Transportation Research Part C-Emerging Technologies最新文献_第4页

A joint, context-aware neural network-based travel demand and scheduling model 基于上下文感知的联合神经网络的出行需求和调度模型

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

Transportation Research Part C-Emerging Technologies

Pub Date : 2026-01-14 DOI: 10.1016/j.trc.2025.105512

Joel Fredriksson, Anders Karlström

Recent advancements in machine learning, and neural networks in particular, have introduced new opportunities for activity-based travel demand modeling and scheduling, providing data-driven alternatives to traditional theory-driven methods. While previous machine learning-based scheduling models have integrated combinations of activity, destination, and mode choice as separate sub-models, none have yet, to the best of our knowledge, unified these components into a single, jointly learned framework.

This paper introduces Skyline-NNjoint, a novel fully neural network-based scheduling model that jointly predicts an agent’s activity, destination, and mode choice decisions at each discrete time step throughout the day. To capture substitution effects and interdependencies among alternatives, the model introduces a Global Context Module (GCM) that enables each alternative to adjust its attractiveness based on the context of all others. While similar context-based approaches have been used in other domains, this is, to the best of our knowledge, the first application of such a mechanism in travel demand modeling. This provides a data-driven approach to relax the Independence of Irrelevant Alternatives (IIA) assumption inherent in multinomial logit models. The effectiveness of the GCM is evaluated by comparing Skyline-NNjoint to a baseline version without it, isolating its contribution to model performance.

The model is trained on travel survey data from Stockholm and evaluated using both cross-entropy loss and simulated daily activity–travel trajectories. Cross-entropy loss results confirm that the GCM improves predictive performance. Simulation results show that Skyline-NNjoint produces patterns of activity participation, trip timing, and mode choice that closely match observed data. Notably, the model accurately reproduces mode distributions across activity purposes, highlighting its capacity to capture interdependencies in joint decision-making.

机器学习，特别是神经网络的最新进展，为基于活动的旅行需求建模和调度带来了新的机会，为传统的理论驱动方法提供了数据驱动的替代方案。虽然以前基于机器学习的调度模型已经将活动、目的地和模式选择的组合作为单独的子模型集成在一起，但据我们所知，还没有一个模型将这些组件统一到一个单独的、共同学习的框架中。本文介绍了Skyline-NNjoint，这是一种新颖的全神经网络调度模型，可以在一天中每个离散时间步联合预测智能体的活动、目的地和模式选择决策。为了捕捉替代效应和替代方案之间的相互依赖性，该模型引入了一个全局上下文模块（GCM），使每个替代方案能够根据所有其他替代方案的上下文调整其吸引力。虽然类似的基于上下文的方法已经在其他领域使用，但据我们所知，这是这种机制在旅行需求建模中的首次应用。这提供了一种数据驱动的方法来放松多项式逻辑模型中固有的不相关选择独立性（IIA）假设。GCM的有效性是通过比较Skyline-NNjoint和没有GCM的基线版本来评估的，隔离GCM对模型性能的贡献。该模型在斯德哥尔摩的旅行调查数据上进行训练，并使用交叉熵损失和模拟的日常活动-旅行轨迹进行评估。交叉熵损失结果证实了GCM提高了预测性能。仿真结果表明，Skyline-NNjoint产生的活动参与、出行时间和模式选择模式与观测数据非常吻合。值得注意的是，该模型准确地再现了跨活动目的的模式分布，突出了其在联合决策中捕获相互依赖性的能力。

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引用次数: 0

Large language model guided deep reinforcement learning for safe autonomous vehicle decision making 基于大语言模型的深度强化学习自动驾驶汽车安全决策

