Transportation Research Part B-Methodological最新文献_第8页

A modified late arrival penalized user equilibrium model and robustness in data perturbation 一个改进的迟到惩罚用户均衡模型和数据扰动的鲁棒性

IF 5.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part B-Methodological

Pub Date : 2025-06-25 DOI: 10.1016/j.trb.2025.103251

Manlan Li , Huifu Xu

In a seminal paper (Watling, 2006), Watling proposes a stochastic variational inequality approach to model traffic flow equilibrium over a network where the transportation time is random and a path is selected by to transport if the user’s expected utility of the transportation of the path is maximized over their paths. A key feature of Watling’s model is that the user’s utility function incorporates a penalty term for lateness and the resulting equilibrium is known as Late Arrival Penalized User Equilibrium (LAPUE). In this paper, we revisit the LAPUE model with a different focus: we begin by adopting a new penalty function which gives a smooth transition of the boundary between lateness and no lateness and demonstrate the LAPUE model based on the new penalty function has a unique equilibrium and is stable with respect to (w.r.t.) small perturbation of probability distribution under moderate conditions. We then move on to discuss statistical robustness of the modified LAPUE (MLAPUE) model by considering the case that the data to be used for fitting the density function may be perturbed in practice or there is a discrepancy between the probability distribution of the underlying uncertainty constructed with empirical data and the true probability distribution in future, we investigate how the data perturbation may affect the equilibrium. We undertake the analysis from two perspectives: (a) a few data are perturbed by outliers and (b) all data are potentially perturbed. In case (a), we use the well-known influence function to quantify the sensitivity of the equilibrium by the outliers and in case (b) we examine the difference between empirical distributions of the equilibrium based on perturbed data and the equilibrium based on unperturbed data. Moreover, we extend the discussions in case (b) to the LAPUE model and the other UE models. To examine the performance of the MLAPUE model and our theoretical analysis of statistical robustness, we carry out some numerical experiments, the preliminary results confirm the statistical robustness as desired.

在一篇开创性的论文（Watling, 2006）中，Watling提出了一种随机变分不等式方法来模拟网络上的交通流平衡，其中运输时间是随机的，如果用户对路径运输的期望效用在其路径上最大化，则选择一条路径进行运输。Watling模型的一个关键特征是用户的效用函数包含了迟到的惩罚项，由此产生的均衡被称为迟到惩罚用户均衡（LAPUE）。本文从不同的角度重新审视了LAPUE模型：我们首先采用了一个新的惩罚函数，它给出了延迟和不延迟之间边界的平滑过渡，并证明了基于新惩罚函数的LAPUE模型在中等条件下对概率分布的（w.r.t）小扰动具有唯一的平衡和稳定。然后，考虑到用于拟合密度函数的数据在实际中可能受到扰动，或者由经验数据构建的潜在不确定性的概率分布与真实概率分布之间存在差异，我们将继续讨论改进的LAPUE （MLAPUE）模型的统计鲁棒性，我们将研究数据扰动如何影响平衡。我们从两个角度进行分析：(a)少数数据受到异常值的扰动；(b)所有数据都可能受到扰动。在情况(a)中，我们使用众所周知的影响函数通过异常值来量化平衡的敏感性，在情况(b)中，我们检查基于扰动数据的平衡的经验分布与基于未扰动数据的平衡之间的差异。此外，我们将案例(b)中的讨论扩展到LAPUE模型和其他UE模型。为了检验MLAPUE模型的性能和我们对统计稳健性的理论分析，我们进行了一些数值实验，初步结果证实了统计稳健性。

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引用次数: 0

A closed-form bounded route choice model accounting for heteroscedasticity, overlap, and choice set formation 考虑异方差、重叠和选择集形成的封闭式有界路径选择模型

IF 5.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part B-Methodological

Pub Date : 2025-06-24 DOI: 10.1016/j.trb.2025.103275

Laurent Cazor , Lawrence Christopher Duncan , David Paul Watling , Otto Anker Nielsen , Thomas Kjær Rasmussen

