Traffic along a freeway varies not only with time but also with space. It is thus essential to model dynamic traffic patterns on the freeway in order to derive appropriate metering control strategies. Existing methods cannot fulfill this task effectively. Due to the learning capability, artificial neural network models are developed to simulate typical time series traffic data and then expanded to capture the inherent time-space interrelations. The augmented-type network is proposed that includes several basic modules intelligently affiliated according to traffic characteristics on the freeway. Inputs to neural network models are traffic states in each time period on the freeway segments while outputs correspond to the desired metering rate at each entrance ramp. The simulation outcomes indicate very encouraging achievements when the proposed neural network model is employed to govern the freeway traffic operations. Also discussed are feasible directions for further improvements.
All Science Journal Classification (ASJC) codes
- Computer Science(all)