The recent developments in the research area of urban transportation and logistics have shown a significant shift towards more scalable, user-centric, and environmentally sustainable solutions. Researchers are increasingly focusing on multi-modal optimization frameworks that integrate various transportation modes, such as e-mobility hubs and micromobility options, to enhance user experience and operational efficiency. These frameworks often employ advanced algorithms, including mixed-integer linear programming and modified graph-based techniques, to handle real-world constraints and large-scale scenarios effectively. Additionally, there is a growing emphasis on robustness and sensitivity analysis in logistics networks, particularly within the Physical Internet paradigm, to ensure resilience against uncertainties and optimize performance across different transportation modes. These advancements collectively aim to provide more flexible, efficient, and sustainable transportation solutions that can adapt to the dynamic needs of urban environments.

Noteworthy papers include one that introduces a multi-modal optimization framework for shared e-mobility, leveraging MILP and a modified Dijkstra's algorithm for scalability and real-time applications. Another notable contribution is a robustness evaluation of a Physical Internet-based intermodal logistic network, employing a Global Sensitivity Analysis to assess system performance under various uncertainties.