Processing Commercial Global Positioning System Data to Develop a Web-Based Truck Performance Measures Program

Abstract

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.

The Application of Smart Phone, Weight-Mile Truck Data to Support Freight-Modeling, Performance Measures and Planning

“…This research was generally aimed at evaluating the feasibility of such data to support a statewide network-performance measures program, and specifically, to monitor truck travel time and system reliability in order to guide freight investment decisions. In this regard, a series of research projects was conducted by McCormack et al (McCormack, 2011;Bassok et al, 2011;Ma, McCormack and Wang, 2011;Y. Zhao, 2000) using data from private GPS vendors.…”

“…Step 1 -Development of a GPS Research Database: The researchers collected commercial-truck GPS data from private vendors for approximately 2,500 trucks in the Puget Sound region for over a year (Ma, McCormack and Wang, 2011). Trucking companies use GPS data from such vendors to dispatch and track their equipment.…”

“…In addition, the algorithm resolved issues with the following (Ma, McCormack and Wang, 2011): Step 3 -Development of Trip-Type Classification Algorithms: This step was required due to the aforementioned limitations of the data. Distance statistics between specified TAZs were collected from the Puget Sound Regional Council (PSRC) and then compared to corresponding statistics from the truck GPS data.…”

“…Distance statistics between specified TAZs were collected from the Puget Sound Regional Council (PSRC) and then compared to corresponding statistics from the truck GPS data. This information was used to classify truck trips into three categories (access trips, local trips and loop trips) based on travel time, distance and stop characteristics (Ma, McCormack and Wang, 2011).…”

Estimation of intercity freight origin-destination matrix using simulated annealing algorithm

“…GPS data show the position of trucks in different areas; however, they are not able to generate load occupation factor, or to determine whether trucks are empty or not and/or some other points. Ma et al (2011), Pinjari et al (2013) and Flaskou et al (2015) were among the researchers who did some studies to produce load flow. However, due to the lack of such data in Iran and the lack of GPS in trucks it is not possible to apply such models.…”