Communications in Transportation Research最新文献_第5页

Customized recursive model for drivers’ navigation compliance behaviors under abnormal events 自定义异常事件下驾驶员导航遵从行为递归模型

IF 12.5 Q1 TRANSPORTATION

Communications in Transportation Research

Pub Date : 2025-06-23 DOI: 10.1016/j.commtr.2025.100187

Kaijie Zou, Yaming Guo, Ke Zhang, Meng Li

In recent years, the resilience of road traffic during abnormal events has drawn considerable attention. Intelligent navigation systems, which proactively guide drivers along optimal routes in such situations, are viewed as a promising solution to facilitate recovery of road network performance. A key question arises: How do drivers choose routes when guided by navigation systems? This study addresses that question by modeling drivers’ decision-making behavior at each decision point using a nested framework. At the upper level, drivers decide whether to strictly follow the route recommended by the navigation system, while at the lower levels, they make route choices in the absence of guidance. A Customized Nested Dynamic Recursive Logit (C-NDRL) model was developed to capture these behaviors. Parameters for both decision levels were jointly estimated using a Broyden-Fletcher-Goldfarb-Shanno (BFGS) Method-based algorithm, and the model was verified on the Sioux-Falls network. The model was then applied to real navigation route and driving trajectory data from Canton, China, for parameter estimation and the analysis of the additional utility provided by navigation. The results indicate that the C-NDRL model significantly outperformed other models. Furthermore, the study quantifies the substantial impact of external environmental factors and navigation-related internal factors on drivers’ compliance on navigation systems, highlighting that during rainstorm days, the additional utility from navigation increases by 17%.

近年来，道路交通在异常事件中的恢复能力受到了广泛的关注。在这种情况下，智能导航系统可以主动引导司机沿着最优路线行驶，这被视为促进道路网络性能恢复的一个有希望的解决方案。一个关键的问题出现了：在导航系统的引导下，司机如何选择路线？本研究通过使用嵌套框架在每个决策点对驾驶员的决策行为进行建模来解决这个问题。在上层，驾驶员决定是否严格按照导航系统推荐的路线行驶；在下层，驾驶员在没有引导的情况下进行路线选择。开发了一个定制的嵌套动态递归Logit （C-NDRL）模型来捕获这些行为。采用基于Broyden-Fletcher-Goldfarb-Shanno （BFGS）方法的算法对两个决策层的参数进行了联合估计，并在Sioux-Falls网络上对模型进行了验证。然后将该模型应用于中国广州的实际导航路线和行驶轨迹数据，进行参数估计和分析导航提供的附加效用。结果表明，C-NDRL模型显著优于其他模型。此外，该研究量化了外部环境因素和与导航相关的内部因素对驾驶员遵守导航系统的实质性影响，强调在暴雨天，导航的额外效用增加了17%。

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引用次数: 0

Towards robust motion control in multi-source uncertain scenarios by robust policy iteration 基于鲁棒策略迭代的多源不确定场景鲁棒运动控制研究

IF 12.5 Q1 TRANSPORTATION

Communications in Transportation Research

Pub Date : 2025-06-20 DOI: 10.1016/j.commtr.2025.100191

Jie Li , Letian Tao , Wenjun Zou , Yuhang Zhang , Bin Shuai , Jingliang Duan , Shengbo Eben Li , Hao Sun , Yiru Wang , Yu Gao , Yuwen Heng , Anqing Jiang

The adoption of neural networks for motion control modules emerges as a critical direction in the advancement of end-to-end autonomous driving. However, few studies have comprehensively addressed the challenges of robustness and generalization in motion control policies, including long-tailed distribution, distribution shift, and sim-to-real gap. In practical applications, motion control performance is compromised by diverse uncertainties, posing substantial challenges to real-world deployment. This work develops a training system to enhance the robustness and generalization of motion control policies when passing through multiple intersections. We first construct a task library comprising 6 driving scenarios, which are allocated to different sampling processes to rebalance the proportion of monotonous and edge scenarios. Next, we formulate a zero-sum game for uncertainties and driving actions with smoothing constraints within the range of observation noise. The driving policy is optimized by the proposed robust policy iteration method for the worst-case performance, which is approximated via Taylor expansion to avoid the computational burden caused by adversarial training on behavior disturbance, where the approximate results decouple model mismatches to ensure robust performance and action smoothness is boosted through penalty function method. Ultimately, the motion control performance and the robustness of driving policy are thoroughly validated by configuring the behavior patterns of traffic participants, ego dynamic parameters, and observation noise intensities in the simulation environment. Physical vehicle experiments on public urban roads further depict the robustness and generalization of the driving policy learned from simulations.

