Study on User Behaviors in Urban Area with Ubiquitous Environment

在本研究中，我们处理了城市空间中具有泛在环境的各种用户行为模型。我们提出了几个城市购物区的行人模型来描述在市中心购物区行走的行人。对于每个行人，我们给出一个场景来定义按顺序访问的商店类别。在我们的模型中，将城市购物区划分为街区，并在每个商店类别上计算商店的大小。根据该场景，对于每一个行人的访问，在城市区域中以概率选择一个购物街区。概率通过使用两个信息来计算。一个是行人当前位置与街区之间的距离。另一个是街区中分类商店的规模。我们将我们的模型应用于横滨的青森市区、山形市区和市中心，通过使用实际行人数量和分类商店数据来评估参数并拟合我们的模型。因此，我们知道这些参数分别代表了青森市和山形市的行人特征。基于行人和用户行为模型，我们开发了一个模拟系统来模拟城市空间中的用户行为，特别是在购物区，包括商店、道路、铁路、火车站和交通信号。本仿真系统是为了分析我们无法处理分析模型的实际用户情况，如移动用户的移动行为，市中心购物区的步行行人在路边的许多商店之间走动，他们在车站乘火车到达和离开。我们观察了流量以及它们如何接受和接收随用户分布而变化的数据包，并讨论了接入点位置策略。通过该模拟系统，我们观察了城市空间中几种接入点的位置模式。

In this study, we deal with various user behavior model within urban space with ubiquitous environment. We propose several pedestrian model s in urban shopping area to describes pedestrians who walk up and down in the downtown shopping area. For each pedestrian, we give a scenario to defines shop categories to visit in order. In our model, the urban shopping area is divided into blocks and the sizes of shops are calculated at each shop categories. According to the scenario, for each visit of a pedestrian, one of the shopping block among the urban area is selected by a probability. The probability is calculated by using two information. One is the distance between the pedestrian's present location and the block. The other is the size of categorized shops included in the block. We applied our model to the urban area of Aomori, Yamagata and Downtown area in Yokohama to evaluate parameters and fit our model by using actual number of pedestrians and categorized shop data. As a result, we know that the parameters suggest the characteristics of Pedestrians in Aomori and Yamagata city respectively. Based on the pedestrian and user behavior models, we developed a simulation system to model user behavior in urban space, especially in shopping area, including shops, roads, railways, railway stations and traffic signals. This simulation system are made to analyze actual user situations which we can't deal analytical models, such as moving behavior of mobile users, walking pedestrians in downtown shopping area who walk around between many shops along roads, their group arrivals and departures by railways at stations. We observe traffic and how they accept and receive packets which changes with user distribution and discuss the access point location policy. By this simulation system, we observe several access points location patterns in a urban space.