Determining an appropriate segment length for highway safety evaluations in low- and middle-income countries (LMICs) poses a significant challenge. This study aims to address this issue by recommending a suitable segment length for such evaluations in India, using a 167 km intercity expressway as a case study. We employed negative binomial (NB) models on datasets segmented from 100 m to 1000 m with 100 m increments. Our findings strongly suggest that segment lengths from 300 m to 700-m suit various safety assessments. However, the study reveals that parameter estimates vary significantly with both segment length and sample size. This highlights the sensitivity of parameters to data aggregation and sample size across different segment lengths, making it difficult to identify a single optimal length. Therefore, we propose selecting the segment length and segmentation approach based on specific local conditions, highway context, data availability and quality. The methodology presented here can guide policymakers in LMICs to make informed choices regarding segment length for safety evaluations, including blackspot identification and treatment on their highways.

A road network aims to facilitate the movement of commuters and goods in a safe, economical, and efficient way that contributes to growth in the economy. Road traffic incidents (RTIs), such as crashes, vehicle breakdowns, hazards, etc., are unexpected events that cause severe traffic congestion, unreliability, and pollution. The existing open-source RTI databases provide information on only a single type of incident, i.e., crashes that too focusing on the fatal ones. Other incidents, such as vehicle breakdowns, are underreported to the transport authorities as they are less severe than road traffic crashes. However, traffic congestion induced by on-road breakdowns is non-trivial, as reported by past studies. Furthermore, the existing RTI databases lack information on incident duration, a variable that indicates the time it takes for the authorities to clear the incident site and bring traffic operations back to normalcy. The increase in duration may reflect either the severity of the incident or/and the delay in emergency services and thus becomes a key indicator for traffic and safety management. Therefore, this paper aims to present the RTI data of the Sydney Greater Metropolitan Area (GMA), Australia, which includes crashes and breakdowns, along with their duration, covering 5.5 years, starting from the 1^st January 2017. The uniqueness of this data is that the RTI duration, i.e., the clearance time of every incident, is provided along with other details, such as vehicles involved, traffic conditions, advisories imposed, etc., over a larger area. Further, the secondary data corresponding to the road network, zonal information, socioeconomic attributes, and travel characteristics collected from various sources were also included. The curated data could be employed to examine the factors influencing RTIs at the micro (individual incident) and macroscopic (zonal) levels.