Journal of Transportation Research

Journal of Transportation Research

Spatial Autocorrelation Analysis of Traffic Accidents Based on Their Severity )Case Study: Qazvin(

Document Type : Original Article

Authors
Mohammadreza Kamalinezhad 1
Amir Abbas Rassafi 2
Hamid Mirzahossein 3
Mehdi Rafati Fard 4
1 M.Sc. Student, Faculty of Engineering, Imam Khomeini International University, Qazvin, Iran.
2 Professor, Faculty of Engineering, Imam Khomeini International University, Qazvin, Iran.
3 Associate Professor, Faculty of Engineering, Imam Khomeini International University, Qazvin, Iran.
4 Assistant Professor, Faculty of Engineering, Imam Khomeini International University, Qazvin, Iran.
10.22034/tri.2025.535878.3360
Abstract
Transportation, as one of the key infrastructures of urban development, plays a significant role in urban economy, politics, and society. At the same time, traffic accidents, as one of the main challenges and issues of this system, have widespread consequences in these areas. Identifying and analyzing them is a fundamental step toward reducing accidents and improving road safety. The analysis of spatial patterns of traffic accidents, as one of the major challenges in urban management and transportation planning, plays a decisive role in identifying critical hotspots and prioritizing safety measures. In this study, using advanced spatial analysis methods,
the spatial distribution pattern of traffic accidents in Qazvin from 2015 to 2017 was examined. The methods used include the Average Nearest Neighbor Distance (ANND) to measure the clustering or dispersion of points, as well as global and local Moran’s I analysis to identify spatial autocorrelation and accident clustering. The results of the ANND analysis indicate that accidents in Qazvin are not randomly distributed and exhibit a strong tendency to form spatial clusters. The global Moran’s index was positive and significant during the study years, indicating the presence of spatial autocorrelation and the clustering of high-risk points. Additionally, local Moran’s analysis, by mapping clustering and significance, more precisely identified critical (HH) and safe (LL) zones. This study not only provides a comprehensive understanding of the spatial patterns of traffic accidents in Qazvin but also serves as a practical tool for urban policymakers and planners to reduce accidents and improve road safety. Identifying high-risk clusters and examining their annual changes can lead to the design of targeted solutions and the evaluation of the effectiveness of past measures.
Keywords
Spatial Autocorrelation
Spatial Analysis of Accidents
Traffic Accidents
Accident Severity
Subjects
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