Rowell, Maura K., Andrea Gagliano, Zun Wang, Anne V. Goodchild, Jeremy L. Sage and Eric L. Jessup. “Improving Statewide Freight Routing Capabilities for Sub-National Commodity Flows.” (2012). University of Washington Doctoral Dissertation.
Truck travel times measure the flow of freight and identify speed trends over time. They are valuable for assessing the efficiency and performance of transportation systems and are essential for planning, designing, and building better transportation facilities.
This article will explore the reliability of the port drayage network. Port drayage is an important component of the marine intermodal system and affects the efficiency of the intermodal supply chain. Sharing and utilizing drayage truck arrival information could improve both port drayage and port operational efficiency. In this article two reliability measures are used to evaluate how the travel time reliability changes with trip origins and across drayage networks. The truck routing choices between Origin-Destination (OD) pairs are examined. A simple method is proposed to predict the 95 percent confidence interval of travel time between any OD pair and is validated with global positioning system (GPS) data. The results presented in this article demonstrate that the proposed travel time prediction method is sufficient for predicting truck arrival time windows at the terminal and can be translated into truck arrival group information.
North American rail terminals need productivity improvements to handle increasing rail volumes and improve terminal performance. This paper examines the benefits of double cycling in wide-span gantry terminals that use automated transfer management systems. The authors demonstrate that the use of double cycling rather than the currently practiced single cycling in these terminals can reduce the number of cycles required to turn a train by almost 50% in most cases and reduce train turn time by almost 40%. This change can provide significant productivity improvements in rail terminals, increasing both efficiency and competitiveness.
Shippers and motor carriers are impacted by and react differently to travel time variability due to their positions within the supply chain and end goals. Through interviews and focus groups these differences have been further examined. Shippers, defined here as entities that send or receive goods, but do not provide the transportation themselves, are most often concerned with longer-term disruptions, which are typically considered within the context of transportation system resilience. Motor carriers, defined here as entities engaged in transporting goods for shippers, are most often concerned with daily travel time variability from events such as congestion. This paper describes the disparity in concerns and the strategies shippers and motor carriers are likely to engage in to address time travel variability. This knowledge allows for a better understanding of how investments to mitigate travel time variability will impact shippers and motor carriers.
Currently, knowledge of actual freight flows in the US is insufficient at a level of geographic resolution that permits corridor-level freight transportation analysis and planning. Commodity specific origins, destinations, and routes are typically estimated from four-step models or commodity flow models. At a sub-regional level, both of these families of models are built on important assumptions driven by the limited availability of data. This study was motivated by a desire to determine whether efforts to gather corridor-level freight movement data will bring significant new insights over current approaches to freight transportation modeling. Through a case study of Washington State’s potato and value added potato products industry, we show that significant insight can be gained by collecting commodity-specific truck trip generation and destination data: the approach allows product specific truck trips to be estimated for each roadway link. When considering a network change, the number of affected trips can be identified, and their re-route distance quantified.
This article identifies the truck routing priorities of freight companies through a survey of Washington state shippers, carriers, and receivers. To elicit these priorities, the survey prompted the respondents to rate 15 items believed to affect route choice decision making with respect to each item’s influence on route choice. Item response theory (IRT) and latent class analysis (LCA) highlights priorities that were common among all survey respondents and priorities that were different among the sample.
Minimizing cost and meeting customer requirements were priorities for all. The influence of other items such as road grade, hours of service limits, and driver availability depended on whether the respondent was best described as a long-haul, local-regional, or urban trucking provider. These three classes of companies were derived from the LCA, and each class has a distinct response pattern to the 15 routing items. This result suggests that truck routing priorities are not constant and uniform across a state’s trucking industry but rather variable and largely dependent on trip length. The paper concludes with practical recommendations as to how these priorities can be implemented within a truck routing model.