Affiliation:
1. Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
2. Faculty of Humanities and Social Sciences, Beijing University of Technology, Beijing 100124, China
Abstract
With the rapid development of the economy, car ownership has grown rapidly, which causes many traffic problems. In recent years, intelligent transportation systems have been used to solve various traffic problems. To achieve effective and efficient traffic management, intelligent transportation systems need a large amount of complete traffic data. However, the current traffic data collection methods result in different forms of missing data. In the last twenty years, although many approaches have been proposed to impute missing data based on different mechanisms, these all have their limitations, which leads to low imputation accuracy, especially when the collected traffic data have a large amount of missing values. To this end, this paper proposes a latent-factor-model-based approach to impute the missing traffic data. In the proposed approach, the spatial information of the road network is first combined with the spatiotemporal matrix of the original traffic data. Then, the latent-factor-model-based algorithm is employed to impute the missing data in the combined matrix of the traffic data. Based on the real traffic data from METR-LA, we found that the imputation accuracy of the proposed approach was better than that of most of the current traffic-data-imputation approaches, especially when the original traffic data are limited.
Funder
National Natural Science Foundation of China
Natural Science Foundation of Beijing Municipality
International Research Cooperation Seed Fund of Beijing University of Technology
Urban Carbon Neutral Science and Technology Innovation Fund Project of Beijing University of Technology
Subject
Earth and Planetary Sciences (miscellaneous),Computers in Earth Sciences,Geography, Planning and Development
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