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• W2954429703 abstract "Urban intersections in India constitute a significant share of pedestrian fatalities. However, model-based prediction of pedestrian fatalities is still in a nascent stage in India. This study proposes an artificial neural network (ANN) technique to develop a pedestrian fatal crash frequency model at the intersection level. In this study, three activation functions are used along with four different learning algorithms to build different combinations of ANN models. In each of these combinations, the number of neurons in the hidden layer is varied by trial and error method, and the best results are considered. In this way, 12 sets of pedestrian fatal crash predictive models are developed. Out of these, Bayesian Regularization Neural Network consisting of 13 neurons in the hidden layer with 'hyperbolic tangent-sigmoid' activation function is found to be the best-fit model. Finally, based on sensitivity analysis, it is found that the 'approaching speed' of the motorized vehicle has the most significant influence on the fatal pedestrian crashes. 'Logarithm of average daily traffic' (ADT) volume is found to be the second most sensitive variable. Pedestrian-vehicular interaction concerning 'pedestrian-vehicular volume ratio' and lack of 'accessibility of pedestrian cross-walk' are found to be approximately as sensible as 'logarithm of ADT'." @default.
• W2954429703 created "2019-07-12" @default.
• W2954429703 creator A5006093808 @default.
• W2954429703 creator A5024859042 @default.
• W2954429703 creator A5082004862 @default.
• W2954429703 date "2019-07-03" @default.
• W2954429703 modified "2023-09-26" @default.
• W2954429703 title "Development of pedestrian crash prediction model for a developing country using artificial neural network" @default.
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• W2954429703 doi "https://doi.org/10.1080/17457300.2019.1627463" @default.
• W2954429703 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31271110" @default.
• W2954429703 hasPublicationYear "2019" @default.
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