Exploring the Spatiotemporal and Behavioral Patterns of Utilitarian E-Bike Users in North America
Mojdeh Azad, John MacArthur, Christopher R CherryConference room
- Travel behavior patterns are important to understand net safety effects of e-bikes
- This study describes a longitudinal machine-learning enabled travel behavior study using a smartphone app that runs passively
- Data collected can assess travel behavior shifts, including mode substitution and trip purpose
- Data collected also assesses rider behavior, including speed traveled and route choice
- Emerging data collection methods can provide insights into user-oriented travel behavior that can assess e-bike rider impacts on traffic safety.
Wed 23:45 - 00:00