Traffic volume imputation using the attention-based spatiotemporal generative adversarial imputation network  

作　　者：Yixin Duan Chengcheng Wang Chao Wang Jinjun Tang Qun Chen 

机构地区：[1]School of Traffic and Transportation Engineering,Central South University,Changsha 410075,China [2]Science&Technology Research&Development Center,Shandong Provincial Communications Planning and Design Institute Group Co.LTD,Jinan 250000,China

出　　处：《Transportation Safety and Environment》2024年第4期54-67,共14页交通安全与环境(英文)

基　　金：funded in part by Key R&D Program of Hunan Province(Grant No.2023GK2014);Key technology projects in the transportation industry(Grant No.2022-ZD6-077);Transportation Science and Technology Plan Project of Shandong Transportation Department(Grant No.2022B62);the Fundamental Research Funds for the Central Universities of Central South University(Grant No.2023ZZTS0683)。

摘　　要：With the increasing development of intelligent detection devices,a vast amount of traffic flow data can be collected from intelligent transportation systems.However,these data often encounter issues such as missing and abnormal values,which can adversely affect the accuracy of future tasks like traffic flow forecasting.To address this problem,this paper proposes the Attention-based Spatiotemporal Generative Adversarial Imputation Network(ASTGAIN)model,comprising a generator and a discriminator,to conduct traffic volume imputation.The generator incorporates an information fuse module,a spatial attention mechanism,a causal inference module and a temporal attention mechanism,enabling it to capture historical information and extract spatiotemporal relationships from the traffic flow data.The discriminator features a bidirectional gated recurrent unit,which explores the temporal correlation of the imputed data to distinguish between imputed and original values.Additionally,we have devised an imputation filling technique that fully leverages the imputed data to enhance the imputation performance.Comparison experiments with several traditional imputation models demonstrate the superior performance of the ASTGAIN model across diverse missing scenarios.

关 键 词：missing data imputation generative adversarial network spatiotemporal traffic flow data attention mechanism 

分 类 号：G63[文化科学—教育学]