GIS-based assessment of pedestrian-vehicle accidents in terms of safety with four different ML models

Geographical spatial analysis and risk prediction based on machine learning for maritime traffic accidents: A case study of Fujian sea area

“…Local environmental and traffic risk factors were considered for the network performance evaluation. Katanalp and Ezgi (2021) conducted microand macro-level evaluations of pedestrian-vehicle crashes. Macro-level findings were obtained with GIS-based density analyzes, and critical road segments were determined.…”

Pedestrian Road Traffic Accidents in Metropolitan Areas: GIS-Based Prediction Modelling of Cases in Mashhad, Iran

“…From a methodological perspective, numerous methods have been used to identify, analyse, or predict accident-prone places. Various models, such as those based on artificial neural networks and machine learning [28,34,35], logistic regression [28], MCDM and analytical hierarchy processes (AHP) [28,[36][37][38] and metaheuristic optimisation techniques [38,39], have introduced approaches to identify spatial associations between socioeconomic and built environment factors and risks such as PTAs in urban areas. For example, logistic regression has been used to determine whether or not there was a relationship between the spatial distribution of PTAs and various predictors in Dhaka, Bangladesh [40], Changsha, China [41], Hong Kong [42], Phoenix, Arizona, United States (US) [43], Iran [28] and a number of other cities, particularly in the US [44].…”

“…For example, logistic regression has been used to determine whether or not there was a relationship between the spatial distribution of PTAs and various predictors in Dhaka, Bangladesh [40], Changsha, China [41], Hong Kong [42], Phoenix, Arizona, United States (US) [43], Iran [28] and a number of other cities, particularly in the US [44]. In some studies [27,35], GISbased models have been used to identify the most significant effective factors involving pedestrian-vehicle accidents as well as APLs in urban areas. Those studies found that the type of land use and road attributes, e.g., the number of intersections, had the strongest PTA impact [35].…”

“…In some studies [27,35], GISbased models have been used to identify the most significant effective factors involving pedestrian-vehicle accidents as well as APLs in urban areas. Those studies found that the type of land use and road attributes, e.g., the number of intersections, had the strongest PTA impact [35]. Markov chain (MC) testing has also been used to model and predict PTAs [45][46][47][48].…”