The field of transportation research is moving towards increased safety, efficiency, and sustainability. Recent studies have focused on leveraging crowd-sourced data, advanced machine learning models, and high-resolution satellite imagery to improve our understanding of transportation phenomena. Notable advancements include the development of novel intention-sharing mechanisms for cooperative electrically power-assisted cycles, scalable dynamic origin-destination demand estimation frameworks, and comprehensive machine learning frameworks for micromobility demand prediction. These innovations have the potential to significantly enhance the safety and efficiency of transportation systems, particularly for vulnerable road users. Noteworthy papers in this area include:

• Evaluating Redundancy Mitigation in Vulnerable Road User Awareness Messages for Bicycles, which proposes an adapted redundancy mitigation mechanism to balance channel load reduction and VRU awareness.
• V2X Intention Sharing for Cooperative Electrically Power-Assisted Cycles, which introduces a novel intention-sharing mechanism for EPACs, enhancing the ETSI VRU Awareness Message protocol.
• A Comprehensive Machine Learning Framework for Micromobility Demand Prediction, which integrates spatial, temporal, and network dependencies for improved micromobility demand forecasting.