IEEE Transactions on Intelligent Transportation Systems最新文献_第2页

ICST-DNET: An Interpretable Causal Spatio-Temporal Diffusion Network for Traffic Speed Prediction 交通速度预测的可解释因果时空扩散网络

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-06-12 DOI: 10.1109/TITS.2025.3574837

Yi Rong;Yingchi Mao;Yinqiu Liu;Ling Chen;Xiaoming He;Guojian Zou;Shahid Mumtaz;Dusit Niyato

{"title":"ICST-DNET: An Interpretable Causal Spatio-Temporal Diffusion Network for Traffic Speed Prediction","authors":"Yi Rong;Yingchi Mao;Yinqiu Liu;Ling Chen;Xiaoming He;Guojian Zou;Shahid Mumtaz;Dusit Niyato","doi":"10.1109/TITS.2025.3574837","DOIUrl":"https://doi.org/10.1109/TITS.2025.3574837","url":null,"abstract":"Traffic speed prediction is significant for intelligent navigation and congestion alleviation. However, making accurate predictions is challenging due to three factors: 1) traffic diffusion, i.e., the spatial and temporal causality existing between the traffic conditions of multiple neighboring roads, 2) the poor interpretability of traffic data with complicated spatio-temporal correlations, and 3) the latent pattern of traffic speed fluctuations over time, such as morning and evening rush. Jointly considering these factors, in this paper, we present a novel architecture for traffic speed prediction, called Interpretable Causal Spatio-Temporal Diffusion Network (ICST-DNET). Specifically, ICST-DNET consists of three parts, namely the Spatio-Temporal Causality Learning (STCL), Causal Graph Generation (CGG), and Speed Fluctuation Pattern Recognition (SFPR) modules. First, to model the traffic diffusion within road networks, an STCL module is proposed to capture both the temporal causality on each individual road and the spatial causality in each road pair. The CGG module is then developed based on STCL to enhance the interpretability of the traffic diffusion procedure from the temporal and spatial perspectives. Specifically, a time causality matrix is generated to explain the temporal causality between each road’s historical and future traffic conditions. For spatial causality, we utilize causal graphs to visualize the diffusion process in road pairs. Finally, to adapt to traffic speed fluctuations in different scenarios, we design a personalized SFPR module to select the historical timesteps with strong influences for learning the pattern of traffic speed fluctuations. Extensive experimental results on two real-world traffic datasets prove that ICST-DNET can outperform all existing baselines, as evidenced by the higher prediction accuracy, ability to explain causality, and adaptability to different scenarios.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9781-9798"},"PeriodicalIF":7.9,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536695","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

VEH-Attack: Stealthy Tracking of Train Passengers With Side-Channel Attack on Vibration Energy Harvesting Wearables veh攻击：利用振动能量收集可穿戴设备的侧通道攻击对火车乘客进行隐形跟踪

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-06-12 DOI: 10.1109/TITS.2025.3576220

Marzieh Jalal Abadi;Sara Khalifa;Mahbub Hassan;Salil Kanhere;Mohamed Ali Kaafar

引用次数: 0

Train Timetabling With Stop Planning and Passenger Distributing Integration Orientated by Railway Capacity and Passenger Service 以铁路运力和客运服务为导向的列车进站规划与旅客分配一体化调度

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-06-11 DOI: 10.1109/TITS.2025.3574789

Ruxin Wang;Lei Nie;Yuyan Tan

{"title":"Train Timetabling With Stop Planning and Passenger Distributing Integration Orientated by Railway Capacity and Passenger Service","authors":"Ruxin Wang;Lei Nie;Yuyan Tan","doi":"10.1109/TITS.2025.3574789","DOIUrl":"https://doi.org/10.1109/TITS.2025.3574789","url":null,"abstract":"In the process of railway operation planning, it is essential to take into account both railway capacity and origin to destination (OD) passenger demand. Stop plan plays a vital role in generating a train timetable with maximum railway capacity and ensuring high-quality service to transport passengers. Therefore, we are addressing the challenge of optimizing both the stop plan and timetable for a group of trains on a railway line, focusing on railway capacity estimation and passenger demand satisfaction. To provide realistic and precise passenger distribution, the preferences of different categories of passengers are given due regard. A classic time-space network describes the integrated problem, based on which a mathematical model is formulated to minimize train occupancy time on the high-speed railway line and maximize passenger kilometers at the same time. A decomposition approach based on Lagrangian relaxation (LR) is suggested to address the problem, which decomposes the integrated scheduling problem into two sub-problems: a train timetabling sub-problem, and a stop planning and passenger distributing sub-problem by dualizing constraints linking the two. A heuristic approach based on genetic algorithms is designed to obtain feasible solutions. The proposed model and approach are shown to generate good solutions efficiently. A series of real-world instances are conducted on the Beijing-Shanghai high-speed railway line in China, and the experimental outcomes show the benefits of optimizing the stop plan. Other related analyses are discussed by comparing results with different total number of stops, heterogeneous and homogeneous cases.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9445-9460"},"PeriodicalIF":7.9,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536509","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

