The dial-a-ride problem (DARP) involves designing vehicle routes to fulfill the door-to-door transportation requests of users where the goal is to minimize costs while satisfying transportation requests. In this paper, we introduce the rich heterogeneous DARP, which extends the generalized heterogeneous DARP to consider a fleet of buses and taxis, multiple depots, time windows at pickup and delivery locations, maximum ride and waiting times, and the possibility of an accompanying person. Our approach is based on a real service in Barcelona, and we also consider the variation in trip duration based on the time of day and day of the week. A predictive model is developed using machine learning techniques to estimate trip durations accurately. We apply our proposal to the daily door-to-door transportation of people with reduced mobility in Barcelona and demonstrate its superiority in terms of costs and quality of service by using the Gurobi optimizer. Additionally, we provide an analysis of the consequences of varying certain features on the costs and quality of service.

中文翻译：

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具有行程时间预测的丰富异构叫车问题

叫车问题（DARP）涉及设计车辆路线来满足用户的门到门运输请求，其目标是在满足运输请求的同时最小化成本。在本文中，我们介绍了丰富的异构 DARP，它扩展了广义异构 DARP，以考虑公共汽车和出租车车队、多个停车场、取货和送货地点的时间窗口、最大乘坐和等待时间以及陪同人员的可能性。我们的方法基于巴塞罗那的真实服务，我们还根据一天中的时间和一周中的日期考虑行程持续时间的变化。使用机器学习技术开发预测模型来准确估计行程持续时间。我们将我们的方案应用于巴塞罗那行动不便人士的日常门到门交通，并通过使用 Gurobi 优化器展示其在成本和服务质量方面的优势。此外，我们还分析了某些功能的变化对服务成本和质量的影响。