Simulation-based adaptive calibration and optimization of intelligent transportation systems for highway congestion management

Abstract

The paper introduces an adaptive optimization framework that is used to calibrate and optimize multiple intelligent transportation strategies such as high occupancy toll (HOT), ramp metering, and variable speed limits. The framework is capable of estimating the OD (origin–destination) matrix, calibrating the model with the experimental data, and conducting system optimization in transportation. This capability is shown with simulation experiments based on the public data of I-95 Florida. The preliminary results show that the revenue increases with target speed up to a point, after which the revenue drops. Also, the throughput on the HOT increases with decreasing target speed. So, to maximize revenue and throughput together, the optimal values are observed at moderate levels of the feasible range. Also, in the presence of accidents, the total throughput is improved by decreasing the toll rate. Optimal level of vehicles on the HOT lanes is examined to minimize vehicle emissions.