Forecasting Traffic Using A3T-GCN

We introduced T-GNNs in Chapter 13, but we did not elaborate on their main application: traffic forecasting. In recent years, the concept of smart cities has become increasingly popular. This idea refers to cities where data is used to manage and improve operations and services. In this context, one of the main sources of appeal is the creation of intelligent transportation systems. Accurate traffic forecasts can help traffic managers to optimize traffic signals, plan infrastructure, and reduce congestion. However, traffic forecasting is a challenging problem due to complex spatial and temporal dependencies.

In this chapter, we will apply T-GNNs to a particular case of traffic forecasting. First, we will explore and process a new dataset to create a temporal graph from raw CSV files. We will then apply a new type of T-GNN to predict future traffic speed. Finally, we will visualize and compare the results to a baseline solution to verify that our...