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    Investigating Risk Factors Affecting Crash Frequency on the Expressways in India: A Random Parameters Negative Binomial Modeling Approach

    Source: Journal of Transportation Engineering, Part A: Systems:;2025:;Volume ( 151 ):;issue: 003::page 04025006-1
    Author:
    Parveen Kumar
    ,
    Jinendra Kumar Jain
    ,
    Gyanendra Singh
    DOI: 10.1061/JTEPBS.TEENG-8491
    Publisher: American Society of Civil Engineers
    Abstract: Despite the high crash rate and rapid expansion of the Indian expressways network, there is a shortage of studies analyzing the risk factors contributing to traffic crashes on these roads. The current study addresses this gap by examining key risk factors associated with traffic volume, traffic composition, expressway geometry, and vehicle overspeeding affecting crash frequency on three expressways of 782 km length. Fixed effects negative binomial (FENB), random parameters negative binomial (RPNB), and correlated random parameters negative binomial (CRPNB) models were utilized with extensive data of 4,342 crashes collected over the period of 2018–2019. Spatial instability among the parameters in crash data from three expressways was identified using the likelihood ratio test. This led to the development of separate models for each expressway to account for the effects of varying road characteristics and traffic conditions on crashes, identifying distinct sets of significant variables. The results showed that the RPNB models outperformed the FENB models, while the CRPNB models offered no significant improvement over the RPNB models across all three expressways. The findings demonstrated that the RPNB model not only effectively deals with the challenges of overdispersion but also accounts for the unobserved heterogeneity in the crash data. The RPNB models identified 13 significant variables, including 5 random parameters. Results showed that segment length, traffic volume, number of lanes, median openings, and bus bay/truck layby were positively correlated with crash frequency, while raised medians with crash barriers, higher proportions of cars and trucks, and wider shoulder width were negatively correlated. The impacts of the diversity in segment length, percentage of trucks, speed limit for cars, cumulative grade change, and interchange segment were also discerned. The findings highlight critical areas for design improvements and policy interventions to enhance safety on Indian expressways under mixed traffic conditions.
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      Investigating Risk Factors Affecting Crash Frequency on the Expressways in India: A Random Parameters Negative Binomial Modeling Approach

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4304826
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    • Journal of Transportation Engineering, Part A: Systems

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    contributor authorParveen Kumar
    contributor authorJinendra Kumar Jain
    contributor authorGyanendra Singh
    date accessioned2025-04-20T10:29:28Z
    date available2025-04-20T10:29:28Z
    date copyright1/10/2025 12:00:00 AM
    date issued2025
    identifier otherJTEPBS.TEENG-8491.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4304826
    description abstractDespite the high crash rate and rapid expansion of the Indian expressways network, there is a shortage of studies analyzing the risk factors contributing to traffic crashes on these roads. The current study addresses this gap by examining key risk factors associated with traffic volume, traffic composition, expressway geometry, and vehicle overspeeding affecting crash frequency on three expressways of 782 km length. Fixed effects negative binomial (FENB), random parameters negative binomial (RPNB), and correlated random parameters negative binomial (CRPNB) models were utilized with extensive data of 4,342 crashes collected over the period of 2018–2019. Spatial instability among the parameters in crash data from three expressways was identified using the likelihood ratio test. This led to the development of separate models for each expressway to account for the effects of varying road characteristics and traffic conditions on crashes, identifying distinct sets of significant variables. The results showed that the RPNB models outperformed the FENB models, while the CRPNB models offered no significant improvement over the RPNB models across all three expressways. The findings demonstrated that the RPNB model not only effectively deals with the challenges of overdispersion but also accounts for the unobserved heterogeneity in the crash data. The RPNB models identified 13 significant variables, including 5 random parameters. Results showed that segment length, traffic volume, number of lanes, median openings, and bus bay/truck layby were positively correlated with crash frequency, while raised medians with crash barriers, higher proportions of cars and trucks, and wider shoulder width were negatively correlated. The impacts of the diversity in segment length, percentage of trucks, speed limit for cars, cumulative grade change, and interchange segment were also discerned. The findings highlight critical areas for design improvements and policy interventions to enhance safety on Indian expressways under mixed traffic conditions.
    publisherAmerican Society of Civil Engineers
    titleInvestigating Risk Factors Affecting Crash Frequency on the Expressways in India: A Random Parameters Negative Binomial Modeling Approach
    typeJournal Article
    journal volume151
    journal issue3
    journal titleJournal of Transportation Engineering, Part A: Systems
    identifier doi10.1061/JTEPBS.TEENG-8491
    journal fristpage04025006-1
    journal lastpage04025006-13
    page13
    treeJournal of Transportation Engineering, Part A: Systems:;2025:;Volume ( 151 ):;issue: 003
    contenttypeFulltext
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