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

Transportation Research Part C-Emerging Technologies

Pub Date : 2026-01-12 DOI: 10.1016/j.trc.2025.105511

Hao Pang, Zhenpo Wang, Guoqiang Li

Deep reinforcement learning (DRL) has shown promising potential for decision-making in autonomous driving. However, it requires extensive interaction with the environment and generally has low learning efficiency. To address these challenges, this paper proposes a novel large language model (LLM) guided deep reinforcement learning (LGDRL) framework for the decision-making problem in autonomous driving. Leveraging the powerful reasoning capabilities of LLMs, an LLM-based driving expert is designed to provide intelligent guidance in the DRL learning process. Subsequently, an innovative expert policy constrained algorithm and a novel LLM-intervened interaction mechanism are developed to efficiently integrate the guidance from the LLM expert to enhance the performance of DRL decision-making policies. Extensive experiments are conducted to evaluate the performance of the proposed LGDRL method. The results demonstrate that our proposed method effectively leverages expert guidance to enhance both learning efficiency and performance of DRL, achieving superior driving performance. Moreover, it enables the DRL agent to maintain consistent and reliable performance in the absence of LLM expert guidance, which is promising for real-world applications. The supplementary videos are available at https://bitmobility.github.io/LGDRL/.

深度强化学习（DRL）在自动驾驶决策方面显示出了巨大的潜力。然而，它需要与环境进行广泛的交互，学习效率一般较低。为了解决这些挑战，本文提出了一种新的大语言模型（LLM）引导深度强化学习（LGDRL）框架来解决自动驾驶中的决策问题。利用llm强大的推理能力，基于llm的驾驶专家可以在DRL学习过程中提供智能指导。随后，提出了一种创新的专家策略约束算法和一种新的LLM干预交互机制，以有效地整合LLM专家的指导，提高DRL决策策略的性能。我们进行了大量的实验来评估所提出的LGDRL方法的性能。结果表明，本文提出的方法有效地利用专家指导，提高了DRL的学习效率和性能，取得了优异的驾驶性能。此外，它使DRL代理能够在没有LLM专家指导的情况下保持一致和可靠的性能，这对于实际应用来说是很有希望的。补充视频可在https://bitmobility.github.io/LGDRL/上获得。

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引用次数: 0

The dynamic system optimal departure time choice problem for a bottleneck with a stochastic capacity: Model formulation and solution algorithm 随机容量瓶颈的动态系统最优出发时间选择问题：模型的表述与求解算法

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

Transportation Research Part C-Emerging Technologies

Pub Date : 2026-01-10 DOI: 10.1016/j.trc.2025.105510

Yao Li , Jie Wang , Zijun Wu , Jiancheng Long

This paper concerns a novel dynamic system optimal departure time choice (DSO-DTC) problem that takes the capacity uncertainty at a bottleneck into account. The existing traditional analytical methods are unable to yield satisfactory results without stringent conditional assumptions. We innovatively discretize and reformulate it with linear programming (LP) and nonlinear programming (NLP), respectively. While the LP problem can be solved exactly with a standard solver like CPLEX, its complexity grows dramatically when the underlying discretization becomes a little finer. Therefore, we propose a sensitivity analysis-based (SAB) algorithm for the NLP problem instead, and further refine this algorithm with sophisticated strategies. Our experimental study demonstrates that the algorithm not only achieves superior solution efficiency and quality but also exhibits enhanced scalability in terms of discretization accuracy when compared to the benchmark solver CPLEX. Besides, the model enables us to efficiently study the impact of bottleneck capacity uncertainty on the performance of the bottleneck and on the efficiency of tolling strategies, which can be hardly achieved by traditional bottleneck model analysis methods.

本文研究了一种考虑瓶颈处容量不确定性的动态系统最优出发时间选择问题。现有的传统分析方法如果没有严格的条件假设，就无法得到令人满意的结果。我们创新性地分别用线性规划（LP）和非线性规划（NLP）对其进行离散化和重新表述。虽然LP问题可以用CPLEX这样的标准求解器精确地解决，但当底层离散化变得更精细时，其复杂性会急剧增加。因此，我们提出了一种基于灵敏度分析（SAB）的算法来代替NLP问题，并使用复杂的策略进一步改进该算法。我们的实验研究表明，与基准求解器CPLEX相比，该算法不仅具有优越的求解效率和质量，而且在离散化精度方面具有增强的可扩展性。此外，该模型可以有效地研究瓶颈容量不确定性对瓶颈性能和收费策略效率的影响，这是传统瓶颈模型分析方法难以实现的。

