Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 10
2972-2977

Traffic data imputation of urban road network based on spatial-temporal residual tensor learning

Li Jinlong1
Li Ruonan2
Wu Pan3
Yu Guangjing1
Xu Lunhui1
1. School of Civil Engineering & Transportation, South China University of Technology, Guangzhou 510641, China
2. College of Computer Science & Technology, Harbin Institute of Technology(Shenzhen), Shenzhen Guangdong 518055, China
3. College of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, China
DOI: 10.19734/j.issn.1001-3695.2023.03.0084

Abstract

To tackle the issue of traffic data loss due to various software/hardware failures in urban road network environments, this paper proposed a traffic data imputation method based on spatial-temporal residual tensor learning(ST-RTL). This method constructed a 3D traffic tensor with missing value to characterize original spatiotemporal attributes of road network maxi-mally. Then it adopted Gibbs sampling to perform a CANDECOMP/PARAFAC(CP) tensor decomposition and low-rank reconstruction of missing traffic data based on the assumption of Gaussian distribution. Considering the residual value produced by the tensor repair process, the study designed a bidirectional residual optimization structure with dynamic iterations to capture the residual spatiotemporal dependencies to enable the accurate repair of the missing traffic data. The experiments took a publicly available Hangzhou metro passenger flow for model construction and validation. The results indicate that when the missing rates are 10%~80%, the three missing scenarios(random, cluster and hybrid missing) have large differences on tensor structure damage, among which cluster missing has the greatest destruction and the evaluation indexes MAPE, RMSE and MAE of ST-RTL lied in 3.1071~7.0371, 16.3779~58.4286 and 3.7434~8.0135; and each indicator of ST-RTL model shows an accelerated increasing trend as the missing rate rises. Compared with the representative baseline models such as HaLRTC, GAIN and BGCP, the ST-RTL exhibits lower performance metrics and stronger stability in the acceptable computational costs, which can provide high-quality basic data for intelligent transportation systems.

Foundation Support

国家自然科学基金资助项目(52072130,11702099)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.03.0084
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 10
Section: Algorithm Research & Explore
Pages: 2972-2977
Serial Number: 1001-3695(2023)10-014-2972-06

Publish History

[2023-05-09] Accepted Paper
[2023-10-05] Printed Article

Cite This Article

李金龙, 李若南, 吴攀, 等. 基于时空残差张量学习的城市路网交通数据修复 [J]. 计算机应用研究, 2023, 40 (10): 2972-2977. (Li Jinlong, Li Ruonan, Wu Pan, et al. Traffic data imputation of urban road network based on spatial-temporal residual tensor learning [J]. Application Research of Computers, 2023, 40 (10): 2972-2977. )

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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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