基于信息熵的自适应多分类器交通数据插值模型

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中图分类号：TP391.7 文献标识码：A DOI：10.7535/hbkd.2025yx03002

Adaptive multi classifier traffic data interpolation model based on information entropy

ZHANG Yunkai1²， 1，2 ， 1，²， （1.Hebei University Road Traffic Perception and Intellgent Application Technology Researchand Develop Center. Shijiazhuang，Hebei O50035，China； 2.Department of Electrical and Information Engineering，Hebei Jiaotong Vocational and Technical College， Shijiazhuang，Hebei O50035，China； 3.School of Artificial Intelligence and Data Science，Hebei University of Technology， Tianjin 300131，China）

Abstract：Toaddress theisse that single traficdata misingvalue imputation modelscannotcomprehensively handle the multi-sourceheterogeneityandcomplexdata volumeof trafficdata，amulti-clasifierimputation modelbasedonadaptive weighting determined by informationentropywas proposed.First，information entropyrepresenting "disorder degree"was introduced toevaluate predictionqualityanddeterminemulti-clasifierweights.Second，adynamicadaptive weightingmethod was designedtoresolve theproblemof differentclassfiers being suitableforvarious samples caused bydeviceheterogeneity. Finall，validationwasconductedonbothpublicandself-collcteddatasets.Theresultsdemonstratethattheproposedmodel achieves significant improvementindetection performancecompared withotherimputation models.Italsoatains highacuracy in experiments on the public Interstate Highway Trafic Flow Dataset，with an F1 of O.778 and a 10% improvement in RMSE，exhibiting strong generalizability.By enabling weights toadaptively evolve withdata streams basedon information entropydetermination，thealgorithmachieves faster detectionspeedand higher accuracy，providing technical references for the establishment of missing value imputation models in traffic data cleaning.

Keywords： data processing；traffc data cleaning;； mising value prediction; information entropy;adaptive weight

随着智慧高速公路的不断发展，高速公路部署了众多终端监测设备来采集种类繁多的数据，如道路数据、车辆数据、气象数据等。（剩余11618字）