A GIS-Based Spatiotemporal Modelling of Urban Traffic Accidents in Tabriz City during the COVID-19 Pandemic

Bakhtiar Feizizadeh, Davoud Omarzadeh, Ayyoob Sharifi, Abolfazl Rahmani, Tobia Lakes and Thomas Blaschke
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Bakhtiar Feizizadeh: Applied GIScience Lab., Humboldt-Universität zu Berlin, 10117 Berlin, Germany
Davoud Omarzadeh: Department of Remote Sensing and GIS, University of Tabriz, Tabriz 516661647, Iran
Ayyoob Sharifi: Graduate School of Humanities and Social Sciences and Network for Education and Research on Peace and Sustainability (NERPS), Hiroshima University, Higashihiroshima 739-8511, Japan
Abolfazl Rahmani: Department of Geography, Hakim Sabzavri University, Sabzevar 9617976487, Iran
Tobia Lakes: Applied GIScience Lab., Humboldt-Universität zu Berlin, 10117 Berlin, Germany
Thomas Blaschke: Department of Geoinformatics (Z-GIS), University of Salzburg, 5020 Salzburg, Austria

Sustainability, 2022, vol. 14, issue 12, 1-20

Abstract: The main aim of the present study was to investigate the spatiotemporal trends of urban traffic accident hotspots during the COVID-19 pandemic. The severity index was used to determine high-risk areas, and the kernel density estimation method was used to identify risk of traffic accident hotspots. Accident data for the time period of April 2018 to November 2020 were obtained from the traffic police of Tabriz (Iran) and analyzed using GIS spatial and network analysis procedures. To evaluate the impacts of COVID-19, we used the seasonal variation in car accidents to analyze the change in the total number or urban traffic accidents. Eventually, the sustainability of urban transport was analyzed based on the demographic and land use data to identify the areas with a high number of accidents and its respective impacts for the local residences. Based on the results, the lockdown measures in response to the pandemic have led to significant reductions in road traffic accidents. From the perspective of urban planning, the spatiotemporal urban traffic accident analysis indicated that areas with high numbers of elderly people and children were most affected by car accidents. As we identified the hotspots of urban traffic accidents and evaluated their spatiotemporal correlation with land use and demography characteristics, we conclude that the results of this study can be used by urban managers and support decision making to improve the situation, so that fewer accidents will happen in the future.

Keywords: urban road; traffic accidents; hotspots mapping; GIS spatial and network analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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