An accurate traffic flow prediction using long-short term memory and gated recurrent unit networks

Abstract

Congestion on roadways is an issue in many cities, especially at peak times, which causes air and noise pollution and cause pressure on citizens. So, the implementation of intelligent transportation systems (ITSs) is a very important part of smart cities. As a result, the importance of making accurate short-term predictions of traffic flow has significantly increased in recent years. However, the current methods for predicting short-term traffic flow are incapable of effectively capturing the complex non-linearity of traffic flow that affects prediction accuracy. To overcome this problem, this study introduces two novel models. The first model uses two long-short term memory (LSTM) units that can extract the traffic flow temporal features followed by four dense layers to perform the traffic flow prediction. The second model uses two gated recurrent unit (GRU) units that can extract the traffic flow temporal features followed by three dense layers to perform the traffic flow prediction. The two proposed models give promising results on performance measurement system (PEMS), traffic and congestions (TRANCOS) dataset that is firstly used as metadata. So, the two models can do this in specific cases and are able to suddenly capture trend changes.

Topology Approach for Crude Oil Price Forecasting of Particle Swarm Optimization and Long Short-Term Memory

“…Considering the specific problem, it is recommended to experiment with different combinations of dense and LSTM units to find the optimal configuration that yields the best performance and generalization on the given task. The selection and number of dense and LSTM units during the construction of neural network models can impact the performance of the models [24] [25]. The configuration and selection of dense and LSTM units in a neural network model can significantly impact its performance.…”