Analysis of Changes in the Activity Prisms of Individuals to Predict a Shared Life Experience Metric Over Different Regions and Sociodemographic Groups

Term Start:

March 1, 2024

Term End:

May 31, 2025




Equity, Metrics, Social Exclusion, Technology

Thrust Area(s):

Data Modeling and Analytic Tools

University Lead:

City College of New York


Mahdieh Allahviranloo

Technology has changed individuals’ travel behavior and time-use in so many ways. As much as it offers variety of benefits to societies, it may add to social exclusion phenomena, since the need for travel is being replaced by a click of a button in cell-phone. People don’t feel the need to leave their home to carry out their tasks. They work from home, they order their items online, and even if they want to attend a meeting, they no longer are obliged to travel. Technology, in fact, creates an invisible bubble around individuals, which the size and the thickness of the bubble may vary across different individuals and households. Wouldn’t this make us feel lonelier and more excluded? Research shows that equity in transportation and mobility is closely tied to happiness and well-being. Ensuring that transportation systems are accessible, affordable, and inclusive can lead to reduced stress level, improved quality of life, better health, and greater opportunities, all of which contribute to greater happiness in communities and societies. Public policies, urban planning, and social factors all play a role in shaping this complex relationship.

In our earlier works, we have discussed about Shared-life Experience (SLE) metric, where we defined it as the likelihood that individuals would interact with others due to their travel patterns; and we also highlighted the importance of travel and access to transportation in having a higher SLE. In this project, we aim to expand the concept in three ways: (a) we define a new SLE metric which is based on the activity prisms of individuals; (b) we analyze the changes in the SLE metric in the individual level over multiple years, using City Wide mobility data that is collected annually; (c) We run a probabilistic analysis to predict changes in the SLE metrics to identify how different regions and different sociodemographic groups will be impacted by. The results of the analysis will identify the most vulnerable areas and groups of people.

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