Are Transportation Network Companies Synergistic with Other Shared Ride Mode Offerings? An Exploratory Analysis of Demand Data from NYC Utilizing High Resolution Spatiotemporal Models

Spurred by technological advances, transportation networks and the mobility offerings for moving people and goods are undergoing transformative and significant changes. A significant operator in the area of moving people is transportation network companies (TNC), they are also commonly referred to by the name of the offering as dynamic ridesharing or ridesourcing. A research need is to comprehensively understand the impacts of TNCs so that transportation systems can be planned and implemented, that effectively respond to changes it brings. A unique feature of TNCs is the ease, efficiency, and effectiveness with which such services can be accessed and consumed, leveraged by technology and innovation. It is important to understand the demand for each of these services individually and to explore the interplay between these services so that policies and planning actions can be implemented to best promote these services and alleviate any negative impacts. The research consisted of a comprehensive exploration of all shared modes (subway, taxi, TNC, bikeshare) in a multivariate framework over multiple years, including incorporating the long-term patterns and incorporating the effects of short term shocks. This exploration was done using dynamic compositional models for time series, the data being aggregated across all of NY City, and would enable informed planning and operations decisions that positively impact all offerings within the shared mode landscape. Details are presented in the manuscript Toman et al. (2019). The next step in the research consisted of exploring the presence of spatial associations at taxi zone level in NYC, for which a comprehensive statistical analysis is scant in the ridesourcing literature. Together, the setup and outcomes of the research will be informative for building and estimating fine-scale spatio-temporal models for characterizing the existing system, as well as for short-term and long-term demand forecasting purposes.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Edition: Final Report
  • Features: Appendices; Figures; Maps; References; Tables;
  • Pagination: 74p

Subject/Index Terms

Filing Info

  • Accession Number: 01724974
  • Record Type: Publication
  • Contract Numbers: 2019 Project 10
  • Files: UTC, NTL, TRIS, ATRI, USDOT
  • Created Date: Dec 12 2019 9:23AM