To evaluate the proposed method, we collect large-scale real-world data, which include accident records, real-time and citi-wide vehicle speeds, road networks, ...
To evaluate the proposed method, we collect large-scale real- world data, which include accident records, real-time and citi- wide vehicle speeds, road networks ...
A novel traffic accident prediction method, namely, STENN, which takes multiple information (Spatial distributions, Temporal dynamics, and External factors) ...
We managed to improve the classification of accident data according to their severity, making viable the usage of the classifier in identifying Brazilian ...
Jan 29, 2021 · This paper proposes a novel Deep Spatio-Temporal Graph Convolutional Network, namely DSTGCN, to predict traffic accidents.
DSTGCN is a graph-based neural network that predicts the risk of traffic accidents in the future. Please refer to our Neurocomputing 2021 paper “Deep Spatio- ...
Missing: Analysis. | Show results with:Analysis.
Oct 22, 2024 · To evaluate the proposed model, we collect large-scale real-world data, including accident records, citi-wide vehicle speeds, road networks, ...
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The rapid development in data science, geographic data collection, and processing methods encourage researchers to evaluate, delineate traffic accident hotspots ...
Jul 29, 2022 · We propose a spatio-temporal model to provide predictions of the number of traffic collisions on any given road segment, to further generate a risk map of the ...
This study proposes a state-of-the-art deep learning-based model that incorporates spatiotemporal information for the short-term crash prediction.