IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i8p3076-d344303.html
   My bibliography  Save this article

Crash Risk Assessment of Off-Ramps, Based on the Gaussian Mixture Model Using Video Trajectories

Author

Listed:
  • Ting Xu

    (College of Transportation Engineering, Chang’an University, Xi’an 710064, China
    College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China)

  • Yanjun Hao

    (College of Transportation Engineering, Chang’an University, Xi’an 710064, China)

  • Shichao Cui

    (College of Transportation Engineering, Chang’an University, Xi’an 710064, China)

  • Xingqi Wu

    (College of Transportation Engineering, Chang’an University, Xi’an 710064, China)

  • Zhishun Zhang

    (College of Transportation Engineering, Chang’an University, Xi’an 710064, China)

  • Steven I-Jy Chien

    (College of Transportation Engineering, Chang’an University, Xi’an 710064, China
    Department of Civil and Environmental Engineering, New Jersey Institute of Technology, Newark, NJ 07102-1982, USA)

  • Yulong He

    (College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China)

Abstract

The focus of this paper is the crash risk assessment of off-ramps in Xi’an. The time-to-collision (TTC) is used for the measurement and cross-comparison of the crash risk of each location. Five sites from the urban expressway in Xi’an were selected to explore the TTC distribution. An unmanned aerial vehicle and a camera were used to collect traffic flow data for 20 min at each site. The parameters, including speed, deceleration rate, truck percentage, traffic volume, and vehicle trajectories, were extracted from video images. The TTCs were calculated for each vehicle. The Gaussian mixture model (GMM) was proposed to predict the TTC probability density functions (PDFs) and cumulative density functions (CDFs) for five sites. The Kolmogorov–Smirnov (K-S) test indicated that the samples followed the estimated GMM distribution. The relationship between the crash risk level and influencing factors was studied by an ordinal logistic regression model and a naive Bayesian model. The results showed that the naive Bayesian model had an accuracy of 86.71%, while the ordinal logistic regression model had an accuracy of 84.81%. The naive Bayesian model outperformed the ordinal logistic regression model, and it could be applied to the real-time collision warning system.

Suggested Citation

  • Ting Xu & Yanjun Hao & Shichao Cui & Xingqi Wu & Zhishun Zhang & Steven I-Jy Chien & Yulong He, 2020. "Crash Risk Assessment of Off-Ramps, Based on the Gaussian Mixture Model Using Video Trajectories," Sustainability, MDPI, vol. 12(8), pages 1-20, April.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:8:p:3076-:d:344303
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/8/3076/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/12/8/3076/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. González, Rosa Marina & Marrero, Gustavo A., 2012. "Induced road traffic in Spanish regions: A dynamic panel data model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(3), pages 435-445.
    Full references (including those not matched with items on IDEAS)

    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. Rahman, Mohammad Lutfur & Baker, Douglas, 2018. "Modelling induced mode switch behaviour in Bangladesh: A multinomial logistic regression approach," Transport Policy, Elsevier, vol. 71(C), pages 81-91.
    2. Dimitropoulos, Alexandros & Oueslati, Walid & Sintek, Christina, 2018. "The rebound effect in road transport: A meta-analysis of empirical studies," Energy Economics, Elsevier, vol. 75(C), pages 163-179.
    3. Bucsky, Péter & Juhász, Mattias, 2022. "Long-term evidence on induced traffic: A case study on the relationship between road traffic and capacity of Budapest bridges," Transportation Research Part A: Policy and Practice, Elsevier, vol. 157(C), pages 244-257.
    4. Siqi Song & Chen-Chieh Feng & Mi Diao, 2020. "Vehicle quota control, transport infrastructure investment and vehicle travel: A pseudo panel analysis," Urban Studies, Urban Studies Journal Limited, vol. 57(12), pages 2527-2546, September.
    5. Rosa M. Gonz lez-Marrero & Rosa M. Lorenzo-Alegr a & Gustavo A. Marrero, 2012. "A Dynamic Model for Road Gasoline and Diesel Consumption: An Application for Spanish Regions," International Journal of Energy Economics and Policy, Econjournals, vol. 2(4), pages 201-209.
    6. Hernández González, Yeray & Corral Quintana, Serafín, 2016. "An integrated assessment of alternative land-based passenger transport policies: A case study in Tenerife," Transportation Research Part A: Policy and Practice, Elsevier, vol. 89(C), pages 201-214.
    7. Özlem Şimşekoğlu & Trond Nordfjærn & Torbjørn Rundmo, 2017. "Predictors of car use habit strength in an urban Norwegian public," Transportation, Springer, vol. 44(3), pages 575-588, May.
    8. Välilä, Timo, 2024. "Fiscal sustainability and the composition of government investment: The case of investment in road infrastructure," Transport Policy, Elsevier, vol. 145(C), pages 105-125.
    9. Corral, Serafin & Hernandez, Yeray, 2017. "Social Sensitivity Analyses Applied to Environmental Assessment Processes," Ecological Economics, Elsevier, vol. 141(C), pages 1-10.

    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:8:p:3076-:d:344303. 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.