Expressway traffic anomaly event detection model based on dual spatio-temporal masks and bidirectional Mamba state space modeling

ZHANG Ding-kai; WANG Yi-zhe; WANG Peng-fei; PENG Qing; DING Lu

doi:10.19818/j.cnki.1671-1637.2025.04.022

Volume 25 Issue 4

Aug. 2025

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ZHANG Ding-kai, WANG Yi-zhe, WANG Peng-fei, PENG Qing, DING Lu. Expressway traffic anomaly event detection model based on dual spatio-temporal masks and bidirectional Mamba state space modeling[J]. Journal of Traffic and Transportation Engineering, 2025, 25(4): 311-327. doi: 10.19818/j.cnki.1671-1637.2025.04.022

Citation:

ZHANG Ding-kai, WANG Yi-zhe, WANG Peng-fei, PENG Qing, DING Lu. Expressway traffic anomaly event detection model based on dual spatio-temporal masks and bidirectional Mamba state space modeling[J]. Journal of Traffic and Transportation Engineering, 2025, 25(4): 311-327. doi: 10.19818/j.cnki.1671-1637.2025.04.022

Citation:

ZHANG Ding-kai, WANG Yi-zhe, WANG Peng-fei, PENG Qing, DING Lu. Expressway traffic anomaly event detection model based on dual spatio-temporal masks and bidirectional Mamba state space modeling[J]. Journal of Traffic and Transportation Engineering, 2025, 25(4): 311-327. doi: 10.19818/j.cnki.1671-1637.2025.04.022

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Expressway traffic anomaly event detection model based on dual spatio-temporal masks and bidirectional Mamba state space modeling

doi: 10.19818/j.cnki.1671-1637.2025.04.022

ZHANG Ding-kai^{1, 2},
WANG Yi-zhe^{1, 2
,
,},
WANG Peng-fei³,
PENG Qing^{1, 2},
DING Lu^{1, 2}

1.
Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai 201804, China
2.
College of Transportation, Tongji University, Shanghai 201804, China
3.
School of Computer Science and Technology, East China Normal University, Shanghai 200062, China

Funds:

National Natural Science Fundation of China 52472350

National Social Science Fundation of China 19XMZ018

Humanities and Social Sciences Research Project of Ministry of Education 20YJAZH130

Zhejiang Provincial Basic Public Welfare Research Program LY23G020007

Zhejiang Provincial Basic Public Welfare Research Program LGF20F020004

General Scientific Research Project of Education of Zhejiang Province Y202147441

More Information

Corresponding author: WANG Yi-zhe (1993-), male, postdoctor, PhD, 16wangyizhe@tongji.edu.cn
Received Date: 2025-01-15
Accepted Date: 2025-04-30
Rev Recd Date: 2025-02-27
Publish Date: 2025-08-28

Abstract

Abstract

To address the challenges in traffic anomaly event detection, a traffic anomaly event detection model was proposed based on dual spatio-temporal masks and bidirectional Mamba state space modeling (DSTBM). Masks on the basis of the temporal and spatial extent of anomaly events and their influence range were introduced. The spatio-temporal dynamic effects on traffic caused by anomalies were learned, and the time periods and spatial regions of mask anomalies and their influence range were adapted automatically. A bidirectional Mamba state space model was designed to integrate multi-scale feature extraction with bidirectional state space modeling. The directionality of state space modeling was extended to capture the short-term dynamic features and multi-scale spatio-temporal dependencies of anomalies, and to reduce detection latency. Additionally, an anomaly scoring method was developed through the combination of the isolation forest algorithm with K-means clustering. Anomaly scores were calculated based on spatio-temporal traffic features to distinguish between normal and abnormal traffic states. The proposed model was evaluated on three real datasets. Experimental results demonstrate that DSTBM outperforms all baseline models in the comparative experiments on the SJC and OKA datasets. The indicator precision rate, recall rate, and F1 score, as the evaluation metric for model accuracy, are enhanced by at least 15.32%, 14.98%, and 15.61%, respectively. The indicator latency rate for measuring the model response speed is reduced by 19.79%. In addition, a domestic case study shows that DSTBM maintains effective anomaly detection capabilities in complex urban road environments. The recall rate is 0.607 and the total latency is 12.2 min when K=10%. This highlights its potential applicability and room for enhancement in high-density and intricate road networks. The proposed DSTBM model can effectively capture complex spatio-temporal dependencies and reduce detection latency for traffic anomalies.
- intelligent traffic,
- traffic anomaly event detection model,
- state space model,
- dual spatio-temporal masking,
- multi-scale feature extraction,
- time series data

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References(40)

References

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Expressway traffic anomaly event detection model based on dual spatio-temporal masks and bidirectional Mamba state space modeling

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