ARCHIVES
Original Article
ML-Based Fair Public Transport Scheduling for Urban and Rural Equity
Samyuktha J1
Safiyanasreen K2
Mohammed Hafil M3
Dhivya T4
1 2 3 4 Department of Artificial Intelligence and data Science, Velalar College of Engineering and Technology, Erode, Tamil Nadu, India.
Published Online: March-April 2026
Pages: 124-132
Cite this article
No DOIReferences
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Transportation Systems, vol. 21, no. 2, pp. 1–10, 2020.
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10. Y. Zheng, F. Liu, and H.-P. Hsieh, “Urban data analysis for smart cities,” IEEE Transactions on Knowledge and Data Engineering, vol.
26, no. 1, pp. 1–14, 2014.
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C, vol. 19, no. 3, pp. 387–399, 2011.
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vol. 59, no. 4, pp. 2421–2430, 2020.
14. Z. Cui, K. Henrickson, R. Ke, and Y. Wang, “Traffic graph convolutional recurrent neural network for traffic forecasting,” IEEE
Transactions on Intelligent Transportation Systems, vol. 21, no. 9, pp. 3848–3858, 2020.
15. X. Shi et al., “Convolutional LSTM network for traffic flow prediction,” Physica A, vol. 580, p. 126172, 2021
Systems, vol. 12, no. 4, pp. 1413–1425, 2011.
2. M. Zhang and X. Zhao, “A data-driven approach for urban bus scheduling optimization,” IEEE Transactions on Intelligent Transportation
Systems, vol. 19, no. 7, pp. 2241–2251, 2018.
3. Y. Zheng, “Urban computing: Concepts, methodologies, and applications,” ACM Transactions on Intelligent Systems and Technology,
vol. 5, no. 3, pp. 1–55, 2014.
4. X. Ma, Z. Tao, Y. Wang, H. Yu, and Y. Wang, “Long short- term memory neural network for traffic speed prediction,” Transportation
Research Part C, vol. 54, pp. 187–197, 2015.
5. Z. Liu, S. Wang, and Y. Liu, “Deep learning-based passenger demand prediction in urban transportation,” IEEE Access, vol. 7, pp.
150256–150265, 2019.
6. H. Chen, X. Wang, and Y. Li, “Fairness-aware machine learning: A survey,” IEEE Transactions on Knowledge and Data Engineering,
vol. 33, no. 4, pp. 1231–1245, 2021.
7. A. Khan, M. A. Khan, and S. U. Khan, “Smart city and intelligent transport systems: A survey,” IEEE Communications Surveys &
Tutorials, vol. 23, no. 3, pp. 1872–1905, 2021.
8. J. Tang, F. Liu, W. Zou, and Y. Wang, “Traffic flow prediction using machine learning methods,” IEEE Transactions on Intelligent
Transportation Systems, vol. 21, no. 2, pp. 1–10, 2020.
9. S. Li, L. D. Xu, and S. Zhao, “The internet of things: A survey,” Information Systems Frontiers, vol. 17, no. 2, pp. 243– 259, 2015.
10. Y. Zheng, F. Liu, and H.-P. Hsieh, “Urban data analysis for smart cities,” IEEE Transactions on Knowledge and Data Engineering, vol.
26, no. 1, pp. 1–14, 2014.
11. M. G. Karlaftis and E. I. Vlahogianni, “Statistical methods versus neural networks in transportation research,” Transportation Research Part
C, vol. 19, no. 3, pp. 387–399, 2011.
12. Y. Duan, Y. Lv, and F.-Y. Wang, “Travel time prediction with LSTM neural network,” Neurocomputing, vol. 268, pp. 139– 148, 2017.
13. M. A. Abdel-Aty and H. Abdalla, “Hybrid ARIMA and neural network model for traffic prediction,” Alexandria Engineering Journal,
vol. 59, no. 4, pp. 2421–2430, 2020.
14. Z. Cui, K. Henrickson, R. Ke, and Y. Wang, “Traffic graph convolutional recurrent neural network for traffic forecasting,” IEEE
Transactions on Intelligent Transportation Systems, vol. 21, no. 9, pp. 3848–3858, 2020.
15. X. Shi et al., “Convolutional LSTM network for traffic flow prediction,” Physica A, vol. 580, p. 126172, 2021
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