Robust Optimization for Dynamic Traffic Assignment Under Demand Uncertainty

This paper considers operation and planning of surface transportation networks in an uncertain environment. A Cell Transmission Model (CTM) with a system optimal objective is used to model traffic dynamics. In particular, we focus on demand uncertainty residing in an appropriate uncertainty set such as box or polyhedral uncertainty set. We formulated an Affinely Adjustable Robust Counterpart (AARC) based linear programming model to study the multi-period transportation planning and operating problem. Simulation experiments show that the AARC solution provides excellent results when compared to Robust Counterpart(RC) solution and sampling based stochastic programming solution.