United States Department of Transportation
i
NY Statewide Behavioral Equity Impact Decision Support Tool with Replica [Supporting Datasets]
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2023-07-04
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Alternative Title:Block-Group Level Mode Choice Parameters for New York City and New York State;Block-Group Level Predicted Mode Share for New York City and New York State;
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Abstract:A NY statewide model choice model is developed to deterministically fit heterogeneous coefficients for trips along each census block-group OD pair conducted by each population segment within a random-utility-consistent framework. The proposed approach is to use inverse optimization (IO) to derive coefficients for each OD pair times population segment as an agent. This is only possible with ubiquitous population data. We call this a group-level agent-based mixed logit (g-AMXL) model, which is an extension of the AMXL model proposed by Ren and Chow (2022). The significance of g-AMXL is as follows. First, g-AMXL takes OD level (instead of individual level) trip data as inputs, which is efficient in dealing with ubiquitous datasets containing millions of observations. Second, preference heterogeneities are based on non-parametric aggregation of coefficients per agent instead of having to assume a distributional fit. Third, since each agent’s representative utility function is fully specified, g-AMXL can be directly integrated into system design optimization models as constraints instead of dealing with simulation-based approaches required by mixed logit (MXL) models. 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 Replica's 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 attributes are 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).
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Content Notes:National Transportation Library (NTL) Curation Note: As this dataset is preserved in a repository outside U.S. DOT control, as allowed by the U.S. DOT’s Public Access Plan (https://doi.org/10.21949/1503647) Section 7.4.2 Data, the NTL staff has performed NO additional curation actions on this dataset. The current level of dataset documentation is the responsibility of the dataset creator. NTL staff last accessed this dataset at its repository URL on 2023-12-07. If, in the future, you have trouble accessing this dataset at the host repository, please email NTLDataCurator@dot.gov describing your problem. NTL staff will do its best to assist you at that time.
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