AbstractThis study aimed to identify the segment-specific risk factors for rear-end crashes on an expressway in India. The fatal crash data were analyzed from August 2012 to October 2018 with road geometry details, speed, volume, and roadside data. Negative binomial (NB) models with a random parameter framework accounted for the unobserved heterogeneity across the segments. A NB model with fixed parameters (FPNB) was used as a base model against basic random parameter NB (RPNB) and correlated random parameter NB (CRPNB) models. The results indicated that rear-end crashes comprised 49% of the total fatal crashes. Truck and car occupants constituted the highest share of victims in rear-end crashes. As per the estimate of log-likelihood for the developed models, CRPNB was best. The results of the RPNB model suggested that speed, Annual Average Daily Traffic (AADT), horizontal alignment radius, and vertical curve length were the significant variables. In the case of the CRPNB model, speed, AADT, and vertical curve length were the significant variables. Segments where a village was present along the expressway were comparatively safer. Segments where entry and exit ramps and underpasses were present and segments with hazards had higher chances of being associated with rear-end crashes. The study’s findings provide valuable insights into proposing interventions for reducing rear-end crashes on expressways in heterogeneous traffic conditions. Detailed investigations with additional data are required at segments with entry-exit ramps and horizontal and vertical curves. To sum up, the findings of this study may assist highway agencies and design engineers in designing new or evaluating existing expressways from a safety viewpoint.