With the rapid advancement of intelligent transportation systems technologies in recent years, an unprecedented opportunity exists for developing countries to develop their transportation system and supporting infrastructure by taking full advantage of the advanced data acquisition and communication technologies available today. This research explored a potential way to assess the level of traffic over the urban network by using travel-time information obtained from the Global Positioning System. The significance of this application is understood: not having to install system detectors midblock on city streets could mean realizing tremendous cost savings and avoiding the disruption to the operation of the complex traffic system that would result from installation and maintenance activities. The feasibility of calibrating a relationship connecting travel time and traffic volume under various roadway and traffic conditions was studied using Monte Carlo simulation and data regression. Reliability analysis was also performed to assess the stability of the volume estimates in anticipation of changes in the model parameters and roadway capacity. Results from the model implementation and reliability analysis have shown that the proposed approach is capable of reliably describing the level of traffic on the roadway network with an error smaller than 5%.