Radio Resource Management and Path Planning in Intelligent Transportation Systems via Reinforcement Learning for Environmental Sustainability
Tarbiat Modares University（塔比亚特·莫达雷斯大学）；Doha University for Science and Technology（多哈科技大学）；TU Braunschweig（布伦瑞克工业大学）；Institute of Communications Technology（通信技术研究所）
S. Norouzi, N. Azarasa, M. R. Abedi, N. Mokari, S. E. Seyedabrishami, H. Saeedi, E. A. Jorswieck
备注：
6 pages, 5 figures, accepted and presented in International Conference on Innovation and Technological Advances for Sustainability
Efficient and dynamic path planning has become an important topic for urban areas with larger density of connected vehicles (CV) which results in reduction of travel time and directly contributes to environmental sustainability through reducing energy consumption. CVs exploit the cellular wireless vehicle-to-everything (C-V2X) communication technology to disseminate the vehicle-to-infrastructure (V2I) messages to the Base-station (BS) to improve situation awareness on urban roads. In this paper, we investigate radio resource management (RRM) in such a framework to minimize the age of information (AoI) so as to enhance path planning results. We use the fact that V2I messages with lower AoI value result in less error in estimating the road capacity and more accurate path planning. Through simulations, we compare road travel times and volume over capacity (V/C) against different levels of AoI and demonstrate the promising performance of the proposed framework.
Latest technological improvements increased the quality of transportation. New data-driven approaches bring out a new research direction for all control-based systems, e.g., in transportation, robotics, IoT and power systems. Combining data-driven applications with transportation systems plays a key role in recent transportation applications. In this paper, the latest deep reinforcement learning (RL) based traffic control applications are surveyed. Specifically, traffic signal control (TSC) applications based on (deep) RL, which have been studied extensively in the literature, are discussed in…
Columbia University（哥伦比亚大学）；Imperial College London（帝国理工学院）；Nokia-Bell Laboratories（诺基亚贝尔实验室）
Intelligent Transportation Systems (ITSs) are envisioned to play a critical role in improving traffic flow and reducing congestion, which is a pervasive issue impacting urban areas around the globe. Rapidly advancing vehicular communication and edge cloud computation technologies provide key enablers for smart traffic management. However, operating viable real-time actuation mechanisms on a practically relevant scale involves formidable challenges, e.g., policy iteration and conventional Reinforcement Learning (RL) techniques suffer from poor scalability due to state space explosion. Motivated…
Intelligent transportation systems (ITSs) are envisioned to be crucial for smart cities, which aims at improving traffic flow to improve the life quality of urban residents and reducing congestion to improve the efficiency of commuting. However, several challenges need to be resolved before such systems can be deployed, for example, conventional solutions for Markov decision process (MDP) and single-agent Reinforcement Learning (RL) algorithms suffer from poor scalability, and multi-agent systems suffer from poor communication and coordination. In this paper, we explore the potential of mutual…
Western University（西安大略大学）；ISSAE-Cnam Liban（黎巴嫩国立应用科学学院-法国国立工艺学院）；CNAM Paris（巴黎法国国立工艺学院）
The growing deployment efforts of 5G networks globally has led to the acceleration of the businesses/services' digital transformation. This growth has led to the need for new communication technologies that will promote this transformation. 6G is being proposed as the set of technologies and architectures that will achieve this target. Among the main use cases that have emerged for 5G networks and will continue to play a pivotal role in 6G networks is that of Intelligent Transportation Systems (ITSs). With all the projected benefits of developing and deploying efficient and effective ITSs come…
University of New South Wales（新南威尔士大学）；University of Sydney（悉尼大学）；Technische Universität Dresden（德累斯顿工业大学）；The Hong Kong Polytechnic University（香港理工大学）
Transportation is the backbone of the economy and urban development. Improving the efficiency, sustainability, resilience, and intelligence of transportation systems is critical and also challenging. The constantly changing traffic conditions, the uncertain influence of external factors (e.g., weather, accidents), and the interactions among multiple travel modes and multi-type flows result in the dynamic and stochastic natures of transportation systems. The planning, operation, and control of transportation systems require flexible and adaptable strategies in order to deal with uncertainty, no…
SIGMA: Symmetry-aware, Intelligent, Geometric, Multi-faced Adaptive Traffic Control for Reliable Intelligent Transportation Systems
Indian Statistical Institute（印度统计研究所）；Sorbonne University Abu Dhabi（阿布扎比索邦大学）；Sorbonne Center for Artificial Intelligence（索邦人工智能中心）；SQC & OR（统计质量控制与运筹学研究室）
Joint Task Offloading, Inference Optimization and UAV Trajectory Planning for Generative AI Empowered Intelligent Transportation Digital Twin
Guangxi University Key Laboratory of Intelligent Networking and Scenario System (School of Information and Communication, Guilin University of Electronic Technology)（广西大学智能组网与场景系统重点实验室（桂林电子科技大学信息与通信学院））；Research Institute of Highway Ministry of Transport（交通运输部公路科学研究院）；School of Automation, Guangdong University of Technology（广东工业大学自动化学院）；School of Architecture and Transportation Engineering, Guilin University of Electronic Technology（桂林电子科技大学建筑与交通工程学院）
Deep Reinforcement Learning for Traffic Light Control in Intelligent Transportation Systems
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Department of Electrical Engineering, Columbia University（哥伦比亚大学电气工程系）；Nokia Bell Labs（诺基亚贝尔实验室）
