Improving Motorway Mobility and Environmental Performance via Vehicle Trajectory Data-Based Control

Abstract

High cost and relatively low reliability of stationary sensors hinder the wide spread of advanced motorway traffic control measures. In this research, we propose a vehicle trajectory data based variable speed limit (VSL) controller to improve mobility and environmental performance of motorways. First, a model based estimator is designed to estimate traffic states using data directly derived from probe vehicle with spacing measurement equipment (PVSMEs). Extended kalman filter (EKF) and METANET model are employed as the data assimilation tool and the process model for the estimator, respectively. Next, we incorporate the estimator with a model predictive control (MPC) to realize optimal VSL control. Finally, a 3.2km stretch in Auckland, New Zealand is selected and simulated to evaluate the proposed VSL under various PVSME penetration rates and traffic scenarios. The simulation results reveal that the PVSME-based VSL controller offers an effective solution to improve mobility and environmental performance of motorways. With an increase in PVSME penetration rates, the mobility and environmental benefits of the PVSME-based VSL increase.