#7699. Traffic prediction using a self-adjusted evolutionary neural network

October 2026publication date
Proposal available till 24-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Mechanical Engineering;
Computer Science Applications;
Electrical and Electronic Engineering;
Transportation;
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Abstract:
Short-term prediction of traffic flow is one of the most essential elements of all proactive traffic control systems. The aim of this paper is to provide a model based on neural networks (NNs) for multi-step-ahead traffic prediction. NNs’ dependency on parameter setting is the major challenge in using them as a predictor. Given the fact that the best combination of NN parameters results in the minimum error of predicted output, the main problem is NN optimization. So, it is viable to set the best combination of the parameters according to a specific traffic behavior.
Keywords:
Genetic algorithm; Neural networks; Self-adjusted framework; Traffic prediction

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