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Citation Information : Transport Problems. Volume 11, Issue 2, Pages 27-36, DOI: https://doi.org/10.20858/tp.2016.11.2.3
License : (CC BY-SA 4.0)
Received Date : 02-December-2015 / Accepted: 08-May-2016 / Published Online: 02-February-2017
Summary. Neural networks’ (NNs) capability of mapping the nonlinear functions of variables describing the behaviour of objects and the simplicity of designing their configuration favours their applications in transport. This paper presents representative examples in the scope of prediction of road traffic parameters, road traffic control, measurement of road traffic parameters, driver behaviour and autonomous vehicles, and transport policy and economics. The features of the solutions are examined. The review shows that feedforward multilayer neural networks are the most often utilised configurations in transportation research. No systematic approach is reported on the optimisation of the NN configurations to achieve a set level of performance in solving modelling tasks.
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