Abstract: Cloud Computing and Artificial Intelligence will enable Intelligent Transportation Safety Systems (ITSS) that ensure a reliable low accident rate in road traffic. ITSS are considered an extension of Intelligent Transportation Systems connected to Cloud Computing with integrated Artificial Intelligence and focus on traffic safety. ITSS detect critical situations, predict dangerous conditions, and inform vehicle and driver assistance systems in real time to avoid accidents, control signal flows, and manage incidents. Today’s configuration of CT and AI can only solve special tasks, for instance, the level of cloud service and the bandwidth of data exchange. However, both technologies can work on a multidisciplinary basis, increase the robustness of work, manage data from various sources, analyze them dynamically and, thus, bring traffic safety to a new level.
The paper applies the original methodology developed for expanding the possible use of Cloud Computing and AI in solving applied problems. Cloud Computing allows integration of data from multiple sources in real time. AI based on interconnection and dynamic development between models of risk, collision, smart camera detection of critical mode conditions, IP-traffic incident detection and routing for traffic management on internal cloud and on-device level of connected vehicles makes it possible to predict modes of accidents with high probability and with a certain reaction time. Although the current architecture of Cloud Computing for ITSS has its limitations, it has enough potential to integrate a sufficiently rich set of modern safety-related solutions.

Keywords: Intelligent Transportation Safety Systems, Cloud Computing For Traffic Safety, AI-Enabled Traffic Management, Real-Time Accident Prediction, Connected Vehicle Safety Systems, Cloud-Based Traffic Data Integration, AI Risk And Collision Models, Smart Camera Traffic Detection, Incident Detection And Management, Low-Latency Safety Analytics, Vehicle-To-Cloud Communication, Predictive Road Safety Analytics, Multisource Traffic Data Fusion, Dynamic Traffic Risk Assessment, AI-Assisted Driver Support, Signal Flow Optimization, IP-Traffic Monitoring, Robust Transportation Safety Architectures, Cloud–Edge ITS Integration, Next-Generation Road Safety Systems.

Downloads: | DOI: 10.17148/IJIREEICE.2025.131227