Ecological Indicators

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Atmospheric Pollution Research

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Cement and Concrete Research

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Journal of Agromedicine

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Journal of Environmental Management

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Water

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Images, videos, and virtual reality have been widely used in the literature to portray complex attributes to survey respondents. It is reasonable to expect immersive videos will be increasingly used in the future due to their decreasing costs and potentially more accurate representation of reality. Nevertheless, the literature has not sufficiently tested their ecological validity, which can be defined as the extent to which the results they produce in a laboratory setting, such as in choice experiments, are close enough to the results that would have been obtained in a real-life setting. The following work presents a comparison of two representation formats, images and immersive videos, to verify if they can elicit the same perceptual responses of pedestrians as real environments. To do this, a survey was carried out using these two formats as well as on-site interviews. Using a MIMIC approach, and after controlling for all relevant sociodemographic variables, results show that perceptions elicited through immersive videos were not different to those elicited in reality in one qualitative variable (perception of safety and security) out of three relevant ones identified. Furthermore, results also show immersive videos consistently induce a smaller distortion than photographs.

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Water

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Transportation Research Part C: Emerging Technologies

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International Journal of Geo-Information

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Journal of Transport Geography

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Previous activity-based studies have shown that behavioural outcomes are the result of complex and multidimensional processes. In this context, identifying and characterizing discrete mobility profiles through the classification of people’s behavior is particularly attractive. By facilitating the interpretation of complex, multidimensional processes, such an exercise could help to efficiently target transport policy decisions. The purpose of this paper is to identify mobility strategy profiles considering four key dimensions: time, space, money, and social interaction. Data from a seven-day activity, travel, expenditure, and social interaction diary applied to a sample of residents from the city of Concepción, Chile, is used. A two-step approach based on machine learning techniques is adopted. First, we use a Self-Organizing Map algorithm to identify seven distinct mobility strategies, each characterized by distinctive behaviors. These profiles are identified through 18 weekday and weekend daily behavior variables measuring the four key dimensions mentioned above, with an explicit focus on transport mode use. In the second step, we use a Decision Tree algorithm to profile the mobility strategies by means of personal and household sociodemographic variables. The results show interesting links among the dimensions of analysis within these profiles, such as connections between monetary expenditure on leisure and daily social interaction, and that profiles with higher private vehicle modal split tend to present higher levels of social interaction with people in their social network than public transit users.

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Journal of Glaciology

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