Travel Time estimation is largely caused by the stochastic process of arrivals and departures of vehicles and its reliability measurements considering important issues for improving operational efficiency and safety for traffic road networks. The exploration of travel time variability and spatio-temporal analysis of urban streets using the Global Positioning System (GPS) concluded that the mixed land uses and travel congestions caused higher travel times and delays. The accessibility indices were increased by increasing access points and decreasing traffic volumes. The Geographic Information System (GIS) networks can produce a model that overcomes some restrictions of accessibility indices. Different prediction models were developed to capture the main parameters related to travel time. It concluded that delay at signalized intersections in terms of stopping delay was the major parameter affecting the total travel time and total delay time of major urban streets. Travel time estimation algorithms based on speed data loop detectors induced insignificant differences when the study route was a relatively short and slow transition from free state to congestion state. Travel time results are affected by the location of sensors and their sparseness, hence estimation errors increase as detector spacing increases.