The Multinomial Logit (MNL) model is widely used in route choice modelling due to its simple closed-form choice probability function. However, MNL assumes that the error terms are independently and identically distributed with infinite support. As a result, it imposes homoscedasticity, meaning that long and short trips share the same error variance, disregards correlations between overlapping routes, and assigns non-zero choice probabilities to all available routes, regardless of their cost. This paper addresses these limitations by developing a closed-form route choice model. We introduce the Bounded q-Product Logit (BqPL) model, which incorporates heteroscedastic error terms with bounded support. The parameter

q

controls the rate at which error term variance increases with trip cost, and routes that violate cost bounds receive zero choice probabilities, implicitly defining the route choice set. Furthermore, we extend the BqPL model to account for correlations between overlapping routes by integrating path size correction terms within the choice probability function, resulting in the Bounded Path Size q-Product Logit (BPSqPL) model. We illustrate the properties of the BPSqPL model on small-scale networks, contrasting it with a range of existing choice models into which it can collapse. We then present a method to estimate the model parameters and standard errors, using bootstrapping. Finally, we estimate the model using a large-scale bicycle route choice case study, comparing its goodness-of-fit, interpretability, and forecasting ability with relevant collapsing models. We also test the impact of the choice set size on the estimated parameters. The results underscore the importance of addressing the three key limitations of the MNL model and demonstrate the effectiveness of the BPSqPL model in doing so.

多项Logit （Multinomial Logit， MNL）模型由于其简单的封闭式选择概率函数，在路径选择建模中得到了广泛的应用。然而，MNL假设误差项是独立的、同分布的，具有无限支持。结果，它施加了同方差性，这意味着长途和短途旅行共享相同的误差方差，忽略了重叠路线之间的相关性，并为所有可用路线分配非零选择概率，而不考虑其成本。本文通过开发一个封闭形式的路径选择模型来解决这些限制。引入有界q-积Logit （Bounded q-Product Logit, BqPL）模型，该模型包含有界支持的异方差误差项。参数q控制错误项方差随行程成本增加的速率，违反成本界限的路由获得零选择概率，隐式地定义了路由选择集。此外，我们扩展了BqPL模型，通过在选择概率函数中集成路径大小校正项来考虑重叠路径之间的相关性，从而得到有界路径大小q-乘积Logit （BPSqPL）模型。我们说明了BPSqPL模型在小规模网络上的特性，并将其与一系列现有的选择模型进行了对比。然后，我们提出了一种方法来估计模型参数和标准误差，使用自举。最后，我们用一个大规模的自行车路线选择案例来评估模型，比较其拟合优度、可解释性和预测能力与相关的崩溃模型。我们还测试了选择集大小对估计参数的影响。研究结果强调了解决MNL模型的三个关键局限性的重要性，并证明了BPSqPL模型在这方面的有效性。

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引用次数: 0

Order dispatching strategy and pricing scheme in ride-sourcing markets with consideration of service cancellation 考虑服务取消的约车市场订单调度策略及定价方案

