@article{ref1,
title="Modelling crash severity outcomes for low speed urban roads using back propagation - artificial neural network (BP - ANN) - a case study in Indian context",
journal="IATSS research",
year="2023",
author="Barman, Santanu and Bandyopadhyaya, Ranja",
volume="47",
number="3",
pages="382-400",
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.<p />",
language="en",
issn="0386-1112",
doi="10.1016/j.iatssr.2023.08.002",
url="http://dx.doi.org/10.1016/j.iatssr.2023.08.002"
}