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

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.

Authors

Institutions

Publication Details

Journal
Journal of Transportation Engineering Part A Systems
Published
2025-01-10
DOI
https://doi.org/10.1061/jtepbs.teeng-8491
Citations
1
Primary Topic
Traffic and Road Safety
Type
article
Field-Weighted Citation Impact
1.30
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Gyanendra Singh, Parveen Kumar, Jinendra Kumar Jain
1 citations
Journal of Transportation Engineering Part A Systems
Traffic and Road Safety
1.30
article

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

Gyanendra Singh, Parveen Kumar, Jinendra Kumar Jain
article en
1 citations

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.

Journal of Transportation Engineering Part A SystemsVol. 151(3)
Deenbandhu Chhotu Ram University of Science and Technology (IN), Malaviya National Institute of Technology Jaipur (IN)
Openalex Percentile: Top 25%
Traffic and Road Safety
1.30
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.