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

A modeling methodology for car-following behaviors of automated vehicles: Trade-off between stability and mobility 自动驾驶车辆跟车行为的建模方法：稳定性与机动性的权衡

IF 6.3 1区工程技术 Q1 ECONOMICS

Transportation Research Part B-Methodological

Pub Date : 2025-10-01 Epub Date: 2025-09-16 DOI: 10.1016/j.trb.2025.103316

Yuqin Zhang , Ke Ma , Zhigang Xu , Hang Zhou , Chengyuan Ma , Xiaopeng Li

Empirical studies have indicated that automated vehicle (AV) automakers tend to prioritize mobility over stability in designing car-following (CF) models, which may raise safety concerns. A likely explanation for this issue is that hardware-induced response delays challenge the ability of the CF models, as designed by automakers, to maintain an equilibrium between stability and mobility. To address these concerns, this study proposes a modeling methodology for the CF model in AVs aimed at achieving a trade-off between stability and mobility. This methodology seeks to identify the optimal parameters that enhance mobility under stability constraints. First, the linear CF model is calibrated using data from 20 commercial AVs produced by multiple automakers, and the unique response delay values of the linear CF model for each AV are identified. Next, the parameter regions ensuring stability are derived theoretically based on the calibrated response delays for each AV. An optimal mobility objective function is constructed to minimize time headway and reaction time, with the boundaries of the stable parameter regions serving as constraints. It allows the selection of CF parameters that maximize mobility while remaining within the stable regions. This proposed modeling method is applied to all AVs, and the optimal parameters are tested in simulations. Simulation results demonstrate that the proposed optimal model effectively dampens oscillations, reduces safety risks, and maintains shorter spacing, thus achieving an ideal trade-off between stability and mobility for AVs.

实证研究表明，自动驾驶汽车（AV）制造商在设计汽车跟随（CF）模型时倾向于优先考虑移动性而不是稳定性，这可能会引发安全问题。对于这个问题，一个可能的解释是，硬件引起的响应延迟挑战了汽车制造商设计的CF模型在稳定性和移动性之间保持平衡的能力。为了解决这些问题，本研究提出了一种自动驾驶汽车CF模型的建模方法，旨在实现稳定性和移动性之间的权衡。该方法旨在确定在稳定性约束下增强机动性的最佳参数。首先，使用多家汽车制造商生产的20辆商用自动驾驶汽车的数据对线性CF模型进行校准，并确定每辆自动驾驶汽车的线性CF模型的唯一响应延迟值。其次，基于标定后的响应延迟，从理论上推导出保证稳定的参数区域，并以稳定参数区域的边界作为约束条件，构造出保证车头时距和反应时最小的最优机动性目标函数。它允许CF参数的选择，最大限度地提高流动性，同时保持在稳定区域内。将所提出的建模方法应用于所有自动驾驶汽车，并对最优参数进行了仿真验证。仿真结果表明，该优化模型有效地抑制了自动驾驶汽车的振荡，降低了安全风险，并保持了较短的间距，从而实现了自动驾驶汽车稳定性和移动性之间的理想平衡。

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引用次数: 0

Routing a fleet of unmanned aerial vehicles: A trajectory optimisation-based framework 无人驾驶飞行器编队的路径选择：基于轨迹优化的框架

IF 6.3 1区工程技术 Q1 ECONOMICS

Transportation Research Part B-Methodological

Pub Date : 2025-10-01 Epub Date: 2025-09-06 DOI: 10.1016/j.trb.2025.103312

Walton P. Coutinho , Jörg Fliege , Maria Battarra , Anand Subramanian

We consider an aerial survey operation in which a fleet of unmanned aerial vehicles (UAVs) is required to visit several locations and then land in one of the available landing sites while optimising some performance criteria, subject to operational constraints and flight dynamics. We aim to minimise the maximum flight time of the UAVs. To efficiently solve this problem, we propose an algorithmic framework consisting of: (i) a nonlinear programming formulation of trajectory optimisation that accurately reflects the underlying flight dynamics and operational constraints; (ii) two sequential trajectory optimisation heuristics, designed to cope with the challenging task of finding feasible flight trajectories for a given route; and (iii) a routing metaheuristic combining iterated local search and a set-partitioning-based integer programming formulation. The proposed framework is tested on randomly generated instances with up to 50 waypoints, showing its efficacy.

