An Android Application for Predicting Traffic Congestion Using Polling Method

Traffic congestion is often a problem in major cities causing economic and social harm, air and sound pollution, as well as delays in daily activities. Existing traffic assistant applications usually provide traffic prediction based on real-time traffic condition and typical traffic record during weekdays. Such a traffic prediction is not applicable to predict traffic for special moments, e.g. long weekends and national holidays, on which the number of vehicle is very much different compared to that on weekdays. To tackle this issue, this paper proposes a method for traffic prediction by combining poll, traffic records, and linear regression. The proposed method is evaluated by conducting a poll to traffic users on one of the roads nearby Telkom University, collecting the traffic record, estimating the future traffic condition using linear regression, and then comparing the predicted traffic condition with that of the actual traffic condition. The level of congestion is measured as the road's level of service. The experiment result shows that the proposed method successfully predicts the traffic condition within the same class of level of service with the actual traffic condition. This confirms that the method is applicable for predicting traffic condition. In this research, the proposed method is also implemented in an android-based mobile application.