IF 5.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part B-Methodological

Pub Date : 2025-06-24 DOI: 10.1016/j.trb.2025.103266

Jing-Peng Wang , Hai Wang , Peng Liu , Hai-Jun Huang

In a ride-sourcing system, dispatching order requests to available drivers entails a comprehensive consideration of factors such as pickup proximity, order rewards, driver rating, safety behavior, passenger preferences, real-time road conditions, and other relevant variables. Inefficient dispatch processes often result in service cancellation by either the customer or the driver. This paper represents a pioneering effort to examine order dispatching strategy and pricing scheme while taking service cancellation behaviors into account. By assuming the platform has limited knowledge of the valuation of service of each customer and the reservation earning rate of each driver, we develop a two-period model that captures the dynamic decision-making processes of multiple stakeholders (customers, drivers, and platform) and formulate the platform’s order-dispatching problem as a stochastic programming model. Within a greedy approximation framework, our analysis reveals the significant implications of pricing scheme for critical performance metrics while considering service cancellation. These include the matching probability (probability of customer-driver acceptance for platform’s match results), the platform’s rewards, and the effects on the platform’s order-dispatching decisions. Specifically, within the realm of linear pricing, the matching probability demonstrates a positive correlation with trip distance, and thereby establishes a consistent dispatching order compared with one that does not consider service cancellation. Conversely, with nonlinear pricing (whether sublinear or superlinear), extended trip distance is generally associated with a reduced matching probability when it exceeds a threshold; this results in prioritizing orders with intermediate trip distances in order-dispatching decisions. Moreover, numerical experiments support that an integration of sublinear, superlinear, and linear pricing is conducive to optimizing rewards across short-, intermediate, and long-distance trips. Finally, scenarios of unimodal distributions of customer’s valuation of service and driver’s reservation earning rate consistently yield the highest rewards, through sublinear, linear, and superlinear pricing schemes.

在叫车系统中，将订单请求发送给可用的司机需要综合考虑各种因素，如取车距离、订单奖励、司机评级、安全行为、乘客偏好、实时路况以及其他相关变量。效率低下的调度过程常常导致客户或司机取消服务。本文开创性地研究了考虑服务取消行为的订单调度策略和定价方案。通过假设平台对每个客户的服务估值和每个司机的预订收益率的了解有限，我们开发了一个两期模型，该模型捕捉了多个利益相关者（客户、司机和平台）的动态决策过程，并将平台的订单调度问题制定为随机规划模型。在贪婪近似框架内，我们的分析揭示了在考虑服务取消时，定价方案对关键性能指标的重要影响。其中包括匹配概率（客户驱动程序接受平台匹配结果的概率）、平台的奖励以及对平台订单调度决策的影响。具体而言，在线性定价领域内，匹配概率与行程距离呈正相关，从而与不考虑服务取消的调度顺序相比，建立了一致的调度顺序。相反，对于非线性定价（无论是亚线性还是超线性），当行程距离超过阈值时，行程距离的延长通常与匹配概率的降低有关；这导致在订单调度决策中优先考虑具有中间行程距离的订单。此外，数值实验支持亚线性、超线性和线性定价的整合有助于优化短途、中程和长途旅行的奖励。最后，通过次线性、线性和超线性定价方案，客户对服务的评价和司机预订收益率的单峰分布场景始终产生最高的奖励。

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引用次数: 0

Moving average vs. exponential smoothing cost-updating filters for day-to-day dynamic assignment: fixed-point stability and bifurcation theoretical analysis 每日动态分配的移动平均与指数平滑成本更新滤波器：定点稳定性和分岔理论分析

IF 5.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part B-Methodological

Pub Date : 2025-06-20 DOI: 10.1016/j.trb.2025.103253

G.E. CANTARELLA , C. FIORI , P. VELONÀ

Deterministic process (DP) models for day-to-day dynamic assignment can be cast in the general two-equation assignment modelling approach, including the following:

- the arc cost updating recursive equation in the case of day-to-day dynamic assignment; instances are exponential smoothing (ES) or moving average (MA) filters;

- the arc flow updating recursive equation in the case of day-to-day dynamic assignment; instances are ES filters.

Even though ES filters for cost updating may well approximate MA filters, somebody in the scientific community argue against the underlying hypothesis of infinite memory for ES filters with respect to MA ones; numerical results support significant differences for small memory depths, say 2 or 3 days.

The main original contribution of this study is a formal fixed-point stability and bifurcation analysis of MA-ES DP models with memory depth 2, and a comparison with ES-ES DP. At this aim the Omega method 2.0, suitable for carrying out general fixed-point stability and bifurcation analysis has been developed and discussed. Extremely long proofs have not been included for brevity. This study focused on methodological aspects; thus, numerical examples were not included.

