McCormack, E. D., & Hallenbeck, M. E. (2005). Options for Benchmarking Performance Improvements Achieved from Construction of Freight Mobility Projects. (No. WA-RD 607.1). Washington State Department of Transportation.
Delivery options have become very diverse with online shoppers demanding faster delivery options (e.g, 2-day delivery, same day delivery options) and more personalized services. For this reason, transportation planners, retailers, and delivery companies are seeking ways to better understand how best to deliver goods and services in urban areas while minimizing disruption to traffic, parking, and building operations. This includes understanding vertical and horizontal goods movements within urban areas.
The goal of this project is to capture the delivery processes within urban buildings in order to minimize these disruptions. This is achieved using a systems approach to understanding the flow of activities and workers as they deliver goods within urban buildings. A mobile application was designed to collect the start and stop times for each task within the delivery process for 31 carriers as they deliver goods within a 62-story office building.
The process flow map helped identify bottlenecks and areas for improvements in the final segment of the delivery operations: the final 50 feet. It also highlighted consistent tasks conducted by all carriers as well as differences with given carrier type. This information is useful to help decision-makers plan appropriately for the design of future cities that encompass a variety of delivery processes.
There are established relationships between urban form and passenger travel, but less is known about urban form and goods movement. The work presented in this paper evaluates how the design of a delivery service and the urban form in which it operates affects its performance, as measured by vehicle miles traveled, CO2, NOx, and PM10 emissions.
This work compares simulated amounts of VMT, CO2, NOx, and PM10 generated by last-mile travel in several different development patterns and in many different goods movement structures, including various warehouse locations. Last-mile travel includes personal travel or delivery vehicles delivering goods to customers. Regression models for each goods movement scheme and models that compare sets of goods movement schemes were developed. The most influential variables in all models were measures of roadway density and proximity of a service area to the regional warehouse.
These efforts will support urban planning for goods movement, inform policies designed to mitigate the impacts of goods movement vehicles, and provide insights into achieving sustainability targets, especially as online shopping and goods delivery become more prevalent.
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.
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. This research collected GPS data from approximately 2,500 trucks in the Puget Sound, Washington, region and evaluated the feasibility of processing these data to support a statewide network performance measures program. The program monitors truck travel time and system reliability and will guide freight investment decisions by public agencies. While other studies have used a limited number of project-specific GPS devices to collect frequent location readings, which permit a fine-grained analysis of specific roadway segments, this study used data that involved less frequent readings but that were collected from a larger number of trucks for more than a year. Automated processing was used to clean and format the data, which encompassed millions of data points. Because a performance measurement program ultimately monitored trips generated by trucks as they travel between origins and destinations, an algorithm was developed to extract this information and geocode each truck’s location to the roadway network and to traffic analysis zones. Measures were developed to quantify truck travel characteristics and performance between zones. To simplify the process and provide a better communications platform for the analysis, the researchers developed a Google Maps-based online system to compute the measures and show the trucks’ routes graphically.
This paper describes commercial vehicle delay, transportation patterns and the commodity profile at the Western Cascade Gateway, the main border crossing between Southwest British Columbia, Canada, and Northwestern Washington, United States. Using five data sources for comparison—a probe vehicle border crossing time data set, a detailed border operations survey data set, loop detector volume counts, manifest sampling, and data from the Bureau of Transportation Statistics, the transportation, trade, and delay patterns can be synthesized to provide a more complete description of regional freight transportation. This context can be used to consider the impact delay has on regional supply chains, and in developing appropriate freight transportation policy solutions for the border.
We utilize an unsupervised learning algorithm called-modes clustering (Huang 1998), which is similar to the better-known-means method (Hartigan and Wong 1979), but with a dissimilarity measure designed for categorical variables (Cao et al. 2012), originally developed for analyzing sequential categorical data such as gene sequences (Goodall 1966), but also amenable to curb zoning types. For a specified, the-modes algorithm finds the top vectors that minimize a distance to all sample vectors in the training dataset. The resulting top modes are representative of distinct clusters of sample vectors, with cluster membership determined by the closest mode. The parameter is chosen through cross-validation by holding out portions of the available training data and finding the smallest that largely minimizes the within-cluster variation in this hold-out set (also called the “elbow method”). We utilize basic matching dissimilarity, as implemented in (Vos 2015). For two vectors and of length, where each element attains categorical values, matching dissimilarity is defined as, where denotes the indicator vector, with value 1 where the bracketed condition is true and 0 otherwise. We’ve chosen this measure of dissimilarity between two sets of categorical variables for a number of reasons: 1) its simplicity, 2) successful use in categorical data clustering (Goodall 1966), and 3) its sensitivity to the ordering of values when vectors and are ordered, specific to how we have chosen to represent curb zoning data.
Through structured interviews with public agency and private company staff and a review of existing pilot project evaluations and curb management guidelines, this study surveys contemporary approaches to curb space management in 14 U.S. cities and documents the challenges and opportunities associated with them. A total of 17 public agencies (including public works departments, transportation agencies, and metropolitan planning organizations) in every census region of the U.S. and 10 technology companies were interviewed.
The results show that the top curb management concerns among public officials are enforcement and communication, data collection and management, and interagency coordination. Interviewees reported success with policies such as allocating zones for passenger pick-ups and drop-offs, incentives for off-peak delivery, and requiring data sharing in exchange for reservable or additional curb spaces. Technology company representatives discussed new tools and technologies for curb management, including smart parking reservation systems, occupancy sensors and cameras, and automated enforcement. Both public and private sector staff expressed a desire for citywide policy goals around curb management, more consistent curb regulations across jurisdictions, and a common data standard for encoding curb information.