融合多种机制的交通时序数据异常检测模型研究

打开文本图片集

中图分类号：TP183 文献标识码：A DOI：10.7535/hbkd.2025yx03003

Research on anomaly detection model for traffic time series data integrating multiple mechanisms

ZHANG Peipei，LIU Jiaqi

（SchoolofEconomicsandManagement，NorthChinaUniversityofScienceandTechnology，Tangshan，HebeiO63oo，China）

Abstract：Toenhancetheanomalyrecognitionabilityof trafictimeseriesdata，ahybrid modelwasconstructed.Firstly，the multi-headatention，residualsandprobabiliticsparseself-atentionwerecombined toformaglobalfeaturerecognition（GFR） module，enhancing theability whilereducingcomputationalcomplexity.Secondly，the dilated convolutionandmulti-scale convolution werecombined to formalocalfeaturerecognition（LFR）module，furtheroptimizing local featureextraction. Thirdly，the FreeRunning training strategy wasused to improve model robustness.Fourthly，the modules and training strategy werecombined withLSTM，whiletheresultof theself-atention mechanismreplaced theLSTM input gate，soas to optimize long sequence memoryabilityand reducecomputing complexity.Finally，a multivariate Gausian distribution probabilityfunction wasused to discriminateanomalies.Theresults show that ading each moduleon the basis of LSTM significantly improvesthemodel'spredictionandanomalydetectionability；Comparedwiththegeneralhybridmodel Transformer-Bi-LSTM，the proposed model hasstronger prediction abilityandlowercomputational complexity.The proposed modelperformsefectivelyinrecognizingboth globalandlocalanomalies in trafictime seriesdata，which provides reference forimproving the operational efficiency and safety of the traffic system.

Keywords：computer neural networks；long sequence time-series data;anomaly detection; attention mechanism;LSTM

在交通领域，交通时序数据异常检测至关重要。（剩余11641字）