Lane Detection on Rainy Nights Based on Memory and Discretization Mechanisms 基于记忆和离散化机制的雨夜车道检测

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-06-10 DOI: 10.1109/TITS.2025.3574763

Yonghang Li;Chang Wang;Yifei Wang;Miao Ren;Jin Niu;Jikang Zhao;Kai Du

{"title":"Lane Detection on Rainy Nights Based on Memory and Discretization Mechanisms","authors":"Yonghang Li;Chang Wang;Yifei Wang;Miao Ren;Jin Niu;Jikang Zhao;Kai Du","doi":"10.1109/TITS.2025.3574763","DOIUrl":"https://doi.org/10.1109/TITS.2025.3574763","url":null,"abstract":"The reflections of multi-colored lights involved on rainy nights present strong uncertainties and abruptness, resulting in a high rate of false and missed detections in existing methods. To solve this issue, this paper proposes a lane detection method based on memory and discretization mechanisms. Firstly, a Memory Fruit-fly-optimizer with Individual Differences (MFID) is innovatively proposed to drive Multi-threshold Otsu (MOtsu)-based multi-class segmentation of lanes, which is a high-dimensional optimization task with real-time and local optimal challenges, for capturing lane clues obscured in multi-intensity reflections, consequently reducing missed detections. Specifically, to solve the challenges inherent in the task, the MFID incorporates a novel memory mechanism to establish fast-converging initial conditions for real-time detection, while creatively considering individual differences to motivate multi-swarm optimization that mitigates local optima risks. After integration, the MFID-MOtsu is constructed for lane segmentation. Subsequently, a dynamic discretization mechanism is proposed to efficiently separate lane edges from interference edges, mitigating accuracy degradation caused by their entanglement. Finally, the false detection issue is greatly reduced through the implementation of adaptive geometric filters. The experimental results demonstrate that the proposed method achieves an average accuracy of 93.21% on rainy nights, indicating an average improvement of 12.7% over state-of-the-art methods. Additionally, without any parameter modifications, the proposed method is applicable to both normal and classic challenging scenes, such as nights, tunnels, rainy days, and shadows. The algorithm achieves an average accuracy of 96.2% and an average detection speed of 46 frames per second.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9529-9541"},"PeriodicalIF":7.9,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536407","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

Dissipating Traffic Waves in Mixed Vehicle Platoons: A Controller-Matching-Based Double-Layer Distributed Model Predictive Control Approach 混合车辆队列交通波的消散：一种基于控制器匹配的双层分布式模型预测控制方法

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-06-10 DOI: 10.1109/TITS.2025.3575540

Panshuo Li;Xingyan Mao;Chao Huang;Pengxu Li

{"title":"Dissipating Traffic Waves in Mixed Vehicle Platoons: A Controller-Matching-Based Double-Layer Distributed Model Predictive Control Approach","authors":"Panshuo Li;Xingyan Mao;Chao Huang;Pengxu Li","doi":"10.1109/TITS.2025.3575540","DOIUrl":"https://doi.org/10.1109/TITS.2025.3575540","url":null,"abstract":"This study proposes a novel distributed model predictive control (DMPC) approach for mixed vehicle platoons (MVPs), which achieves driving safety, asymptotic stability, and traffic wave dissipation simultaneously. The longitudinal dynamics model and safety constraints are first established for each vehicle. The MVPs are divided into several sub-platoons according to the distribution of connected-and-automated vehicles (CAVs) and human-driven vehicles (HDVs). Then, a compound controller to ensure the head-to-tail string stability of MVPs is constructed as a reference controller for the subsequent design of the double-layer distributed model predictive controller (DL-DMPC). To describe the behavior of human drivers, a car-following model specific to HDVs is developed. On the basis of the designed compound controller and the description of HDVs, the DL-DMPC is proposed. The first layer improves tracking performance and satisfies the state constraints of each CAV under different communication topologies through an optimization problem. The second layer utilizes controller-matching technology to ensure the asymptotic stability of vehicle platoons and dissipate traffic waves. With the above double-layer structure, the DL-DMPC can simultaneously address multiple tasks and is applicable in various communication topologies. Simulations and analyses based on the NGSIM dataset are conducted in various scenarios to validate the effectiveness of the developed approach.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9326-9340"},"PeriodicalIF":7.9,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536500","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

