Communications - Scientific Letters of the University of Zilina 2023, 25(2):D45-D53 | DOI: 10.26552/com.C.2023.036

Developing Pedestrian Fatality Prediction Models Using Historical Crash Data: Application of Binary Logistic Regression and Boosted Tree Mechanism

Malaya Mohanty ORCID...1, *, Bitati Sarkar2, Piotr Gorzelańczyk ORCID...3, Rachita Panda1, Srinivasa Rao Gandupalli ORCID...4, Ampereza Ankunda1
1 School of Civil Engineering, KIIT Deemed to be University, Bhubaneswar, India
2 Department of Civil Engineering, National Institute of Technology, Silchar, India
3 Department of Engineering, Stanislaw Staszic University of Applied Sciences, Pila, Poland
4 GITAM School of Technology, GITAM Deemed to be University, Visakhapatnam, India

Pedestrian fatality rate plays a key role in examining effectiveness of the road safety. The present study attempts to examine the effect of various categories of accused vehicles and the average 85th percentile speed at accident location on the pedestrian crash fatality. The study also attempts to develop pedestrian crash severity models using the binary logistic regression and boosted trees technique. Historical crash data, along with the video recording technique at accident sites, have been utilized for the present study. From regression equations, it is observed that when the heavy vehicle (HV) hits a pedestrian as compared to two-wheeler (2W), the average chance of death increases 2.44 times. According to the Boosted tree model, the contribution of speed is 60 %, whereas the contribution of category of accused vehicle is 40 % for pedestrian fatality prediction. The study should help in planning better strategies like all red time at intersections or pedestrian foot over bridge at critical locations.

Keywords: Pedestrian fatalities, vehicle category, 85th percentile speed, binary logistic model, boosted tree model
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The authors received no financial support for the research, authorship and/or publication of this article.

Conflicts of interest:


The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Received: November 25, 2022; Accepted: February 28, 2023; Prepublished online: March 21, 2023; Published: April 6, 2023  Show citation

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Mohanty, M., Sarkar, B., Gorzelańczyk, P., Panda, R., Gandupalli, S.R., & Ankunda, A. (2023). Developing Pedestrian Fatality Prediction Models Using Historical Crash Data: Application of Binary Logistic Regression and Boosted Tree Mechanism. Communications - Scientific Letters of the University of Zilina, 25(2), D45-53. doi: 10.26552/com.C.2023.036
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