On Smart Mobility and Data Stream Mining
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-25312-6_44
Reference16 articles.
1. Al-Kanj, L., Nascimento, J., Powell, W.B.: Approximate dynamic programming for planning a ride-hailing system using autonomous fleets of electric vehicles. Eur. J. Oper. Res. 284(3), 1088–1106 (2020)
2. Almannaa, M.H., Elhenawy, M., Guo, F., Rakha, H.A.: Incremental learning models of bike counts at bike sharing systems. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 3712–3717. IEEE (2018)
3. Arentze, T.A.: Adaptive personalized travel information systems: a Bayesian method to learn users’ personal preferences in multimodal transport networks. IEEE Trans. Intell. Transp. Syst. 14(4), 1957–1966 (2013)
4. Bartlett, Z., Han, L., Nguyen, T.T., Johnson, P.: A novel online dynamic temporal context neural network framework for the prediction of road traffic flow. IEEE Access 7, 153533–153541 (2019)
5. Del Ser, J., Sanchez-Medina, J.J., Vlahogianni, E.I.: Introduction to the special issue on online learning for big-data driven transportation and mobility. IEEE Trans. Intell. Transp. Syst. 20(12), 4621–4623 (2019). https://doi.org/10.1109/TITS.2019.2955548
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