Short-Term Traffic Flow Prediction Using Neuro-Genetic Algorithms

Abstract

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 real traffic flow data obtained from the ATMS Testbed in Orange County, California. Both temporal and spatial effects were found to be essential for proper prediction. Results obtained indicate that the prediction errors vary inversely with the extent of the spatial contribution, and that the inclusion of three loop stations in both directions of the subject station is sufficient for practical purposes. Also, the longer the extent of prediction, the more the predicted values tend toward the mean of the actual, for which case the optimal look-back interval also shortens. The results also indicate that the level of data aggregation/resolution should be comparable to the prediction horizon for best accuracy. The model performed acceptably using both simulated and real data. The model also showed potential to be superior to such other well-known neural network models as the Multi layer Feed-forward (MLF) when applied to the same problem.