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引用次数: 0

Dynamic on-demand delivery with spatial divisions of labor 动态按需配送，实现空间分工

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

Transportation Research Part C-Emerging Technologies

Pub Date : 2026-01-09 DOI: 10.1016/j.trc.2025.105509

Yue Yang , André van Renssen , Mohsen Ramezani

The rapid evolution of on-demand delivery services has significantly influenced traditional logistics, particularly in urban areas where there is a surge in customer demand for timely and efficient delivery of small to medium-sized parcels. This paper investigates the concept of spatial divisions of labor in on-demand deliveries where the delivery network is partitioned into several operational regions, each managed by designated couriers. To facilitate parcel transshipment between regions, a set of accessible lockers is strategically placed at the shared borders of adjacent regions. We introduce and address a multi-hop delivery with spatial divisions of labor (MDSDL) problem, which involves dynamic parcel-courier dispatching and routing to minimize total operational costs. Within a rolling-horizon decision framework, the MDSDL problem is decomposed into two interdependent subproblems: (i) Region-Level Path Optimization (RLPO) that determines the coarse-grained, multi-hop path each parcel should take through the network, from its pickup region to its destination region, via the lockers, with the objective of minimizing delivery lateness. This path specifies the sequence of service regions a parcel must traverse to reach its final destination. (ii) Courier Route Optimization (CRO) that manages fine-grained, intra-region dispatching and routing by assigning incoming pickup and drop-off tasks to local couriers, who each has a continuously updated schedule. Subsequently, we develop a novel heuristic approach to dynamically solve RLPO and CRO in real time considering a rolling-horizon formulation. Extensive comparative experiments are conducted to demonstrate the advantages of the proposed approach. Implementing spatial divisions of labor not only enhances system efficiency and reduces operational costs but also improves the customer experience by reducing lateness and shortening ready-to-pickup times.

按需递送服务的迅速发展对传统物流产生了重大影响，特别是在城市地区，客户对及时、有效递送中小型包裹的需求激增。本文研究了按需配送中空间分工的概念，其中配送网络被划分为几个操作区域，每个区域由指定的快递员管理。为了方便不同地区之间的包裹转运，在邻近地区的共同边界处策略性地设置了一套无障碍储物柜。我们介绍并解决了一种具有空间分工的多跳交付（MDSDL）问题，该问题涉及动态包裹递送和路由，以最大限度地降低总运营成本。在滚动水平决策框架中，MDSDL问题被分解为两个相互依赖的子问题：(i)区域级路径优化（RLPO），确定每个包裹应该通过网络的粗粒度多跳路径，从其取件区域到目的地区域，通过储物柜，目标是最小化交付延迟。此路径指定包裹到达最终目的地必须经过的服务区域序列。（ii）快递路线优化（CRO）管理细粒度的、区域内的调度和路线，将进站取件和送件任务分配给当地的快递员，他们每个人都有一个不断更新的时间表。随后，我们开发了一种新的启发式方法，考虑滚动地平线公式，实时动态求解RLPO和CRO。大量的对比实验证明了该方法的优越性。实施空间分工不仅可以提高系统效率和降低运营成本，还可以通过减少延迟和缩短准备提货时间来改善客户体验。

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引用次数: 0

Multi-reservoir traffic dynamics: Outflow network MFD and state estimation with sparse traffic data 多水库交通动力学：基于稀疏交通数据的流出网络MFD和状态估计

