Dynamic Mode Choice of Commuters in an Agent-Based Simulation Model with Inductive Learning Machines
This study applies an agent-based approach to modeling a transport system. Using the advantage of agentbased models of being validated at an individual level, a social dilemma of travel mode choice is modeled and viewed as a complex system. An inductive learning machine is combined with an evolutionary approach to simulate traveler learning. A user-equilibrium point predicted by conventional analysis is reached and stabilized. The stable situation is produced by interaction among agents and by behavioral change of each agent, without a central or external rule that organizes objectives of the system. The study shows conditions that may produce other stable situations besides the user equilibrium point. An emergent situation combined with traveler sensitivity to payoff differences is influential.