2026-09-20T03:11:17Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1621322025-07-30T03:07:52Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Duarte, Fábio
Raghavan, Manish
El-Sisi, Kareem H.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Urban Studies and Planning
2025-07-29T17:19:40Z
2025-07-29T17:19:40Z
2025-05
2025-06-05T13:44:09.118Z
https://hdl.handle.net/1721.1/162132
https://orcid.org/0000-0003-0015-8433
In this thesis, I investigate which variables have the strongest influence on an individual's travel mode choice depending on the purpose and level of urgency (leisure, essential, emergency) of the trip. I analyze the relationship between spatiotemporal costs conditioned by demographic segmentation using data on population mobility patterns in auto-centric Los Angeles and multimodal New York City. Through a synergistic three-pronged methodology consisting of spatial (time and distance analysis complemented by a spatial interaction model), statistical (multinomial logistic regression model), and machine learning-based (graph neural networks and extreme gradient boosting) analysis, I explore the multifaceted nature of decision-making processes in different urban environments. The hidden patterns revealed by artificial intelligence show that distance is the key determinant of mode choice, depending on the urban form of the city and its adaptation to multimodality.
M.C.P.
S.M.
Massachusetts Institute of Technology
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https://rightsstatements.org/page/InC-EDU/1.0/
Miles Matter: Demographics, Distance, and Decision-Making
Thesis
application/pdf
Master
Master in City Planning
Master of Science in Electrical Engineering and Computer Science
Publication
unknown
unknown
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<Abstract>In this thesis, I investigate which variables have the strongest influence on an individual's travel mode choice depending on the purpose and level of urgency (leisure, essential, emergency) of the trip. I analyze the relationship between spatiotemporal costs conditioned by demographic segmentation using data on population mobility patterns in auto-centric Los Angeles and multimodal New York City. Through a synergistic three-pronged methodology consisting of spatial (time and distance analysis complemented by a spatial interaction model), statistical (multinomial logistic regression model), and machine learning-based (graph neural networks and extreme gradient boosting) analysis, I explore the multifaceted nature of decision-making processes in different urban environments. The hidden patterns revealed by artificial intelligence show that distance is the key determinant of mode choice, depending on the urban form of the city and its adaptation to multimodality.</Abstract>
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