Transportation Research Part C-Emerging Technologies最新文献_第6页

A hybrid ranking and selection procedure to solve simulation-based discrete network design problems 一种解决基于仿真的离散网络设计问题的混合排序和选择程序

IF 7.6 1区工程技术 Q1 TRANSPORTATION SCIENCE & TECHNOLOGY

Transportation Research Part C-Emerging Technologies

Pub Date : 2025-12-20 DOI: 10.1016/j.trc.2025.105438

Jinbiao Huo , Zhiyuan Liu , Ziyuan Gu , Churong Chen

With the rapid advancements of simulation techniques, simulation-based traffic management and control have gained increasing popularity. This study specifically targets the solution algorithm for simulation-based discrete network design problems (S-DNDPs). Previous research has often tackled S-DNDPs using surrogate-based optimization approaches. However, existing methods face challenges in accuracy due to discrete decision variables and insufficiently address the stochasticity and high computational costs of traffic simulators. Consequently, there is a pressing need to develop an efficient and effective method for S-DNDPs. To this end, this study introduces a novel simulation-based optimization framework, namely the ranking and selection (R&S) procedure, to solve S-DNDPs. The R&S procedure, deemed to be a variant of the enumeration method, does not rely on (approximate) gradient information of the S-DNDP. Instead, it explores all feasible solutions, intelligently allocates computational resources, and selects the optimal one accordingly. To efficiently solve S-DNDPs, a hybrid R&S procedure is proposed by incorporating a Bayesian screening procedure in a popular R&S procedure (i.e., the optimal computing budget allocation, OCBA). In addition, a self-adaptive scheme is developed to determine the computational budget in each iteration. This study demonstrated that in comparison with OCBA, the proposed R&S procedure achieves a higher lower bound of the probability of correct selection, leading to a more efficient allocation of computational resources. To address the scalability issue, this study provides a simple yet effective extension of the hybrid R&S procedure, enabling the method to be applied to large-scale problems where computational resources may not suffice to perform exhaustive evaluations. The proposed methods, including the hybrid R&S procedure and its extension, are validated on a real-world S-DNDP. Experimental results demonstrate the superior performance of the proposed methods against benchmark methods.

随着仿真技术的飞速发展，基于仿真的交通管理与控制越来越受到人们的欢迎。本研究特别针对基于仿真的离散网络设计问题（s - dndp）的求解算法。以前的研究通常使用基于代理的优化方法来解决s - dndp。然而，现有的方法由于决策变量的离散性而在精度上面临挑战，并且不能充分解决交通模拟器的随机性和高计算成本。因此，迫切需要开发一种高效的S-DNDPs方法。为此，本研究引入了一种新颖的基于仿真的优化框架，即排名和选择（R&；S）程序来求解S- dndp。R&；S过程被认为是枚举方法的一种变体，它不依赖于S- dndp的（近似）梯度信息。相反，它探索所有可行的解决方案，智能地分配计算资源，并相应地选择最优解决方案。为了有效地求解S- dndps，提出了一种混合R&；S程序，在流行的R&；S程序（即最优计算预算分配，OCBA）中加入贝叶斯筛选程序。此外，还提出了一种自适应方案来确定每次迭代的计算预算。本研究表明，与OCBA相比，所提出的R&；S过程实现了更高的正确选择概率下界，从而更有效地分配了计算资源。为了解决可扩展性问题，本研究提供了混合R&；S过程的简单而有效的扩展，使该方法能够应用于计算资源可能不足以执行详尽评估的大规模问题。所提出的方法，包括混合R&；S过程及其扩展，在实际的S- dndp上进行了验证。实验结果表明，与基准方法相比，所提出的方法具有优越的性能。

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引用次数: 0

Privacy risk of transportation location-Based data: Re-identification and de-anonymization 基于交通位置数据的隐私风险：再识别和去匿名化

