System Monitoring Traffic Density Application based on Neural Network Algorithms

Ari Wijayanti, Nur Adi Siswandari, Haniah Mahmudah, Okkie Puspitorini, Chusnul Chotimah


The increase in vehicle traffic density in a city correlates with increasing private vehicle usage. This problem is caused by the lack of public transport services. In other side, the volume of private vehicles increases the traffic density and because problems are jammed in rush hour traffic. Use of the application can help road users to know the traffic conditions at real time. The research has developed a mobile application of information about traffic condition. This application is created using Android software programming, Android Development Tool (ADT) integrated with Google Maps so it can display information points from jammed location. The information presented in the form of the location name, location coordinates (latitude, longitude), the average vehicle speed (Km / h) which pass through the area and the traffic status in the form of a solid, jammed or smoothly. The data was predicted by Backpropagation Neural Network. The performance has seen on the size of MSE (Mean Square Error). The result is the smallest MSE are 8,91x10-24, it means the chosen method has a predictability that is very close to the actual conditions of traffic situation



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