Publications

Summary:
The rapid growth in e-commerce activities and the constant specialization of industries have aroused an unparalleled demand for trucking in urban areas, leading to growing concern over its interference to the transportation system.

Summary:
This paper presents a framework to classify and mitigate roadway bottlenecks and that is designed to improve freight mobility. This is in recognition that roadway operations for trucks are under studied, truck-only bottlenecks are often not identified and freight-specific problem areas are therefore often overlooked. The framework uses four-steps: Step 1: identifies and locates the roadway sections where vehicle travel time is in excess of what would normally occur.

Summary:
The demand for truck transportation increases alongside growth in population and economic activity. As both truck and passenger traffic outstrip roadway capacity, the result is congestion, which the freight community experiences as truck bottlenecks. This NCHRP project produced a Guidebook that provides state-of-the-practice information to transportation professionals on practices and measures for identifying, classifying, evaluating, and mitigating truck freight bottlenecks.

Summary:
Community resilience depends on the resilience of the lifeline infrastructure and the performance of the disaster-related functions of local governments. State and federal resilience plans and guidelines acknowledge the importance of the transportation system as a critical lifeline in planning for community resilience and in helping local governments to set recovery goals.

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:
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:
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:
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:
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.

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:
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:
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:
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:
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:
This paper quantifies the benefits to drayage trucks and container terminals from a data-sharing strategy designed to improve operations at the drayage truck-container terminal interface. This paper proposes a simple rule for using truck information to reduce container rehandling work and suggests a method for evaluating yard crane productivity and truck transaction time.