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AI-Powered Smart Mobility System for Urban Cities: Integrating Electric Cycles, IoT Tracking, and Real-Time Route Optimization
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence, IoT, Smart Mobility
DOI: 10.64388/IREV9I12-1718519
Abstract
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.
Keywords
Electric Cycles, Smart Mobility, IoT Tracking, Route Optimization, Artificial Intelligence, Smart Cities, Urban Transportation, Last-Mile Connectivity, Corporate Mobility, Green Transport.
References
[1] R. Singh and P. Chauhan, "Barriers to Electric Two-Wheeler Adoption in Indian Tier-2 Cities: A Consumer Perspective," Transportation Research Part A, vol. 154, pp. 120–138, 2021.
[2] H. Zheng, Y. Liu, and J. Wang, "Deep Reinforcement Learning for Bike-Sharing Fleet Rebalancing," IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 8, 2022.
[3] S. Nair and R. Srinivasan, "Smart City Mobility Projects in India: Success Factors and Failure Modes," Urban Studies, vol. 59, no. 12, pp. 2541–2558, 2022.
[4] A. Kumar, P. Mishra, and S. Rao, "Predictive Maintenance of Shared Electric Cycles Using IoT Sensor Data and Gradient Boosting," Journal of Cleaner Production, vol. 385, 2023.
[5] World Health Organization India, "Urban Air Quality and Non-Motorized Transport: Evidence from Indian Cities," WHO India Technical Report, 2023.
[6] Autodesk & Bajaj Research, "Generative Design for Electric Two-Wheeler Frames: Weight Reduction and Structural Optimization," Industry Research Paper, 2024.
[7] Ministry of Housing and Urban Affairs, Government of India, "Smart Cities Mission: Mobility Projects Annual Report," New Delhi, 2023.
[8] A. Woodcock, P. Edwards, C. Tonne, et al., "Public Health Benefits of Cycling Strategies in European Cities," The Lancet, vol. 370, no. 9592, pp. 1930–1943, 2009.
[9] NITI Aayog, "India Electric Mobility Transformation Report 2023," Government of India, 2023.
[10] C. Ratti and M. Claudel, The City of Tomorrow: Sensors, Networks, Hackers, and the Future of Urban Life. Yale University Press, 2016.
[11] Nagpur Smart City Development Corporation, "Nagpur Smart City Annual Progress Report 2024," Nagpur Municipal Corporation, 2024.
[12] M. Shaheen and A. Cohen, "Shared Micromobility Policy Toolkit," University of California, Berkeley, Transportation Sustainability Research Center, 2022.
How to cite this paper
@article{1718519,
author = {Atharva Kudale, Prof. Netraja Muley},
title = {AI-Powered Smart Mobility System for Urban Cities: Integrating Electric Cycles, IoT Tracking, and Real-Time Route Optimization},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {12},
pages = {96-103},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1718519.pdf},
abstract = {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.},
keywords = {Electric Cycles, Smart Mobility, IoT Tracking, Route Optimization, Artificial Intelligence, Smart Cities, Urban Transportation, Last-Mile Connectivity, Corporate Mobility, Green Transport.},
month = {June},
doi = {https://doi.org/10.64388/IREV9I12-1718519}
}