Machine learning algorithms performance evaluation in traffic flow prediction

The important reasons for traffic congestion are classified into some categories. Some of the main reasons for traffic congestion are work zones, weather, special events, traffic incidents. This bad condition of weather makes changes the behavior in the driver and the traffic flow is affected. Accurate traffic prediction is important for the road user's traffic system administrators. Communication technology is influenced by various domains. Due to the advancement of technology machine learning concepts are used in traffic forecasting. This proposed model uses four types of machine learning concepts like DAN(Deep Autoencoder), DBN(Deep Belief Network), RF(Random Forest), and LSTM (Long Short Term Memory). The performance of the proposed model can be measured by using the accuracy, precision, recall, and error value metrics of machine learning approaches. From the four approaches, LSTM generates 95.2% accuracy.