United States Department of Transportation
i
Truck Activity Analysis Using GPS Data
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2019-07-01
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[PDF-26.33 MB]
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Edition:Final Report
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Abstract:This project investigated large streams of truck Global Positioning System (GPS) data for statewide freight modeling and planning applications in Arkansas. Approximately 5% of the trucks represented movements entirely within state boundaries with average trip lengths of 30 miles and durations of 1 hour; 22% represented in and outbound movements with trip lengths of 75 to 110 miles and durations of 1.5 to 2 hours; 69% represented movements crossing state boundaries with a stop inside the state with average trip lengths of 130 miles and durations of 2.5 hours; and 4% represented pass through movements of 265 miles and 4.5 hours on average. Coverage, defined as the percent of the truck population represented by the data sample, ranged from 8% on US highways to 9% on interstates with an average coverage of 8.5% across all roadway types, and from 6% in District 3 (Southwest Arkansas) to 17% in District 9 (Northwest Arkansas) with an average coverage of 9% across all districts. By time of day (TOD), highest coverage occurred during the early morning and evening for all functional class and all days of the week. By day of week, the coverage was evenly distributed, with a minor peak in coverage on weekends. Performance measures including travel time reliability, percent of the interstate system experiencing congestion, etc. were calculated for the state‐maintained roadway network. Interactive maps depicting these features were developed and published online using Environmental Systems Research Institute’s Arc‐Geographic Information System (ESRI ArcGIS) map tools along with an implementation guide detailing the use of the online map interfaces. Three case studies showcased unique uses of the truck GPS data including: (a) truck parking usage patterns; (b) travel time delays for trucks resulting from accidents; (c) spatial impacts of inland waterway ports. Truck GPS data uniquely fills a freight data gap by capturing spatial and temporal travel patterns that cannot be discerned from existing sources (e.g. WIM, surveys, etc.). Noting the coverage, the data was spatially and temporally representative. There is still concern about a lack of coverage of particular industries such as lumber/logging that tend to be operated by independent owner‐operators, rather than large, managed fleets.
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