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Traffic Safety and Built Environment

Bangladesh is one of the most hazardous countries in the world in terms of road crashes. According to police reports, around 2,800 or more road crashes occur in this country every year. Moreover, pedestrian crashes have become a major safety concern in urban areas. From 1998 to 2013, more than 10,000 crashes occurred in Dhaka, the capital and megacity of Bangladesh, and 4,514 pedestrians died in those crashes. To improve this critical situation, researchers have been trying to identify the contributory factors behind crashes through studies at both microscopic and macroscopic levels. It is possible to improve road safety by altering the built environment of urban areas, considering its relationship to crash occurrences, since the built environment has a strong direct influence on traffic volume, speed, and conflicts. Therefore, understanding the contributory built environment factors behind crashes is very important for taking effective countermeasures to reduce crashes, and consequently, improve road safety, including the safety of pedestrians. This study:

  • Explored the effects of human, vehicle, roadway, built environment, and natural environment-related factors on pedestrian and motorcycle crash severity in Dhaka using binary logistic regression.

  • Developed a spatial regression modeling framework that integrates global non-spatial regression (linear regression, logistic regression), global spatial regression (spatial lag model, spatial error model), and local spatial regression (GWR, MGWR, GWLR) models to address spatial autocorrelation and explore the spatially heterogeneous effects of the built environment on pedestrian crash occurrences and severity.


  • Zafri, N. M., & Khan, A. (2023). Using geographically weighted logistic regression (GWLR) for pedestrian crash severity modeling: Exploring spatially varying relationships with natural and built environment factors. IATSS Research, Elsevier. [DOI]

  • Hossain, A., Sun, X., Zafri, N. M., & Codjoe, J. (2023). Investigating pedestrian crash patterns at high-speed intersection and road segments: Findings from the unsupervised learning algorithm. International Journal of Transportation Science and Technology, Elsevier. [DOI]

  • Zafri, N. M., & Khan, A. (2022). A spatial regression modeling framework for examining relationships between the built environment and pedestrian crash occurrences at macroscopic level: A study in a developing country context. Geography and sustainability, Elsevier. [DOI]

  • Rahman, M. H.*, Zafri, N. M.*, Akter, T., & Pervaz, S. (2021). Identification of factors influencing severity of motorcycle crashes in Dhaka, Bangladesh using binary logistic regression model. International Journal of Injury Control and Safety Promotion, Taylor & Francis. [DOI] [*Shared First Author]

  • Zafri, N. M., Prithul, A. A., Baral, I., & Rahman, M. (2020). Exploring the factors influencing pedestrian-vehicle crash severity in Dhaka, Bangladesh. International Journal of Injury Control and Safety Promotion, Taylor & Francis. [DOI]


Proposed spatial regression modeling framework

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Spatially varying relationships between pedestrian crash severity and crash-inducing factors (natural and built environment related)

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Spatiotemporal pattern of traffic crash hotspot

Project Details

Project Timeline: 2020-2022

Project Type: Postgraduate thesis of Niaz Mahmud Zafri

Funding: Committee for Advanced Studies and Research (CASR), Bangladesh University of Engineering and Technology (BUET)

Main Team Members:

  • Niaz Mahmud Zafri, Assistant Professor, Department of Urban and Regional Planning, BUET

  • Dr. Asif-Uz-Zaman Khan, Professor, Department of Urban and Regional Planning, BUET

  • Ivee Baral, Postgraduate Student, City in Regional Planning, Georgia Institute of Technology

  • Md Hamidur Rahman, PhD Student in Community and Regional Planning, University of Texas at Austin

  • Shahrior Pervaz, PhD Candidate, Department of Civil, Environmental & Construction Engineering, University of Central Florida

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