编者按

在本文章中，我们对交通运筹领域顶刊《Transportation Research Part B: Methodological》于2025年6-7月份正式发布的文章中进行了精选（共10篇），并总结其基本信息，旨在帮助读者快速洞察行业最新动态。本月TR-B发文聚焦交通流量管理、铁路运输系统与公共交通网络优化等领域，研究了客货混运的优化问题、交通分配问题以及铁路网络项目调度等。方法涵盖了博弈论、双层优化、鲁棒优化、生成式算法及数据驱动模型等，旨在提高交通效率、减少运输成本并应对运输需求的不确定性。

文章1

● 题目：Robust train carriage planning for mixed transportation of passengers and uncertain freights in a high-speed railway network

高速铁路网络中客货混运的鲁棒列车编组与车厢安排

● 原文链接：https://doi.org/10.1016/j.trb.2025.103216

● 作者：

Chuntian Zhang (a) (b), Zhou Xu (b), Lixing Yang (a), Ziyou Gao (a), Yuan Gao (c)

  (a)School of Systems Science, Beijing Jiaotong University, Beijing, 100044, China

  (b)Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

  (c)School of Management and Economics, Beijing Institute of Technology, Beijing, 100081, China

● 发布时间：2025-6

● 摘要：

Mixed transportation of passengers and freights is an effective strategy for reducing environmental pollution and improving the service level of railway systems. This study addresses the problem of robust train composition and carriage arrangement for the mixed transportation of passengers and freights in a high-speed railway (HSR) network. Specifically, a network-based robust optimization (RO) model is introduced to address the uncertainty in freight demand while considering deterministic passenger demand. The model utilizes space–time network representations to characterize the movements of passengers and freights. To account for various potential scenarios, a polyhedral uncertainty set is integrated into the model. Moreover, we develop a novel exact algorithm called B-C&CG, which utilizes the strengths of Benders decomposition for solving the deterministic passenger sub-problem and the strengths of column-and-constraint generation (C&CG) for solving the robust freight sub-problem. This provides an efficient solution to the RO model formulated for our problem. The objective is to optimize the train operating cost, passenger generalized travel cost, and the worst-case freight travel cost simultaneously. Additionally, a series of numerical experiments based on the real-world instance in a HSR network are conducted to verify the effectiveness of the developed B-C&CG algorithm and the advantages of the proposed RO model. The results demonstrate that (i) the newly developed algorithm outperforms both the Benders decomposition algorithm and the hybrid algorithm (B-BC&CG) in terms of computing time, where the latter differs from B-C&CG by using both Benders decomposition and C&CG to handle the robust freight sub-problem; (ii) the degree of conservatism can be controlled by altering parameters related to uncertain freight demand; (iii) the proposed RO model can improve the worst-case solutions under polyhedral uncertainty set, compared to nominal and stochastic programming models.

客货混运是一种有效的策略，能够减少环境污染并提高铁路系统的服务水平。本研究解决了在高速铁路（HSR）网络中进行客货混运时，鲁棒列车编组与车厢安排的问题。具体而言，本文引入了一个基于网络的鲁棒优化（RO）模型，来应对货物需求的不确定性，同时考虑确定性的客运需求。该模型利用时空网络表示来描述乘客和货物的流动。为了应对各种潜在情境，模型中融入了一个多面体不确定集。此外，我们开发了一种新型精确算法，称为B-C&CG，该算法结合了Benders分解法的优势来求解确定性客运子问题，以及列约束生成（C&CG）方法的优势来求解鲁棒货运子问题。这为我们的问题中鲁棒优化模型的求解提供了一种高效的解决方案。研究目标是同时优化列车运营成本、乘客综合旅行成本和最坏情况的货物旅行成本。此外，我们基于高速铁路网络中的实际实例进行了系列数值实验，以验证所开发的B-C&CG算法的有效性以及所提鲁棒优化模型的优势。结果表明：（i）新开发的算法在计算时间方面优于Benders分解算法和混合算法（B-BC&CG），后者通过结合Benders分解和C&CG来处理鲁棒货运子问题；（ii）通过改变与不确定货运需求相关的参数，可以控制保守程度；（iii）与标称编程和随机编程模型相比，所提出的鲁棒优化模型可以在多面体不确定集下改善最坏情况的解决方案。

