Traffic Congestion Identification and Reduction

Abstract

Historical data is increasingly becoming more purposeful in the field of intelligent traffic systems. Traffic congestion is one of the major challenges in most cities around the world. It increases fuel wastage, monetary losses, and life-endangering. Historical trajectory data may be a suitable solution to reduce congestion on the road in VANET. This approach is a combination of data mining historical trajectory data to detect and predict traffic congestion and VANET in reducing the detected congestion events. The trajectories are first preprocessed before they are clustered using traj clustering algorithm. Congestion detection is then done for each cluster based on a certain speed threshold and also time duration of that particular event. The experiments are done on a data set which recorded traffic trajectories for one day and the same experiment is iterated on data sets for the day two and three respectively day and the detected congestion incidents are recorded in terms of their coordinates and time period in which they occurred. After detecting the congestion, we have also proposed a traffic congestion reduction solution. Results and simulations show that our proposed mechanism is suitable in VANET.