在运动控制模块中采用神经网络是端到端自动驾驶技术发展的关键方向。然而，很少有研究全面解决运动控制策略的鲁棒性和泛化挑战，包括长尾分布、分布移位和模拟到真实的差距。在实际应用中，运动控制性能受到各种不确定性的影响，对现实世界的部署构成了重大挑战。本工作开发了一个训练系统，以增强通过多个交叉口时运动控制策略的鲁棒性和泛化性。我们首先构建了一个包含6个驾驶场景的任务库，将这些场景分配到不同的采样过程中，以平衡单调场景和边缘场景的比例。其次，在观测噪声范围内，我们建立了一个具有平滑约束的不确定性和驱动行为的零和博弈。采用提出的鲁棒策略迭代方法对最坏情况下的驾驶策略进行优化，通过Taylor展开进行近似，避免了对行为干扰进行对抗性训练带来的计算负担，其中近似结果解耦模型失配以保证鲁棒性能，并通过惩罚函数方法增强动作平滑性。最后，通过配置仿真环境中交通参与者的行为模式、自我动态参数和观察噪声强度，彻底验证了运动控制性能和驾驶策略的鲁棒性。在城市公共道路上的实体车辆实验进一步描述了从模拟中学习到的驾驶策略的鲁棒性和泛化性。

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引用次数: 0

A sustainable multi-objective framework for multi-phased, capacitated vertiport siting with land use integration 一个可持续的多目标框架，用于多阶段、有能力的垂直机场选址和土地利用整合

IF 12.5 Q1 TRANSPORTATION

Communications in Transportation Research

Pub Date : 2025-06-18 DOI: 10.1016/j.commtr.2025.100186

Hao Wu , Shahriar Iqbal Zame , Tao Guo , Qing-Long Lu , Constantinos Antoniou

This research presents a multi-objective optimization framework for incremental siting of capacitated vertiports that integrates real land use data and aims to maximize generalized cost savings while minimizing infrastructure costs and emissions. The multi-phased siting framework uniquely facilitates the gradual evolution of Urban Air Mobility (UAM) operations from initial electric vertical takeoff and landing vehicles (eVTOLs) to more advanced modular flying vehicles (MFVs). This phased technological progression provides a practical pathway toward fully operational flying cars while ensuring feasible infrastructure adaptability across these transitions. Applied to the Munich metropolitan area, the framework demonstrates that multi-phased siting, particularly a 4-phased strategy, yielding about 1.315 × 10⁵ euros higher daily net profits. Specifically, compared to base single-phased approach, the 4-phased strategy delivers substantial marginal improvements across key metrics for an exemplary operating day: 1.3 × 10⁴ euros in generalized travel cost savings, 15 t in emissions reductions, and a 0.9% increase in UAM mode share. Beyond four phases, the benefits diminish relative to increased complexity. A full factorial analysis examining capacity constraints and infrastructure costs reveals that ignoring either factor leads to impractical outcomes-unconstrained capacity results in demand exceeding 60-fold capacity, while disregarding infrastructure costs generates negative net profits due to overinvestment. The analysis identifies an optimal infrastructure cost subsidy range of 20%–40%, balancing performance gains with economic sustainability. These findings enable integrated planning that effectively balances operational efficiency, system-wide environmental externalities, and economic viability through optimized cost allocation and phased investment strategies.

