| Abstrakt: |
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. • BP-ANN is used to model day and night crash severity factors for low-speed urban roads using 5 year crash data of Patna India • Appropriate BP-ANN model architecture obtained by analysing misclassification rates for different possible architectures. • Urban Mid-Block segments witnessed higher severities during day and controlled intersections are safer • Motorcycle crashes are observed to have higher severities even during daytime • Broken and narrow pavement was observed to increase severity of daytime crashes [ABSTRACT FROM AUTHOR] |
