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An Introduction to the Chair of Traffic Process Automation [Its Research Lab] IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2024-03-07 Meng Wang, Angelika Hirrle, Matthias Körner, Susanne Wunsch, Yikai Zeng, Yisheng Lv
Please send your proposal on profiling research activities of your or other intelligent transportation systems research groups and labs for the “ITS Research Labs” column to Yisheng Lv at yisheng.lv@ ia.ac.cn.
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Share Your Preprint Research with the World! IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2024-03-06
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The Impactful Achievements of the IEEE ITSS [President’s Message] IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2024-03-06 Cristina Olaverri-Monreal
In this last message as president of the IEEE Intelligent Transportation Systems Society (ITSS) for the years 2022 and 2023, I would like to express my heartfelt gratitude to each and every member of our esteemed Society. It has been an incredible journey serving alongside your dedication and passion.
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What’s Next After Big Models? Small Models? Agents? Other Things? [Editor’s Column] IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2024-03-06 Yisheng Lv
The last couple of years have seen a steady stream of bigger and better big models. But what’s next after the current high-water mark—big models? Small models? Agents? Or other things? In intelligent transportation systems, the same question stands. What’s next after big transportation models? Another wave of small transportation models? Agents in real and virtual worlds? Or something else?
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Roadside Sensors for Traffic Management IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2024-01-24 Lawrence A. Klein
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How to Guarantee Driving Safety for Autonomous Vehicles in a Real-World Environment: A Perspective on Self-Evolution Mechanisms IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2024-01-09 Shuo Yang, Yanjun Huang, Li Li, Shuo Feng, Xiaoxiang Na, Hong Chen, Amir Khajepour
A succession of accidents shows that production vehicles with autonomous driving systems do not work safely in real-world environments, especially when facing unseen scenarios. Therefore, how to ensure that autonomous systems drive more safely becomes a challenge. Thanks to the self-learning ability of human beings, human drivers can gradually learn how to drive from a driving test with typical and
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Tech RXIV: Share Your Preprint Research with the World! IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2024-01-04
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Think Ahead, Jump Above: Farewell 2023! Welcome 2024! [Editor’s Column] IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2024-01-04 Yisheng Lv
2023 was my first year as editor-in-chief of IEEE Intelligent Transportation Systems Magazine ( ITSM ). For the past year, I have had many touching moments, reflections, and expectations, and I’m very happy to share them with you.
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Highlights and Achievements of IEEE ITSC 2023: Bridging Innovation, Culture, and Excellence in the IEEE ITSS [President’s Message] IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2024-01-04 Cristina Olaverri-Monreal
The IEEE Intelligent Transportation Systems Conference (ITSC 2023), the most recent flagship conference of the IEEE Intelligent Transportation Systems Society (ITSS), has just concluded. It was hosted in the city of Bilbao, nestled in the heart of the Basque Country region. This vibrant metropolis harmonizes modernity with tradition, offering rich cultural diversity and breathtaking landscapes.
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Drive Like a Machine: A Green Reflection of Red Flag Acts for Intelligent Vehicles [History and Perspectives] IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2024-01-04 Fei-Yue Wang
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
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The UCLA Mobility Labs [Its Research Lab] IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2024-01-04 Jiaqi Ma, Yisheng Lv
The University of California, Los Angeles (UCLA) Mobility Lab is dedicated to harnessing system theories and tools, such as artificial intelligence, control theory, robotics, machine learning, and optimization, to innovate and develop advanced mobility technologies and solutions for smart cities, particularly intelligent vehicular and transportation systems. We leverage the university environment and
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Call for Distinguished Lecturer Nominations [Society News] IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2024-01-04 Matthew Barth
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
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A Survey on Datasets for the Decision Making of Autonomous Vehicles IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2024-01-01 Yuning Wang, Zeyu Han, Yining Xing, Shaobing Xu, Jianqiang Wang
Autonomous vehicles (AVs) are expected to reshape future transportation systems, and decision making is one of the critical modules toward high-level automated driving. To overcome those complicated scenarios that rule-based methods could not cope with well, data-driven decision-making approaches have aroused more focus. The datasets to be used in developing data-driven methods dramatically influence