文章2

● 题目：A bi-level optimization model for project scheduling and traffic flow routing in railway networks

铁路网络中项目调度与交通流路径选择的双层优化模型

● 原文链接：https://doi.org/10.1016/j.trb.2025.103213

● 作者：

Tomas Lidén (a), Filip Kristofersson (a), Martin Aronsson (b)

  (a)Swedish National Road and Transport Research Institute (VTI), Box 556 85, SE-102 15 Stockholm, Sweden

  (b)RISE Research Institutes of Sweden AB, Sweden

● 发布时间：2025-6

● 摘要：

Long-term tactical infrastructure planning for a transportation network consists of deciding on renewals and major maintenance works. Such projects constitute large budget volumes and will impair the available traffic capacity during their execution, especially for railway systems. Quantitative methods that schedule and coordinate infrastructure projects together with traffic flow adaptations is however largely lacking today.

This paper addresses the joint planning of temporary capacity restrictions and traffic flow adaptions during track work closures, by proposing a bi-level optimization model which separates the problem into project scheduling (upper level) and traffic assignment (lower level). The latter model uses a novel traffic flow formulation for routing volumes of trains through the transportation network under the capacity restrictions given by the project scheduling. An aggregated network is used together with time discretized into uniform periods, which makes it possible to treat large national planning problems with a planning horizon of up to a year and a period length of a couple hours. The computational properties are evaluated, both for the individual models, and for their joint usage. Furthermore, results from applying the models on two case studies, concerning Northern and South-Western Sweden, are presented.

The main conclusion is that the model formulations are capable of solving realistic planning cases and to provide support for capacity planners at an infrastructure manager, even for a large national railway. The results show that a good overview over the collective traffic impact is obtained, but also that details of particular traffic relations or capacity usage over individual network links and their variation over time can be studied. One major deficiency has been identified in the flow-based traffic assignment model, which can lead to incoherent train flows over long traveling distances and many time periods.

运输网络的长期战术基础设施规划包括决策更新和重大维修工作。这类项目涉及大额预算，并且在执行过程中会影响可用的交通容量，尤其是铁路系统。现有的定量方法在调度和协调基础设施项目与交通流适应方面仍然缺乏有效的解决方案。

本文通过提出一个双层优化模型，解决了铁路工程施工期间临时容量限制和交通流适应的联合规划问题。该模型将问题分为项目调度（上层）和交通分配（下层）。下层模型采用了一种新型的交通流量公式，用于在项目调度所给定的容量限制下，通过运输网络进行列车流量的路径选择。采用聚合网络并将时间离散化为均匀的周期，这使得可以处理大规模的国家级规划问题，规划期可长达一年，每个周期为几小时。评估了计算特性，既针对各个模型，也针对它们的联合使用。此外，本文还呈现了将这些模型应用于瑞典北部和西南部两个案例研究的结果。

主要结论是，模型的公式能够解决现实的规划案例，并为基础设施管理者的容量规划提供支持，即使是在一个大型国家铁路系统中。结果显示，该模型可以为集体交通影响提供良好的概览，同时也能研究特定交通关系或单个网络链路的容量使用及其随时间的变化。研究中还发现，基于流量的交通分配模型存在一个主要缺陷，即可能导致列车流在长途旅行和多个时段内出现不一致的流动情况。

文章3

● 题目：On the calibration of stochastic car following models

随机跟车模型的标定研究

● 原文链接：https://doi.org/10.1016/j.trb.2025.103224

● 作者：

Shirui Zhou (a) (b), Shiteng Zheng (c), Tu Xu (d), Martin Treiber (e), Junfang Tian (a) (b), Rui Jiang (c)

  (a)Institute of Systems Engineering, College of Management and Economics, Tianjin University, No. 92 Weijin Road, Nankai District, 300072, Tianjin, China

  (b)Laboratory of Computation and Analytics of Complex Management Systems (CACMS), Tianjin University, No. 92 Weijin Road, Nankai District, 300072, Tianjin, China

  (c)School of Systems Science, Beijing Jiaotong University, No. 3 Shangyuancun, Haidian District, 100044, Beijing, China

