Robust Transportation Network Design Modeling with Regret Value

Abstract

Based on long-term transportation network design decisions and potential parameter variations, it is important that demand uncertainty is considered in transportation modeling. In this paper, we present a novel robustness measure that combines the two objectives by minimizing the expected travel time while bounding the relative regret in each scenario facing uncertain origin-destination demand (OD demand). The concept of α-robust solution is introduced into the transportation network design problem. We propose a robust transportation network design model with regret value constraints, then design an algorithm based on the genetic algorithm to solve the problem and obtain the optimal solutions for different regret values. Finally, numerical results based on the Nguyen-Dupuis network validate the effectiveness of the algorithm. While detailed analysis on trade-offs, between the expected travel time and the maximum regret value, shows that large reductions in maximum regret do not necessarily result in a great increase in expected travel time. Meanwhile, we compared the robust model presented with the stochastic model and numerical examples demonstrate that the robust planning network is more reliable and less risky than the stochastic model if demand uncertainty is considered in modeling.