A novel self-organizing fuzzy rule-based system for modelling traffic flow behaviour

  • Authors:

  • C. Quek;M. Pasquier;B. Lim

  • Affiliations:

  • Centre for Computational Intelligence (C2I), Nanyang Technological University, School of Computer Engineering, Block N4, #2A-32, Nanyang Avenue, Singapore 639798, Singapore;Centre for Computational Intelligence (C2I), Nanyang Technological University, School of Computer Engineering, Block N4, #2A-32, Nanyang Avenue, Singapore 639798, Singapore;Centre for Computational Intelligence (C2I), Nanyang Technological University, School of Computer Engineering, Block N4, #2A-32, Nanyang Avenue, Singapore 639798, Singapore

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2009

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Abstract

The study and development of transportation systems have been a focus of attention in recent years, with many research efforts directed in particular at modelling traffic behaviour from both macroscopic and microscopic points of views. Although many statistical regression models of road traffic relationships have been formulated, they have proven to be unsuitable due to multiple and ill-defined traffic characteristics. Alternative methods such as neural networks have thus been sought but, despite some promising results, their design remains problematic and implementation is equally difficult. Another salient issue is that the opaqueness of trained networks prevents understanding the underlying models. Hybrid neuro-fuzzy rule-based systems, which combine the complementary capabilities of both neural networks and fuzzy logic, constitute a more promising technique for modelling traffic flow. This paper describes the application of a specific class of neuro-fuzzy system known as the Pseudo Outer-Product Fuzzy-Neural Network using Truth-Value-Restriction method (POPFNN-TVR) for modelling traffic behaviour. This approach has been shown to perform better on such problems than similar architectures. The results obtained highlight the capability of POPFNN-TVR in fuzzy knowledge extraction for modelling inter-lane relationships in a highway traffic stream, as well as in generalizing from sample data, as compared to traditional feed-forward neural networks using back-propagation learning. The model thus obtained automatically can be understood, analysed, and readily applied for transportation planning.