Front tracking transition system model with controlled moving bottlenecks and probabilistic traffic breakdowns

Abstract

Cell-based approximations of PDE traffic models are widely used for traffic prediction and control. However, in order to represent the traffic state with good resolution, cell-based models often require a short cell length, which results in a very large number of states. We propose a new transition system traffic model, based on the front tracking method for solving the LWR PDE model. Assuming piecewise-linear flux function and piecewise-constant initial conditions, this model gives an exact solution. Furthermore, it is easier to extend, has fewer states and, although its dynamics are intrinsically hybrid, is faster to simulate than an equivalent cell-based approximation. The model is extended to enable handling moving bottlenecks as well as probabilistic traffic breakdowns and capacity drops at static bottlenecks. A control strategy that utilizes controlled moving bottlenecks for bottleneck decongestion is described and tested in simulation. It is shown that we are able to keep the static bottleneck in free flow by creating controlled moving bottlenecks at specific instances along on the road, and using them to regulate the incoming traffic flow.