A Digital Twin Architecture for Intelligent Public Transportation Systems: A FIWARE-Based Solution
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-60796-7_12
Reference29 articles.
1. Amato, F., Di Martino, S., Mazzocca, N., Nardone, D., Rocco di Torrepadula, F., Sannino, P.: Bus passenger load prediction: challenges from an industrial experience. In: Karimipour, F., Storandt, S. (eds.) W2GIS 2022. LNCS, vol. 13238, pp. 93–107. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-06245-2_9
2. Bao, L., Wang, Q., Jiang, Y.: Review of digital twin for intelligent transportation system. In: 2021 International Conference on Information Control, Electrical Engineering and Rail Transit (ICEERT), pp. 309–315 (2021). https://doi.org/10.1109/ICEERT53919.2021.00064
3. Bin, Y., Zhongzhen, Y., Baozhen, Y.: Bus arrival time prediction using support vector machines. J. Intell. Transp. Syst. 10(4), 151–158 (2006)
4. Cirillo, F., Solmaz, G., Berz, E.L., Bauer, M., Cheng, B., Kovacs, E.: A standard-based open source IoT platform: FIWARE. IEEE Internet Things Mag. 2(3), 12–18 (2019). https://doi.org/10.1109/IOTM.0001.1800022
5. Conde, J., Munoz, J., Alonso, A., Lòpez-Pernas, S., Salvachua, J.: Modeling digital twin data and architecture: a building guide with FIWARE as enabling technology. IEEE Internet Comput. 26, 7–14 (2021). https://doi.org/10.1109/MIC.2021.3056923
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