Efficient active-passive vehicle coordination in multimodal transportation networks

This dataset accompanies a study submitted to "Transportation Research Part E: Logistics and Transportation Review", focusing on a modular, demand-responsive transport system. The system uses passive, modular containers, or “pods,” that are transported across multiple vehicle types like trucks, trains, and boats to fulfill door-to-door customer requests. The study's main objective is to improve customer satisfaction and reduce operator costs, thus providing a more flexible alternative to traditional public transportation systems. The unique challenge lies in synchronizing the movements of both the pods and the vehicles they rely on, which creates interdependencies between routes. To address this, a mixed-integer linear program (MILP) and an adaptive large neighborhood search (ALNS) heuristic were developed, alongside a clustering approach to accelerate solution times for real-world applications. This dataset validates the performance of these optimization methods, providing a practical framework for testing advanced multimodal routing solutions.

Since no standard benchmark data existed for this novel system, the dataset consists of five custom instance sets - A to E - with increasing levels of complexity and sizes, containing 30 instances each. The instances include randomly generated hubs in a two-dimensional space, with minimum pairwise distances maintained for spatial realism. The transportation network is constructed by assigning edges randomly within each modality (e.g., road, rail) while ensuring full connectivity. Additionally, each base and pod is assigned a capacity, and time windows for customer requests vary, with each subsequent set having a longer time window range. Travel times are computed as the Euclidean distance between hubs divided by a constant speed factor, with adjustments for loading and boarding times to reflect real-world operational delays.

本数据集配套于一篇投往《运输研究E辑：物流与交通评论》（Transportation Research Part E: Logistics and Transportation Review）的研究，聚焦于模块化需求响应型运输系统。该系统采用被动式模块化集装箱（pods），可通过卡车、火车、船舶等多种交通工具完成点对点的客户配送需求。本研究的核心目标为提升客户满意度、降低运营方成本，以此为传统公共交通系统提供更具灵活性的替代方案。其独特挑战在于同步舱格及其依托运载工具的运行轨迹，由此引发路径间的相互依存关系。为此，本研究开发了混合整数线性规划（mixed-integer linear program, MILP）模型与自适应大邻域搜索（adaptive large neighborhood search, ALNS）启发式算法，并配套聚类方法以加速实际应用场景下的求解速度。本数据集用于验证上述优化方法的性能，为测试先进多式联运路径规划方案提供了实用框架。

由于该新型系统尚无标准基准数据集，本数据集共包含5套自定义实例集（A至E），实例复杂度与规模依次递增，每套包含30个实例。实例包含二维空间中随机生成的枢纽节点，为保证空间真实性，节点间两两距离均设置了最小阈值。运输网络通过在各运输模态（如公路、铁路）内随机生成边来构建，并确保网络完全连通。此外，每个枢纽站点与舱格均被赋予容量属性，客户需求的时间窗设置各不相同，且后续每套实例集的时间窗范围均有所扩大。出行时间通过枢纽节点间的欧氏距离除以恒定速度系数计算得出，并通过调整装卸与登乘时间来反映实际运营中的延误情况。