Robust Traffic Control Using a First Order Macroscopic Traffic Flow Model

Traffic control is at the core of research in transportation engineering because it one best practices for reducing traffic congestion. It has been shown recent years that problem involving Lighthill-Whitham-Richards (LWR) model can be formulated as a Linear Programming (LP) given corresponding initial conditions and parameters fundamental diagram are fixed. However, uncertain when studying actual problems. This paper presents stochastic programming formulation boundary chance constraints, to capture uncertainty conditions. Different objective functions explored using this framework, proposed validated by conducting case studies both single highway link network. In addition, accuracy relaxed optimal results proved Monte Carlo simulation.