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.

• Record URL:
  http://www.ntc.umd.edu/node/141

Language

• English

Project

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

  DTRT13-G-UTC30

  NTC2015-SU-R-06

• 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

• TRT Terms: Real time information; Traffic forecasting; Travel time; Uncertainty; Value of time; Volatility
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting;

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