Set of New Traffic-Responsive Ramp-Metering Algorithms and Microscopic Simulation Results

Abstract

In this paper, we present a novel switching traffic-responsive ramp-metering controller that adapts to the different traffic dynamics under different congestion conditions-free-flow or congested. The approach of multirate linear quadratic control with integral action (LQI) is employed to compensate for disturbances and to accommodate the difference between the model sampling time and the metering-rate update interval. In addition, a queue length regulator is designed to prevent the queue from exceeding the ramp storage capacity and yield improved performance over the currently used ad hoc "queue-override" scheme. Subsequently, a queue length estimator is designed to provide feedback to the queue length regulator, using the queue-detector speed data that are available in the field. A local ramp-metering control strategy is proposed to achieve the control goal of reducing the spatial and temporal span of the congestion, while satisfying the on-ramp storage capacity constraints, using locally available information. Test results on a calibrated microscopic traffic simulator demonstrate the performance and effectiveness of the switching ramp-metering controller, the queue length estimator and regulator, and the overall control strategy. The Total Vehicle and Passenger Congestion Delays are both reduced by 16%, while the Total Travel Time is improved by 5.6%. As a comparison, simulation results of ALINEA are also presented.

Reinforcement Learning for Ramp Control: An Analysis of Learning Parameters

“…Considering the difficulties of real site evaluation, a macroscopic traffic flow model asymmetric cell transmission model (ACTM) is selected for our test. ACTM has been used for ramp control strategies development and evaluation in some recent studies [17][18][19] because of the low computational demand and stable performance on mimicking real traffic on motorways. Furthermore, this deterministic model can avoid the stochastic influence of the traffic flow model when the ramp agent is analyzed.…”

TOPL: Tools for Operational Planning of Transportation Networks

“…These include (1) increasing demand by (say) five percent relative to the base case; (2) modeling an incident in a particular cell by reducing the capacity of the cell by one or two lanes for a certain time period; (3) a demand management scheme that reduces on-ramp flows at some locations by a certain amount. TOPL provides several ramp metering control laws, including ALINEA [9] and those described in [10]. Running each specified scenario with and without ramp metering in place provides an estimate of the benefits of ramp metering.…”