92

format, and quality. Adequately processing the probe data and fur-ther developing useful truck performance information generationand visualization tools have proved to be ongoing challenges forresearchers (1).

The Washington State Department of Transportation, Transporta-tion Northwest at the University of Washington, and the Washing-ton Trucking Associations partnered on a research effort to collectand analyze GPS truck data from commercial in-vehicle fleetmanagement systems used in the central Puget Sound region. TheGPS information, which is in essence a by-product of truckingindustry operations, was collected and evaluated for its usefulnessin supporting a long-term truck-based freight network performancemonitoring program. The Washington State Department of Trans-portation intends to use this program to guide planning and construc-tion decisions. The data, which are from GPS devices installed forthe convenience of the trucking industry, were collected infrequentlybut involved a large number of trucks over a long period of time.In this effort’s study area, data were collected from approximately2,500 trucks per day.

This paper discusses the steps required in processing these data,addressing data limitations, and developing usable performance mea-sures. The data had to be acquired from diverse sources, and soft-ware that could handle extremely large databases had to be developed.Bad data due to GPS signal loss or inaccuracy had to be removed,and the data had to be effectively assigned (geocoded) to the PugetSound area roadway network.

Development of a method for identifying truck travel patternswas crucial for the project, since the success of a truck-orientedperformance measures program depends on extracting useful trucktravel time and speed, roadway location, and stop location infor-mation. A performance measures program ultimately monitors travelgenerated by trucks as they respond to shippers’ business needs bypicking up goods at origins and dropping them off at destinations.In view of the importance of individual truck origin–destination(O-D) pairs, an algorithm for extracting the pairs from the GPS read-ings was developed, which formed the basis for the performancemeasures.

Once each truck’s geocoded location was mapped to the PugetSound region’s roadway network and then assigned to traffic analy-sis zones (TAZs), truck freight performance measures that focused onzonal travel times and speeds could be calculated. These measures,analyzed over time, would determine a freight system’s reliability.A Google Maps–based truck performance system was implementedto provide a user-friendly and interactive interface for agency staff touse in accessing the GPS data and monitoring trucking movementsin the Puget Sound region.

Processing Commercial Global PositioningSystem Data to Develop a Web-BasedTruck Performance Measures Program

Xiaolei Ma, Edward D. McCormack, and Yinhai Wang

Although trucks move larger volumes of goods than other modes oftransportation, public agencies know little about their travel patternsand how the roadway network performs for trucks. Trucking companiesuse data from the Global Positioning System (GPS) provided by com-mercial vendors to dispatch and track their equipment. This researchcollected GPS data from approximately 2,500 trucks in the PugetSound, Washington, region and evaluated the feasibility of processingthese data to support a statewide network performance measures pro-gram. The program monitors truck travel time and system reliabilityand will guide freight investment decisions by public agencies. Whileother studies have used a limited number of project-specific GPS devicesto collect frequent location readings, which permit a fine-grained analy-sis of specific roadway segments, this study used data that involved lessfrequent readings but that were collected from a larger number of trucksfor more than a year. Automated processing was used to clean and for-mat the data, which encompassed millions of data points. Because a per-formance measurement program ultimately monitored trips generatedby trucks as they travel between origins and destinations, an algorithmwas developed to extract this information and geocode each truck’slocation to the roadway network and to traffic analysis zones. Measureswere developed to quantify truck travel characteristics and perfor-mance between zones. To simplify the process and provide a bettercommunications platform for the analysis, the researchers developed aGoogle Maps–based online system to compute the measures and showthe trucks’ routes graphically.

Data from the Global Positioning System (GPS) used by truckingcompanies for fleet management have only recently been exploredas a source of truck performance data for public agencies. Althoughcommercial systems making use of GPS data are the most compre-hensive national source of truck travel data available for quantifyingtruck performance, their data cannot be used directly in calculatingtruck performance measures, and considerable processing is re-quired. Problems include limitations in terms of reading frequency,

X. Ma, Department of Civil and Environmental Engineering, University of Wash-ington, Box 354802, Seattle, WA 98105. E. D. McCormack, Box 354802, andY. Wang, Box 352700, Department of Civil and Environmental Engineering, Uni-versity of Washington, Seattle, WA 98195-2700. Corresponding author: E. D.McCormack, edm@u.washington.edu.

