Search

The effectiveness of a stay-at-home order depends on the speed of behavioral changes that are triggered by risk perception. Probability neglect bias, one of the cognitive biases, may lead people to engage in social distancing. However, there is no empirical evidence of the relationship between probability neglect bias and social distancing. This study aims to examine the relationship between individual differences in susceptibility to probability neglect bias and the level of social distancing practice during the early stages of the COVID-19 outbreak in Japan. The level of engagement in social distancing was defined as the narrowing of life-space mobility. We conducted a web-based questionnaire survey among 1000 adults living in central Tokyo, Japan, at the beginning of the pandemic outbreak. Our results show that people had a strong fear of infection in the early pandemic stages. Approximately 60% of our subjects were influenced by probability neglect bias. People susceptible to probability neglect bias engaged in social distancing more intensely than those who were not susceptible after the state of emergency was lifted., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Authors. Published by Elsevier Ltd.)

In Japan, mobility restrictions were enforced by the government to abate the spread of COVID-19. The current study examined whether experiences of such mobility restrictions affected motivation for future going-out activities. To this end, we conducted a one-time online survey of 1,000 adults in Tokyo to measure going-out activities in four different time periods at once: before the spread of infection, during and after the emergency declaration, and after the end of the pandemic (future desire). In addition, to examine the impact of preferences for online services that make it easier to stay home, we measured the usage history of online services to obtain food during the mobility restriction period. Results indicated that desire for going-out activities after the end of the pandemic increased compared with those before the pandemic, particularly for leisure-related purposes. In addition, the use of online services to obtain food tended to suppress the increased desire for future going-out activities, although this effect was not significant. In conclusion, mobility restrictions resulted in motivational arousal for going-out activities after the end of the pandemic. Our findings indicate that psychological reactance plays a role in determining going-out activities in the future., Competing Interests: HS, MS, and TY were employed by Toyota Central R&D Laboratories. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Sakai, Shimizu, Yoshimura and Hato.)

Smartphones are becoming increasingly popular day-by-day. Modern smartphones are more than just calling devices. They incorporate a number of high-end sensors that provide many new dimensions to smartphone experience. The use of smartphones, however, can be extended from the usual telecommunication field to applications in other specialized fields including transportation. Sensors embedded in the smartphones like GPS, accelerometer and gyroscope can collect data passively, which in turn can be processed to infer the travel mode of the smartphone user. This will solve most of the shortcomings associated with conventional travel survey methods including biased response, no response, erroneous time recording, etc. The current study uses the sensors' data collected by smartphones to extract nine features for classification. Variables including data frequency, moving window size and proportion of data to be used for training, are dealt with to achieve better results. Random forest is used to classify the smartphone data among six modes. An overall accuracy of 99.96% is achieved, with no mode less than 99.8% for data collected at 10 Hz frequency. The accuracy is observed to decrease with decrease in data frequency, but at the same time the computation time also decreases.