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

Transportation Research Part C-Emerging Technologies

Pub Date : 2026-01-08 DOI: 10.1016/j.trc.2025.105504

Omid Mousavizadeh, Mehdi Keyvan-Ekbatani

This study introduces an innovative framework for traffic state estimation in multi-reservoir networks, tackling the challenges posed by sparse data in urban traffic networks. By integrating Floating Car Data (FCD) and Loop Detector Data (LDD), the framework estimates traffic dynamics, such as inflows, outflows, transfer flows, and accumulation, without requiring detailed trip information or path flow distribution methods. A step forward in this study is a method for the estimation of internal outflow and transfer flows, enabling the direct estimation of outflow Network Macroscopic Fundamental Diagrams (outflow-NMFDs) which overcomes the shortcomings of outflow-NMFD estimation through the full set of sensors or indirect estimation via the production-NMFD. As argued, the outflow-NMFD is deemed the more robust foundation for the accumulation-based modelling in multi-reservoir systems. Accordingly, the estimated outflow-NMFD is used to build the accumulation-based model, in which the model outputs are combined with sparse real-time measurements, improving the model’s alignment with actual traffic conditions. The framework’s adaptability allows it to function effectively under varying levels of probe vehicle penetration, making it suitable for real-world scenarios where data availability can be inconsistent or limited. Simulation results validate the model’s robustness in capturing both steady-state and dynamic traffic behaviours, maintaining high accuracy even during abrupt demand changes. Furthermore, the framework performs reliably under stochastic conditions, demonstrating its resilience to daily traffic fluctuations. By reducing dependence on widespread sensor deployment across the network or its boundaries, this cost-effective approach offers a practical solution for real-time traffic monitoring and management in multi-reservoir systems, even when the boundaries of the system are not fixed in the network.

本研究提出了一种创新的多水库网络交通状态估计框架，解决了城市交通网络中数据稀疏带来的挑战。通过整合浮动车数据（FCD）和环路检测器数据（LDD），该框架可以估计交通动态，如流入、流出、换乘流量和累积，而不需要详细的行程信息或路径流分布方法。本研究进一步提出了一种估算内部流出和转移流量的方法，可以直接估算流出网络宏观基本图（outflow- nmfd），克服了通过一整套传感器估算流出网络宏观基本图或通过生产网络宏观基本图间接估算流出网络宏观基本图的缺点。如上所述，流出量nmfd被认为是多储层系统中基于累积的建模的更强大的基础。据此，利用估算出的流量nmfd建立基于累积的模型，将模型输出与稀疏实时测量相结合，提高了模型与实际交通状况的一致性。该框架的适应性使其能够在不同探测车辆渗透水平下有效运行，使其适用于数据可用性不一致或有限的现实场景。仿真结果验证了该模型在捕获稳态和动态交通行为方面的鲁棒性，即使在需求突变时也能保持较高的准确性。此外，该框架在随机条件下运行可靠，证明了其对日常流量波动的弹性。通过减少对网络或其边界上广泛部署的传感器的依赖，这种经济有效的方法为多水库系统的实时流量监控和管理提供了一种实用的解决方案，即使系统的边界在网络中不固定。

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引用次数: 0

U-Aerodrome: Data-driven and risk-bounded airspace reconfiguration for safe integration of urban air mobility at aerodrome U-Aerodrome：数据驱动和风险有限的空域重构，用于机场城市空中机动性的安全集成

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

Transportation Research Part C-Emerging Technologies

Pub Date : 2026-01-07 DOI: 10.1016/j.trc.2025.105506

Xinting Hu , Bizhao Pang , Sameer Alam , Mir Feroskhan

Urban Air Mobility (UAM) offers promising solutions for alleviating urban congestion and enabling seamless air transportation. However, its integration near aerodromes is limited by static no-fly zones and traditional airspace management practices. Existing boundary-setting methods often depend on oversimplified assumptions about trajectory distributions or apply rigid spatial constraints, which can lead to safety risks and inefficient airspace utilization. To address these limitations, this study introduces U-Aerodrome, a data-driven and risk-bounded airspace reconfiguration framework designed to support the safe and flexible integration of UAM operations near controlled aerodromes. The approach employs procedure-based trajectory classification and equal-altitude sampling to ensure equitable and non-biased representation of flight patterns. It further incorporates probabilistic boundary estimation that accommodates both Gaussian and non-Gaussian distributions, as well as a time-dependent boundary update mechanism responsive to dynamic traffic demand. The framework is validated using real-world data collected from Singapore Changi Airport. Results show that U-Aerodrome reduces missed detections and conservative volume compared to a purely Gaussian baseline, yielding 30.95 % average safety improvement and 15.25 % higher availability. The time-dependent mechanism further reduces unnecessary restrictions by an additional 20.02 % on average compared with baselines assuming static boundaries. The framework supports flexible and statistically grounded planning for safe UAM access near aerodromes.

