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Publicação
Exploring city mobility through travel diaries: a non-negative tensor factorization approach
Autores
Orientador(es)
Resumo(s)
In the EU 64% of everyday people's transport is made by private vehicles (Palm, 2022.), but
that leads to today's CO2 emissions of private road vehicles being responsible for 74% of all
emissions related to transport (IEA. 2020). That's why there should be an effort to enhance
the usage of public transportation, which was agreed upon by all the EU members who
signed the Green New Deal (European Environment Agency, 2021). In this research, the
status of the public transit network will be explored, and the pain points will be exploited to
recognize the necessary changes required to facilitate this shift. This will be done using a
non-negative tensor factorization approach which will help to better understand complex
and multidimensional travel diaries.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
Palavras-chave
Travel diary Tensor factorization Mobility patterns Non-negative Tucker Non-negative PARFAC SDG 11 - Sustainable cities and communities