Risky zones in urban area: an analysis using fault tree and risk matrix method

Dalai, Bahuguna; Landge, Vishrut S.

doi:10.1007/s41062-021-00696-6

Cited by 2 publications

(1 citation statement)

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“…On the other hand, traffic violations are greatly affected by road type, the number of lanes, speed limit, volume etc. [ 26 , 27 , 28 ] We construct a traffic violation feature set to predict its spatiotemporal distribution. The traffic violation feature set contains historical traffic violation features, external environmental features and urban functional features.…”

Section: Introductionmentioning

confidence: 99%

GATR: A Road Network Traffic Violation Prediction Method Based on Graph Attention Network

Zhou

Wang

Zhang

et al. 2023

IJERPH

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Prediction of traffic violations plays a key role in transportation safety. Combining with deep learning to predict traffic violations has become a new development trend. However, existing methods are based on regular spatial grids which leads to a fuzzy spatial expression and ignores the strong correlation between traffic violations and road network. A spatial topological graph can express the spatiotemporal correlation more accurately and then improve the accuracy of traffic violation prediction. Therefore, we propose a GATR (graph attention network based on road network) model to predict the spatiotemporal distribution of traffic violations, which adopts a graph attention network model combined with historical traffic violation features, external environmental features, and urban functional features. Experiments show that the GATR model can express the spatiotemporal distribution pattern of traffic violations more clearly and has higher prediction accuracy (RMSE = 1.7078) than Conv-LSTM (RMSE = 1.9180). The verification of the GATR model based on GNN Explainer shows the subgraph of the road network and the influence degree of features, which proves GATR is reasonable. GATR can provide an important reference for prevention and control of traffic violations and improve traffic safety.

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Section: Introductionmentioning

confidence: 99%

GATR: A Road Network Traffic Violation Prediction Method Based on Graph Attention Network

Zhou

Wang

Zhang

et al. 2023

IJERPH

View full text Add to dashboard Cite

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Identifying Hazardous Crash Locations Using Empirical Bayes and Spatial Autocorrelation

Mekonnen

Sipos

Krizsik

2023

IJGI

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Identifying and prioritizing hazardous road traffic crash locations is an efficient way to mitigate road traffic crashes, treat point locations, and introduce regulations for area-wide changes. A sound method to identify blackspots (BS) and area-wide hotspots (HS) would help increase the precision of intervention, reduce future crash incidents, and introduce proper measures. In this study, we implemented the operational definitions criterion in the Hungarian design guideline for road planning, reducing the huge number of crashes that occurred over three years for the accuracy and simplicity of the analysis. K-means and hierarchical clustering algorithms were compared for the segmentation process. K-means performed better, and it is selected after comparing the two algorithms with three indexes: Silhouette, Davies–Bouldin, and Calinski–Harabasz. The Empirical Bayes (EB) method was employed for the final process of the BS identification. Three BS were identified in Budapest, based on a three-year crash data set from 2016 to 2018. The optimized hotspot analysis (Getis-Ord Gi*) using the Geographic Information System (GIS) technique was conducted. The spatial autocorrelation analysis separates the hotspots, cold spots, and insignificant areas with 95% and 90% confidence levels.

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Risky zones in urban area: an analysis using fault tree and risk matrix method

Cited by 2 publications

References 50 publications

GATR: A Road Network Traffic Violation Prediction Method Based on Graph Attention Network

GATR: A Road Network Traffic Violation Prediction Method Based on Graph Attention Network

Identifying Hazardous Crash Locations Using Empirical Bayes and Spatial Autocorrelation

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