Learning an Active Inference Model of Driver Perception and Control: Application to Vehicle Car-Following 学习驾驶员感知与控制的主动推理模型：在车辆跟车中的应用

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-06-10 DOI: 10.1109/TITS.2025.3574552

Ran Wei;Alfredo Garcia;Anthony McDonald;Gustav Markkula;Johan Engstrom;Matthew O'Kelly

{"title":"Learning an Active Inference Model of Driver Perception and Control: Application to Vehicle Car-Following","authors":"Ran Wei;Alfredo Garcia;Anthony McDonald;Gustav Markkula;Johan Engstrom;Matthew O'Kelly","doi":"10.1109/TITS.2025.3574552","DOIUrl":"https://doi.org/10.1109/TITS.2025.3574552","url":null,"abstract":"In this paper we introduce a general estimation methodology for learning a model of human perception and control in a sensorimotor control task based upon a finite set of demonstrations. The model’s structure consists of (i) the agent’s internal representation of how the environment and associated observations evolve as a result of control actions and (ii) the agent’s preferences over observable outcomes. We consider a model’s structure specification consistent with active inference, a theory of human perception and behavior from cognitive science. According to active inference, the agent acts upon the world so as to minimize surprise defined as a measure of the extent to which an agent’s current sensory observations differ from its preferred sensory observations. We propose a bi-level optimization approach to estimation which relies on a structural assumption on prior distributions that parameterize the statistical accuracy of the human agent’s model of the environment. To illustrate the proposed methodology, we present the estimation of a model for car-following behavior based upon a naturalistic dataset. Overall, the results indicate that learning active inference models of human perception and control from data is a promising alternative to closed-box models of driving.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9475-9490"},"PeriodicalIF":7.9,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536295","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

Joint Optimization of Transit Network Design, Timetable, and Passenger Assignment With Exact Transfer Behavior Modeling 基于精确换乘行为模型的公交网络设计、时刻表和乘客分配联合优化

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-06-09 DOI: 10.1109/TITS.2025.3573284

Yunyi Liang;Constantinos Antoniou;Mohammad Sadrani;Jinjun Tang

{"title":"Joint Optimization of Transit Network Design, Timetable, and Passenger Assignment With Exact Transfer Behavior Modeling","authors":"Yunyi Liang;Constantinos Antoniou;Mohammad Sadrani;Jinjun Tang","doi":"10.1109/TITS.2025.3573284","DOIUrl":"https://doi.org/10.1109/TITS.2025.3573284","url":null,"abstract":"This study investigates the problem of joint optimization of transit network design, timetable, and passenger assignment with exact transfer behavior modeling. The problem is formulated as a bi-level mixed-integer bilinear program to capture passengers’ realistic path choice behavior. The upper-level model aims to minimize the weighted sum of the cost of bus route construction, bus route operation, bus station construction, travel time of passengers, the delay caused by failures in aboarding to the bus trips at the origin, the delay caused by failures in transfer between the bus trips, and the overflow delay when the bus trip operates at capacity. The lower-level model aims to minimize the travel time of passengers. The travel time of passengers is formulated as the sum of the waiting time for boarding, the transfer time, and the in-vehicle travel time. The passenger transfer time and the delay caused by failures in transfer between the bus trips are formulated with exact modeling of passenger transfer behavior. This bi-level mixed-integer bilinear program is transformed into an equivalent mixed-integer bilinear program with equilibrium constraints using Karush-Kuhn-Tucker conditions. To seek a solution of good quality to the proposed model while not requiring a large amount of computer memory, a Benders decomposition algorithm integrated with piecewise linearization is developed. A numerical application demonstrates that the proposed model is able to achieve 3.49% lower total cost than the baseline model assuming passenger transfer time to be half of the headway.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9263-9276"},"PeriodicalIF":7.9,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536552","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