Transportation Research Record: Journal of the Transportation Research Board,No. 2246, Transportation Research Board of the National Academies, Washington,D.C., 2011, pp. 92–100.DOI: 10.3141/2246-12

LITERATURE REVIEW

Several previous efforts have used GPS data to measure truck per-formance. A recent study by the American Transportation ResearchInstitute concluded that GPS data from trucks can be processed to pro-vide average travel rates along major long-distance U.S. corridors (2).The data used for that study were purchased from GPS vendors, andthe spot speeds from GPS devices were aggregated over time toidentify truck bottlenecks. McCormack and Hallenbeck (3) testedthe use of truck GPS data for measuring freight movements alongspecific roadway corridors in Washington State. The study, whichused 25 portable data collection GPS loggers with 5-s location read-ings, concluded that GPS data can provide an indication of roadwayperformance. Greaves and Figliozzi (4) described algorithms forprocessing raw data to identify trip ends. Their study, which col-lected data from 30 trucks, also discussed the potential uses and lim-itations of GPS technology in urban freight modeling and planning.The Ontario Ministry of Transportation has an ongoing arrangementwith a private vendor to use GPS data from its commercial fleetmanagement systems. This vendor provides GPS data from approx-imately 30,000 trucks each weekday (5, 6). IBI Group used GPS datafrom the same vendor to monitor truck freight performance at Ontarioand Québec border crossings (7 ). A series of metrics to examinefreight system performance was developed.

A number of researchers have documented the processing of GPSdata and the retrieval of trip information to detect O-D patterns and tocalculate travel times. Quiroga and Bullock proposed a new method-ology for performing travel time studies that used GPS and geographicinformation system (GIS) technologies. They documented the datacollection, reduction, and reporting procedures used to produce mea-sures of effectiveness for travel times at various levels of resolution,including the roadway system, corridors, and local roads (8). Du andAultman-Hall (9) developed an automatic trip end identification algo-rithm that uses a combination of maximum and minimum dwell times,heading changes, and a check for distance between the GPS points.This process was used to increase the accuracy of trip rate information.Schuessler and Axhausen (10) described a postprocessing procedurefor cleaning and smoothing raw GPS data and identified trip activityand trip modes automatically by using fuzzy logic. Hunter et al. (11)used GPS-instrumented test vehicles and developed an algorithm thatidentified the traversal time between intersections for a GPS devicemounted in a probe vehicle to calculate travel times on urban arterialstreets. Zheng et al. (12) used GPS to monitor transit vehicle move-ments along signalized arterial corridors to evaluate a transit signalpriority system deployed in Snohomish County in Washington State.

A number of these research efforts relied on GPS devices withhigh reporting frequencies but small numbers of probe vehicles toidentify trip ends and calculate travel times. While these studies typ-ically applied some level of automated processing to clean and orga-nize the data, the postprocessing also included manual processes tofix or remove data with problems.

Ma, McCormack, and Wang 93

DATA ACQUISITION

The first step of this study was to collect truck GPS data. Obtainingthe data directly from trucking companies was initially attemptedbut proved to be unworkable. While most companies agreed to sharetheir data, it was difficult to work out the technical details with thecompanies’ in-house data staff (if they existed) for transferring thedata. The authors reconsidered and determined that it would be easierto obtain data from the GPS device vendors. This approach had anumber of advantages:

• The vendors had technical staff experienced in sending outGPS data.

• Each vendor was able to provide GPS data from multiple truck-ing companies.

• The vendors were interested in the performance measures pro-gram because it represented a way to obtain additional value fromtheir GPS data.

• A contract was drawn up with each vendor, which replacedvoluntary participation with a business relationship.

Purchasing data directly from the vendors, however, also haddisadvantages:

• The data were collected for the benefit of the trucking compa-nies and might not be ideal for the freight performance measuresprogram.

