Efficient management of traffic flow in urban environments presents a significant challenge, exacerbated by dynamic changes and the sheer volume of data generated by modern transportation networks. Traditional centralized traffic management systems often struggle with scalability and privacy concerns, hindering their effectiveness. This paper introduces a novel approach by combining Federated Learning (FL) and Meta-Learning (ML) to create a decentralized, scalable, and adaptive traffic management system. Our approach, termed Meta-Federated Learning, leverages the distributed nature of FL to process data locally at the edge, thereby enhancing privacy and reducing latency. Simultaneously, ML enables the system to quickly adapt to new traffic conditions without the need for extensive retraining. We implement our model across a simulated network of smart traffic devices, demonstrating that Meta-Federated Learning significantly outperforms traditional models in terms of prediction accuracy and response time. Furthermore, our approach shows remarkable adaptability to sudden changes in traffic patterns, suggesting a scalable solution for real-time traffic management in smart cities. This study not only paves the way for more resilient urban traffic systems but also exemplifies the potential of integrated FL and ML in other real-world applications.

In the modern world, the development of Artificial Intelligence (AI) has contributed to improvements in various areas, including automation, computer vision, fraud detection, and more. AI can be leveraged to enhance the efficiency of Autonomous Smart Traffic Management (ASTM) systems and reduce traffic congestion rates. This paper presents an Autonomous Smart Traffic Management (STM) system that uses AI to improve traffic flow rates. The system employs the YOLO V5 Convolutional Neural Network to detect vehicles in traffic management images. Additionally, it predicts the number of vehicles for the next 12 hours using a Recurrent Neural Network with Long Short-Term Memory (RNN-LSTM). The Smart Traffic Management Cycle Length Analysis manages the traffic cycle length based on these vehicle predictions, aided by AI. From the results of the RNN-LSTM model for predicting vehicle numbers over the next 12 hours, we observe that the model predicts traffic with a Mean Squared Error (MSE) of 4.521 vehicles and a Root Mean Squared Error (RMSE) of 2.232 vehicles. After simulating the STM system in the CARLA simulation environment, we found that the Traffic Management Congestion Flow Rate with ASTM (21 vehicles per minute) is 50\% higher than the rate without STM (around 15 vehicles per minute). Additionally, the Traffic Management Vehicle Pass Delay with STM (5 seconds per vehicle) is 70\% lower than without STM (around 12 seconds per vehicle). These results demonstrate that the STM system using AI can increase traffic flow by 50\% and reduce vehicle pass delays by 70\%.

Existing research on AI-based traffic management systems, utilizing techniques such as fuzzy logic, reinforcement learning, deep neural networks, and evolutionary algorithms, demonstrates the potential of AI to transform the traffic landscape. This article endeavors to review the topics where AI and traffic management intersect. It comprises areas like AI-powered traffic signal control systems, automatic distance and velocity recognition (for instance, in autonomous vehicles, hereafter AVs), smart parking systems, and Intelligent Traffic Management Systems (ITMS), which use data captured in real-time to keep track of traffic conditions, and traffic-related law enforcement and surveillance using AI. AI applications in traffic management cover a wide range of spheres. The spheres comprise, inter alia, streamlining traffic signal timings, predicting traffic bottlenecks in specific areas, detecting potential accidents and road hazards, managing incidents accurately, advancing public transportation systems, development of innovative driver assistance systems, and minimizing environmental impact through simplified routes and reduced emissions. The benefits of AI in traffic management are also diverse. They comprise improved management of traffic data, sounder route decision automation, easier and speedier identification and resolution of vehicular issues through monitoring the condition of individual vehicles, decreased traffic snarls and mishaps, superior resource utilization, alleviated stress of traffic management manpower, greater on-road safety, and better emergency response time.

A vehicle detection system is an important component of modern transportation, security, and infrastructure systems. Vehicle detection systems must be accurate and reliable to ensure their safety and efficiency. One of the most promising technologies for vehicle detection is Lidar, combined with the power of machine learning. A pulsed laser is used to measure distances using Lidar, or Light Detection and Ranging, a remote sensing technology. From autonomous vehicles to traffic management systems, Lidar provides high-resolution data combined with various applications.

Originally published on Towards AI. In recent years, air traffic has become a serious issue in the world. Delays in air traffic are caused by factors such as air system delays, security delays, airline delays, late aircraft delays, and weather delays. Air Traffic Control (ATC) will become more complex in the future decades as aviation grows and becomes more complex, and it must be improved to ensure aviation safety. Nowadays, Artificial Intelligence (AI) plays an important role in data management and ATC decision-making.

Americans wasted a whopping 3.4 billion hours in 2021 thanks to traffic, according to research from connected car analytics company INRIX, which also noted that this equates to 36 hours lost per person. The numbers are clear: Even with drops in traffic thanks to new travel patterns in the wake of the pandemic, we still lose an entire workweek each year to traffic. Soon enough, artificial intelligence (AI) may be able to alleviate--or fully solve--the problem. Today, researchers and companies are working to develop AI-powered systems that tackle the problem of traffic from a number of angles. For instance, Intelligent Traffic Control of Tel Aviv, Israel, has developed a solution that collects data from traffic cams, then regulates traffic signals to optimally route vehicles.

As thousands of Washington, D.C. drivers headed to Arlington National Cemetery for the Armistice Day ceremony, they found themselves stuck in the world's first traffic jam. On November 11, 1921, the congestion trapped motorists in their cars for hours--along with one very displeased President Harding, whose limousine had been caught up in the middle of it all. People were frustrated, tired, and unaware that they were making history. Just 100 years later, urban traffic chaos persists. But AI traffic management systems may offer a new solution to this century-old problem, while at the same time addressing the sustainability challenges of the future.

The smart transportation market size is estimated to reach $130 billion by 2024. This reflects a CAGR growth of 20 percent from 2018 till 2024. Even though such a development pace can hardly be called explosive, smart transportation systems are gaining momentum worldwide. Rapid urbanization, always-connected vehicles, environment protection initiatives, and traffic optimization technologies are the primary triggers of new mobility. City residents and authorities expect smart infrastructures to emerge since improved road and passenger safety is a must in the so-desired smart cities. Smart transportation consists of smart infrastructures capable of providing passengers and drivers with advanced services for better coordinated and more efficient transportation networks.

A key part of realizing the future of commercial drones will be drone traffic management: An integrated way to manage airspace for UAV. That's the goal of a recent trial in Japan led by NEDO (National Institute of New Energy and Industrial Technology Development Organization) to develop a drone traffic management system for multiple drone operators to fly in the same airspace safely. The trial, closely watched in the industry, brings together several prominent companies and consortiums, including ANRA Technologies, BIRD INITIATIVE, NEC Corporation, All Nippon Airways (ANA), and other partners. It will take place above Wakkanai City in Japan using ANRA's airspace and delivery management software platforms. The testbed is part of an ongoing R&D effort led by NEDO with the aim of integrating drone traffic management and creating a blueprint for a nationwide traffic management system.

Ill-managed intersections are the primary reasons behind the increasing traffic problem in urban areas, leading to nonoptimal traffic-flow and unnecessary deadlocks. In this paper, we propose an automated intersection management system that extracts data from a well-defined grid of sensors and optimizes traffic flow by controlling traffic signals. The data extraction mechanism is independent of the optimization algorithm and this paper primarily emphasizes the later one. We have used MiniZinc modeling language to define our system as a constraint satisfaction problem which can be solved using any off-the-shelf solver. The proposed system performs much better than the systems currently in use. Our system reduces the mean waiting time and standard deviation of the waiting time of vehicles and avoids deadlocks.