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Aug 30, 2023
Traffic Graph Convolutional Network with Residual Connection for Accident Severity Prediction
Authors: Ke Zhang [email protected], Meng Li [email protected], Qingquan Liu [email protected], and Yaming Guo [email protected]Author Affiliations
Publication: CICTP 2023
ABSTRACT
Urban traffic accidents have seriously affected people’s life and property. The characteristics of road structure have direct impacts on the severity of traffic accidents, while traditional machine learning algorithms are difficult to take into account the complex road network structure. Therefore, research on an effective method to extract deep information within the road network is necessary. This paper proposes a traffic graph convolutional network with residual connection (ResTGC), which can deepen the structure of the neural network and effectively extract vital information through residual connection. By evaluating a data set collected from Oklahoma City, the proposed ResTGC outperforms a series of machine learning algorithms, such as SVM, GBDT, Adaboost, GCN, and GraphSAGE, with an improvement of over 6.1%. The remarkable classification ability can effectively identify the risk degree of various road network structures, which can provide a vital reference for the vehicle routing problem, municipal planning, and so on.
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Published online: Aug 30, 2023
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ASCE Technical Topics:
- Algorithms
- Artificial intelligence and machine learning
- Computer programming
- Computing in civil engineering
- Engineering fundamentals
- Highway and road management
- Highway and road structures
- Highway transportation
- Infrastructure
- Mathematics
- Models (by type)
- Traffic accidents
- Traffic analysis
- Traffic engineering
- Traffic management
- Traffic models
- Traffic safety
- Transportation engineering
- Transportation management
- Transportation networks
Authors
Affiliations
1Dept. of Civil Engineering, Tsinghua Univ., Beijing, China. Email: [email protected]
2Dept. of Civil Engineering, Tsinghua Univ., Beijing, China. Email: [email protected]
3Dept. of Civil Engineering, Tsinghua Univ., Beijing, China. Email: [email protected]
4Dept. of Civil Engineering, Tsinghua Univ., Beijing, China. Email: [email protected]
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Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.