ACTIVE TRAFFIC MANAGEMENT IN V2X HIGHWAY ENVIRONMENT

Active traffic management systems have emerged over the past decades as a leading paradigm in the field of transportation research and development. The growing need for modernizing traffic management and infrastructure is driven by technological advancements and the increasing push for more integrated collaboration between stakeholders. Additionally, there is an urgent need to address traffic congestion, which has been identified as a significant issue due to its negative impact on economic progress, increased emissions, and elevated road safety risks. Historically, various strategies have been adopted to address traffic challenges, but the rapid technological advancements across the transportation sector necessitate a timely update of these strategies to maximize the benefits of new technological resources. This thesis aims to develop a control system for variable speed limits (VSL), exploring the broad range of technologies surrounding active traffic management strategies. The thesis ultimately proposes an effective solution and outlines the integration process of these technologies to support the development of the final product. The proposed solution strategically utilizes traffic simulation technologies and integrates available and customized resources. Microsimulation models were employed to act as a surrogate for real-world scenarios, with a critical scenario implemented in microsimulation software to analyze the effects of traffic congestion. The chosen reference scenario simulates the impact of peak hour traffic at a bottleneck caused by lane narrowing. After examining traffic behavior in this critical scenario, macroscopic traffic simulation tools were used to develop a model capable of providing reliable estimates of traffic evolution in response to variations in inflow. At the core of the final solution is a variable speed limit controller based on Non-Linear Programming (NLP) optimization techniques. The controller is designed to optimize the macroscopic traffic model by determining the optimal speed limits to regulate traffic flow. By using NLP optimization, the controller minimizes traffic congestion by adjusting speed limits dynamically in response to real-time traffic conditions. Once developed, the VSL controller was integrated into the microsimulation software to evaluate its effectiveness in a dynamic environment. The results demonstrate that the controller performs efficiently, leading to a reduction in the time vehicles spend at low speeds, thereby significantly decreasing emissions. While further refinement of the system is possible, the results show that the NLP-based controller has a notable positive impact on traffic management, offering a promising approach to reducing congestion and improving the overall efficiency of traffic networks.