• The data presented an ongoing cost.• Because the vendors were not accustomed to selling data

to a university, each relationship required a sometimes protractedcontracting process.

• Because protecting the privacy of clients for the vendor’s truck-ing company was critical, nondisclosure agreements were required,which involved attorneys and slowed the project.

Data acquisition contracts were signed with three GPS vendors.Each vendor collected and managed truck GPS data differently. Anoverview of the data acquired from each vendor is shown in Table 1.Common to each data set were variables that included longitude, lat-itude, truck ID (hashed for privacy in some cases), travel heading,and a date and time stamp. The three vendors jointly provided GPSdata from approximately 2,500 trucks traveling in the Puget Soundregion each day.

DATA STORAGE AND PROCESSING

To manage and analyze the truck GPS data, an efficient mechanism forreceiving, cleaning, storing, and manipulating the GPS data on a data-base server was essential. Because of the diversity and large volumesof the GPS data, a scalable database with flexible data formats had to

TABLE 1 GPS Data Overview by Vendor

Average Total Frequency of GPS Vendor Daily Records Total Trucks Readings (min) Data Type

Vendor A 94,000 Approximately 2,500 per day 5–15 In-vehicle GPS with a cellular connection

Vendor B 12,000 25 0.5 In-vehicle GPS with a cellular connection

Vendor C 3,000 60 1–5 GPS cell phone

be created. This required relational tables storing different vendors’GPS data, which could be combined to supply the data needed for truckO-D identification, GIS processing, and performance measurement.The truck GPS data processing flowchart is shown in Figure 1.

Database Development

An initial challenge was that each vendor’s data used different for-mats and communication methods. Data from Vendors A and Bwere periodically obtained via File Transfer Protocol (FTP) services,and data from Vendor C were acquired manually by e-mail everymonth. The research team developed a database system that playeda crucial role in acquiring and archiving these heterogeneous GPSdata. For Vendors A and B, a scheduled fetch program was run at theback end to connect with the vendors’ FTP servers and retrieve andupload the data into databases at 1-month intervals. For Vendor C, aseparate program was written to check data availability periodically.When data were received, this program automatically imported theminto databases. Along with these programs, the researchers devel-oped a data feed and archiving service to place each vendor’s datain a separate database.

More than 100,000 location readings were downloaded into thedatabase each day, and querying and analyzing such a large and grow-ing database as part of a performance measures program could becumbersome. To optimize the database, a program was developed tosplit the three vendors’ databases into three monthly data sets auto-matically. Common information from each vendor’s data set wasformatted into standard columns, which contained only the fieldsnecessary for a freight performance measures development program.This reduced data processing time. After several programming iter-ations, it became possible to query nearly 10 million rows of datawithin 1 s.

Truck Location Mapping

The trucks’ GPS locations (determined on the basis of the latitude andlongitude reported by the GPS) had to be geocoded onto the roadnetwork for analysis by GIS software. However, the accuracy of theGIS in mapping geocoded location data to a road network typicallysuffered from errors caused by spatial mismatches between the basenetwork and the latitude and longitude points, as well as other prob-lems such as assignment confusion related to overpasses and frontageroads. These issues typically required postprocessing and error check-ing. Because of the large size of the data set, manual checking for

94 Transportation Research Record 2246

errors was impossible. A computer program was developed to iden-tify suspect points automatically for further checking or elimination.For data quality control, a set of rules was developed and imple-mented in the computer program, which was also designed to use aGIS scripting language to assign each truck’s GPS readings to thecorrect roadway segments.

A section of I-90 through the Seattle, Washington, area served asa case study to develop and test the geocoding process. GPS datafrom Vendor A on I-90 from May 4 to May 23, 2009, were retrievedand processed in the computer program. Use of a 100-ft (30-m)buffer from the road midpoint helped in assigning each geocodedlocation reading to the nearest roadway. Heading data for the truckswere then checked to associate the GPS travel bearing with the roadsegment’s bearing. A 15-degree heading range was selected becausethis reasonably captured trips along the roadway while excludingtruck travel on overpasses and intersecting roads. The process provedeffective in mapping trucks’ geocoded locations to roadways, withabout 60% of the points passing the screening process.

