Publications by authors named "Neda Kamboozia"
Article Synopsis
- The research investigates the impact of streetscape and land use on urban accidents in Mashhad from 2017 to 2021, focusing on three different urban zones.
- The study found that commercial areas had the highest accident rates, three times those of residential areas, with 75% of these accidents occurring in open streetscapes.
- Machine learning models were used for analysis, with Random Forest Regression achieving the highest accuracy at 85%, followed by Extreme Boost Gradient Regression at 81%, and Multilayer Neural Network Perceptron at 75%.
Statistical analysis and accident prediction models leading to pedestrian injuries and deaths on rural roads in Iran.
Int J Inj Contr Saf Promot
December 2020
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.
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