日常动态分配的确定性过程（DP）模型可以采用一般的双方程分配建模方法，包括：-日常动态分配情况下的电弧成本更新递归方程；例如指数平滑（ES）或移动平均（MA）滤波器；-在日常动态分配的情况下，弧流更新递归方程；实例是ES过滤器。尽管用于成本更新的ES滤波器可能很接近MA滤波器，但科学界有人反对ES滤波器相对于MA滤波器具有无限记忆的基本假设；数值结果支持小内存深度（例如2或3天）的显著差异。本研究的主要原创性贡献是对记忆深度为2的MA-ES DP模型进行了形式不动点稳定性和分岔分析，并与ES-ES DP进行了比较。为此，提出并讨论了适用于一般不动点稳定性和分岔分析的Omega方法2.0。为简洁起见，没有包括非常长的证明。这项研究侧重于方法方面；因此，不包括数值例子。

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引用次数: 0

Market size and fare-free public transit in theory 市场规模和公共交通免费理论

IF 5.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part B-Methodological

Pub Date : 2025-06-17 DOI: 10.1016/j.trb.2025.103265

Lewis J. Lehe, Ayush Pandey

Studies of fare-free public transit claim it is most convenient in small communities, because their transit systems tend to have lower farebox recovery ratios and more available capacity. This paper offers a rationale for these tendencies by working through a static model of a bus route with boarding/alighting delays, crowding and elastic demand that scales with a “market size” parameter. Due to externalities passengers impose, ridership arises as an equilibrium outcome given a certain market size and the agency’s choice of fare and fleet size. When the fare and fleet size are chosen to satisfy the First- and Second-Order Conditions for maximizing social surplus, the farebox recovery ratio, number of passengers on each bus and the rate passengers board each bus are all smaller in smaller markets. An extension explores capacity choice.

对免费公共交通的研究表明，在小社区最方便，因为他们的交通系统往往有更低的车费回收率和更大的可用容量。本文通过一个公交车路线的静态模型，为这些趋势提供了一个基本原理，该模型包含上车/下车延误、拥挤和弹性需求，并随“市场规模”参数缩放。由于乘客施加的外部性，客流量作为给定一定市场规模和代理商选择的票价和机队规模的均衡结果产生。当选择满足社会剩余最大化的一阶和二阶条件的票价和车队规模时，在较小的市场中，车费箱回收率、每辆公交车上的乘客人数和每辆公交车上的乘客率都较小。扩展探索容量选择。

引用次数: 0

A systematic review of node models for macroscopic network loading of traffic flows 交通流宏观网络负荷节点模型的系统综述

IF 5.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part B-Methodological

Pub Date : 2025-06-12 DOI: 10.1016/j.trb.2025.103273

Xiaolin Gong, Mark P.H. Raadsen, Michiel C.J. Bliemer

Node models in macroscopic network loading procedures are used to distribute competing flows arising at motorway ramps, junctions, and intersections, influencing congestion and queuing delays. Despite decades of research on macroscopic node model development, a comprehensive literature review on their characteristics, categories, emerging trends, and further research opportunities does not yet exist. This study fills this gap by conducting a systematic literature review on the node models in macroscopic network loading frameworks. We identify representative characteristics of node models and then systematically classify and interpret those characteristics across existing node model studies present in the literature. We propose six general principles for node models and explore five extension categories characterising additional features. This paper makes two contributions to the field. Firstly, it provides a comprehensive classification of node model research, grounded in the proposed principles and extension categories. This classification is substantiated by relevant references and culminates in the development of a node model classification table. Secondly, it identifies future research directions and opportunities, providing guidance and insights for researchers and practitioners engaged in the study of macroscopic node models.

宏观网络加载过程中的节点模型用于分配高速公路匝道、路口和交叉路口产生的竞争流，影响拥堵和排队延迟。尽管对宏观节点模型发展的研究已经进行了数十年，但尚未对其特征、类别、新兴趋势和进一步研究机会进行全面的文献综述。本研究对宏观网络加载框架中的节点模型进行了系统的文献综述，填补了这一空白。我们确定节点模型的代表性特征，然后系统地分类和解释文献中现有节点模型研究中的这些特征。我们提出了节点模型的六个一般原则，并探索了描述附加特征的五个扩展类别。本文对这一领域有两个贡献。首先，基于提出的原则和可拓范畴，对节点模型研究进行了全面的分类。这种分类得到了相关文献的证实，最终形成了节点模型分类表。其次，确定未来的研究方向和机会，为从事宏观节点模型研究的研究者和实践者提供指导和见解。