  (d)Laboratory of Public Safety Risk Governance, Zhejiang Police College, No. 555, Binwen Road, Binjiang District, Hangzhou, 310053, Zhejiang, China

  (e)Institute for Transport and Economics, Dresden University of Technology, Würzburger Str. 35, Dresden, D-01062, Saxony, Germany

● 发布时间：2025-6

● 摘要：

Recent empirical and theoretical findings highlight the critical role of stochasticity in car-following (CF) dynamics. Although several stochastic CF models have been proposed, their calibration remains relatively underexplored compared to deterministic models. This article addresses this gap by utilizing four stochastic CF models to conduct a comprehensive evaluation of two existing calibration methods—minimizing multiple runs mean error (MRMean) and maximum likelihood estimation (MLE) as well as a newly proposed method, minimizing multiple runs minimum (MRMin) error, based on synthetic trajectories. Results show that MRMean and MLE exhibit significant biases in estimating the ground truth values of stochastic model parameters, while MRMin achieves nearly zero estimation errors. Specifically, MRMean eliminates stochasticity, transforming models into deterministic ones, whereas MRMin successfully separates aleatoric errors caused by randomness and epistemic errors caused by parameters, as demonstrated through a theoretical error analysis. Furthermore, CF experiments conducted in an identical driving environment reveal that differences in spacing are more pronounced than differences in speed. Calibration against experimental trajectories verifies the conclusions drawn from synthetic trajectories and theoretical analysis. Additionally, the covariance matrix of parameters is estimated using bootstrap sampling, highlighting MRMin’s ability to capture the inherent stochasticity of CF behavior. These findings deepen our understanding of CF stochasticity and provide a robust framework for calibrating stochastic models.

最近的实证和理论研究突显了随机性在跟车（CF）动态中的关键作用。尽管已经提出了几种随机跟车模型，但与确定性模型相比，其标定研究仍然相对较少。本文通过利用四种随机跟车模型，进行综合评估，研究了两种现有的标定方法——最小化多次运行均值误差（MRMean）和最大似然估计（MLE），以及一种新提出的方法——最小化多次运行最小值误差（MRMin），并基于合成轨迹进行验证。结果表明，MRMean和MLE在估计随机模型参数的真实值时存在显著偏差，而MRMin能够实现几乎零的估计误差。具体来说，MRMean消除了随机性，将模型转化为确定性模型，而MRMin则成功地分离了由随机性引起的偶然误差和由参数引起的认知误差，这一点通过理论误差分析得到了证明。此外，在相同驾驶环境下进行的跟车实验表明，间距差异比速度差异更加明显。通过对实验轨迹的标定验证了合成轨迹和理论分析得出的结论。此外，使用自助法抽样估计参数的协方差矩阵，突出了MRMin在捕捉跟车行为固有随机性方面的能力。这些发现加深了我们对跟车随机性的理解，并为标定随机模型提供了一个稳健的框架。

文章4

● 题目：Capacity drop at active bottlenecks: An empirical study based on trajectory data

主动瓶颈处的容量下降：基于轨迹数据的实证研究

● 原文链接：https://doi.org/10.1016/j.trb.2025.103218

● 作者：

Yu Han (a), Jiarui Wu (a), Fan Ding (a), Zhibin Li (a), Pan Liu (a), Ludovic Leclercq (b)

