IEEE Transactions on Intelligent Transportation Systems最新文献_第3页

A Moving Authority Generation Switch Decision Method for Virtual Coupling High-Speed Train 虚拟耦合高速列车移动权限生成切换决策方法

IF 9.1 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-10-01 Epub Date: 2026-06-22 DOI: 10.1109/TITS.2026.3703416

Yichen Dun;Wei ShangGuan;Baigen Cai;Feijie Gong

{"title":"A Moving Authority Generation Switch Decision Method for Virtual Coupling High-Speed Train","authors":"Yichen Dun;Wei ShangGuan;Baigen Cai;Feijie Gong","doi":"10.1109/TITS.2026.3703416","DOIUrl":"https://doi.org/10.1109/TITS.2026.3703416","url":null,"abstract":"Virtual coupling is an effective way to improve the capacity of high-speed railway. Moving authority generation is important for high-speed train operation safety and efficiency, which would have great significance for train operation with small distance in virtual coupling. In this paper, a framework of moving authority generation switch between the existing train operation control mode and train-to-train communication based mode has been established to the high-speed train operation control under disturbance in virtual coupling. Then, different logit models have been used to analyze the train-to-train communication mode applicable condition and mine the causes of moving authority switch to select the feature factors of moving authority switch decision. And a Convolutional Neural Network-Gated Recurrent Unit neural network module is designed and used to make decision for train moving authority generation switch. A simulation dataset of virtual coupling train trajectory with disturbance is collected from the model predictive control based train operation control. The experiment has been conducted with the simulation dataset, where the neural network feature factors of moving authority switch decision are selected. On the basis of moving authority switch causes analysis, the effectiveness of Convolutional Neural Network-Gated Recurrent Unit module in moving authority switch decision has been verified and tested. Moreover, the comparison among different moving authority generation conditions have been taken, and the effectiveness of the proposed decision method in train operation safety and efficiency has been verified.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 10","pages":"12880-12894"},"PeriodicalIF":9.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Crosswalk Trajectory Prediction via an Intent-Trajectory Integrated Framework for Autonomous Driving 基于意图-轨迹集成框架的自动驾驶人行横道轨迹预测

IF 9.1 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-10-01 Epub Date: 2026-07-13 DOI: 10.1109/TITS.2026.3707642

Yanhang Lin;Junheng Yang;Weiying Chen;Xiaodong Zang;Qiang Luo

{"title":"Crosswalk Trajectory Prediction via an Intent-Trajectory Integrated Framework for Autonomous Driving","authors":"Yanhang Lin;Junheng Yang;Weiying Chen;Xiaodong Zang;Qiang Luo","doi":"10.1109/TITS.2026.3707642","DOIUrl":"https://doi.org/10.1109/TITS.2026.3707642","url":null,"abstract":"The increasing complexity of automated urban environments demands accurate modeling of pedestrian crossing behavior. Existing approaches often rely on human annotations or vehicle-centric views, limiting their ability to predict pedestrian intentions in dynamic scenarios. In this paper, we propose an Intent-Trajectory Integrated Pedestrian Crossing Prediction framework (ITI-PCP) that explicitly couples high-level semantic intention inference with multimodal trajectory generation. The proposed framework introduces a Dynamic Bayesian Network to model pedestrian crossing intent in an interpretable and temporally evolving manner, enabling early detection of intention shifts prior to significant trajectory deviations. To effectively capture heterogeneous interaction densities in complex traffic scenes, we further design an Adaptive Spatio-Temporal Encoder with dynamic grid resolution selection, combined with a coarse-to-fine multi-scale grid-based prediction scheme that balances multimodality and spatial continuity. Experiments on the ETH/UCY datasets show that ITI-PCP reduces ADE and FDE by 39.1% and 33.3%, respectively, compared to Trajectron++, and achieves a lower KDE-NLL score (–1.24) than AgentFormer and PECNet. On the NuScenes dataset, the model maintains strong performance over a 4-second prediction horizon, demonstrating robust generalization to realistic autonomous driving scenarios. Ablation studies further confirm the effectiveness of each proposed module.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 10","pages":"13024-13039"},"PeriodicalIF":9.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Interpretable Framework for Quantifying and Predicting Driver's Situation Awareness at the Moment of Takeover Request in Conditionally Automated Driving 有条件自动驾驶接管请求时刻驾驶员态势感知量化与预测的可解释框架