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Human-Like Decision Making at Unsignalized Intersections Using Social Value Orientation IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2023-12-27 Yan Tong, Licheng Wen, Pinlong Cai, Daocheng Fu, Song Mao, Botian Shi, Yikang Li
With the commercial application of automated vehicles (AVs), the sharing of roads between AVs and human-driven vehicles (HVs) will become a common occurrence in the future. While research has focused on improving the safety and reliability of autonomous driving, it’s also crucial to consider collaboration between AVs and HVs. Human-like interaction is a required capability for AVs, especially at common
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A Survey of Integrated Simulation Environments for Connected Automated Vehicles: Requirements, Tools, and Architecture IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2023-12-21 Vitaly G. Stepanyants, Aleksandr Y. Romanov
Automated and connected vehicles are emerging in the market. Currently, solutions are being proposed to use these technologies for cooperative driving, which can significantly improve road safety. Vehicular safety applications must be tested before deployment. It is challenging to verify and validate them in the real world. Therefore, simulation is used for this purpose. Modeling this technology necessitates
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DERNet: Driver Emotion Recognition Using Onboard Camera IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2023-12-07 Dingyu Wang, Shaocheng Jia, Xin Pei, Chunyang Han, Danya Yao, Dezhi Liu
Driver emotion is considered an essential factor associated with driving behaviors and thus influences traffic safety. Dynamically and accurately recognizing the emotions of drivers plays an important role in road safety, especially for professional drivers, e.g., the drivers of passenger service vehicles. However, there is a lack of a benchmark to quantitatively evaluate the performance of driver
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The Exponential Scaling Law for Taxi Mobility Patterns IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2023-11-17 Mingchao Li, Ge Guo, Xinxin Zhang
Given the influence of different travel modes on urban transportation planning, such as private cars and public transportation, it is crucial to accurately identify the law of mobility under a specific travel mode. However, existing mobility laws, from the gravity law to the visitation law, describe the properties of human mobility based on various travel modes at a large spatial scale and do not capture
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Center for Sustainable Road Freight [ITS Research Lab] IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2023-11-07 Xiaoxiang Na, Yisheng Lv
Please send your proposal on profiling research activities of your or other ITS research groups and labs for the “ITS Research Labs” column to Yisheng Lv at yisheng.lv@ia.ac.cn.
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Large Transportation Models on the Horizon: Challenges and Issues [Editor’s Column] IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2023-11-06 Yisheng Lv
Following large language models, the development of large models in industries is becoming more intensified by the day. Without exception, large transportation models, including traffic perception and cognition for large-scale road networks and autonomous driving models, have been issued one after another, kicking off a new round of competition characterized by “big model + big data + big computing
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ITS Applications That Prioritize Human Interaction [President’s Message] IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2023-11-06 Cristina Olaverri-Monreal
As we navigate toward the future, the integration of human awareness, interaction, and user-friendliness within intelligent transportation systems (ITS) becomes increasingly vital. Designing ITS technologies to prioritize the well-being and preferences of individuals and communities is paramount, ensuring transportation experiences that are safe, convenient, and enjoyable. By embracing human-centric
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IEEE ITSC 2022 [Conference Activities] IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2023-11-06 Nan Zhang, Naiqi Wu, Lingxi Li, Yonglin Tian, Xiao Wang, Brendan Morris
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
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Destination-Aware Coordinated Ramp Metering for Preventing Off-Ramp Queue Spillover and Mainstream Congestion IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2023-10-25 Cheng Zhang, Wei Ma, Jing Zhao, Chengyuan Ma, Yuelong Su, Xiaoguang Yang
Freeway congestion usually occurs in on-ramp areas; however, it also occurs around off-ramp areas and can even spill back onto the main line, which is common on many congested urban freeways during peak hours. Nevertheless, traditional ramp metering strategies, one of the most successful approaches to reducing freeway congestion, have not considered the off-ramp traffic dynamics. Therefore, this study
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A Dynamic Transformation Car-Following Model for the Prediction of the Traffic Flow Oscillation IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2023-10-20 Shan Fang, Lan Yang, Xiangmo Zhao, Wei Wang, Zhigang Xu, Guoyuan Wu, Yang Liu, Xiaobo Qu
