Road traffic prediction using context-aware random forest based on volatility nature of traffic flows

Nowadays short-term traffic prediction is of great interest in Intelligent Transportation Systems (ITS). To come up with an effective prediction model, it is essential to consider the time-dependent volatility nature of traffic data. Inspired by this understanding, this paper explores the underlying trend of traffic flow to differentiate between peak and non-peak traffic periods, and finally makes use of this notion to train separate prediction model for each period effectively. It is worth mentioning that even if time associated with the traffic data is not given explicitly, the proposed approach will strive to identify different trends by exploring distribution of data. Once the data corresponding trends are determined, Random Forest as prediction model is well aware of data context, and hence, it has less chance of getting stuck in local optima. To show the effectiveness of our approach, several experiments are conducted on the data provided in the first task of 2010 IEEE International Competition on Data Mining (ICDM). Experimental results are promising due to the scalability of the proposed method compared to the results given by the top teams of the competition.