Publications

Summary:
This paper presents a systematic methodology for identifying and ranking bottlenecks using probe data collected by commercial GPS fleet management devices. This methodology is based on the hypotheses that truck speed distributions can be represented by either a unimodal or bimodal probability density function, and it proposes a new reliability measure for evaluating roadway performance.

Summary:
The Port of Seattle surveyed drayage truckers serving the port in 2006, 2008, and surveyed drivers again in 2013 in partnership with the University of Washington. This thesis describes the methodology used to survey drayage drivers at the Port of Seattle, describes the economic conditions of drayage drivers at the port and changes in economic conditions since previous surveys, and attempts to model driver earnings based on other driver characteristics.

Summary:
Although trucks move larger volumes of goods than other modes of transportation, public agencies know little about their travel patterns and how the roadway network performs for trucks. Trucking companies use data from the Global Positioning System (GPS) provided by commercial vendors to dispatch and track their equipment.

Summary:
Global positioning system (GPS) devices that are installed in trucks and used for fleet management are increasingly common. Raw data from these devices present an opportunity for public agencies to use these trucks as probe vehicles to better monitor roadway operations and to quantify transportation system efficiency.

Summary:
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.

Summary:
This paper describes the development of a systematic methodology for identifying and ranking bottlenecks using probe data collected by commercial global positioning system fleet management devices mounted on trucks. These data are processed in a geographic information system and assigned to a roadway network to provide performance measures for individual segments.

Summary:
This article considers the effectiveness of a truck appointment system in improving yard efficiency in a container terminal. This research uses the truck appointment information obtained from an appointment system to improve import container retrieval operation and reduce container rehandles by adopting an advanced container location assignment algorithm. By reducing container rehandles, the terminal could improve yard crane productivity and reduce truck transaction time.

Summary:
Freight Performance Measures (FPM) are of interest to transportation planning agencies. One of the key tools that aids in the study of freight system activity is the data from Global Positioning System (GPS) devices located in trucks and cars. While commercially available GPS data has a common basic output format, the level of aggregation of the raw data, impacts the data’s ultimate usability and applications.

Summary:
Truck probe data collected by global positioning system (GPS) devices has gained increased attention as a source of truck mobility data, including measuring truck travel time reliability. Most reliability studies that apply GPS data are based on travel time observations retrieved from GPS data. The major challenges to using GPS data are small, nonrandom observation sets and low reading frequency.

Summary:
Establishment level employment data indicate that the warehousing industry has experienced rapid growth and restructuring since 1998. This restructuring has resulted in geographic shifts at the national, regional, and local scales. Uneven growth in warehousing establishments across the Pacific Northwest has likely exerted a significant impact on the regional transportation system, but the extent of these transportation impacts remains unknown.

Summary:
The objective of this paper is to develop a methodology for forecasting freeway vehicle travel time reliability for transportation planning using probe GPS data. Travel time reliability is measured using the coefficient of variation of the GPS spot (instantaneous) speed distribution. The proposed approach establishes relationships between travel time reliability and roadway traffic density in order to forecast reliability given future traffic conditions.

Summary:
Predicting truck (heavy vehicle) travel time is a principal component of freight project prioritization and planning. However, most existing travel time prediction models are designed for passenger vehicles and fail to make truck specific forecasts or use truck specific data. Little is known about the impact of this limitation, or how truck travel time prediction could be improved in response to freight investments with an improved methodology.

Summary:
In automated container terminals, rail based horizontal transfer systems are newly proposed and regarded to be more suitable to intermodal transportation [1]. However, improvements are required in operations scheduling in rail based transfer automated container terminals (RBT-ACT) to take advantage of the infrastructure improvement [2].

Summary:
Travel demand models are used to aid infrastructure investment and transportation policy decisions. Unfortunately, these models were built primarily to reflect passenger travel and most models in use by public agencies have poorly developed freight components. Freight transportation is an important piece of regional planning, so regional models should be improved to more accurately capture freight traffic.

Summary:
Freight systems are a critical yet complex component of the transportation domain. Understanding the dynamic of freight movements will help in better management of freight demand and eventually improve freight system efficiency. This paper presents a series of data-mining algorithms to extract an individual truck’s trip-chaining information from multi-day GPS data.