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ICTD 2025

Data Sensing & Analytics

POSTER RECEPTION (View list of posters here)

Impact of Employer Transportation Programs and Social Factor on Commute Mode Choices in Denver

Tuesday, June 10, 2025
4:45 PM – 6:00 PM MT
Abstract: Using survey data from over 3,000 downtown Denver employees, this study explores the impact of employer transportation benefits programs on commute mode choices among downtown Denver employees. We also consider the impacts with respect to social factors such as age groups, gender, and income levels. Descriptive and data mining analyses were conducted, employing techniques like frequency distribution, Spearman correlation, and logistic regression to quantify the relationships between transportation programs, social factors, and commute mode choices. The findings of the study indicate that employees in Downtown Denver with access to free or subsidized parking are 195% more likely to drive to work. Similarly, employees with intermediate stops were 363% more likely to drive to the office. The results also suggest that employees with access to secure bike parking are 789% more likely to bike to the office. Furthermore, employees with free or subsidized public transit passes are 106% and 56% more likely to use transit to work, respectively. Finally, employees with access to remote work policy are 188% more likely to telecommute. In term of social factors, male respondents were found to be 217% more likely to bike, 31% more likely to use transit, and 47% less likely to telecommute. Employees in younger age groups are more likely to bike and less likely to drive and use transit. Finally, employees in high income level are more likely drive and telecommute, however, less likely to use transit. These findings are essential in designing effective transportation programs for employers.

Learning Objectives:

Attendees can expect to learn the following from this session:
  • List and describe the various employer transportation benefits programs and their impacts on commute mode choices among downtown Denver employees.
  • Analyze the influence of social factors, including age, gender, and income level, on commute mode preferences and the likelihood of different commuting behaviors.
  • Demonstrate how to apply descriptive and data mining techniques, such as frequency distribution, Spearman correlation, and logistic regression, to assess relationships between transportation programs, social factors, and commuting choices.