Despite their heavy use of the road transportation system, little data is available on trip generation rates for trucks. In this paper, truck trip rates from grocery stores are used in a case study to evaluate and compare two simple methods for collecting data on truck trip generation: telephone interviews and manual counts. The findings from this study showed that grocery stores generated an average of 18 truck trips per day on a typical peak period weekday. The results also showed that a combination of telephone interviews and manual counts was more effective than telephone interviews alone. Information from the telephone interview guided the manual counts and provided a baseline measurement of counts. However, the interviews underreported truck trips when compared to the manual observations.

The Washington State Department of Transportation (WSDOT), Transportation Northwest at the University of Washington (UW), and the Washington Trucking Associations (WTA) have partnered on a research effort to collect and analyze global positioning systems (GPS) truck data from commercial, invehicle, truck fleet management systems. This effort was funded by the Washington State Legislature, and its purpose is to develop a statewide freight performance measures program for use by WSDOT. This document reviews the program’s previous phases and provides details about the latest phase of the program. The report also provides references to the technical documents that support the program.

A number of trucking companies use Global Positioning System (GPS) devices for fleet management. Data extracted from these devices can provide valuable traffic information such as spot (instantaneous) speeds and vehicle trajectory. However, the accuracy of GPS spot speeds has not been fully explored, and there is concern about their use for estimating truck travel speed. This concern was addressed by initially comparing GPS spot speeds with speeds estimated from dual-loop detectors. A simple speed estimation method based on GPS spot speeds was devised to estimate link travel speed, and that method was compared with space mean speed estimation based on GPS vehicle location and time data. The analysis demonstrated that aggregated GPS spot speeds generally matched loop detector speeds and captured travel conditions over time and space. Speed estimation based on GPS spot speeds was sufficiently accurate in comparison with space mean speeds, with a mean absolute difference of less than 6%. It is concluded that GPS spot speed data provide an alternative for measuring freight corridor performance and truck travel characteristics.

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.

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.