AI-Powered Smart Mobility System for Urban Cities: Integrating Electric Cycles, IoT Tracking, and Real-Time Route Optimization

Urban transportation in Indian cities is facing a dual crisis: growing congestion that slows commuter movement and rising vehicular pollution that damages public health. Petrol-powered two-wheelers and cars dominate short-distance urban trips that could be completed far more efficiently by electric cycles. This paper proposes and evaluates an AI-powered smart mobility system that integrates low-maintenance electric cycles with IoT-based real-time tracking and machine learning route optimization. The system is designed for deployment across smart city public infrastructure and corporate campuses. We examine the system architecture, its AI components, the design principles that minimize maintenance cost and repair complexity, and the health and environmental outcomes that result from large-scale adoption. Comparative data drawn from Indian urban mobility studies and global smart city deployments demonstrate that the proposed system can reduce last-mile travel cost by up to 70%, lower maintenance expenditure compared to petrol vehicles, and contribute measurable improvements in urban air quality and commuter physical health. This paper argues that AI-enabled electric cycle networks are not a future possibility but an implementable solution for Indian cities today.