Advanced Volatility Models for Improving Travel Time Prediction

Travel time effectively measures freeway traffic conditions. Easy access to this information provides the potential to alleviate traffic congestion and to increase the reliability in road networks. Accurate travel time information through Advanced Traveler Information Systems (ATIS) can provide guidance for travelers’ decisions on departure time, route, and mode choice, and reduce travelers’ stress and anxiety. In addition, travel time information can be used to present the current or future traffic state in a network and provide assistance for transportation agencies in proactively developing Advanced Traffic Management System (ATMS) strategies. Despite its importance, it is still a challenging task to model and estimate travel time, as traffic often has irregular fluctuations. These fluctuations result from the interactions among different vehicle-driver combinations and exogenous factors such as traffic incidents, weather, demand, and roadway conditions. Travel time is especially sensitive to the exogenous factors when operating at or near the roadway’s capacity, where congestion occurs. Small changes in traffic demand or the occurrence of an incident can greatly affect the travel time. As it is impossible to take into consideration every impact of these unpredictable exogenous factors in the modeling process, travel time prediction problem is often associated with uncertainty. This research uses innovative data mining approaches such as advanced statistical and machine learning algorithms to study uncertainty associated with travel time prediction.

• Record URL:
  • https://mti.umd.edu/sites/mti.umd.edu/files/documents/NTC2015-SU-R-06%20Ali%20Haghani_0.pdf
• 
• Summary URL:
  • https://mti.umd.edu/project/ntc2015-su-r-06-advanced-volatility-models-improving-travel-time-prediction
• Supplemental Notes:
  • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  National Transportation Center @ Maryland

  1173 Glenn L. Martin Hall
  University of Maryland
  College Park, Maryland  United States  20742

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Authors:
  • Haghani, Ali
  • Zhang, Yanru
• Publication Date: 2015-2

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Final Report
• Features: Figures; References; Tables;
• Pagination: 62p

Subject/Index Terms

• TRT Terms: Advanced traffic management systems; Advanced traveler information systems; Highway traffic control; Mathematical models; Real time information; Stochastic processes; Traffic congestion; Traffic forecasting; Travel time
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01667221
• Record Type: Publication
• Report/Paper Numbers: NTC2015-SU-R-06
• Contract Numbers: NTC2015-SU-R-06
• Files: UTC, TRIS, ATRI, USDOT
• Created Date: Apr 25 2018 11:14AM