  (a)School of Transportation, Southeast University, 211189, Nanjing, China

  (b)LICIT-ECO7, Université Gustave Eiffel, ENTPE, F-69675, Lyon, France

● 发布时间：2025-6

● 摘要：

Capacity drop, a traffic phenomenon indicating that the discharge flow from a queue is lower than the pre-queue flow, is commonly observed at freeway bottlenecks. In the literature, the majority of empirical studies on capacity drop rely on aggregated traffic flow data. To fully understand the mechanism behind capacity drop, it is essential to analyze trajectory data, which captures the microscopic behavior of individual vehicles. However, the availability of high-quality trajectory data covering both sufficient spatial and temporal scope is limited. Consequently, existing theories and mechanisms to explain capacity drop from the perspective of vehicle behavior are predominantly analytical, lacking direct evidence to validate their impacts on contributing to capacity drop. This paper fills this gap by conducting a comprehensive empirical analysis of capacity drop using high-resolution trajectory data extracted from videos recorded by unmanned aerial vehicles. The empirical analysis examines the relative effects of various capacity drop mechanisms and reveals the following findings: (i) The late responses of hesitant vehicles during the acceleration process significantly contribute to capacity drop; (ii) the impact of response delay on queue discharge rate is more pronounced at lower congestion speeds; (iii) response delays primarily result from deceleration during car-following, followed by lane changes, with their combined effect having a more pronounced triggering impact. These findings are subsequently validated using data collected from another site. The findings presented in this paper are valuable for developing more accurate microscopic traffic simulation models and designing more effective traffic management and control strategies.

容量下降是指排队流量低于排队前流量的一种交通现象，通常在高速公路瓶颈处观察到。现有文献中，大多数关于容量下降的实证研究依赖于聚合交通流数据。为了深入理解容量下降背后的机制，必须分析轨迹数据，因为它能捕捉到单个车辆的微观行为。然而，涵盖足够空间和时间范围的高质量轨迹数据较为稀缺。因此，现有的容量下降理论和机制主要是分析性的，缺乏直接证据来验证这些机制对容量下降的影响。本文通过使用无人机录制的视频提取的高分辨率轨迹数据，进行了一项关于容量下降的全面实证分析。实证分析考察了不同容量下降机制的相对影响，并揭示了以下发现：（i）在加速过程中，犹豫车辆的迟缓反应显著贡献于容量下降；（ii）反应延迟对排队流量的影响在较低的拥堵速度下更为明显；（iii）反应延迟主要源于跟车过程中的减速，其次是换道，且二者的联合效应对容量下降有更为显著的触发影响。随后，这些发现通过另一地点收集的数据得到了验证。本文提出的发现对于开发更精确的微观交通仿真模型以及设计更有效的交通管理与控制策略具有重要价值。

文章5

● 题目：Exploiting modularity for co-modal passenger-freight transportation

利用模块化技术实现客货共运

● 原文链接：https://doi.org/10.1016/j.trb.2025.103217

● 作者：

Hongyu Zheng (a), Jiayang Li (b), Jane Lin (c), Yu (Marco) Nie (d)

  (a)Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, Knoxville, TN 37996, USA

  (b)Department of Data and Systems Engineering, The University of Hong Kong, Hong Kong Special Administrative Region, China

  (c)Department of Civil, Materials, and Environmental Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA

  (d)Department of Civil and Environmental Engineering, Northwestern University, 2145 Sheridan Road, Evanston, IL 60208, USA

● 发布时间：2025-6

● 摘要：

Using a game theoretic approach, this paper explores a futuristic passenger-freight co-modality system that leverages autonomous modular vehicle (AMV) technology. In our model, a transit operator and a freight carrier operate within a stylized city, transporting passengers and parcels, respectively. The freight carrier can rent the transit operator’s underutilized transport capacity during off-peak periods through a market mechanism. By analyzing the design problems of both the operator and the carrier, we characterize their willingness-to-trade function, which defines the feasible region for a two-player game. We formulate four distinct market mechanisms, each corresponding to a different type of game. The first two are leader–follower Stackelberg games, differing in which player assumes the leadership role. The third mechanism features iterative negotiation between both players until equilibrium is achieved, while the fourth assumes full cooperation. Our results indicate that in the Stackelberg games, the leader captures all the benefits of co-modality, whereas neither player benefits in the negotiation game. Moreover, the carrier-led Stackelberg game proves more efficient than the operator-led one. Finally, while regulatory interventions such as price caps can promote a more equitable benefit distribution in the Stackelberg framework, similar outcomes are attainable without intervention in the cooperative game.

