Aug 2026· International Journal of Technology and Emerging Research· 0 citations· 20 references
TL;DR
The findings show that AI technologies, such as smart traffic signals, real-time monitoring, and data analysis, can improve traffic flow and help reduce accidents.
Abstract
This study explores the role of Artificial Intelligence (AI) in smart traffic monitoring and road accident prevention. The main objective is to understand how AI can improve road safety by monitoring traffic, reducing congestion, and preventing accidents. The study discusses the importance of AI-based traffic management systems, common causes of road accidents, and the challenges involved in implementing these technologies. Information was collected from research articles, government reports, and other reliable sources. The findings show that AI technologies, such as smart traffic signals, real-time monitoring, and data analysis, can improve traffic flow and help reduce accidents. The study also highlights the need for public awareness, proper infrastructure, and government support for the successful implementation of AI-based traffic systems. In conclusion, Artificial Intelligence has the potential to make transportation safer and more efficient by supporting better traffic management and reducing road accidents.
Keywords: artificial intelligence; Smart Traffic Management; Road Safety; Traffic Monitoring; Accident Prevention; Intelligent Transportation Systems
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.
Shaikh Amra Bano, Kamal, Pramod Kumar Soni et al.· International journal of com...· 0 citations
Road traffic accidents remain a major public safety challenge, necessitating intelligent prediction systems that can accurately assess accident risk under dynamic traffic and environmental conditions. This study helps to design a smart system of predicting traffic-related risks and accidents with the help of the Random Forest algorithm based on the analysis of lane traffic density, weather, and signal status. The system estimates the risk of accidents in real time to facilitate proactive safety and risk conscious traffic control. Materials and Methods: The study utilizes historical traffic datasets collected from Kaggle and Data.gov. There are Two experimental groups are considered: Group 1 uses the XGBoost algorithm with a sample size of 3,000 instances of traffics for traffic prediction and signal timing analysis. Group 2 uses the proposed Random Forest Algorithm-based prediction system with a sample size of 3,000 instances of traffic. Results: The accident risk prediction model was a proposed random forest model which attained accuracy of 95.2% including 94.9% precision, 97.2% recall and the F1- score of 96.1% which is superior to the XGBoost model that reported an accuracy of 83.5. Even though the Random Forest model had a reduced prediction error (5.1%) than XGBoost (8.4%), the difference was not statistically significant at the 95% confidence level (p = 0.3). The 4-lane accident risk prediction system proposed on the basis of the Random Forests is an accurate prediction of the probability of accidents on a lane based on the parameters of traffic and weather. It is also applicable in real time traffic safety and decision support applications because it remains stable even in dynamic conditions.
N.Magendiran, Brajesh, S.Vinothini et al.· 2026 6th International Confe...· 0 citations
The high rate of urbanization has caused a high growth in the number of vehicles, which has produced a congestion, wastage on time, and fuel, as well as pollution to the environment. No longer applicable because of the dynamic character of modern urban traffic, the traditional traffic management systems based on the use of the non-informative control mechanisms and low real-time flexibility. The Intelligent Traffic Management Systems (ITMS) have become a very important part of an intelligent city system, as it intends to use the latest technologies that include Artificial Intelligence (AI), Internet of Things (IoT), machine learning, cloud computing and big data analysis to make traffic flow in the city more efficient and safer. In this paper, complete research on Intelligent Traffic Management Systems in smart cities has been made. It dwells upon the development of traffic management, the enabling technologies, system architecture, and methodologies. An elaborate literature review indicates the latest developments and outlines the gaps in research. The proposed approach will combine real-time data collection, predictive analysis, and responsive signal modulation to improve the traffic flow. The mathematical models and performance evaluation measures have been addressed to measure the system effectiveness. The findings indicate that intelligent systems are very effective in minimizing congestion, travelling time as well as emissions over traditional methods. Lastly, issues, constraints, and research prospects are given to facilitate long-term and viable implementation of ITMS in intelligent city setups.
Grace Ndlovu· International Journal of Mod...· 0 citations
With the development of smart cities, the intelligentization of transportation infrastructure such as highways and highspeed railways connecting smart cities has become an inevitable requirement. However, influenced by factors such as geological conditions, precipitation, and construction techniques, slope collapses and landslides pose significant risks to urban and transportation infrastructure safety. To effectively prevent these risks from escalating into disasters that cause severe losses to people's lives and property, traditional measurement methods struggle to meet the operational requirements of infrastructure for "real-time perception, intelligent analysis, and rapid assessment." Based on new technologies like 5G, the Internet of Things, and measurement robots, intelligent monitoring technology has emerged as a novel solution for safety risk prevention. This technology integrates functions such as data collection, data transmission, data calculation and analysis, assessment, and forecasting, enabling intelligent prevention of risks throughout the entire lifecycle of slopes. Grounded in the context of smart city development, this study explores and practices intelligent monitoring technology for transportation infrastructure slopes, providing theoretical support and practical experience for its large-scale application.
Lijing Cao, Siyuan Yao· International Conference on...· 0 citations
An intelligent monitoring system for roads and bridges based on Internet of Things technology that provides an intelligent and scalable technical solution for the full life-cycle management of roads and bridges, applicable to the regular monitoring and emergency response of large-scale transportation infrastructure such as expressway bridges, urban overpasses, and long tunnels.
Road accidents are a leading cause of injury and death around the world due to the delayed emergency response and drunk
driving, without the availability of real-time monitoring systems. Traditional accident-reporting procedures rely on
manual communication and lead to longer rescue times and limited prospects of timely medical support. In order to solve
these problems, this research introduces an intelligent smart accident detection and instant alert generation system based
on an embedded system and wireless communication technologies. This proposed solution combines the use of Arduino
Uno, vibration sensors, alcohol sensors, GPS modules, GSM communication, Wi-Fi connectivity, LCD displays, and
buzzer units to enable real-time monitoring of vehicles and automated emergency responses.
The system is constantly assessing the state of the vehicle and driver's actions in real time. The vibration sensor senses
anomaly of the intensity of impact in case of collision and can immediately begin the mechanism for the detection of the
accident. The GPS module gets the exact geographical position of the vehicle and the GSM module automatically sends
out emergency call messages of the accident and the geographical coordinates of the accident to preprogrammed
emergency contacts. The framework is also designed to include an alcohol monitoring feature that detects alcohol
impaired driving conditions, and provides warnings to prevent alcohol impaired driving. Furthermore, Wi-Fi connectivity
allows for cloud-based monitoring and integration with IoT, which can facilitate real-time data analysis and intelligent
transportation solutions.
Daripally Karthik, M. Ramesh, Dr. L Jagadeesh Naik· International Journal of Dru...· 0 citations
