A Light Weight Traffic Volume Prediction Approach Based on Finite Traffic Volume Data
作者机构：Faculty of Information TechnologyBering University of TechnologyBeijing100124China Shanghai Ocean UniversityShanghai201306China Academy of Opto-electronicsChinese Academy of SciencesBeijing100094China
出 版 物：《Journal of Systems Science and Systems Engineering》 (系统科学与系统工程学报（英文版）)
年 卷 期：2023年第32卷第5期
页 面：603-622页
核心收录：
学科分类：08[工学] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0823[工学-交通运输工程] 081202[工学-计算机软件与理论]
基 金：supported by the National Natural Science Foundation of China(No.62276011,62072016) the Natural Science Foundation of Beijing Municipality(No.4212016) Urban Carbon Neutral Science and Technology Innovation Fund Project of Beijing University of Technology(No.040000514122608)
主 题：Short-term traffic volume prediction tensor Tucker decomposition finite traffic volume data
摘 要：As one of the key technologies of intelligent transportation systems, short-term traffic volume prediction plays an increasingly important role in solving urban traffic problems. In the last decade, many approaches were proposed for the traffic volume prediction from different perspectives. However, most of these approaches are based on a large amount of historical data. When there are only finite collected traffic data, they cannot be well trained, so the prediction accuracy of these approaches will be poor. In this paper, a tensor model is proposed to capture the change patterns of continuous traffic volumes. From collected traffic volume data, the element data are extracted to update the corresponding elements of the tensor model. Then, a tucker decomposition and gradient descent based algorithm is employed to impute the missing elements of the tensor model. After missing element imputation, the tensor model can be directly applied to the short-term traffic volume prediction through searching the corresponding elements of the model and the storage cost of the model is low. Our model is evaluated on real traffic volume data from PeMS dataset, which indicates that our model has higher traffic volume prediction accuracy than other approaches in the situation of finite traffic volume data.
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