Short term traffic speed prediction on a large road network

Sammanfattning: Traffic flow speed prediction has been an important element in the application of intelligent transportation system (ITS). The timely and accurate traffic flow speed prediction can be utilized to support the control, management, and improvement of traffic conditions. In this project, we investigate the short term traffic flow speed prediction on a large highway network. To eliminate the vagueness, we first give a formal mathematical definition of traffic flow speed prediction problem on a road network. In the last decades, traffic flow prediction research has been advancing from the theoretically well established parametric methods to nonparametric data-driven algorithms, like the deep neural networks. In this research, we give a detailed review of the state-of-art prediction models appeared in the literature.However, we find that the road networks are rather small in most of the literature, usually hundreds of road segments. The highway network in our project is much larger, consists of more than eighty thousand road segments, which makes it almost impossible to use the models in the literature directly. Therefore, in this research, we employ the time series clustering method to divide the road network into different disjoint regions. After that, several prediction models include historical average (HA), univariate and vector Autoregressive Integrated Moving Average model (ARIMA), support vector regression (SVR), Gaussian process regression (GPR), Stacked Autoencoders (SAEs), long short-term memory neural networks (LSTM) are selected to do the prediction on each region. We give a performance analysis of selected models at the end of the thesis.