Source-linked AI summary
A Comparative Analysis of E-Scooter and E-Bike Usage Patterns: Findings from the City of Austin, TX
Mohammed Hamad Almannaa, Huthaifa I. Ashqar, Mohammed Elhenawy, Mahmoud Masoud, Andry Rakotonirainy, Hesham Rakha
TL;DR
Cities face growing transportation demand and micromobility safety concerns, while evidence comparing e-scooter and dockless e-bike user patterns remains limited. The paper analyzes Austin trip speeds by weekday and time of day, finding higher average speeds for e-bikes and similar weekly but different hourly patterns.
Problem
The paper addresses limited fair comparison of e-scooter and bike-share user behavior amid transportation demand and micromobility safety concerns.
Source: INTRODUCTION · pp. 3–4, RELATED WORK · p. 7Method
The authors analyze Austin e-scooter and dockless e-bike trip speeds by day of week and time of day using CA-based clustering and consensus clustering.
Source: INTRODUCTION · p. 5, METHODOLOGY · p. 9Results
E-bike average trip speeds are 3.01–3.44 m/s, higher than e-scooter speeds of 2.19–2.78 m/s; weekly patterns are similar, but hourly patterns differ.
Source: DISCUSSION AND CONCLUSIONS · p. 15Takeaways & Limitations
The Austin findings offer policy-makers, operators, and researchers evidence about shared-road users’ patterns relevant to safety, transportation, health, and recreation.
Source: DISCUSSION AND CONCLUSIONS · p. 16The analysis is limited to Austin because this was the only dataset of this kind available to the authors’ knowledge.
Source: DISCUSSION AND CONCLUSIONS · p. 15
Abstract
from arXiv · showhide
E-scooter-sharing and e-bike-sharing systems are accommodating and easing the increased traffic in dense cities and are expanding considerably. However, these new micro-mobility transportation modes raise numerous operational and safety concerns. This study analyzes e-scooter and dockless e-bike sharing system user behavior. We investigate how average trip speed change depending on the day of the week and the time of the day. We used a dataset from the city of Austin, TX from December 2018 to May 2019. Our results generally show that the trip average speed for e-bikes ranges between 3.01 and 3.44 m/s, which is higher than that for e-scooters (2.19 to 2.78 m/s). Results also show a similar usage pattern for the average speed of e-bikes and e-scooters throughout the days of the week and a different usage pattern for the average speed of e-bikes and e-scooters over the hours of the day. We found that users tend to ride e-bikes and e-scooters with a slower average speed for recreational purposes compared to when they are ridden for commuting purposes. This study is a building block in this field, which serves as a first of its kind, and sheds the light of significant new understanding of this emerging class of shared-road users.
INTRODUCTION
Cities face growing transportation demand and congestion, while micromobility is expanding amid safety concerns. This study examines e-scooter and dockless e-bike users’ average trip speeds across days and times in Austin.
INTRODUCTION
- Traffic growth challenges cities’ ability to provide high-quality transportation, and short-distance trips are often made by personal car or rideshare.The paper frames policy, infrastructure, transport modes, and commuters’ choices as areas that may need change.Source: INTRODUCTION · p. 3
- Bike-sharing can serve recreational and non-recreational trips, while reducing private-car trips is associated with lower carbon energy use and emissions.Prior work cited in the introduction also reports potential transportation, health, and environmental benefits.Source: INTRODUCTION · pp. 3–4
- Dock-based bike-sharing supports varied trip purposes, but station imbalance can prevent users from picking up or returning bicycles.Dockless systems instead distribute bikes along sidewalks and allow users to leave them within a defined zone.Source: INTRODUCTION · pp. 3–4
- E-scooter sharing grew rapidly and attracted policy-maker and investor attention to safety, management, and operations.The paper reports more than two million users and entry into over 80 city schemes since 2017.Source: INTRODUCTION · p. 4
- Limited regulation and awareness of risks raise safety concerns about micromobility, with U.S. facial and head injuries from these devices tripling over ten years.The cited national database reports 2,325 incidents in 2008 and 6,957 in 2018; 66% of those treated were not wearing helmets.Source: INTRODUCTION · pp. 4–5
- The study compares average trip speed by day of week and time of day for e-scooters and dockless e-bikes using Austin trip data.The dataset was collected in Austin in 2018–2019.Source: INTRODUCTION · p. 5
RELATED WORK
Prior work examined bike-share demographics and trip purposes, and e-scooter roles in transport and safety. The authors identify limited fair comparisons and no temporal analysis of users’ speed across these modes.
RELATED WORK
- Prior bike-share research examined user demographics and found differences between reported gender balance and usage rates, alongside studies of long- and short-term members.Work-trip patterns and trip purposes varied by membership duration, while other research identified factors such as gender, age, location, ethnicity, and weather.Source: RELATED WORK · pp. 5–6
- Prior bike-share temporal research had not analyzed cyclists’ speed with respect to a time event.The paper positions its analysis of speed over time as addressing this gap.Source: RELATED WORK · p. 6
- Earlier e-scooter studies investigated vehicle replacement, public-transit support, and usage segments, while another study considered safety risks in e-bicycle and e-scooter sharing.The authors say these efforts did not analyze both systems’ user behavior with respect to operational temporal patterns.Source: RELATED WORK · pp. 6–7
- Earlier comparisons of e-scooter and dock-based bike-share usage were limited by the systems’ different parking arrangements, and temporal user-behavior analysis remained unaddressed.The authors note that one comparison found bikes used for work commuting while e-scooters were not, but dock-based systems require returns to stations.Source: RELATED WORK · p. 7
DATA SET
The analysis uses Austin’s public dataset of e-bike and e-scooter trips collected from December 2018 to May 2019, filtering out trips outside specified distance and duration limits.
