Machine Learning Solutions to Vehicular Traffic Congestion

Traffic management of metropolitan cities in India is becoming a challenging factor day by day. Traffic congestion and improper management leads to rise in accident cases in the city. Efficiency of existing traffic management solutions is decreasing, as the number of private vehicles is on the rise. In the context of increasing complexity of urban traffic and to reduce the accident rise, a machine learning solution is proposed. It provides predictive analysis of traffic in a given area using Supervised Learning techniques such as Back Propagation Neural Network (BPN). The work discusses about an android application that makes use of real-time traffic data and predicts the traffic densities of entire map area in an offline mode. It also specifically suggests best routes from source to destination based on the traffic data. The bigger picture here is the reduction of congested roads all over the city. This mechanism will also help to minimize the battery consumption of mobile devices.