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引用次数: 0

End-to-end logistics in metropolitan areas: A stochastic dynamic order-assignment and dispatching problem 都市圈端到端物流：一个随机动态订单分配与调度问题

IF 5.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part B-Methodological

Pub Date : 2025-06-06 DOI: 10.1016/j.trb.2025.103249

M. Arya Zamal , Albert H. Schrotenboer , Tom Van Woensel

The growth of e-commerce requires efficient integration of first-mile pickup, middle-mile consolidation, and last-mile delivery. These so-called integrated end-to-end logistics operations are particularly visible in metropolitan areas where fast delivery services are in high demand. Inspired by real-world practices at our industry partner, this paper introduces the Stochastic Dynamic Order-Assignment and Dispatching Problem (SDOA-DP). It concerns stochastic and dynamic pickup-and-delivery orders arising at an end-to-end logistics delivery platform, for which the company, as a decision maker, needs to determine in real-time how to assign orders to middle-mile linehaul schedules and when to dispatch first- and last-mile two-echelon vehicle routes. We model the SDOA-DP as a Markov Decision Process and propose a novel solution approach based on a parameterized Cost Function Approximation (CFA) for order assignment in the middle mile and a parameterized Adaptive Large Neighborhood Search (ALNS) for vehicle dispatch and two-echelon routing in the first and last-mile. The CFA balances the cost of using linehauls with the time slack available for first- and last-mile planning while ensuring time windows are met. The parameterization in the ALNS ensures that we balance routing cost and delivery speed by limiting the frequency and timing of dispatching vehicle routes. We learn the best value of the parameterization using Bayesian optimization. Computational experiments show that our approach yields a 22% on-average improvement compared to a baseline policy. If we learn a single best parameterization for various system settings, we observe almost as good cost savings, showing that our approach is robust and reliable for practitioners. Finally, we applied our method to a case study of our industry partner and showed that our method could potentially reduce daily costs by 30.5% across various operational contexts.

电子商务的发展需要第一英里提货、中间英里整合和最后一英里配送的高效整合。这些所谓的一体化端到端物流业务在快速配送服务需求旺盛的大都市地区尤为明显。受我们的行业合作伙伴的实际实践启发，本文介绍了随机动态顺序分配和调度问题（SDOA-DP）。它涉及端到端物流配送平台上产生的随机动态取货订单，作为决策者的公司需要实时确定如何将订单分配到中间一英里的线路运输计划中，以及何时调度第一英里和最后一英里的两级车辆路线。我们将SDOA-DP建模为一个马尔可夫决策过程，并提出了一种新的解决方法，该方法基于参数化成本函数近似（CFA）用于中间英里的订单分配和参数化自适应大邻域搜索（ALNS）用于车辆调度和第一英里和最后一英里的两级路由。CFA平衡了使用线路的成本和第一英里和最后一英里计划的空闲时间，同时确保满足时间窗口。ALNS中的参数化通过限制调度车辆路线的频率和时间，保证了调度成本和配送速度的平衡。我们用贝叶斯优化来学习参数化的最佳值。计算实验表明，与基线策略相比，我们的方法产生了平均22%的改进。如果我们为不同的系统设置学习一个单一的最佳参数化，我们观察到几乎同样好的成本节约，表明我们的方法对于从业者来说是健壮和可靠的。最后，我们将我们的方法应用到我们的行业合作伙伴的案例研究中，并表明我们的方法可以在各种操作环境中潜在地减少30.5%的日常成本。

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引用次数: 0

Rolling stock shunt operation planning in urban rail transit depots with maintenance consideration 考虑维修的城市轨道交通车辆段车辆分流作业规划

