Traffic volume forecasting using the LSTM time series model

Time series models also have various other business applications, such as predicting stock prices, forecasting the demand for products, or predicting the number of passengers per hour at an airport.

One of the most commonly used applications of such a model (which you must have used unknowingly while driving) is traffic predictions. In this section, we will try to predict the traffic volume for Interstate 94, which can be used by ride share companies such as Uber, Lyft, and/or Google Maps for predicting traffic and the time required to reach a destination for both the drivers and ride share customers. The traffic volume varies notoriously by hour (depending on the time of day, office hours, commute hours, and so on), and a time series model is helpful in making such predictions.

In this use case, we will use the Metro Interstate Traffic Volume dataset to build a multi-layer stacked LSTM model and forecast the traffic...