Abstract
This work describes the architecture of the back-end engine of a real-time traffic data processing and satellite navigation system. The role of the engine is to process real-time feedback, such as speed and travel time, provided by in-vehicle devices and derive real-time reports and traffic predictions through leveraging historical data as well. We present the main building blocks and the versatile set of data sources and processing platforms that need to be combined together to form a working and scalable solution. We also present performance results focusing on meeting system requirements keeping the need for computing resources low. The lessons and results presented are of value to additional real-time applications that rely on both recent and historical data.
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References
Djuric, N., Radosavljevic, V., Coric, V., Vucetic, S.: Travel speed forecasting by means of continuous conditional random fields. Transp. Res. Rec. 2263, 131–139 (2011)
Gao, Y., Sun, S., Shi, D.: Network-scale traffic modeling and forecasting with graphical lasso. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds.) ISNN 2011. LNCS, vol. 6676, pp. 151–158. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21090-7_18
Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 3rd edn. Morgan Kaufmann, Burlington (2011)
Harinarayan, V., Rajaraman, A., Ullman, J.D.: Implementing data cubes efficiently. In: Proceedings of the 1996 ACM SIGMOD, pp. 205–216 (1996)
Kim, I.K., Wang, W., Qi, Y., Humphrey, M.: Empirical evaluation of workload forecasting techniques for predictive cloud resource scaling. In: 9th IEEE International Conference on Cloud Computing, CLOUD, pp. 1–10 (2016)
Li, C.S., Chen, M.C.: Identifying important variables for predicting travel time of freeway with non-recurrent congestion with neural networks. Neural Comput. Appl. 23, 1611–1629 (2013). https://doi.org/10.1007/s00521-012-1114-z
Michailidou, A., Gounaris, A.: Bi-objective traffic optimization in geo-distributed data flows. Big Data Res. 16, 36–48 (2019)
Pandey, V., Kipf, A., Neumann, T., Kemper, A.: How good are modern spatial analytics systems? PVLDB 11(11), 1661–1673 (2018)
Qiao, W., Haghani, A., Hamedi, M.: Short-term travel time prediction considering the effects of weather. Transp. Res. Rec. J. Transp. Res. Board 2308, 61–72 (2012)
Toliopoulos, T., Gounaris, A., Tsichlas, K., Papadopoulos, A., Sampaio, S.: Parallel continuous outlier mining in streaming data. In: 5th IEEE International Conference on Data Science and Advanced Analytics, DSAA, pp. 227–236 (2018)
Vlahogianni, E.I., Karlaftis, M.G., Golias, J.C.: Short-term traffic forecasting: where we are and where we’re going. Transp. Res. Part C Emerg. Technol. 43, 3–19 (2014)
Yu, J., Wu, J., Sarwat, M.: Geospark: a cluster computing framework for processing large-scale spatial data. In: Proceedings of the 23rd SIGSPATIAL, pp. 70:1–70:4 (2015)
Acknowledgements
This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH - CREATE - INNOVATE (project code: T1EDK-01944).
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Toliopoulos, T. et al. (2020). Developing a Real-Time Traffic Reporting and Forecasting Back-End System. In: Dalpiaz, F., Zdravkovic, J., Loucopoulos, P. (eds) Research Challenges in Information Science. RCIS 2020. Lecture Notes in Business Information Processing, vol 385. Springer, Cham. https://doi.org/10.1007/978-3-030-50316-1_4
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