[1]ZHANG Yankong,LU Jiapin,ZHANG Shuaichao,et al.A short-term risk prediction method for urban traffic accidents based on road network[J].CAAI Transactions on Intelligent Systems,2020,15(4):663-671.[doi:10.11992/tis.201910002]
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A short-term risk prediction method for urban traffic accidents based on road network

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 4 Page number: 663-671 Column: 学术论文—智能系统 Public date: 2020-07-05
Title:
A short-term risk prediction method for urban traffic accidents based on road network
Author(s):
ZHANG Yankong1;  LU Jiapin1;  ZHANG Shuaichao2;  JI Xiaopeng2;  LUO Yuetong1;  CHEN Wei2
1. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230000, China;
2. College of Computer Science and Technology, Zhejiang University, Hangzhou 310018, China
Keywords:
GCNN; traffic accident; accident mode; multi-source data; risk forecasting; road network structure; LSTM; smart city
CLC:
TP18
DOI:
10.11992/tis.201910002
Abstract:
Urban traffic accidents usually occur on public roads. However, the existing traffic accident risk prediction algorithms determine the prediction space unit by regularizing grid of the prediction area, which leads to low prediction accuracy and low practicability. Taking road sections as the prediction unit, this paper constructs a short-term traffic accident risk prediction method based on road network structure (TARPBRN) by using graph convolution and long short-term memory network. This method can predict the traffic accident risk in a short period of the designated section, so as to carry out targeted governance and reduce the occurrence of traffic accidents. In this paper, traffic accident data from Xihu District, Hangzhou city are used to train the model, and four econometric models and three existing deep learning prediction algorithms are compared. The experimental results show that the proposed algorithm is superior to the existing ones in accuracy, precision and false negative rate (FNR).
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