Using a surrogate safety approach to prioritize hazardous segments in a rural highway in a developing country. (July 2020)

• Record Type: Journal Article
• Authors: Mahmud, S.M. Sohel; Ferreira, Luis; Hoque, Md. Shamsul; Tavassoli, Ahmad
• Is Part Of: IATSS research. Volume 44:Number 2(2020)
• Page Start: 132
• Page End: 141
• Publication Date: 2020-07
• Subjects: Crash prediction models -- Poisson model -- Random parameter -- Zero-inflated -- Developing countries -- Heterogeneous or mixed traffic; Traffic safety -- Periodicals; Transportation and state -- Periodicals; Verkeersveiligheid; Internationale organisaties; Traffic safety; Transportation and state; Periodicals; 363.1256
• DOI: 10.1016/j.iatssr.2019.11.002
• Languages: English
• ISSNs: 0386-1112

Abstract

Abstract: Road safety is a global concern particularly in developing countries where some road sections are disproportionately more vulnerable in terms of the frequency and severity of crashes. Other than using historical crash data based reactive approaches, those sections need to be identified proactively, so that mitigation measures can be applied. Moreover, those approaches are sometimes questioned mainly due to data reliability issues in developing countries. The study reported here is aimed at highlighting the applicability of traffic conflict techniques as surrogate safety measures to identify those sections of a rural highway in a developing country, which are most likely at risk. An adapted framework is demonstrated to identify traffic conflicts using combined surrogate indicators acknowledging the limited resources and facilities in developing countries. A new model is put forwarded using a count data modelling approach. Both fixed and random parameters model derivatives have been explored as an alternative methodological approach to relate the factors affecting the number and probability of conflicts. The partial effects of individual independent variables were estimated to gain a better insight of their impact. The results show that the model can predict high risk segments in terms of probability of conflicts as well as safety risk, as well as prioritize road sections according to the likelihood of their safety level. The model provides a less expensive alternative to the collection of historical crash data in order to identify hazardous road locations or black spots on two-lane highways in developing countries. 1 Highlights: A methodological approach using surrogate safety measures to identify and prioritizethose at risk road segments. Fixed and random parameter Poisson models have been explored to relate the factors affecting the probability of conflicts. Data has been collected from a section national highway with heterogeneous traffic environment in Bangladesh. The partial effect of each influencing factor has been quantified for both fixed and random parameter models. Performance of the proposed model has been analysed.