IF 5.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part B-Methodological

Pub Date : 2025-06-05 DOI: 10.1016/j.trb.2025.103252

Dian Wang , Ling Yao , Andrea D’Ariano , Shuguang Zhan , Lisha Wang

This study investigates the daily rolling stock shunt operation planning in a rail depot. Given the layout of a depot and the rolling stocks (that arrive at and depart from this depot within an operation day) with given maintenance schedule, the studied problem lies in determining: 1) the position where each rolling stock is parked, outside washed, and/or maintained, and 2) the conflict-free shunting plan of rolling stocks to move within a depot. We transform the track-circuits in the depot into different multi-layer directed graphs to illustrate the shunting processes of rolling stocks. By means of these graphs, we formulate the studied problem as a mixed integer linear programming model by considering a more general variant of four requirements that make sense in practice but are not or rarely considered in previous works and by presenting a flexible technique to model the track capacity, to reduce the additional shunting movements of rolling stocks. Besides, we design a two-stage decomposition manner to efficiently solve real-life problem instances, wherein the problem in each stage is addressed by a presented logic-based Benders decomposition algorithm enhanced by customized acceleration mechanisms. Finally, a set of realistic and real-life instances with different scales (derived from the largest depot of the Chongqing Rail Transit Line 3 in China) are investigated. Computational results demonstrate that our best algorithm solves a real-life instance to optimality in approximately 8 s that is considerably shorter than the time of rail staffs to solve this instance manually, thus our approach can provide strong automatical computer-aided decision supports. Our approach is also very efficient in optimizing another objective that is also widely used, and can provide management insights to rail staffs.

本文以某铁路车辆段为研究对象，探讨车辆段车辆每日分流作业计划。给定一个车辆段的布局和在给定维修计划下到达和离开该车辆段的车辆，所研究的问题在于确定：1)每辆车辆停放、外洗和/或维修的位置；2)车辆在车辆段内移动的无冲突调车计划。我们将车辆段的轨道电路转换成不同的多层有向图来说明车辆的调车过程。通过这些图，我们将所研究的问题表述为一个混合整数线性规划模型，通过考虑在实践中有意义但在以前的工作中没有或很少考虑的四个要求的更一般的变体，并通过提出一种灵活的技术来建模轨道容量，以减少车辆的额外调车运动。此外，我们设计了一种两阶段分解方法来有效地解决实际问题实例，其中每个阶段的问题都由基于逻辑的Benders分解算法来解决，并通过定制的加速机制来增强。最后，研究了一组不同规模的现实和现实案例（来自中国重庆轨道交通3号线最大的车辆段）。计算结果表明，我们的最佳算法在大约8秒内解决了一个实际实例的最优性，这比铁路工作人员手动解决该实例的时间要短得多，因此我们的方法可以提供强大的自动计算机辅助决策支持。我们的方法在优化另一个也被广泛使用的目标方面也非常有效，可以为铁路工作人员提供管理见解。

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引用次数: 0

Data-driven optimization for container ship bunkering management under fuel price uncertainty 燃油价格不确定条件下集装箱船加注管理数据驱动优化

IF 5.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part B-Methodological

Pub Date : 2025-06-04 DOI: 10.1016/j.trb.2025.103250

Xuecheng Tian , Shuaian Wang , Yan Liu , Ying Yang

Fuel prices are a crucial and volatile component of operational costs in maritime transportation. This paper optimizes container ship bunkering decisions under the uncertainty of multi-port fuel prices, using data-driven optimization frameworks that integrate machine learning and mathematical programming models. We address two primary challenges: (i) incorporating spatiotemporal correlations between multi-port fuel prices into predictive models, and (ii) determining the most effective data-driven modeling framework for this problem. To address the first challenge, we develop a two-channel long short-term memory model specifically designed to capture the spatiotemporal dependencies of multi-port fuel prices. For the second challenge, we construct two data-driven modeling frameworks for ship bunkering management: a two-stage contextual deterministic programming model with point predictions (TDP framework) and a multistage contextual stochastic programming model with distributional estimates (MSD framework). Through comprehensive computational experiments using both real-world and synthetic data, we obtain two crucial insights: (i) accounting for the spatiotemporal correlations among multi-port fuel prices significantly improves the accuracy of fuel price predictions; and (ii) the TDP framework is more suited to container shipping routes with fewer ports, while the MSD framework offers advantages in contexts with a higher number of ports.

