A proactive parallel complex event processing method for large-scale intelligent transportation systems

Intelligent Transportation Systems (ITS) is one of the important application areas of the Internet of Things (IoT). The key issue is how to process the huge events generated by IoT system to support ITS. In this paper a proactive parallel complex event processing method is proposed for congestion control in large-scale ITS. A Bayesian model averaging method is used to obtain accurate predictions under different event context. Based on the predictive analysis, a parallel Markov decision processes model is designed to support decision making for large-scale ITS. An optimized parallel policy iteration algorithm is proposed based on state partition and policy decomposition. The experimental evaluations show that this method has good accuracy and scalability when used to process congestion control in large-scale ITS. © 2014 SERSC.