We explore the use of deep learning and deep reinforcement learning for optimization problems in transportation. Many transportation system analysis tasks are formulated as an optimization problem - such as optimal control problems in intelligent transportation systems and long term urban planning. Often transportation models used to represent dynamics of a transportation system involve large data sets with complex input-output interactions and are difficult to use in the context of optimization. Use of deep learning metamodels can produce a lower dimensional representation of those relations …
Traffic signal control is an important and challenging real-world problem, which aims to minimize the travel time of vehicles by coordinating their movements at the road intersections. Current traffic signal control systems in use still rely heavily on oversimplified information and rule-based methods, although we now have richer data, more computing power and advanced methods to drive the development of intelligent transportation. With the growing interest in intelligent transportation using machine learning methods like reinforcement learning, this survey covers the widely acknowledged trans…
In this paper, the necessity for application-oriented development and evaluation of Joint Communication and Sensing (JC&S) applications, especially in transportation, is addressed. More specifically, an integrative evaluation chain for immersively testing JC&S location capabilities, reaching from early-stage testing, over model- and scenario-enabled ray tracing simulation, to real-world evaluation (laboratory and field testing) is presented. This includes a discussion of both challenges and requirements for location-aware applications in Intelligent Transportation Systems. Within this scope, a…
Shahrood University of Technology（沙赫鲁德理工大学）；Louisiana State University（路易斯安那州立大学）；Ton Duc Thang University（同塔大学）；Oxford Brookes University（牛津布鲁克斯大学）；Bauhaus University Weimar（魏玛包豪斯大学）；Queensland University of Technology（昆士兰科技大学）；Duy Tan University（岘港大学（越南东塔大学））；J. Selye University（塞莱大学）；Obuda University（奥布达大学）
Prediction models in mobility and transportation maintenance systems have been dramatically improved through using machine learning methods. This paper proposes novel machine learning models for intelligent road inspection. The traditional road inspection systems based on the pavement condition index (PCI) are often associated with the critical safety, energy and cost issues. Alternatively, the proposed models utilize surface deflection data from falling weight deflectometer (FWD) tests to predict the PCI. Machine learning methods are the single multi-layer perceptron (MLP) and radial basis fu…
A traffic monitoring system is an integral part of Intelligent Transportation Systems (ITS). It is one of the critical transportation infrastructures that transportation agencies invest a huge amount of money to collect and analyze the traffic data to better utilize the roadway systems, improve the safety of transportation, and establish future transportation plans. With recent advances in MEMS, machine learning, and wireless communication technologies, numerous innovative traffic monitoring systems have been developed. In this article, we present a review of state-of-the-art traffic monitorin…
Ground-based Augmentation System (GBAS) augments Global Navigation Satellite Systems (GNSS) to support the precision approach and landing of aircraft. To guarantee integrity, existing single-frequency GBAS utilizes position-domain geometry screening to eliminate potentially unsafe satellite geometries by inflating one or more broadcast GBAS parameters. However, GBAS availability can be drastically impacted in low-latitude regions where severe ionospheric conditions have been observed. Thus, we developed a novel geometry-screening algorithm in this study to improve GBAS availability in low-lati…
Vehicle-to-everything (V2X) communication and services have been garnering significant interest from different stakeholders as part of future intelligent transportation systems (ITSs). This is due to the many benefits they offer. However, many of these services have stringent performance requirements, particularly in terms of the delay/latency. Multi-access/mobile edge computing (MEC) has been proposed as a potential solution for such services by bringing them closer to vehicles. Yet, this introduces a new set of challenges such as where to place these V2X services, especially given the limit …
In this paper, we propose a closely spaced multi-antenna system with \textit{passive} self-interference cancellation (\textit{p}-SIC) of $\approx 90$ dB between the transmitter and receiver antenna for full-duplex application. The \textit{p}-SIC is achieved by field confinement near individual antennae using shorted metallic vias and the application of U-shaped perturbation in the ground plane. The \textit{p}-SIC technique is initially implemented in a 1-Tx and 1-Rx antenna system and explained using transmission line-based theory. Further, it is extended to 1-Tx and 2-Rx configurations. Here …
This paper reports a detailed experimental characterization of optical performances of Visible Light Communication (VLC) system using a real traffic light for ultra-low latency, infrastructure-to-vehicle (I2V) communications for intelligent transportation systems (ITS) protocols. Despite the implementation of long sought ITS protocols poses the crucial need to detail how the features of optical stages influence the overall performances of a VLC system in realistic configurations, such characterization has rarely been addressed at present. We carried out an experimental investigation in a reali…
This paper analyzes the smart charging system for dealing with issues related to large parking garages, and analyzes the relevant technical standards of intelligent charging piles application and comprehensive transportation hubs. It mainly includes the number, area and charging method of the charging piles installed in the garages. New forms of construction management is conceived, and the investment and construction are reinforced, apportioning all charging system property rights to investment parties. Simultaneously, the investors take full responsibility for the future management and opera…
