Due to increasing energy consumption and its associated costs, all industries try to reduce energy consumption and increase the efficiency. One of the main industries in this field is the traortation industry. Therefore, the subjects that aimed to reduce fuel consumption in the traortation industry and on the other hand lead to a reduction in air pollution, are the challenging subjects in the research. Hybrid electric vehicles are an important solution to reduce fuel consumption and emissions. The performance of the power management strategy in a hybrid electric vehicle depends on powertrain structure (e.g., parallel or series), the degree of hybridization (or the relative capacities of internal combustion engine and the energy storage system) and the amount of available preview information of the ahead path. The focus of this dissertation is to design an appropriate control strategy aimed to reduce fuel consumption in rail and road traortation. A new and effective method in this field is “look-ahead” control approach. The main logic of this approach is to use ahead path information (such as road gradients, curves of the road, traffic information, weather information, etc.) in the control decisions. In the look-ahead control, a part of the ahead road is considered as the look-ahead window and the ahead path information will be the average information of the points within this window. Based on this calculated information, the desired speed will be modified which is the input command for cruise control system. Travel time increment is considered as a constraint in the problem. In this thesis, the look-ahead algorithm is upgraded so that the speed limits of the path are considered other than the gradient data. Fine tuning of the look-ahead controller parameters requires to check the profiles of ahead road slope and speed limit. The working conditions of the look-ahead controller can be justify; TEXT-INDENT: 18pt; MARGIN: 0cm 0cm 0pt; unicode-bidi: embed; DIRECTION: ltr" On the other hand, the concept of hybrid vehicle is extended to any conventional vehicle that uses the look-ahead control approach. In the proposed concept, a kinetic energy storage (KES) has been considered for each vehicle. The kinetic energy storage has no physical existence but also exist in the background of any vehicle movement. The identification and use of the KES depends to control system ability. The most important feature of the KES is the efficiency of 100% and no investment cost. In the first half of this thesis, the implementation of the look-ahead controller in the rail traortation is followed. Considering the weakness of the existing models for simulation of diesel electric train and their inability to calculate fuel consumption directly, a new model is proposed. The main components of the locomotive are considered in this model including diesel generator and the electric motors and also the energy flow between them. In this model, the diesel engine parameters are tuned based on the experimental tests conducted in Raja Company. Therefore, the amount of fuel consumption can be calculated more precisely. The fuel consumption in this model is consistent with the experimental fuel consumption and the locomotive’s catalog information. The simulation results of the diesel-electric train motion using the look-ahead control is presented that shows the reduction in fuel consumption and on the other hand travel time increment (within permissible limits). The effectiveness of look-ahead control is compared with the dynamic programming algorithm. The series structure for hybrid diesel-electric powertrain is designed (by adding a Li-Ion battery pack) and the look-ahead control approach has been implemented. In the second half of this thesis, the proposed upgraded look-ahead control is implemented for the conventional and hybrid intercity buses as a fuzzy algorithm with the ability of fuzzy gain scheduling based on road pattern recognition. The simulation results indicate a significant reduction in fuel consumption. Keywords: Diesel-electric hybrid train, Hybrid bus, Fuel consumption, Look-ahead control, Fuzzy control, Genetic algorithm.