In recent years, there are regions where accidents caused by elderly individuals driving have become a social issue. Automobiles are a crucial means of transportation in daily life, therefore, alternative transportation options are necessary to reduce driving opportunities for the elderly. Public buses are one of the useful means of transportation for moving within the city. However, it is not guaranteed that public buses are running appropriate routes for the citizens. To verify the appropriateness of the bus routes, we investigated the citizens’ mobility patterns by analyzing their location information in Nobeoka City. The results of the investigation enabled us to find different mobility patterns among the citizens compared to the current bus routes.

Smartphones have become more ubiquitous than ever. People are installing various applications on their smart-phone to fit into their lifestyle. Existing research shows that there are patterns within the ways people access those applications, whether it involves particular locations, particular ranges of time, or many other factors. In this research, through a collaboration with a commercial company, we collected usage data from a popular smartphone application that gives its users access to digital flyers information for shops and supermarkets throughout Japan. Our early experiments found that the pattern information contained inside the data could be used to authenticate users. In this research, we are proposing a behavioral authentication model implementing customized learning classifier systems to search through vast amount of possible patterns to authenticate users of the application. Our early findings for this ongoing research demonstrate that our model can feasibly be a good alternative for additional authentication factor to implicitly authenticate users beyond the initial registration.