Enhancing model-based feedback perimeter control with data-driven online adaptive optimization

Abstract

Most feedback perimeter control approaches that are based on the Macroscopic Fundamental Diagram (MFD) and are tested in detailed network structures restrict inflow from the external boundary of the network. Although such a measure is beneficial for the network performance, it creates virtual queues that do not interact with the rest of the traffic and assumes small unrestricted flow (i.e. almost zero disturbance). In reality, these queues can have a negative impact to traffic conditions upstream of the protected network which is not modelled. In this work an adaptive optimization scheme for perimeter control of heterogeneous transportation networks is developed and the aforementioned boundary control limitation is dropped. A nonlinear model is introduced that describes the evolution of the multi-region system over time, assuming the existence of well-defined MFDs. Multiple linear approximations of the model (for different set-points) are used for designing optimal multivariable integral feedback regulators. Since the resulting regulators are derived from approximations of the nonlinear dynamics, they are further enhanced in realtime with online learning/adaptive optimization, according to performance measurements. An iterative data-driven technique is integrated with the model-based design and its objective is to optimize the gain matrices and setpoints of the multivariable perimeter controller based on real-time observations.

Effective Multi-region Traffic Control and Demand Management Using an Overlay Route-Reservation Scheme

“…Utilizing the NFD dynamics within homogeneous regions increases the modeling accuracy and ensures a relatively low implementation overhead since it does not require extensive traffic information (e.g., the per-link densities, speeds and flows) [11], [10]. However, the use of gating and perimeter control approaches may not always be beneficial for the overall network [12], [13]. If gated links do not have sufficient space for queuing, queues due to gating may obstructs the upstream network destinations and the benefits will be reduced as the queue length cannot be efficient estimated.…”

Exploring the impact of autonomous vehicles in urban networks and potential new capabilities for perimeter control

“…The traffic flow contains a mix of human piloted and automated vehicles. The test case network is a replica of the network used in [11] and corresponds to a part of the CBD of Barcelona in Spain (Figure 1). A traffic scenario is carefully constructed to contain a mix of free flow and congested traffic conditions.…”

Network traffic flow optimization under performance constraints

“…In Yildirimoglu et al (2015), route choice models under dynamical constraint have been included to perimeter ow control decisions. Note that, as reported in Mahmassani et al (2013); Yildirimoglu and Geroliminis (2014); Kouvelas et al (2017), route choice e ects can in uence the trip length distribution of vehicles in the network and thereby approximation of out ow might experience some errors. In Haddad (2017b) optimal control for maximum queue length inclusion on aggregated inter-regional boundary queues is considered.…”