A Review of the Prediction Method for Intelligent Transport System

The management for Intelligent Transport System (ITS) is an important control because it reduces traffic time, the safety of fuel energy, the accident decreased and so on. A key of ITS control is the transport system prediction method that helps to manage the travel time, traffic flow and traffic congestion. This paper proposes the review of prediction method that researchers evaluate the many algorithms for the experiment. The information and the method to predict are the key factors for achieving the goal. The traditional algorithm is a parametric method such as autoregressive moving average model (ARMA), autoregressive integrated moving average models (ARIMA) and vector auto regression (VAR). Non-parametric is a method that helps to support for process development of ITS such as neural network, machine learning and deep learning, etc. Both methods utilize the data but apply the different method to process.