The rapid growth of micromobility is gradually transforming urban travel patterns. Electric bicycles and scooters are now effective alternatives to traditional modes of transportation, helping to reduce traffic congestion and pollutant emissions. However, integrating these vulnerable users into cooperative intelligent transportation systems (C-ITS) poses new challenges in terms of connectivity, service continuity, mobility management, and communications security. Vehicle-to-Everything (V2X) communications are a key component of C-ITS architectures. They rely primarily on ITS-GS technologies, based on the IEEE 802.11p standard, and on next-generation cellular networks such as SG NR-V2X. Although these technologies have complementary characteristics, their effective deployment in micromobility environments remains a major challenge due to the dynamic mobility of users, the power constraints of onboard equipment, and the variability of radio conditions. This thesis focuses on evaluating and improving the performance of ITS communication technologies designed for micromobility. Initially, several representative use cases were studied in an ITS-GS-based VANET environment, including adaptive speed control, dynamic geofencing, and energy optimization of communication infrastructure. Intelligent management mechanisms for Roadside Units (RSUs), including the use of mobile RSUs, have been proposed to improve network coverage while reducing energy consumption. The results show a reduction in RSU energy consumption of up to 44.5%, while ensuring the continuity of V2X communications. Subsequently, a hybrid ITS-GS/SG architecture was developed to take advantage of the complementarity between low-latency local communications and wide-area cellular networks. An intelligent selection strategy a radio interface selection method, based on deep reinforcement learning and the Double Deep Q-Network (DDQN) algorithm, was proposed to dynamically optimize the choice of communication interface. This approach improves communication reliability, packet delivery rate, throughput, and energy efficiency in dynamic urban environments. Evaluations showed a packet delivery rate (PDR) of 99% and a 15.6% reduction in energy consumption.
Finally, this thesis addresses the issue of vertical handover in hybrid ITS-GS/SG architectures. An innovative approach to handover management, based on offline reinforcement learning and the Conservative Q-Learning (CQL) algorithm, is introduced. The proposed solution, called RIMA-HO, leverages historical data to reduce unnecessary transitions between radio technologies while ensuring service continuity and the quality of V2X communications. The RIMA-HO solution reduces unnecessary handovers and improves service continuity, with an average energy reduction of 16.3%, reaching 41.8% in the most favorable scenarios. The proposed contributions are validated through simulations conducted in a co-simulation environment integrating SUMO and OMNeT++, as well as using real-world mobility and communication traces. The results demonstrate significant improvements in connectivity, energy efficiency, communication stability, and mobility management. This thesis thus contributes to the development of intelligent solutions that promote the secure and efficient integration of micromobility into future cooperative transportation systems.