An Effective Hyperparameter Optimization Algorithm for DNN to Predict Passengers at a Metro Station

As one of the public transportation systems, metro is certainly an indispensable part in urban areas of a metropolis today. Several successful results have shown that deep learning technologies might provide an effective way to predict the number of passengers at a metro station. However, most information systems based on deep learning technologies are usually designed and tuned manually by using domain knowledge and trial-and-error; thus, how to find out a set of suitable hyperparameters for a deep neural network (DNN) has become a critical research issue. To deal with the problem of hyperparameter setting for a DNN in solving the prediction of passengers at a metro station, a novel metaheuristic algorithm called search economics for hyperparameter optimization is presented to improve the accuracy rate of such a prediction system. The basic idea of the proposed algorithm is to divide the solution space into a set of subspaces first and then assign a different number of search agents to each subspace based on the "potential of each subspace." The potential is estimated based on the objective values of the searched solutions, the objective values of the probe solutions, and the computation time invested in each subspace. The proposed method is compared with Bayesian, random forest, support vector regression, DNN, and DNN with different hyperparameter search algorithms, namely, grid search, simulated annealing, and particle swarm optimization. The simulation results using the data provided by the government of Taipei city, Taiwan, indicate that the proposed method outperforms all the other forecasting methods compared in this article in terms of the mean absolute percentage error.