IF 7.6 1区工程技术 Q1 TRANSPORTATION SCIENCE & TECHNOLOGY

Transportation Research Part C-Emerging Technologies

Pub Date : 2025-12-18 DOI: 10.1016/j.trc.2025.105495

Yi Zhang, Yaobang Gong, Madeline Shi, Xianfeng Yang

In the era of ubiquitous connectivity, vast quantities of location-based data are continuously generated, profoundly impacting both online services and offline research applications. Although this data drives significant advancements in fields such as autonomous vehicle trajectory control, customized recommendations, and travel behavior analysis, it simultaneously raises critical privacy concerns, notably the risk of individual re-identification from anonymized datasets. Previous research focused on pattern-based re-identification within isolated datasets, without effectively linking anonymized data to real-world identities and investigating the real-world implications of such privacy breaches. Moreover, the intrinsic mechanisms underlying the vulnerability of location-based datasets to de-anonymization attacks remain underexplored. This study addresses these gaps by proposing an inference model specifically tailored for individual re-identification and de-anonymization attacks on privacy-protected transportation location-based datasets. Additionally, a mathematical framework is introduced to quantify the de-anonymization rate and systematically analyze dataset vulnerability to such privacy attacks. Experimental evaluations conducted on datasets of varying sizes validate the effectiveness of our approach, demonstrating successful re-identification even when standard privacy protections are in place. Our findings emphasize the urgent need for enhanced privacy-preserving strategies and offer a comprehensive basis for understanding and mitigating privacy risks associated with location-based data.

在无所不在的连接时代，大量基于位置的数据不断产生，深刻影响着线上服务和线下研究应用。尽管这些数据推动了自动驾驶车辆轨迹控制、定制建议和出行行为分析等领域的重大进步，但同时也引发了关键的隐私问题，特别是匿名数据集的个人重新识别风险。以前的研究主要集中在孤立的数据集中基于模式的再识别，没有有效地将匿名数据与现实世界的身份联系起来，也没有调查这种隐私泄露的现实世界影响。此外，基于位置的数据集易受去匿名化攻击的内在机制仍未得到充分探讨。本研究通过提出一个专门针对基于隐私保护的交通位置数据集的个人再识别和去匿名化攻击量身定制的推理模型来解决这些差距。此外，还引入了一个数学框架来量化去匿名化率，并系统地分析数据集对此类隐私攻击的脆弱性。在不同大小的数据集上进行的实验评估验证了我们方法的有效性，即使在标准隐私保护到位的情况下，也证明了成功的重新识别。我们的研究结果强调了加强隐私保护策略的迫切需要，并为理解和减轻与基于位置的数据相关的隐私风险提供了全面的基础。

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引用次数: 0

Dual-mode control for dedicated-lane networks in mixed semi-autonomous vehicle traffic 混合半自主车辆交通专用车道网络的双模控制

