A Systematic Mapping Study On Multi-Algorithm Methods For Optimizing Transportation Systems

Abstract

The integration of multi-algorithm methods has emerged as a transformative approach in addressing complex challenges within modern transportation systems. This study presents a systematic mapping review to explore the application, effectiveness, and potential advancements of multi-algorithm techniques across diverse transportation domains, including road, rail, air, and maritime transport. By synthesizing findings from 23 selected studies, this research identifies key algorithmic paradigms, such as machine learning (ML), genetic algorithms (GA), optimization models and hybrid frameworks, and their functional roles in enhancing decision making, resource allocation, and system efficiency. The analysis reveals that multi-algorithm systems offer significant advantages in managing uncertainty, processing large-scale datasets, and generating high-probability solutions for real-time operations. In particular, ML algorithms demonstrate robust capabilities in predictive maintenance and demand forecasting, while GA-based approaches excel in dynamic environments such as traffic signal optimization and UAV path planning. Despite these advances, critical challenges persist, including the need for high-quality data, scalable algorithm design, and seamless integration with existing infrastructure. Furthermore, certain promising methods such as the whale optimization algorithm (WOA) and graph neural networks (GNN) remain underutilized, highlighting opportunities for future exploration. This study underscores the necessity for interdisciplinary collaboration and methodological innovation to overcome deployment barriers and enhance the sustainability of intelligent transportation systems (ITS). Ultimately, multi-algorithm approaches have substantial potential to drive the evolution of transportation networks toward greater efficiency, resilience, and adaptability in an increasingly complex and dynamic mobility landscape.