AI-Driven Cloud Computing Framework for Traffic Prediction and Sustainable Urban Development in Smart Cities
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I4P114Keywords:
Smart City Traffic Management, Cloud-Enabled Artificial Intelligence, AI-Driven Urban Mobility, Predictive Traffic Management Systems, Real-Time Traffic Control, Intelligent Incident Response, Urban Demand Management, Mobility-As-A-Service (Maas), Cloud-Based Decision Support Systems, Data-Driven Traffic Optimization, Sustainable Urban Mobility Models, Metropolitan Traffic Analytics, AI Deployment In Cloud Environments, Traffic Prediction Models, Smart Transportation Architectures, Energy-Efficient Urban Mobility, Environmental Impact Reduction, Resilient Transportation Systems, Integrated Traffic Control Frameworks, AI-Enabled Smart CitiesAbstract
Smart cities increasingly leverage cloud-based artificial intelligence to address urban challenges and promote sustainability. This research explores the integration of cloud computing infrastructure with AI algorithms for predictive traffic management systems. The proposed framework utilizes real-time data from IoT sensors, traffic cameras, and mobile devices to forecast congestion patterns and optimize traffic flow. Machine learning models deployed on cloud platforms enable scalable processing of massive datasets, facilitating dynamic route optimization and reducing carbon emissions. The system demonstrates significant improvements in traffic efficiency, air quality, and energy consumption. By combining predictive analytics with cloud-enabled AI, this approach provides municipalities with cost-effective solutions for sustainable urban planning and intelligent transportation management in modern metropolitan environments.
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