IDENTIFICATION OF ORIGINS AND DESTINATIONS

Developing a performance measures program requires an under-standing of the travel patterns of trucks as they use the roadway net-work. This requires an efficient and accurate process for identifyingeach location where a truck stops to complete the productive trans-action that defines the purpose of each truck’s trip. Because GPSdevices record a range of stops, a methodology was needed to dif-ferentiate between traffic-based (unintended) stops at intersectionsor in congestion and intended stops that corresponded to a truck’sorigin and destination. Several studies that have analyzed GPS truckdata have attempted to separate traffic stops from O-D stops on thebasis of the stop duration (i.e., dwell time) (4, 9–11). In these stud-ies, the origins and destinations identified by stop duration were alsomanually examined to identify any unusual situations or problems.

Because of the large size of the GPS data sets in this study, anautomatic and efficient O-D identification algorithm was required.The research team initially developed a dwell time threshold to iden-tify trucks’ origins and destinations. Determining the dwell time set-ting was crucial, since a threshold setting that was too long or too shortcould either miss or incorrectly segment trips. An appropriate dwelltime depends on traffic conditions in a given area (9). McCormackand Hallenbeck’s (3) truck GPS research completed previously inWashington State determined 3 min to be a reasonable dwell thresh-old. The 3-min period filtered out most trucks’ non-O-D stops for

EmailVendor C

CSV Files

Vendor B and Vendor C

FTP Server

Database ServerDRIVE Net Web Server

FTP Server

Vendor A Database

FTP

Vendor B Database

Vendor B Database

O-D Identification

GIS Processing

Freight Performance

Statistics

FIGURE 1 GPS data processing flowchart (CSV � comma-separated values).

traffic signals, since most signals have a shorter cycle length. Inaddition, traffic congestion in which a truck is stationary for morethan 3 min is unusual in the Puget Sound region. In some cases, a 3-min stop might include a driver’s break or fueling stop, but thesestops were considered part of a truck’s daily travel pattern.

However, observation of outliers and examination of travel betweenspecific TAZs revealed that a number of short stops of less than 3 minwere not recorded as destinations but should have been. Furtheranalysis indicated that a number of trucks traveled significant dis-tances to a different TAZ, made a short stop, and then continued toanother TAZ for a final destination stop. This problem was addressedby taking advantage of the fact that the largest database’s (Vendor A)GPS data feeds included engine park and stop information. A stopwas therefore defined as either a truck with its engine off or in parkstatus with the engine on. In this case, any parked stop when theengine remained on could also be considered an origin or destina-tion. The algorithm was consequently revised to include all of these“parked” stops. Furthermore, the revised O-D identification algo-rithm included programming that successfully resolved issues ofsignal loss, signal jiggle, and abnormal trips.

Signal Loss

GPS signals may be blocked when overhead obstructions such astall buildings prevent GPS devices from communicating effectivelywith the GPS satellites (4, 13). In this study, such blockage resultedin a few lost O-D data pairs. Fortunately, vendors of GPS devicescompensate for short-term signal loss by waiting until an adequatenumber of satellites are available before reporting a position. Duringthe short blockage period, it is reasonable to assume that a truck con-tinues to travel at a constant speed, since the GPS points recordedbefore and after the signal loss can be used to calculate the averagespeed. For this study, a threshold speed limit of 5 mph was selected.If the average calculated speed was below this threshold, the programdetermined that a trip had ended in the area of signal loss.

Signal Jiggle

In many cases, when a series of GPS points for a truck is mapped ina GIS system, the points may fluctuate around a position when atruck idles (known as jiggle), which creates a false report of move-ment (14). This is due to GPS signal inaccuracy. To address this issue,the distance between consecutive GPS points was used to refine theO-D algorithm. If the difference of longitude and latitude for twoconsecutive GPS points was less than 0.000051 degree (about 65 ftor 20 m), a flag was tagged to the record marking it as a possible des-tination point. When the average speed for this trip was calculated,the delay time caused by the fluctuations was subtracted to achievea more accurate result.

