Authors - Ekene Michael Ogbejesi, Ming Jiang Abstract - The rapid growth of e-commerce and smart city infrastructures has intensified the operational challenges associated with last-mile delivery systems. Last-mile logistics remains the most expensive and operationally complex component of modern supply chains due to traffic congestion, inefficient routing, fluctuating environmental conditions, and increasing customer delivery expectations. This study presents a scalable data fusion and machine learning platform designed to optimize smart city last-mile delivery operations through predictive analytics and route optimization techniques. The proposed framework integrates heterogeneous datasets including geospatial coordinates, delivery agent profiles, weather conditions, traffic density, and vehicle characteristics to improve delivery efficiency and operational decision-making. A comprehensive preprocessing and feature engineering pipeline was developed, incorporating Haversine distance computation and categorical feature transformation. Multiple machine learning models including XGBoost, LightGBM, Random Forest Regression, and Neural Networks were evaluated for delivery time prediction using historical logistics data. The dataset comprised 1,000 delivery records with features including distance, traffic conditions, vehicle type, and time-based variables. Data preprocessing involved handling missing values, outlier treatment, and feature scaling. Experimental evaluation demonstrated that the Random Forest model achieved the strongest predictive performance with a Root Mean Square Error (RMSE) of 25.15 minutes, Mean Absolute Error (MAE) of 19.28 minutes, and an R² score of 0.76. The predictive model was integrated with a Traveling Salesman Problem (TSP)-based optimization module using a nearest-neighbor heuristic algorithm to generate efficient multi-stop delivery routes. The framework was deployed as an interactive Streamlit application supported by Folium geospatial visualization. Results demonstrate that the proposed framework improves delivery prediction accuracy, route planning efficiency, and operational scalability within intelligent transportation systems.
