Statistical analysis and accident prediction models leading to pedestrian injuries and deaths on rural roads in Iran
Neda Kamboozia, Mahmoud Ameri, Seyed Mohsen Hosseinian
Abstract
The purpose of this study was to develop models to predict the severity of pedestrian accidents on rural roads of Guilan, Iran. Therefore, the probability of occurrence of any type of accidents was measured using the accident data from March 2014 to March 2019. Eleven independent variables affecting the severity of pedestrian accidents as well as statistical analysis such as the frequency analysis, Friedman test and factor analysis, and modeling including multiple logistic regression and artificial neural networks using multi-layer perceptron (MLP) and radius basis function (RBF) have been used. Results of modeling and analysis of pedestrian accidents in different methods showed each of the methods depending on their function investigated the severity of accidents with different point of view and had different results. As a result, putting the output results together, the best measures can be suggested to increase the safety of pedestrians on the rural roads of Guilan.