Short-Term Traffic Flow Prediction Using Neuro-Genetic Algorithms

This paper presents a new short-term traffic flow prediction system based on an advanced Time Delay Neural Network (TDNN) model, the structure of which is synthesized using a Genetic Algorithm (GA). The model predicts flow and occupancy values at a given freeway section based on contributions from their recent temporal profile (over a few minutes) as well the spatial profile (including inputs from neighboring upstream and downstream sections). An in-depth investigation of the variables pertinent to traffic flow prediction was conducted examining the extent of the “look-back” in time interval, the extent of prediction in the future, the extent of spatial contribution, the resolution of the input data, and their effects on prediction accuracy. The model’s performance is validated using both simulated and