Article
Engineering, Civil
Saeed Rahmani, Asiye Baghbani, Nizar Bouguila, Zachary Patterson
Summary: Graph neural networks (GNNs) have gained popularity in the field of intelligent transportation systems (ITS) due to their ability to analyze graph-structured data. However, there is currently no comprehensive review of recent advancements and future research directions in all transportation areas. This survey provides an overview of GNN studies in ITS, exploring various applications such as traffic forecasting, demand prediction, autonomous vehicles, intersection management, parking management, urban planning, and transportation safety. It also identifies domain-specific research directions, opportunities, challenges, and previously overlooked research opportunities in edge and graph learning, multi-modal models, and unsupervised and reinforcement learning methods for developing more powerful GNNs. The survey also highlights popular baseline models and datasets for each transportation domain.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xiaocong Chen, Lina Yao, Julian McAuley, Guanglin Zhou, Xianzhi Wang
Summary: This article provides a timely and comprehensive overview of recent trends in deep reinforcement learning (DRL) in recommender systems. It discusses the motivation for applying DRL in recommender systems, presents a taxonomy and summary of current DRL-based recommender systems, and explores emerging topics and open issues. The survey serves as an introductory material for readers from academia and industry and identifies notable opportunities for further research.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Engineering, Civil
Ammar Haydari, Yasin Yilmaz
Summary: Latest technological improvements have enhanced the quality of transportation. The emergence of new data-driven approaches has opened up new research directions for control-based systems in various domains, including transportation, robotics, IoT, and power systems. This paper presents a survey of traffic control applications based on deep reinforcement learning (RL). It extensively discusses different problem formulations, RL parameters, and simulation environments for traffic signal control (TSC) applications. The survey also covers autonomous driving applications studied with deep RL models, categorizing them based on application types, control models, and algorithms studied. The paper concludes with a discussion on challenges and open questions in deep RL-based transportation applications.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Li Zhu, Cheng Chen, Hongwei Wang, F. Richard Yu, Tao Tang
Summary: Urban Rail Transit Systems (URTS) are becoming increasingly important in modern public transportation, but many existing URTS still lack advanced intelligence. Machine Learning (ML) has shown great potential in enhancing URTS through tasks like perception, prediction, and optimization. Utilizing ML techniques can improve service quality and performance.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Review
Transportation Science & Technology
Nishant Kumar, Martin Raubal
Summary: This survey presents the current state of deep learning applications in tasks related to detection, prediction, and alleviation of congestion. Recurring and non-recurring congestion are discussed separately, revealing inherent challenges and gaps in the current research.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2021)
Article
Computer Science, Theory & Methods
Jose Mena, Oriol Pujol, Jordi Vitria
Summary: This study introduces the importance of uncertainty estimation in machine learning systems and analyzes how uncertainty can be measured in classification systems based on deep learning. The study also provides an overview of practical considerations in different applications and highlights the properties that should be considered when developing metrics.
ACM COMPUTING SURVEYS
(2022)
Article
Computer Science, Artificial Intelligence
Manqing Dong, Feng Yuan, Lina Yao, Xianzhi Wang, Xiwei Xu, Liming Zhu
Summary: This survey provides a systematic summary of three categories of trust issues in recommender systems and focuses on the work based on deep learning techniques.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Theory & Methods
Pengzhen Ren, Yun Xiao, Xiaojun Chang, Po-Yao Huang, Zhihui Li, Brij B. Gupta, Xiaojiang Chen, Xin Wang
Summary: Researchers have shown relatively lower interest in active learning compared to deep learning, but with the increasing demand for large-scale high-quality annotated datasets, active learning is receiving more attention. This article provides a comprehensive survey on deep active learning, including a formal classification method, an overview of existing work, and an analysis of developments from an application perspective.
