H2O-Based Stochastic Gradient Descent Grid Search Using Ridge Regression Techniques for Traffic Flow Forecasting

Abstract

Efficient and exact traffic flow forecasting is critical for intelligent transportation systems. The number of model generations increases the computational complexity of deep neural network (DNN) models. Overfitting occurs when hyperparameters are used excessively to train neural network models, and this has a major influence on prediction accuracy. To address these limitations, this approach employed H2O grid-based stochastic gradient descent with ridge regression in deep neural network (GSGDRR-DNN). This technique efficiently distributes memory across several clusters, runs independently, and simultaneously creates numerous DNN models. To remove multicollinearity and achieve better computational efficiency and lower variance, GSGDRR-DNN utilizes stochastic gradient descent (SGD) with ridge regression in the H2O cluster. Finally, we evaluate the performance of the recommended GSGDRR-DNN approach against several DNN methods, including LSTM, Bi-LSTM, GRU, and current state-of-the-art methods. Additionally, the run-time performance of the parallel GSGDRR-DNN model was compared with the run-time performance of the sequential GSGDRR-DNN model. The suggested system has a minimum MSE, a minimum RMSE, a minimum MAE, a minimum RMSLE, and maximum R² values of 0.012, 0.108, 0.096, 0.015, and 0.99. This demonstrates that the GSGDRR-DNN model of traffic flows outperforms other state-of-the-art approaches in terms of prediction accuracy.

Keywords:
Deep Neural Network (DNN); Ridge Regression; Grid Search; Stochastic Gradient Descent; Parallel Distributed Processing; Traffic flow forecasting

HIGHLIGHTS

H2O's parallel grid SGD approach builds a series of DNN models in parallel.

Ridge regression reduces multicollinearity and minimizes variance and bias.

The SGD parallel grid returns optimal hyperparameters for DNN training.

Parallel DNN models predict traffic flows with minimal error metrics.