Real-Time Prediction of Freeway Occupancy for Congestion Control

Abstract

Accurate traffic prediction is critical for effective control of on-ramp traffic (ramp metering). During congestion, traffic shock waves propagate back and forth between the detectors, and traffic becomes inherently non-stationary and difficult to predict. Recently, several adaptive non-linear time series prediction methods have been developed in statistics and in artificial neural networks. We applied these methods to develop real-time prediction of freeway occupancy during congestion periods, from current and time-lagged observations of occupancy at several (neighboring) detector stations. This study used the following function estimation methodologies for real-time occupancy prediction: two statistical techniques, multivariate adaptive regression splines (MARS) and projection pursuit regression; two neural network methods, multi-layer perceptrons (MLP) and constrained topological mapping (CTM). All these methods were applied to freeway occupancy data collected on I-35W during morning rush hours. Data collected on one day was used for training (model estimation), whereas the data collected on a different day was used for testing, i.e., estimating the quality of prediction (generalization). Results for this study indicate that the proposed methodology provides 10-15% more accurate prediction of traffic during congestion periods than the approach currently used by Minnesota DOT.