Advanced Volatility Models for Improving Travel Time Prediction

In order to provide meaningful traffic information to both travelers and traffic managers, it is critical to develop accurate and reliable traffic prediction algorithms that not only reduce absolute value of prediction error but also take into consideration the uncertainty associated with travel time prediction. The objective of this research is to identify and model uncertainties associated with travel time prediction and develop models for short term forecasting of the traffic state. Most existing travel time prediction methods only provide a point value as the prediction result which does not represent the uncertainty issues. Instead of providing a point value (an average of travel time during a certain time interval), a prediction interval based approach is proposed. The prediction interval represents likeliness of capturing true value of the future travel time. In other words, a prediction interval is an estimated range that captures the future observation, with a prescribed probability, given the current available observations.


  • English


  • Status: Completed
  • Funding: $120000.00
  • Contract Numbers:



  • Sponsor Organizations:

    National Transportation Center @ Maryland

    1173 Glenn L. Martin Hall
    University of Maryland
    College Park, Maryland  United States  20742
  • Project Managers:

    Zhang, Lei

  • Performing Organizations:

    University of Maryland, College Park

    Department of Civil and Environmental Engineering
    1173 Glenn Martin Hall
    College Park, MD  United States  20742
  • Principal Investigators:

    Haghani, Ali

  • Start Date: 20150101
  • Expected Completion Date: 0
  • Actual Completion Date: 20151231
  • Source Data: RiP Project 39049

Subject/Index Terms

Filing Info

  • Accession Number: 01593956
  • Record Type: Research project
  • Source Agency: National Transportation Center @ Maryland
  • Contract Numbers: DTRT13-G-UTC30, NTC2015-SU-R-06
  • Files: UTC, RIP
  • Created Date: Mar 21 2016 1:00AM