Unraveling Urban Form and Collision Risk: The Spatial Distribution of Traffic Accidents in Zanjan, Iran

Mohsen Kalantari, Saeed Zanganeh Shahraki, Bamshad Yaghmaei, Somaye Ghezelbash, Gianluca Ladaga and Luca Salvati
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Mohsen Kalantari: Department of Human Geograhy and Spatial Planning, Faculty of Earth Sciences, Shahid Beheshti University, 1613778314 Tehran, Iran
Saeed Zanganeh Shahraki: Department of Human Geography, University of Tehran, 1613778314 Tehran, Iran
Bamshad Yaghmaei: Department of Remote Sensing and Geographical Information Systems, Faculty of Earth Sciences, Shahid Beheshti University, 1613778314 Tehran, Iran
Somaye Ghezelbash: Faculty of Earth Sciences, Shahid Beheshti University, 1613778314 Tehran, Iran
Gianluca Ladaga: Istituto Nazionale per l’Assicurazione Contro gli Infortuni sul Lavoro (INAIL), Viale Vincenzo Verrastro 3/C, I-85100 Potenza, Italy
Luca Salvati: Department of Economics and Law, University of Macerata, Via Armaroli 43, I-62100 Macerata, Italy

IJERPH, 2021, vol. 18, issue 9, 1-15

Abstract: Official statistics demonstrate the role of traffic accidents in the increasing number of fatalities, especially in emerging countries. In recent decades, the rate of deaths and injuries caused by traffic accidents in Iran, a rapidly growing economy in the Middle East, has risen significantly with respect to that of neighboring countries. The present study illustrates an exploratory spatial analysis’ framework aimed at identifying and ranking hazardous locations for traffic accidents in Zanjan, one of the most populous and dense cities in Iran. This framework quantifies the spatiotemporal association among collisions, by comparing the results of different approaches (including Kernel Density Estimation (KDE), Natural Breaks Classification (NBC), and Knox test). Based on descriptive statistics, five distance classes (2–26, 27–57, 58–105, 106–192, and 193–364 meters) were tested when predicting location of the nearest collision within the same temporal unit. The empirical results of our work demonstrate that the largest roads and intersections in Zanjan had a significantly higher frequency of traffic accidents than the other locations. A comparative analysis of distance bandwidths indicates that the first (2–26 m) class concentrated the most intense level of spatiotemporal association among traffic accidents. Prevention (or reduction) of traffic accidents may benefit from automatic identification and classification of the most risky locations in urban areas. Thanks to the larger availability of open-access datasets reporting the location and characteristics of car accidents in both advanced countries and emerging economies, our study demonstrates the potential of an integrated analysis of the level of spatiotemporal association in traffic collisions over metropolitan regions.

Keywords: urban transportation; traffic accidents; spatiotemporal interactions; hazardous locations (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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