我们考虑一种空中测量操作，其中一组无人驾驶飞行器（uav）需要访问多个地点，然后在一个可用的着陆点着陆，同时优化一些性能标准，受操作约束和飞行动力学。我们的目标是尽量减少无人机的最大飞行时间。为了有效地解决这一问题，我们提出了一个算法框架，包括：(i)精确反映潜在飞行动力学和操作约束的轨迹优化的非线性规划公式；（ii）两个顺序轨迹优化启发式算法，设计用于处理寻找给定航线可行飞行轨迹的挑战性任务；（iii）结合迭代局部搜索和基于集合划分的整数规划公式的路由元启发式算法。在随机生成的多达50个航路点的实例上测试了该框架的有效性。

引用次数: 0

Robust vessel traffic scheduling with uncertain Berth Service Times in a Seaport 海港泊位服务时间不确定的鲁棒船舶交通调度

IF 6.3 1区工程技术 Q1 ECONOMICS

Transportation Research Part B-Methodological

Pub Date : 2025-10-01 Epub Date: 2025-09-09 DOI: 10.1016/j.trb.2025.103294

Runqing Zhao , Shengnan Shu , Lingxiao Wu , Shuai Jia

We consider a planning horizon during which a set of vessels visit a seaport for cargo transshipment. To access the designated berths, vessels should travel from the anchorage ground to the port basin by passing through a navigation channel. As soon as the vessels have completed the cargo transshipment, they need to travel from the port basin back to the anchorage ground through the navigation channel again. Navigation channel traffic is affected by the tidal effect and is bottlenecked by the limited capacity. The incoming vessels may wait for the tide to enter the channel after arriving at the anchorage ground; while the outgoing vessels need to wait for the tide to enter the channel upon completion of cargo transshipment. During these operations, the port operators need to assign tidal windows for vessels to travel into or out of the port, as well as the berthing and unberthing times of vessels, in order to minimize the overall operating cost. We formulate the problem as a two-stage robust optimization model, considering the uncertain vessel service times at berths. By exploiting the problem structure, we develop an adapted column and constraint generation algorithm framework, where the second-stage problem is solved by an enumeration-based method for generating candidate vessel service sequences and a dynamic programming algorithm for allocating the uncertainty budgets to vessels. The computation experiments show that our proposed algorithm generates optimal solutions within acceptable computation times (less than 30 s), and performs better than well established benchmark methods in terms of both worst-case performance and mean performance metrics. Several managerial insights can be derived from our experimental results to guide port operations in terms of the application of the robust models and benefits to the industry.

我们考虑一个规划范围，在此期间，一组船只访问海港进行货物转运。船舶进入指定泊位，应当从锚地经航道进入港池。船舶一旦完成货物转运，就需要再次从港盆通过航道驶回锚地。航道交通受潮汐效应的影响，又因容量有限而成为瓶颈。进入航道的船舶到达锚地后，可以等待潮水进入航道；而出港船只在完成货物转运后，则需要等待潮水进入航道。在这些操作中，港口运营商需要为船舶分配进出港口的潮汐窗口，以及船舶的停泊和离港时间，以最大限度地降低总体运营成本。考虑泊位船舶服务时间的不确定性，将该问题表述为两阶段鲁棒优化模型。通过利用问题结构，我们开发了一种适用的列和约束生成算法框架，其中第二阶段问题通过基于枚举的方法生成候选船舶服务序列和动态规划算法分配不确定性预算来解决。计算实验表明，我们提出的算法在可接受的计算时间内（小于30秒）产生最优解，并且在最坏情况性能和平均性能指标方面都优于现有的基准方法。从我们的实验结果中可以得出一些管理见解，以指导港口在应用稳健模型方面的操作，并为行业带来好处。

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引用次数: 0

Robust planning for electric vehicle charging stations under congestion 拥堵条件下电动汽车充电站的稳健规划

