Title: Connected Vehicular Transportation: Data Analytics and Traffic-dependent Networking,
Abstract: With onboard operating systems becoming increasingly common in vehicles, the
real-time broadband infotainment and Intelligent Transportation System (ITS)
service applications in fast-motion vehicles become ever demanding, which are
highly expected to significantly improve the efficiency and safety of our daily
on-road lives. The emerging ITS and vehicular applications, e.g., trip
planning, however, require substantial efforts on the real-time pervasive
information collection and big data processing so as to provide quick decision
making and feedbacks to the fast moving vehicles, which thus impose the
significant challenges on the development of an efficient vehicular
communication platform. In this article, we present TrasoNET, an integrated
network framework to provide realtime intelligent transportation services to
connected vehicles by exploring the data analytics and networking techniques.
TrasoNET is built upon two key components. The first one guides vehicles to the
appropriate access networks by exploring the information of realtime traffic
status, specific user preferences, service applications and network conditions.
The second component mainly involves a distributed automatic access engine,
which enables individual vehicles to make distributed access decisions based on
access recommender, local observation and historic information. We showcase the
application of TrasoNET in a case study on real-time traffic sensing based on
real traces of taxis.