DATA SET
- The dataset contains about 6 million e-bike and e-scooter trips collected in Austin from December 3, 2018, to May 20, 2019.Each trip is represented by a row.Source: DATA SET · p. 7
- Each trip record includes 18 features, including vehicle type, duration, distance, start and end times, and geographic identifiers.The listed fields also include hour, day of week, month, year, council district, and census tract.Source: DATA SET · p. 8
- The analysis retained trips at least 0.1 miles and less than 500 miles long, with durations under 24 hours.The data are publicly available from the City of Austin.Source: DATA SET · pp. 7–8
METHODOLOGY
The methodology uses CA-based clustering to examine average trip-speed patterns by weekday and time of day, with consensus clustering used to select the number of clusters.
METHODOLOGY
- The CA-based clustering algorithm matches clusters and data points to minimize intra-cluster distances while maximizing cluster purity.The method models the two sets as players with ranked preferences and continues matching until convergence.Source: METHODOLOGY · pp. 8–9
- Consensus clustering estimates the number of clusters by measuring agreement across resampled clustering runs, aiming for more robust partitions than a single run.The study applies it to determine cluster counts for average trip speed across days and times.Source: METHODOLOGY · p. 9
RESULTS
The analysis compares e-scooter and dockless e-bike usage patterns by day of week and time of day using consensus clustering, CA-based clustering, and Wilcoxon rank-sum tests.
RESULTS
- The study uses consensus clustering to select cluster counts, CA-based clustering to group usage patterns, and Wilcoxon rank-sum tests to compare the resulting distributions.The tests assess whether the two clusters have equal medians.Source: RESULTS · p. 10
- The analysis applies these steps separately to e-scooter and dockless e-bike patterns by day of week and time of day.The dataset covers Austin, Texas, from December 3, 2018 to May 20, 2019.Source: RESULTS · p. 10
Day-of-week
Consensus clustering selected two clusters for both vehicle types in the day-of-week analysis; the supplied figures and table describe the analysis outputs.
Day-of-week
- k=2 is the most stable model order for both e-bikes and e-scooters in the day-of-week analysis.The flattest consensus-index curve identifies the most stable order.Source: Day-of-week · p. 10, Figure 3 · p. 11
- Figure 4 presents the day-of-week clusters separately for e-bikes and e-scooters.The caption identifies panels (a) and (b) as e-bikes and e-scooters, respectively.Source: Figure 4 · p. 12
- Table 2 reports the mean and standard deviation for each e-bike and e-scooter cluster.Source: Table 2
Time-of-day
Consensus clustering selected two clusters for both vehicle types in the time-of-day analysis, whose average-speed patterns differ across hours.
Time-of-day
- k=2 is the most stable model order for both e-bikes and e-scooters in the time-of-day analysis.The flattest consensus-index curve identifies the most stable order.Source: Time-of-day · p. 12, Figure 5 · p. 13
- The time-of-day average-speed patterns differ, with e-bike and e-scooter clusters spanning distinct hour ranges.E-bike clusters average 3.32 m/s from midnight to 11 AM and 3.09 m/s for the rest of the day except 6–7 PM; e-scooter clusters average 2.78 m/s from 3 AM to noon and 2.19 m/s for the rest of the day.Source: Time-of-day · p. 13, Figure 6 · p. 14, Table 3 · p. 14
- Figure 6 presents the time-of-day clusters for e-bikes and e-scooters based on average trip speed.Source: Figure 6 · p. 14
- Table 3 reports the mean and standard deviation for each e-bike and e-scooter cluster.Source: Table 3 · p. 14
DISCUSSION AND CONCLUSIONS
The study compares shared e-bike and e-scooter usage patterns in the United States, finding higher average trip speeds for e-bikes and different temporal patterns by hour. Its findings are limited by the available Austin dataset, and patterns may shift across locations and seasons.
DISCUSSION AND CONCLUSIONS
- The study addresses safety concerns around emerging shared micro-mobility modes by examining differences in e-bike and e-scooter usage patterns.Source: DISCUSSION AND CONCLUSIONS · p. 15
- E-bike average trip speeds range from 3.01–3.44 m/s, compared with 2.19–2.78 m/s for e-scooters.The authors also report similar weekday-versus-weekend patterns but different hourly patterns, with slower speeds associated with recreational than commuting periods.Source: DISCUSSION AND CONCLUSIONS · p. 15
- The analysis uses Austin data because it was the only dataset of this kind available to the authors.The authors suggest that data from other U.S. locations could strengthen the study.Source: DISCUSSION AND CONCLUSIONS · p. 15
- The authors expect usage patterns might shift with data covering different locations and seasons.They identify broader geographic and seasonal coverage as a future direction.Source: DISCUSSION AND CONCLUSIONS · pp. 15–16