Communications in Transportation Research最新文献_第7页

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

Communications in Transportation Research Pub Date : 2025-12-01 Epub 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

{"title":"Towards robust motion control in multi-source uncertain scenarios by robust policy iteration","authors":"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","doi":"10.1016/j.commtr.2025.100191","DOIUrl":"10.1016/j.commtr.2025.100191","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100191"},"PeriodicalIF":12.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Privacy-preserving personalized pricing and matching for ride hailing platforms 为网约车平台提供保护隐私的个性化定价和匹配

IF 14.5

Communications in Transportation Research Pub Date : 2025-12-01 Epub Date: 2025-09-11 DOI: 10.1016/j.commtr.2025.100205

Bing Song, Sisi Jian

{"title":"Privacy-preserving personalized pricing and matching for ride hailing platforms","authors":"Bing Song, Sisi Jian","doi":"10.1016/j.commtr.2025.100205","DOIUrl":"10.1016/j.commtr.2025.100205","url":null,"abstract":"<div><div>This research addresses the growing concern of balancing personalized services with data privacy in the ride-hailing industry. While personalized pricing and matching strategies, fueled by travelers’ personal data, can optimize platform revenue, they also expose users and platforms to significant privacy risks. The correlation between personalized pricing, waiting times, and personal information might be exploited by third-party agents to infer sensitive user attributes, resulting in potential economic losses for the platform and severe consequences for users, including compromised privacy and potential discrimination. Existing privacy protection methods often fall short in providing robust and quantifiable guarantees. To overcome these limitations, this study introduces a privacy-preserving approach for personalized pricing and matching within ride-hailing platforms. The proposed approach leverages the bounded Laplace (BL) mechanism and parallel composition to inject noise into the order price and waiting time feedback provided to travelers. This study rigorously demonstrates that the proposed approach satisfies differential privacy. Furthermore, the proposed approach outperforms other classic privacy-preserving methods in terms of platform revenue. This superior performance is validated through extensive numerical experiments using realistic ride-hailing data.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100205"},"PeriodicalIF":14.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Communications in Transportation Research Pub Date : 2025-12-01 Epub Date: 2025-06-10 DOI: 10.1016/j.commtr.2025.100183

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

{"title":"A multiagent social interaction model for autonomous vehicle testing","authors":"Shihan Wang , Ying Ni , Chengsheng Miao , Jian Sun , Jie Sun","doi":"10.1016/j.commtr.2025.100183","DOIUrl":"10.1016/j.commtr.2025.100183","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100183"},"PeriodicalIF":12.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Flying cars and urban air mobility: Redefining cities in three dimensions 飞行汽车和城市空中交通：在三维空间重新定义城市

IF 14.5

Communications in Transportation Research Pub Date : 2025-12-01 Epub Date: 2025-10-01 DOI: 10.1016/j.commtr.2025.100213

Di Lv , Kai Wang , Shulu Chen , Xiaobo Qu

引用次数: 0

Scalable and reliable multi-agent reinforcement learning for traffic assignment 交通分配的可扩展和可靠的多智能体强化学习

IF 14.5

Communications in Transportation Research Pub Date : 2025-12-01 Epub Date: 2025-11-21 DOI: 10.1016/j.commtr.2025.100225

Leizhen Wang , Peibo Duan , Cheng Lyu , Zewen Wang , Zhiqiang He , Nan Zheng , Zhenliang Ma

{"title":"Scalable and reliable multi-agent reinforcement learning for traffic assignment","authors":"Leizhen Wang , Peibo Duan , Cheng Lyu , Zewen Wang , Zhiqiang He , Nan Zheng , Zhenliang Ma","doi":"10.1016/j.commtr.2025.100225","DOIUrl":"10.1016/j.commtr.2025.100225","url":null,"abstract":"<div><div>The evolution of metropolitan cities and increasing travel demand impose stringent requirements on traffic assignment methods. Multi-agent reinforcement learning (MARL) approaches outperform traditional methods in modeling adaptive routing behavior without requiring explicit system dynamics, making them attractive for real-world deployment. However, existing MARL frameworks face scalability and reliability challenges when managing large-scale networks with substantial and variable demand. This study proposes MARL-OD-DA, a novel framework that redefines agents as origin–destination (OD) pair routers and employs a continuous simplex-constrained action space. This reformulation reduces the agent population from <em>O</em>(<em>N</em>) (number of travelers) to <em>O</em>(|<em>D</em>|) (number of OD pairs), achieving at least two orders of magnitude fewer agents in practice while preserving convexity and enabling efficient adaptation to demand variation, thus significantly improving scalability. In contrast to prior MARL studies constrained to small-sized networks (up to 70 nodes, 2100 travelers) and fixed demand, MARL-OD-DA is validated on medium-sized networks (up to 416 nodes, 1406 OD pairs, and 360,600 travelers) under varying demand scenarios, demonstrating substantial improvements in scalability and applicability. To further enhance reliability, the framework integrates a Dirichlet-based policy, action pruning, and a relative gap-based reward. Theoretical analysis demonstrates that the Dirichlet-based policy reduces gradient bias, stabilizes variance, and enables sparse routing decisions, in contrast to the commonly used softmax-based policy. Experiments on three benchmark networks show that MARL-OD-DA significantly improves assignment quality and convergence speed. On the SiouxFalls network, the trained agents converge within 10 iterations during deployment, reducing the relative gap by 94.99% compared to conventional baselines.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100225"},"PeriodicalIF":14.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multimodal traffic assignment from privacy-protected OD data 多模式交通分配从隐私保护OD数据

