Congestion prediction of Urban traffic Employing SRBDP

Traffic congestion in the urban area has become a serious social problem. In order to alleviate congestion and reduce economic costs, it is important to predict the current and future traffic flows of road network nodes. Unfortunately, there are some challenges that make this work difficult. Firstly, the traffic flow is changeable and difficult to predict. Secondly, there are massive traffic data, but the quality of data is not high. For instance, when the traffic lights are just turning green, the speed of the vehicle can't represent the speed of the road vehicle. To this end, this paper proposes a traffic congestion prediction and detection algorithm based on data analysis. One of the most important parts of our algorithm is a short-term prediction method to predict the future traffic flow, which is called SRBDP. Moreover, we designed data characteristics and use clustering methods and a small amount of human intervention to determine the historical data congestion situation. Finally, we demonstrate the effectiveness of our algorithm through a real traffic data set.