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"Traffic control"
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Institutional reform of air navigation service providers : a historical and economic perspective
'Institutional Reform of Air Navigation Service Providers' deals with the changes that have taken place in this major, technologically progressive industry as many countries moved away from direct provision by the government to forms of corporate of private provision. The author provides an up-to-date institutonal and economic analysis of air navigation service providers' efforts to reform their governance and funding structures under these changes.
Book
A Simulation-Based Traffic Signal Control for Congested Urban Traffic Networks
Traffic congestion in urban networks may lead to strong degradation in the utilization of the network infrastructure, which can be mitigated via suitable control strategies. This paper studies and analyzes the performance of an adaptive traffic-responsive strategy that controls the traffic light parameters in an urban network to reduce traffic congestion. A nearly optimal control formulation is adopted to avoid the curse of dimensionality occurring in the solution of the corresponding Hamilton–Jacobi–Bellman (HJB) optimal control problem. First, an (approximate) solution of the HJB is parametrized via an appropriate Lyapunov function; then, the solution is updated at each iteration in such a way to approach the nearly optimal solution, using a close-to-optimality index and information coming from the simulation model of the network (simulation-based design). Simulation results obtained using a traffic simulation model of the network Chania, Greece, an urban traffic network containing many varieties of junction staging, demonstrate the efficiency of the proposed approach, as compared with alternative traffic strategies based on a simplified linear model of the traffic network. It is shown that the proposed strategy can adapt to different traffic conditions and that low-complexity parametrizations of the optimal solution, a linear and a bimodal piecewise linear strategy, respectively, provide a satisfactory trade-off between computational complexity and network performance.
Journal Article
Complexity science in air traffic management
\"Air traffic management (ATM) comprises a highly complex socio-technical system that keeps air traffic flowing safely and efficiently, worldwide, every minute of the year. Over the last few decades, several ambitious ATM performance improvement programmes have been undertaken. Such programmes have mostly delivered local technological solutions, whilst corresponding ATM performance improvements have fallen short of stakeholder expectations. In hindsight, this can be substantially explained from a complexity science perspective: ATM is simply too complex to address through classical approaches such as system engineering and human factors. In order to change this, complexity science has to be embraced as ATM's 'best friend'. The applicability of complexity science paradigms to the analysis and modelling of future operations is driven by the need to accommodate long-term air traffic growth within an already-saturated ATM infrastructure\"--Provided by publisher.
Book
Traffic signal active control method for short-distance intersections
Aiming at the existing problems about the overflow prevention goal and the overall traffic efficiency guarantee being difficult to optimize at the same time in the signal control process of short-distance intersections scenario, this paper proposes a traffic signal active control method based on key state prediction. In order to construct the key state evolution trend of short-distance intersection scenarios, this paper proposes the concept of overflow index for short-distance road sections and designs the prediction method of overflow index. In order to perform fast computation and solution for the active control scheme, this paper builds a solution algorithm based on deep reinforcement learning and optimizes the problem of reward sparsity in the algorithm, which improves the ability of active control in terms of state space and reward function. The experimental results show that this method can not only ensure the overall traffic efficiency of short-distance intersections and reduce the travel delay but also can actively sense the change of overflow state, improve the overflow prevention and control ability of the target scenario, and reduce the overflow risk.
Journal Article
IoT-Simulated Digital Twin with AI Traffic Signal Control for Real-Time Traffic Optimization in SUMO
Urban traffic congestion leads to longer travel times, economic losses, and increased pollution. Recent advances in the Internet of Things (IoT) provide detailed real-time traffic data, yet testing adaptive control strategies directly on live networks remains costly and risky. To address this challenge, we propose an IoT-driven digital twin framework for the design and evaluation of AI-based traffic management systems. The framework is implemented in the Simulation of Urban MObility (SUMO) and uses its Python 3.14.2 API to emulate a dense network of IoT sensors that stream real-time information on vehicle density, queue lengths, and waiting times. This simulated IoT data feeds an AI agent that adapts traffic signal control in real time. The agent is trained with a composite reward function to jointly minimise vehicle waiting times and emissions. Its performance is compared with fixed-time and vehicle-actuated control under varying traffic demand scenarios. Results demonstrate the effectiveness of combining IoT-based simulation with AI control, providing a safe and scalable pathway towards the real-world deployment of intelligent traffic management systems.
Journal Article
A review of the next generation air transportation system : implications and importance of system architecture
The Next Generation Air Transportation System's (NextGen) goal is the transformation of the U.S. national airspace system through programs and initiatives that could make it possible to shorten routes, navigate better around weather, save time and fuel, reduce delays, and improve capabilities for monitoring and managing of aircraft. A Review of the Next Generation Air Transportation provides an overview of NextGen and examines the technical activities, including human-system design and testing, organizational design, and other safety and human factor aspects of the system, that will be necessary to successfully transition current and planned modernization programs to the future system. This report assesses technical, cost, and schedule risk for the software development that will be necessary to achieve the expected benefits from a highly automated air traffic management system and the implications for ongoing modernization projects. The recommendations of this report will help the Federal Aviation Administration anticipate and respond to the challenges of implementing NextGen.
Book
Multi-Agent Reinforcement Learning for Traffic Signal Control: A Cooperative Approach
The rapid growth of urbanization and the constant demand for mobility have put a great strain on transportation systems in cities. One of the major challenges in these areas is traffic congestion, particularly at signalized intersections. This problem not only leads to longer travel times for commuters, but also results in a significant increase in local and global emissions. The fixed cycle of traffic lights at these intersections is one of the primary reasons for this issue. To address these challenges, applying reinforcement learning to coordinating traffic light controllers has become a highly researched topic in the field of transportation engineering. This paper focuses on the traffic signal control problem, proposing a solution using a multi-agent deep Q-learning algorithm. This study introduces a novel rewarding concept in the multi-agent environment, as the reward schemes have yet to evolve in the following years with the advancement of techniques. The goal of this study is to manage traffic networks in a more efficient manner, taking into account both sustainability and classic measures. The results of this study indicate that the proposed approach can bring about significant improvements in transportation systems. For instance, the proposed approach can reduce fuel consumption by 11% and average travel time by 13%. The results of this study demonstrate the potential of reinforcement learning in improving the coordination of traffic light controllers and reducing the negative impacts of traffic congestion in urban areas. The implementation of this proposed solution could contribute to a more sustainable and efficient transportation system in the future.
Journal Article
Phase-free traffic signal control for balanced flow in sensor-limited environments
This paper introduces a phase-free traffic signal control system designed to improve both efficiency and equity in sensor-limited environments. While traditional Adaptive Traffic Signal Control (ATSC) effectively reduces delays, it often results in inequitable green time allocation, particularly under oversaturated conditions. To address this issue, this study proposes a Cell Transmission Model (CTM)-based approach for estimating queue lengths beyond the detection zones of point sensors in congested conditions. By exchanging traffic information between adjacent intersections in distributed environments, the proposed approach estimates real-time queue lengths and waiting times for each traffic movement. The phase-free system dynamically allocates green time to balance these estimates, ensuring more equitable and efficient traffic management. The system was evaluated through numerical experiments on a two-intersection network and a 3 × 3 grid network, where it achieved a 15% reduction in average control delay and small deviations in the level of service between movements compared to traditional control systems. The results demonstrate the system’s potential for real-world applications, particularly in urban areas with uneven traffic flows and limited sensor coverage. By addressing the dual objectives of maximizing throughput and ensuring equitable treatment of all traffic movements, the proposed control system provides a scalable solution for modern urban traffic networks.
Journal Article