The field of vehicle routing and urban transportation is witnessing significant advancements with the integration of machine learning and artificial intelligence. Researchers are exploring innovative approaches to improve the efficiency and effectiveness of vehicle routing problems (VRPs) and urban transportation systems. One notable direction is the development of lifelong learning frameworks that enable neural solvers to adapt to diverse contexts and problem sizes. Another area of focus is the application of large language models (LLMs) and graph neural networks (GNNs) to tackle complex transportation challenges. These advancements have the potential to revolutionize the way we approach urban transportation and logistics. Noteworthy papers in this area include:

• Lifelong Learner, which presents a novel lifelong learning framework for solving VRPs in distinct contexts.
• Edge-Selector Model, which proposes a hybrid machine learning and metaheuristic mechanism for solving VRPs.
• Entropy-Constrained Strategy Optimization, which introduces a hierarchical multi-agent framework for urban flood response.
• TransLLM, which develops a unified multi-task foundation framework for urban transportation via learnable prompting.