Short-Term Freeway Traffic Flow Prediction: Bayesian Combined Neural Network Approach

TLDR

Short‑term traffic flow prediction is a critical concern for intelligent transportation systems, and existing models perform well only in specific periods, motivating a combined approach. This study introduces a Bayesian combined neural network that adaptively assigns credit to single neural network predictors for improved short‑term traffic flow forecasting. Two single predictors—back‑propagation and radial‑basis‑function neural networks—are linearly combined, with credit values computed by a heuristic algorithm based on conditional probability and Bayes’ rule, and evaluated on Singapore Ayer Rajah Expressway data using MAAPE, variance of AP error, and probability of percentage error. The combined model consistently outperforms individual predictors and dynamically tracks online performance to select the best predictors for each time period.

Abstract

Short-term traffic flow prediction has long been regarded as a critical concern for intelligent transportation systems. On the basis of many existing prediction models, each having good performance only in a particular period, an improved approach is to combine these single predictors together for prediction in a span of periods. In this paper, a neural network model is introduced that combines the prediction from single neural network predictors according to an adaptive and heuristic credit assignment algorithm based on the theory of conditional probability and Bayes’ rule. Two single predictors, i.e., the back propagation and the radial basis function neural networks are designed and combined linearly into a Bayesian combined neural network model. The credit value for each predictor in the combined model is calculated according to the proposed credit assignment algorithm and largely depends on the accumulative prediction performance of these predictors during the previous prediction intervals. For experimental test, two data sets comprising traffic flow rates in 15-min time intervals have been collected from Singapore’s Ayer Rajah Expressway. One data set is used to train the two single neural networks and the other to test and compare the performances between the combined and singular models. Three indices, i.e., the mean absolute percentage error, the variance of absolute percentage error, and the probability of percentage error, are employed to compare the forecasting performance. It is found that most of the time, the combined model outperforms the singular predictors. More importantly, for a given time period, it is the role of this newly proposed model to track the predictors’ performance online, so as to always select and combine the best-performing predictors for prediction.