An Enhanced Motorway Control System for Mixed Manual/Automated Traffic Flow

Abstract

Recent advances in vehicle automation technologies have opened up new perspectives for motorway traffic condition monitoring and macroscopic controls. When sufficient automated vehicles (AVs) are present, they can provide wide-ranging and spatiotemporally detailed traffic information. Furthermore, AVs can be programmed to comply with traffic laws and optimized to smooth traffic flows. Thus, deploying AVs are expected to address the issues encountered by the existing variable speed limit (VSL) systems. However, the appealing scenario that 90% or more AVs are on motorways is seen as a long-term goal. Therefore, this article explores the utilization of AV technologies in VSL systems under mixed traffic conditions where AVs coexist with manually driven vehicles. In this article, a VSL system using AVs as a source of data is presented. More specifically, an extended Kalman filter-based data assimilation method is proposed to estimate system variables (i.e., density, speed, critical density, and compliance rate) from the collected AV data. Following this, a model predictive control scheme is adopted to solve the optimal AV-data-based VSL control problem on the basis of estimated system variables. Finally, the efficiency of the proposed system is verified against a real motorway section in New Zealand.