本研究提出了一个多目标优化框架，用于集成实际土地利用数据的可再生垂直机场的增量选址，旨在最大限度地节省总体成本，同时最大限度地降低基础设施成本和排放。多阶段选址框架独特地促进了城市空中交通（UAM）从最初的电动垂直起降车辆（evtol）到更先进的模块化飞行车辆（mfv）的逐步发展。这种分阶段的技术进步为飞行汽车的全面运行提供了切实可行的途径，同时确保了在这些过渡过程中可行的基础设施适应性。将该框架应用于慕尼黑大都市区，结果表明，多阶段选址，特别是四阶段策略，每天的净利润约为1.315 × 105欧元。具体来说，与基本的单阶段方法相比，4阶段策略在典型工作日的关键指标上取得了实质性的边际改进：节省1.3 × 104欧元的总体差旅成本，减少15吨的排放量，UAM模式的份额增加0.9%。超过四个阶段后，收益就会随着复杂性的增加而减少。考察产能约束和基础设施成本的全因子分析表明，忽视任何一个因素都会导致不切实际的结果——不受约束的产能导致需求超过产能的60倍，而忽视基础设施成本则会因过度投资而产生负净利润。分析确定了20%-40%的最优基础设施成本补贴范围，以平衡绩效收益和经济可持续性。这些发现有助于通过优化成本分配和分阶段投资策略，有效地平衡运营效率、全系统环境外部性和经济可行性。

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引用次数: 0

Situational awareness using set-based estimation and vehicular communication: An occluded pedestrian-crossing scenario 使用基于集合的估计和车辆通信的态势感知：一个闭塞的行人过街场景

IF 12.5 Q1 TRANSPORTATION

Communications in Transportation Research

Pub Date : 2025-06-11 DOI: 10.1016/j.commtr.2025.100190

Vandana Narri , Amr Alanwar , Jonas Mårtensson , Henrik Pettersson , Fredrik Nordin , Karl Henrik Johansson

The safety of unprotected road-users is crucial in any urban traffic. Occlusions and blind spots in the field-of-view of a vehicle can lead to unsafe situations. In this work, a specific pedestrian-crossing scenario is considered with an occlusion in the ego-vehicle's field-of-view. A novel framework is presented to enhance situational awareness based on vehicle-to-everything (V2X) communication to share perception data between vehicle and roadside units. It leverages set-based estimation utilizing a computationally efficient algorithm, for which the pedestrian is guaranteed to be located in a constrained zonotope. The proposed method has been validated through both simulation and real experiments. The real experiments are carried out on a test track using Scania autonomous vehicles.

在任何城市交通中，无保护的道路使用者的安全至关重要。车辆视野中的遮挡和盲点会导致不安全的情况。在这项工作中，考虑了一个特定的行人过街场景，在自我车辆的视野中遮挡。提出了一种基于车联网（V2X）通信增强态势感知的新框架，以在车辆和路边单元之间共享感知数据。它利用一种计算效率高的算法，利用基于集合的估计，保证行人位于受限的分区中。通过仿真和实际实验验证了该方法的有效性。真正的实验是在斯堪尼亚自动驾驶汽车的测试轨道上进行的。

引用次数: 0

DFUN-KDF: Efficient and robust decentralized federated framework for UAV networks via knowledge distillation and filtering DFUN-KDF：基于知识蒸馏和过滤的高效鲁棒的去中心化无人机网络联邦框架

IF 12.5 Q1 TRANSPORTATION

Communications in Transportation Research

Pub Date : 2025-06-11 DOI: 10.1016/j.commtr.2025.100173

Wenyuan Yang , Yuhang Liu , Xinlin Leng , Hanlin Gu , Gege Jiang , Xiaochuan Yu , Xiaochun Cao

Unmanned aerial vehicles (UAVs) are increasingly crucial across various fields. There is a growing interest in using federated learning (FL) methods to enhance the efficiency of UAV operations. Nevertheless, incumbent methods remain encumbered by significant drawbacks, including high energy consumption from extensive parameter exchanges, the imperative for homogeneous networks, and sensitivity to single-point failures. These difficulties are compounded by the unreliable nature of communication channels and the current inability to effectively manage the diversity of UAV models, highlighting the imperative for more resilient and adaptable FL solutions. To address these issues, we propose an efficient and robust decentralized FL framework for heterogeneous UAV networks. Our framework first leverages the knowledge distillation where UAVs transmit embeddings instead of model parameters to reduce the number of transmission parameter. UAVs update their local models using embeddings generated by other UAVs, which also enables UAVs with diverse architectures to participate in training. Moreover, our framework incorporates a filtering mechanism to remove malicious embeddings, ensuring resilience against adversities in UAV networks. Extensive experiments on various datasets validate the effectiveness and practical deployment potential of our framework.