IF 6.3 1区工程技术 Q1 ECONOMICS

Transportation Research Part B-Methodological

Pub Date : 2025-10-01 Epub Date: 2025-08-27 DOI: 10.1016/j.trb.2025.103291

Yongzhen Li , Jia Shu , Chengyao Wang , Ting Wu , Yinghui Wu

The last decades have witnessed the rise of electric vehicle (EV) sales, accompanied by a growing demand for readily accessible public EV charging facilities. Unlike refueling a fossil fuel vehicle, charging an EV requires significantly more time, which may lead to congestion if the public charging infrastructure is not well-designed. In this paper, we study the strategic planning of public EV charging stations, aiming to place chargers with a limited investment budget to maximize the coverage of uncertain charging demand. To ensure service quality under possible congestion, we introduce two types of chance constraints to mitigate long waiting times and reduce demand loss in situations with limited waiting space. Given the challenges in accurately estimating charging demand and charging time, we apply a robust approach to model this problem with uncertain charging demand arrival and service rates. The robust model is then reformulated into an equivalent mixed integer linear program of moderate size, which is tractable by commercial solvers. A case study based on data from Nanjing demonstrates the effectiveness of the proposed robust approach and provides insights into real-world applications. Extensions with a general charging process and decentralized driver selection of charging stations are also discussed and verified through extensive numerical experiments, which indicates the stable performance of the proposed approach under general settings.

过去几十年见证了电动汽车（EV）销量的增长，同时对公共电动汽车充电设施的需求也在不断增长。与化石燃料汽车加油不同，电动汽车充电需要更多的时间，如果公共充电基础设施设计不当，可能会导致拥堵。本文研究了公共电动汽车充电站的战略规划，目的是在投资预算有限的情况下放置充电站，以最大限度地覆盖不确定的充电需求。为了确保在可能出现的拥塞情况下的服务质量，我们引入了两种类型的机会约束，以减少在有限的等待空间下的长等待时间和减少需求损失。考虑到在准确估计充电需求和充电时间方面存在的挑战，我们采用了一种鲁棒的方法来对充电需求到达和服务费率不确定的问题进行建模。然后将鲁棒模型重新表述为一个中等大小的等效混合整数线性规划，该规划可由商业求解器处理。基于南京数据的案例研究证明了所提出的稳健方法的有效性，并为实际应用提供了见解。讨论了一般充电过程下的扩展和充电站驾驶员选择的分散，并通过大量的数值实验验证了该方法在一般设置下的稳定性能。

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引用次数: 0

Deep learning-based travel choice prediction with provable and adaptable fairness guarantees 基于深度学习的可证明和适应性公平性保证的出行选择预测

IF 6.3 1区工程技术 Q1 ECONOMICS

Transportation Research Part B-Methodological

Pub Date : 2025-10-01 Epub Date: 2025-09-12 DOI: 10.1016/j.trb.2025.103318

Zhiwei Chen , Yufei Xu , Srinivas Peeta

Deep Learning (DL) models offer substantial potential for travel choice predictions but are often plagued by algorithmic unfairness where disadvantaged population groups such as racial minorities and low-income populations often receive disproportionately worse prediction outcomes (e.g. accuracy) compared to their counterparts. Studies to address this issue in the transportation domain are relatively new and they fail to provide provable fairness guarantees and cannot address the diverse interpretations of fairness in practice. This study introduces a novel DL approach that provides provable fairness guarantees while being adaptable to various fairness standards. It embeds statistical hypothesis testing within a practical equality constraint to control disparities in prediction accuracy across different population groups, thus providing provable and adaptable fairness guarantees. This approach results in a threshold modification problem, formulated as a mixed-integer non-linear programming model that is proven to be NP-hard. To allow for efficient problem solving, theoretical properties of the threshold modification problem are investigated, enabling the decomposition of the original problem into smaller, more manageable subproblems. This decomposition provides insights into the problem's structure and enables the development of an efficient "Accuracy-First-Threshold-Second " algorithmic framework. Within this framework, an exact solution method is proposed to achieve optimal solutions, whereas a heuristic method, incorporating a sandwich algorithm and a bounded-enumeration algorithm, is designed to efficiently approximate near-optimal solutions. Extensive experiments demonstrate the computational performance of the proposed solution algorithms as well as the ability of the proposed fair DL approach to provide provable and adaptable fairness guarantees for travel choice predictions. This study offers a flexible and theoretically robust solution to fairness in travel choice prediction, with potential applications for enhancing equity in transportation systems.