IF 7.6 1区工程技术 Q1 TRANSPORTATION SCIENCE & TECHNOLOGY

Transportation Research Part C-Emerging Technologies

Pub Date : 2025-12-17 DOI: 10.1016/j.trc.2025.105491

Hao Guan , Xiangdong Chen , Qiang Meng , Chin Long Mak

The urban mobility landscape is evolving from traditional vehicles (TVs) to a mix including semi-autonomous vehicles (semi-AVs), which provide automation functions yet still require human oversight. A key characteristic of semi-AVs, often overlooked in existing mixed traffic studies, is their dual-mode driving capability, allowing control to switch between human drivers and semi-autonomous systems. Our study addresses this gap by focusing on mixed semi-AV traffic environments and leveraging the unique dual-mode driving capability of semi-AVs to enhance overall urban network efficiency. We propose a novel dual-mode control methodology for mixed semi-AV traffic, implementing dedicated lanes to avoid the complex interactions between autonomously driven semi-AVs (AD-SAVs) and manually driven vehicles. The critical AD-SAV proportion that maximizes network flow is identified by modeling the traffic dynamics of the dedicated-lane network using a three-dimensional macroscopic fundamental diagram (3D-MFD), providing a practical reference for effectively managing mixed traffic. A dual-mode pricing system is developed to dynamically adjust vehicle proportions through an adaptive pricing strategy. This strategy is encapsulated in an optimization model that utilizes a mixed logit model to determine the optimal pricing adjustments. Utilizing the analytical properties of the mixed traffic characterized by the 3D-MFD, an equivalent stage-wise reformulation model is constructed for real-time computation. By dynamically optimizing the proportion of AD-SAVs, our approach ensures efficient lane utilization, enhances overall traffic flow, and reduces congestion without requiring costly infrastructure modifications. Through extensive simulations, the proposed dual-mode control strategy demonstrates significant improvements in traffic efficiency, contributing a scalable, cost-effective solution for integrating semi-AVs into urban transportation networks.

城市交通格局正在从传统汽车（电视）演变为包括半自动驾驶汽车（semi-AVs）在内的混合汽车，半自动驾驶汽车提供自动化功能，但仍需要人工监督。在现有的混合交通研究中经常被忽视的半自动驾驶汽车的一个关键特征是其双模式驾驶能力，允许在人类驾驶员和半自动系统之间切换控制。我们的研究通过关注混合半自动驾驶交通环境，并利用半自动驾驶汽车独特的双模式驾驶能力来提高整体城市网络效率，从而解决了这一差距。本文提出了一种新的半自动驾驶混合交通双模式控制方法，通过实现专用车道来避免自动驾驶半自动驾驶（ad - sav）和手动驾驶车辆之间的复杂交互。利用三维宏观基本图（3D-MFD）对专用车道网络的交通动态进行建模，确定网络流量最大化的关键AD-SAV比例，为有效管理混合交通提供实用参考。设计了一种双模式定价系统，通过自适应定价策略对车辆比例进行动态调整。该策略被封装在一个优化模型中，该模型利用混合logit模型来确定最优定价调整。利用3D-MFD所表征的混合交通的解析特性，构建了等效的分阶段重构模型，用于实时计算。通过动态优化ad - sav的比例，我们的方法确保了有效的车道利用率，提高了整体交通流量，减少了拥堵，而不需要昂贵的基础设施改造。通过大量的仿真，提出的双模式控制策略证明了交通效率的显著提高，为将半自动驾驶汽车整合到城市交通网络中提供了一种可扩展的、经济高效的解决方案。

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引用次数: 0

Learning to retrieve containers: A scale-diverse deep reinforcement learning approach for the container retrieval problem 学习检索容器：针对容器检索问题的一种不同尺度的深度强化学习方法

IF 7.6 1区工程技术 Q1 TRANSPORTATION SCIENCE & TECHNOLOGY

Transportation Research Part C-Emerging Technologies

Pub Date : 2025-12-17 DOI: 10.1016/j.trc.2025.105496

Woo-Jin Shin, Inguk Choi, Sang-Hyun Cho, Hyun-Jung Kim

This study addresses the container retrieval problem (CRP), a key challenge in the storage yards of automated container terminals where operational efficiency directly affects vessel turnaround time and yard congestion. In storage yards, containers are stacked vertically to maximize space utilization; however, accessing one located below others requires relocating the blocking containers, leading to additional crane movements and delays. The CRP involves retrieving containers from multiple bays in a specified order while minimizing the total working time of the yard crane, with relocation position decisions being critical. The CRP poses several practical challenges: despite being

NP

-hard, real-world instances often involve hundreds of containers, requiring high-quality solutions in real time; yard configurations also vary widely and change frequently, demanding methods that adapt effectively to arbitrary layouts. We propose a novel deep reinforcement learning approach incorporating (1) a size-agnostic network architecture, enabling a single trained network to handle diverse yard configurations, and (2) a scale-diverse learning framework, which trains on a various yard scales using a normalized loss to improve generalization and scalability. Experiments on well-known benchmarks with several hundred containers show that the proposed method substantially outperforms existing baselines across a wide range of yard sizes. It also scales to instances with thousands of containers and maintains strong performance in dynamic settings where retrieval orders are revealed online. Solutions are produced within a second for realistic instances, confirming its effectiveness and practical applicability in real-world automated container terminals. The implementation and datasets used in this study are publicly available in the GitHub repository: https://github.com/operagang/CRP_RL.