本文采用博弈论方法探讨了一种未来的客货共运系统，该系统利用自主模块化车辆（AMV）技术。在我们的模型中，交通运营商和货运承运商分别在一个简化的城市中运营，分别运输乘客和包裹。货运承运商可以通过市场机制，在非高峰期租用交通运营商未充分利用的运输能力。通过分析运营商和承运商的设计问题，我们刻画了他们的交易意愿函数，该函数定义了双人博弈的可行区域。我们提出了四种不同的市场机制，每种机制对应一种不同类型的博弈。前两种是领导者-跟随者的斯塔克尔伯格博弈，区别在于哪个玩家承担领导角色。第三种机制涉及双方的迭代谈判，直到达成均衡，而第四种假设完全合作。我们的研究结果表明，在斯塔克尔伯格博弈中，领导者获得了所有的共运收益，而在谈判博弈中，双方都没有受益。此外，承运商主导的斯塔克尔伯格博弈比运营商主导的更为高效。最后，虽然价格上限等监管干预措施可以促进斯塔克尔伯格框架下更公平的收益分配，但在合作博弈中，通过不干预同样可以获得类似的结果。

文章6

● 题目：Online adaptive shockwave detection and inpainting based on vehicle trajectory data: rigorous algorithm design and theory development

基于车辆轨迹数据的在线自适应交通冲击波检测与修复：严格算法设计与理论发展

● 原文链接：https://doi.org/10.1016/j.trb.2025.103225

● 作者：

Chenlu Pu, Lili Du

Department of Civil and Coastal Engineering, University of Florida

● 发布时间：2025-7

● 摘要：

Traffic shockwaves, as the boundary of distinct traffic states, capture the temporal-spatial characteristics of traffic fluctuation formation and propagation. Monitoring shockwaves facilitates real-time traffic management and control to improve traffic efficiency and safety. However, detecting shockwaves is challenging due to the complex nature of traffic dynamics and limited data collection. Existing methods either require prior knowledge of shockwaves to detect them in specific traffic scenarios or are capable of detecting only partial shockwaves with approximated propagation speed. To address these limitations, this study develops an eFfective online ShockWave deTection and Inpainting approach using vehicle trajectory data (labeled as SWIFT) collected in broad traffic scenarios. Briefly, first noticing the correlation between turning points for piecewise linear regression and breakpoints on each individual trajectory curve where a vehicle experiences significant speed changes, we develop a novel automatic breakpoint identification method by renovating the piecewise linear regression with shockwave features’ constraint. Next, we design an adaptive data-driven online shockwave detection approach that operates without any prior knowledge of shockwaves. This approach sequentially classifies and connects breakpoints based on shockwave propagation characteristics to generate distinct piecewise linear shape shockwave traces with mathematically guaranteed error bounds. Considering the shockwaves detected from data-driven approaches are usually incomplete, we establish the theoretical foundation including critical definitions, corollaries, and a theorem to guide shockwave inpainting and missing shockwave revealing based on the geometry representation of shockwave features. Built upon that, we develop a generative algorithm that verifies shockwave endpoints one by one based on partial trajectory data to repair incomplete shockwaves and reveal missing shockwaves. The numerical experiments using the NGSIM dataset demonstrated the accuracy, adaptiveness, and robustness of the SWIFT under various data collection settings (e.g., penetration rates, detection window sizes, sampling intervals) and different traffic scenarios.

交通冲击波作为不同交通状态的分界线，捕捉了交通波动形成与传播的时空特征。监测冲击波有助于实施实时交通管理与控制，从而提升通行效率与交通安全。然而，由于交通动态的复杂性和数据采集的局限性，冲击波检测面临严峻挑战。现有方法需依赖冲击波先验知识以识别特定交通场景的冲击波，或仅能通过近似传播速度检测局部冲击波。为突破这些局限，本研究基于多场景车辆轨迹数据开发了一种高效的在线冲击波检测与修复方法（简称SWIFT）。

研究框架如下：首先，通过观察分段线性回归转折点与车辆速度显著变化的轨迹曲线断点之间的关联性，本研究创新性地提出一种约束冲击波特征的分段线性回归模型，构建了新型自动断点识别方法。随后，设计了一种无需冲击波先验知识的自适应数据驱动在线检测方法，该方法根据冲击波传播特性对断点进行序贯分类与关联，生成具有数学误差保证的线性分段冲击波轨迹。

针对数据驱动方法检测的冲击波常不完整的问题，本研究建立了包含核心定义、推论及定理的理论体系，基于冲击波的几何特征表征指导修复缺失冲击波。在此基础上，开发了一种生成式算法，可依据局部轨迹数据逐一验证冲击波端点，实现不完整冲击波修复与隐藏冲击波重现。利用NGSIM数据集开展的数值实验表明，SWIFT方法在不同数据采集条件（如渗透率、检测窗口尺寸、采样间隔）及多样化交通场景下均表现出精确性、适应性与鲁棒性。

