Big Transportation Data Analytics

Traffic volume data is crucial in many applications, including transportation operation analysis, congestion management, accident prevention, etc. Yet an extensive capture of accurate volume information on a large-scale network can be difficult and costly. This research focuses on hourly traffic volume prediction in a statewide network using spatial-temporal features and heterogenous data sources. The authors present a classic machine learning technique - support vector machine (SVM) and compare its efficiency for traffic volume prediction with traditional estimation method. Further, the study develops an innovative spatial prediction method. The method is built off a state-of-the-art tree ensemble model - extreme gradient boosting tree (XGBoost) - to handle the large-scale features and hourly traffic volume samples. Moreover, spatial dependency among road segments is considered using graph theory. Specifically, the authors created a traffic network graph leveraging probe trajectory data and implemented a graph-based approach - breadth first search (BFS) - to search neighboring sites in this graph for computing spatial dependency. The proposed spatial dependency feature is subsequently incorporated as a new feature fed into XGBoost. The proposed methods are applied to 101 continuous count station (CCS) sites in the State of Utah. Prediction accuracy and training time are compared across the proposed models.

  • Record URL:
  • Record URL:
  • Supplemental Notes:
    • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
  • Corporate Authors:

    University of Utah, Salt Lake City

    Department of Civil and Environmental Engineering
    Salt Lake City, UT  United States 

    Mountain-Plains Consortium

    North Dakota State University
    Fargo, ND  United States  58108

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Authors:
    • Liu, Xiaoyue Cathy
    • Yi, Zhiyan
  • Publication Date: 2021-3


  • English

Media Info

  • Media Type: Digital/other
  • Edition: Final Report
  • Features: Appendices; Figures; References; Tables;
  • Pagination: 42p

Subject/Index Terms

Filing Info

  • Accession Number: 01769067
  • Record Type: Publication
  • Report/Paper Numbers: MPC-543, MPC 21-428
  • Created Date: Apr 5 2021 10:55AM