|Table of Contents|

Short-term traffic flow forecasting based on hybrid model

《南京理工大学学报》（自然科学版）[ISSN:1005-9830/CN:32-1397/N]

Issue:
2014年02期
Page:
246-251
Research Field:
Publishing date:

Info

Title:
Short-term traffic flow forecasting based on hybrid model
Author(s):
Shen Guojiang1; Zhu Yun2; Qian Xiaojie2; Hu Yue2
1.College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China; 2.State Key Laboratory of Industrial Control Technology,Zhejiang University,Hangzhou 310027,China
Keywords:
interrupted flow;  short-term traffic flow forecasting;  Kalman filter model;  radical basis function neural network;  inertia factors
PACS:
TP18
DOI:
-
Abstract:
In view of that the traffic flow of the urban road is a nonlinear and uncertaint interrupted flow,a hybrid model for the short-term traffic flow is put forward to overcome the shortage of the lower forecasting accuracy of the single model.This model consists of two sub-models,the Kalman filter model and the radical basis function neural network model,so the steady-state problem of the neural network model in the huge traffic flow and the low accuracy problem of the Kalman filter model in the unsteady traffic flow can be all solved.An inertia factor is introduced in the process of combining to ensure the stability of the hybrid model.The simulation result shows that the hybrid model is feasible and effective.

References:

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Last Update: 2014-04-30