SSGRU: A novel hybrid stacked GRU-based traffic volume prediction approach in a road network

As a potential solution to relieve traffic congestions and help build a more safe traffic system, traffic flow prediction methods are given much attention in recent years. In previous studies, it can be found the machine learning (ML)-based methods are widely used in volume predictions of single roads. However, when applied in a more complicated road network, they usually show low efficiency and need to pay higher computing costs. To solve this problem, an innovative ML-based model, named Selected Stacked Gated Recurrent Units model (SSGRU), is proposed in this paper, which is mainly in allusion to road network traffic flow. There are mainly two parts in this model: one is used to do spatial pattern mining based on linear regression coefficients, and the other one includes a stacked gated recurrent unit (SGRU), which is essential for multi-road traffic flow prediction. As the basic unit, a simple tree structure is adopted to approximate the given road network. Particularly, we implemented our model into both suburban and urban traffic contexts, to prove its high adaptability. The whole evaluation process is based on seven different traffic volume data sets recorded at the 15-min interval, chosen from the England Highways. The results show that our model has higher accuracy than others when applied to a multi-road input infrastructure for all road scenarios.