燃料价格是海上运输业务成本中一个关键且不稳定的组成部分。本文利用数据驱动的优化框架，结合机器学习和数学规划模型，对多港燃油价格不确定性下的集装箱船加油决策进行了优化。我们解决了两个主要挑战：(i)将多港口燃料价格之间的时空相关性纳入预测模型，以及（ii）为该问题确定最有效的数据驱动建模框架。为了解决第一个挑战，我们开发了一个双通道长短期记忆模型，专门用于捕捉多港口燃料价格的时空依赖性。对于第二个挑战，我们构建了两个数据驱动的船舶加油管理建模框架：一个带点预测的两阶段上下文确定性规划模型（TDP框架）和一个带分布估计的多阶段上下文随机规划模型（MSD框架）。通过使用真实世界和合成数据的综合计算实验，我们获得了两个重要的见解：(i)考虑多港口燃料价格之间的时空相关性显著提高了燃料价格预测的准确性；以及（ii） TDP框架更适合港口较少的集装箱航线，而MSD框架在港口较多的情况下具有优势。

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引用次数: 0

The ridesharing routing problem with flexible pickup and drop-off points 具有灵活上下车点的拼车路线问题

IF 5.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part B-Methodological

Pub Date : 2025-05-25 DOI: 10.1016/j.trb.2025.103234

Zuhayer Mahtab , Shichun Hu , Maged Dessouky , Fernando Ordoñez

In major metropolitan areas, ride-sharing systems can help reduce traffic congestion and increase the transportation system’s efficiency. In this paper, we propose a Branch-and-Price based approach for solving the ride-share routing problem with flexible pickup and drop-off points. We assume a ride-sharing system where drivers have their own origins and destinations, where all the drivers’ and passengers’ information is known beforehand, and all the problem data information is static and deterministic. We assume that drivers can pick up or drop off passengers from or to flexible meeting points that are within a passenger’s walking time limit from their origin or destination and are determined on a continuous plane. We formulate a mixed integer nonlinear model for routing and selecting pickup and drop-off points. Our solution approach decomposes this problem in two: selecting pickup and drop-off points and a rideshare routing problem. We develop an efficient algorithm to select the best pickup and drop-off points and show computationally that it is more efficient at finding pickup and drop-off points than considering a fixed set of discrete meeting points. To evaluate the performance of our approach, we perform numerical experiments on a San Francisco Taxicab dataset. Results show that our approach is efficient, solving instances with up to 600 points within 31 CPU minutes. For these datasets, incorporating flexible pickup and drop-off points can reduce the total vehicle travel time of the rideshare system by 4% on average.

在大城市，拼车系统可以帮助减少交通拥堵，提高交通系统的效率。在本文中，我们提出了一种基于分支和价格的方法来解决具有灵活上下车点的拼车路线问题。我们假设在一个拼车系统中，司机有自己的出发地和目的地，所有司机和乘客的信息都是事先已知的，所有的问题数据信息都是静态的和确定性的。我们假设司机可以在灵活的集合点接送乘客，这些集合点在乘客从起点或目的地出发的步行时间限制内，并且确定在一个连续的平面上。我们建立了一个混合整数非线性模型，用于选择取货点和落货点。我们的解决方法将这个问题分解为两个：选择接送点和乘车路线问题。我们开发了一种有效的算法来选择最佳的接送点，并通过计算表明，它比考虑一组固定的离散会面点更有效地找到接送点。为了评估我们的方法的性能，我们在旧金山出租车数据集上进行了数值实验。结果表明，我们的方法是有效的，在31 CPU分钟内解决了多达600个点的实例。对于这些数据集，结合灵活的上下车点可以将拼车系统的总车辆行驶时间平均减少4%。

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引用次数: 0