Although trucks move the largest volume and value of goods in urban areas, relatively little is known about their travel

patterns and how the roadway network performs for trucks. Global positioning systems (GPS) used by trucking

companies to manage their equipment and staff and meet shippers’ needs capture truck data that are now available to the

public sector for analysis. The Washington State Department of Transportation (WSDOT), Transportation Northwest

(TransNow) at the University of Washington (UW), and the Washington Trucking Associations (WTA) have partnered on

a research effort to collect and analyze GPS truck data from commercial, in-vehicle, truck fleet management systems used

in the central Puget Sound region. The research project is collecting commercially available GPS data and evaluating

their feasibility to support a state truck freight network performance monitoring program. WSDOT is interested in using

this program to monitor truck travel times and system reliability, and to guide freight investment decisions.

The success of the truck freight performance measurement program will depend on developing the capability to

 efficiently collect and process GPS devices’ output

 extract useful truck travel time and speed, roadway location, and stop location information and

 protect the identity of the truckers and their travel information so that business sensitive information is

not released.

While earlier studies have evaluated commercial vehicles’ travel characteristics by using GPS devices, these

researchers did not have access to commercial fleet data and had to estimate corridor travel speeds from a limited number

of portable GPS units capable of making frequent (1-to-60-second) location reads (Quiroga and Bullock 1998, Greaves

and Figliozzi 2008, Due and Aultman-Hall 2007). This read frequency permitted a fine-grained analysis of truck

movements on specific segments of the road network but did not provide enough data points to reliably track regional or

corridor network performance.

This research project is taking a different approach. The data analyzed in this project are drawn from GPS devices

installed to meet the trucking sector’s fleet management needs. So the truck locations are collected less frequently (typically every 5 to 15 minutes) but are gathered from a much larger number of trucks over a long period of time. The

researchers are collecting data from 2,000 to 3,000 trucks per day for one year in the central Puget Sound region.

This report discusses the steps taken to build, clean, and test the data collection and analytic foundation from which the

UW and WSDOT will extract network-based truck performance statistics. One of the most important steps of the project

has been to obtain fleet management GPS data from the trucking industry. Trucking companies approached by WSDOT

and the UW at the beginning of the study readily agreed to share their GPS data, but a lack of technical support from the

firms made data collection difficult. The researchers overcame that obstacle by successfully negotiating contracts with

GPS and telecom vendors to obtain GPS truck reads in the study region. The next challenge was to gather and format the

large quantities of data (millions of points) from different vendors’ systems so that they could be manipulated and

evaluated by the project team. Handling the large quantity of data meant that data processing steps had to be automated,

which required the development and validation of rule-based logic that could be used to develop algorithms.

Because a truck performance measures program will ultimately monitor travel generated by trucks as they respond to

shippers’ business needs, picking up goods at origins (O) and dropping them off at destinations (D), the team developed

algorithms to extract individual truck's O/D information from the GPS data. The researchers mapped (geocoded) each

truck’s location (as expressed by a GPS latitude and longitude) to its actual location on the Puget Sound region’s roadway

network and to traffic analysis zones (TAZs) used for transportation modeling and planning.

The researchers reviewed truck freight performance measures that could be extracted from the data and that focused on

travel times and speeds, which, analyzed over time, determine a roadway system's reliability. Because the fleet

management GPS data from individual trucks typically consist of infrequent location reads, making any one truck an

unreliable probe vehicle, the researchers explored whether data from a larger quantity of trucks could compensate for

infrequent location reads. To do this, the project had to evaluate whether the spot (instantaneous) speeds recorded by one

truck’s GPS device could be used in combination with spot speeds from other trucks on the same portion of the roadway

network.

The utility of spot speeds and the GPS data in general was evaluated in a case study of a three-week construction

project on the Interstate-90 (I-90) bridge. The accuracy of the spot speeds was then validated by comparing the results

with speed data from WSDOT's freeway management loop system (FLOW).

The researchers also explored methods for capturing regional truck travel performance. The approach identified zones

that were important in terms of the number of truck trips that were generated. Trucks’ travel performance as they traveled

between these economic zones could then be monitored over time and across different times of day.