Abnormal Trips

GPS data often contain abnormal trips caused by vehicles leaving anarea or GPS multipath effects, in which the GPS signals are reflectedoff the surface of objects located between the GPS satellites and theGPS device (13, 14). In an effort to deal with these data problems,the O-D identification algorithm was also programmed to detectabnormal trips. Such trips were flagged. This secondary processdetected the following:

• Extremely short trips between GPS readings for an individualtruck,

Ma, McCormack, and Wang 95

• False trips in which the elapsed truck travel time was zero,• Trips with extremely high speed,• Trips in which a truck’s origin or destination was external to

the study area and could not be captured by the O-D algorithm, and• Trips in which a truck left the study area and then returned.

The flagged trips could be removed from any truck performanceprogram calculations that required travel time and travel speed.Figure 2 illustrates the O-D identification algorithm as implementedin Java.

The O-D algorithm was validated by using 1 month of VendorA’s data. This data set contained 3 million GPS records and filledabout 1 GB of file space. The O-D algorithm ran on an Intel PentiumXeron processor and required about 1 h to complete; it resulted in 358,692 O-D trips, with 6,443 abnormal trips flagged during thesecond-round trip processing. To validate the accuracy of the fun-damental assumptions behind the O-D algorithm, several truck IDswere randomly selected from the database, and their entire dailytrips were geocoded onto a road network in GIS software. Eachtruck’s trip was followed from stop to stop along the road network,and stops were explored by using Google Earth, Google Maps, andaerial photographs. This process examined the O-D locations detectedby the algorithm to determine whether the trucks’ estimated travelcorresponded to locations with truck terminals, warehouses, or otherreasonable freight delivery and pickup locations. In this test, the O-Dalgorithm was successful.

TRIP TYPE CLASSIFICATION

Different types of trucks have different trip characteristics. This dis-tinction is useful for targeting performance measures. Distance sta-tistics calculated from the roadway network of the Puget SoundRegional Council’s (PSRC’s) travel demand model were used todistinguish among different truck trip types. The PSRC data providetrucks’ optimal travel time and distance between zones, and an impor-tant assumption is that most truckers travel an economically effi-cient (optimal) route between an origin and a destination. On thebasis of this assumption, the average travel distance and travel timefor all GPS trucks between each pair of zones were compared withPSRC’s travel distance for the same pair of zones.

This process indicated that a few trips had notably longer GPS dis-tances than the PSRC network distances. These truck trips remainedinternal to the study area, did not include any stops longer than 3 min,and did not contain instances in which the truck was placed into a“parked” status. Investigation indicated that a few GPS trucks madedeliveries without the need to park. One example was a trash con-tainer company that delivered large empty containers to a con-struction site. Because the containers were tilted off the back of thetrucks, the driver never needed to stop or even get out of the vehicleto make a delivery, and the GPS never recorded a parked or stoppedstatus.

This comparison between the average trip travel distance and PSRCnetwork distance indicated that the truck trips could be classified intoseveral general categories:

• Access trips: Trucks had clearly defined origins and destina-tions, and each stop could be defined by a park event or a stop longerthan 3 min. This was the most common trip type (53% of all trips).

• Local trips: If the average trip travel distance was less than 0.5 mi, a trip was identified as a local trip. These were trips duringwhich the driver dropped off packages without stopping longer than

3 min. These trucks, typically small package delivery trucks, wereinvolved in about 38% of all trips.

• Loop trips: If the average trip travel distance was at least twotimes longer than the PSRC network distance, a trip was identified asa loop trip. These trucks completed their business without the needto stop or place the truck into park. Only a few trucks fit this category(9%), and these were typically garbage or construction trucks.

96 Transportation Research Record 2246

DEVELOPING TRUCK PERFORMANCE MEASURES

The GPS truck data were collected to quantify truck travel todevelop performance measures. Assigning the truck trip O-D datato TAZs allows the quantification of regional travel between thezones, and this can provide an effective platform for evaluating

Connect to database

Record j moving?

