A Bayesian regularized neural network approach to short-term traffic speed prediction

Abstract

Short term traffic speed prediction is very important in intelligent transportation systems. Neural networks have been widely used for traffic speed prediction. However, the classical neural network usually lacks satisfactory generalization ability, which usually results in an imprecise prediction of traffic speed. Regularization is an essential technique to improve the generalization ability of neural network. Regularization is realized by adding a weight decay function to the energy function of the neural network. One of the key problems of the regularization technique is how to decide the parameter of the weight decay function. In this paper, the Bayesian technique is used to optimize these regularization parameters and a Bayesian regularized neural network (BRNN) used for traffic speed prediction is proposed. The speed prediction model was validated by the real-world traffic speeds of the Hangzhou city collected from the floating car system. The experimental results show that the proposed method is able to improve the generalization ability of neural networks, and can achieve better prediction results than several traditional prediction models.

Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks

“…A. Khotanzad and N. Sadek applied a multilayer perceptron (MLP) and a fuzzy neural network (FNN) to high-speed network traffic prediction; the results indicate that NN performs better than the autoregressive model [ 30 ]. C. Qiu et al developed a Bayesian-regularized NN to forecast short-term traffic speeds [ 31 ]. X. Ma [ 11 ] proposed a congestion prediction method that is based on recurrent neural networks and restricted Boltzmann machines (RNN-RBM) for a large-scale transportation network that included 515 road links.…”

Urban traffic flow online prediction based on multi‐component attention mechanism

“…To this effect, Khotanzad and Sadek [29] applied multi‐layer sensing and fuzzy NNs to predict fast‐path networks, which resulted in better performance than the parametric regression model. Qiu et al constructed a Bayesian regularised NN to predict short‐term velocity [30], and Ma et al put forward a large‐scale congestion prediction model based on a recurrent NN (RNN) and proposed a restricted Boltzmann machine [31]. Tao et al used NN to predict the short‐term travel time during incidents using data collected from a highway corridor in the United States [32].…”

Freeway Short-Term Travel Speed Prediction Based on Data Collection Time-Horizons: A Fast Forest Quantile Regression Approach

“…In addition, researchers have compared various models such as SVR, ANN, bayesian regularized neural network (BRNN) and SARIMA to forecast short-term speed and achieved prediction accuracy estimates comparable to our proposed method. In these studies, authors have demonstrated the predicted travel speed trend during off-peak hours and peak hours of the day and captured traffic nonlinearity in arbitrary time horizons [81][82][83]. To evaluate the predictive accuracy of the models at different time intervals, the performance metrics of the model were also presented in Tables 2 and 3.…”