Modelling a Social Dilemma of Commuters' Mode Choice with Inductive Learning Machines

Abstract

This study attempts to apply an agent-based approach to modelling a transportation system. Utilizing the advantage of agent-based model of being validated at an individual level, a social dilemma situation of travel mode choice is modeled and viewed as a complex system. Inductive-learning’scapability of travelers is used and combined with an evolutionary approach in order to simulate travelers’ learning process. Auser-equilibrium point as predicted by conventional equilibrium analysis could be reached and stabilized. The stable situation is produced by interaction process among agents and by behavioral change process of each agent, without a central or external rule that organizes objective function of the system. The study also revealed some conditions that may produce other stable situations in addition to the user equilibrium point. An emergent situation combined with travelers’ sensitivity to payoff differences is observed to be influential.