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Block-group level predicted mode share for New York City and New York State - MaRDI portal

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Block-group level predicted mode share for New York City and New York State

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DOI10.5281/zenodo.8105151Zenodo8105151MaRDI QIDQ6723779

Dataset published at Zenodo repository.

Author name not available (Why is that?)

Publication date: 4 July 2023

Copyright license: No records found.



We provide two datasets of predicted mode share, onefor New York City and another for New York State. Each row contains the mode proportion of trips along a census block group-level OD pair made by one of the four population segments: low-income, not low-income, students, and senior population. Six trip modes are considered: private auto, public transit (such as buses, light rail, and subways), on demand auto (taxi or TNC services such as Uber or Lyft), biking (including e-bike), walking, and carpool. The prediction is based on GLAM logit model calibrated with Replicas statewide synthetic population dataset. The in-sample prediction accuracyis quite competitive, with an overall accuracy of 90.28% in New York State and 88.63% in New York City. For more details of the model, please refer to our Github repository:BUILTNYU/GLAM-Logit (github.com)






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