| dc.description.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. | |
