Graph Neural Network via Dynamic Weights and LSTM with Attention for Traffic Forecasting

In the problem of predicting values, various methods, regression or recurrent techniques are used, which can predict values based on previous ones with great efficiency. In our research, we propose our own algorithm to predict the situation on the streets, which makes it possible to create a system capable of determining the fastest and optimal routes for emergency vehicles in the event of a sudden reported accident, taking into account the occurrence of unexpected traffic jams or congestion on the road. Recently, Graph Neural Network (GNN), which can analyze and process relationships in graphs, have gained popularity. These layers use various aggregation functions to collect information and exchange it between different nodes and their neighbors. In addition, popular networks such as Long Short-Term Memory Network (LSTM), Gated Recurrent Unit model (GRU) or classical Recurrent Neural Network (RNN) are used in the problem of time series forecasting. In our research, we propose an algorithm consisting of two parts: the first part is responsible for spatial processing of data using graph layers and a novel aggregation function using dynamic weights, the second part: temporal processing using LSTM with additional attention. We show that in the problem of traffic volume forecasting, spatial analysis is also important, where streets, intersections can be correlated with each other, and transferring this information to the LSTM model improves and generates better results. In the end, our architecture outperforms typical and popular solutions that do not include attention mechanisms, obtaining MAE = 6.33, RMSE = 3.52, MAPE = 8.99% for the PeMSD7(M) dataset and MAE = 6.64, RMSE = 3.68, MAPE = 9.43% for the PeMSD7(L) dataset.