本研究解决了集装箱回收问题（CRP），这是自动化集装箱码头堆场的一个关键挑战，其操作效率直接影响船舶周转时间和堆场拥堵。在堆场中，集装箱垂直堆放，最大限度地利用空间；然而，进入位于其他集装箱下方的集装箱需要重新安置阻塞的集装箱，导致额外的起重机移动和延误。CRP包括按照指定的顺序从多个货舱中取出集装箱，同时最大限度地减少堆场起重机的总工作时间，重新定位的决定至关重要。CRP提出了几个实际挑战：尽管np困难，但现实世界的实例通常涉及数百个容器，需要实时提供高质量的解决方案；院子的配置也变化很大，变化频繁，要求方法有效地适应任意布局。我们提出了一种新的深度强化学习方法，包括：(1)一个大小无关的网络架构，使单个训练的网络能够处理不同的码位配置；(2)一个规模不同的学习框架，它使用归一化损失在不同的码位尺度上进行训练，以提高泛化和可扩展性。在数百个集装箱的知名基准测试中进行的实验表明，所提出的方法在广泛的码码尺寸范围内大大优于现有的基线。它还可以扩展到具有数千个容器的实例，并在在线显示检索顺序的动态设置中保持强大的性能。针对实际情况，解决方案在一秒钟内生成，证实了其在实际自动化集装箱码头中的有效性和实用性。本研究中使用的实现和数据集可在GitHub存储库中公开获取：https://github.com/operagang/CRP_RL。

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引用次数: 0

In-plant autonomous mobile robot scheduling and routing problem considering battery consumption model 考虑电池消耗模型的厂内自主移动机器人调度与路由问题

IF 7.6 1区工程技术 Q1 TRANSPORTATION SCIENCE & TECHNOLOGY

Transportation Research Part C-Emerging Technologies

Pub Date : 2025-12-15 DOI: 10.1016/j.trc.2025.105488

Zhaofang Mao , Zhanghuang Xie , Kan Fang , Dian Huang , Zhao Zhao

The evolving landscape of larger and increasingly intricate systems calls for the adoption of diverse and adaptable autonomous mobile robots (AMRs) to meet the movement requirements within a factory. To examine the battery consumption characteristics of AMRs, our study introduces a novel perspective by investigating the relationship between carrying capacity and battery consumption. We propose to model this relationship using simulation techniques, thus enabling our study to better aligns with real-world applications. The primary objective of this study is to develop optimal charging strategies within the constraints of limited charging stations while simultaneously minimizing overall tardiness. We develop a mixed integer programming (MIP) model for the AMR scheduling and routing problem with battery consumption model (AMRSRP-BCM). A dynamic operator sequence reheating simulated annealing hyper-heuristic (DOS-RSAH) algorithm is designed to solve this problem. Extensive experiments are conducted to demonstrate the robustness of our model and algorithm while sensitivity analysis is presented to show the application of our study, and valuable insights are gained.

