2300(16-01) - National Performance Management Research Data Set (NPMRDS) – Speed Data Validation For Traffic Performance Measurement

Urban traffic congestion is common and the cause for loss of productivity (due to trip delays) and higher risk to passenger safety (due to increased time in the automobile), not to mention an increase in fuel consumption, pollution, and vehicle wear. The fiduciary effect is a tremendous burden for citizens and states alike. One way to alleviate these ill effects is increasing state roadway and highway capacity. Doing so, however, is cost prohibitive. A better option is improving performance measurements in an effort to manage current roadway assets, improve traffic flow, and reduce road congestion. Variables like segment travel time, speed, delay, and origin-to-destination trip time are measures frequently used to monitor traffic and improve traffic flow on the state roadways. In 2014, the Oklahoma Department of Transportation (ODOT) was given access to the Federal Highway Administration's (FHWA’s) National Performance Management Research Data Set (NPMRDS), which includes average travel times divided into contiguous segments with travel time measured every 15 minutes. Travel times are subsequently segregated into passenger vehicle travel time and freight travel time. Both types of time are calculated using global positioning system (GPS) location transmitted by way of participating drivers traveling along interstate highways, namely called probe data. However, NPMRDS contains all unfiltered reported travel times including outliers caused by factors not related to road segment traffic flows and congestions. Road closure, presence of traffic light on an interstate, road incidents, and/or road surface condition due to weather are few examples of external factors that demonstrate influence on travel time and its calculation, yet are not directly related to roadway design. In addition to information available in the NPMRDS, ODOT collects traffic data at various segments across the state’s roadways and highways in an effort to monitor traffic volume to better design the state roadways. Ninety-two ODOT automatic vehicle control (AVC) and weigh in motion (WIM) sites currently collect vehicle volume, speed, classification, and weight data. The research team will develop “Big data analytics” algorithms to organize and analyze very large sets of travel time data (we call big data) to discover hidden patterns and useful information undetected in large dataset, and identify and remove speed data outliers. It is an aid to better understand the information contained within datasets and serves as a tool for extracting insight that is most crucial to achieve the project goals and make critical informed decisions. The use of Big Data has become the basis of competition and growth, enhancing productivity and creating significant value through knowledge and insight, particularly for organizations dealing with large sets of continuously generated data.


    • English


    • Status: Completed
    • Funding: $90745
    • Contract Numbers:


      SPR Implementation 2300(16-01)

    • Sponsor Organizations:

      Oklahoma Department of Transportation

      200 NE 21st Street
      Oklahoma City, OK  United States  73105
    • Project Managers:

      Johnson, Daryl

    • Performing Organizations:

      University of Oklahoma, Norman

      School of Civil Engineering and Environmental Science
      202 West Boyd Street, Room 334
      Norman, OK  United States  73019
    • Principal Investigators:

      Refai, Hazem

    • Start Date: 20161001
    • Expected Completion Date: 20170930
    • Actual Completion Date: 20171229

    Subject/Index Terms

    Filing Info

    • Accession Number: 01578490
    • Record Type: Research project
    • Source Agency: Oklahoma Department of Transportation
    • Contract Numbers: SPRY-0010(69)RS, SPR Implementation 2300(16-01)
    • Files: RiP, STATEDOT
    • Created Date: Oct 22 2015 2:28PM