Agent-Based Modeling and Simulation for the Bus-Corridor Problem in a Many-to-One Mass Transit System

摘要: With the growing problem of urban traffic congestion, departure time choice is becoming a more important factor to commuters. By using multiagent modeling and Bush-Mosteller reinforcement learning model, we simulated day-to-day evolution commuters’ on many-to-one mass transit system during morning peak period. To start with, verified model by comparison with traditional analytical methods. Then formation process equilibrium investigated additionally. Seeing validity some initial assumptions were relaxed two groups experiments carried out considering heterogeneity memory limitations. The results showed that heterogeneous distribution broader has lower at different people behave in pattern. When each commuter limited memory, fluctuations exist evolutionary dynamics system, hence an ideal can hardly be reached. This research helpful acquiring better understanding commuter’s commuting period; approach also provides effective way explore complicated phenomena.