An Approach based on Geographic Weighted Regression Models in Predicting Pedestrian Crashes using Exposure and Environmental Variables

Document Type : Scientific - Research

Authors
Seyed Ahmad Almasi 1
Hamid Reza Behnood 2
Arezou Hajrajabi 3
1 PhD Candidate, Highways and Transportation, Faculty of Engineering, Imam Khomeini International University, Qazvin, Iran
2 Assistant Professor, Highways and Transportation, Faculty of Engineering, Imam Khomeini International University, Qazvin, Iran
3 Assistant Professor, Statistics, Faculty of Basic Sciences, Imam Khomeini International University, Qazvin, Iran
10.22119/jte.2022.322963.2577
Abstract
Pedestrians are one of the most vulnerable users of urban roads that are directly exposed to accidents. Traffic crashes tend to be spatially dependent and is a phenomenon known as spatial dependence. The objectives of this study include identifying alternative variables for pedestrians in urban roads and identifying accident-prone areas using pedestrian exposure variables, as well as demonstrating the efficiency of spatial models in predicting pedestrian crashes. In this study, in the first step, which is the identification of exposure variables, several statistical methods have been used to identify these variables. Also, the frequency of pedestrian crashes has been predicted using six widely used models of spatial statistics, which have been evaluated based on pedestrian crash data in Tehran for the years 1396-1398. The results of this study show that the prediction of the frequency of pedestrian crashes using Poisson regression models with zero-inflated geographical weight and distribution of zero-inflated negative geographical binomials has better results based on model selection criteria than other models. This study has shown the dispersion and density of pedestrian accidents without having the volume of pedestrians and thus can be done by taking safety measures in places prone to pedestrian crashes, social costs and casualties. In this study, the use of various geographical weighted regression models to evaluate the relationship between sociological variables and crash frequencies at the area level was appropriate. Comparison of the performance of geographic weighted regression models shows the existence of significant spatial heterogeneity in the analysis.

Keywords

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Quarterly Journal of Transportation Engineering
Volume 15, Issue 1 - Serial Number 58
Autumn 2023
Pages 3253-3277

  • Receive Date 03 January 2022
  • Revise Date 27 June 2022
  • Accept Date 06 August 2022