IF 9.1 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-10-01 Epub Date: 2026-06-23 DOI: 10.1109/TITS.2026.3703843

Xiaoyuan Ma;Chuanliang Shen;Shuai Zhan;Yi Li;Zhenhai Gao;Hongyu Hu

{"title":"An Interpretable Framework for Quantifying and Predicting Driver's Situation Awareness at the Moment of Takeover Request in Conditionally Automated Driving","authors":"Xiaoyuan Ma;Chuanliang Shen;Shuai Zhan;Yi Li;Zhenhai Gao;Hongyu Hu","doi":"10.1109/TITS.2026.3703843","DOIUrl":"https://doi.org/10.1109/TITS.2026.3703843","url":null,"abstract":"The driver’s situation awareness (SA) at the moment of a takeover request (TOR) is essential for safe control transition in conditionally automated driving. To address limitations in existing methods for SA quantification and prediction, this study proposes an interpretable framework based on the analysis of SA’s formation process and critical role during takeovers. First, an eXtreme Gradient Boosting (XGBoost) model was constructed to predict takeover performance (TOP). SHapley Additive exPlanations (SHAP) was employed to provide local interpretability and identify the contribution of readily measurable SA Level 1 to TOP, based on which SA Level 2 and 3 were operationalized, thereby enabling the continuous quantification of overall SA. Subsequently, another XGBoost model was developed to predict the quantified SA, and SHAP’s global interpretability was used to optimize model performance and analyze the key features’ main and interaction effects on SA. A takeover simulation experiment was conducted to construct the necessary dataset. Results indicated that the quantified SA was able to match the reasonable distribution patterns of SA across different takeover results. The XGBoost model outperformed four comparison models in SA prediction. In addition, the key features’ main and interaction effects on SA were broadly supported by existing studies, confirming the rationality and effectiveness of the SA prediction model. This research provides support for driver state monitoring and the formulation of takeover strategies in conditionally automated driving.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 10","pages":"13325-13342"},"PeriodicalIF":9.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Controllable Risk Scenario Generation From Human Crash Data for Autonomous Vehicle Testing 基于人类碰撞数据的自动驾驶汽车可控风险场景生成

IF 9.1 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-10-01 Epub Date: 2026-07-03 DOI: 10.1109/TITS.2026.3707288

Qiujing Lu;Xuanhan Wang;Runze Yuan;Wei Lu;Xinyi Gong;Shuo Feng

{"title":"Controllable Risk Scenario Generation From Human Crash Data for Autonomous Vehicle Testing","authors":"Qiujing Lu;Xuanhan Wang;Runze Yuan;Wei Lu;Xinyi Gong;Shuo Feng","doi":"10.1109/TITS.2026.3707288","DOIUrl":"https://doi.org/10.1109/TITS.2026.3707288","url":null,"abstract":"Ensuring the safety of autonomous vehicles (AV) requires rigorous testing under both everyday driving and rare, safety-critical conditions. A key challenge lies in simulating environment agents, including background vehicles (BVs) and vulnerable road users (VRUs), that behave realistically in nominal traffic while also exhibiting risk-prone behaviors consistent with real-world accidents. However, real-world accident data involving AVs remain extremely scarce, whereas large-scale human driving data provide a richer source of safety-critical events due to the vast cumulative driving mileage worldwide. In light of this, we propose Controllable Risk Agent Generation (CRAG), a framework that learns safety-critical behavior patterns from human crash data, combined with a nominal behavior prior, to enable agents to act adversarially toward the AV while remaining physically plausible. Extensive experiments show that CRAG improves diversity compared to existing baselines, while also enabling controllable generation of risk scenarios for targeted and efficient evaluation of AV robustness.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 10","pages":"13257-13270"},"PeriodicalIF":9.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Graph-Benders Decomposition for Integrated Electric Trucks Routing, Cargo Loading, and V2G Scheduling 集成电动卡车路线、货物装载和V2G调度的弯曲图分解

