Multivariate machine learning-based prediction models of freeway traffic flow under non-recurrent events

This paper concerns multivariate machine learning-based prediction models of freeway traffic flow under non-recurrent events. Five model architectures based on the multi-layer percep-tron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), CNN-LSTM and Autoencoder LSTM networks have been developed to predict traffic flow under a road crash and the rain. Using an input dataset with five features (the flow rate, the speed, and the density, road incident and rainfall) and two standard metrics (the Root Mean Square error and the Mean Absolute error), models' performance is evaluated.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/ 4.0/).

Automated summary

This paper presents multivariate machine learning-based prediction models for freeway traffic flow under non-recurrent events. Five different model architectures, including MLP, CNN, LSTM, CNN-LSTM, and Autoencoder LSTM networks, are developed to predict traffic flow under road crashes and rainfall. The models' performance is evaluated using an input dataset with five features (flow rate, speed, density, road incident, and rainfall) and two standard metrics (Root Mean Square error and Mean Absolute error).