ACM COMPUTING SURVEYS
(2022)
Article
Engineering, Civil
Guang-Li Huang, Arkady Zaslavsky, Seng W. Loke, Amin Abkenar, Alexey Medvedev, Alireza Hassani
Summary: Context awareness enhances data for applications and enables algorithms to sense changes in data streams. Context-aware machine learning is used in intelligent services and has been successful in intelligent transportation systems. This paper reviews recent studies in context-aware machine learning for transportation, discussing contextual data, applications, learning methods, and proposing a context-aware machine learning architecture for addressing existing gaps.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Information Systems
Ke He, Dan Dongseong Kim, Muhammad Rizwan Asghar
Summary: Network-based Intrusion Detection System (NIDS) is vital for defending against network attacks, but it is susceptible to adversarial attacks that manipulate input examples. This article reviews the literature on NIDS, adversarial attacks, and defence mechanisms, highlighting the challenges in launching and detecting adversarial attacks against NIDS.
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS
(2023)
Article
Computer Science, Artificial Intelligence
Yang Tang, Chaoqiang Zhao, Jianrui Wang, Chongzhen Zhang, Qiyu Sun, Wei Xing Zheng, Wenli Du, Feng Qian, Juergen Kurths
Summary: This review focuses on the applications of learning-based monocular approaches in ego-motion perception, environment perception, and navigation in autonomous systems. It discusses the shortcomings of existing classical visual simultaneous localization and mapping (vSLAM) solutions and the necessity to integrate deep learning techniques. It also reviews visual-based environmental perception and understanding methods based on deep learning and explores visual navigation based on learning systems, including reinforcement learning and deep reinforcement learning. Challenges and promising directions in related research of learning systems in computer science and robotics are examined and concluded.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Information Systems
Issam Damaj, Salwa K. Al Khatib, Tarek Naous, Wafic Lawand, Zainab Z. Abdelrazzak, Hussein T. Mouftah
Summary: This article discusses the challenges and requirements of using high-performance hardware devices and machine learning techniques in Intelligent Transportation Systems (ITS). By reviewing relevant literature and proposing a performance evaluation framework, it lays the foundation for developing suitable hardware devices and technological infrastructure, as well as bridging the gap between research and real-world deployments.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Automation & Control Systems
Yao Deng, Tiehua Zhang, Guannan Lou, Xi Zheng, Jiong Jin, Qing-Long Han
Summary: The rapid development of artificial intelligence, specifically deep learning technology, has propelled advancements in autonomous driving systems. However, these systems are increasingly threatened by various types of attacks. This survey comprehensively analyzes potential attacks on autonomous driving systems and presents state-of-the-art defense mechanisms, while also suggesting promising research directions for improving safety.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Xiangli Yang, Zixing Song, Irwin King, Zenglin Xu
Summary: This paper provides a comprehensive survey on deep semi-supervised learning methods, including model design and unsupervised loss functions. It categorizes existing methods into different types and reviews 60 representative methods with a detailed comparison. The paper also discusses the shortcomings of existing methods and proposes heuristic solutions.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Engineering, Civil
Taiyuan Gong, Li Zhu, F. Richard Yu, Tao Tang
Summary: Edge intelligence (EI) is a research hotspot that empowers intelligent transportation systems (ITS). By pushing AI to the network edge, EI enables ITS AI applications to have lower latency, higher security, less pressure on the backbone network, and better use of edge big data.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Industrial
Haobin Li, Giulia Pedrielli, Loo Hay Lee, Ek Peng Chew
FLEXIBLE SERVICES AND MANUFACTURING JOURNAL
(2017)
Article
Automation & Control Systems
Juxin Li, Weizhi Liu, Giulia Pedrielli, Loo Hay Lee, Ek Peng Chew
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2018)
Article
Computer Science, Artificial Intelligence
Chunjiang Zhang, Kay Chen Tan, Loo Hay Lee, Liang Gao
Article
Automation & Control Systems
Taiguang Gao, Min Huang, Qing Wang, Mingqiang Yin, Wai Ki Ching, Loo Hay Lee, Xingwei Wang