IF 9.1 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-10-01 Epub Date: 2026-07-03 DOI: 10.1109/TITS.2026.3707595

Chengwei Lou;Hangxing Zhang;Jiaze Ma;Zongsheng Li;Zhiyuan Guo;Lu Zhang;Wei Tang;Bo Zhang;Jin Yang

{"title":"A Graph-Benders Decomposition for Integrated Electric Trucks Routing, Cargo Loading, and V2G Scheduling","authors":"Chengwei Lou;Hangxing Zhang;Jiaze Ma;Zongsheng Li;Zhiyuan Guo;Lu Zhang;Wei Tang;Bo Zhang;Jin Yang","doi":"10.1109/TITS.2026.3707595","DOIUrl":"https://doi.org/10.1109/TITS.2026.3707595","url":null,"abstract":"The tightening of global environmental regulations is accelerating the adoption of Electric Trucks (ETs) in logistics; however, the integrated optimization of their routing, charging schedules, and cargo loading presents a formidable challenge. This complex coupling inherently introduces a Generalized Multiple Knapsack Problem and results in a Mixed-Integer Linear Programming model that is typically NP-hard and computationally demanding for standard solvers. To address this, we propose a novel Graph-Benders algorithm based on an exact algorithmic framework that strategically decomposes the problem into an ET routing master problem and a Vehicle-to-Grid (V2G) scheduling subproblem, reformulated within a graph-based modeling approach. This structure ensures all delivery tasks are fulfilled while tightening the feasible region for grid coordination. By synergizing feasibility and optimality cuts from the linearly relaxed subproblem with no-good cuts to address integer infeasibility, the method efficiently eliminates infeasible solutions,yielding a high-quality near-optimal solution, which is at least as good as the optimal V2G schedule for the best feasible routes. Through experiments in various geographic scenarios and split-delivery tasks, the algorithm demonstrates its versatility and achieves a breakthrough in efficiency without sacrificing solution quality compared to Gurobi. In large-scale tests, for instances with 19,585 decision variables, the proposed Graph-Benders decomposition yields a superior, high-quality near-optimal solution in merely 28 seconds, outperforming Gurobi’s 10,000-second time-limit result. Notably, even at a scale of 127,489 variables, the algorithm yields high-quality near-optimal solutions in only 130 seconds.Consequently, the proposed algorithm effectively tackles the strong coupling of large-scale ET routing and V2G coordination, offering a powerful tool for practical low-carbon logistics deployment.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 10","pages":"13298-13309"},"PeriodicalIF":9.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Comprehensive Survey on Road Damage Detection: Datasets, Methods, and Platforms 道路损伤检测的综合研究：数据集、方法和平台

IF 9.1 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-10-01 Epub Date: 2026-08-12 DOI: 10.1109/TITS.2026.3710986

