Connected Vehicular Transportation: Data Analytics and Traffic-Dependent Networking

Abstract

With onboard operating systems becoming increasingly common in vehicles, the realtime broadband infotainment and intelligent transportation system (ITS) service applications in fast-moving vehicles become ever demanding, and they are 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 in real-time pervasive information collection and big data processing to allow quick decision making and feedback to fast-moving vehicles, which imposes significant challenges on the development of an efficient vehicular communication platform. In this article, we present TrasoNET, an integrated network framework that provides real-time intelligent transportation services to connected vehicles by exploring the data analytics and networking techniques. TrasoNET is built upon two key components. The first guides vehicles to the appropriate access networks by exploring the real-time status of local traffic, specific user preferences, service applications, and network conditions. The second mainly involves a distributed automatic access engine, which enables individual vehicles to make distributed access decisions based on recommendations, local observations, and historic information. We highlight the application of TrasoNET in a case study on real-time traffic sensing based on real traces of taxis.