An efficient traffic forecasting system based on spatial data and decision trees

The rapid proliferation of Global Position Service (GPS) devices and mounting number of traffic monitoring systems employed by municipalities have opened the door for advanced traffic control and personalized route planning. Most state of the art traffic management and information systems focus on data analysis, and very little has been done in the sense of prediction. In this article, we devise an efficient system for the prediction of peak traffic flow using machine learning techniques. In the proposed system, the traffic flow of a locality is predicted with the aid of the geospatial data obtained from aerial images. The proposed system comprises of two significant phases: Geospatial data extraction from aerial images, and traffic flow prediction using See5.0 decision tree. Firstly, geographic information essential for traffic flow prediction are extracted from aerial images like traffic maps, using suitable image processing techniques. Subsequently, for a user query, the trained See5.0 decision tree predicts the traffic state of the intended location with relevance to the date and time specified. The experimental results portray the effectiveness of the proposed system in predicting traffic flow.