城市空中交通（UAM）为缓解城市拥堵和实现无缝空中运输提供了有前途的解决方案。然而，它在机场附近的整合受到静态禁飞区和传统空域管理实践的限制。现有的边界设置方法往往依赖于对轨迹分布的过于简化的假设或应用刚性的空间约束，这可能导致安全风险和低效的空域利用。为了解决这些限制，本研究引入了U-Aerodrome，这是一个数据驱动和风险有限的空域重构框架，旨在支持受控机场附近UAM操作的安全和灵活集成。该方法采用基于程序的轨迹分类和等高度抽样，以确保公平和无偏见地表示飞行模式。它进一步结合了适应高斯和非高斯分布的概率边界估计，以及响应动态交通需求的时变边界更新机制。该框架使用从新加坡樟宜机场收集的真实数据进行验证。结果表明，与纯高斯基线相比，U-Aerodrome减少了漏检和保守体积，平均安全性提高了30.95%，可用性提高了15.25%。与假设静态边界的基线相比，依赖时间的机制进一步减少了不必要的限制，平均减少了20.02%。该框架支持机场附近安全UAM通道的灵活和统计基础规划。

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引用次数: 0

CONTINA: Confidence interval for traffic demand prediction with coverage guarantee CONTINA：具有覆盖保证的交通需求预测的置信区间

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

Transportation Research Part C-Emerging Technologies

Pub Date : 2025-12-30 DOI: 10.1016/j.trc.2025.105502

Chao Yang , Xiannan Huang , Shuhan Qiu , Yan Cheng

Accurate short-term traffic demand prediction is critical for the operation of traffic systems. Besides point estimation, the confidence interval of the prediction is also of great importance. Many models for traffic operations, such as shared bike rebalancing and taxi dispatching, take into account the uncertainty of future demand and require confidence intervals as the input. However, existing methods for confidence interval modeling rely on strict assumptions, such as unchanging traffic patterns and correct model specifications, to guarantee enough coverage. Therefore, the confidence intervals provided could be invalid, especially in a changing traffic environment. To fill this gap, we propose an efficient method, CONTINA (Conformal Traffic Intervals with Adaptation) to provide interval predictions that can adapt to external changes. By collecting the errors of interval during deployment, the method can adjust the interval in the next step by widening it if the errors are too large or shortening it otherwise. Furthermore, we theoretically prove that the coverage of the confidence intervals provided by our method converges to the target coverage level. Experiments across four real-world datasets and prediction models demonstrate that the proposed method can provide valid confidence intervals with shorter lengths. Our method can help traffic management personnel develop a more reasonable and robust operation plan in practice. And we release the code, model and dataset in GitHub.

准确的短期交通需求预测对交通系统的运行至关重要。除了点估计外，预测的置信区间也很重要。许多交通运营模型，如共享单车再平衡和出租车调度，都考虑了未来需求的不确定性，并需要置信区间作为输入。然而，现有的置信区间建模方法依赖于严格的假设，如不变的交通模式和正确的模型规范，以保证足够的覆盖。因此，所提供的置信区间可能是无效的，特别是在不断变化的流量环境中。为了填补这一空白，我们提出了一种有效的方法CONTINA （Conformal Traffic Intervals with Adaptation）来提供能够适应外部变化的区间预测。该方法通过收集部署时的间隔误差，如果误差太大，则可以扩大间隔，否则可以缩短间隔，从而在下一步调整间隔。进一步，从理论上证明了该方法提供的置信区间的覆盖收敛于目标覆盖水平。在四个实际数据集和预测模型上的实验表明，该方法可以提供较短长度的有效置信区间。该方法可以帮助交通管理人员在实践中制定更合理、更稳健的运营计划。我们在GitHub上发布代码、模型和数据集。

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引用次数: 0

Uncertainty quantification for joint demand prediction of multi-modal ride-sourcing services using spatiotemporal Mixture-of-Expert neural network 基于时空混合专家神经网络的多模式拼车服务联合需求预测的不确定性量化

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

Transportation Research Part C-Emerging Technologies

Pub Date : 2025-12-29 DOI: 10.1016/j.trc.2025.105507

Xiaobing Liu , Yu Duan , Yangli-ao Geng , Yun Wang , Qingyong LI , Xuedong Yan , Ziyou Gao

