volumes as a Modal Split Parameter," Sustainability, MDPI, vol. 12(24), pages 1-21, December.
    2. Asghari, Mohammad & Jaber, Mohamad Y. & Mirzapour Al-e-hashem, S.M.J., 2023. "Coordinating vessel recovery actions: Analysis of disruption management in a liner shipping service," European Journal of Operational Research, Elsevier, vol. 307(2), pages 627-644.
    3. Nakata, Makoto & Yamauchi, Atsuo & Tanimoto, Jun & Hagishima, Aya, 2010. "Dilemma game structure hidden in traffic flow at a bottleneck due to a 2 into 1 lane junction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(23), pages 5353-5361.
    4. McCrea, Jennifer & Moutari, Salissou, 2010. "A hybrid macroscopic-based model for traffic flow in road networks," European Journal of Operational Research, Elsevier, vol. 207(2), pages 676-684, December.
    5. Nestor Goicoechea & Luis María Abadie, 2021. "Optimal Slow Steaming Speed for Container Ships under the EU Emission Trading System," Energies, MDPI, vol. 14(22), pages 1-25, November.
    6. Li, Xiaopeng & Wang, Xin & Ouyang, Yanfeng, 2012. "Prediction and field validation of traffic oscillation propagation under nonlinear car-following laws," Transportation Research Part B: Methodological, Elsevier, vol. 46(3), pages 409-423.
    7. Bo Yang & Yao Wu & Weihua Zhang & Jie Bao, 2020. "Modeling Collision Probability on Freeway: Accounting for Different Types and Severities in Various LOS," Sustainability, MDPI, vol. 12(18), pages 1-13, September.
    8. Wang, Xiao & Jiang, Rui & Li, Li & Lin, Yi-Lun & Wang, Fei-Yue, 2019. "Long memory is important: A test study on deep-learning based car-following model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 786-795.
    9. Coifman, Benjamin & Ponnu, Balaji, 2020. "Adjacent lane dependencies modulating wave velocity on congested freeways-An empirical study," Transportation Research Part B: Methodological, Elsevier, vol. 142(C), pages 84-99.
    10. Chandle Chae & Youngho Kim, 2023. "Investigation of Following Vehicles’ Driving Patterns Using Spectral Analysis Techniques," Sustainability, MDPI, vol. 15(13), pages 1-15, July.
    11. Zhang, H.M. & Kim, T., 2005. "A car-following theory for multiphase vehicular traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 39(5), pages 385-399, June.
    12. Georgia Perakis & Guillaume Roels, 2006. "An Analytical Model for Traffic Delays and the Dynamic User Equilibrium Problem," Operations Research, INFORMS, vol. 54(6), pages 1151-1171, December.
    13. Rabia Soomro & Irfan Ahmed Memon & Agha Faisal Habib Pathan & Waqas Ahmed Mahar & Noman Sahito & Zulfiqar Ali Lashari, 2022. "Factors That Influence Travelers’ Willingness to Adopt Bus Rapid Transit (Green Line) Service in Karachi," Sustainability, MDPI, vol. 14(16), pages 1-35, August.
    14. Rehborn, Hubert & Klenov, Sergey L. & Palmer, Jochen, 2011. "An empirical study of common traffic congestion features based on traffic data measured in the USA, the UK, and Germany," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4466-4485.
    15. Kaur, Ramanpreet & Sharma, Sapna, 2018. "Modeling and simulation of driver’s anticipation effect in a two lane system on curved road with slope," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 110-120.
    16. Yifan Pan & Yongjiang Wang & Baobin Miao & Rongjun Cheng, 2022. "Stabilization Strategy of a Novel Car-Following Model with Time Delay and Memory Effect of the Driver," Sustainability, MDPI, vol. 14(12), pages 1-20, June.
    17. Xiangyang Cao & Bingzhong Zhou & Qiang Tang & Jiaqi Li & Donghui Shi, 2018. "Urban Wasteful Transport and Its Estimation Methods," Sustainability, MDPI, vol. 10(12), pages 1-15, December.
    18. Jaeung Cha & Jinwoo Lee & Changhee Lee & Yulseong Kim, 2021. "Legal Disputes under Time Charter in Connection with the Stranding of the MV Ever Given," Sustainability, MDPI, vol. 13(19), pages 1-25, September.
    19. Hongxing Zhao & Ruichun He & Xiaoyan Jia, 2019. "Estimation and Analysis of Vehicle Exhaust Emissions at Signalized Intersections Using a Car-Following Model," Sustainability, MDPI, vol. 11(14), pages 1-25, July.
    20. Mohamed Ali Kammoun & Sadok Turki & Nidhal Rezg, 2020. "Optimization of Flight Rescheduling Problem under Carbon Tax," Sustainability, MDPI, vol. 12(14), pages 1-19, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:23:p:9888-:d:451524. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.