United States Department of Transportation
i
The Effect of Weather Events on Truck Traffic Patterns Using Fixed and Mobile Traffic Sensors
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2017-12-20
[PDF-1.61 MB]
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Edition:Final Report
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Abstract:Severe weather conditions, i.e. snowfall, floods, ice storms, etc. can have major effects on traffic volumes along the highway network. Unlike passenger vehicles, which may choose not to travel during inclement weather, freight trucks need to adhere to delivery schedules requiring them to alter their route rather than cancel a trip. While previous studies have modeled the effects of weather on total traffic volumes, very few studies have examined the effect of weather on truck volumes. Due to differences in travel behaviors between passenger and freight trucks, the study of weather effects on truck volumes requires advanced modeling techniques that are able to capture effects over space. This study applies spatial regression techniques to develop a predictive model that relates variations in truck traffic patterns to weather conditions, with a focus on extreme weather events. The study uses traffic classification data from six Weigh-in-Motion (WIM) stations and weather data from six weather stations in Arkansas. The study shows that, as expected, reduction in truck volume occurs due to extreme weather events such as snowfall, fog, hail, winter storms, flash flood, etc. Notably, through the spatial model, the negative spatial autocorrelation parameter explains that reductions in truck volume at one site are countered by higher truck volumes at neighboring sites- thus explaining rerouting behaviors of trucks. The study finds extreme cold events (i.e. snow) reduces daily truck volumes by approximately 22% while heavy rainfall, flood, flash flood reduces daily truck volumes by 13%, compared to average daily truck traffic. The study 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.
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