Publisher
Springer Nature Switzerland
Reference55 articles.
1. Yi, Z., Liu, X.C., Markovic, N., Phillips, J.: Inferencing hourly traffic volume using data-driven machine learning and graph theory. Comput. Environ. Urban. Syst 85, 101548 (2021)
2. Saha, R., Tariq, M.T., Hadi, M.: Deep learning approach for predictive analytics to support diversion during freeway incidents. Transp. Res. Rec 2674(6), 480–492 (2020)
3. Georgiou, H., et al.: Moving objects analytics: survey on future location & trajectory prediction methods [Unpublished manuscript]. arXiv preprint arXiv:1807.04639, pp. University of Piraeus (2018)
4. Miller, J.: Dynamically computing fastest paths for intelligent transportation systems. IEEE. Intell. Transp. Syst. Mag 1(1), 20–26 (2009)
5. Mikluščák, T., Gregor, M., Janota, A.: Using neural networks for route and destination prediction in intelligent transport systems. In: Mikulski, J. (ed.) International Conference on Transport Systems Telematics. TST 2012: Telematics in the Transport Environment, pp. 380–387, Springer, Heidelberg (2012)