  Walking is a fundamental activity of human life, not only for moving between
places but also for interacting with surrounding environments. While walking to
destinations, pedestrians may acquaint themselves with attractions such as
artworks, shop displays, and public events. If such attractions are tempting
enough, pedestrians opt to stop walking in order to join the attractions. In
crowded conditions, the existence of attractions may considerably affect the
pedestrian flow patterns. One can see that the attracted pedestrians often
impede passerby flow near the attractions during peak hour in a shopping
center. During shopping holidays such as Black Friday in the United States,
shoppers are competing for very attractive products in stores and such behavior
might lead to pedestrian incidents.
  The work presented in this dissertation reproduced those phenomena in
numerical simulations by developing mathematical models of attracted pedestrian
movements. In the models, the crowd preference and the attractiveness of the
attraction were taken into account in order to reflect the characteristics of
attractions and pedestrian traffic. Furthermore, this dissertation suggested
possible explanations of the predicted phenomena based on the interactions
between pedestrians and attractions. The presented models can be applied and
extended, for example, to optimize pedestrian flow in shopping centers and
museums.
