Sustainable mobility development requires the optimization of existing transportation infrastructure. In particular, ubiquitous traffic information systems have the potential to optimize the use of the transportation network. The system must provide accurate and reliable traffic information in real-time to optimize mobility choices. Successful implementations are also valuable tools for traffic management agencies. The thesis studies how the emergence of Internet services and location based services on mobile devices enable the development of novel Intelligent Transportation Systems which estimate and broadcast traffic conditions in arterial networks. Sparsely sampled probe data is the main source of arterial traffic data with the prospect of broad coverage in the near future. The small number of vehicles that report their position at a given time and the low sampling frequency require specific models and algorithms to extract valuable information from the available data. On the one hand, the variability of traffic conditions in urban networks, caused mainly by the presence of traffic lights, motivates a statistical approach of arterial traffic dynamics. On the other hand, an accurate modeling of the physics of arterial traffic from hydrodynamic theory (formation and dissolution of horizontal queues) ensures the physical validity of the model. The thesis proposes to integrate the dynamical model of arterial traffic in a statistical framework to integrate noisy measurements from probe vehicle data and estimate physical parameters, which characterize the traffic dynamics. In particular, the thesis derives and estimates the probability distributions of vehicle location and of travel time between arbitrary locations. The thesis leverages the data and the infrastructure developed by the Mobile Millennium project at the University of California, Berkeley to validate the models and algorithms. The results underline the importance to design statistical models for sparsely sampled probe vehicle data in order to develop the next generation of operation large-scale traffic information systems
