Welfare, sustainability, and equity evaluation of the New York City Interborough Express using spatially heterogeneous mode choice models
<|reference_start|>Welfare, sustainability, and equity evaluation of the New York City Interborough Express using spatially heterogeneous mode choice models: The Metropolitan Transit Authority (MTA) proposed building a new light rail route called the Interborough Express (IBX) to provide a direct, fast transit linkage between Queens and Brooklyn. An open-access synthetic citywide trip agenda dataset and a block-group-level mode choice model are used to assess the potential impact IBX could bring to New York City (NYC). IBX could save 28.1 minutes to potential riders across the city. For travelers either going to or departing from areas close to IBX, the average time saving is projected to be 29.7 minutes. IBX is projected to have more than 254 thousand daily ridership after its completion (69% higher than reported in the official IBX proposal). Among those riders, more than 78 thousand people (30.8%) would come from low-income households while 165 thousand people (64.7%) would start or end along the IBX corridor. The addition of IBX would attract more than 50 thousand additional daily trips to transit mode, among which more than 16 thousand would be switched from using private vehicles, reducing potential greenhouse gas (GHG) emissions by 29.28 metric tons per day. IBX can also bring significant consumer surplus benefits to the communities, which are estimated to be $1.25 USD per trip, or as high as $1.64 per trip made by a low-income traveler. While benefits are proportionately higher for lower-income users, the service does not appear to significantly reduce the proportion of travelers whose consumer surpluses fall below 10% of the population average (already quite low).<|reference_end|>
@article{yang2024welfare,,
    title={Welfare, sustainability, and equity evaluation of the New York City
  Interborough Express using spatially heterogeneous mode choice models},
    author={Hai Yang, Hongying Wu, Lauren Whang, Xiyuan Ren, Joseph Y. J. Chow},
    journal={arXiv preprint arXiv:2408.01562},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.01562},
    primaryClass={cs.CY}
}