Research on Travel Time Estimation Based on Tensor Decomposition

Estimation of travel time is a key factor of traffic guidance and transportation management. However, it is impossible to collect the time of passing all roads during the data collection phase. Due to the sparseness of data, the prediction accuracy of existing methods is usually not satisfactory. In this paper, the authors consider introducing the concept of tensor to make full use of spatial-temporal traffic patterns. A model based on tensor decomposition for estimating the travel time from the citywide perspective is proposed. This model is comprised of three major components: travel time tensor construction, travel time tensor factorization, and unconstrained optimization. Different dates, time periods, and road segments are modeled as three dimensions of tensors. An example analysis of the monitoring data collected is provided. This paper verifies conditions in which missing data at different degrees, the accuracy of the model. Results show the model can achieve great prediction accuracy.

Language

  • English

Media Info

  • Media Type: Web
  • Pagination: pp 246-254
  • Monograph Title: CICTP 2020: Transportation Evolution Impacting Future Mobility

Subject/Index Terms

Filing Info

  • Accession Number: 01767320
  • Record Type: Publication
  • ISBN: 9780784483053
  • Files: TRIS, ASCE
  • Created Date: Dec 9 2020 3:01PM