Integration of AI-Driven Multimodal Transport Systems for Optimizing Real-Time Urban and Intercity Mobility Solutions.

Abstract

The escalating demands of urbanization and globalization have placed immense pressure on existing transport infrastructures, necessitating the evolution of smarter, integrated, and sustainable mobility solutions.This study explores the integration of Artificial Intelligence (AI)-driven multimodal transport systems as a transformative approach to optimize real-time urban and intercity mobility.At the broader level, the research investigates how AI technologies-such as machine learning, computer vision, and natural language processing-can intelligently manage data from diverse transportation modes, including buses, trains, ride-shares, bicycles, and pedestrian pathways.The convergence of these modes through centralized, intelligent platforms enables seamless trip planning, efficient resource allocation, and improved commuter experiences.Focusing on the operational dimension, the study emphasizes real-time optimization capabilities powered by AI models that dynamically respond to traffic patterns, service disruptions, weather conditions, and passenger demands.Reinforcement learning algorithms are deployed to support adaptive routing, demand forecasting, and congestion management across multiple networks.Furthermore, the integration of geospatial data, Internet of Things (IoT) sensors, and edge computing enhances responsiveness and situational awareness, facilitating predictive analytics and automated decisionmaking in transport control centers.A case-based evaluation in metropolitan and intercity corridors demonstrates significant improvements in travel time reduction, fuel efficiency, and service reliability.The framework also supports inclusive mobility by integrating accessibility-focused solutions and equitable service distribution.This study concludes that AI-integrated multimodal transport systems represent a crucial pillar in the future of smart mobility, enabling governments and stakeholders to transition from fragmented transit systems to coordinated, intelligent ecosystems that are resilient, sustainable, and user-centric.

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