Review of road accident analysis using GIS technique

Investigation of annual trucks fatal accidents in El Hajeb province of Morocco using TRIZ-Ishikawa diagram

A stretch of road in the province of EL HAJEB, located in the central-south region of Morocco, is classified among the roadways experiencing an accumulation of fatal traffic accidents, with a particular involvement of freight transport vehicles. Investigation reports elaborated for these fatal accidents specify that these accidents occurred when drivers lost control of their vehicles due to brake system failures, resulting in multiple fatalities. However, these investigation reports did not provide root causes of this phenomena. Scientific research efforts in this field are directed toward preventive solutions and proposing a comprehensive analytical approach. This study aims to elucidate the mechanisms behind these specific accident phenomena on the identified stretch in the city of EL HAJEB. To achieve the study's objective and identify the triggering or contributing factors of these failures, we employed a novel approach combining the TRIZ and Ishikawa tools. This is a systematic methodology for analyzing potential causes of accidents, allowing us to clarify the intricacies of the specific phenomena leading to accidents while systematizing the analysis process, thus contributing to enhancing the effectiveness of investigative teams. This article contributes to introducing a new analytical tool in the field of accident analysis.

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

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.

GIS-based crash hotspot identification: a comparison among mapping clusters and spatial analysis techniques

Knowing the locations of traffic crash hotspots can provide us with valuable insights into the root causes of crash occurrence over the area under study. This knowledge helps decision-makers to better assess the risk associated with road crashes and, as a result, help them to propose more effective countermeasures in order to reduce the annual crash rate. Nonetheless, identifying the areas with the highest potential of crash occurrence is a complicated task. In this regard and within this study, five various types of hotspot identification techniques, consisting of Average Nearest Neighbor, Getis-Ord Gi*, Global Moran's I, kernel density estimation (KDE) and mean centre, were compared to each other, using three different performance measures, including Predictive Accuracy Index (PAI), Recapture Rate Index (RRI) and hit rate. According to the results, the most accurate model with the highest PAI values (PAI = 1.61 and 1.76), Moran's I, had the third-highest reliability value (RRI = 1.003). On the other hand, while the Gi* method was the most precise and reliable technique with the highest RRI value (RRI = 1.121), it showed the second-lowest accuracy (PAI= 0.83 and 0.74). Overall, it seems that Moran's I method is superior to other methods in locating hotspots, which is not only the most accurate technique but also precise enough to rely on.