Improving the Service Quality of Bike Sharing Systems via the Analysis of Real-Time User Data

Bike sharing is an innovative urban transportation alternative that provides citizens fast access to bicycles for inner-city commuting. These types of systems are known for bringing significant benefits to its users in the form of a healthy and efficient transportation option, and to cities as an effective way of reducing CO₂ emissions and traffic. The principal objective of this project is to develop a predictive statistical framework to efficiently estimate the ability of a bike-sharing system to serve incoming bike requests. By mining user data collected from the system's smartphone app, an operator can utilize the proposed models to predict the likelihood that any potential user who desires to use the system decides to do so under the given the system's conditions the user encounters.

• Record URL:
  https://www.buffalo.edu/transinfo/Research/transportation-operations/BikeSharingSustems.html

Language

• English

Project

• Status: Active
• Funding: $75000
• Contract Numbers:

  DTRT13-G-UTC48

• Sponsor Organizations:

  State University of New York, Buffalo

  212 Ketter Hall
  Buffalo, NY  United States  14260

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Project Managers:

  Walteros, Jose

• Performing Organizations:

  Transportation Informatics University Transportation Center

  State University of New York, Buffalo
  Buffalo, NY  United States  14260
• Principal Investigators:

  Walteros, Jose

• Start Date: 20170901
• Expected Completion Date: 20180831
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Bicycle commuting; Bicycles; Data mining; Mathematical prediction; Mobile applications; Quality of service; Real time information; Statistical analysis; Urban transportation; Vehicle sharing
• Subject Areas: Data and Information Technology; Pedestrians and Bicyclists; Planning and Forecasting; Public Transportation;

Filing Info

• Accession Number: 01652077
• Record Type: Research project
• Source Agency: Transportation Informatics University Transportation Center
• Contract Numbers: DTRT13-G-UTC48
• Files: UTC, RiP
• Created Date: Nov 27 2017 2:28PM