不断发展的更大、更复杂的系统要求采用多样化和适应性强的自主移动机器人（amr）来满足工厂内的运动要求。为了研究自动驾驶汽车的电池消耗特性，本研究引入了一个新的视角，即研究承载能力与电池消耗之间的关系。我们建议使用仿真技术对这种关系进行建模，从而使我们的研究能够更好地与现实世界的应用保持一致。本研究的主要目标是在有限充电站的约束下，制定最优充电策略，同时使整体延迟最小化。针对电池消耗模型（AMRSRP-BCM）下的AMR调度和路由问题，建立了一个混合整数规划（MIP）模型。针对这一问题，设计了一种动态算子序列再加热模拟退火超启发式算法（DOS-RSAH）。我们进行了大量的实验来证明我们的模型和算法的鲁棒性，同时提出了敏感性分析来展示我们的研究的应用，并获得了有价值的见解。

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引用次数: 0

Joint optimization of pricing, seat allocation and overbooking for high-speed railway system under demand uncertainty 需求不确定性下高速铁路系统定价、座位分配与超售联合优化

IF 7.6 1区工程技术 Q1 TRANSPORTATION SCIENCE & TECHNOLOGY

Transportation Research Part C-Emerging Technologies

Pub Date : 2025-12-15 DOI: 10.1016/j.trc.2025.105492

Jing Xu , Lianbo Deng , Yihan Gao , Wei Liu

This paper examines the integrated optimization of pricing, seat allocation, and overbooking strategies in high-speed railway (HSR) operations. Overbooking helps address inefficiencies arising from empty seats due to passenger no-shows or last-minute cancellations. The complexity of the problem stems from two main factors: (i) the interdependence of pricing, seat allocation, and overbooking decisions, which jointly influence railway system performance; and (ii) the uncertainties associated with passenger demand and no-show behavior. To tackle these complexities, we develop a two-stage stochastic programming model aimed at maximizing expected railway profit. In the first stage, the model determines HSR pricing and seat allocation, including overbooking, while the second stage addresses potential denied boarding due to overbooking, based on the first-stage decisions. To solve the model, we employ a sample average approximation method and introduce a tailored progressive hedging algorithm (PHA). Additionally, we adapt commonly used surrogate-based optimization methods, such as Kriging and radial basis function models, for comparative analysis. Numerical studies on both a small-scale example and a real-world HSR line reveal that the proposed joint optimization significantly boosts railway profit across various demand and no-show scenarios, with the PHA solution approach outperforming surrogate-based methods in terms of both solution quality and computational efficiency.

本文研究了高速铁路运营中定价、座位分配和超售策略的综合优化。超额预订有助于解决由于乘客未到或最后一刻取消而导致的空座问题。问题的复杂性源于两个主要因素：(i)定价、座位分配和超售决策的相互依赖性，它们共同影响铁路系统的绩效；（ii）与乘客需求和未到行为相关的不确定性。为了解决这些复杂性，我们开发了一个两阶段的随机规划模型，旨在最大化铁路的预期利润。在第一阶段，该模型确定高铁定价和座位分配，包括超售，而第二阶段则基于第一阶段的决策，解决因超售而被拒绝登机的可能性。为了求解该模型，我们采用了样本平均逼近法，并引入了定制的渐进式套期保值算法（PHA）。此外，我们采用常用的基于代理的优化方法，如Kriging和径向基函数模型，进行比较分析。对一个小规模实例和一条真实高铁线路的数值研究表明，所提出的联合优化方法显著提高了铁路在各种需求和未到货情况下的利润，PHA求解方法在求解质量和计算效率方面都优于基于代理的方法。

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引用次数: 0

Towards full-scenario safety evaluation of automated vehicles: A volume-based method 面向自动驾驶车辆全场景安全评估：基于体积的方法

