Traffic flow forecasting using hybrid time series models

Abstract

The Intelligent Transportation System (ITS) is one of the component of various smart city technologies that improve transportation and commuting procedures. The goal of ITS is to solve traffic issues, particularly traffic congestion. New models and frameworks for forecasting traffic flow have been quickly developed in recent years, along with the integration of Artificial Intelligence approaches such as machine learning, to improve the performance of traffic flow prediction. Forecasting refers to the data at a specific point of time in the future. Time series analysis is a process of developing models to understand the data and to make extrapolation into the future. The traffic flow forecasting is a time series forecasting problem. A detailed survey on time series forecasting models for traffic flow prediction has been carried out. The time series forecasting deals with analysing the time series data in order to make predictions and thereby facilitate the strategic decision making in traffic management. newline