As demands for convenient and comfortable mobility grow, transportation network companies (TNCs) begin to diversify the ride-hailing services they offer. Modes that are offered now include ride-pooling (RP), non-ride-pooling (NP), and a third “bundled” option, which combines RP and NP. This emerging bundled option allows riders to be served via either RP or NP mode, whichever becomes available first. This paper examines the added complexity that a ride-hailing service platform faces when it introduces a third bundled option. Incorporating the predicted pooling information in the near future, a ride-matching problem is dedicated to matching vehicles with riders under various scenarios over a number of matching iterations. We formulate the multi-period ride-matching problem using an integer linear programming model with multiple objectives and to make dispatching decisions based on certain matching criteria. The complexity of the problem requires resolution via a two-stage Kuhn-Munkres (2-KM) algorithm, whose robustness is verified by computational tests. Some interesting insights are obtained: (1) how the bundled option impacts system performance metrics depends on whether the supply is sufficient or not; (2) there is an optimal value of the criterion of the maximum pickup time that maximizes the ride-pooling time savings.

随着对便利和舒适出行需求的增长，交通网络公司 (TNC) 开始使他们提供的乘车服务多样化。现在提供的模式包括拼车 (RP)、非拼车 (NP) 和第三个“捆绑”选项，它结合了 RP 和 NP。这种新兴的捆绑选项允许通过 RP 或 NP 模式（以先可用者为准）为乘客提供服务。本文研究了网约车服务平台在引入第三种捆绑选项时所面临的额外复杂性。结合在不久的将来预测的池化信息，一个骑行匹配问题致力于通过多次匹配迭代在各种场景下匹配车辆与乘客。我们使用具有多个目标的整数线性规划模型来制定多周期乘车匹配问题，并根据某些匹配标准做出调度决策。问题的复杂性需要通过两阶段 Kuhn-Munkres (2-KM) 算法解决，其稳健性已通过计算测试验证。获得了一些有趣的见解：（1）捆绑选项如何影响系统性能指标取决于供应是否充足；(2) 最大上车时间的准则存在一个最优值，可以最大限度地节省拼车时间。获得了一些有趣的见解：（1）捆绑选项如何影响系统性能指标取决于供应是否充足；(2) 最大上车时间的准则存在一个最优值，可以最大限度地节省拼车时间。获得了一些有趣的见解：（1）捆绑选项如何影响系统性能指标取决于供应是否充足；(2) 最大上车时间的准则存在一个最优值，可以最大限度地节省拼车时间。