IF 14.5

Communications in Transportation Research Pub Date : 2025-12-01 Epub Date: 2025-12-02 DOI: 10.1016/j.commtr.2025.100223

Guoyang Qin , Shidi Deng , Qi Luo , Jian Sun

{"title":"Multimodal traffic assignment from privacy-protected OD data","authors":"Guoyang Qin , Shidi Deng , Qi Luo , Jian Sun","doi":"10.1016/j.commtr.2025.100223","DOIUrl":"10.1016/j.commtr.2025.100223","url":null,"abstract":"<div><div>The (static) traffic assignment (TA) problem, which computes network equilibrium flows from origin–destination (OD) demand under flow conservation, is central to transportation modeling. As multimodal transportation systems (MTSs) grow, sharing detailed OD data – such as trip counts, timestamps, and routes – raises serious privacy concerns. Differential privacy (DP) has emerged as the leading standard for releasing such data, offering adjustable protection beyond traditional anonymization. However, current methods mostly apply extrinsic DP by adding noise to aggregate OD matrices before release, without fully addressing its effects on traffic modeling. This reveals TA’s unpreparedness for privacy-protected data and calls for redesigned methods that operate reliably under such constraints. To fill this gap, we propose the privacy-preserving traffic assignment (PPTA) framework, which embeds DP intrinsically within the TA process. Instead of externally perturbing aggregate demand, PPTA injects structured noise at the individual trip level. This preserves privacy while ensuring equilibrium feasibility through chance-constrained optimization, unifying privacy protection and traffic assignment. The framework supports various discrete choice models and noise types, using a moment-based approximation to boost computational efficiency. Our results show PPTA attains a privacy-utility balance beyond extrinsic methods, enabling robust, privacy-aware multimodal routing, network design, and pricing.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100223"},"PeriodicalIF":14.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Driving under the sun: Future of solar buses in Hong Kong, China 在阳光下驾驶：中国香港太阳能巴士的未来

IF 12.5

Communications in Transportation Research Pub Date : 2025-12-01 Epub Date: 2025-03-12 DOI: 10.1016/j.commtr.2025.100168

Zhuowei Wang , Yiyang Peng , Hongxing Yang , Anthony Chen

引用次数: 0

Few-shot learning for novel object detection in autonomous driving 基于少镜头学习的自动驾驶新目标检测

IF 12.5

Communications in Transportation Research Pub Date : 2025-12-01 Epub Date: 2025-07-16 DOI: 10.1016/j.commtr.2025.100194

Yifan Zhuang , Pei Liu , Hao Yang , Kai Zhang , Yinhai Wang , Ziyuan Pu

{"title":"Few-shot learning for novel object detection in autonomous driving","authors":"Yifan Zhuang , Pei Liu , Hao Yang , Kai Zhang , Yinhai Wang , Ziyuan Pu","doi":"10.1016/j.commtr.2025.100194","DOIUrl":"10.1016/j.commtr.2025.100194","url":null,"abstract":"<div><div>Artificial intelligence and advanced sensing technologies have significantly advanced the intelligent transportation system and autonomous vehicles. Perception, a critical component, extracts real-time traffic information essential for various system functionalities, such as agent behavior prediction. However, the quality of information derived from perception greatly influences overall system performance. This study focuses on enhancing perception robustness in autonomous vehicles, particularly in detecting rare objects, which pose a challenge due to limited training samples. While deep learning-based vision methods have shown promising accuracy, they struggle with rare object detection. To address this, we propose a few-shot learning training strategy tailored for improved detection accuracy of rare or novel objects. Additionally, we design a one-stage object detector for efficient object detection in autonomous driving scenarios. Experiments on a self-driving dataset augmented with rare objects alongside the popular few-shot object detection (FSOD) benchmark, the pattern analysis, statical modeling, and computational learning PASCAL Visual Object Classes (PASCAL-VOC), demonstrate state-of-the-art accuracy in rare categories and superior inference speed compared to alternative algorithms. Furthermore, we investigate the impact of intra-class variance on detection accuracy, providing insights for data annotation in the preparation stage.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100194"},"PeriodicalIF":12.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A hybrid centralized-decentralized traffic control framework for unmanned aerial vehicles in urban low-altitude airspace 城市低空空域无人机集中-分散混合交通控制框架

