Predicting Link Travel Speed in Urban Road Networks Using Variational Mode Decomposition

Predicting travel speeds in the urban road networks is a challenging subject due to its uncertainty stemming from travel demand, geometric condition, traffic signals, and other exogenous factors. This uncertainty appears in the data as non-linearity, non-stationarity, and volatility, and it also creates a spatiotemporal heterogeneity of the link travel speed by combining with the correlation of the neighbor links. In this study, the authors propose a hybrid model using variational mode decomposition (VMD) to investigate and mitigate the uncertainty of urban travel speeds. VMD allows the travel speed data to be divided into orthogonal and oscillatory sub-signals, called modes. The regular components are extracted as the low-frequency modes, and the irregular components presenting uncertainty are transformed into a combination of modes that is more predictable than the original uncertainty. For the prediction, the VMD decompose the travel speeds data into modes, and they are predicted respectively and summed to represent the predicted travel speed. The evaluation results in the urban road networks show that a hybrid model outperformed the benchmark models in congested area and in general. The improvement in performance increases significantly over specific link-days which generally are hard to predict. To explain the significant variance of the prediction performance according to each link and each day, correlation analysis between the properties of modes and the performance of the model is conducted. Based on the results, discussion on the interpretation of the correlation analysis and future research are addressed.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee ABJ30 Standing Committee on Urban Transportation Data and Information Systems.
  • Corporate Authors:

    Transportation Research Board

  • Authors:
    • Kim, Eui-Jin
    • Park, Ho-Chul
    • Kho, Seung-Young
    • Kim, Dong-Kyu
  • Conference:
  • Date: 2019


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References;
  • Pagination: 6p

Subject/Index Terms

Filing Info

  • Accession Number: 01697517
  • Record Type: Publication
  • Report/Paper Numbers: 19-00525
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 7 2018 9:29AM