Efficient and reliable goods movement via our nation’s highway system is critical to the nation’s economy and quality of life. Truck mobility is one of the key performance measures for evaluating the conditions of goods movement and supporting freight planning. Truck GPS data can be useful in developing truck mobility measures and providing insights into freight planning. This dissertation employs truck GPS data and proposes a set of methodologies for measuring and forecasting truck mobility performance, with particular emphases on truck travel time and travel time reliability. It also examines how GPS data can be used to support freight planning, using the analysis of impacts of a tolling project on truck mobility and routing as a case study. The first part of this dissertation investigates how to measure truck travel time reliability given the characteristics of GPS data. An improved spot-speed distribution based travel time reliability measure is proposed. The proposed approach is compared with a number of commonly applied reliability measures. The correlations among these measures reveal that the reliability measures are not highly correlated, demonstrating that different measures provide different conclusions for the same underlying data and traffic conditions. The author presents recommendations of the appropriate measures for different applications. Quantitative freight project prioritization processes require both pre- and post-investment truck mobility performance. Therefore, the second part of this dissertation develops quantitative methods for forecasting truck specific travel time and travel time reliability. For travel time prediction, a speed-density based approach is proposed to predict truck travel time associated with segment density changes. Traffic regimes are segmented using a cluster analysis approach. The travel time estimates are compared with two widely applied traditional methodologies. The results demonstrate that the proposed method is able to estimate more accurate travel times. For reliability prediction, we analyze the changes of GPS spot speed distribution in response to different traffic conditions. A relationship between truck spot speed distribution coefficient of variation and segment density is proposed to forecast reliability. The approach is transferrable and sheds a light on forecasting travel time reliability. The third part of this dissertation focuses on examining how GPS data can be used to assist freight planning. The SR-520 toll bridge in the City of Seattle, Washington is selected as the case study. We quantify the toll project impacts on truck mobility and route choice. Truck GPS data is used to evaluate route choice and travel speed along SR-520 and the alternate toll-free route I-90. A logit model is developed to determine the influential factors in truck routing. The results indicate that travel time, travel time reliability and toll rate are all influential factors during both peak and off-peak periods. The values of truck travel time during different time periods are estimated, and the values vary with the definition of peak and off-peak periods. This dissertation provides decision makers with useful guidance and information on using GPS data for truck mobility measurement and forecasting. It also demonstrates the capability of GPS data in supporting freight planning.
Wang, Zun (2014). Truck GPS Data in Freight Planning: Methodologies and Applications for Measurement and Forecasting. University of Washington Doctoral Dissertation.