Traffic Prediction of Congested Patterns

  • H. RehbornEmail author
  • Sergey L. Klenov
  • M. Koller
Reference work entry
Part of the Encyclopedia of Complexity and Systems Science Series book series (ECSSS)


Data mining or physics of traffic: two different approaches to traffic prediction

In “data mining” approaches, reproducible features of measured traffic data are identified and analyzed through the use of various learning systems like neural networks, regression techniques, time series, wavelet analysis, Bayesian networks, etc. The nature of the traffic phenomena does not play a key role for this reproducible traffic feature learning. Instead, reproducible phenomena of the input data are learned by those methods. In contrast, “physics of traffic” approaches understand, explain, and model these reproducible features of measured traffic data. Then features of traffic phenomena are used as the basis for traffic prediction methods.

Fuel consumption

Traffic congestions have a specific impact on the fuel and/or energy consumption of vehicles: this consumption is traffic state dependent on freeways and on urban roads. Predictions of the traffic states offer the chance to develop...


Primary Literature

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Research and Development RD/USN, HPC: 059-X901Daimler AGSindelfingenGermany
  2. 2.Department of PhysicsMoscow Institute of Physics and TechnologyMoscowRussia

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