A simulation heuristic for traveler- and vehicle-discrete dynamic traffic assignment

A dynamic traffic assignment problem is considered where travelers are modeled as integral decision makers and network flow is composed of integral vehicles. As travel behavior affects network conditions and network conditions affect travel behavior, a complex model system results. The versatility of the considered model class has led to increasing practical interest (“agent-based simulation”) but also complicates the development of solvers for mutually consistent travel behavior and network conditions that represent possible long-term states of a transport system. Continuum flow assignment techniques are not applicable to this model class. This work starts out from a Nikaido-Isoda gap function for the traveler- and vehicle-discrete dynamic traffic assignment problem. A tractable but rather uninformative upper bound on this gap function is derived. A reformulation is presented that violates this bound as little as possible while ensuring that the reformulated bound carries relevant information for the subsequently developed new assignment heuristic. The proposed approach is formally related to and experimentally compared with relevant methods from the literature. It is found to exhibit superior performance in nontrivial case studies for Stockholm (Sweden), Oslo (Norway), and Berlin (Germany).