Abstract
Traffic congestion is one of the problems for cities around the world due to the rapid increasing of vehicles in urbanization. Traffic flow prediction is of a great importance for Intelligent Transport System (ITS) which helps to optimize the traffic regulation of a transportation in the city. Nowadays, several researches have been studied so far on traffic flow prediction, accurate prediction has not yet been exploited by most of existing studies due to the impact of inability to effectively deal with spatial temporal features of the times series data. Traffic information in transportation system will also be affected by different factors. In this research we intended to study various models for Traffic flow prediction on the basis machine learning and deep learning approaches. Factors affecting the performance of traffic flow prediction intensity are studied as well. Benchmark performance evaluation metrics are also reviewed. Generally, this manuscript covers relevant methods and approaches, review the state-of-art works with respect to different traffic flow prediction technique help researchers in exploring future directions so as to realize robust traffic flow prediction.
Machine Learning and Deep Learning Models for Traffic Flow Prediction: A Survey
