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

Aditya Kumar Sharma

doi:10.36948/ijfmr.2022.v04i06.16031

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

Author(s)	Aditya Kumar Sharma
Country	United States
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.
Keywords	Machine Learning Algorithms, School Bus Routing Optimization, Environmental Sustainability, Computational Transportation Logistics, Dynamic Route Adaptation, Efficiency and Cost Reduction
Field	Engineering
Published In	Volume 4, Issue 6, November-December 2022
Published On	2022-12-28
DOI	https://doi.org/10.36948/ijfmr.2022.v04i06.16031
Short DOI	https://doi.org/gtpxbk

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

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