采用人机协作决策方法调度具有灵活发车时间的定制公交车

IF 6.3 1区 工程技术 Q1 ECONOMICS
Tao Liu , Hailin You , Konstantinos Gkiotsalitis , Oded Cats
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引用次数: 0

摘要

公共交通机构需要利用新兴技术,在从叫车服务到共享微型交通等颠覆性交通服务日益增多的交通环境中保持竞争力。定制公交(CB)是一种创新型公交系统,它利用数字出行平台提供先进、个性化和灵活的需求响应型公交服务。规划和运营定制公交系统的挑战之一是在满足乘客个性化出行需求的同时,高效、实用地调度定制公交车辆。以往的研究假设 CB 乘客首选的上车或下车时间是在预先确定的硬时间窗口内,而这个时间窗口是固定不变的。然而,最近的一些研究表明,引入软弹性时间窗可以进一步降低运营成本。考虑到软弹性时间窗口,本研究首先提出了一种基于近邻的乘客到车辆分配算法,将 CB 乘客分配到车辆行程,并生成所需的车辆服务行程。然后,提出了一个新颖的双目标整数编程模型,以优化 CB 运营成本(以车队规模衡量)和服务水平(以乘客出发时间偏差惩罚成本衡量)。通过使用商业优化求解器和基于赤字函数的图形化车辆调度技术,对模型进行了重构,以使双目标模型可以求解。开发了一种新颖的两阶段人机协同优化方法,利用机器智能和人类智能协同解决问题,以生成更实用的帕累托最优 CB 调度结果。实际 CB 系统的计算结果证明了所提出的优化模型和求解方法的有效性和优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Human-Machine collaborative decision-making approach to scheduling customized buses with flexible departure times

Public transport agencies need to leverage on emerging technologies to remain competitive in a mobility landscape that is increasingly subject to disruptive mobility services ranging from ride-hailing to shared micro-mobility. Customized bus (CB) is an innovative transit system that provides advanced, personalized, and flexible demand-responsive transit service by using digital travel platforms. One of the challenging tasks in planning and operating a CB system is to efficiently and practically schedule a set of CB vehicles while meeting passengers’ personalized travel demand. Previous studies assume that CB passengers’ preferred pickup or delivery time is within a pre-defined hard time window, which is fixed and cannot change. However, some recent studies show that introducing soft flexible time windows can further reduce operational costs. Considering soft flexible time windows, this study first proposes a nearest neighbour-based passenger-to-vehicle assignment algorithm to assign CB passengers to vehicle trips and generate the required vehicle service trips. Then, a novel bi-objective integer programming model is proposed to optimize CB operation cost (measured by fleet size) and level of service (measured by passenger departure time deviation penalty cost). Model reformulations are conducted to make the bi-objective model solvable by using commercial optimization solvers, together with a deficit function-based graphical vehicle scheduling technique. A novel two-stage human–machine collaborative optimization methodology, which makes use of both machine intelligence and human intelligence to collaboratively solve the problem, is developed to generate more practical Pareto-optimal CB scheduling results. Computation results of a real-world CB system demonstrate the effectiveness and advantages of the proposed optimization model and solution methodology.

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来源期刊
CiteScore
13.20
自引率
7.80%
发文量
257
审稿时长
9.8 months
期刊介绍: Transportation Research: Part A contains papers of general interest in all passenger and freight transportation modes: policy analysis, formulation and evaluation; planning; interaction with the political, socioeconomic and physical environment; design, management and evaluation of transportation systems. Topics are approached from any discipline or perspective: economics, engineering, sociology, psychology, etc. Case studies, survey and expository papers are included, as are articles which contribute to unification of the field, or to an understanding of the comparative aspects of different systems. Papers which assess the scope for technological innovation within a social or political framework are also published. The journal is international, and places equal emphasis on the problems of industrialized and non-industrialized regions. Part A''s aims and scope are complementary to Transportation Research Part B: Methodological, Part C: Emerging Technologies and Part D: Transport and Environment. Part E: Logistics and Transportation Review. Part F: Traffic Psychology and Behaviour. The complete set forms the most cohesive and comprehensive reference of current research in transportation science.
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