Given the sparse uncertainty and highly imbalanced distribution of origin-destination demand in ride-sourcing, probabilistic prediction methods offer rich information to enhance operation decision-making reliability. However, existing uncertainty quantification methods face critical limitations: parametric approaches struggle with irregularities of real-world demand distributions. In contrast, nonparametric methods based on quantile regression are scarcely implemented and suffer from quantile crossing, compromising probabilistic consistency. Furthermore, current approaches seldom consider the crucial inter-service correlations among ride-sourcing services. This paper introduces the Spatiotemporal Mixture-of-Experts Residual-based Multi-quantile Regressive Network (ST-MoE-RMQRN).¹ This novel hybrid framework integrates distribution-free quantile estimation with service correlation modeling. Our three main contributions are: 1) a Residual-based Multi-quantile Regressor that employs monotonicity-constrained residual fitting to ensure non-crossing quantiles while preserving distributional flexibility; 2) an environment-aware MoE architecture that captures service correlations through multiple shared-expert networks, addressing potential demand switches among service modals with sensitivity to temporal heterogeneity and exogenous contextual factors such as weather conditions; 3) comprehensive validation on two real-world ride-sourcing datasets demonstrating that our approach consistently outperforms recent probabilistic predictive models in both deterministic and probabilistic forecast metrics, including the Spatiotemporal Zero-Inflated Negative Binomial Graph Neural Network (STZINB-GNN). Additionally, data-wise and component-wise ablation studies confirm the effectiveness of the entire framework and each part of it, showing that explicitly modeling inter-service dependencies substantially improves both deterministic accuracy and probabilistic calibration. Our model is highly efficient in representing extreme demand fluctuations and data-sparse scenarios, which promise to address existing operational inefficiencies faced by transitioning Transportation Network Companies from reactive to anticipatory decision-making.

考虑到网约车需求的稀疏不确定性和高度不均衡分布，概率预测方法提供了丰富的信息，提高了运营决策的可靠性。然而，现有的不确定性量化方法面临着严重的局限性：参数化方法与现实世界需求分布的不规则性作斗争。相比之下，基于分位数回归的非参数方法很少实现，并且存在分位数交叉，影响概率一致性。此外，目前的方法很少考虑乘车外包服务之间至关重要的服务间相关性。本文介绍了基于时空专家混合残差的多分位数回归网络（ST-MoE-RMQRN）该框架将无分布分位数估计与服务相关建模相结合。我们的三个主要贡献是：1)基于残差的多分位数回归器，它采用单调性约束残差拟合来确保不交叉分位数，同时保持分布的灵活性；2)环境感知的MoE架构，通过多个共享专家网络捕获服务相关性，解决服务模式之间潜在的需求转换，对时间异质性和外生上下文因素（如天气条件）敏感；3)在两个现实世界的打车数据集上进行全面验证，表明我们的方法在确定性和概率预测指标上始终优于最近的概率预测模型，包括时空零膨胀负二项图神经网络（STZINB-GNN）。此外，数据和组件的消融研究证实了整个框架及其每个部分的有效性，表明显式建模服务间依赖关系大大提高了确定性精度和概率校准。我们的模型在表示极端需求波动和数据稀疏场景方面非常高效，有望解决交通网络公司从被动决策向预期决策转变所面临的现有运营效率低下问题。

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引用次数: 0

Overcoming computational challenges in air transportation: A quantum computing perspective of the status quo and future applicability 克服航空运输中的计算挑战：现状和未来适用性的量子计算视角

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

Transportation Research Part C-Emerging Technologies

Pub Date : 2025-12-27 DOI: 10.1016/j.trc.2025.105505

Zhuoming Du, Sebastian Wandelt, Xiaoqian Sun

Recent research breakthroughs in quantum computing, such as Microsoft’s topological qubits, hold the promise of revolutionizing complex optimization problems, particularly in the air transportation industry. This study aims to estimate the mid-term scalability of quantum computing in air transportation, focusing on prevalent optimization problems including network design, airline scheduling, and gate assignment. These problems are computationally intensive and often intractable for classical computers due to their highly combinatorial nature. We develop a framework to assess the potential scalability of quantum algorithms for these problems, considering factors such as qubit count and error rates. Our findings suggest that significant advancements in quantum hardware and algorithms are necessary before quantum computing can outperform classical methods in this domain. Therefore, while quantum computing offers a promising tool for solving complex optimization problems in air transportation, its real-world application remains a distant goal. We believe that our work helps guiding researchers and industry professionals in their pursuit of quantum-enhanced air transport solutions.

