基于贝叶斯网络的青少年对社区互助支持系统的态度调查

S. Matsumoto, Nobuyuki Ohigashi
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引用次数: 0

摘要

一些广岛市民居住在环城山坡的郊区住宅区,但居住环境不适合以老年人为主的“弱势道路使用者”。为了改善他们的生活环境,我们展示了信息共享系统的概念,以支持他们生活在公共交通不发达的地区。此外,我们还构建了一个名为MASS的web服务来支持他们的日常生活。我们的系统的作用是促进当地社区人们之间的相遇,提供资源共享的机会,产生一些交通工具,以互助的方式解决日常生活中的困难。年轻人的积极参与对于使我们的系统对弱势道路使用者更有效至关重要,因为大多数技能将由年轻人提供。通过对以往工作的分析,揭示了青年在当地社区技能分享意识的趋势,并发现了量化方法II的局限性。因此,在本文中,为了讨论我们的系统作为一个业务的连续性,我们对当地社区年轻人的资源共享意识进行了态度调查,并使用贝叶斯网络进行了分析。从实验结果中,我们再次确认了之前的研究结果,并进一步明确了我们目前无法明确的因素之间的关系。此外,我们使用构建的贝叶斯网络进行了数值实验,得到了支持几个假设的结果,并揭示了对MASS使用有强烈影响的因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Attitude Survey of Youth for Mutual Aid Support System for Local Community Using Bayesian Network
Some Hiroshima citizens live in suburban residential estates at the slope of mountains surrounding the city, but the living environment is unsuitable for “vulnerable road users” mainly elderly people. To improve their living environments, we have shown the concept of information sharing system to support them living in regions where public transport is not sufficiently developed. In addition, we have constructed a web service to support their daily life named MASS. Our system’s role is to facilitate the encounter between local community people, provide the opportunity of resource sharing, generate some kinds of transport means, and solve the difficulties in daily life by mutual aid. Young people’s active participation is essential to make our system more effective for vulnerable road users because most of the skills will be provided by young people. The analysis of the previous work unveiled a trend of the youth consciousness about skill sharing in their local community and found the limitation of Quantification Method Type II. Therefore, in this paper, to discuss our system’s continuity as a business, we conducted an attitude survey on young people’s awareness of resource sharing in their local community and analyzed it with Bayesian network. From the experimental result, we re-confirmed the findings of the previous research and further clarified the relationship between the factors that we could not clarify so far. In addition, we conducted numerical experiments using the constructed Bayesian network, obtained results that support several hypotheses, and revealed factors that have a strong influence on the use of MASS.
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