Y

Flag origin

Get the first GPSrecord j=0

j=j+1N

j=j+1

Record j is the lastrecord?

N

Y

Done

Moving?

Error trip?

External trip?

N

N

Abnormaltrip flag

Y

Y

Engine status is off?

Y

N

External trip?

Error trip?

N

Abnormaltrip flag

Y

Y

GPS signalloss?

Calculate travel timeand travel speed

Flag destination

N

Calculate the travelspeed between record

j-1 and record j+1

Speed<=5mph

Y

Y

N

Trip type/identification(loop, local, or access trip)

FIGURE 2 O-D identification algorithm flowchart.

regional truck travel. The advantage of examining zone-to-zonetravel with GPS data is that this process can be used to monitor net-work performance between economically important areas even whentruck drivers choose multiple connecting routes. Another benefit ofthe zone-level analysis is that the most economically importantzones with the most truck trips should also be represented by thelargest numbers of O-D pairs.

A number of quantitative performance measures were calculatedfor zone-to-zone travel. Because the GPS data provided, at a basiclevel, travel distance and speed for the probe trucks, all the measureswere derived from these numbers and were often compared withideal or free-flow travel conditions. The literature survey completedas part of this research, as well as studies by others (7, 15), suggesteda number of performance metrics that can be applied to the GPS datato analyze truck performance between zones.

Free-Flow Speed and Nominal Travel Time

Free-flow speed and nominal travel time performance measures aredefined as the truck travel speed and time under uncongested traf-fic conditions. In this research, the calculation of free-flow truckspeed was derived from the PSRC truck freight analysis model. Thefree-flow travel time excluded intrazonal travel to the origin anddestination TAZs, so free-flow travel time was calculated on thebasis of centroid-to-centroid (zone center to zone center) traveltime during the night (from 11:00 p.m. to 5:00 a.m.), when road-ways were likely to be uncongested (16). The free-flow travel speedcan be derived by dividing the centroid-to-centroid distance by thefree-flow travel time.

Average Travel Time

Average travel time (ATT) represents the averaged trucks’ traveltime (arithmetic mean) between one origin and destination pair.The ATT depends on trucks’ route choices and the traffic condi-tions. For a single trip, a travel time performance measure was cal-culated by taking the difference between the origin time stamp andthe destination time stamp recorded by the GPS device and com-paring that with an average of all trips with the same origin anddestination.

Average Travel Speed

For each trip between one O-D pair, trucks normally do not travelthe same distance; therefore, a large variance in travel time may beobserved. To provide a more accurate and descriptive measure,average travel speed is used. This measure was calculated as thearithmetic mean of all trip speeds. Each trip speed was calculatedby dividing the distance by the travel time between each O-D pair.The distance between each O-D pair was precisely recorded by theGPS device.

Average Travel Distance

Average travel distance is the arithmetic mean of travel distances forall the trips. For most trips, travel distance can be read directly fromthe odometer recorded by the GPS device mounted in each truck.

Ma, McCormack, and Wang 97

Variability of Travel Time and Variability of Travel Speed

Variability of travel time and variability of travel speed determinethe reliability of the freight movement for all trips between eachzonal O-D pair. They are the ratios of the standard deviation oftravel time (travel speed) to the average travel time (travel speed)aggregated over a sample of road sections.

xth Percentile Travel Time

For this research, 95th percentile travel time was adopted.

Travel Time Index

The travel time index is the ratio of the average travel time to thetravel time under free-flow conditions.

Buffer Time Index

The buffer time index is the ratio of the 95th percentile travel timeto the average travel time. This index indicates how much additionaltime is needed to ensure an on-time arrival with 95% probability.

Planning Time Index

The planning time index is the ratio of the 95th percentile travel timeto the nominal travel time (corresponding to free-flow speeds),which can be thought of as the 95th percentile travel time index. Forexample, a planning time index of 1.6 means that for 95% of all tripsto arrive on time, truckers should allow a travel time that is 1.6 timeslonger than the nominal travel time.

