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

This study utilised multi-year data from 5354 incidents to predict pedestrian-road traffic accidents (PTAs) based on twelve socioeconomic and built-environment factors. The research employed the logistic regression model (LRM) and the fuzzy-analytical hierarchy process (FAHP) techniques to evaluate and assign weights to each factor. The susceptibility map for PTAs is generated using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Subsequently, the probability of accidents in 2020 was predicted using real multi-year accident data and the Markov chain (MC) and cellular automata Markov chain (CA-MC) models, with the prediction accuracy assessed using the Kappa index. Building upon promising results, the model was extrapolated to forecast the probability of accidents in 2023. The findings of the LRM demonstrated the significance of the selected variables as predictors of accident likelihood. The prediction approaches identified areas prone to high-risk accidents. Additionally, the Kappa for no information (KNO) statistical value was calculated for both the MC and CA-MC models, which yielded values of 0.94 and 0.88, respectively, signifying a high level of accuracy. The proposed methodology is generalizable, and the identification of high-risk locations can aid urban planners in devising appropriate preventive measures.

Automated summary

This study used multi-year data to predict pedestrian-road traffic accidents based on socioeconomic and built-environment factors. Logistic regression and fuzzy-analytical hierarchy process techniques were used to evaluate and assign weights to each factor. The susceptibility map for accidents was generated using the Technique for Order of Preference by Similarity to Ideal Solution. Accidents in 2020 were predicted using real accident data and Markov chain and cellular automata Markov chain models, with high prediction accuracy assessed by the Kappa index. The proposed methodology is generalizable and can aid urban planners in identifying high-risk locations and implementing preventive measures.