Abstract
This paper presents a model of traffic event streams processing. Events are generated by the developed spatiotemporal traffic simulator for real highway networks. The simulator is designed according to a distributed architecture based on mobile agents. It generates a flow of vehicles, assigning them to trips according to a model using geographic data. The highway network is equipped with sensors that generate events when passing vehicles. The event stream is processed in real time by agents to estimate the current traffic state to inform users via traffic message-variable panels. The architecture of the real-time event processing system is based on Kafka Stream Processing. To evaluate the performance of our model, we carried out a simulation of the traffic of a year in 24 h with a constraint of 25 million of Vehicles Kilometer per Day, producing an events density of 9485.2 Events/s. The proposed real-time processing topology shows that the estimation error does not exceed 5% for segments length less than 12 km.
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References
Diakaki, C., Papageorgiou, M., Papamichail, I., Nikolos, I.K.: Overview and analysis of vehicle automation and communication systems from a motorway traffic management perspective. Transp. Res. A, Policy Pract. 75, 147–165 (2015)
de Fabritiis, C., Ragona, R., Valenti, G.: Traffic estimation and prediction based on real time floating car data,” In: Proceedings of IEEE Conference on Intelligent Transportation Systems, Beijing, China, 2008, pp. 197–203 (2008)
Ge, J.I., Orosz, G.: Dynamics of connected vehicle systems with delayed acceleration feedback. Transp. Res. C, Emerging Technol. 46, 46–64 (2014)
Kesting, A., Treiber, M., Schonhof, M., Helbing, D.: Adaptive cruise control design for active congestion avoidance. Transp. Res. C, Emerging Technol. 16(6), 668–683 (2008)
Lo, S.-C., Hsu, C.-H.: Cellular automata simulation for mixed manual and automated control traffic. Math. Comput. Modell. 51(7/8), 1000–1007 (2010)
Soriguera Martí, F.: Short-term prediction of highway travel time using multiple data sources. In: Highway Travel Time Estimation With Data Fusion. Springer Tracts on Transportation and Traffic, vol. 11. Springer, Heidelberg (2016)
Mohammed, A.F., Humbe, V.T., Chowhan, S.S.: A review of big data environment and its related technologies. In: 2016 International Conference on Information Communication and Embedded Systems (ICICES), Chennai, 2016, pp. 1–5 (2016)
Liu, C.H., Zhang, Z., Huang, Y., Leung, K.K.: Distributed and real-time query framework for processing participatory sensing data streams. In: 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security, and 2015 IEEE 12th International Conference on Embedded Software and Systems, New York, NY, 2015, pp. 248–253 (2015)
Katsifodimos, A., Schelter, S.: Apache flink: stream analytics at scale. In: 2016 IEEE International Conference on Cloud Engineering Workshop (IC2EW), Berlin, p. 193 (2016). https://doi.org/10.1109/ic2ew.2016.56
Batyuk, A., Voityshyn, V.: Apache storm based on topology for real-time processing of streaming data from social networks. In: IEEE First International Conference on Data Stream Mining & Processing (DSMP), Lviv, 2016, pp. 345–349 (2016). https://doi.org/10.1109/dsmp.2016.7583573
Xhafa, F., Naranjo, V., Caballé, S.: Processing and analytics of big data streams with Yahoo!S4. In: IEEE 29th International Conference on Advanced Information Networking and Applications, Gwangiu, 2015, pp. 263–270 (2015). https://doi.org/10.1109/aina.2015.194
Salloum, S., Dautov, R., Chen, X., et al.: Int. J. Data Sci. Anal. 1, 145 (2016). https://doi.org/10.1007/s41060-016-0027-9
Vohra, D.: Using Apache Kafka. In: Pro Docker. Apress, Berkeley, CA (2016)
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Daaif, A., Bouattane, O., Youssfi, M., Snineh, S.M. (2019). An Efficient Traffic Monitoring Model Using a Stream Processing Platform Based on Smart Highways Events Generator. In: Mizera-Pietraszko, J., Pichappan, P., Mohamed, L. (eds) Lecture Notes in Real-Time Intelligent Systems. RTIS 2017. Advances in Intelligent Systems and Computing, vol 756. Springer, Cham. https://doi.org/10.1007/978-3-319-91337-7_4
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DOI: https://doi.org/10.1007/978-3-319-91337-7_4
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