A Hybrid Prediction Algorithm for Traffic Speed Prediction

Abstract

Many types of data can be regarded as time series data. Therefore time series data predictions are applied in a wide range of domains, such as investment, traffic prediction, etc. Traffic status prediction can be used for congestion avoidance and travel planning. We solve the problem of predicting traffic status by time series prediction. The time series data prediction problem is that given a query time and time series data, we intend to predict the data value at the query time. Usually, a query time will be a future time. In this paper, we propose a hybrid prediction algorithm which exploits regression-based and clustering-based prediction methods. Explicitly, regression-based prediction is accurate when the query time is not too far from the current time. Note that time series data may have some similar shapes or trends. To capture the similar shapes hidden in this data, we utilize clustering concepts. Using these clusters, we could further discover their sequential relationships. As such, if the query time is far away from the current time, we utilize the above cluster sequential relationships to predict the possible similar cluster. From the similar cluster, the data value at the query time is obtained. Note that the hybrid algorithm aggregates the above two methods using one threshold that decides which method to use. If the time difference between the query time and the current time is smaller than the prediction length threshold, hybrid prediction uses regression-based prediction. Otherwise, our hybrid algorithm uses clustering-based prediction. To prove our proposed methods, we have carried out a set of experiments on real data sets to compare the accuracy of the methods. The results of the experiments prove that our proposed methods are both accurate and practical.