Development of district-wise crash prediction model in Bangladesh
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-structura...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2020-01-01
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| Series: | Cogent Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/23311916.2020.1762525 |
| _version_ | 1797718594552856576 |
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| author | Soumik Nafis Sadeek Shakil Mohammad Rifaat |
| author_facet | Soumik Nafis Sadeek Shakil Mohammad Rifaat |
| author_sort | Soumik Nafis Sadeek |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T08:52:38Z |
| format | Article |
| id | doaj.art-295fb71fbabb493c9b89d82a5c388d21 |
| institution | Directory Open Access Journal |
| issn | 2331-1916 |
| language | English |
| last_indexed | 2024-03-12T08:52:38Z |
| publishDate | 2020-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj.art-295fb71fbabb493c9b89d82a5c388d212023-09-02T16:14:51ZengTaylor & Francis GroupCogent Engineering2331-19162020-01-017110.1080/23311916.2020.17625251762525Development of district-wise crash prediction model in BangladeshSoumik Nafis Sadeek0Shakil Mohammad Rifaat1IUBAT – International University of Business Agriculture and TechnologyIslamic University of Technology (IUT)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.http://dx.doi.org/10.1080/23311916.2020.1762525road infrastructuredemographicweathercrash severitypoisson regression |
| spellingShingle | Soumik Nafis Sadeek Shakil Mohammad Rifaat Development of district-wise crash prediction model in Bangladesh Cogent Engineering road infrastructure demographic weather crash severity poisson regression |
| title | Development of district-wise crash prediction model in Bangladesh |
| title_full | Development of district-wise crash prediction model in Bangladesh |
| title_fullStr | Development of district-wise crash prediction model in Bangladesh |
| title_full_unstemmed | Development of district-wise crash prediction model in Bangladesh |
| title_short | Development of district-wise crash prediction model in Bangladesh |
| title_sort | development of district wise crash prediction model in bangladesh |
| topic | road infrastructure demographic weather crash severity poisson regression |
| url | http://dx.doi.org/10.1080/23311916.2020.1762525 |
| work_keys_str_mv | AT soumiknafissadeek developmentofdistrictwisecrashpredictionmodelinbangladesh AT shakilmohammadrifaat developmentofdistrictwisecrashpredictionmodelinbangladesh |