The retrieval of intra-day trend and its influence on traffic prediction

Abstract

In this paper, we discuss three problems that occur within short-term traffic prediction when the information from only a single point loop detector is used. First, we analyze the retrieval of intra-day trend for traffic flow series and determine whether this retrieval process improves traffic prediction. We compare different highway traffic prediction models that use either the original traffic flow series or the residual time series with the intra-day trend removed. Test results indicate that the prediction performance MAY be significantly improved in the latter scenario. Second, we address two other related questions: the influence of missing data and traffic breakdown prediction. We show that the Probabilistic Principal Component Analysis (PPCA) method, which also utilizes the intra-day trend of traffic flow series, can be a useful tool in imputing the missing data. It can simultaneously ensure that the prediction error remains at an acceptable level, especially when the missing ratio is relatively low. We also show that almost all the known predictors have hidden assumptions of smoothness and, thus, cannot predict the burst points that deviate too far from the intra-day trend. As a result, traffic breakdown points can only be identified but not predicted.