Discovering Potential Market for the Integration of Public Transportation and Emerging Shared-Mobility Services

This project conducted a comprehensive data analysis to answer two questions: (1) Who are the potential demands with high probability to use intermodal services provided by hybrid systems? (Task 1); (2) Where and when are the supply gaps to coordinate public transit services with shared-mobility service? (Task 2). To accomplish these research objectives, the project team draws from several data sources and approaches to conduct research from both the demand and supply perspectives. Specifically, Task 1 of this study investigated the influential factors that affect transit users’ choices of access and egress modes, including transportation network company (TNC) or taxi, drive alone (PNR), carpool (KNR, carpool or shuttle), and micromobility modes (bike-sharing, scooters, etc.), using a transit onboard survey conducted in Spring 2017 for the Orlando metropolitan area. Task 2 of this study used the transit trip data and ridesharing trajectory data in the second ring region of Chengdu, China to develop innovative data analysis and machine learning approaches to explore the transit service gaps in both flexibility and coverage. Together, these efforts provide a snapshot to better understand the potential service gaps and demands (such as first/last mile gaps and demand) for promoting hybrid mobility that integrates shared mobility and public transit. Overall, the outputs of this project will increase the use of sustainable transportation modes, which may reduce urban congestion, emission, and energy consumptions. Thus, the success of this project will help establish an eco-friendly transportation system.

• Dataset URL:
  https://doi.org/10.5281/zenodo.4035109
• Record URL:
  https://www.eng.ufl.edu/stride/wp-content/uploads/sites/153/2021/05/STRIDE-Project-F2-Final-Report-1-1.pdf
• 
• Summary URL:
  https://stride.ce.ufl.edu/project-f2/
• Summary URL:
  https://stride.ce.ufl.edu/wp-content/uploads/2021/05/Project-F2-Brief-Du-Final-Accessible.pdf
• Dataset URL:
  https://rosap.ntl.bts.gov/view/dot/58430
• Record URL:
  https://rosap.ntl.bts.gov/view/dot/57440
• Supplemental Notes:
  • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program. Supporting datasets available at: https://doi.org/10.5281/zenodo.4035109; https://rosap.ntl.bts.gov/view/dot/58430
• Corporate Authors:

  University of Florida, Gainesville

  Department of Civil and Coastal Engineering
  Gainesville, FL  United States  32611-6580

  Florida International University, Miami

  Department of Civil and Environmental Engineering
  Miami, FL  United States  33174

  Southeastern Transportation Research, Innovation, Development and Education Center (STRIDE)

  University of Florida
  365 Weil Hall
  Gainesville, FL  United States  32611

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Authors:
  • Du, Lili
  • 0000-0003-1740-1209
  • Jin, Xia
  • 0000-0002-8660-3528
  • Qiu, Jiahua
  • 0000-0002-3025-5878
  • Wang, Peng
  • 0000-0002-0269-7310
  • Azimi, Ghazaleh
  • 0000-0001-5646-6908
  • Rahimi, Alireza
  • 0000-0002-4555-7799
  • Lee, Ming
  • 0000-0001-6856-6367
  • Davis, William J
• Publication Date: 2021-5-7

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Final Report
• Features: Appendices; Figures; Maps; References; Tables;
• Pagination: 83p

Subject/Index Terms

• TRT Terms: Access; Data analysis; Machine learning; Public transit; Ridesharing; Ridesourcing; Shared mobility; Surveys
• Geographic Terms: Chengdu (China); Orlando (Florida)
• Subject Areas: Passenger Transportation; Planning and Forecasting; Public Transportation;

Filing Info

• Accession Number: 01771626
• Record Type: Publication
• Report/Paper Numbers: Project F2
• Contract Numbers: 69A355174710
• Files: UTC, NTL, TRIS, ATRI, USDOT
• Created Date: May 21 2021 10:54AM