TY - CHAP
T1 - Development of Neural Network Based Traffic Flow Predictors Using Pre-processed Data
AU - Chan, Kit Yan
AU - Yiu, Ka Fai Cedric
PY - 2014/1/1
Y1 - 2014/1/1
N2 - Neural networks have commonly been applied for traffic flow predictions. Generally, the past traffic flow data captured by on-road detector stations, is used to train the neural networks. However, recently research mostly focuses on development of innovative neural networks, while it lacks development of mechanisms on pre-processing traffic flow data priors on tr aining in order to obtain more accurate neural networks. In this chapter, a simple but effective training method is proposed by incorporating the mechanisms of back-propagation algorithm and the exponential smoothing method, which is proposed to pre-process traffic flow data before training purposes. The pre-processing approach intends to aid the back-propagation algorithm to develop more accurate neural networks, as the pre-processed traffic flow data is more smooth and continuous than the original unprocessed traffic flow data. This approach was evaluated based on some sets of traffic flow data captured on a section of the freeway in Western Australia. Experimental results indicate that the neural networks developed based on this pre-processed data outperform those that are developed based on either original data or data which is preprocessed by the other pre-processing approaches.
AB - Neural networks have commonly been applied for traffic flow predictions. Generally, the past traffic flow data captured by on-road detector stations, is used to train the neural networks. However, recently research mostly focuses on development of innovative neural networks, while it lacks development of mechanisms on pre-processing traffic flow data priors on tr aining in order to obtain more accurate neural networks. In this chapter, a simple but effective training method is proposed by incorporating the mechanisms of back-propagation algorithm and the exponential smoothing method, which is proposed to pre-process traffic flow data before training purposes. The pre-processing approach intends to aid the back-propagation algorithm to develop more accurate neural networks, as the pre-processed traffic flow data is more smooth and continuous than the original unprocessed traffic flow data. This approach was evaluated based on some sets of traffic flow data captured on a section of the freeway in Western Australia. Experimental results indicate that the neural networks developed based on this pre-processed data outperform those that are developed based on either original data or data which is preprocessed by the other pre-processing approaches.
KW - Data cleansing
KW - Data processing
KW - Intelligent traffic management
KW - Neural network
KW - Time-series forecasting
KW - Traffic flow predictions
UR - http://www.scopus.com/inward/record.url?scp=84896513067&partnerID=8YFLogxK
U2 - 10.1007/978-94-017-8044-5_8
DO - 10.1007/978-94-017-8044-5_8
M3 - Chapter in an edited book (as author)
SN - 9789401780438
T3 - Intelligent Systems, Control and Automation: Science and Engineering
SP - 125
EP - 138
BT - Optimization and Control Methods in Industrial Engineering and Construction
PB - Kluwer Academic Publishers
ER -