Graduation Year


Document Type




Degree Granting Department

Civil and Environmental Engineering

Major Professor

Abdul R. Pinjari, Ph.D.

Committee Member

Pei-Sung Lin, Ph.D.

Committee Member

Jason J. Bittner, M.P.A.


ATRI, Conversion Algorithm, Cube Software, Florida Statewide Model (FLSWM), ODME


An accelerated growth in the volume of freight shipped on Florida's highways has led to a significant increase in truck traffic, influencing traffic operations, safety, and the state of repair of highway infrastructure. Traffic congestion in turn has impeded the speed and reliability of freight movement on the highway system. Appropriate planning and decision making processes are necessary to address these issues. However, a main challenge in establishing such processes is the lack of adequate data on statewide freight movements. As traditional data sources on freight movement are either inadequate or no longer available, new sources of data must be investigated.

A recently available source of data on nationwide freight flows is based on a joint venture by the American Transportation Research Institute (ATRI) and the Federal Highway Administration (FHWA) to develop and test a national system for monitoring freight performance measures on key corridors in the nation. This data is obtained from trucking companies who use GPS-based technologies to remotely monitor their trucks. The database contains GPS traces of a large number of trucks as they traveled through the national highway system. This provides unprecedented amounts of data on freight truck movements throughout the nation (and in Florida). Such truck GPS data can potentially be used to support planning, operation, and management processes associated with freight movements. Further, the data can be put to better use when used in conjunction with other freight data obtained from other sources.

The overarching goal of this thesis is to investigate the use of large streams of truck-GPS data from the American Transportation Research Institute (ATRI) for the estimation of statewide freight truck flows in Florida. To this end, first, an algorithm was devised to convert large streams of raw GPS data into a database of truck trips. The algorithm was applied to four months of ATRI's truck-GPS data comprising over 145 Million GPS records to derive a database of more than 1.2 million truck trips starting and/or ending in Florida. This database was used to analyze truck travel characteristics and origin-destination truck flow patterns for different geographical regions in Florida. The resulting database was used in conjunction with the GPS data to analyze the extent to which ATRI's data represents observed truck traffic flows in the state. It was found that at an aggregate level, almost 10% of heavy truck traffic flows in Florida is captured in the ATRI data.

Finally, the database of truck trips derived from ATRI's truck-GPS data was combined with observed heavy truck traffic volumes at different locations within and outside Florida to derive an origin-destination (OD) table of truck flows within, into, and out of the state. To this end, first, the truck trip database developed from ATRI's truck-GPS data was converted into a seed OD table at the TAZ-level spatial resolution used in FLSWM. Subsequently, a mathematical procedure called origin-destination matrix estimation (ODME) method was employed to combine the OD flow table generated from the ATRI data with observed truck traffic volume information at different locations within and outside Florida. The OD table of truck flows estimated from this procedure can be used for a variety of purposes, including the calibration and validation of the heavy truck modeling components of FLSWM.