Advancing School Bus Routing: a Machine Learning Approach for Enhanced Efficiency, Safety, and Sustainability

Abstract

The optimization of school bus routing presents a multifaceted challenge that seeks to enhance efficiency, safety, and environmental sustainability in student transportation. Traditional methods, while foundational, often fall short in addressing the dynamic complexities and scalability required for modern school bus logistics. This research paper explores the application of machine learning (ML) algorithms as a superior alternative for optimizing school bus routes. By integrating techniques such as Genetic Algorithms (GA), Ant Colony Optimization (ACO), Neural Networks (NN), and Reinforcement Learning (RL), this study proposes a novel ML-based model that aims to outperform traditional route optimization methods across efficiency, cost-effectiveness, and environmental impact. The methodology involves a comprehensive examination of machine learning algorithms suitable for route optimization, with a focus on a hybrid model combining GA and NN to predict and adapt to real-time traffic conditions. A detailed comparative analysis demonstrates the model's significant improvements in reducing travel times, distances, and operational costs through a real-world case study. This research not only contributes to the transportation logistics literature by showcasing the advantages of ML in school bus routing but also opens avenues for future innovation in integrating real-time data and exploring algorithmic efficiency for broader applications. The findings underscore the transformative potential of ML in crafting more efficient, economical, and sustainable transportation solutions, marking a significant step forward in the application of advanced computational techniques to practical logistics challenges.