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Abstract for presentation (Poster or Podium) with a Paper in the Conference Proceedings

Social Equity, Justice, and Welfare

Transportation Equity Analysis of Bikeshare Use Among Different Sociodemographic Groups

Sunday, June 16, 2024
3:00 PM - 4:15 PM ET
Room: Galleria
Abstract:

Different data related to bikeshare program usage have been collected by many transportation organizations. However, few studies have investigated various aspects of bikeshare among different races and focused on the comparison of sociodemographic variables that affect these trips. The main goal of this study is to investigate the bikeshare program usage, number of bike trips, and number of bike trips for exercise among different races. Bikeshare program participation among different sociodemographic groups is also investigated. To conduct the statistical analysis, we use the latest NHTS data, which is the largest and the most valid national transportation-related data in the U.S. The novelties of this study are exploring the newly bike-related variables in the  NHTS, and focus on the White, African-American, and Asian population by weighting the NHTS data. The analyses show that, African-American population have the lowest average number of bike trips compare to White and Asian population. Among African-American and Asian population, females had the highest 30-day average bikeshare program compared to males. Moreover, among African-American and Asian population, individuals with household income more than $100,000 had the highest 30-day average bikeshare program participation compared to other income groups. This study’s  primary contribution to the state of the art is to provide a deeper insight into bike-travel behavior among different races in the U.S., which can assist the authorities and transportation planners in prioritizing investment in bike infrastructure equitably.

Learning Objectives:

Attendees can expect to learn the following from this session:
  • Gain an understanding of the significance of bicycle safety as a critical issue in transportation and the need to analyze factors influencing the severity of bicycle-involved crashes.
  • Learn about the development of a Bayesian network model and its application in analyzing Health and Safety Information System (HSIS) data to provide valuable insights into rider safety.
  • Explore two scenarios that highlight the impact of different variables, such as lighting, reflective clothing, road design, and infrastructure on the probability of fatal bicycle crashes.