Traffic Management for Intelligent Vehicle-Highway Systems Using Model-Based Predictive Control

In this paper the authors present an integrated traffic management and control approach for Intelligent Vehicle Highway Systems (IVHS). These IVHS consist of interacting roadside controllers and intelligent vehicles that are organized in platoons with short intraplatoon distances, and larger distances between platoons. All vehicles are assumed to be fully automated, i.e., throttle, braking, and steering commands are determined by an automated on-board controller. The proposed control approach is based on a hierarchical traffic management and control architecture for IVHS, and it also takes the connection and transition between the non-automated part of the road network and the IVHS into account. In particular, the authors combine dynamic speed limits and lane allocation for the platoons on the IVHS highways with access control for the on-ramps using ramp metering, and the authors propose a model-based predictive control approach to determine optimal speed limits and lane allocations as well as optimal release times for the platoons at the on-ramps. In order to illustrate the potential of the proposed traffic control method, we apply it to a simple simulation example in which the aim is to minimize the total time all vehicles spend in the network by optimally assigning dynamic speed limits, lane allocations, and on-ramp release times to the platoons. For the case study the platoon-based approach results in a performance improvement of about 9 % compared to the situation with controlled human drivers.