Methods to Analyze and Predict Interstate Travel Time Reliability – Phase II
Accurate prediction of reliability measures would help state departments of transportation (DOTs) set performance targets and communicate the progress toward those targets as required by the Moving Ahead for Progress in the 21st Century (MAP-21) Act. A recent Virginia Transportation Research Council (VTRC) research study “Methods to Analyze and Predict Interstate Travel Time Reliability” developed linear quantile mixed models (LQMMs) and generalized random forest (GRF) models to analyze travel time reliability influencing factors and predict reliability of the interstate system during peak traffic periods. This project will implement the recommendations developed in that research study for the interstate system, including developing detailed step-by-step data preparation and modeling guidance, applying the GRF approach developed using INRIX TMC network to the National Performance Management Research Data Set (NPMRDS) network, and expanding the GRF approach to cover the weekday midday and weekend daytime periods using NPMRDS.
Language
- English
Project
- Status: Active
- Funding: $138557
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Contract Numbers:
120025
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Sponsor Organizations:
Virginia Transportation Research Council
530 Edgemont Road
Charlottesville, VA United States 22903 -
Performing Organizations:
Virginia Transportation Research Council
530 Edgemont Road
Charlottesville, VA United States 22903 -
Principal Investigators:
Zhao, Mo
Appiah, Justice
- Start Date: 20210816
- Expected Completion Date: 20221231
- Actual Completion Date: 0
Subject/Index Terms
- TRT Terms: Forecasting; Information processing; Interstate highways; Travel time
- Identifier Terms: National Performance Management Research Data Set (NPMRDS)
- Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;
Filing Info
- Accession Number: 01778896
- Record Type: Research project
- Source Agency: Virginia Department of Transportation
- Contract Numbers: 120025
- Files: RIP, STATEDOT
- Created Date: Aug 10 2021 10:21AM