无人驾驶飞行器（uav）在各个领域越来越重要。使用联邦学习（FL）方法来提高无人机操作效率的兴趣越来越大。然而，现有的方法仍然存在明显的缺点，包括大量参数交换带来的高能耗、同质网络的必要性以及对单点故障的敏感性。这些困难由于通信渠道的不可靠性质和目前无法有效管理无人机模型的多样性而复杂化，突出了更具弹性和适应性的FL解决方案的必要性。为了解决这些问题，我们提出了一个高效、鲁棒的异构无人机网络分散FL框架。我们的框架首先利用无人机传输嵌入而不是模型参数的知识蒸馏来减少传输参数的数量。无人机使用其他无人机生成的嵌入来更新其本地模型，这也使具有不同架构的无人机能够参与训练。此外，我们的框架结合了一种过滤机制来去除恶意嵌入，确保无人机网络在逆境中的弹性。在各种数据集上的大量实验验证了我们的框架的有效性和实际部署潜力。

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引用次数: 0

Integrating micro and macro traffic control for mixed autonomy traffic 混合自治交通宏微观一体化控制

IF 12.5 Q1 TRANSPORTATION

Communications in Transportation Research

Pub Date : 2025-06-10 DOI: 10.1016/j.commtr.2025.100188

Tingting Fan , Jieming Chen , Edward Chung

During the transition to fully autonomous traffic systems, managing mixed traffic consisting of connected automated vehicles (CAVs) and human-driven vehicles (HDVs) is imperative. Existing macroscopic and microscopic strategies have shown effectiveness in alleviating highway congestion. However, the integration of these strategies for mixed autonomy traffic remains under-explored. This study proposes a hybrid flow and trajectory control (HFTC) strategy that combines a macroscopic control, ramp metering (RM), with a microscopic control, cooperative merging (CM) for CAV trajectory optimization in mixed traffic scenarios. Specifically, the RM control considers CAV-penetration-dependent dynamics to regulate ramp flow, and the CM utilizes a centralized optimization model to enhance CAV merging trajectories. Independently implementing RM or CM proved effective only under heavy or moderate traffic flow, whereas our proposed integrated strategy, HFTC, demonstrated greater adaptability and suitability under various traffic conditions. Additionally, the impacts of CAV penetration rates and traffic flows on performance of different control strategies are thoroughly explored. Simulation results indicate that under low and moderate traffic conditions, microscopic control can be comparable to macroscopic control given sufficient CAV integration, while under heavy traffic flows, macroscopic control cannot be replaced by microscopic control.

在向全自动交通系统过渡的过程中，管理由联网自动驾驶汽车（cav）和人类驾驶汽车（HDVs）组成的混合交通势在必行。现有的宏观和微观策略在缓解公路拥堵方面都显示出了效果。然而，将这些策略整合到混合自主交通中仍有待探索。针对混合交通场景下CAV的轨迹优化问题，提出了一种将宏观控制匝道计量（RM）与微观控制协同归并（CM）相结合的流轨混合控制策略。具体来说，RM控制考虑了CAV穿透相关的动力学来调节坡道流，CM利用集中优化模型来增强CAV合并轨迹。独立实施RM或CM被证明仅在交通流量较大或中等的情况下有效，而我们提出的综合策略HFTC在各种交通条件下表现出更大的适应性和适用性。此外，深入探讨了自动驾驶汽车渗透率和交通流量对不同控制策略性能的影响。仿真结果表明，在低、中等交通条件下，如果CAV积分足够，微观控制可以与宏观控制相媲美，而在大交通流量下，宏观控制无法被微观控制所取代。

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引用次数: 0

A multiagent social interaction model for autonomous vehicle testing 自动驾驶汽车测试中的多智能体社会交互模型

