Evaluation of city-scale built environment policies in New York City with an emerging-mobility-accessible synthetic population

Brian Y. He, Jinkai Zhou, Ziyi Ma, Joseph Y.J. Chow, Kaan Ozbay

Research output: Contribution to journalArticlepeer-review

Abstract

With the rise of smart cities, a number of new mobility services have emerged to drive changes in built environment policies. Up-to-date demand models are needed to capture the impact of these policies on emerging mobility-enabled travel patterns. The study explores modeling requirements to assess the impact of such built environment policies. A synthetic population of New York City with a tour-based nested logit mode choice model was developed with accessibility to bikesharing and ride hail services via smartphone ownership. The model results suggest Manhattanites have a value of time of $29/h, consistent with the literature. Smartphone ownership is positively influenced by income and negatively influenced by age, and in turn negatively impacts Citi Bike ridership relative to other modes available. The synthetic population is also applied to analyze two city-scale built environment scenarios: a hypothetical Amazon headquarter deployment and a Citi Bike service expansion. If Amazon succeeded in Long Island City, it would have increased the number of trips to/from that neighborhood by 239%, of which FHVs would grow by over 441% and transit by 294%. It would have led to an increase of peak morning trips from 5000 up to at least 8000. Citi Bike's expansion plan would grow ridership by 92%, and if they were able to expand efficiently throughout NYC this would grow further to 210% over the baseline.

Original languageEnglish (US)
Pages (from-to)444-467
Number of pages24
JournalTransportation Research Part A: Policy and Practice
Volume141
DOIs
StatePublished - Nov 2020

Keywords

  • Built environment
  • Emerging mobility
  • New York City
  • Synthetic population
  • Tour-based model

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Transportation
  • Management Science and Operations Research

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