Car-following (CF) behavior is a fundamental of traffic flow modeling; it can be used for the virtual testing of connected and automated vehicles and the simulation of various types of traffic flow, such as free flow and traffic oscillation. Although existing CF models can replicate the free flow well, they are incapable of simulating complicated traffic oscillation, and it is difficult to strike a
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Overview of Cooperative Fault-Tolerant Control Driven by the Full Information Chain of Intelligent Connected Vehicle Platoons Under the Zero-Trust Framework: Opportunities and Challenges IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2023-10-19 Darong Huang, Yuhong Na, Yang Liu, Zhenyuan Zhang, Bo Mi
The zero-trust framework is a potential solution to address complex dynamic behaviors, information interactions, complex network topologies, and environmental security threats to groups, such as the safety of the intelligent connected vehicle (ICV) platoon. In addition, it addresses the challenges faced due to increased traffic of intelligent vehicles, communication in critical infrastructure, and
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Toward Directed Spatiotemporal Graph: A New Idea for Heterogeneous Traffic Prediction IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2023-10-11 Yixuan Ku, Chen Guo, Kangshuai Zhang, Yunduan Cui, Hongfeng Shu, Yang Yang, Lei Peng
Enhancing the accuracy of traffic prediction relies on building a graph that effectively captures the intricate spatiotemporal correlations in traffic data. It is a widely observed phenomenon that different urban traffic activities exhibit an asymmetric mutual influence. However, existing methods for graph construction largely overlook this characteristic. To bolster prediction performance, this article
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Predictive Information Multiagent Deep Reinforcement Learning for Automated Truck Platooning Control IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2023-10-06 Renzong Lian, Zhiheng Li, Boxuan Wen, Junqing Wei, Jiawei Zhang, Li Li
Human-leading automated truck platooning has been an effective technique to improve traffic capacity and fuel economy and eliminate uncertainties of the traffic environment. Aiming for a tradeoff between the dynamic response of car following and energy-efficient platooning control, a predictive information multiagent soft actor–critic (PI-MASAC) control framework is proposed for a human-leading automated
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Traffic Origin-Destination Flow-Inspired Dynamic Urban Arterial Partition for Coordinated Signal Control Using Automatic License Plate Recognition Data IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2023-10-02 Siping Ke, Yinpu Wang, Chengchuan An, Zhenbo Lu, Jingxin Xia
The partition of a long urban arterial is necessary for efficient and reliable traffic signal coordination. Previous studies have utilized fixed detector data or aggregated vehicle trajectory data to measure the correlation between adjacent intersections for an arterial partition. However, the appropriate partition of a long urban arterial remains a challenge because of the difficulty in quantifying
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A Proportional Allocation Model for Parking Reservation Systems Considering Entrance Capacity Constraints IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2023-10-02 Xun-You Ni, Daniel Jian Sun, Qing-Chang Lu, Qian Chen
Parking reservation systems allocate large parking flows to popular parking lots, which may increase the queue length and waiting time at the entrances of those parking lots. To alleviate or even solve the queuing problem, this article proposes a moving amendment method to determine the allowable parking flow entering parking lots at each allocation time. Based on this, a system-oriented proportional
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Multilevel Self-Training Approach for Cross-Domain Semantic Segmentation in Intelligent Vehicles IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2023-09-13 Yung-Yao Chen, Sin-Ye Jhong
The use of intelligent vehicle technology is increasing; however, this technology requires further improvement. Semantic segmentation enables intelligent vehicles to understand the environment. Although advances have been achieved in a deep learning model for semantic segmentation, large-scale real-world datasets with manual pixel-level annotations, which are expensive, are required for adequately
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History Makes the Future: Iterative Learning Control for High-Speed Trains IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2023-09-13 Shuai Gao, Qijiang Song, Hao Jiang, Dong Shen
With the development of high-speed rail transportation, the automatic train operation (ATO) of high-speed trains (HSTs) has attracted considerable attention in the fields of both theoretical research and engineering practice. The core task of ATO is trajectory tracking. As an intelligent control method that imitates human learning behavior, iterative learning control (ILC) has been widely applied to
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Collaborative Perception in Autonomous Driving: Methods, Datasets, and Challenges IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2023-09-12 Yushan Han, Hui Zhang, Huifang Li, Yi Jin, Congyan Lang, Yidong Li
