Jul 2026· International journal of computer information systems and industrial management applications· 0 citations
TL;DR
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, and their applications for traffic flow prediction, adaptive traffic signal control, vehicle and pedestrian detection, accident prediction, driver behaviour monitoring, and prioritisation of emergency vehicles are discussed.
Abstract
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.
- Rapid urbanization, increasing vehicle ownership, and the growing complexity of urban transportation networks have intensified challenges related to traffic congestion, travel delays, fuel consumption, environmental pollution, and road traffic accidents. Conventional traffic management systems, which primarily rely on fixed-time traffic signal control and manual monitoring, often fail to respond effectively to dynamic traffic conditions and accident-prone situations. The integration of the Internet of Things (IoT) with deep learning has emerged as a promising approach for developing intelligent transportation systems capable of real-time monitoring, adaptive traffic control, and proactive accident prediction. This paper proposes an Intelligent Traffic Management and Accident Prediction Framework Using IoT and Deep Learning that integrates heterogeneous IoT devices, edge computing, cloud computing, and hybrid deep learning models to improve traffic efficiency and road safety. The proposed framework employs smart cameras, Global Positioning System (GPS) devices, roadside units, Radio Frequency Identification (RFID) sensors, connected vehicles, and environmental sensors to collect real-time traffic data. Data are preprocessed at the edge to reduce latency before being transmitted to cloud platforms for large-scale storage and deep learning analysis. A hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture is adopted to capture both spatial and temporal traffic characteristics for congestion forecasting and accident risk prediction. The framework supports adaptive traffic signal control, intelligent route optimization, and early warning generation for traffic management authorities and road users. A comprehensive review of recent studies demonstrates that integrating IoT with deep learning significantly improves prediction accuracy, decision-making speed, and transportation efficiency compared with conventional approaches. The research also identifies key implementation challenges, including data heterogeneity, cyber security, privacy preservation, model explain ability, and scalability. The proposed research framework provides a scalable and intelligent solution for next-generation smart transportation systems and offers a foundation for future research on explainable artificial intelligence, federated learning, digital twins, and autonomous connected vehicles within intelligent transportation environments.
Mubarak Jibril Yeldu, Abubakar Jibo Magayaki, A. Gulumbe et al.· Iconic research and engineer...· 0 citations
Conventional traffic management systems mainly depend on decision-tree and rule-based methods for controlling traffic flow. These methods generally provide only moderate accuracy and are often slow in identifying traffic conditions in real time. In addition, they have limited capability to adapt to changing traffic patterns and face difficulties when processing large volumes of data. To overcome these drawbacks, an Intelligent Traffic Management System can be developed using Artificial Intelligence (AI) and Internet of Things (IoT) technologies. Sensors and cameras installed on roads continuously collect data related to traffic flow, vehicle density, and congestion levels. Based on the collected information, traffic signals can be adjusted dynamically to improve vehicle movement and reduce delays. This helps in lowering fuel consumption and minimizing air pollution caused by traffic congestion. The system can also provide signal priority for emergency vehicles such as ambulances and suggest alternate routes for other vehicles. Further improvement can be achieved by applying deep learning techniques, which offer better vehicle detection accuracy, faster processing, and effective real-time operation. These techniques also support scalability and adaptability in changing traffic environments. As a result, the proposed system can reduce congestion, improve road safety, and contribute to efficient urban transportation management.
S. Murugaraj, S. S. Mallika, Ch. Gayathri· International Conference on...· 0 citations
A sustainable and equity-oriented framework for adaptive traffic signal control, in which data from road sensors, traffic cameras, GPS, and public transport systems are integrated with a short-term traffic flow prediction model and a reinforcement learning algorithm, demonstrates that traffic efficiency, environmental sustainability, and transport equity can be integrated within a unified control logic.
Mohammad Iqbal Khairandish, M. O. Khairandish· 0 citations
The findings confirm that traffic management systems based on deep learning can contribute significantly to the improvement of urban mobility, environmental impact, and road safety.
Ibrahim A. Lawal· International Journal of Art...· 0 citations
The proposed model addresses challenges such as limited training data and different road conditions, helping improve its reliability in practical situations, and demonstrates how artificial intelligence can support modern transportation by providing faster, more accurate, and efficient accident detection while contributing to safer roads and better traffic monitoring.
CHENCHETI INDHU, Dr.M.Ramesh· International Journal of Eng...· 0 citations
: 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.
Inam Ullah, Z. Haider, Omar Almomani et al.· Computers, Materials & C...· 0 citations
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