Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 8
2340-2345

Multimodal spatial-temporal point processes for traffic accident event prediction

Peng Wenchuanga,b
Guo Shengnana,b
Wan Huaiyua,b
Lin Youfanga,b
a. School of Computer & Information Technology, b. Beijing Key Laboratory of Traffic Data Analysis & Mining, Beijing Jiaotong University, Beijing 100044, China
DOI: 10.19734/j.issn.1001-3695.2022.12.0799

Abstract

Traffic accident event prediction is of great importance to build intelligent transportation systems. However, traffic accident event data occurring in the continuous time domain contains temporal and spatial modal information with different semantic characteristics and different uncertainty, so the traditional sequence models cannot fully describe the spatial-temporal correlation of traffic accident events, and it is difficult to achieve accurate traffic accident prediction. So this paper proposed a multimodal spatial-temporal point process(MSTPP) model. And the model designed a seq2seq framework with dual decoders. It proposed decay-aware long short-term memory networks(DLSTM) in the encoder for encoding traffic accident event sequences in the continuous time domain, effectively fusing different modal information and modelling the asynchronicity of event sequences. In the decoding stage, it used two specially designed decoders to handle the difference between the two modalities. Extensive experiments on two real-world datasets demonstrate the superiority of MSTPP against the state-of-the-art baseline methods with regard to both the next accident happening time prediction and region prediction tasks.

Foundation Support

博士后面上基金资助项目(2021M700365)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.12.0799
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 8
Section: Algorithm Research & Explore
Pages: 2340-2345
Serial Number: 1001-3695(2023)08-015-2340-06

Publish History

[2023-03-02] Accepted Paper
[2023-08-05] Printed Article

Cite This Article

彭文闯, 郭晟楠, 万怀宇, 等. 面向交通事故预测的时空多模态点过程 [J]. 计算机应用研究, 2023, 40 (8): 2340-2345. (Peng Wenchuang, Guo Shengnan, Wan Huaiyu, et al. Multimodal spatial-temporal point processes for traffic accident event prediction [J]. Application Research of Computers, 2023, 40 (8): 2340-2345. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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