Flood risk communication is imperative to aiding people’s decision making in flood situations. These warnings can be communicated through navigation applications on mobile devices. The current study investigated how flood-depth information affected drivers’ actions given flood warnings from a mobile navigation application in a driving simulator. This study manipulated the type of flood warning presented to the participants in the driving scenarios and measured their actions given a potentially flooded roadway. Participants experienced six drives with different flood warning conditions. Results indicated that providing flood depth information helped drivers accurately estimate the depth of the flood and their perceived risks; including more detailed information was helpful for drivers to make informed decisions regarding a flooded roadway. We suggest that designers include flood depth information to help drivers accurately perceive the depth and risk regarding a flooded roadway.
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The increasing threat of inland flooding due to precipitation changes and floodplain development necessitates efficient real-time flood detection and communication methods. While automated floodwarning systems facilitate such communication, they are susceptible to errors like false alarms and misses, which could undermine drivers’ trust during flood events. This study examined how system accuracy and error type impact perceived system reliability, as well as drivers’ trust and behaviors. Our results showed that both false alarms and misses lowered drivers’ perceived system reliability, and drivers were more inclined to follow recommendations from a system with higher reliability compared to one with low reliability. Misses and false alarms influenced drivers’ reliance and compliance behaviors differently. These findings help predict how system reliability level and error type shape drivers’ responses to automated flood-warning systems, potentially contributing to their design and calibration.
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Flood warnings can be communicated through mobile devices and should convey enough information to keep the user safe during a flood situation. However, the amount of detail included in the warning, such as the depth of the flood, may vary. The purpose of this study was to investigate how to best inform drivers of floods to keep them protected. Participants were tasked to drive to a restaurant in a driving simulator after receiving instructions and a type of flood information warning during each scenario (flood, no flood, flood of 6 inches, flood of 6 inches maximum). We found that participants accepted the alternate route more when in a scenario with a flood present compared to the no-flood scenario. These results deepened the understanding of human decisionmaking and can guide future flood warning designs to keep drivers protected from flooded roadways
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Photo sharing has become increasingly easy with the rise of social media. Social networking sites (SNSs), such as Instagram and Facebook, are well known for their image-sharing capabilities. However, this brings the concern of photo privacy, such as who may see the images of a user who is included in a post. Photo privacy settings offer detailed and more secure ways to share a user’s photos, however, this would require SNS users to understand these settings. To better grasp users’ understanding of photo privacy settings, we conducted a structured interview with Instagram users. We found that users were aware of the majority of the privacy settings asked about and that they accurately perceived their photo privacy safety based on their knowledge of photo privacy settings.