文章7

● 题目：A novel multi-objective evolutionary algorithm for transit network design and frequency-setting problem considering passengers’ choice behaviors under station congestion

一种考虑车站拥挤下乘客选择行为的公共交通网络设计与发车频率设置问题的多目标进化算法

● 原文链接：https://doi.org/10.1016/j.trb.2025.103238

● 作者：

Mingzhang Liang (a) (b), Min Xu (c), Shuaian Wang (b)

  (a)School of Transportation, Southeast University, Nanjing 210096, China

  (b)Department of Logistics and Maritime Studies, Faculty of Business, The Hong Kong Polytechnic University, Hung Hom, Hong Kong

  (c)Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong

● 发布时间：2025-7

● 摘要：

The transit network design and frequency-setting problem (TNDFSP) plays a critical role in urban transit system planning. Due to the conflict between the level of service and operating costs, extensive research has been conducted to obtain a set of trade-off solutions between the interests of users and operators. However, most studies ignored the effects of station congestion in TNDFSP, resulting in unrealistic solutions or a failure to achieve optimal design schemes. Therefore, this study investigates the multi-objective optimization of TNDFSP considering users’ choice behaviors under station congestion. To address the problem, a multi-objective bilevel optimization model is first formulated. The upper level is a bi-objective optimization model with two conflicting objectives: minimizing users’ cost and minimizing operator’s cost. The lower-level problem is a passenger assignment problem under station congestion. Moreover, a novel multi-objective evolutionary algorithm based on objective space decomposition (MOEA-OSD) is proposed to solve the complex problem. When dealing with multi-objective optimizations, a decomposition mechanism is developed to convert the problem into multiple subproblems. These subproblems are optimized using an evolutionary approach with newly designed selection process and elite preservation strategy to achieve desirable convergence and diversity. The computational results obtained using Mandl’s benchmark demonstrate the efficacy of MOEA-OSD and the advantage of the proposed model in achieving more comprehensive trade-off solutions.

公共交通网络设计与发车频率设置问题（TNDFSP）在城市公共交通规划中至关重要。由于服务水平与运营成本之间存在冲突，已有研究多致力于在用户与运营者利益之间获取一组权衡解。然而，大多数研究忽略了车站拥挤对 TNDFSP 的影响，导致解不够现实或难以获得最优设计方案。为此，本文在考虑车站拥挤条件下的用户选择行为的基础上，研究 TNDFSP 的多目标优化问题。首先构建一个多目标双层优化模型：上层为双目标优化，目标为最小化乘客成本与最小化运营者成本；下层为考虑车站拥挤的乘客分配问题。进一步地，提出一种基于目标空间分解的全新多目标进化算法（MOEA-OSD）来求解该复杂问题。在处理多目标优化时，通过分解机制将原问题转化为多个子问题，并采用包含新设计的选择过程与精英保留策略的进化方法进行优化，以兼顾收敛性与解集多样性。基于 Mandl 基准网络的计算结果表明，MOEA-OSD 具有良好效果，所提模型在获得更全面的权衡解方面具有优势。

文章8

● 题目：Post-disruption lane reversal optimization with surrogate modeling to improve urban traffic resilience

基于代理建模的扰动后车道反转优化以提升城市交通韧性

● 原文链接：https://doi.org/10.1016/j.trb.2025.103237

● 作者：

Qing-Long Lu (a) (d), Wenzhe Sun (b), Cheng Lyu (a), Jan-Dirk Schmöcker (c), Constantinos Antoniou (a)

  (a)Chair of Transportation Systems Engineering, Technical University of Munich, Munich, 80333, Germany

  (b)Business School, University of Shanghai for Science and Technology, Shanghai, 200093, China

  (c)Department of Urban Management, Kyoto University, Kyoto, 615-8540, Japan

  (d)Department of Civil and Environmental Engineering, National University of Singapore, Singapore, 117576, Singapore

