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Accid Anal Prev. 2018 Mar 12. pii: S0001-4575(18)30106-4. doi: 10.1016/j.aap.2018.03.002. [Epub ahead of print]

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

Author information

1
Department of Civil Engineering, University of Texas at Arlington, 416 Yates St., 425 Nedderman Hall, Arlington, TX, 76019, United States. Electronic address: kate.hyun@uta.edu.
2
Department of Civil and Environmental Engineering, Intelligent Transportation Systems Lab., 77 Massachusetts Avenue, Building 1-180, Massachusetts Institute of Technology, United States. Electronic address: kjeong@mit.edu.
3
Institute of Transportation Studies, 4000 Anteater Instruction and Research Building (AIRB), University of California, Irvine, Irvine, CA, 92697, United States. Electronic address: ytok@uci.edu.
4
Department of Civil and Environmental Engineering, 4014 Anteater Instruction and Research Building (AIRB), Institute of Transportation Studies, University of California, Irvine, Irvine, CA, 92697, United States. Electronic address: sritchie@uci.edu.

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.

KEYWORDS:

Conditional logistic regression; Crash risk; Inductive loop detector; Truck; Vehicle interactions

PMID:
29544655
DOI:
10.1016/j.aap.2018.03.002

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