Vehicle Longitudinal Stochastic Control for Connected and Automated Vehicle Platooning in Highway Systems 高速公路系统互联自动车辆队列的车辆纵向随机控制

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-06-09 DOI: 10.1109/TITS.2025.3572555

Min Dai;Chaozhong Wu;Jianghui Wen

{"title":"Vehicle Longitudinal Stochastic Control for Connected and Automated Vehicle Platooning in Highway Systems","authors":"Min Dai;Chaozhong Wu;Jianghui Wen","doi":"10.1109/TITS.2025.3572555","DOIUrl":"https://doi.org/10.1109/TITS.2025.3572555","url":null,"abstract":"The vehicle platoon control for highway traffic can help to improve traffic flow efficiency, enhance traffic safety, and reduce fuel consumption. In previous platoon control research, most of the driving behaviors are described using deterministic car-following models. Nevertheless, random factors that may come from the vehicle power train and additional stimuli, can have a greater impact on platoon control in highway traffic. In this paper, a novel stochastic control method is proposed based on a stochastic car-following model which considers microscopic driving behavior. Firstly, the stochastic car-following model is designed that fully accounts for the impact of random factors on platoon control. Secondly, an optimal objective function is constructed and the Hamilton-Jacobi-Bellman equation is used to solve this stochastic control problem, thereby completing the upper-level controller design and obtaining the optimal desired acceleration of the vehicle. Thirdly, the stochastic stability method is applied to analyze the proposed model and obtain the stochastic stability conditions satisfied by the model. Finally, tests are conducted for three different scenarios: stable speed, acceleration, and deceleration with additive and multiplicative noise, as well as the case where the lead vehicle’s speed is based on real vehicle trajectory data. These tests validate the stability and effectiveness of the stochastic car-following model predictive control method from the perspective of control strategy and model respectively. The experimental results show that under the stable parameter conditions of the model, the connected and automated vehicle platoon can achieve accurate speed tracking and maintain an appropriate safe distance in highway system.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9563-9578"},"PeriodicalIF":7.9,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536292","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

HiMo: End-to-End Congestion Control for High Speed Rail Data Networking 高速铁路数据网络的端到端拥塞控制

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-06-06 DOI: 10.1109/TITS.2025.3558776

Chenren Xu;Yuhan Zhou;Jing Wang;Ruihan Li;Lingyang Song;Guangyu Zhu

{"title":"HiMo: End-to-End Congestion Control for High Speed Rail Data Networking","authors":"Chenren Xu;Yuhan Zhou;Jing Wang;Ruihan Li;Lingyang Song;Guangyu Zhu","doi":"10.1109/TITS.2025.3558776","DOIUrl":"https://doi.org/10.1109/TITS.2025.3558776","url":null,"abstract":"The highly variable nature of cellular networks challenges end-to-end network transmissions in achieving low-latency and high-throughput performance. In high-speed rail (HSR) networks, the intermittent connectivity and capacity dynamics imposed by high client mobility further add complexity and difficulty in providing seamless service. While congestion control algorithms (CCAs) play an essential role in ensuring optimal network performance, prior works on congestion control have predominantly concentrated on enhancing network performance within stationary or low-mobility mobile networks without considering frequent disconnections and highly dynamic network capacities imposed by HSR networks, resulting in severe RTT inflation and slow loss recovery. In this paper, we argue that a dedicated transport layer protocol is necessary for high-mobility scenarios. We propose an end-to-end low-latency congestion control algorithm HiMo for HSR networks that reacts to abrupt bandwidth changes quickly, handles frequent handovers, and is immediately deployable. Our trace-driven emulation on real-world datasets demonstrates that HiMo can reduce 51.3% 95th-percentile latency with comparable throughput on high-speed rail networks, compared to state-of-the-art CCAs.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 6","pages":"7715-7725"},"PeriodicalIF":7.9,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243777","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 Multi-Environment Freespace Detection Method Based on Range Scale Map 基于距离比例尺地图的多环境自由空间检测方法

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-06-04 DOI: 10.1109/TITS.2025.3568165

Siyuan Shao;Kunyang Wu;Genyuan Xing;Yang Liu;Guanyu Zhang

引用次数: 0