Evaluating the Effect of Time Series Segmentation on STARIMA-Based Traffic Prediction Model

As the interest for developing intelligent transportation systems increases, the necessity for effective traffic prediction techniques becomes profound. Urban short-term traffic prediction has proven to be an interesting yet challenging task. The goal is to forecast the values of appropriate traffic descriptors such as average travel time or speed, for one or more time intervals in the future. In this paper a novel and efficient short-term traffic prediction approach based on time series analysis is provided. Our idea is to split traffic time series into segments (that represent different traffic trends) and use different time series models on the different segments of the series. The proposed method was evaluated using historical GPS traffic data from the city of Berlin, Germany covering a total period of two weeks. The results show smaller traffic prediction error, in terms of travel time, with respect to two basic time series analysis techniques in the relevant literature.