● 发布时间：2025-7

● 摘要：

Rapid post-disruption recovery is essential but challenging, given the complex interactions between vehicular flows and the network supply. Simulation-based methods are widely used to assist the planner with realistic user-system interactions in the recovery measure optimization, though the application to large-scale transportation networks remains computationally expensive. This study explores the feasibility of using surrogate models as a time-efficient alternative to resource-intensive simulations. Lane reversal control is employed as a novel recovery measure and an optimization framework prioritizing systematic recovery is developed. A resilience loss indicator based on macroscopic fundamental diagram (MFD) dynamics is used to evaluate the real-time performance of the transportation system. The proposed surrogate model, therefore, also focuses on approximating recovery evaluation indicators, i.e., the resilience loss, other than link flows and density. The surrogate model contains a dynamic analytical network model and a Gaussian process regression (GPR) model. The former provides the analytical resilience loss and considers the temporal correlation of network changes resulting from time-dependent lane reversal decisions. The latter captures the difference between simulated and analytical resilience losses. Experiments are conducted on a large real-world road network in Kyoto City. The proposed approach demonstrates its efficacy by mitigating traffic resilience loss by about 6% under scenarios of 15 and 20 controllable links with a mere five algorithm iterations, requiring only 150 simulation runs. We also illustrate a trade-off between recovery performance and control resources that more controllable links unnecessarily offer better resilience improvement given the short decision-making duration and the very tight computational budget.

交通网络在受扰后实现快速恢复至关重要，但由于车流与网络供给之间的复杂相互作用，这一目标具有挑战性。基于仿真的方法广泛用于在恢复措施优化中刻画真实的用户—系统交互，但应用于大尺度交通网络时计算代价高昂。本文探索以代理模型作为一种时间高效、资源节约的替代方案。我们采用车道反向控制作为新型恢复手段，并构建了优先系统性恢复的优化框架。基于宏观基本图（MFD）动力学的韧性损失指标用于评估交通系统的实时性能。因此，所提出的代理模型重点逼近的是恢复评估指标——即韧性损失——而非仅仅是路段流量与密度。该代理模型由动态解析网络模型与高斯过程回归（GPR）模型组成：前者给出解析的韧性损失，并考虑由时变车道反向决策引起的网络变化的时间相关性；后者捕捉仿真与解析韧性损失之间的差异。我们在京都市的大规模真实道路网络上开展实验。结果表明，在可控路段数量为15与20的情景下，仅经过5次算法迭代（共150次仿真运行），即可将交通韧性损失降低约6%，验证了所提方法的有效性。我们还揭示了恢复效果与控制资源之间的权衡：在决策时间短且计算预算极为紧张的条件下，增加可控路段数量并不必然带来更好的韧性提升。

文章9

● 题目：Liner fleet deployment and slot allocation problem: A distributionally robust optimization model with joint chance constraints

班轮船队部署与舱位分配问题：带联合机会约束的分布鲁棒优化模型

● 原文链接：https://doi.org/10.1016/j.trb.2025.103236

● 作者：

Tao Zhang (a), Shuaian Wang (b), Xu Xin (a) (b)

  (a)School of Economics and Management, Tongji University, Shanghai, 200092, PR China

  (b)Department of Logistics and Maritime Studies, Faculty of Business, The Hong Kong Polytechnic University, Hung Hom, 999077, Hong Kong Special Administrative Region of China

● 发布时间：2025-7

● 摘要：

In this paper, we address the classical liner fleet deployment and slot allocation joint optimization problem in the maritime field with uncertain container transportation demand. We relax the assumption in existing studies that the demand distribution function is known because container transportation demand is deeply affected by the world’s economic and political landscape. With the help of advances in distributionally robust optimization theory, we develop a two-stage data-driven robust chance-constrained model. This distribution-free model requires only limited historical demand data as input and jointly optimizes the class (i.e., capacity) and number of liners assigned on each route and the scheme for allocating containers on each leg to maximize the profit (container transportation revenue minus fleet operating costs, voyage costs, and capital costs) of the liner company. The joint chance constraint in the model requires that the transportation demand of the contract shipper be satisfied with a pre-determined probability. We then reformulate the model as a second-order cone programming and design a customized algorithm to explore the global optimal solution based on the outer approximation algorithm framework. This paper can serve as a baseline distribution-free model for solving liner fleet deployment and slot allocation joint optimization problems.

