| Research Abstract |
In this research, we study congested phenomena in which customers behave with intelligence. So far congested phenomena, for example in telecommunication systems, computer systems production lines and car traffics, have been studied and analyzed mostly using models such as queuing models and car traffic models. In these phenomena, customers' behaviors are somewhat restricted by physical constraints, and so we may assume customers obey simple rules in the models.
However for congested phenomena of other types, we might have to take more complicated customers' behaviors or judgements into account in our model because customers have more freedom in these phenomena. Here we consider the following phenomena :
a) pedestrians' walks on a crossing
b) passengers' movements at getting on and off a train
c) users' behavior at internet terminal
d) users' behavior at mobile phones
In a), we develop a model in which each pedestrian anticipates future collisions and chooses best direction and best speed for
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walking without any collisions. Using this model, we derive a relation between pedestrian density and mean speed through simulation experiments. The relation is very similar to the one observed from real data of pedestrians. This shows our model captures major aspects of pedestrians' behavior.
In b), we develop a model in which each passenger tries to minimize his/her potential function which reflects the distances from and the sights of other passengers. Further the model incorporates incentives concerning to getting on/off. Using this model, we derive a relation through simulation "between the congestion of a train and the time required for some number of passengers to get off. The result shows that when the half of passengers get off the time is longer than the case where all passengers get off. This phenomena has been already known but probably this is the first result which evaluate the phenomena quantitavely.
In c), we observe traffic data passing through the gate of LAN of Tokyo Institute of Technology and develop a model for the traffic. However, the model cannot reflect users' behavior sufficiently for some reason. This is a theme for future study.
In d), it is very difficult to capture users' behavior since we cannot get suitable data. Less
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