Traffic demand and longer term forecasting from real-time observations
We optimize traffic signal timing sequences for a section of a traffic net-work in order to reduce congestion based on anticipated demand. The system relieson the accuracy of the predicted traffic demand in time and space which is carriedout by a neural network. Specifically, we design, train, and evaluate three differentneural network models and assert their capability to describe demand from tra