A Traffic Prediction Using Machine Learning: Literature Survey

Abstract

Accurate calculation of the commute cost is crucial for the government to decide whether housing subsidy will be provided to disadvantaged workers, or to create a new method that can reduce the commute cost of the disadvantaged workers by offering mass transit. Many studies have already proven that machine learning can predict traffic and commute times. This literature survey focuses on finding the algorithms that can calculate traffic precisely and efficiently. While various algorithms can be used to predict the traffic, this literature survey focuses on using Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), which are based on the Recurrent Neural Networks (RNNs) architecture, for predicting the traffic.

Keywords: Machine learning, gated recurrent unit, long short-term memory, recurrent neural networks, traffic prediction