Citation

Barman S, Bandyopadhyaya R. IATSS Res. 2023; 47(3): 382-400.

Copyright

(Copyright © 2023, International Association of Traffic and Safety Sciences, Publisher Elsevier Publishing)

DOI

10.1016/j.iatssr.2023.08.002

PMID

unavailable

Abstract

This work analyses influence of road, weather and crash-specific factors on crash severity outcomes for low-speed urban midblock sections and intersections, for day and night time, using Backpropagation-Artificial Neural Network (BP-ANN). Five-year crash data (2015-2019) from 82Km urban road network of Patna, India was used for the study. The road factors include pavement width, distress condition, marking; shoulder type, condition; road section type as mid-block, intersection and intersection control. Weather factors include season of crash, fog or rain at crash time. Crash factor include collision partner, type and crash time. The most appropriate BP-ANN model architecture was estimated using Misclassification-Rate. It was observed that midblock segments witness higher severities during daytime, whereas intersections witness higher severities during night. Controlled intersections are safer compared to un-controlled intersections. Pavement distress greatly increase the chance of higher severities. Narrow roads record greater severities during day due to lack of surveillance.

Keywords

Backpropagation – Artificial neural network (BP-ANN); Low speed urban road; Severity outcomes