Highlights This paper assesses crash risk considering vehicles interactions with trucks under different traffic conditions. Vehicle interaction metrics are developed based on the combination of leading and following vehicle types. Individual vehicle data are collected from Inductive Loop Detectors (ILDs). A case-control analysis is conducted using conditional logistic models. Results highlight that vehicle interactions involved with trucks are significantly related to the crash likelihood.

    Abstract Trucks have distinct driving characteristics in general traffic streams such as lower speeds and limitations in acceleration and deceleration. As a consequence, vehicles keep longer headways or frequently change lane when they follow a truck, which is expected to increase crash risk. This study introduces several traffic measures at the individual vehicle level to capture vehicle interactions between trucks and non-trucks and analyzed how the measures affect crash risk under different traffic conditions. The traffic measures were developed using headways obtained from Inductive Loop Detectors (ILDs). In addition, a truck detection algorithm using a Gaussian Mixture (GM) model was developed to identify trucks and to estimate truck exposure from ILD data. Using the identified vehicle types from the GM model, vehicle interaction metrics were categorized into three groups based on the combination of leading and following vehicle types. The effects of the proposed traffic measures on crash risk were modeled in two different cases of prior- and non-crash using a case-control approach utilizing a conditional logistic regression. Results showed that the vehicle interactions between the leading and following vehicle types were highly associated with crash risk, and further showed different impacts on crash risk by traffic conditions. Specifically, crashes were more likely to occur when a truck following a non-truck had shorter average headway but greater headway variance in heavy traffic while a non-truck following a truck had greater headway variance in light traffic. This study obtained meaningful conclusions that vehicle interactions involved with trucks were significantly related to the crash likelihood rather than the measures that estimate average traffic condition such as total volume or average headway of the traffic stream.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Assessing crash risk considering vehicle interactions with trucks using point detector data


    Contributors:

    Published in:

    Publication date :

    2018-03-01


    Size :

    9 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Assessing the Risk of Crash for Trucks on Onset Yellow

    A. Sharma / N. P. Burnett | NTIS | 2012


    Comparing the Crash Risk of Vehicle-Pedestrian Interactions using Autonomous Vehicle Data

    Lanzaro, Gabriel / Fu, Chuanyun / Sayed, Tarek | IEEE | 2023


    Crash risk factors for interstate large trucks in North Carolina

    Teoh, Eric R. / Carter, Daniel L. / Smith, Sarah et al. | Elsevier | 2017