Within the scope of this study, original methods were developed to determine driving cycles for local intracity buses and were used in the subsequent hybridization analyses. In this regard, power-train and vehicle dynamics models of aforementioned vehicles were established in the virtual environment. The algorithms of the energy management systems of the electric/hybrid vehicles have been investigated from the literature and analyses have been carried out to determine the benefits of advanced methods such as the Equivalent Consumption Minimization Strategy (ECMS) compared to simpler rule based methods. In particular, an original method based on adaptive- ECMS, which consists in scheduling the equivalency factor according to the real-time driving cycle has been developed. Within the scope of the method, the parameters of the energy management system were adapted to the traffic density information provided by the vehicle tracking system. In other words, in the virtual environment, the speed profile for the road segment where the ego vehicle is about to travel is assumed to be known, using the vehicle tracking system speed data of vehicles that travelled on the same road segment in the recent past. Thereby, the calibration of the hybrid energy system algorithms is made possible by using driving cycles calculated for the road segment under interest. By using this method, it was found that fuel consumption savings up to 40% fuel consumption were possible for intracity driving cycles.