Authors : K. Abirami; R. Joel; Vishal M.; Sunil Kumar K.

Volume/Issue : Volume 11 - 2026, Issue 5 - May

Google Scholar : https://tinyurl.com/bdhpn4cz

Scribd : https://tinyurl.com/mrmvf54x

DOI : https://doi.org/10.38124/ijisrt/26may1146

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Rapid urbanization has intensified pressure on public transportation networks, exposing critical deficiencies in fleet management, passenger safety, and real-time operational responsiveness. UrbanFlow AI presents a next-generation smart urban mobility platform that integrates live vehicle tracking, artificial intelligence-driven occupancy prediction, dynamic fleet reallocation, and emergency response coordination within a unified system. The platform employs bidirectional Long Short-Term Memory (BiLSTM) deep learning architectures to forecast crowd density, enabling proactive coach recommendations and route optimization. A dedicated SOS emergency module provides one-touch distress alerts covering five incident categories, while an eco-tracking layer incentivizes sustainable commuter behavior through gamified reward mechanisms. On the administrative side, a Digital Twin simulation engine allows operators to model demand surges under real-world scenarios including inclement weather, public festivals, and large-scale sporting events. An AI-powered CCTV alert system continuously monitors for overcrowding, suspicious activity, and maintenance anomalies, enabling rapid operational intervention. Experimental evaluations demonstrate that the proposed system achieves crowd prediction accuracy of 93.6%, reduces average passenger wait time by 28.3%, decreases fleet idle time by 34.0%, and achieves SOS alert dispatch latency below 3 seconds compared to conventional transit management approaches. This paper presents the system architecture, methodology, performance benchmarks, and broader implications of AI-integrated urban mobility for smarter, safer, and more sustainable cities.

Keywords : Urban Mobility, Artificial Intelligence, Real-Time Tracking, Occupancy Prediction, Fleet Management, Digital Twin, Emergency SOS, Eco-Tracking, Smart Transportation, Deep Learning, BiLSTM, CCTV Anomaly Detection.

References :

1. W. McKinsey Global Institute, "Urban World: Mapping the Economic Power of Cities," McKinsey & Company, 2020.
2. United Nations, "World Urbanization Prospects: The 2018 Revision," UN Department of Economic and Social Affairs, 2019.
3. L. Chen and W. Xu, "Deep Learning for Urban Bus Occupancy Forecasting: A Bidirectional LSTM Approach," Journal of Intelligent Transportation Systems, vol. 27, no. 4, pp. 312-328, 2023.
4. M. Cilingiroglu, "Quantitative AI Methods for Predictive Analytics Using Deep Learning," Computational AI Review, vol. 8, no. 2, pp. 112-134, 2023.
5. R. Sharma, N. Patel, and K. Reddy, "Reinforcement Learning for Dynamic Fleet Reallocation in Public Transit Networks," Transportation Research Part C, vol. 138, Article 103621, 2022.
6. R. Kotecha, "AI-Driven Innovations in Urban Mobility, Fleet Efficiency, and Commuter Experience," Journal of Smart Transportation Technology, vol. 12, no. 1, pp. 45-67, 2025.
7. S. Kumar and P. Nair, "AI-Powered CCTV Anomaly Detection for Urban Transit Safety," IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 2, pp. 890-904, 2024.
8. A. Mehta, "Personalised Coach Recommendations in Smart Public Transport: A User Study," Smart Cities Journal, vol. 6, no. 1, pp. 45-62, 2023.
9. N. Abbas, C. Cohen, and B. Mosk, "AI and Real-Time Data Integration in Smart City Systems," Urban Intelligence Bulletin, vol. 12, no. 3, pp. 55-74, 2024.
10. R. Gupta, "Digital Twin Applications in Urban Mobility Planning," FinTech and Smart Infrastructure Quarterly, vol. 5, no. 2, pp. 78-95, 2024.
11. N. Adlakha and A. Katal, "Real-Time Monitoring Systems for Smart Transport," in Proc. 2021 International Conference on System, Computation, Automation and Networking (ICSCAN), IEEE, pp. 1-5, 2021.
12. S. M. Bartram, J. Branke, and M. Motahari, "Artificial Intelligence in Asset Management," CFA Institute Research Foundation, doi: 10.2139/ssrn.3692805, 2020.
13. N. Abbas, C. Cohen, D. J. Grolleman, and B. Mosk, "Artificial Intelligence Can Make Markets More Efficient and More Volatile," IMF Blog, Oct. 2024.
14. S. Hochreiter and J. Schmidhuber, "Long Short-Term Memory," Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997.
15. V. Mnih et al., "Human-Level Control Through Deep Reinforcement Learning," Nature, vol. 518, pp. 529-533, 2015.