IF 12.5 Q1 TRANSPORTATION

Communications in Transportation Research

Pub Date : 2025-06-10 DOI: 10.1016/j.commtr.2025.100183

Shihan Wang , Ying Ni , Chengsheng Miao , Jian Sun , Jie Sun

Social interaction capability (SIC) is essential for autonomous vehicles (AVs) when they interact with surrounding vehicles, as the ability of understanding and reacting to the behaviors of other road users can significantly enhance AVs’ rapid deployment. Virtual simulation testing is a core approach for evaluating AVs, including their SIC, on the basis of traffic simulation models. However, existing simulation models focus mainly on generating accurate vehicle trajectories and do not explicitly model the high-level sociality nature of interaction decisions that guide specific movements. This study aims to address this gap by developing a multiagent simulation model for the social interaction of human driving behavior on the basis of the multiagent imitation learning (MAIL) approach, which is referred to as the Social-MAIL model. Specifically, to quantify the sociality of decisions, we introduce social value orientation into the reward function to quantify cooperation or competition intent and guide the generation of social driving behaviors. Furthermore, to fully depict the complex interaction environment, we develop a heterogeneous policy network with temporal‒spatial attention mechanisms to describe the impact of multiple interactive objects and historical states on driving behavior. Through training and validation on the SinD dataset, we demonstrate that, compared with a set of baseline models, the proposed Social-MAIL model can accurately capture complex and time-varying social intent and reproduce the most realistic vehicle trajectories and macroscopic traffic flow characteristics at intersections. Moreover, we apply the Social-MAIL model for evaluating the SIC of AVs via comparison experiments.

社交互动能力（Social interaction capability， SIC）是自动驾驶汽车与周围车辆互动的关键，因为对其他道路使用者行为的理解和反应能力可以显著提高自动驾驶汽车的快速部署能力。虚拟仿真测试是基于交通仿真模型对自动驾驶汽车（包括其SIC）进行评估的核心方法。然而，现有的仿真模型主要侧重于生成准确的车辆轨迹，并没有明确地模拟指导特定运动的交互决策的高级社会性。本研究旨在通过开发基于多智能体模仿学习（MAIL）方法的人类驾驶行为社会互动的多智能体仿真模型来解决这一差距，该模型被称为social -MAIL模型。具体而言，为了量化决策的社会性，我们将社会价值取向引入奖励函数，量化合作或竞争意图，引导社会驱动行为的产生。此外，为了充分描述复杂的交互环境，我们开发了一个具有时空注意机制的异构策略网络来描述多个交互对象和历史状态对驾驶行为的影响。通过在SinD数据集上的训练和验证，我们证明，与一组基线模型相比，所提出的social - mail模型可以准确地捕捉复杂和时变的社交意图，并再现最真实的交叉口车辆轨迹和宏观交通流特征。此外，我们还通过对比实验，将Social-MAIL模型应用于av的SIC评价。

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引用次数: 0

A parsimonious model for classifying the traffic state of urban road networks: A two-stage regression approach 城市道路网络交通状态分类的简化模型：两阶段回归方法

IF 12.5 Q1 TRANSPORTATION

Communications in Transportation Research

Pub Date : 2025-06-05 DOI: 10.1016/j.commtr.2025.100185

Wei Huang , Dalin Tang , Xin Qiao , Guojun Chen

An effective method of traffic state classification is crucial for managing urban traffic congestion. Existing methods usually assume a given number of state categories, which is not flexible if real applications are required to define different state levels. In this study, a parsimonious statistical model is derived and validated for classifying urban traffic states. The model is developed on the basis of a large-scale empirical travel speed dataset from five cities in China. First, a hybrid clustering method that integrates DBSCAN and natural breaks is used to derive traffic state classification under various numbers of state categories. The classification results are then compiled to conduct the subsequent regression analysis. Second, a two-stage regression approach is proposed to investigate the correlation between the number of state categories and the classification criteria (i.e., state thresholds that separate one state level from another). In the first stage, a significant linear relationship between the classification criteria of adjacent traffic states is derived (

\bar{R^{2}}

= 0.80, P < 0.001). In the second stage, a significant correlation between the slope, intercept, and number of state categories is derived (

\bar{R^{2}}

= 0.95, P < 0.001). On the basis of the two-stage regression analysis, a novel parsimonious statistical model is developed. Third, the developed model is evaluated with three performance indicators, namely, the mean squared error (MSE), mean absolute error (MAE), and mean relative error (MRE). The claffication accuracy is further validated via a case study on the speed data of Foshan Avenue North road. We suggest that the model can be used to assist flexible decision-making support with different levels of detail.

