Recent years there has been increasing concern about the rider demand responsive systems and the vehicular ad hoc networks. On one hand, centralised taxi platforms such as Uber and Didi Taxi are popular and changing our daily life; on the other hand, vehicles are equipped with more and more sensors and are capable to calculate, store, and communicate with other vehicles or road side units, forming vehicleto-vehicle or vehicle-to-infrastructure communications. However, little effort has been devoted to integrating these two fields. In this paper, we propose a distributed public vehicle (PV) system that integrates the rider demand responsive system and ad hoc vehicular technologies, where the concept of fog computing and vehicular sensing are adopted for the system design. The challenges lie in that the PV scheduling problem itself is NP-hard, and careful design of scheduling and cooperation schemes among nodes are needed as they are ubiquitously connected at the edge of networks. The proposed PV system adopts a heuristic request insertion algorithm and a cooperative strategy among vehicle nodes, fog nodes, and the cloud to dispatch requests and to schedule routes for PVs. Experimental studies on real-world data sets demonstrate that the proposed scheme achieves higher service ratio of requests and better efficiency than other transit methods. Furthermore, the distributed vehicular sensing is demonstrated to be capable of collecting feasible metadata for scheduling applications. To the best of our knowledge, this paper is the first report on the integration of fog nodes and vehicular sensing for the rider request responsive scheduling systems.
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