Zhida Wang;Zhaoke Huang;Qi Zhao;Renjie Xu;Hong Yan

{"title":"A Comprehensive Survey on Road Damage Detection: Datasets, Methods, and Platforms","authors":"Zhida Wang;Zhaoke Huang;Qi Zhao;Renjie Xu;Hong Yan","doi":"10.1109/TITS.2026.3710986","DOIUrl":"https://doi.org/10.1109/TITS.2026.3710986","url":null,"abstract":"Road damage detection has emerged as a strategic cornerstone of modern infrastructure management, directly impacting transportation safety, economic sustainability, and urban resilience. This review provides a comprehensive overview of state-of-the-art methodologies integrating diverse sensors (including cameras, LiDAR, ground penetrating radar, and accelerometers) with cutting-edge deep neural networks (such as CNNs, Transformers, and YOLO models) for precise classification and localization of road defects, including cracks, potholes, and subsurface anomalies. We also examines publicly available datasets, such as road damage detection dataset-series and UAV-Benchmark-Dataset, and discuss emerging themes in synthetic data generation and lightweight architectures aimed to mitigate dataset scarcity and improve model robustness. By synthesizing technical breakthroughs with real-world applications, this review provides a foundational reference for researchers and practitioners aiming to develop intelligent, scalable solutions for road infrastructure monitoring, ultimately contributing to safer, more sustainable transportation ecosystems.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 10","pages":"11949-11965"},"PeriodicalIF":9.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Discovering Cyclists' Street Visual Preferences From Shared Bike Trajectories and Street View Images Using XAI-Enhanced Inverse Reinforcement Learning 利用xai增强的逆强化学习从共享自行车轨迹和街景图像中发现骑车者的街道视觉偏好

IF 9.1 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-10-01 Epub Date: 2026-06-15 DOI: 10.1109/TITS.2026.3699424

Kezhou Ren;Meihan Jin;Huiming Liu;Mengwei Xin;Yongxi Gong;Yu Liu

{"title":"Discovering Cyclists' Street Visual Preferences From Shared Bike Trajectories and Street View Images Using XAI-Enhanced Inverse Reinforcement Learning","authors":"Kezhou Ren;Meihan Jin;Huiming Liu;Mengwei Xin;Yongxi Gong;Yu Liu","doi":"10.1109/TITS.2026.3699424","DOIUrl":"https://doi.org/10.1109/TITS.2026.3699424","url":null,"abstract":"Cycling has gained global popularity due to its health benefits and positive impacts on urban areas. While prior work has examined how urban built environments influence cyclists’ behaviors, cyclists’ route-choice mechanisms remain insufficiently understood largely due to overlooking the complex and continuous nature of cycling. To address this gap, we introduce a data-driven framework that integrates Maximum Entropy Deep Inverse Reinforcement Learning (MEDIRL) with explainable artificial intelligence (XAI). This framework is designed to quantify and interpret cyclists’ street visual preferences, which are their preferences for the street-level built environment during cycling. Our approach uses MEDIRL to approximate reward functions that reflect cyclists’ street visual preferences by integrating dockless bike-sharing (DBS) trajectories with street view images (SVIs). Subsequently, we apply XAI techniques to reveal the complex effects of street visual elements on those quantified street visual preferences. We validate our framework in the Bantian Subdistrict, Shenzhen. The results demonstrate that the quantified street visual preferences significantly outperform shortest-path models and other baselines in explaining cyclists’ route decision-making. Besides, cyclists exhibit strong preferences towards elements associated with perceived safety, street enclosure, and cycling comfort. Moreover, our detailed analysis discovers nonlinear relationships among these street visual elements, providing a scalable, data-driven foundation for understanding cyclists’ behaviors and guiding streetscape design.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 10","pages":"12863-12879"},"PeriodicalIF":9.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Achieving Socially-Aware Car-Following in Mixed Traffic: A Safe Multi-Task Deep Reinforcement Learning Approach 在混合交通中实现社会意识汽车跟随：一种安全的多任务深度强化学习方法

IF 9.1 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-10-01 Epub Date: 2026-06-15 DOI: 10.1109/TITS.2026.3697403

