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

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.

Analyzing urban crash incidents: An advanced endogenous approach using spatiotemporal weights matrix

“…They used Geographically Weighted Negative Binomial Regression in their analysis. Concurrently, Soltani and Askari (2017) Feizizadeh et al (2022) employed the distances between crash records to define an SWM and applied a Kernel Density Estimation method to identify the risk of traffic accident hotspots. Finally, Gilardi et al (2022) returned to the first-order contiguity concept at the street level, incorporating a Bayesian multivariate space-time model similar to that used by Ma et al (2017).…”

“…• Population (Almasi & Behnood, 2022;Alves et al, 2021;Feizizadeh et al, 2022;Huang et al, 2016;Mahmud et al, 2019;Wu et al, 2024;Zhou et al, 2022) • Median household income (Huang et al, 2019) • Land use (Almasi & Behnood, 2022;Feizizadeh et al, 2022;Sandoval-Pineda et al, 2022;Wang, Yuan, et al, 2019) • POI (Almasi & Behnood, 2022;Sandoval-Pineda et al, 2022;Wang, Yuan, et al, 2019;Zhou et al, 2022) • Street level (Alves et al, 2021;Feizizadeh et al, 2022;Huang et al, 2016) • Zonal level (Almasi & Behnood, 2022;Huang et al, 2016Huang et al, , 2019Mahmud et al, 2019;Sandoval-Pineda et al, 2022;Wang, Yuan, et al, 2019;Wu et al, 2024;Zhou et al, 2022) Weather data • Precipitation, snow depth, temperature, wind speed, visibility, cloud cover (Alves et al, 2021;Hasan et al, 2022) • Street level (Alves et al, 2021) • Zonal level (Hasan et al, 2022) According to Retting et al (1999) while VMT measures the total miles driven by vehicles within a particular area or over a specific period. Furthermore, running red lights, by either drivers or pedestrians (Retting et al, 1999), can increase crash rates.…”

“…Socioeconomic factors are also critical to understanding the causes of car accidents. Prior research has revealed that population, employment rates, and the number of uneducated residents (Almasi & Behnood, 2022;Alves et al, 2021;Feizizadeh et al, 2022;Huang et al, 2016;Mahmud et al, 2019;Wu et al, 2024;Zhou et al, 2022), as well as median household income (Huang et al, 2019), correlate with pedestrian accidents. In addition, specific land use and points of interest, such as hospitals and schools, have been shown to affect traffic accidents in TAZs, according to studies conducted by Almasi and Behnood (2022) Some studies mentioned in Table 2 focus on constructing models for macro-scale accident prediction, using TAZ as the unit of analysis.…”

“…More recently, Feizizadeh et al. (2022) employed the distances between crash records to define an SWM and applied a Kernel Density Estimation method to identify the risk of traffic accident hotspots. Finally, Gilardi et al.…”

“…, Feizizadeh et al (2022),Sandoval-Pineda et al (2022),Wang, Yuan, et al (2019) andZhou et al (2022) Hasan et al (2022). andAlves et al (2021) have also suggested that weather conditions like rain, snow, frost, and fog can negatively impact accidents due to decreased visibility and difficulty controlling a vehicle.…”