Literature review revealed application of various techniques for efficient use of existing resources in road transport sector vehicles, operators and related facilities. This issue assumes bigger dimensions in situations where there are multiple routes and the demand in the routes is highly fluctuating over the day. The application of the existing techniques as reported in literature addresses above issues to a considerable extent. However the main draw back in existing techniques is lack of proper uninterrupted information about vehicles and demand available at a central place for allocation of vehicles in different roads and huge computational times required for processing. Cloud computing is a recently developed processing tool that is used in effective utilization of resources in transport sector under dynamic resource allocation. Since the demand fluctuates at different times in different routes, mobile cloud computing techniques are being used to address the issues related to effective resource allocation. This paper attempts at, making a detailed study of application of mobile cloud computing techniques in transport sector for dynamic resource allocation and to identify the limitations there in, with a view to address the limitations. Creating identical clouds at various strategic points and mobile feeding of information to each cloud, creation of a central processing place called as traffic manager, releasing vehicle / driver allocation orders of the traffic manager etc, are some of the essential features of the proposed mobile cloud computing. Simulation studies are made by considering case studies and the results are compared with real time values. The proposed mobile cloud computing makes use of JRE processing. The required algorithms along with coding, flow diagram and the output are presented. It has been observed that the proposed mobile cloud computing processing offers uninterrupted service with minimum preprocessing, processing and post processing time. Further the proposed method can be used for handling large number of vehicles / routes with case and hence is more efficient than the existing methods of dynamic resource
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