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2018)
Article
Engineering, Industrial
Byung Kwon Lee, Loo Hay Lee, Ek Peng Chew
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2018)
Article
Operations Research & Management Science
Xin Jia Jiang, Yanhua Xu, Chenhao Zhou, Ek Peng Chew, Loo Hay Lee
TRANSPORTATION SCIENCE
(2018)
Article
Operations Research & Management Science
Chenhao Zhou, Ek Peng Chew, Loo Hay Lee
TRANSPORTATION SCIENCE
(2018)
Article
Engineering, Industrial
Yinchao Zhu, Giulia Pedrielli, Loo Hay Lee
Article
Economics
Lu Hu, Juan Xiu Zhu, Yuan Wang, Loo Hay Lee
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2018)
Article
Automation & Control Systems
Hui Xiao, Hu Chen, Loo Hay Lee
Article
Automation & Control Systems
Yijie Peng, Jie Xu, Loo Hay Lee, Jianqiang Hu, Chun-Hung Chen
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2019)
Article
Computer Science, Information Systems
Xiaohu Qian, Min Huang, Loo Hay Lee, Xingwei Wang, Shanshan Tang
Proceedings Paper
Computer Science, Theory & Methods
Chenhao Zhou, Haobin Li, Weizhi Liu, Aloisius Stephen, Loo Hay Lee, Ek Peng Chew
2018 WINTER SIMULATION CONFERENCE (WSC)
(2018)
Proceedings Paper
Automation & Control Systems
Chenhao Zhou, Haobin Li, Loo Hay Lee, Ek Peng Chew
2018 IEEE 14TH INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING (CASE)
(2018)
Article
Transportation Science & Technology
Yue Zhao, Liujiang Kang, Huijun Sun, Jianjun Wu, Nsabimana Buhigiro
Summary: This study proposes a 2-population 3-strategy evolutionary game model to address the issue of subway network operation extension. The analysis reveals that the rule of maximum total fitness ensures the priority of evolutionary equilibrium strategies, and proper adjustment minutes can enhance the effectiveness of operation extension.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Hongtao Hu, Jiao Mob, Lu Zhen
Summary: This study investigates the challenges of daily storage yard management in marine container terminals considering delayed transshipment of containers. A mixed-integer linear programming model is proposed to minimize various costs associated with transportation and yard management. The improved Benders decomposition algorithm is applied to solve the problem effectively and efficiently.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Zhandong Xu, Yiyang Peng, Guoyuan Li, Anthony Chen, Xiaobo Liu
Summary: This paper studied the impact of range anxiety among electric vehicle drivers on traffic assignment. Two types of range-constrained traffic assignment problems were defined based on discrete or continuous distributed range anxiety. Models and algorithms were proposed to solve the two types of problems. Experimental results showed the superiority of the proposed algorithm and revealed that drivers with heightened range anxiety may cause severe congestion.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Chuanjia Li, Maosi Geng, Yong Chen, Zeen Cai, Zheng Zhu, Xiqun (Michael) Chen
Summary: Understanding spatial-temporal stochasticity in shared mobility is crucial, and this study introduces the Bi-STTNP prediction model that provides probabilistic predictions and uncertainty estimations for ride-sourcing demand, outperforming conventional deep learning methods. The model captures the multivariate spatial-temporal Gaussian distribution of demand and offers comprehensive uncertainty representations.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Benjamin Coifman, Lizhe Li
Summary: This paper develops a partial trajectory method for aligning views from successive fixed cameras in order to ensure high fidelity with the actual vehicle movements. The method operates on the output of vehicle tracking to provide direct feedback and improve alignment quality. Experimental results show that this method can enhance accuracy and increase the number of vehicles in the dataset.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Mohsen Dastpak, Fausto Errico, Ola Jabali, Federico Malucelli
Summary: This article discusses the problem of an Electric Vehicle (EV) finding the shortest route from an origin to a destination and proposes a problem model that considers the occupancy indicator information of charging stations. A Markov Decision Process formulation is presented to optimize the EV routing and charging policy. A reoptimization algorithm is developed to establish the sequence of charging station visits and charging amounts based on system updates. Results from a comprehensive computational study show that the proposed method significantly reduces waiting times and total trip duration.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)