  We discuss how to efficiently forward data in vehicular networks. Existing
solutions do not make full use of trajectory planning of nearby vehicles, or
social attributes. The development of onboard navigation system provides
drivers some traveling route information. The main novelty of our approach is
to envision sharing partial traveling information to the encountered vehicles
for better service. Our data forwarding algorithm utilizes this lightweight
information under the delusive paths privacy preservation together with the
social community structure in vehicular networks. We assume that data
transmission is carried by vehicles and road side units (RSUs), while cellular
network manages and coordinates relevant global information. The approximate
destination set is the set of RSUs that are often passed by the destination
vehicle. RSU importance is raised by summing encounter ratios of RSUs in the
same connected component. We first define a concept of space-time
approachability which is derived from shared partial traveling route and
encounter information. It describes the capability of a vehicle to advance
messages toward destination. Then, we design a novel data forwarding algorithm,
called approachability based algorithm, which combines the space-time
approachability with the social community attribute in vehicular networks. We
evaluate our approachability based algorithm on data sets from San Francisco
Cabspotting and Shanghai Taxi Movement. Results show that the partially shared
traveling information plays a positive role in data forwarding in vehicular
networks. Approachability based data forwarding algorithm achieves a better
performance than existing social based algorithms in vehicular networks.
