DocumentCode :
1700290
Title :
Traffic flow forecasting based on ant colony neural network
Author :
Pang, Qingle ; Zhang, Min
Author_Institution :
Sch. of Inf. & Electron. Eng., Shandong Inst. of Bus. & Technol., Yantai, China
fYear :
2010
Firstpage :
4706
Lastpage :
4710
Abstract :
As intelligent transportation systems (ITS) are implemented widely throughout the world, managers of transportation systems have access to large amounts of real-time status data. A variety of methods and techniques have been developed to forecast traffic flow. The traffic flow forecasting model based on neural network has been applied widely in ITS because of its high forecasting accuracy and self-learning ability. But the problems of neural network such as the difficult of designing optimal structure and weak global searching ability limit seriously its applications. So the traffic flow forecasting based on ant colony neural network is proposed. The ant colony algorithm, which has a powerful global searching ability, is applied to solve the problem of tuning both network structure and parameters of a feedforward neural network. First, the ant colony neural network algorithm is introduced in detail. Then, the presented approach is effectively applied to solve traffic flow forecasting. The simulation experiments show that the presented traffic flow forecasting based on ant colony neural network can simplify the structure of neural network greatly and improve the forecasting accuracy significantly.
Keywords :
automated highways; neural nets; road traffic; ant colony neural network; feedforward neural network; global searching ability; intelligent transportation systems; self-learning ability; traffic flow forecasting; Accuracy; Artificial neural networks; Feedforward neural networks; Forecasting; Predictive models; Transportation; Tuning; Intelligent transportation systems; ant colony algorithm; neural network; traffic flow forecasting;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Control and Automation (WCICA), 2010 8th World Congress on
Conference_Location :
Jinan
Print_ISBN :
978-1-4244-6712-9
Type :
conf
DOI :
10.1109/WCICA.2010.5554931
Filename :
5554931
Link To Document :
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