Estimating Path Choice Models through Floating Car Data

Offloading Floating Car Data through V2V Communication

During a century of existence, vehicles transformed from mechanical master pieces into consumer products with more and more computing power and sensing capabilities. Current vehicles include tens or even hundreds of Electronic Control Units (ECUs) that gather important amounts of information from the embedded sensors, and use this information to control different parts of the vehicles or to pro...

Supporting augmented floating car data through smartphone-based crowd-sensing

Intelligent extended floating car data collection

The elaboration of data collected by vehicles moving on road network is relevant for traffic management and for private service providers, which can bundle updated traffic information with navigation services. Floating data, in its extended acceptation, contains not only time and location provided by a positioning system, but also information coming from various vehicle sensors. In this paper w...

Traffic State Estimation Using Floating Car Data

There is an increasing availability of floating car data both historic, in the form of trajectory datasets and real-time, in the form of continuous data streams. This paves the way for several advanced traffic management services such as current traffic state estimation, congestion and incident detection and prediction of the short-term evolution of traffic flow. In this paper, we present an an...

Travel Time Estimation Using Floating Car Data

This project explores the use of machine learning techniques to accurately predict travel times in city streets and highways using floating car data (location information of user vehicles on a road network). The aim of this report is twofold, first we present a general architecture of solving this problem, then present and evaluate few techniques on real floating car data gathered over a month ...