Development of Neural Network Based Traffic Flow Predictors Using Pre-processed Data

Kit Yan Chan, Ka Fai Cedric Yiu

Research output: Chapter in book / Conference proceedingChapter in an edited book (as author)Academic researchpeer-review

4 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationOptimization and Control Methods in Industrial Engineering and Construction
PublisherKluwer Academic Publishers
Number of pages14
ISBN (Print)9789401780438
Publication statusPublished - 1 Jan 2014

Publication series

NameIntelligent Systems, Control and Automation: Science and Engineering
ISSN (Print)2213-8986
ISSN (Electronic)2213-8994


  • Data cleansing
  • Data processing
  • Intelligent traffic management
  • Neural network
  • Time-series forecasting
  • Traffic flow predictions

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Mechanical Engineering
  • Control and Optimization


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