最近在量子计算方面的研究突破，如微软的拓扑量子比特，有望彻底改变复杂的优化问题，特别是在航空运输行业。本研究旨在评估量子计算在航空运输中的中期可扩展性，重点关注网络设计、航班调度和登机口分配等常见优化问题。这些问题是计算密集型的，由于它们的高度组合性，对于经典计算机来说往往是难以处理的。我们开发了一个框架来评估这些问题的量子算法的潜在可扩展性，考虑到诸如量子比特计数和错误率等因素。我们的研究结果表明，在量子计算能够超越该领域的经典方法之前，量子硬件和算法的重大进步是必要的。因此，虽然量子计算为解决航空运输中复杂的优化问题提供了一个有前途的工具，但其在现实世界中的应用仍然是一个遥远的目标。我们相信，我们的工作有助于指导研究人员和行业专业人士寻求量子增强航空运输解决方案。

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引用次数: 0

Robotic sorting systems: Robot management and layout design optimization 机器人分拣系统：机器人管理和布局设计优化

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

Transportation Research Part C-Emerging Technologies

Pub Date : 2025-12-26 DOI: 10.1016/j.trc.2025.105500

Tong Zhao , Xi Lin , Fang He , Hanwen Dai

In the contemporary logistics industry, automation plays a pivotal role in enhancing production efficiency and expanding industrial scale. In particular, autonomous mobile robots have become integral to modernization efforts in warehouses. One noteworthy application in robotic warehousing is the robotic sorting system (RSS), which is distinguished by its cost-effectiveness, simplicity, scalability, and adaptable throughput control. Previous research on RSS efficiency often assumed an ideal robot management system, ignoring potential traffic delays and assuming constant travel times. To address this gap, we introduce a novel robot traffic management method, named Rhythmic Control for the Sorting Scenario (RC-S), for RSS operations, along with an analytical estimation formula that establishes the quantitative relationship between system performance and configurations. Simulations validate that RC-S reduces average service time by 10.3 % compared to the classical cooperative A* algorithm, while also improving throughput and runtime. Based on the performance analysis of RC-S, we develop a layout optimization model that considers system configurations, desired throughput, and costs to minimize expenses and determine the optimal layout. Numerical studies show that facility costs dominate at lower throughput levels, while labor costs prevail at higher throughput levels. Additionally, due to traffic efficiency limitations, an RSS is well-suited for small-scale operations like end-of-supply-chain distribution centers.

在现代物流业中，自动化对提高生产效率和扩大产业规模起着举足轻重的作用。特别是，自主移动机器人已经成为仓库现代化工作不可或缺的一部分。机器人仓储中一个值得注意的应用是机器人分拣系统（RSS），其特点是成本效益高、简单、可扩展和适应性强的吞吐量控制。以往的RSS效率研究通常假设一个理想的机器人管理系统，忽略潜在的交通延迟，并假设恒定的旅行时间。为了解决这一差距，我们引入了一种新的机器人交通管理方法，称为排序场景的节奏控制（RC-S），用于RSS操作，以及一个分析估计公式，该公式建立了系统性能和配置之间的定量关系。仿真结果表明，RC-S算法与传统的协同A*算法相比，平均服务时间减少了10.3%，同时提高了吞吐量和运行时间。基于RC-S的性能分析，我们建立了考虑系统配置、期望吞吐量和成本的布局优化模型，以最小化费用并确定最佳布局。数值研究表明，设备成本在低吞吐量水平下占主导地位，而劳动力成本在高吞吐量水平下占主导地位。此外，由于交通效率的限制，RSS非常适合像供应链末端配送中心这样的小规模操作。

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