Traffic parameters estimation from the analysis of connected car data

Traffic flow theory is grounded on the theoretical analysis of time-space vehicle trajec- tories to describe the movement of individual vehicles and the relative movement of the neighbour vehicles. Nevertheless, this theoretical point of view has rarely been considered in the empirical analysis of traffic data, since the available information was typically the aggregated spot measurements of flows, speeds and occupancies provided by inductive loop detectors. This limitation begins to be overcome with the advances in ICT applications. In particular, the vehicles known as probe vehicles are able to acquire wide-ranging and spa- tiotemporally detailed information about their positions via GPS, but they can not directly supply volume-related variables such as flow and density. Cooperative cars with commu- nication capabilities with other vehicles and/or the infrastructure represent a step forward because they also allow to track the vehicles in their surrounding. In this project we de- scribe and analyze the experiment conducted in Barcelona with a small fleet of cooperative vehicles equipped with a set of radars that identify the vehicles within their detection zone. The gathered data were used to build and calibrate the emulation of the functions of such vehicles in a microscopic simulation model, which allowed to emulate fleet data on a large scale that goes far beyond what the reduced fleet of vehicles could capture. Finally, this data leads us to explore and evaluate methodological approaches for the estimation of the fundamental traffic variables, namely the flow, density and average speed, based on Edie’s definitions