Dynamic Route Optimization for Urban Waste Collection

Abstract: Efficient waste collection is a critical aspect of urban management. This paper presents a dynamic route optimization system tailored for garbage collection trucks in urban environments. The proposed approach uses synthetic data generation and vehicle routing algorithms to reduce fuel consumption, minimize travel time, and improve operational efficiency. The solution also includes a dashboard for real-time visualization and analysis of route data. Results show an improvement in efficiency compared to traditional static routing systems. This system can be applied to smart city waste management, enabling proactive decision-making and efficient resource allocation. By integrating real-time traffic data, bin fill levels, and vehicle capacity constraints, the system adapts dynamically to changing conditions in the urban landscape. A reinforcement learning model further enhances the system's ability to predict and prioritize waste collection based on historical patterns. Simulation testing with SUMO demonstrates the model’s viability under realistic conditions. The architecture is modular and scalable, making it suitable for deployment across various city infrastructures