Memory Effects and Scaling Properties of Traffic Flows
arXiv:physics/0603252 · doi:10.1209/epl/i2005-10291-y
Abstract
Traffic flows are studied in terms of their noise of sound, which is an easily accessible experimental quantity. The sound noise data is studied making use of scaling properties of wavelet transforms and Hurst exponents are extracted. The scaling behavior is used to characterize the traffic flows in terms of scaling properties of the memory function in Mori-Lee stochastic differential equations. The results obtained provides for a new theoretical as well as experimental framework to characterize the large-time behavior of traffic flows. The present paper outlines the procedure by making use of one-lane computer simulations as well as sound-data measurements from a real two-lane traffic flow. We find the presence of conventional diffusion as well as 1/f-noise in real traffic flows at large time scales.
3 figures
References in corpus (11)
- Statistical Physics of Vehicular Traffic and Some Related Systems
- Discrete stochastic models for traffic flow
- Emergent Traffic Jams
- Determination of the Hurst Exponent by Use of Wavelet Transforms
- Traffic on complex networks: Towards understanding global statistical properties from microscopic density fluctuations
- Mechanical restriction versus human overreaction triggering congested traffic states
- Separating internal and external dynamics of complex systems
- An empirical test for cellular automaton models of traffic flow
- Simple model for 1/f noise
- Violation of the fluctuation-dissipation theorem for fast superdiffusion
- Steady state solutions of hydrodynamic traffic models