IF 12.5

Communications in Transportation Research Pub Date : 2025-12-01 Epub Date: 2025-07-17 DOI: 10.1016/j.commtr.2025.100195

Xiangdong Chen , Shen Li , Meng Li

{"title":"A hybrid centralized-decentralized traffic control framework for unmanned aerial vehicles in urban low-altitude airspace","authors":"Xiangdong Chen , Shen Li , Meng Li","doi":"10.1016/j.commtr.2025.100195","DOIUrl":"10.1016/j.commtr.2025.100195","url":null,"abstract":"<div><div>Urban air mobility (UAM) represents a transformative approach to alleviating ground-level congestion by transitioning from two-dimension (2D) to three-dimension (3D) transportation systems. Envisioned as a safe, sustainable, and efficient mode of urban transit, UAM leverages aerial space to reduce dependence on traditional road infrastructure while addressing traffic congestion challenges in urban mobility. However, the rapid growth in aerospace transportation demand, coupled with the complexity of managing large-scale unmanned aerial vehicle (UAV) operations in 3D airspace, challenges the effectiveness of traditional traffic management systems. To address these challenges, this study proposes a hybrid framework for UAV air traffic control that integrates centralized and decentralized approaches. A 3D air traffic network is modeled in low-altitude airspace, capturing detailed 2D and 3D conflict relationships. The concept of a “virtual flight container” (VFC) is introduced to regulate UAV space–time trajectories, ensuring conflict-free, low-delay operations while minimizing real-time computational requirements, especially in high demands. The problem is addressed using a bi-level optimization approach: The upper level focuses on solving the traffic assignment problem, considering airway capacity constraints, while the lower level designs space–time trajectories to ensure conflict-free operations and enhance traffic efficiency, thereby complementing the traffic control scheme. Numerical experiments validate the proposed framework, highlighting its effectiveness in improving traffic efficiency and network throughput. Key insights are provided regarding the role of network structure, the placement of take-off and landing points, and control parameters in optimizing UAM operations.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100195"},"PeriodicalIF":12.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144654928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hierarchical Bayesian threshold excess model for real-time vehicle-based conflict prediction in dynamic traffic environ-ments 动态交通环境下基于车辆冲突实时预测的层次贝叶斯阈值过剩模型

IF 14.5

Communications in Transportation Research Pub Date : 2025-12-01 Epub Date: 2025-09-18 DOI: 10.1016/j.commtr.2025.100210

Léah Camarcat , Yuxiang Feng , Nicolette Formosa , Mohammed Quddus

{"title":"Hierarchical Bayesian threshold excess model for real-time vehicle-based conflict prediction in dynamic traffic environ-ments","authors":"Léah Camarcat , Yuxiang Feng , Nicolette Formosa , Mohammed Quddus","doi":"10.1016/j.commtr.2025.100210","DOIUrl":"10.1016/j.commtr.2025.100210","url":null,"abstract":"<div><div>Vehicle-based collision risk assessment methods often exhibit a tradeoff between simplifying assumptions in physics-based models and the interpretability challenges of learning algorithms. To tackle this, methods based on Extreme Value Theory (EVT) have gained momentum in recent years, but there is a lack of studies employing EVT for vehicle-based applications. This paper proposes a new, context-aware conflict prediction algorithm using a hierarchical Bayesian threshold excess model. Contextual traffic data are integrated with vehicle sensor data to improve the robustness and accuracy of the model. The feasibility of real-time deployment is also examined by optimising computational efficiency, leveraging several implementations of the Hamiltonian Monte Carlo No-U-Turn Solver (NUTS). The results demonstrate that including traffic covariates improves the model goodness-of-fit by 4.80% in terms of Deviance Information Criterion, and generalisability with a decrease of 1.36% in mean absolute error. However, partially pooled models, while enhancing goodness-of-fit, result in a reduction of generalisation capabilities. Additionally, the No-U-Turn Sampler compiled in JAX demonstrated sufficient performance for both online training and inference, thus making this methodology a feasible solution for real-time deployment in vehicle-based applications.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100210"},"PeriodicalIF":14.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0