An informed user equilibrium dynamic traffic assignment problem in a multiple origin-destination stochastic network

•Propose a novel mathematical programming model for the discrete-time information-based stochastic user equilibrium (ISUE) DTA problem where the real-time information accounts for the (stochastic) uncertainty in demand and network capacity.•Mathematically show the relationship between ISUE and the information-based stochastic system optimal (ISSO) objective, and prove that the ISSO objective is approximately the ISUE objective as the time step goes to zero, given the satisfaction of the FIFO constraint.•Prove the impact of information on different solutions: “ISSO”, “without-information SO” and “without- information UE”, and show that the total system travel time increases in that order respectively. The performance of the ISUE solution could be as good as ISSO solution, or worst than without-information UE solution.•Develop a link-based incremental solution method to effectively solve the information-based UE-DTA problem without using any path-based variables and constraints. We develop in this paper a comprehensive linear mathematical framework to study the benefit of real-time information and the impact of resulting user adaptive route choice behaviours on network performance. The framework formulates the information-based stochastic user equilibrium (ISUE) dynamic traffic assignment (DTA) problem for a multiple origin-destination (OD) network. Using the framework, we prove the linkage between the user equilibrium (UE) and system optimal (SO) solutions underpinned by the first-in-first-out (FIFO) principle. This important property then enables us to develop an incremental loading method to obtain the ISUE solutions efficiently by solving a sequence of linear programs. Moreover, the proposed method is more scalable that avoids a huge enumeration of paths in large-scale networks as done in path-based methods of the existing literature on this topic. We show via numerical examples the impact of information on both route choices and network performance, and demonstrate the significant improvements in the obtained ISUE solution both in terms of accuracy and computational complexity.