Adaptive demand-diversion prediction for integrated control of freeway corridors

Integrated freeway corridor control, which includes efficient real-time management of freeway traffic diversion onto less congested arterials, is one of the most cost-effective ways to cope with freeway congestion. Because traffic diversion is influenced by ramp metering and intersection signal timing, the effectiveness of an integrated corridor control strategy draws on its ability to predict the diversion resulting from the control in real time. An adaptive demand-diversion predictor is developed that reflects the drivers’ choice behavior in a rapidly changing traffic environment. The new method explicitly treats the time-variant effects of control on the traffic demand to be predicted by combining behavioral modeling with filtering. Behavioral demand-diversion models and an extended Kalman filter are developed, with the filter continuously updating the model parameters with the most recent prediction error. The method was applied in several freeway entrance ramps of the Minneapolis-St. Paul metropolitan area freeway system to predict the demand-diversion of traffic flow approaching the ramp area in real time. Following extensive testing and evaluation, the method was incorporated in a new demand-responsive control logic for the online control of freeway corridors.