An Automated Hyperparameter Search-Based Deep Learning Model for Highway Traffic Prediction

Auto machine learning recently has been introduced as a trending technique for learning applications, including smart transportation. In this study, we focus on applying auto-machine learning for hyperparameter tuning to learn traffic datasets at the main regions of highway systems. Particularly, deep learning models have been recently introduced as emergent methods for traffic prediction. However, training deep learning models requires expensive works (e.g., time-consuming and human expertise), especially in terms of determining the configurations of hyperparameters in the models. In this regard, this paper introduces an automated framework for hyperparameter tuning to learn traffic datasets at an ecosystem in terms of reducing time-consuming tasks. Specifically, we first propose the HyperNet framework, using advanced data science techniques (e.g., Bayesian optimization and meta-learning) for the automated hyperparameter search process. Then, a deep learning model with the long short term memory network based on the HyperNet framework has presented for learning the temporal variation of traffic datasets at main regions of highway traffic systems. Regarding the experiment, we take data from the Korean highway system into account as a case study to evaluate the proposed approach. The evaluation indicates promising results of the proposed framework for learning multiple datasets of the traffic highway systems.