IF 7.6 1区工程技术 Q1 TRANSPORTATION SCIENCE & TECHNOLOGY

Transportation Research Part C-Emerging Technologies

Pub Date : 2025-12-12 DOI: 10.1016/j.trc.2025.105485

Hang Zhou , Chengyuan Ma , Shiyu Shen , Zhaohui Liang , Xiaopeng Li

With the rapid development of automated vehicles (AVs) in recent years, commercially available AVs are increasingly demonstrating high-level automation capabilities. However, most existing AV safety evaluation methods are primarily designed for simple maneuvers such as car-following and lane-changing. While suitable for basic tests, these methods are insufficient for assessing high-level automation functions deployed in more complex environments. First, these methods typically use crash rate as the evaluation metric, whose accuracy heavily depends on the quality and completeness of naturalistic driving environment data used to estimate scenario probabilities. Such data is often difficult and expensive to collect. Second, when applied to diverse scenarios, these methods suffer from the curse of dimensionality, making large-scale evaluation computationally intractable. To address these challenges, this paper proposes a novel framework that is designed for the safety ranking of AVs and has the potential to be extended to full-scenario applications. A unified model is first introduced to standardize the representation of diverse driving scenarios. This modeling approach constrains the dimension of most scenarios to a regular highway setting with three lanes and six surrounding background vehicles, significantly reducing dimensionality. To further avoid the limitations of the probability-based method, we propose a volume-based evaluation method that quantifies the proportion of risky scenarios within the entire scenario space. For car-following and lane-changing scenarios, we prove that the set of safe scenarios has convex properties under specific settings, enabling exact volume computation. Experimental results validate the effectiveness of the proposed volume-based method in representative scenarios, including car-following and lane-changing, using both AV behavior models from existing literature and six production AV models calibrated from field-test trajectory data in the Ultra-AV dataset.

随着近年来自动驾驶汽车（AVs）的快速发展，商用自动驾驶汽车越来越显示出高水平的自动化能力。然而，大多数现有的自动驾驶汽车安全性评估方法主要是针对车辆跟随和变道等简单操作而设计的。虽然这些方法适合于基本测试，但对于评估部署在更复杂环境中的高级自动化功能来说是不够的。首先，这些方法通常使用碰撞率作为评估指标，其准确性在很大程度上取决于用于估计场景概率的自然驾驶环境数据的质量和完整性。这类数据的收集往往困难且昂贵。其次，当应用于各种场景时，这些方法受到维度诅咒的影响，使得大规模评估在计算上变得难以处理。为了应对这些挑战，本文提出了一个新的框架，该框架专为自动驾驶汽车的安全排名而设计，并有可能扩展到全场景应用。首先引入统一的模型，对不同驾驶场景的表示进行标准化。这种建模方法将大多数场景的维度限制为具有三条车道和六辆周围背景车辆的常规高速公路设置，大大降低了维度。为了进一步避免基于概率方法的局限性，我们提出了一种基于体积的评估方法，该方法量化了整个场景空间中风险场景的比例。对于车辆跟随和变道场景，我们证明了安全场景集在特定设置下具有凸特性，从而实现精确的体积计算。实验结果验证了所提出的基于体积的方法在代表性场景中的有效性，包括车辆跟随和变道，使用现有文献中的自动驾驶行为模型和Ultra-AV数据集中的现场测试轨迹数据校准的六个生产自动驾驶模型。

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引用次数: 0

Using connected and automated vehicles to attenuate disturbances in freeway traffic 使用联网和自动化车辆来减弱高速公路交通中的干扰

IF 7.6 1区工程技术 Q1 TRANSPORTATION SCIENCE & TECHNOLOGY

Transportation Research Part C-Emerging Technologies

Pub Date : 2025-12-12 DOI: 10.1016/j.trc.2025.105497

Benjamin Coifman , Yuan Liu

Congested traffic is characterized by disturbances propagating upstream through the queued vehicles. This work seeks to use connected and automated vehicles (CAVs) to mitigate slow and stop waves in congested freeway traffic with the goals of improving safety by reducing unexpected deceleration waves while improving fuel efficiency and emissions by reducing the need to accelerate after disturbances pass. To smooth out traffic disturbances, the CAV implements a jam absorption strategy that integrates the instantaneous state information from the downstream vehicles to forecast the trajectory of the CAV’s leader and proactively respond to changes in state that have not yet reached the lead vehicle. Using empirical vehicle trajectory data, it is shown that the methodology can rapidly nullify stop and slow waves. Examples include the conversion of a 10 sec stop wave to steady and smoothly flowing traffic over a span of 15 CAVs.

