LSTM for future forecast

Traffic Flow Prediction Using Neural Network & Roll Forward Validation

Traffic flow prediction with big data & deep learning

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Traffic flow as a part of transportation management and control, has become more data intensive work. Forecasting traffic flow primarily depends on historical and real-time traffic data collected from various sensor sources, including inductive loops, radars, cameras, mobile Global Positioning System, crowd sourcing, social media, etc.

We all are aware of the fact that, the traffic follows a very particular pattern for week days. In the morning the traffic flow increases and reaches a peak (rush hour) before slowing down rest of the day till evening or late afternoon. Traffic flow depends on time dependent and spatial correlations. Data driven traffic management system is effective when we combine time dependent and spatial correlations and other variables such as news data as alternate data. Therefore, we need an effective data mining process with data integration (merging of data from multiple sources) prior to loading into a data warehouse.

Here, we will experiment with feed forward neural network using a simple time dependent data. Below 1 year…