Analysis of Young People’s Attitudes toward Mutual Aid Support System in Local Community Using Sensitivity Analysis of Bayesian Network

  • Shimpei Matsumoto Hiroshima Institute of Technology
  • Nobuyuki Ohigashi Hiroshima Institute of Technology
Keywords: vulnerable road users, resource-sharing, mutual assistance, local community activation, Bayesian network, sensitivity analysis

Abstract

We have shown the concept of an information-sharing system to support vulnerable road users living in the suburban slope residential areas where public transport is scarce. Then we also have constructed a web service to support their daily life named MASS. The role of MASS is to facilitate the encounter between local community people and to provide the opportunity of resource sharing for solving the difficulties in daily life by mutual assistance. To effectively solve the problems of vulnerable road users, mainly older people with MASS, young people’s active participation is essential because most of the resources of skills will be provided by young people. Therefore, to discuss our system’s continuity as a general service, the previousresearch has conducted an attitude survey on young people’s awareness of resource sharing in their local community and analyzed it with Bayesian networks. From the analysis, the previous research has shown the relationship between the factors, which are not clarified so far, and obtained results that support several hypotheses. However, the previous research has analyzed only the results of evaluating MASS from a subjective view and has not dealt with the survey results of evaluating MASS from an objective viewpoint. Furthermore, each explanatory variable’s strength concerning the objective variable (each one’s evaluation about MASS) was not sufficiently clear. This study aims to analyze the sensitivity of each explanatory variable for the objective variable in the constructed model of Bayesian networks and perform inference using the model. From the experiment, we were able to clarify the strength of each explanatory variable quantitatively.

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Published
2022-02-28
Section
Technical Papers