Prediction of Hourly Vehicle Flows by Optimized Evolutionary Fuzzy Rules
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.
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.
- 7.Krömer, P., Platos, J.: Simultaneous prediction of wind speed and direction by evolutionary fuzzy rule forest. In: International Conference on Computational Science, ICCS 2017, 12-14 June 2017, Zurich, Switzerland, pp. 295–304 (2017)Google Scholar
- 10.Osorio, C., Selvam, K.K.: Solving large-scale urban transportation problems by combining the use of multiple traffic simulation models. Transp. Res. Procedia 6, 272–284 (2015). 4th International Symposium of Transport Simulation (ISTS 2014) Selected Proceedings, Ajaccio, France, 1-4 June 2014Google Scholar
- 11.Pan, B., Demiryurek, U., Shahabi, C.: Utilizing real-world transportation data for accurate traffic prediction. In: IEEE 12th International Conference on Data Mining, pp. 595–604 (2012)Google Scholar
- 12.Pasi, G.: Fuzzy sets in information retrieval: state of the art and research trends. In: Bustince, H., Herrera, F., Montero, J. (eds.) Fuzzy Sets and Their Extensions: Representation, Aggregation and Models, Studies in Fuzziness and Soft Computing, vol. 220, pp. 517–535. Springer, Heidelberg (2008)CrossRefGoogle Scholar
- 19.Zheng, B., Chen, J., Xia, S., Jin, Y.: Data analysis of vessel traffic flow using clustering algorithms. In: International Conference on Intelligent Computation Technology and Automation (ICICTA) 2008, vol. 2, pp. 243–246. IEEE (2008)Google Scholar