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Emerging Technologies in Multi-Modal Networks: Graph Theory Solutions

Emerging Technologies in Multi-Modal Networks: Graph Theory Solutions
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Author(s): Suvarna Mohanrao Nade (Sandip University, Nashik, India)and Renu Praveen Pathak (Sandip University, Nashik, India)
Copyright: 2026
Pages: 38
Source title: Integrating Sustainability and Autonomous Transportation for Enhanced Urban Mobility
Source Author(s)/Editor(s): Ergunova Olga Titovna (Peter the Great St. Petersburg Polytechnic University, Russia), Dinesh Elango (University of Arizona, USA), Pawan Kumar (Lovely Professional University, India), Preeti Mehra (Campbell College, Canada & Lovely Professional University, India)and Rajesh Verma (Lovely Professional University, India)
DOI: 10.4018/979-8-3373-0882-1.ch007

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Abstract

Multi-modal transportation networks are critical to modern logistics and urban mobility, requiring seamless integration across diverse transportation modes. Emerging technologies, such as artificial intelligence (AI), the Internet of Things (IoT), and big data, offer transformative opportunities for optimizing these networks. Graph theory, a robust mathematical framework, is a powerful tool for addressing complex challenges like route optimization, congestion management, and resource allocation in multi-modal systems. This chapter explores the confluence of these technologies and graph theory, highlighting innovative solutions for enhancing efficiency, sustainability, and resilience in transportation networks. Case studies and a synthesis of recent research underscore the role of graph-based algorithms in enabling adaptive and intelligent transportation systems while identifying future trends and research opportunities.

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