A chance-constrained dial-a-ride problem with utility-maximising demand and multiple pricing structures
A chance-constrained dial-a-ride problem with utility-maximising demand and multiple pricing structures
复制标题
具有效用最大化需求和多种定价结构的机会约束的叫车问题
DOI：
10.1016/j.tre.2021.102601
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
D. Rey
中科院分区：
文献类型：
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作者：
Xiaotong Dong;Joseph Y. J. Chow;S. Waller;D. Rey
经典的拨号乘车问题（DARP）的目的是设计最小成本的路由，以适应一组用户的请求下的约束条件下，在运营规划水平，用户的偏好和收益管理往往被忽视。在本文中，我们提出了一种机制，接受/拒绝用户请求的需求响应运输（DRT）的背景下，替代交通方式的代表效用的基础上。我们考虑效用最大化的用户，并提出了一个混合整数规划制定的机会约束的DARP（CC-DARP），通过Logit模型捕捉用户的偏好。我们进一步引入基于类的用户组，并考虑各种DRT服务的定价结构。一个定制的本地搜索为基础的启发式和数学的开发，以解决建议的CC-DARP。我们报告的数字结果DARP基准的实例和现实的案例研究的基础上，纽约市黄色出租车行程数据。在105个基准测试实例上进行的计算实验，最多96个节点，使用建议的局部搜索启发式和数学方法，分别产生2.59%和0.17%的平均利润差距。现实的案例研究结果表明，分区票价结构是优化收入和乘客量方面的最佳策略。拟议的CC-DARP制定提供了一个新的决策支持工具，以告知DRT系统在战略规划层面上的收入和车队管理。
The classic Dial-A-Ride Problem (DARP) aims at designing the minimum-cost routing that accommodates a set of user requests under constraints at an operations planning level, where users’ preferences and revenue management are often overlooked. In this paper, we present a mechanism for accepting/rejecting user requests in a Demand Responsive Transportation (DRT) context based on the representative utilities of alternative transportation modes. We consider utility-maximising users and propose a mixed-integer programming formulation for a Chance Constrained DARP (CC-DARP), that captures users’ preferences via a Logit model. We further introduce class-based user groups and consider various pricing structures for DRT services. A customised local search based heuristic and a matheuristic are developed to solve the proposed CC-DARP. We report numerical results for both DARP benchmarking instances and a realistic case study based on New York City yellow taxi trip data. Computational experiments performed on 105 benchmarking instances with up to 96 nodes yield average profit gaps of 2.59% and 0.17% using the proposed local search heuristic and matheuristic, respectively. The results obtained on the realistic case study reveal that a zonal fare structure is the best strategy in terms of optimising revenue and ridership. The proposed CC-DARP formulation provides a new decision-support tool to inform on revenue and fleet management for DRT systems on a strategic planning level.