As an important component of transportation system, public transportation accounts for a considerable proportion in the whole traffic flow. The public transportation vehicles can be categorized into two categories as follows: one with determinate trajectories and schedules such as bus, tramway, and light rail; the other with flexible and variable running paths, such as taxis. In this paper, we firstly present a destination-gathering-based driving path prediction method for taxis, which can make taxis’ driving paths prescient in the initial stage of carrying passengers every time. Compared with ordinary vehicles, public transportation vehicles have such features as long time running on roads and no privacy-protection need, and thus their trajectories can be opened. Through utilizing the features above, we propose a novel public-transportation-assisted data delivery scheme (PTDD) used to improve the performance of data delivery of Vehicular Delay Tolerant Networks (VDTNs). Simulation results based on a real map demonstrate the effectiveness of the proposed scheme. 1. Introduction With the rapid development of wireless communication technology, new types of wireless networks and applications are appearing constantly. In this context, vehicular networks have gradually become an important research field in wireless communication and received broad attention from both industry and academy [1, 2]. As vehicle nodes’ high speed mobility and uneven distribution, vehicular networks have dynamic and changing network topology, which make it difficult to maintain persistent connection among vehicle nodes. To improve data delivery performance, vehicular networks widely adopt Delay Tolerant Networking (DTN) technology, and thus Vehicular Delay Tolerant Networks (VDTNs) emerges. VDTNs have evolved from DTNs and are formed by cars and any supporting fixed nodes. As an important component of Intelligent Transportation Systems (ITS) [3], VDTNs promise a wide range of valuable applications including real time traffic estimation for trip planning, mobile access to Internet, and in-time dissemination of emergency information such as accidents and pavement collapses. To realize the applications above, one of key research topics is to design effective and efficient data delivery schemes. Therefore, many schemes have been presented to solve the problem in recent years. Among the existing schemes, some works mainly take advantage of geographic position information, such as GPSR [4] and CAR [5]. The performances of these protocols mainly depend on the network connectivity, and
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