The traffic engineering community currently faces the advent of a new generation of Intelligent Transportation Systems (ITS), known as cooperative systems. More specifically, the recent developments of connected and autonomous vehicles, i.e. cooperative vehicles, are expected to cause a societal shift, changing the way people commute on a daily basis and relate to transport in general. The research presented in this dissertation is motivated by the need for proper understanding of the possible inputs of cooperative vehicles in a traffic stream. Beyond legal aspects regarding the introduction of such vehicles and considerations on standardization and harmonization of the communication norms, the research focuses on the use of communication for highway traffic flow homogenization. In particular, the selected approach for the introduction of cooperation inherits from the theory of traffic flow and the recent developments of microscopic traffic models. Cooperation can first be introduced as a form of multi-anticipation, which can either come from drivers' behaviors or from communication. A mathematical framework for investigating the impact of perturbations into a steady-state traffic is proposed for the class of time continuous car-following models. Linear stability analyses are refined for forward and backward multi-anticipation, exploring the underlying importance of considering upstream information. The linear stability analyses for all wavelengths can be deepened by the mean of the graphical root locus analysis, which enables comparisons and design of strategies of cooperation. The positive influence of bilateral cooperation and of added linear control terms are highlighted. Weakly non-linear analyses are also performed, and the equations of solitary waves appearing at the frontier of the instability domain are obtained. A simple condition over the partial derivatives of the dynamical system is found to determine the acceleration regime of the leading edge of the travelling wave. Following these analytical results, one aim is to simulate a realistic traffic thereby reproducing the driving behavior variability. A Next Generation Simulation trajectory dataset is used to calibrate three continuous car-following models. A methodology involving data filtering, robust calibration, parameters estimation and sampling of realistic parameters is detailed, and allows realistic traffic with stop-and-go waves appearances to be replicated. Based on these simulated trajectories, previous analytical results are confirmed, and the growing perturbations are removed for various coverage rates of cooperative vehicles and adequately tuned cooperative strategies. Finally the issue of information reliability is assessed for a mixed fleet of cooperative and non-cooperative vehicles. The modeling choice consists in building a three layers multi-agent framework that enables the following properties to be defined: the physical behavior of vehicles, the communication possibilities, and the trust each vehicle -or agent- has in another vehicle information or in itself. The investigation of trust and communication rules allow the model to deal with high rates of disturbed cooperative vehicles sensors and to learn in real time the quality of the sent and received information. It is demonstrated that appropriate communication and trust rules sensibly increase the robustness of the network to perturbations coming from exchanges of unreliable information.