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21st Century Operations Using 21st Century Technologies

Regional Assessment of Weather Impacts on Freight

Executive Summary

This project follows up on a study completed for the Federal Highway Administration (FHWA) in 2012 that provided a national estimate of weather-related delay affecting the trucking industry. The initial estimate indicated that weather-related delay costs the industry $8 billion to $9 billion annually. The goal of this study is to conduct a more detailed assessment of the impacts of adverse weather on freight movement in 13 diverse geographic regions, including both urban and rural corridors. The objective is to provide greater temporal and geographic detail than the first phase study allowed, and to use the results to refine the previously developed weather adjustment factors. These factors can be applied in future work to refine the national estimate and can be used at the local or regional level to hep develop traffic management strategies for adverse weather.

After thoroughly reviewing the sources of available data on weather and freight, the project selected the following 13 regions and roadway segments to assess the regional impacts of weather on freight. The project also took freight movement patterns, weather patterns, economic diversity, and regional population size—among other factors—into consideration in the selection process.

  • Atlanta, Georgia: The I-285 Beltway.
  • Chicago, Illinois: I-57 from I-94 to the north and the Kankakee/Iroquois county line to the south.
  • Columbus, Ohio: I-70 from I-75 to the west and the Licking/Muskingum county line to the east.
  • Denver, Colorado: I-70 from State Route (SR)-191 in Grand, Utah to the west and the Elbert/Lincoln county line to the east.
  • Lake Tahoe, California: I-80 from I-5 to the west and the California/Nevada border to the east.
  • Lexington, Kentucky: I-64 from I-265 to the west and the Bath/Rowan county line to the east.
  • Newark, New Jersey: I-78 from I-476 to the east and I-95 to the west.
  • Oklahoma City, Oklahoma: I-35 from I-44 to the north and U.S. 70 to the south.
  • Pittsburgh, Pennsylvania: I-79 from I-80 to the north and the Pennsylvania/West Virginia border to the south.
  • Raleigh, North Carolina: I-40 from the Davie/Forsyth county line to the east and the Johnston/Sampson county line to the west.
  • Rapid City, South Dakota: I-90 from the Wyoming/South Dakota State line to the west and SR-45 (Kimball) to the east.
  • Salt Lake City, Utah: I-80 from the Nevada/Utah border to the west and the Utah/Wyoming border to the east.
  • Seattle, Washington: I-90 from I-5 to the west and I-82 to the east.

The project designed a methodology to associate weather events with freight activity using three categories of data: travel-time data, weather data, and freight data. For travel-time data, the project used the National Performance Management Research Data Set (NPMRDS) downloaded from HERE's online service, which provided data on average travel times for all vehicles, average travel times for passenger vehicles, and average travel times for freight vehicles along the National Highway System (NHS). The project used the National Oceanic and Atmospheric Administration’s (NOAA) Storm Events Database (hereafter the “storm database”) and the National Land Data Assimilation Systems (NLDAS) as its primary sources of data on weather events, including event type, State, county, date, time, and magnitude. Finally, for freight data, the project used FHWA's Freight Analysis Framework Version 3.5 (FAF3.5), which provided estimates of tonnage, value, and domestic ton-miles by region of origin and destination. In order to amass a significant sample size of weather events associated with highway travel and freight activity, the project associated weather events with roadway segment based on county. Since the weather events analyzed were predominately large storms, the likelihood that a weather event impacted roadways within the same county was relatively high. Still, this assumption introduced some uncertainty into the methodology, which is discussed further in the presentation of the results.

The results indicated that, overall, weather events have a significant negative impact on traffic speeds—and, therefore, the freight industry—when analyzed at the regional level. In this report's study areas alone, decreased traffic speeds due to weather events on the highways analyzed were estimated to cost the freight industry $3.8 million per year. These calculations were relative to the average cost of trucking, which includes both good and bad weather. These findings support the earlier estimates and—by examining 13 regions in the United States that vary in terms of their weather, population size, and economies—this report demonstrates how these national trends impact individual regions.

The regional analysis allowed for a more detailed investigation of how the impacts of weather on freight vary by weather event, highway type, time of day, and region size. The key takeaways from the regional analysis are listed below, followed by a discussion of important considerations and limitations of the analysis and directions for further research. All key takeaways are overall findings from the analysis of all 13 regional study areas.

  • Weather events that fall into the categories of Ice and Snow, Fog, Flood, Wind, Rain, and Extreme Temperature were, together, associated with the vast majority of traffic speed decreases during weather events, as well as costs to the freight industry from weather-related delay.
  • Ice and Snow events were associated with over half of all lost time due to decreased traffic speeds during weather events and are the most costly for the freight industry (costing over 25 dollars per segment hour and over 25 cents per truck per segment).
  • Weather events exert the largest negative impacts on traffic speeds between hour 0 and hour 1. However, small decreases in speed are also seen in the hours leading up to a weather event, and moderate decreases in speed are still seen up to four hours after the event.
  • Throughout the day, irregular flow highways (highways that experience morning and evening rush hours) suffer more than even flow highways in terms of loss of speed during weather events.
  • Time of day matters—all highway types suffer more in terms of loss of speed during weather events that occur during morning and evening rush hour periods.
  • Highways in smaller regions (where region size is based on population size and economic intensity) suffer less than highways in medium and large regions in terms of loss of speed during weather events.

While the analysis was able to detect these trends looking across the 13 regions, it is important to understand the limitations of the methodology and data used in order to best apply these insights. The association of traffic speeds with weather events based on county means that it is not certain that traffic speeds on a given roadway were always directly impacted by the associated weather event. The eight-hour timeframe applied to each weather event (four hours before and after hour 0) also limits the analysis as storms vary in length, but it was necessary to establish a common timeframe in order to have consistency in the analysis. Ideally, the analysis would track each storm individually for the time that it occurred, but the data processing requirements for such an approach were significant and not feasible within project resources. Nonetheless, given the sample size amassed using these assumptions, the research team was able to determine the significant trends listed above.

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