Collaborative perception is essential to address occlusion and sensor failure issues in autonomous driving. In recent years, theoretical and experimental investigations of novel works for collaborative perception have increased tremendously. So far, however, few reviews have focused on systematical collaboration modules and large-scale collaborative perception datasets. This article reviews recent
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Driving the Future: Highlights From the 2023 IEEE Intelligent Vehicles Symposium [President’s Message] IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2023-09-06 Cristina Olaverri-Monreal
This year, the IEEE Intelligent Vehicles Symposium (IV), the flagship conference of the IEEE Intelligent Transportation Systems (ITS) Society, took place in June, in Anchorage, AK, USA. The event witnessed remarkable engagement and participation from researchers and scholars worldwide, attracting a significant number of paper submissions from various countries. After a rigorous review process, 300
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The Story of IEEE ICVES: The Dark Days Before China’s Boom in New Energy Vehicles [History and Perspectives] IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2023-09-06 Fei-Yue Wang
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
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Artificial Intelligence-Generated Content in Intelligent Transportation Systems: Learning to Copy, Change, and Create! [Editor’s Column] IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2023-09-06 Yisheng Lv
As artificial intelligence (AI), cloud computing, and chip technologies surge forward, AI-generated content (AIGC) is changing incredibly with each passing day. From the perspective of cognition and learning, just as the human learning process goes from imitation to improvement and then to creation, AIGC is going through the process of moving from sensing and twinning the real world (learning to copy)
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Infrastructure Perception and Control Laboratory [ITS Research Lab] IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2023-09-06 Yisheng Lv
U.S. Department of Energy national laboratories host world-class advanced computing and modeling capabilities. The National Renewable Energy Labowratory (NREL) inaugurated the Infrastructure Perception and Control Laboratory (IPC Lab) to advance mobility reach into real-world applications.
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A Survey of Decision-Making Safety Assessment Methods for Autonomous Vehicles IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2023-09-08 Zhaowen Pang, Zhenbin Chen, Jiayi Lu, Mengyue Zhang, Xinjie Feng, Yuyi Chen, Shichun Yang, Yaoguang Cao
How to drive safely in complex real-world traffic settings has long been a question and challenge for autonomous vehicles (AVs). Decision-making systems (DecSs) are the core of AVs, and their safety and rationality are crucial. Several decision-making techniques and algorithms have been applied to AVs; however, they are still subject to limitations and deficiencies, making it impossible to fully guarantee
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Joint Defocus Deblurring and Superresolution Learning Network for Autonomous Driving IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2023-09-07 Rui Wang, Chunjie Zhang, Xiaolong Zheng, Yisheng Lv, Yao Zhao
With the development of autonomous driving and computer vision, the importance of high-quality images is increasingly prominent. However, in practical applications, due to lighting, device response speed, distance, and other factors, the image captured by the onboard camera has a variety of degradations. One of the most common degradations is the combination of defocus blur and low resolution. But
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Multiairport Departure Scheduling via Multiagent Reinforcement Learning IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2023-09-06 Kaiquan Cai, Ziqi Li, Tong Guo, Wenbo Du
With the sharp increase in air traffic demand, the terminal area near airports encounters severe saturation, leading to unprecedented safety concerns. Departure scheduling is the paramount commitment to reduce terminal area congestion. To meet the excessive traffic flow, collaborative departure scheduling by several cooperative airports of a multiairport system has become a new trend for future air
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Research on System Architecture, Basic Platform, and Development Path of Autonomous Intelligent High-Speed Railway System IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2023-09-06 Ping Li, Yuhao Zhu, Yi Liu, Chenkun Jin
As the informatization, digitization, and intelligence of railways continues to progress, new advantages, such as massive data resources and rich application scenarios, promote the generation of the autonomous intelligent high-speed railway system (AIHSRS). Based on an analysis of the current state of autonomous transportation system research, this article proposes an overall architecture of the AIHSRS
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Model Predictive Control-Based Speed Profile Optimization of a Freight Train Group With a Hierarchical Algorithm IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2023-08-30 Liu Yang, Xubin Sun, Zemin Yao, Weifeng Zhong, Biao Liu, Xianjin Huang
Once a freight train is delayed on a busy railway under a quasi-moving-block signaling system, the speed of its following trains may fluctuate, without proper adjustment. Updating the reference speed profile of the delayed freight train is an urgent task to improve the train operation performance and rail utilization ratio. To this end, this article proposes a speed profile optimization method for
