This paper focuses on a decision making of evacuation start timing. This primary objective is to account for a dynamics of heterogeneity in expected utility. Decision-makers don't have the correct expected utilities at the time of inexperienced disasters. We extend a dynamic discrete choice model for the dynamics of heterogeneity. Our parameter estimation method assumes the expected utilities as parameters and get the perceived and feasible expected utilities. The model performance is demonstrated using evacuation behavior data in 2011 Great East Japan Earthquake. The variance of the expected utilities increases when it is closer to the tsunami arrival time. It shows that the behaviors of evacuees diverge as time progresses.
The activity of low vision people is multifarious because of difference of their visual function, ability of walking and consciousness of mobility. The characteristic of low vision's visual function and activity is not considered enough, when the environment is improved. We have to consider not only relationship between low vision person's visual function and activity but also environmental factors. The purpose of this study is to apply the concept of ICF (International Classification of Functioning, Disability and Health) to evaluation of activity of low vision people. Especially, we consider the relationship between visual function, activity and environmental factors. As a result, the public transportation facility was important for activity of low vision people who were able to walk alone.
Recently, several transport policies for bicycles are introduced such as construction of bicycle lane on roadway and one-way operation for bicycle traffic. It is considered that the traffic accident probability of bicycles which run right side of road is larger than those which run left side of road. However, it is based on the traffic accident probability on each intersection, and it is not based on the traffic accident probability between origin and destination for bicycle users. In this research, the traffic accident probability of bicycles by running position and direction considering the number of road crossings between origin and destination for bicycle users is estimated.
Relinquishment of driving licences is regarded as a measure to prevent elderly drivers' traffic crashes. Those who completed relinquishment often suffer from constraints on transportation. In order to consider measures to support these people, it is essential to understand what the act of relinquishing driving licences actually means to them. Thus, the present study aimed to conduct a life story research of two elderly people in order to clarify the meaning. There were two major findings. First, it was shown that there are cases in which the meaning of the act can be understood only through the understanding of the stories of the entire life span. Second, the two subjects were found to be similar in the sense that their acts of relinquishing licences can be interpreted as the opportunity to strengthen the motivation to make their lives more coherent. Practical implications of the findings are also discussed.
Recently expectations for the utilization of location-based services have been increasing. However, their practical use has been limited because of the difficulty of privacy protection. Although population statistics derived from operational data of mobile phone network have also attracted much attention, essential information about human mobility is eliminated by the aggregation process. Under these circumstances, this paper proposes a novel method to produce pseudo-data on human mobility and activity by integrating non-identifiable statistics. This data is composed of numerous virtual agents generated by a stochastic model and fulfills the given statistical features. The performance of the proposed model was examined by applying it to a real data set and comparing the results with Mobile Spatial Statistics which is estimated population using mobile phone data. The results show the hourly population estimated by the proposed model has good reproducibility for any age-groups and sex.
This study conducted questionnaire research in Osaka city in order to examine the effect of the media which serves information for people regarding the Osaka Metropolis plan. As a result, people in contact with the media, such as TV or newspaper which the pros and cons are shown, do not change their opinion because it is difficult to sift the genuine from the false. Based on the elaboration likelihood model, when elaboration is high, the central persuasive route is more likely to occur; conversely, when the elaboration is low, the peripheral route is more likely to occur. In this case, it is suggested that people who referred to the central route changed their opinion to disapprove the Osaka Metropolis plan, and people who referred to the peripheral route changed their opinion to approve the plan.
Recently, in various fields, there is an increasing need for human behavior understanding in the city environment. However, there are problems with the behavior analysis using the position data. One of the problems is that the semantic information, such as transportation mode or trip purpose, is not attached to the data. Another problem is that, because of the privacy protection, personal attributes of people can not be obtained. In this paper, we propose a method to estimate personal attributes and semantic information from the position data. Specifically, the estimation by the machine learning uses only features obtained from the position data. By using only the unsupervised learning, it can estimate without a correct labeled data. In the experiment using people flow data in the Tokyo urban area, we consider the effectiveness of the technique.
More than half of traffic accidents on arterial road networks occur at intersections. To enhance traffic safety, it is essential to identify the risk factors with qualitative manner, and to reform the configuration of intersections and improve the traffic management for removing the risk factors. In this study, first we collected the data of intersection geometric attributes and lane configurations by using virtual database (cf. Google Earth and Google Street View), which was integrated with the data of traffic accidents, road networks, and traffic volume. Then, Poisson regression models were applied to statistically identify the risk factors on categorized traffic accidents. Finally, the expected number of reduction of traffic accidents are estimated based on the regression models when the reformations are applied to the intersections in Kagawa. As a result, those findings are obtained; (i) the geometric attributes of intersections are significantly varied among prefectures (Kagawa, Shiga, and Aichi), which can be considered as regional characteristics; (ii) as the size of intersections becomes large, the risk of traffic accidents becomes worse, which implies that downsizing of intersection may contribute to improve the traffic safety; (iii) according to the proposed reforming scenario for intersections in Kagawa, there is a potential that the total number of traffic accidents could be reduced by more than 35%.