深度学习（DL）模型为旅行选择预测提供了巨大的潜力，但经常受到算法不公平的困扰，弱势群体，如少数民族和低收入人群，与同行相比，往往会得到不成比例的更差的预测结果（例如准确性）。在交通运输领域解决这一问题的研究相对较新，它们未能提供可证明的公平保证，也无法解决实践中对公平的各种解释。本研究提出了一种新的深度学习方法，该方法提供了可证明的公平保证，同时适应各种公平标准。它将统计假设检验嵌入到实际的平等约束中，以控制不同人群之间预测精度的差异，从而提供可证明和可适应的公平性保证。这种方法导致了一个阈值修改问题，它被表述为一个被证明是np困难的混合整数非线性规划模型。为了有效地解决问题，研究了阈值修改问题的理论性质，从而将原始问题分解为更小、更易于管理的子问题。这种分解提供了对问题结构的洞察，并使开发高效的“精度-第一-阈值-第二”算法框架成为可能。在此框架下，提出了精确解方法来获得最优解，而结合三明治算法和有界枚举算法的启发式方法来有效地逼近近最优解。大量的实验证明了所提出的解决算法的计算性能，以及所提出的公平深度学习方法为旅行选择预测提供可证明和可适应的公平性保证的能力。本研究为出行选择预测中的公平性问题提供了一个灵活且理论上稳健的解决方案，在提高交通系统公平性方面具有潜在的应用前景。

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引用次数: 0

Optimizing continuous-time berth allocation, time-variant quay crane and yard assignment 优化连续泊位分配、时变码头起重机和堆场分配

IF 6.3 1区工程技术 Q1 ECONOMICS

Transportation Research Part B-Methodological

Pub Date : 2025-10-01 Epub Date: 2025-09-15 DOI: 10.1016/j.trb.2025.103317

Zhiyuan Yang , Miaomiao Wang , Shuaian Wang , Lu Zhen

Efficient container terminal operations depend on the coordinated use of three key resources: berths, quay cranes (QCs), and yard space. Decisions involving these components are highly interrelated. Berth allocation affects QC scheduling, which in turn influences yard-side transport. However, the majority of the literature treat these problems separately or under simplifying assumptions such as discrete berth allocation, time-invariant QC allocation, or omission of yard assignment. To the best of our known, this paper is the first to formulate a unified continuous-time optimization model that integrates continuous berth allocation, time-variant QC scheduling, and yard space assignment. To solve our proposed comprehensive decision model, we develop an exact algorithm and accelerate this by designing some novel valid inequalities and M-tightening techniques. The algorithmic efficiency and the benefits of considering the aforementioned decision features are validated through computational experiments. In addition, sensitivity analyses are conducted to derive potentially useful managerial insights.

高效的集装箱码头运营依赖于三个关键资源的协调使用：泊位、码头起重机（qc）和堆场空间。涉及这些组成部分的决策是高度相互关联的。泊位分配影响QC调度，进而影响场边运输。然而，大多数文献单独处理这些问题或简化假设，如离散泊位分配，定常QC分配，或遗漏码分配。据我们所知，本文首次建立了统一的连续时间优化模型，该模型集成了连续泊位分配、时变QC调度和堆场空间分配。为了解决我们提出的综合决策模型，我们开发了一个精确的算法，并通过设计一些新的有效不等式和m收紧技术来加速该算法。通过计算实验验证了算法的效率和考虑上述决策特征的好处。此外，还进行敏感性分析，以获得潜在有用的管理见解。

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引用次数: 0

Real-time vehicle location estimation in signalized networks using partial connected vehicle trajectory data 基于部分互联车辆轨迹数据的信号网络实时车辆位置估计

