The booking service, a key feature of autonomous public transport vehicle (APTV) systems, has been designed to introduce a new, real-time, on-demand, and reliable element to service improvement, similar to ride-hailing. However, the current APTV system has yet to fully realize the potential of a smart public transport service in optimizing the balance between supply and demand. This study proposes a real-time, multi-objective programming model that aims to minimize three key factors: passenger waiting times, timetable deviations, and fleet size. Recognized as an NP-hard problem, the model is linearized to reduce computational complexity, with real-time demands tracked through a rolling horizon method. A predict-then-optimize approach is introduced to enable timely responses to new bookings. A customized two-phase algorithm incorporating three enhancements − valid cuts, Monte Carlo simulation, and neighborhood and local search − significantly improves solution efficiency. A case study in Auckland, New Zealand, evaluates the proposed approach. The findings reveal significant improvements in booking service performance, with two scenarios achieving a 35 % and 27 % reduction in passenger waiting time and a 13 % and 12 % decrease in fleet size compared to the current conventional bus line. These results were attained with minimal deviations from the original schedule, validating the effectiveness of the developed methodology.

中文翻译：

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自动驾驶公共交通车辆的实时调度优化，以满足预订需求

预订服务是自动驾驶公共交通车辆 （APTV） 系统的一个关键功能，旨在引入一种新的、实时的、按需的和可靠的服务改进元素，类似于叫车。然而，目前的 APTV 系统尚未完全实现智能公共交通服务在优化供需平衡方面的潜力。本研究提出了一种实时、多目标的编程模型，旨在最大限度地减少三个关键因素：乘客等待时间、时刻表偏差和车队规模。该模型被认为是 NP 困难问题，它被线性化以降低计算复杂性，并通过滚动水平法跟踪实时需求。引入了一种预测后优化的方法，以便及时响应新预订。自定义的两阶段算法包含三个增强功能（有效割平面、蒙特卡洛模拟以及邻域和局部搜索），可显著提高求解效率。新西兰奥克兰的一个案例研究评估了所提出的方法。研究结果显示，预订服务性能有了显著改善，与目前的传统公交线路相比，两种情况分别实现了乘客等待时间减少 35% 和 27%，车队规模减少了 13% 和 12%。这些结果是在与原始计划的偏差最小的情况下获得的，验证了所开发方法的有效性。