Minimum GPS Sample Size

To estimate link travel time or speed, the minimum required GPStruck sample size had to be determined. This information was used todevelop statistical significance performance measures. With the GPSprobe data, the travel time between TAZs fluctuated more than didthe travel speed because of the variability in TAZ area size. In com-parison with travel time, link speed was independent of link length andcould be measured easily and objectively. Cheu et al. (17) and Chenand Chien (18) investigated probe GPS vehicle population and sam-ple size for speed estimation and provided an equation to calculatethe sample size as follows:

where

α = significance level,tα/2,n−1 = t-value from two-tailed t-distribution with n − 1 degrees

of freedom for a confidence level of 1 − α,x– = mean travel speed,�r = user-selected allowable relative error in the estimate of

the mean speed, ands = sample speed standard deviation.

nt s

xn

r

=⎛⎝⎜

⎞⎠⎟

−α / , ( )2 1

2

1�

However, this is not a closed-form equation, and an iterativeprocedure has to be applied because the t-statistic is dependent onsample size.

Nezamuddin et al. (19) and Li et al. (20) demonstrated in their sam-ple size studies that if a large sample is available, the z-statistic can beused instead of the t-statistic, which requires a one-step calculation,and the equation can be written as follows:

Here, zα/2 is the z-statistic for a given confidence level 1 − α, whichdoes not rely on sample size. However, note that Equation 1 remainsthe most reliable way of calculating sample size and is preferredwhenever a reliable estimate of standard deviation can be obtained.

To simplify the calculation, Equation 2 was used to estimate thesample size because of the large number of GPS trucks in the database.One caution in applying this sample size estimation equation is thatboth equations are based on the assumption that the speed of vehicles(or travel time) follows a normal distribution. Chen and Chien (18)found that other factors can affect the distribution, including road-way geometrics and traffic volumes on the link. For example, incongested conditions, the travel speed may not follow a normal dis-tribution, and the above estimation equations may not be applicable.However, the above equations are widely used and are expected toprovide reliable sample size estimates (21).

ONLINE TRUCK PERFORMANCE MEASURESSYSTEM IMPLEMENTATION

Once a series of performance metrics had been developed, an onlinesystem was designed to make the measures readily usable by stafffrom the Washington State Department of Transportation and otherpublic agencies. A web-based freight module was developed on thebasis of an existing data interoperation and analysis systems plat-form known as Digital Roadway Interactive Visualization and Eval-uation Network (DRIVE Net). DRIVE Net is a community-basedprogram developed by the Smart Transportation Application andResearch Laboratory at the University of Washington (22).

Figure 3 shows the online truck freight performance measures sys-tem flowchart. The GPS data server stored the raw data received fromthe GPS vendors. After the O-D identification program processed thedata, they were automatically imported into the online database. Theprogram detected each trip end, calculated the travel time and speed,and completed data quality checks. Zonal freight travel time, speed,and distance data used by the PSRC travel demand model were col-lected in the PSRC database module, which was used as the road net-work reference for developing truck performance measures. ThePSRC mapping data were also stored in the TAZ GIS database mod-ule for visualization in Google Maps. On the server side, the truckperformance measure module calculated statistics by using data fromthe truck GPS and PSRC databases. The calculated performance met-rics were transferred to the client side through a Google Web Toolkitmodule, which is a development tool for building and optimizingcomplex browser-based applications (23). The inputs received fromusers were sent to the JavaScript Interpreter to display in GoogleMaps or were further processed and calculated on the server side.

The resulting online system allows users to calculate performancemeasures easily between zones. By specifying the origin and desti-

nz s

xr

= ⎛⎝⎜

⎞⎠⎟

α / ( )2

2

2�

98 Transportation Research Record 2246

nation of interest in the online systems, users are able to track thetrucks’ movements and generate performance metrics, such as num-bers of trips, average travel time and speed, and travel time index.An example of the output is shown in Figure 4. That screen captureshows truck performance measures calculated between a warehousedistrict and the Port of Seattle and displayed along the left side ofthe screen. When users click a marker on the map, a small windowpops up (also shown in Figure 4) with a brief description of therecorded GPS point, including device ID, spot speed, time stamp,moving status, mileage, and direction. Users can track any one truck’smovement by clicking a “show GPS route” button, which will dis-play the routing information for an individual truck. The sample sizewas based on a 95% confidence level and incorporated into theonline system. This allows users to determine the data reliability andaccuracy for a selected zone pair.

