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In this paper, we present and analyze an algorithm for mapping discrete GPS data gathered from vehicles to a continuous flow of data to determine the time to traverse a road section. Vehicle-tracking devices are installed in 80 probe vehicles in the Anchorage area, and a specific roadway section was chosen as a test section. Drivers for this study drove from before the start of the test roadway section past the end of the test roadway section, measuring the time to travel from the start to the finish of the test roadway section. The vehicle-tracking devices report speed and location every 10 seconds. From this data, we calculated the amount of time to traverse the test roadway section using our proportional model and compared it to the actual amount of time it took to traverse the test roadway section. We performed the analysis assuming the vehicle-tracking devices were reporting location every 10 seconds, 20 seconds, 30 seconds, 40 seconds, 50 seconds, and 60 seconds. With an average actual time to traverse the test roadway section of 2 minutes 28 seconds, the error rate based on the proportional model was between 1.8%-9.2% (2.7-13.1 seconds), based on how frequently the vehicle was reporting its location. Merely taking the average speed on the edge from the vehicle reporting its speed and location during those same durations had an error rate between 14.2%-25.8% (24.7-41.1 seconds). Our results show that the proportional model has a small error rate (1.8% with 10 second reporting time) and can accurately represent the time to traverse roadway sections based on discrete readings from a small number of probe vehicles.