Sustainable Urban Mobility: Developing Eco-friendly Transportation Systems with AI Integration

DOI: https://doi.org/10.63345/ijrmeet.org.v10.i9.3

Dr Sandeep Kumar

SR University

Hasanparthy, Telangana 506371 India

Abstract
Sustainable urban mobility presents multifaceted challenges tied to growing traffic congestion, vehicle emissions, and fluctuating energy demands. Integrating artificial intelligence (AI) into urban transportation offers transformative opportunities to address these challenges systematically using data-driven insights and adaptive control. This enhanced abstract deepens the overview by exploring the synergies among advanced AI techniques—such as ensemble machine learning for traffic forecasting, multi-objective optimization for route planning, and hierarchical reinforcement learning for real-time signal coordination—alongside considerations for electric vehicle (EV) charging strategies. We include discussions on leveraging high-resolution spatiotemporal data from Internet of Things (IoT) sensors and connected vehicles, which empower predictive models to capture latent traffic patterns and demand spikes. Moreover, we reflect on the role of federated learning for preserving data privacy while enabling cross-jurisdiction collaborations, and the implications of edge computing to reduce latency in critical control loops. Our findings, derived from a comprehensive simulation framework, demonstrate that such AI-integrated systems can lower carbon dioxide emissions by up to 17.2%, reduce average travel times by 12%, and improve network throughput by 8%. Finally, we outline deployment considerations—ranging from interoperability standards and cybersecurity to stakeholder engagement—underscoring both the promise and the complexity of realizing AI-driven sustainable mobility by 2022.

Keywords
Sustainable urban mobility, AI integration, eco‑friendly transportation, route optimization, traffic forecasting

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