Data Imputation for Detected Traffic Volume of Freeway Using Regression of Multilayer Perceptron

Traffic volume data are the important part of research and application of intelligent transportation systems (ITS). However, data loss often happens due to various factors in the real world, which may cause large deviations in prediction or bad accuracy of optimizations. Imputation is a valid way to handle missing values. A multilayer perceptron-multivariate imputation of chain equation (MLP-MICE) regression imputation method optimized by the limit-memory-BFGS algorithm is proposed, considering the temporal and spatial characteristics of traffic volume. Also, 32 groups of simulated imputation experiments based on the detected traffic volume of road sections in the Guangdong freeway system are conducted, which take the scenarios of continuous missing and jumped missing into account. The results of the experiments show that the MLP-MICE can optimize the imputation performance in the missing value of traffic volume with the MAPE of imputation results from 6.38% to 30%. Meanwhile, the proposed model has higher imputation accuracy for the traffic volume data with a lower degree of mutation. Lastly, the performance of the proposed model of imputation in short-term traffic volume prediction is discussed using the support vector machine. The results of it show that the MAPE of prediction under the proposed model is much lower than all-zero imputation. Therefore, the proposed model in this study is positive on improving the accuracy of traffic volume prediction and intelligent traffic control and management.

• Record URL:
  • https://doi.org/10.1155/2022/4840021
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/5121625
• Supplemental Notes:
  • © 2022 Xiang Wang et al.
• Authors:
  • Wang, Xiang
  • Ma, Yingying
  • Huang, Shengwen
  • Xu, Yu
• Publication Date: 2022-5

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: Article ID 4840021
• Serial:
  • Journal of Advanced Transportation
  • Volume: 2022
  • Publisher: John Wiley & Sons, Incorporated
  • ISSN: 0197-6729
  • EISSN: 2042-3195
  • Serial URL: http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)2042-3195
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Freeways; Neural networks; Regression analysis; Traffic data; Traffic volume
• Geographic Terms: Guangdong (China)
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01846870
• Record Type: Publication
• Files: TRIS
• Created Date: May 25 2022 9:35AM