This paper investigates the real-time customized bus (CB) route optimization problem, which aims to maximize the service rate for clients and profits for operators. The on-road bus has a flexible route, which can be updated based on the real-time data and route optimization solutions. A two-phase framework is established. In phase 1, the vehicle-related data including existing route and schedule, client-related data involving pick-up/drop-off location, and time windows are collected once receiving a new CB request. The second phase optimizes the bus route by establishing three nonlinear programming models under the given data from phase 1. A concept of profit difference is introduced to decide the served demand. To improve computation efficiency, a real-time search algorithm is proposed that the neighboring buses are tested one by one. Finally, a numerical study based on Sioux Falls network reveals the effectiveness of the proposed methodology. The results indicate that the real-time route optimization can be achieved within the computation time of 0.17–0.38 seconds.

中文翻译：

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使用实时数据进行定制的公交路线优化

本文研究了实时定制公交（CB）路线优化问题，旨在最大程度地为客户提供服务率并为运营商带来利润。公路公交车具有灵活的路线，可以根据实时数据和路线优化解决方案进行更新。建立了一个两阶段的框架。在阶段1中，一旦接收到新的CB请求，便会收集与车辆相关的数据，包括现有路线和时间表，与客户相关的数据（包括上落地点）以及时间窗口。第二阶段通过在阶段1的给定数据下建立三个非线性规划模型来优化公交路线。引入了利润差异的概念来确定服务需求。为了提高计算效率，提出了一种实时搜索算法，对相邻的总线进行逐一测试。最后，基于苏福尔斯网络的数值研究表明了该方法的有效性。结果表明，可以在0.17–0.38秒的计算时间内实现实时路由优化。