AI-based smart waste collection route optimization system

Efficient collection of solid waste has become a significant challenge in urban areas due to increasing population density and waste generation. Conventional waste collection approach followed a rigid path and time based strategy resulting in heavy utilization of fuel, high operation cost, improper resource utilization, delay in waste collection in critical places. Intelligent data driven waste collection approach is thus required. This paper demonstrates an Artificial Intelligence (AI)-based smart waste collection route optimization system combining machine learning based prediction and classical vehicle routing approach. The framework is built to predict the waste bin fill-levels using an Extreme Gradient Boosting (XGBoost) regressor which identifies bins which require collection. Based on the projected demands for collection a Capacitated Vehicle Routing Problem (CVRP) is built and solved using Google OR-Tools to develop an efficient route that is also optimized in terms of fuel utilization while satisfying vehicle capacity constraints. The entire framework is developed using geospatial information data set of 11,058 waste bins and various waste processing facilities provided by Greater Chennai Corporation (GCC). The system is compared using metrics namely, collection distance, fuel consumption and its environmental impact. The current framework overcomes the problem of a reactive traditional system with inefficient prediction by presenting an intelligent and dynamic waste management system, proving that scalable AI-driven route optimization can help in supporting efficient smart city waste management.