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Comparing E-bike and Conventional Bicycle Use Patterns in a Public Bike Share System: A Case Study of Richmond, VA

Yifan Yang, Elliott Sloate, Nashid Khadem, Celeste Chavis, Vanessa Frias Martinez

TL;DR

This study compares pedelec and conventional bicycle usage within Richmond's RVA Bike Share to quantify differences in trip length, duration, speed, elevation, origin-destination patterns, and roadway use. By integrating GPS-based map-matching, OpenStreetMap road-type labeling, and elevation data, the authors compute key travel-behavior metrics and apply Welch's $t$-tests to identify statistically significant differences. Pedelecs are associated with longer distances, shorter trip times, and higher speeds, along with lower uphill elevation changes, and they show greater use on major roads and cycleways; origin-destination patterns indicate exercise- and recreation-oriented trips dominate for both bike types. These findings suggest e-bikes extend mobility within docked bike-share systems, particularly in flat urban terrain, with implications for infrastructure planning and mobility policy in similar cities.

Abstract

The results show that pedelecs are generally associated with longer trip distances, shorter trip times, higher speeds, and lower rates of uphill elevation change. The origin-destination analysis considering the business, mixed use, residential, and other uses shows extremely similar trends, with a large number of trips staying within either business or residential locations or mixed use. The roadway use analysis shows that pedelecs are used farther outside of the city than bikes.

Comparing E-bike and Conventional Bicycle Use Patterns in a Public Bike Share System: A Case Study of Richmond, VA

TL;DR

This study compares pedelec and conventional bicycle usage within Richmond's RVA Bike Share to quantify differences in trip length, duration, speed, elevation, origin-destination patterns, and roadway use. By integrating GPS-based map-matching, OpenStreetMap road-type labeling, and elevation data, the authors compute key travel-behavior metrics and apply Welch's -tests to identify statistically significant differences. Pedelecs are associated with longer distances, shorter trip times, and higher speeds, along with lower uphill elevation changes, and they show greater use on major roads and cycleways; origin-destination patterns indicate exercise- and recreation-oriented trips dominate for both bike types. These findings suggest e-bikes extend mobility within docked bike-share systems, particularly in flat urban terrain, with implications for infrastructure planning and mobility policy in similar cities.

Abstract

The results show that pedelecs are generally associated with longer trip distances, shorter trip times, higher speeds, and lower rates of uphill elevation change. The origin-destination analysis considering the business, mixed use, residential, and other uses shows extremely similar trends, with a large number of trips staying within either business or residential locations or mixed use. The roadway use analysis shows that pedelecs are used farther outside of the city than bikes.
Paper Structure (17 sections, 8 figures, 9 tables)

This paper contains 17 sections, 8 figures, 9 tables.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Bike Share Programs
  4. Introduction of E-bikes
  5. Impacts on Travel Behavior
  6. Methods and Material
  7. Study Site: RVA Bike Share
  8. Trip Data
  9. Data Summary
  10. Membership Types
  11. Methodology
  12. Results
  13. Spatio-Temporal Analysis
  14. Trip Analysis by Types of Origin and Destination Locations Visited
  15. Roadway Use
  16. ...and 2 more sections

Figures (8)

  • Figure 1: RVA Bike Share system. The basemap was generated with Esri ArcGIS and the following sources: Esri, HERE, Garmin, GeoTechnologies, Inc., USGS and EPA.
  • Figure 2: Total number of trips to and from station (min = 3 trips, max = 1208). The basemap was generated with Bing Maps and with TomTom as its basemap data source.
  • Figure 3: Bike and trip characteristics over the study period (first week of each month) segmented by bike type.
  • Figure 4: Total number of weekday and weekend trips per hour of the day.
  • Figure 5: Trip summary by trip type (touring vs. O-D).
  • ...and 3 more figures