Travel to intercity transportation hubs, such as railway stations and airports, can be the most troublesome and inefficient part of the entire air/railway travel journey, as travelers often carry large luggage and have stringent arrival time requirements. Taking public transportation, such as metro and bus services, is inconvenient to carry luggage and less reliable in arrival time while taking taxi services could be less economical. As a result, providing reliable and convenient yet economical on-demand first-mile services for travelers to intercity transportation hubs is essential. This paper proposes a ridesharing approach for the first-mile transport system for travelers heading towards the intercity transportation hub and develops a mixed-integer linear programming (MILP) model with the objective of minimizing the total operating costs for ridesharing service operators. The MILP model considers (1) large luggage that may occupy seats when the car trunk is not large enough to place them; (2) passengers’ requirements on arrival time and ride time; and (3) travel time uncertainty ensuring that riders’ arrival time and ride time can be satisfied. A tailored adaptive large neighborhood search algorithm with an acceleration strategy is developed for obtaining robust near-optimal solutions within a reasonable time. To assess the solution quality, the MILP model is reformulated as a set-partitioning model, and the column generation algorithm is leveraged to determine a tight lower bound; a greedy algorithm is introduced to obtain an upper bound. Computational experiments on Shanghai South Railway Station demonstrate that ridesharing is an effective strategy for reducing overall travel costs while meeting the first-mile travel demand. In addition, it is essential to consider luggage and travel time uncertainty for determining ridesharing schemes.

前往火车站和机场等城际交通枢纽可能是整个航空/铁路旅行旅程中最麻烦和效率最低的部分，因为旅客通常携带大件行李并且对到达时间有严格的要求。乘坐公共交通工具，如地铁和公共汽车服务，携带行李不方便，到达时间不太可靠，而乘坐出租车服务可能不太经济。因此，为前往城际交通枢纽的旅客提供可靠、方便且经济的按需第一英里服务至关重要。本文针对前往城际交通枢纽的旅客的第一英里交通系统提出了一种拼车方法，并开发了一个混合整数线性规划 (MILP) 模型，旨在最大限度地降低拼车服务运营商的总运营成本。MILP模型考虑（1）当汽车后备箱不够大放置时可能占据座位的大件行李；（二）旅客对到达时间和乘车时间的要求；(3) 行程时间的不确定性，确保骑手的到达时间和乘车时间可以得到满足。开发了一种具有加速策略的定制自适应大邻域搜索算法，用于在合理的时间内获得稳健的近似最优解。为了评估解决方案的质量，MILP 模型被重新表述为一个集合划分模型，并且利用列生成算法来确定严格的下限；引入贪心算法以获得上限。上海南站的计算实验表明，拼车是在满足第一英里出行需求的同时降低整体出行成本的有效策略。此外，在确定拼车方案时，必须考虑行李和旅行时间的不确定性。