Xiao Wen;Jingyuan Zhou;Sisi Jian

{"title":"Achieving Socially-Aware Car-Following in Mixed Traffic: A Safe Multi-Task Deep Reinforcement Learning Approach","authors":"Xiao Wen;Jingyuan Zhou;Sisi Jian","doi":"10.1109/TITS.2026.3697403","DOIUrl":"https://doi.org/10.1109/TITS.2026.3697403","url":null,"abstract":"Current research on autonomous vehicle (AV) car-following control primarily emphasizes optimizing AV performance without considering interactions between AVs and human-driven vehicles (HDVs). This oversight can lead to socially non-compliant AV behaviors that confuse following HDVs, destabilize traffic flow, and increase congestion and crash risks. Moreover, while most studies employ safe deep reinforcement learning (DRL) methods focused solely on AV safety, we argue that AVs, which are equipped with superior sensing and decision-making capabilities, should proactively contribute to the safety of following HDVs. To address these limitations, we propose a socially aware AV car-following controller based on social value orientation (SVO). Given the broad spectrum of SVO, we design a safe multi-task DRL (SMTDRL) framework that includes a single-task DRL backbone, a curriculum-based multi-task learning strategy for efficient cross-SVO knowledge transfer, and a differentiable safety module using control barrier functions (CBFs) to ensure safety for both the AV and its HDV followers. Comprehensive simulations demonstrate that SMTDRL improves traffic flow stability, ride comfort, and fuel economy while enforcing strict safety constraints at the platoon level, outperforming both single-task DRL and baseline multi-task DRL approaches. For example, in safety-critical scenarios, SMTDRL achieves a 100% collision-free rate, significantly outperforming baseline multi-task DRL approaches. In terms of traffic performance, it reduces the average cumulative damping ratio by approximately 7% compared to single-task DRL models. Furthermore, SMTDRL exhibits strong generalizability to unseen SVO values, highlighting its robust adaptability in mixed traffic environments.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 10","pages":"13097-13112"},"PeriodicalIF":9.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Context-Aware Evaluation of Signal Timing Interventions on Red-Light Running Using High-Resolution Traffic Controller Data 基于高分辨率交通控制数据的红灯信号配时干预的情境感知评估

IF 9.1 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-10-01 Epub Date: 2026-07-17 DOI: 10.1109/TITS.2026.3710415

B M Tazbiul Hassan Anik;Mohamed Abdel-Aty;Zubayer Islam;Yang-Jun Joo

{"title":"Context-Aware Evaluation of Signal Timing Interventions on Red-Light Running Using High-Resolution Traffic Controller Data","authors":"B M Tazbiul Hassan Anik;Mohamed Abdel-Aty;Zubayer Islam;Yang-Jun Joo","doi":"10.1109/TITS.2026.3710415","DOIUrl":"https://doi.org/10.1109/TITS.2026.3710415","url":null,"abstract":"Red-light running (RLR) is a persistent traffic safety issue that contributes to thousands of crashes annually. Improperly configured yellow and red-clearance intervals are key contributors to RLR violations, yet existing timing strategies often fail to adapt to real-world variability in driver behavior and traffic conditions. This study develops a novel, data-driven framework that integrates high-resolution Automated Traffic Signal Performance Measures (ATSPM) data with Causal Forest modeling to evaluate the impact of yellow and red-clearance interval adjustments on unintentional RLR events. Using event-level data from eight intersections in Hillsborough County, Florida, we estimate both Average Treatment Effects (ATEs) and Conditional Average Treatment Effects (CATEs) across varying operational conditions. Key findings show that yellow interval extensions are significantly more effective than red-clearance interval changes, with a one-second increase in yellow time reducing RLR violation probability by up to 30 percentile points in high-risk scenarios. A counterfactual simulation further demonstrates that targeting only 37–49% of the highest-risk cycles can capture 100% of the achievable RLR reductions, enabling cost-effective, data-informed deployment of timing interventions. The proposed framework enables adaptive, hour-by-hour, and movement-specific timing recommendations, offering a scalable and practical solution for intersection safety improvement. By supporting data-informed, resource-conscious decision-making, this study advances proactive traffic signal management and RLR mitigation strategies.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 10","pages":"12557-12572"},"PeriodicalIF":9.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149096972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CFENN: A Deep Learning Framework for Image-Based Pantograph Contact Force Monitoring 基于图像的受电弓接触力监测的深度学习框架

IF 9.1 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-10-01 Epub Date: 2026-02-09 DOI: 10.1109/TITS.2026.3659758

Huayu Duan;Qiufeng Yi;Hao Ni;Jiaqi Ye;Ning Zhao;Jintao Liu;Edward Stewart;Roger Dixon

引用次数: 0