Multi-Agent Autonomous Control Framework for Intelligent Transportation Infrastructure

AI and IoT Based Smart Traffic Management for Intelligent Transportation Systems

Urban transportation networks are essential infrastructures that support the movement of people and goods in modern cities. However, rapid urbanization, population growth, and the increasing number of vehicles have led to serious challenges such as traffic congestion, road accidents, increased travel time, and environmental pollution. Conventional traffic management systems rely mainly on fixed-time signal control strategies, which are unable to adapt to dynamic and real-time traffic conditions, resulting in inefficient traffic flow and underutilization of road infrastructure. To overcome these limitations, Artificial Intelligence (AI) and the Internet of Things (IoT) have been integrated into Intelligent Transportation Systems (ITS) to enable intelligent and adaptive traffic management. IoT-enabled devices such as smart cameras, inductive loop detectors, GPS modules, and environmental sensors continuously collect real-time traffic parameters including vehicle density, speed, queue length, and traffic flow. These heterogeneous data streams are transmitted through wireless communication networks to cloud or edge computing platforms for real-time data processing and analysis. AI-based algorithms, including machine learning and deep learning models, analyze the collected traffic data to identify spatiotemporal traffic patterns, predict congestion levels, and optimize traffic signal timings. Advanced techniques such as reinforcement learning enable adaptive traffic signal control by dynamically adjusting signal phases based on current traffic conditions, thereby reducing vehicle delays and improving intersection throughput. Furthermore, Vehicle-to-Everything Communication (V2X) facilitates communication between vehicles, infrastructure, and pedestrians, enabling cooperative traffic management and improved road safety. Despite these advancements, challenges such as high deployment costs, data privacy concerns, and interoperability issues remain. Continuous research in AI-driven IoT frameworks and secure communication technologies is essential to achieve efficient, scalable, and sustainable smart transportation systems.

Intelligent Urban Traffic Management Using Artificial Intelligence and Machine Learning Methods and Algorithms: Challenges and Opportunities

Recent Advances in Artificial Intelligence for Smart Vehicles and Intelligent Transportation Systems

: The growing need for intelligent, information-based, and automated transportation systems has been brought about by the rapid progress of intelligent vehicles and Intelligent Transportation Systems (ITS). Artificial Intelligence (AI) has emerged as an indispensable asset for the challenges and opportunities of today’s transportation, improving decision-making, flexibility, and system efficiency. This survey examines breakthroughs in AI techniques applied to smart vehicles and ITS between 2019 and 2026, focusing on Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), Federated Learning (FL), and Computer Vision. The survey also includes the integration of AI with enabling technologies, such as the Internet of Things (IoT), edge computing, and cloud computing, in this instance, to create real-time distributed intelligence in transportation networks. Also, the application fields of autonomous driving, intelligent traffic control, Advanced Driver Assistance Systems (ADAS), intelligent parking, and Vehicle-to-Everything (V2X) communication are covered. The survey showed that other key challenges stemming from data heterogeneity, scale, latency, security, privacy, and model interpretability would need to be resolved for stable deployment. Lastly, AI-fueled Smart Cities, 5G/6G-powered transportation, Digital Twins, and Explainable Artificial Intelligence (XAI) are briefly mentioned as future research directions and novelties. This survey offers a comprehensive overview of AI-based transportation systems and will be of interest to researchers in the field.

Artificial Intelligence Models for Smart Traffic Management and Road Safety

High traffic volume, urbanization and car ownership have exacerbated traffic congestion, travel time and road accidents; these are some of the problems facing modern transportation systems. Artificial Intelligence (AI) has become a viable solution, allowing intelligent, adaptive and data-informed traffic management. This paper provides an overview of the most prominent types of AI models employed in smart traffic control and road safety, such as supervised and unsupervised machine learning, deep learning, computer vision, and reinforcement learning. It discusses their applications for traffic flow prediction, adaptive traffic signal control, vehicle and pedestrian detection, accident prediction, driver behaviour monitoring, and prioritisation of emergency vehicles. The research also covers the features of Vehicle-to-Everything (V2X) communication, connected vehicles, the Internet of Things (IoT), and Intelligent Transportation Systems (ITS), as well as their potential for enhancing transportation efficiency and road safety. In addition, the paper points out potential roadblocks for the implementation of AI such as data quality, computational complexity, cybersecurity, privacy, infrastructure cost, and model interpretability. Finally, future research directions are outlined, highlighting explainable AI, generative AI, digital twins and integration of intelligent transportation systems in smart cities for sustainable cities. Overall, the review shows that AI can revolutionize traditional transportation systems, turning them into intelligent networks that can help alleviate congestion, lower the risk of accidents, optimize traffic flow, and facilitate safer and more sustainable urban mobility.

Intelligent Resource Allocation in Smart Cities Using Multi-Agent Reinforcement Learning

A Multi-Agent Reinforcement Learning (MARL) framework for intelligent resource allocation across transportation, energy, water, healthcare, emergency response, and communication systems is proposed, which enhances resource utilization, reduces energy consumption and response time, improves system reliability, and supports scalable urban operations.

Intelligent Transportation Systems Using Vehicle-to-Everything (V2X) Communication

The proposed framework brings in intelligent decision-making algorithms and edge-enabled V2X communication to enable dynamic traffic control, accident prevention, route optimization, and emergency response coordination to enhance transportation systems and road safety.