A zone-based network O-D matrix tool is also available in thissystem. By specifying the date, users can generate a variety of O-Dmatrices based on total trip numbers, average travel time, and aver-age travel distance, and they can output a comma-separated valuesfile designed for use by modelers.

CONCLUSIONS

Processing steps and algorithms to apply to data collected fromcommercial GPS devices installed in trucks were developed. Theseefforts established a foundation for a Washington State freight per-formance measures program. The main challenge with the commer-cially available GPS data was that they were collected for truck fleetmanagement business needs and were not designed for a public-sector performance measures program. As a result, the data setsincluded a large number of individual trucks but collected GPSlocation readings less frequently than has been done previously toevaluate some urban network travel times. This research demon-strated the feasibility of obtaining commercial GPS truck data fromrelatively low-cost sources and that these data can be processedto create usable truck performance measures designed for publicagencies.

Acquiring the data was problematic initially, but the researchersfound that purchasing GPS data and services directly from GPS fleetmanagement vendors worked well.

Processing the raw GPS data into a form that could be used fora performance monitoring program required a number of steps.Each vendor’s data set was large and organized differently, whichrequired the development of an automated process to acquire andformat the raw GPS data into a working database that would allowlocation of truck travel patterns on the roadway network and analysisof their travel times and speeds.

Because identifying the travel patterns of truck trips is an impor-tant aspect of monitoring truck performance, a rule-based algorithmthat automatically distinguished operational truck stops from originand destination stops was developed. The algorithm also flaggedabnormal trips so that they could be accounted for when perfor-mance measurement statistics were developed. This O-D algorithmwas validated and modified by using network maps and aerial photo-graphs in GIS software and by comparing their results with networktravel statistics used for a regional travel model.

The GPS data were then capable of being used to monitor trans-portation network performance between economically importantareas or zones. The advantage of GPS data is that they can capturethe routes used by trucks to travel between zones. A web-based

Client Computers (Browsers)

Web Server

Compiling Module

Data Storage Module

Data from ThreeVendors

GPSDatabase

Java Servlet and JSPCompiled by GWT

Javascript Interpreter/ AJAX

Freight Performance Measures Module

PerformanceMeasuresGenerator

Data Processing ModuleO-D Identification

User Input Module

Information Display Module

Google MapsAPI

Display

PSRCDatabase

TAZ GISDatabase

GIS Processi ng

FIGURE 4 Truck freight performance measures screen shot.

FIGURE 3 Online truck freight performance measures system flowchart (API � application programming interface;GWT � Google Web Toolkit; JSP � JavaServer Pages).

performance measures system was designed so that public agencystaff can use the GPS data to evaluate truck performance. The sys-tem is based in Google Maps and provides a user-friendly interfacefor visualizing the GPS data and generating zonal truck performancemetrics. In addition, this system can be used to track the trucks’movements and their routing choices.

Although the web-based truck performance measures platformhas resulted in a usable foundation for a statewide performancemonitoring program, improvements and system expansions are underway. The current data stream will be expanded to include new GPSvendors and statewide data. This will require the development of morerobust and versatile data processing and quality control steps. Withthese additional data, segment-based truck performance measuresmay be possible, which will require new processing steps.

Because of privacy concerns, some aspects of the web-based truckperformance program will not be made available to end users. A ver-sion that outputs aggregated data will be designed for agency staffand public users. Further research will be completed to investigatethe utility of this system to transportation professionals.

ACKNOWLEDGMENTS

The authors acknowledge support and funding from the Washing-ton State Department of Transportation, Transportation Northwestat the University of Washington, and the Washington TruckingAssociations.

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The Freight Transportation Data Committee peer-reviewed this paper.