Prediction of Road Traffic Flow Based on Deep Recurrent Neural Networks

Abstract

Traffic congestion is a major issue for developed countries; therefore, research into the prediction of road traffic flow is vital. Deep neural networks, such as deep recurrent neural networks, are now being explored for road traffic flow prediction. However, what deep architecture is the most appropriate remains unanswered. Previous research into deep recurrent neural networks fails to compare them to other deep models; instead, comparisons are made with simple shallow models. To compound this issue, standard performance metrics assess a model's success solely on its accuracy. No consideration is given to computational cost. Furthermore, optimisation of a neural network's architecture can be difficult. There is no standard or analytical method to determine their correct structure. This often leads to sub-optimal architectures being used. Therefore, deep neural networks should be assessed on how sensitive the model is to architectural changes. In this paper, we have examined three recurrent neural networks (a standard recurrent, a long short-term memory, and a gated recurrent unit) to determine how they perform on time-series data based on a real dataset. We compared their accuracy, training time, and sensitivity to architectural change. Additionally, we developed a new performance metrics, Standardised Accuracy and Time Score (STATS), which standardises the accuracy and training time into a comparable score, allowing an overall score to be awarded. The experimental results show that, based on the STATS, the gated recurrent unit produced the highest overall performance and accuracy score. Furthermore, its prediction was most stable against architectural changes. Conversely, the long short-term memory was the least stable model.