Empirical Formulation of Highway Traffic Flow Prediction Objective Function Based on Network Topology

Arsalan Rahi, Soodamani Ramalingam

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Abstract

Accurate Highway road predictions are necessary for timely decision making by the transport authorities. In this paper, we propose a traffic flow objective function for a highway road prediction model. The bi-directional flow function of individual roads is reported considering the net inflows and outflows by a topological breakdown of the highway network. Further, we optimise and compare the proposed objective function for constraints involved using stacked long short-term memory (LSTM) based recurrent neural network machine learning model considering different loss functions and training optimisation strategies. Finally, we report the best fitting machine learning model parameters for the proposed flow objective function for better prediction accuracy.
Original languageEnglish
Article number29
Pages (from-to)7158-7170
JournalInternational Journal of Advanced Research in Science, Engineering and Technology
Volume5
Issue number10
Publication statusPublished - 5 Nov 2018

Keywords

  • Intelligent Transportation Systems, Machine Learning, LSTM, Flow Estimation, Hyper Parameter Optimisation.

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