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.