![]() Data related to urban road crashes for a 12-year reference period from 2008 to 2019 were used. The aim of this study was to enhance the focus on urban road safety by providing an illustrative spatial and temporal overview on the road crashes occurred in the cities and towns of Romania and their effects on the people involved. Urban road traffic crashes are some of the most difficult issues to tackle by the local administrative planning and development authorities in Europe. Sustainable urban mobility and road safety have been both a challenge and a priority at the European level for two decades now. 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. 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. Based on the results, the lockdown measures in response to the pandemic have led to significant reductions in road 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 their respective impacts on the local residences. To evaluate the impacts of COVID-19, we used the seasonal variation in car accidents to analyze the change in the total number of urban traffic accidents. 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. The severity index was used to determine high-risk areas, and the kernel density estimation method was used to identify the risk of traffic accident hotspots. The main aim of the present study was to investigate the spatiotemporal trends of urban traffic accident hotspots during the COVID-19 pandemic. The developed methodology identifies sections of arterial roads-Strand Road and AJC Bose Road in Kolkata and Gota Road in Ahmedabad, as the critical hotspot links that require urgent intervention. The proposed three-step integrated methodology is novel and has never been used to simultaneously identify and prioritize the critical pedestrian crash locations as it has been done in the present study. Finally, Hotspot Identification (HSID) methods, i.e., Equivalent Property Damage Only (EPDO) and Upper-tail Critical Tests are used to rank the road links based on spatio-temporal crash severity leading to the identification of links needing urgent interventions. Secondly, space-time cube and emerging hotspot analysis are carried out to predict crash hotspots along urban streets. Firstly, available multi-year crash data from two cities in India is digitized, and the spatial autocorrelation tool is used to determine the pedestrian crash hotspots. This study proposes a three-step methodology to identify current and future critical pedestrian crash hotspots. Recent years have witnessed an increasing interest among the scientific community to analyze and enhance pedestrians' safety in an environment dominated by motor vehicles. Pedestrians are one of the most vulnerable road users globally. In addition, the finding has a potential engineering application value, and it is of great significance to the sustainable development of Wujiang. The results illustrate that the traffic crash hotspots of road intersections are primarily distributed in the Northeast area of Wujiang’s major urban area, while the crash cold spots are concentrated in the Southwest of Wujiang, which points out the direction for crash prevention. Finally, different types of crash hotspots, as well as their evolution patterns over time, are determined. The analysis process identifies the high incidence locations of traffic crashes, then presents the spatial change trend and statistical significance of the crash locations. Then, a small sized-city of China (i.e., Wujiang) is selected as the case study, and the historical traffic crash data occurring at the road intersections of Wujiang for the year 2016 are analyzed by the proposed method. These analyses are all conducted by the corresponding toolbox of ArcGIS 10.5. With the objective to prevent and reduce road traffic crashes, this study proposes a comprehensive spatiotemporal analysis method that integrates the time-space cube analysis, spatial autocorrelation analysis, and emerging hot spot analysis for exploring the traffic crash evolution characteristics and identifying crash hot spots. Road traffic safety is a key concern of transport management as it has severely restricted Chinese economic and social development. ![]()
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