Paper Title:
Traffic Flow Forecasting Based on Chaos Neural Network
  Abstract

Traffic flow forecasting has become an emphasis question for discussion in traffic engineering domain and one kernel study in Intelligent Transportation System. After ensuring the traffic flow has the characteristic of chaos, traffic flow real data have been used to reconstruct phase space. Calculate the saturation phase space embedding dimension and maximal Lyapunov exponent. By above all, a chaos neural network model is constructed, which can make high precision short-term forecast for the nonlinear big-lagged system even by imperfect and variation inputs. At last, a forecasting example provides that the traffic flow forecasting based on chaos neural network is validity and feasibility.

  Info
Periodical
Edited by
Qi Luo
Pages
1236-1240
DOI
10.4028/www.scientific.net/AMM.20-23.1236
Citation
Y. Y. Zhang, S. S. Yang, Q. Cai, P. Sun, "Traffic Flow Forecasting Based on Chaos Neural Network", Applied Mechanics and Materials, Vols. 20-23, pp. 1236-1240, 2010
Online since
January 2010
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