One of the key innovations in this work is how the traffic state is forecasted using a model that is much simpler than prior work in this area while still being able to dynamically adapt to changes in the evolving traffic state. This paper also explores the impacts of varying the look ahead range, acceleration limits, and the parameters of the forecasting model. It is shown that the method is robust, still performing well when the forecasting model’s calibration is poor. While the different permutations result in small changes, in all the experiments the methodology continues working as intended: the first CAV was able to nullify the stop waves and subsequent CAVs continued to anneal the disturbances.

拥挤交通的特征是干扰通过排队车辆向上游传播。这项工作旨在使用联网和自动驾驶车辆（cav）来缓解拥挤的高速公路交通中的减速波和停车波，目标是通过减少意外减速波来提高安全性，同时通过减少干扰通过后的加速需求来提高燃油效率和排放。为了消除交通干扰，CAV实施了一种拥堵吸收策略，该策略集成了来自下游车辆的瞬时状态信息，以预测CAV领导者的轨迹，并主动响应尚未到达领先车辆的状态变化。利用经验车辆轨迹数据，表明该方法可以快速消除停止波和慢波。示例包括将10秒的停止波转换为15个cav跨度内的稳定和平稳流动的交通。这项工作的关键创新之一是如何使用比该领域先前工作简单得多的模型来预测交通状态，同时仍然能够动态适应不断变化的交通状态的变化。本文还探讨了改变前视范围、加速度限制和预测模型参数的影响。结果表明，该方法具有较强的鲁棒性，在预测模型标定较差的情况下仍然具有较好的效果。虽然不同的排列会导致微小的变化，但在所有的实验中，该方法都按照预期继续工作：第一个CAV能够消除停止波，随后的CAV继续退火干扰。

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引用次数: 0

On-demand DAR system considering traffic dynamics and network partitioning 考虑流量动态和网络划分的按需雷达系统

IF 7.6 1区工程技术 Q1 TRANSPORTATION SCIENCE & TECHNOLOGY

Transportation Research Part C-Emerging Technologies

Pub Date : 2025-12-11 DOI: 10.1016/j.trc.2025.105493

Cong Quoc Tran, Shang Jiang, Mehdi Keyvan-Ekbatani

On-demand shared mobility systems (e.g., pooled ride-hailing, dial-a-ride, and carpooling) could enhance urban mobility by providing more sustainable door-to-door transportation options, connecting communities to public transport hubs and combining the advantages of public transportation and private services. Among these, dial-a-ride (DAR) systems represent a classical form of shared mobility; however, most DAR models traditionally rely on traffic-independent travel times. The challenges lie in the complexity of incorporating traffic conditions and stakeholders’ involvement in the decision-making process, particularly under dynamic traffic settings. This study proposes a congestion-aware DAR framework to maximize the system’s profit while considering the dynamic nature of traffic conditions, real-time passenger requests and their sharing preferences. The proposed framework is able to capture the heterogeneity of traffic patterns by incorporating a regional dynamic traffic assignment (DTA) with a novel network partitioning strategy. Solution algorithms (i.e., quasi-dynamic partition selection, MSA-based algorithm for the regional DTA problem and dynamic on-demand DAR algorithm) are developed and numerically tested using the central business district network of Christchurch City, New Zealand, considering specific traffic scenarios. Numerical tests and comparative results demonstrate the practical applicability and operational efficiency (i.e., utilization rates of vehicles and rejection rates of customers’ requests) of the regional-based dynamic on-demand DAR system compared to the traditional dynamic DAR approach. Managerial insights and potential research directions are also provided.