IF 6.3 1区工程技术 Q1 ECONOMICS

Transportation Research Part B-Methodological

Pub Date : 2025-10-01 Epub Date: 2025-08-20 DOI: 10.1016/j.trb.2025.103292

Shaocheng Jia , S.C. Wong , Wai Wong

Real-time vehicle location estimation is essential for diverse transportation applications, such as travel time estimation, arrival pattern estimation, and adaptive signal control. Existing connected vehicle-based studies rely on either black-box neural networks requiring large training datasets or computationally intensive time-continuous movement simulations grounded in car-following models. However, they often overlook the distinct vehicle location patterns in source lanes, which define network boundaries and experience random arrivals, and intermediate lanes, situated between intersections and receiving traffic discharged from upstream. These patterns are critical for accurate vehicle location estimation. To address these limitations, this study proposes a generic and fully analytical CV-based vehicle location (CVVL) model for estimating vehicle locations within a signalized lane in a network using readily available partial CV trajectory data. The proposed model is applicable to any signal timing, traffic demand, and CV penetration rate and consists of two sub-models: CVVL-S and CVVL-I. The CVVL-S sub-model estimates vehicle locations in source lanes, where vehicle distribution tends to be relatively homogeneous owing to random arrivals. In contrast, the CVVL-I sub-model focuses on estimating vehicle locations in intermediate lanes, where sequential discharges from different upstream lanes can lead to the formation of multiple platoons, adding complexity to vehicle location estimation. The proposed model decomposes the complex task into three sequential sub-problems: identifying candidate platoons (CPs), estimating the number of vehicles in each CP, and determining the spatial distribution of vehicles within each CP. Extensive numerical experiments were conducted under various traffic conditions, CV penetration rates, and times of interest using the VISSIM platform and the real-world Next Generation Simulation dataset. The results demonstrate that the proposed CVVL model achieved improvements of 0–45 %, 0–37 %, and 4–34 % in precision, recall, and F1 score, respectively, compared with the competing method. These results highlight the model’s potential to enhance the accuracy and reliability of various downstream applications.

实时车辆位置估计对于各种交通应用至关重要，如行程时间估计、到达模式估计和自适应信号控制。现有的基于互联汽车的研究要么依赖于需要大量训练数据集的黑盒神经网络，要么依赖于基于汽车跟随模型的计算密集型时间连续运动模拟。然而，他们往往忽略了源车道上不同的车辆定位模式，源车道定义了网络边界并随机到达，而中间车道位于十字路口之间并接收上游排放的车辆。这些模式对于准确估计车辆位置至关重要。为了解决这些限制，本研究提出了一种通用的、完全分析的基于CV的车辆定位（CVVL）模型，该模型使用易于获得的部分CV轨迹数据来估计网络中信号车道内的车辆位置。该模型适用于任何信号配时、流量需求和CV渗透率，包括CVVL-S和CVVL-I两个子模型。CVVL-S子模型估计源车道上的车辆位置，由于随机到达，源车道上的车辆分布趋于相对均匀。相比之下，cvv1 - i子模型侧重于估计中间车道的车辆位置，其中来自不同上游车道的连续排放可能导致形成多个排，增加了车辆位置估计的复杂性。该模型将复杂的任务分解为三个连续的子问题：识别候选队列（CPs），估计每个CP中的车辆数量，以及确定每个CP内车辆的空间分布。使用VISSIM平台和真实世界的下一代模拟数据集，在各种交通条件、CV渗透率和兴趣时间下进行了大量的数值实验。结果表明，与同类方法相比，所提出的CVVL模型在查全率、查全率和F1分数上分别提高了0 - 45%、0 - 37%和4 - 34%。这些结果突出了该模型在提高各种下游应用的准确性和可靠性方面的潜力。

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引用次数: 0

Promoting carpooling on car-hailing platforms: Order allocation and motivating subsidy 促进网约车平台的拼车：订单分配和激励补贴

IF 5.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part B-Methodological

Pub Date : 2025-09-01 Epub Date: 2025-07-22 DOI: 10.1016/j.trb.2025.103282

Rui Yan , Yuwen Chen , Baolong Liu , Xuege Wang

This paper investigates an order allocation problem for an online car-hailing platform, including solo-ride and carpooling orders. Compared to solo rides, carpooling provides convenience, reduces emissions, and lowers traveling costs for passengers. However, drivers are unwilling to fulfill carpooling requests due to e.g., extra waiting and detour time to pick up carpooling passengers, and potential disputes and complaints from passengers. Therefore, carpooling brings operational challenges to car-hailing platforms in motivating drivers to serve the carpooling orders and allocating orders to the assign (drivers receive orders reactively) and inform (drivers claim orders proactively) order-dispatching systems. In promoting carpooling services, platforms are willing to provide subsidies to seize the market. In this regard, our study explores the scenario where a car-hailing platform maximizes service-quality-related platform performance by providing subsidies to drivers and optimizing the carpooling order allocation and the matching radius strategies. By taking Didi Chuxing as an example, we build G/M/1-family queueing models to maximize the platform performance measure. Our analysis derives the structure of optimal carpooling order allocation and the threshold subsidy to balance the drivers’ payoff in the two systems at equilibrium. We conduct numerical experiments and sensitivity analysis to simulate close-to-reality cases and find 90% of the carpooling orders should be sent to the assign system with a matching radius of

3 \sim 5 km

. For robustness check, we also discuss the cases where the platform’s profit is the objective and the detour time endogenously depends on the matching radius and the order arrival rate. To ensure Pareto improvement for the platform, the drivers, and the passengers, we also apply the

ɛ

-constraint method to find the Pareto-improvement sets and the corresponding strategies.

