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Mobility behaviors have been captured mainly by (i) Call Detail Records (CDRs) or Mobile Network Data generated by providers, and by (ii) smartphone applications.
The literature suggests that energy balance is shaped by where food is accessed, mobility behaviors, beliefs, preferences, socioeconomic characteristics, and attributes of the built environment.
In recent years, the design and deployment of persuasive interventions for inducing sustainable urban mobility behaviors has become a very active research field, leveraging on the pervasive usage of social media and mobile apps by citizens in their daily life.
By reflecting on our results and design experiences so far, we aim to provide better guidance for future development of more effective solutions supporting citizens' adoption of sustainable mobility behaviors in urban settings.
These data are used to generate a pool of mobility behaviors, i.e., sets of daily driving behavior patterns.
For example, a parent may change her/his mobility behaviors in order to take the children to the doctor or to stay at home with the sick children.
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This is because mobile communication systems focus merely on the mobility behavior of mobile users.
Although these variations bring back uncertainty in the act of VHO decision making, they can be utilized to extract precious information about propagation environment and mobility behavior of mobile user [4].
Two key parameters, and, define the mobility behavior of the mobile nodes.
Simulation study has demonstrated our proposed scheme can meet user's requirement even mobile users have different mobility behavior.
Information on human mobility behavior derived from mobile phones has been shown to be an invaluable source to leverage within the public health domain, both at an aggregated and individual level [17].
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