按需共享交通系统（例如，拼车、叫车和拼车）可以通过提供更可持续的门到门交通选择，将社区与公共交通枢纽连接起来，并结合公共交通和私人服务的优势，增强城市机动性。其中，打车（DAR）系统代表了一种经典的共享出行形式；然而，大多数DAR模型传统上依赖于与交通无关的旅行时间。挑战在于将交通状况和利益相关者参与决策过程的复杂性，特别是在动态交通环境下。本研究在考虑交通状况的动态性、实时乘客需求及其共享偏好的情况下，提出了一个拥堵感知DAR框架，以实现系统利润最大化。该框架通过将区域动态流量分配（DTA）与新颖的网络划分策略相结合，能够捕获流量模式的异质性。以新西兰基督城中央商务区网络为例，结合具体交通场景，开发了求解算法（拟动态分区选择、基于msa的区域DTA问题算法和动态按需DAR算法）并进行了数值测试。数值试验和对比结果表明，与传统的动态DAR方法相比，基于区域的动态按需DAR系统的实用性和运行效率（即车辆利用率和客户请求拒绝率）。并提供了管理见解和潜在的研究方向。

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引用次数: 0

Consolidation of urban freight transportation and public transit systems: a generic strategic framework using Continuum Approximation 城市货运和公共交通系统的整合：使用连续统近似的一般战略框架

IF 7.6 1区工程技术 Q1 TRANSPORTATION SCIENCE & TECHNOLOGY

Transportation Research Part C-Emerging Technologies

Pub Date : 2025-12-11 DOI: 10.1016/j.trc.2025.105490

Mohammadhosein Pourgholamali , Mohammad Miralinaghi , Alireza Talebpour , Samuel Labi

This study proposes a consolidated transportation system (CTS) in which the public transit fleet is leveraged to transport packages in an urban area. Under the proposed CTS, the freight company pays a fee to use a transit fleet’s unused capacity for its last-mile delivery operations. A decision-making framework is developed to identify the set of transit lines and stations that optimize the CTS operation. This framework also identifies optimal service zones for the selected transit stations and the warehouses. The proposed decision-making framework is formulated as an optimization problem and solved using a Continuum Approximation (CA) technique and a customized Adaptive Large Neighborhood Search (ALNS). The performance of the proposed algorithm is evaluated by comparing its solutions with those obtained from CPLEX, demonstrating both accuracy and efficiency. The numerical results show some significant benefits for freight companies due to delivery cost reduction and revenue generation for transit agencies. Also, the effects of the different cost components on CTS’s economic feasibility are shown and measured through sensitivity analyses. It is shown that mini-depot opening costs and transit shipment fees not only determine the overall savings for freight companies but also directly affect the revenue generated for transit agencies. Furthermore, the analyses highlight inherent trade-offs between the economic benefits of the CTS and potential reductions in the level of service experienced by transit passengers. Overall, this study demonstrates the promising benefits of integrating public transit and last-mile delivery to enable a more efficient transportation system.

本研究提出了一个综合运输系统（CTS），其中公共交通车队被利用在城市地区运输包裹。根据拟议的CTS，货运公司支付费用，使用运输车队未使用的运力进行最后一英里的交付业务。制定了一个决策框架，以确定优化CTS运营的公交线路和车站。该框架还确定了选定的中转站和仓库的最佳服务区域。提出的决策框架是一个优化问题，并使用连续近似（CA）技术和自适应大邻域搜索（ALNS）进行求解。通过将该算法的解与CPLEX算法的解进行比较，验证了算法的准确性和效率。数值结果表明，由于运输成本的降低和运输机构的创收，货运公司获得了一些显著的好处。通过敏感性分析，揭示了不同成本构成对CTS经济可行性的影响。研究表明，小型仓库的开设成本和中转运输费用不仅决定了货运公司的总体节省，而且直接影响了中转机构的收入。此外，分析强调了CTS的经济效益与过境乘客所体验的服务水平可能下降之间的内在权衡。总的来说，这项研究证明了整合公共交通和最后一英里交付以实现更高效的交通系统的有希望的好处。

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