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

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.

I. Introduction

With the rapid development of intelligent transportation systems (ITS), many traffic data are collected by various types of traffic equipments, such as loop detectors, cameras, loop detectors, etc. Extensive traffic data collected from these equipments make it possible to develop traffic condition prediction models. Predicted traffic information in the near future can provide traffic authorities traffic control assistance and give travelers route guidance information. Therefore, the short-term traffic prediction models have become a hotspot in ITS in the past decade.