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Block-group level mode choice parameters for New York City and New York State - MaRDI portal

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Block-group level mode choice parameters for New York City and New York State

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DOI10.5281/zenodo.8113817Zenodo8113817MaRDI QIDQ6723780

Dataset published at Zenodo repository.

Author name not available (Why is that?)

Publication date: 4 July 2023

Copyright license: No records found.



Description

We provide two datasets of census block group-level mode choice parameters for New York City and New York State.The parameters are estimated by GLAM logit model using Replicas synthetic population datasets (For details of the GLAM logit model, please refer to BUILTNYU/GLAM-Logit (github.com)). Each row contains a set of mode choice parameters for each block-group OD pair and 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.Parameters of twelve mode attributesare estimated, including, auto travel time, transit in-vehicle time, transit access time, transit egress time, number of transit transfers, non-vehicle travel time, trip cost, and five alternative specific constants (setting carpool as the reference level). In New York City, the average value of time (VOT) of low-income population is 21.67$/hour, the average VOT of not low-income population is 28.05$/hour, the average VOT of student population is 10.96$/hour, and the average VOT of senior population is 10.93$/hour.In New York State, the average value of time (VOT) of low-income population is 9.63$/hour, the average VOT of not low-income population is 13.95$/hour, the average VOT of student population is 7.40$/hour, and the average VOT of senior population is 6.26$/hour. The empirical distribution of agent-level parameters is neither Gumbel nor Gaussian, whichreveals a regional divergence of the value of time and mode preference, indicating potential inequity issues in the transportation system. This is infeasible for conventional discrete choice models (DCMs) to capture.






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