Data Driven Urban Traffic Prediction for Winter Performance Measurements

Prediction of traffic speed drop under severe weather in an urban setting is important in measuring the performance of winter highway maintenance programs in the city. This work is built on our previous and current work on point level modeling and prediction of traffic speed drops during weather for performance evaluation in rural areas. INRIX and Wavetronix traffic data and limited weather information were used to develop models for detecting abnormal traffic patterns and predicting traffic speed and volume at any location on a network. Multivariate quantiles were estimated for the INRIX observations, and the INRIX data were compared with the estimated quantiles to identify abnormal traffic patterns in both space and time. An online interactive app was developed to visualize the results and inform decisions about winter maintenance. A dynamic Bayesian model was implemented at two Wavetronix sensor locations where weather information was available, with the corresponding median curve as the baseline. The fitting results were satisfactory. The INRIX data’s spatial structure was explored, and curve Kriging was used to predict traffic speed and volume at any location. The prediction method worked well.

Language

  • English

Project

  • Status: Completed
  • Funding: $192496.00
  • Contract Numbers:

    DTRT13-G-UTC37

  • Sponsor Organizations:

    Midwest Transportation Center

    Iowa State University
    2711 S Loop Drive, Suite 4700
    Ames, IA  United States  50010-8664

    Department of Transportation

    Office of the Assistant Secretary for Research and Technology
    1200 New Jersey Avenue, SE
    Washington, DC  United States  20590

    Iowa Department of Transportation

    800 Lincoln Way
    Ames, IA  United States  50010
  • Managing Organizations:

    Midwest Transportation Center

    Iowa State University
    2711 S Loop Drive, Suite 4700
    Ames, IA  United States  50010-8664
  • Performing Organizations:

    Iowa State University, Ames

    Institute for Transportation
    2711 South Loop Drive, Suite 4700
    Ames, Iowa  United States  50010-8664
  • Principal Investigators:

    Kaiser, Mark

    Zhu, Zhengyuan

  • Start Date: 20140801
  • Expected Completion Date: 20161231
  • Actual Completion Date: 0
  • Source Data: RiP Project 39357

Subject/Index Terms

Filing Info

  • Accession Number: 01560407
  • Record Type: Research project
  • Source Agency: Midwest Transportation Center
  • Contract Numbers: DTRT13-G-UTC37
  • Files: UTC, RiP
  • Created Date: Apr 15 2015 1:01AM