An Adaptive-Agent Simulation Analysis of a Simple Transportation Network

抄録

In general, transportation system consists of many drivers who choose the route, learning based on their experiences and information provided. In this study, drivers are assumed to reason and learn inductively based on their experiences. We develop an agent-based transportation system simulation model. In the model, the agent learns which route to choose based on his experiences. We shall call such a learning agent an adaptive agent. We examine the behavior of agents and network flow through the simulation. The results of the numerical experiments can be summarized as follows: 1) the system converges to Wardrop equilibrium; 2) the grades (the number of times of choosing the fastest route) are various among agents; 3) the difference of the grades occurs contingently; 4) agents who choose the route randomly deteriorate the system’s stability excessively.