Development of district-wise crash prediction model in Bangladesh. Issue 1 (1st January 2020)

• Record Type: Journal Article
• Authors: Sadeek, Soumik Nafis; Rifaat, Shakil Mohammad
• Editors: Giunta, Marinella
• Is Part Of: Cogent engineering. Volume 7:Issue 1(2020)
• Publication Date: 2020-01-01
• Subjects: Road infrastructure -- demographic -- weather -- crash severity -- poisson regression; Engineering -- Periodicals; Technology -- Periodicals; Engineering; Technology; Periodicals; 620
• DOI: 10.1080/23311916.2020.1762525
• Languages: English
• ISSNs: 2331-1916

Abstract

Abstract: Every year severity of road crash shows substantial evidence that Bangladesh is a crash-prone country. Different factors are directly and indirectly linked with crash occurrences. Thus, developing a crash prediction model is of necessity to explore the effects of various structural and non-structural elements associated with transportation system on crashes in the context of Bangladesh. Considering all districts (64) of Bangladesh, 13 types of road infrastructures, different types of roads and highways, socio-economic and demographic factors, and weather conditions have been taken as predictors to identify their inimical effect on crash occurrence. Poisson Regression models have been developed for different crash types described in the crash data. In this research, an apt focus is given on the effect of road infrastructures on crash severity. The result displays that Reinforced Cement Concrete (RCC) and Pre-stressed Concrete (PC) built infrastructures are harmful for fatal and grievous crashes whereas structures designed with both concrete and steel, e.g., Steel Beam and RCC Slab (SBRS), Truss with RCC Slab (TRS), Baily with Steel Deck (BSD) are found less detrimental to any type of crash severity. National highways, non-surveyed roads, paved roads are found hazardous. Also, sex ratio and large household size tend to increase fatal crash. Moreover, rainfall and fog, both have strong influence on crash occurrence.