Developing a spatio-temporal interactions model for car crashes using a novel data-driven AHP-TOPSIS

Ghezelbash, Somaye; Ghezelbash, Reza; Kalantari, Mohsen

doi:10.1016/j.apgeog.2023.103151

Cited by 9 publications

(1 citation statement)

References 63 publications

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“…Afghari synthesized two models, crash count and crash severity, to develop a weighted risk-scoring methodology to more effectively identify serious injuries and fatalities in accident hotspots [4]. Ghezelbash applied kernel density estimation, combined with a hierarchical analysis method and an ideal solution similarity order preference technique based on both accident locations and spatio-temporal interactions, to provide a more comprehensive identification of accident hotspots [5]. Hu analysed pedestrian accidents in Changsha using kernel density analysis combined with binary logistic regression and tree-based modelling, and found that pedestrian accidents showed several cluster distributions in urban spaces, and were significantly correlated with population density, road network density, and social service activities [6].…”

Section: Introductionmentioning

confidence: 99%

Accident Severity Analysis of Traffic Accident Hot Spot Areas in Changsha City Considering Built Environment

Yan,

Hu,

et al. 2024

Sustainability

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Examining the impacts of accident characteristics and differentiated built environment factors on accident severity at inherent accident hotspots within cities can help managers to adjust traffic control measures through urban planning and design, thereby reducing accident casualties. In this study, time series clustering was used to identify traffic accident hotspots in Changsha City. Based on the hotspot identification results, Kruskal–Wallis tests were used to select differentiated built environment factors among different accident areas within the city. A severity analysis model for road crashes in Changsha’s hotspots, taking into account the built environment, was constructed using a Light gradient boosting machine (LightGBM). In addition, Shapley additive explanations (SHAP) were used to reveal the influences of accident characteristics and built environment factors on accident severity. The results showed that different accident characteristics and built environment factors affect accident severity in different urban accident areas. Factors such as type of accident, visibility, period of time, land use mixing degree, population density, density of commercial places, and density of industrial places showed varying degrees of importance in influencing accident severity, while the overall impact trends remained consistent. On the other hand, transportation accessibility, road network density, landform, and accident location showed significant differences in their impacts on accident severity between different accident areas within the city.

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Section: Introductionmentioning

confidence: 99%