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Prediction of Hourly Vehicle Flows by Optimized Evolutionary Fuzzy Rules

  • Pavel KrömerEmail author
  • Jana Nowaková
  • Martin Hasal
  • Jan Platoš
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 554)

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.

Notes

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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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Pavel Krömer
    • 1
    Email author
  • Jana Nowaková
    • 1
  • Martin Hasal
    • 2
  • Jan Platoš
    • 1
  1. 1.Department of Computer ScienceVŠB Technical University of OstravaOstravaCzech Republic
  2. 2.IT4InnovationsVŠB Technical University of OstravaOstravaCzech Republic

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