本文研究了一个网约车平台的订单分配问题，包括专车订单和拼车订单。与单独出行相比，拼车提供了便利，减少了排放，并降低了乘客的出行成本。然而，司机不愿意满足拼车的要求，例如，额外的等待和绕路去接拼车的乘客，以及潜在的争议和乘客的投诉。因此，拼车给网约车平台带来了运营上的挑战，即激励司机服务拼车订单，并将订单分配给分配（司机主动接受订单）和通知（司机主动认领订单）的订单调度系统。在推广拼车服务时，平台愿意提供补贴以抢占市场。为此，本研究探讨了网约车平台通过向司机提供补贴、优化拼车订单分配和匹配半径策略，实现与服务质量相关的平台绩效最大化的场景。以滴滴出行为例，构建G/M/1族排队模型，实现平台性能指标最大化。我们的分析导出了两种系统在均衡状态下的最优拼车顺序分配结构和平衡司机收益的阈值补贴。我们通过数值实验和灵敏度分析来模拟接近现实的情况，发现90%的拼车订单应该发送到匹配半径为3 ~ 5km的分配系统。为了检验鲁棒性，我们还讨论了平台利润为目标，绕路时间内生取决于匹配半径和订单到达率的情况。为了保证平台、司机和乘客的帕累托改进，我们还应用了约束方法来寻找帕累托改进集和相应的策略。

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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 : 2025-09-01 Epub 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

Aircraft recovery with precancellation 飞机回收与预先取消

IF 5.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part B-Methodological

Pub Date : 2025-09-01 Epub Date: 2025-07-12 DOI: 10.1016/j.trb.2025.103279

Yi Su , Kexin Xie , Lei Huang , Xiaoning Zhang , Chutian Chen , Zhe Liang

Airlines often adopt a wait-and-see strategy for disruptions, resulting in canceling flights at the last moment. This not only incurs extra compensation costs but also significantly affects passengers’ travel experiences. To mitigate these losses, we introduce the concept of flight precancellation, which is defined as canceling flights one to several days before departure. To make precancellation decisions with respect to stochastic future weather conditions, we develop a two-stage stochastic model aimed at minimizing the overall recovery cost. To solve this model, we design a Lagrangian dual decomposition (LDD) approach, which efficiently decomposes the model into scenario-independent submodels. These submodels are then solved by a column generation framework. Additionally, we propose a dual-based variable evaluation strategy (DVS) to accelerate the solving process of LDD. We evaluate the effectiveness and efficiency of our model and algorithms using real operational data from three airlines, which are tested via real typhoon data. The computational results show that LDD can obtain optimal linear programming (LP) solutions and near-optimal integer programming (IP) solutions. Compared with the baseline column generation algorithm, the solution times for LDD and LDD-DVS are reduced by 41% and 46%, respectively. Additionally, tests conducted on real typhoon data demonstrate that, by incorporating precancellation decisions, it achieves an average cost savings of 17% compared with solutions that consider only real-time cancellation decisions.

航空公司通常对航班中断采取观望策略，导致在最后一刻取消航班。这不仅会产生额外的赔偿成本，而且会严重影响乘客的出行体验。为了减少这些损失，我们引入了航班提前取消的概念，它被定义为在起飞前一到几天取消航班。为了针对未来的随机天气条件做出预取消决策，我们开发了一个旨在使总恢复成本最小化的两阶段随机模型。为了求解该模型，我们设计了一种拉格朗日对偶分解（LDD）方法，该方法有效地将模型分解为与场景无关的子模型。然后通过列生成框架求解这些子模型。此外，我们提出了一种基于双变量评估策略（DVS）来加速LDD的求解过程。我们使用来自三家航空公司的真实运营数据来评估我们的模型和算法的有效性和效率，这些数据通过真实的台风数据进行了测试。计算结果表明，LDD可以得到最优线性规划（LP）解和近最优整数规划（IP）解。与基线列生成算法相比，LDD和LDD- dvs的求解次数分别减少了41%和46%。此外，对真实台风数据进行的测试表明，与仅考虑实时取消决策的解决方案相比，通过纳入预取消决策，该解决方案平均节省了17%的成本。

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