AI-Driven Model Predictive Control for Smart Waste Collection Routing in Urban Environments

Abstract: Efficient municipal waste collection is still a big problem in cities that are growing quickly because more trash is being made, and routing plans are not working well. Traditional waste collection systems often use set schedules or simple threshold-based systems, which can cause vehicles to make unnecessary trips, raise operational costs, and cause bins to overflow. This paper puts forward a framework for smart waste collection routing called AI-Driven Model Predictive Control (AI-MPC). The suggested method combines model predictive control with artificial intelligence-based waste prediction to make routing decisions that are both flexible and proactive. The control system can predict when bins will reach critical fill levels by looking at smart bin data to see how waste is likely to build up in the short term. Then, the predictive information is used to make an MPC-based routing strategy that finds the best waste collection routes while keeping travel distance to a minimum and stopping overflow. The framework was tested in a Python-based simulation environment, where the performance of the suggested AI-MPC method was compared to that of traditional static and threshold-based routing methods. The simulation results show that the suggested method greatly improves routing efficiency, cuts down on overflow events, and makes overall waste collection performance better. The suggested AI- MPC framework is a smart and scalable way to manage waste in smart cities.