Abstract
The prediction of traffic situation at different time periods is essential for intelligent management of transportation systems and represents a key concept of smart cognitive environments. Road traffic is a complex dynamic system with many stochastic elements and many internal and external dependencies. Real–world traffic patterns in large cities are very complicated to model and simulate analytically. Road traffic monitoring, on the other hand, can be easily achieved by inexpensive sensing and monitoring systems and is often readily available. It can be even obtained as a by–product of other transportation services, for example, toll collection. In this work, we use a modified version of a recent machine–learning method, evolutionary fuzzy rules, to learn location–specific estimators of hourly traffic flow at specific locations.
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Acknowledgement
This work was supported by the European Regional Development Fund under the project AI&Reasoning (reg. no. CZ.02.1.01/0.0/0.0/15_003/0000466), by the Czech Science Foundation under the grant no. GJ16-25694Y, and by the project SP2018/126 of the Student Grant System, VŠB-Technical University of Ostrava.
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Krömer, P., Nowaková, J., Hasal, M., Platoš, J. (2020). Prediction of Hourly Vehicle Flows by Optimized Evolutionary Fuzzy Rules. In: Zelinka, I., Brandstetter, P., Trong Dao, T., Hoang Duy, V., Kim, S. (eds) AETA 2018 - Recent Advances in Electrical Engineering and Related Sciences: Theory and Application. AETA 2018. Lecture Notes in Electrical Engineering, vol 554. Springer, Cham. https://doi.org/10.1007/978-3-030-14907-9_31
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