A review of neural networks applied to transport

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.