一种有效的交通状态分类方法对于管理城市交通拥堵至关重要。现有的方法通常假设给定数量的状态类别，如果实际应用程序需要定义不同的状态级别，这是不灵活的。本文提出了一种简洁的城市交通状态分类统计模型，并进行了验证。该模型是在中国五个城市的大规模经验旅行速度数据的基础上开发的。首先，采用DBSCAN和自然中断相结合的混合聚类方法，推导出不同数量状态类别下的流量状态分类。然后对分类结果进行编译，进行后续的回归分析。其次，提出了一种两阶段回归方法来研究状态类别数量与分类标准（即将一个状态级别与另一个状态级别分开的状态阈值）之间的相关性。在第一阶段，导出相邻交通状态分类标准之间的显著线性关系(R2¯= 0.80,P <；0.001)。在第二阶段，推导出斜率、截距和状态类别数量之间的显著相关性(R2¯= 0.95,P <；0.001)。在两阶段回归分析的基础上，建立了一种新的简约统计模型。第三，用均方误差（MSE）、平均绝对误差（MAE）和平均相对误差（MRE）三个性能指标对模型进行评价。以佛山大道北路车速数据为例，进一步验证了该方法的准确率。我们建议，该模型可用于辅助灵活的决策支持与不同层次的细节。

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引用次数: 0

Next leap in the sustainable transport revolution: Identifying gaps and proposing solutions for hydrogen mobility 可持续交通革命的下一个飞跃：确定差距并提出氢交通的解决方案

IF 12.5 Q1 TRANSPORTATION

Communications in Transportation Research

Pub Date : 2025-05-29 DOI: 10.1016/j.commtr.2025.100180

Fangjie Liu , Muhammad Shafique , Xiaowei Luo

Amid escalating global climate concerns, the reliance of the transportation sector on high-carbon fossil fuels urgently demands sustainable alternatives. Hydrogen has emerged as a potent solution because of its zero-emission usage, but its overall impact hinges on its full life cycle, which this review comprehensively examines. This article delves into the environmental, economic, and safety dimensions of hydrogen as an alternative fuel by systematically reviewing the life cycle assessment (LCA) literature across the production, storage, delivery, and usage phases, with a focus on electrolysis and natural gas reforming methods, among others. A key insight from this study is the critical importance of considering the entire delivery system holistically rather than isolating the delivery phase. Many studies have overlooked two important aspects: first, the distribution of hydrogen as a product itself is often underemphasized; second, the integration of storage and delivery (the “storage-delivery nexus”) is crucial since separating them can lead to misleading conclusions about cost and emissions. For example, while certain delivery methods may appear cost-effective, their associated storage processes (such as hydrogenation and dehydrogenation in liquid organic hydrogen carrier systems) can have significant emission impacts. To address these gaps, this study introduces a novel “surface-level” LCA framework to enhance the assessment of the environmental impacts of hydrogen, promoting a more integrated understanding of the storage-delivery system. This framework aims to provide more accurate insights into hydrogen's life cycle, thereby facilitating better-informed policy-making and technological advancements. This study underscores the imperative for robust policy support, public engagement, and continuous innovation to overcome these barriers, advocating for strategic initiatives that bolster the sustainability and adoption of hydrogen mobility, particularly in hydrogen fuel cell vehicles (HFCVs).

