A review of neural networks applied to transport

https://doi.org/10.1016/0968-090X(95)00009-8Get rights and content

Abstract

This paper attempts to summarise the findings of a large number of research papers concerning the application of neural networks to transportation. A brief introduction to neural networks is included, for the benefit of readers unfamiliar with the techniques. Because the subject is so young, some of the papers appear only in conference proceedings or other less formal publications. I make no apology for this; I felt it was important to cover as much of the contemporary work as was possible.

The paper surveys both the application areas found to be fruitful and the range of neural network paradigms which have been used. Not surprisingly, multilayer feedforward networks such as backpropagation have so far been by far the most popular, but there are signs of a growing diversity; practitioners using neural networks are urged to seek out the less well known paradigms and experiment with them themselves.

A particular weakness noted in much of the work is the informal approach taken to detailed analysis of the results of the research. It is postulated that a more rigorous approach to matters such as comparison with other techniques and also the methodology used to design the neural networks would help a clearer picture to emerge as to best practice and future research directions.

References (64)

  • S.D. Clark et al.

    The use of neural network and time series modes for short term forecasting: a comparative study

  • A. Collins et al.

    Aircraft noise and residential property values, an artificial neural network approach

    J. Transp. Econ. Policy

    (1994)
  • J.D. Crisman et al.

    The warp machine on NAVLAB

    IEEE Trans. Patt. Anal. Mach. Intell

    (1991)
  • G. Demuth et al.

    Obstacle avoidance using neural networks

  • M.S. Dougherty et al.

    Short term inter-urban traffic forecasts using neural networks

  • M.S. Dougherty et al.

    A behavioural model of driver route choice using neural networks

  • M.S. Dougherty et al.

    The use of neural networks to recognise and predict traffic congestion

    Traff. Engng Contr.

    (1993)
  • M.S. Dougherty et al.

    Using neural networks to recognise predict and model traffic

  • K.A. Duliba

    Contrasting neural nets with regression in predicting performance in the transportation industry

  • A. Faghri et al.

    Evaluation of artificial neural network applications in transportation engineering

    Transportation Research Record

    (1992)
  • A. Faghri et al.

    Roadway seasonal classification using neural networks

  • S. Grossberg

    Adaptive pattern recognition and universal recording: (1) parallel development and coding of neural feature detectors

    Biol. Cybernet.

    (1976)
  • J. Hajek et al.

    Comparison of rule-based and neural network solutions for a structured selection problem

    Transportation Research Record

    (1993)
  • R. Hartani et al.

    Regulation de trafic de lignes de metro basee sur la logique floue et les reseaux de neurones.

  • R. Hecht-Nielsen

    Theory of the backpropagation neural network

  • R. Hecht-Nielsen
  • B.C. Heymans et al.

    Determining maximum traffic flow using back propagation

  • D. Hginyen et al.

    The truck backer-upper: an example of self-learning in neural networks

  • J.J. Hopfield

    Neural networks and physical systems with emergent collective computational abilities

  • J. Hua et al.

    Traffic mark classification using artificial neural networks

  • J. Hua et al.

    Dynamic traffic pattern classification using artificial neural networks

    Transportation Research Record 1399

    (1993)
  • J. Hua et al.

    Application of artificial neural networks to IVHS

  • Cited by (0)

    View full text