Intelligent short term traffic forecasting using deep learning models with Bayesian contextual hyperband tuning

Abstract

An intelligent transport system (ITS) is fully valuable only if it can dynamically and aptly integrate all the latest cutting-edge technologies. An ITS focuses on providing services like promptly offering real-time road traffic information to interested parties, finding ways to reduce the average waiting time and offer secure and reliable services for commuters using past statistics. Short-term traffic prediction is one such area in which the research community has focused in the past decade. Existing models developed for prediction has scope to improve in terms of accuracy and training time. There is a necessity to develop a best-performing model that is computationally affluent to train with the optimal hyperparameter configuration as input which leads to improved performance. This article proposes a model that captures the traffic flow trend present in the past data to predict the flow for a future time interval. This model is an amalgamation of seasonal global trend (SGT) model and long short-term memory (LSTM) model with attention mechanism. A novel hyperparameter tuning algorithm is also proposed which is based on multi-armed bandit strategy with context, incorporating the right trade-off between exploitation and exploration of the hyperparameter space using successive halving. Experimental results conducted proves that our forecast model in combination with the proposed hyperparameter tuning

Adaptive Bayesian contextual hyperband: A novel hyperparameter optimization approach

“…SVM has conditional hyperparameters, RF and neural network (NN) each have multiple hyperparameters of various types, whereas K-nearest neighbor (KNN) has just one discrete parameter. Hyperband (HB) [18] Bayesian Optimization Hyperband (BOHB) [19] Bayesian Contextual Hyperband (BCHB) [28] Adaptive BCH Each experiment uses 5-fold cross-validation on the chosen HPO methodologies. While mean squared error (MSE) is employed for the regression job using the Boston Housing dataset, accuracy is the assessment metric for the classification task using the Modified National Institute of Standards and Technology database (MNIST) and Canadian Institute for Advanced Research, 10 classes (CIFAR-10) datasets.…”

“…The hyperparameter tuning process is iterative and accepts the complete space of hyperparameters as input. In our earlier work [28], we had formulated an algorithm named Bayesian contextual hyperband (BCHB) that incorporates a surrogate model and an acquisition function to select the next best configuration to run based on the observed loss values in the previous iterations. An initial surrogate model constructed using default hyperparameters forms the basis of the algorithm.…”

“…An initial surrogate model constructed using default hyperparameters forms the basis of the algorithm. The inner loop of the algorithm (see BCHB algorithm of [28]) executes r i times (the number of resources allocated for a configuration) for each iteration i to update the surrogate model with the next-best hyperparameter configuration and associated loss value. The acquisition function S is used to acquire the best hyperparameter which is denoted by h * .…”