Sustainable Urban Mobility for Road Information Discovery-Based Cloud Collaboration and Gaussian Processes
Ali Louati, Hassen Louati, Elham Kariri, Wafa Neifar, Mohammed A. Farahat, Heba M. El-Hoseny, Mohamed K. Hassan, Mutaz H. H. Khairi- Management, Monitoring, Policy and Law
- Renewable Energy, Sustainability and the Environment
- Geography, Planning and Development
- Building and Construction
A novel cloud-based collaborative estimation framework for traffic management, utilizing a Gaussian Process Regression approach is introduced in this work. Central to addressing contemporary challenges in sustainable transportation, the framework is engineered to enhance traffic flow efficiency, reduce vehicular emissions, and support the maintenance of urban infrastructure. By leveraging real-time data from Priority Vehicles (PVs), the system optimizes road usage and condition assessments, contributing significantly to environmental sustainability in urban transport. The adoption of advanced data analysis techniques not only improves accuracy in traffic and road condition predictions but also aligns with global efforts to transition towards more eco-friendly transportation systems. This research, therefore, provides a pivotal step towards realizing efficient, sustainable urban mobility solutions.