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Transportation Efficiency Evaluation Under the Policies of Energy Savings and Emissions Reduction IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2023-08-22 Qiwei Xie, Kun Shi, Xiao Wu, Wuling Huang, Xiaolong Zheng, Yongjun Li
The transportation industry is considered the foundation and bridge of national economic development, enabling the growth of the social economy. However, it also consumes a considerable amount of energy, resulting in high levels of carbon dioxide (CO 2 ) emissions. As a component of China’s vigorous promotion of energy-saving and emissions-reducing policies in recent years, it is crucial to maximize
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Optimized Multicar Dynamic Route Planning Based on Time-Hierarchical Graph Model IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2023-08-21 Qing Song, Xiaolei Li, Chao Gao, Zhen Shen, Gang Xiong
Traffic congestion has become a major concern in most cities all over the world. The proper guidance of cars with an effective route planning method has become a fundamental and smart way to alleviate congestion under existing urban road facilities. Current route planning methods mainly focus on a single car, but ignoring the dynamic effect between cars may lead to severe congestion during the actual
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Entropy Feature Fusion-Based Diagnosis for Railway Point Machines Using Vibration Signals Based on Kernel Principal Component Analysis and Support Vector Machine IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2023-08-14 Yongkui Sun, Yuan Cao, Peng Li, Shuai Su
Railway point machines are the key equipment that controls the train route and affects the safety of train operation. Complex and harsh working environments lead to frequent failures, accounting for 40% of the total failures of the railway signaling system. Thus, it is an urgent task to present an intelligent fault diagnosis approach. Considering the easy acquisition and anti-interference characteristics
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Spatiotemporal K-Nearest Neighbors Algorithm and Bayesian Approach for Estimating Urban Link Travel Time Distribution From Sparse GPS Trajectories IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2023-08-09 Wenwen Qin, Mingfeng Zhang, Wu Li, Yunyi Liang
Travel time distribution (TTD) estimation on urban arterial links with sparse trajectory data is a practically important while substantially challenging subject. Although several methods have been proposed to estimate link TTDs, the applications of the existing methods are often limited by their shortcomings, such as the needs for extra road geometric features, signal control plans, model assumptions
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Multi-Sensor Fusion and Cooperative Perception for Autonomous Driving: A Review IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2023-08-04 Chao Xiang, Chen Feng, Xiaopo Xie, Botian Shi, Hao Lu, Yisheng Lv, Mingchuan Yang, Zhendong Niu
Autonomous driving (AD), including single-vehicle intelligent AD and vehicle–infrastructure cooperative AD, has become a current research hot spot in academia and industry, and multi-sensor fusion is a fundamental task for AD system perception. However, the multi-sensor fusion process faces the problem of differences in the type and dimensionality of sensory data acquired using different sensors (cameras
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When Blockchain Meets Urban Rail Transit: Current Prospects, Case Studies, and Future Challenges IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2023-07-27 Hao Liang, Li Zhu, F. Richard Yu
Thanks to the vigorous development of artificial intelligence, urban rail transit (URT) is undergoing a new round of intelligent upgrades. While its intelligence level is improving, URT suffers from a weak trust foundation, high data sharing costs, and low collaboration efficiency. Driven by outstanding features of decentralization, resilience against tampering, and traceability, blockchain can provide
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A Particle Swarm Optimization-Based Online Optimization Approach for Virtual Coupling Trains With Communication Delay IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2023-07-25 Yuxuan Guo, Xuan Pei, Xiaolin Luo, Hongjie Liu, Tao Tang, Taogang Hou
Virtual coupling allows multiple trains to run synchronously in a virtually coupled formation via train-to-train (T2T) wireless communication. The distance between virtual coupling trains can be reduced greatly to improve the line capacity. However, the time delay in T2T communication cannot be avoided, and the delay may fluctuate due to the uncertainties in the train operation, which will negatively
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An Unsupervised Learning Approach for Robust Denied Boarding Probability Estimation Using Smart Card and Operation Data in Urban Railways IEEE Intell. Transp. Syst. Mag. (IF 3.6) Pub Date : 2023-07-11 Kerem S. Tuncel, Haris N. Koutsopoulos, Zhenliang Ma
Urban railway systems in many cities are facing increasing levels of crowding and operating near capacity. Crowding at stations and on trains is a concern due to its impact on safety, service quality, and operating efficiency. Denied boarding is becoming a key measure of the impact of near-capacity operations on customers, and it is fundamental for calculating other performance metrics, such as the