The shoulder width, as a geometric element, plays a crucial role in enhancing highway safety. Research from high-income countries indicates that improving shoulders on highways leads to substantial safety benefits. However, the safety effectiveness of paved shoulders for low- and middle-income countries (LMICs) highway contexts has limited evidence. This study evaluated the safety effectiveness of the paved shoulder width on 61 km, four-lane, divided rural intercity highways in India. The first objective was to evaluate highway crash patterns using data from 2016 to 2019. The second objective was to evaluate the safety effectiveness of paved shoulder width using the case-control approach. The findings of this study demonstrate a consistent decline in the likelihood of crashes as the shoulder’s width increases within the range of zero to 2.5 m for the 100 m segment length and zero to 1.7 m for the 500 m segment length. Nevertheless, model estimates indicate an increased crash risk for shoulders wider than 2.5 m. The results also suggested that the odds ratio for paved shoulder widths ranging from no shoulder to 2.5 m is likely to follow the crash modification factor from the highway safety manual. The findings of this study hold significant implications for the design policy of shoulder width on rural highways in LMICs.

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.