随着全球气候担忧的加剧，交通运输部门对高碳化石燃料的依赖迫切需要可持续的替代品。氢已经成为一种强有力的解决方案，因为它的零排放使用，但它的整体影响取决于它的整个生命周期，这篇综述全面考察了。本文通过系统地回顾生产、储存、输送和使用阶段的生命周期评估（LCA）文献，重点讨论了电解和天然气重整方法等，深入研究了氢作为替代燃料的环境、经济和安全方面的问题。从这项研究中得出的一个关键见解是，从整体上考虑整个交付系统而不是孤立地考虑交付阶段是至关重要的。许多研究忽视了两个重要方面：首先，氢作为产品本身的分布往往被低估；其次，储存和运输的整合（“储存-运输联系”）是至关重要的，因为将它们分开可能会导致关于成本和排放的误导性结论。例如，虽然某些输送方法可能看起来具有成本效益，但它们相关的储存过程（例如液体有机氢载体系统中的加氢和脱氢）可能会对排放产生重大影响。为了解决这些差距，本研究引入了一种新的“表面水平”LCA框架，以加强对氢的环境影响的评估，促进对储存-输送系统的更综合的理解。该框架旨在为氢的生命周期提供更准确的见解，从而促进更明智的政策制定和技术进步。这项研究强调了强有力的政策支持、公众参与和持续创新的必要性，以克服这些障碍，倡导采取战略举措，加强氢交通的可持续性和采用，特别是在氢燃料电池汽车（HFCVs）方面。

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引用次数: 0

Traffic simulation optimization considering driving styles 考虑驾驶风格的交通仿真优化

IF 12.5 Q1 TRANSPORTATION

Communications in Transportation Research

Pub Date : 2025-05-29 DOI: 10.1016/j.commtr.2025.100181

Yunyang Shi , Tong Wu , Tan Guo , Jinbiao Huo , Ziyuan Gu , Yifan Dai , Zhiyuan Liu

Parameter calibration is essential for ensuring the accuracy of microscopic traffic simulations. The expected speed is a critical parameter that characterizes behaviors of vehicles in most simulation models, which is influenced by road traffic conditions and the driving characteristics of different drivers. Most existing parameter calibration methods typically concentrate on micro-level parameters such as time headway and lane change motivation, while overlooking the calibration of vehicle expected speeds in consideration of driver behavior habits. This study combines data from highway electronic toll collection (ETC), gantries, and 100-m mileage average speed data, and proposes a method for calibrating vehicle expected speed that considers driving style clustering. The Gaussian mixture model (GMM) algorithm is used to develop driver models with three distinct driving styles: aggressive, moderate, and conservative. To ensure driving diversity and enhance parameter calibration efficiency, we rebuild vehicle driving models and representative parameters based on the classification results. Moreover, the Bayesian optimization algorithm is modified in conjunction with a microscopic traffic simulation model to perform automatic calibration of expected speeds. Experiments conducted on the Shanghai–Hangzhou–Ningbo highway demonstrate that the proposed method significantly reduces the mean absolute percentage error (MAPE) from 20.2% (using default parameters) to 3.1%. Additionally, in the model robustness test, the MAPE reaches 5.01%, indicating a certain level of stability and scalability. This method proposes a tailored calibration method accounting for the heterogeneous driving behaviors of micro-traffic simulation models, achieving satisfactory calibration results for simulation models in highway scenarios.

参数标定是保证微观交通仿真精度的关键。在大多数仿真模型中，期望速度是表征车辆行为的关键参数，它受到道路交通条件和不同驾驶员驾驶特性的影响。现有的参数校准方法大多集中在车头时距和变道动机等微观参数上，而忽略了考虑驾驶员行为习惯对车辆预期速度的校准。本研究结合高速公路电子收费站（ETC）、龙门台和百公里平均车速数据，提出了一种考虑驾驶风格聚类的车辆预期车速标定方法。采用高斯混合模型（Gaussian mixture model， GMM）算法建立了三种不同驾驶风格的驾驶员模型：进取型、温和型和保守型。为保证驾驶多样性，提高参数标定效率，基于分类结果重构车辆驾驶模型和代表性参数。此外，结合微观交通仿真模型对贝叶斯优化算法进行了改进，实现了期望速度的自动标定。在沪杭甬高速公路上进行的实验表明，该方法将平均绝对百分比误差（MAPE）从20.2%（使用默认参数）显著降低到3.1%。此外，在模型稳健性检验中，MAPE达到5.01%，表明具有一定的稳定性和可扩展性。该方法针对微交通仿真模型的异质性驾驶行为，提出了一种定制化的校准方法，对高速公路场景下的仿真模型取得了满意的校准结果。

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引用次数: 0