Pick-up and delivery operations are an essential part of urban goods movements. However, rapid urban growth, increasing demand, and higher customer expectations have amplified the challenges of urban freight movement. In recent years, the industry has emphasized improving “last-mile” operations with the intent of focusing on what has been described as the last leg of the supply chain.
Many urban planning efforts have supported development in dense, mixed-use areas, but tools are not widely available to help understand the relationship between urban form and goods movement. A review is presented on the status of urban goods movement forecasting models to account for the impacts of density and mixed land use. A description is given of a series of forecasting model runs conducted with state-of-the-practice tools available at the Puget Sound Regional Council.
This report summarizes the work completed under the SHRP2 (Strategic Highway Research Program 2) Local Freight Data program. Supply chain firm interviews and truck counts were conducted to better understand the Food Distribution System in the Puget Sound. Interviews explored key business challenges, operations, and potential responses to natural gas incentives.
Freight transport is a challenging economic sector, as it is essential for the functioning ofproduction and distribution systems but and the same time is at the origin of many nuisances such as congestion, greenhouse gas emissions, pollution and noise. When responding to these issues, planners dispose nowadays of a growing body of freight data that can be used for the implementation of policies towards achieving smart mobility.
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 multiday GPS data.
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.
Bicycling is being encouraged across the US and the world as a low-impact, environmentally friendly mode of transportation. In the US, many states and cities, especially cities facing congestion issues, are encouraging cycling as an alternative to automobiles. However, as cities grow and consumption increases, freight traffic in cities will increase as well, leading to higher amounts of interactions between cyclists and trucks.
Readers who were teenagers in the 1980s may remember driving to a Sam Goody store to buy music. You probably also remember your disappointment when sometimes the tape or CD wasn’t in stock when you arrived. Perhaps you returned to your car and headed for Tower Records to try your luck there.
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.
While recent urban planning efforts have focused on the management of growth into developed areas, the research community has not examined the impacts of these development patterns on urban goods movement.