Techniques for Mining Truck Data to Improve Freight Operations and Planning, TransNow, Final Report

Abstract. Freight plays an increasingly valuable role in the national economy, and a growing percentage of freight— measured by both total volume and market value—is being moved along the highway system by truck. An important part of the research process to fully understand the impacts of these increased truck volumes on the transportation network is fueled by collecting and analyzing freight data. With the adoption of just-in-time supply chain management solutions, and increasing congestion on urban, rural, and intercity motorways, better knowledge of freight movements can serve to improve highway operations. The real-time data generated by the development of travel time algorithms can be provided to commercial vehicle operators to enable them to minimize the delay associated with goods movement, and assist in streamlining the logistics planning process. Increased knowledge of truck travel patterns has the potential to increase overall highway safety, lead to better-managed maintenance operations, provide cost savings to public agencies, validate investments in intelligent transportation systems (ITS), and improve long-range planning and forecasting. This paper explores techniques that use current ITS technologies such as video image processing and loop detector data algorithms to collect and verify short and long vehicle coun