Multi-agent fare optimization model of two modes problem and its analysis based on edge of chaos

This paper proposes a new framework of fare optimization & game model for studying the competition between two travel modes (high speed railway and civil aviation) in which passengers' group behavior is taken into consideration. The small-world network is introduced to construct the multi-agent model of passengers' travel mode choice. The cumulative prospect theory is adopted to depict passengers' bounded rationality, the heterogeneity of passengers' reference point is depicted using the idea of group emotion computing. The conceptions of “Langton parameter” and “evolution entropy” in the theory of “edge of chaos” are introduced to create passengers' “decision coefficient” and “evolution entropy of travel mode choice” which are used to quantify passengers' group behavior. The numerical simulation and the analysis of passengers' behavior show that (1) the new model inherits the features of traditional model well and the idea of self-organizing traffic flow evolution fully embodies passengers' bounded rationality, (2) compared with the traditional model (logit model), when passengers are in the “edge of chaos” state, the total profit of the transportation system is higher.