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Noise and Incomplete Data in Temporal Modeling Literature Review

Written By Sunday, April 17, 2022 Add Comment Edit

Abstract

The missing traffic information has caused great obstacles and interference to farther inquiry, such equally traffic flow prediction, which affects the traffic authorities' judgment for the real traffic performance state of road network and the new control strategies. It is very disquisitional to select the imputation methods with skilful performance for maintaining the integrity and effectiveness of the traffic data. A large number of literatures have developed many methods to repair missing traffic information, nonetheless lacking systematic comparison of these methods and an overview of the state-of-the-art development in imputation methods. In this newspaper, extensive enquiry on imputation methods are sorted out and synthesized, the mechanism of missing traffic data is analyzed, and various algorithms in repairing missing data are systematically reviewed, highlighted some challenges and potential solutions. The purpose is to provide a structural diagram of the current recovery engineering for missing traffic information, clearly pointing out the advantages and disadvantages of these methods, and helping researchers to conduct better exploration on the incomplete traffic information.

Keywords

  • Missing traffic data
  • Imputation method
  • Tensor decomposition

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Author data

Affiliations

  1. Schoolhouse of Civil Engineering and Transportation, S China Academy of Technology, Guangzhou, Guangdong, Prc

    Pan Wu, Lunhui Xu & Zilin Huang

Corresponding author

Correspondence to Lunhui Xu .

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© 2020 Springer Nature Singapore Pte Ltd.

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Cite this paper

Wu, P., Xu, 50., Huang, Z. (2020). Imputation Methods Used in Missing Traffic Data: A Literature Review. In: Li, K., Li, W., Wang, H., Liu, Y. (eds) Artificial Intelligence Algorithms and Applications. ISICA 2019. Communications in Computer and Information Scientific discipline, vol 1205. Springer, Singapore. https://doi.org/10.1007/978-981-15-5577-0_53

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  • DOI : https://doi.org/10.1007/978-981-xv-5577-0_53

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