本文针对海运领域在集装箱运输需求不确定条件下的经典“班轮船队部署与舱位分配”联合优化问题展开研究。鉴于集装箱运输需求深受全球经济与政治格局影响，本文放宽了既有研究中“需求分布已知”的假设。借助分布鲁棒优化理论的进展，我们构建了一个两阶段、数据驱动的鲁棒机会约束模型。该“分布无关”模型仅需有限的历史需求数据作为输入，并联合优化各航线所配置班轮的船型（即运力规模）与数量，以及各航段的集装箱分配方案，以最大化班轮公司的利润（即集装箱运输收入减去船队运营成本、航次成本与资本成本）。模型中的联合机会约束要求以预设概率满足签约托运人的运输需求。随后，我们将模型等价重构为二阶锥规划（SOCP），并在外近似算法框架下设计了定制化算法以寻求全局最优解。本文可作为求解班轮船队部署与舱位分配联合优化问题的基线分布无关模型。

文章10

● 题目：Modeling instantaneous queuing effects in the traffic assignment problem with consideration of demand fluctuations in the modeling period

考虑建模期内需求波动的交通分配问题中瞬时排队效应建模

● 原文链接：https://doi.org/10.1016/j.trb.2025.103248

● 作者：Yuxin Shi (a), William H.K. Lam (a), Hao Fu (b), H.W. Ho (c), Mei Lam Tam (a), Wei Ma (a)

● 发布时间：2025-7

● 摘要：

Instantaneous traffic queues, introducing fluctuated queuing delays, significantly affect the journey times and route choices of travelers particularly during the peak hour periods in congested road networks. Distinguished from average residual queues in static traffic assignment problems, instantaneous queues form and dissipate within minuscule time frames of a modeling period, triggered by an inflow exceeding link capacity, owning to instantaneous demand fluctuations. The inclusion of instantaneous queuing effects is of paramount importance, given their more frequent occurrence compared to residual queues. However, little attention has been given to these instantaneous queuing effects in static traffic assignment models for strategic planning. To fill this gap, this paper proposes a novel instantaneous traffic assignment (ITA) model to incorporate instantaneous queuing effects in congested road networks, accounting for within-period demand fluctuations. The enhanced ITA modeling framework is proposed encompassing two fixed-point problems for network loading and a logit-based stochastic user equilibrium assignment model. The ITA model is formulated as an equivalent variational inequality problem. The mathematical properties of the proposed model such as the stability of the unique solutions can be rigorously proved. An improved method of successive weighted average algorithm with an adaptive step size is developed to solve the proposed model in order to facilitate the examination of instantaneous queuing effects in real-world contexts for large-scale networks. Numerical examples are conducted to demonstrate the merits and efficacy of the proposed ITA model. The feasibility and applicability of the proposed model in reality are further illustrated in case studies of three different road networks.

在拥堵路网的高峰时段，瞬时交通排队会引入波动的排队延误，显著影响出行者的出行时间与路径选择。不同于静态交通分配问题中的平均残余队列，瞬时队列由于流入量瞬时超过路段通行能力而在极短时间内形成与消散，其根源在于建模期内的需求波动。鉴于瞬时排队较残余队列更为频繁地出现，将其纳入模型至关重要。然而，战略规划场景下的静态交通分配模型对瞬时排队效应关注不足。为弥补这一空白，本文提出一种新的瞬时交通分配（ITA）模型，在拥堵路网中将建模期内需求波动导致的瞬时排队效应纳入考虑。所提出的增强型 ITA 建模框架包含两个用于网络加载的定点问题以及一个基于 Logit 的随机用户均衡分配模型。ITA 模型被表述为一个等价的变分不等式问题，并对其数学性质（如解的唯一性与稳定性）给出了严格证明。为求解该模型，本文提出改进的带自适应步长的逐次加权平均（MSWA）算法，以便在大规模路网的实际情境中检验瞬时排队效应。数值算例展示了所提 ITA 模型的优点与有效性，而三个不同道路网络的案例研究进一步说明了模型在现实中的可行性与适用性。