The Effects of Weather Events on Truck Traffic Using Fixed and Mobile Traffic Sensors

With freight tonnage expected to grow by 43% over the next 25 years, it is increasingly important to direct resources towards the maintenance and operation of an efficient freight transportation system. In the Southern Plains region, severe weather conditions such as wind, ice, and snowfall can have major effects on traffic volumes along the highway network. Understanding the impacts of extreme weather events on the spatial and temporal traffic patterns of freight trucks, in particular, can assist state and regional transportation agencies in developing freight-oriented programs and policies for road and winter maintenance, structural and geometric pavement design, highway life cycle analysis, and long range transportation planning. While previous studies have modeled the effects of weather on total traffic volumes, limited research in this area has been conducted for freight trucks. The distinction between freight and passenger volumes is essential, as each exhibits different behavioral characteristics in response to severe weather conditions and thus impacts the transportation system in different ways. For freight truck traffic, rather than volume reductions across the network resulting from cancelled trips, rerouting decisions causing shifts in route and time of departure may be more likely to occur. The goal of this study is to develop a predictive model that relates variations in truck traffic patterns to weather conditions, with a focus on extreme weather events. The research will accomplish the following objectives: (1) to develop a spatial regression model to explain and predict the impact of weather events on truck traffic volumes, (2) to fuse fixed truck traffic sensor measurements with mobile sensor data to produce estimates of population level vehicle miles of travel/vehicle hours of travel (VMT/VHT) impacts of weather events, and (3) to predict annual average daily truck traffic (AADTT) and VMT/VHT impacts based on forecasts of extreme weather events. This research is unique in that the model will augment truck traffic data gathered from Weigh-In-Motion (WIM) sensors with truck Global Positioning Systems (GPS) data to predict VMT/VHT impacts. Further, unlike previous work which was limited to multiple regression techniques, this study will explore spatial regression models which correct for spatial autocorrelation that exists in explanatory variables due to spatial differences in transportation network density and land uses. Ultimately, this research will help leverage existing freight data sources to support freight transportation planning and decision making.


  • English


  • Status: Completed
  • Funding: $60671
  • Contract Numbers:

    SPTC 15.1-20/DTRT13-G-UTC36

  • Sponsor Organizations:

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Managing Organizations:

    Southern Plains Transportation Center

    University of Oklahoma
    201 Stephenson Pkwy, Suite 4200
    Norman, OK  United States  73019
  • Performing Organizations:

    University of Arkansas, Fayetteville

    Board of Trustees
    Fayetteville, AR  United States 
  • Principal Investigators:

    Hernandez, Sarah

  • Start Date: 20160301
  • Expected Completion Date: 20171231
  • Actual Completion Date: 20171220
  • USDOT Program: University Transportation Centers Program

Subject/Index Terms

Filing Info

  • Accession Number: 01607673
  • Record Type: Research project
  • Source Agency: University of Oklahoma, Norman
  • Contract Numbers: SPTC 15.1-20/DTRT13-G-UTC36
  • Files: UTC, RiP
  • Created Date: Aug 17 2016 1:28PM