The Future of E-Bikes on Public Lands:

A Human Factors Field Study at

Minute Man National Historical Park

Final Report 

August 2023 

Source: USDOT Volpe Center
E-Bikes in Public Lands: A Human Factors Field Study – Final Report

August 2023

2 

Notice

This document is disseminated under the sponsorship of the U.S. Department of Transportation 

(USDOT) in the interest of information exchange. The U.S. Government assumes no liability for the 

use of the information contained in this document. 

The U.S. Government does not endorse products or manufacturers. Trademarks or manufacturers’ 

names appear in this report only because they are considered essential to the objective of the 

document. They are included for informational purposes only and are not intended to reflect a 

preference, approval, or endorsement of any one product or entity. 

Quality Assurance Statement

The Federal Highway Administration (FHWA) provides high-quality information to serve 

Government, industry, and the public in a manner that promotes public understanding. Standards and 

policies are used to ensure and maximize the quality, objectivity, utility, and integrity of its 

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ensure continuous quality improvement. 
E-Bikes in Public Lands: A Human Factors Field Study – Final Report

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TECHNICAL REPORT DOCUMENTATION PAGE

1. Report No.

FHWA-FLH-23-005

2. Government Accession No. 3. Recipient’s Catalog No.

4. Title and Subtitle

The Future of E-Bikes on Public Lands:

How to Effectively Manage a Growing Trend

5. Report Date

November 2022

6. Performing Organization Code:

DOT-VNTSC (VOLPE)

7. Author(s)

Jonah Chiarenza ORCID: 0000-0002-0799-5560

Ian Berg, Clark Calabrese, Dr. Daniel Flynn, Kirby Ledvina,

Ben Rasmussen, Robert “RJ” Rittmuller Michael Rodriguez,

and Jared Young

8. Performing Organization Report No.

NA

9. Performing Organization Name and Address

U.S. DOT Volpe National Transportation Systems Center

55 Broadway, Kendall Square

Cambridge, MA 02142

10. Work Unit No. V0323

11. Contract or Grant No.

Intra-Agency Agreement 69056720N300012,

HW72B120

12. Sponsoring Agency Name and Address

U.S Department of Transportation

Federal Highway Administration

1200 New Jersey Ave, SE

Washington, DC 20590

13. Type of Report and Period

Final Report, March 2022 – August 2023

14. Sponsoring Agency Code

15. Supplementary Notes

Project management provided by Western Federal Lands Highway Division

Seth English-Young and Roxanne Bash

16. Abstract

Electric-assist bicycles (e-bikes) are growing in popularity, and their use has increased on public lands managed by

federal, state, and local agencies. Evaluation of e-bike use on public lands is hindered by a dearth of scientific

research. This paper presents the results of a human factors field study that evaluated participant speed and behavior

on an unpaved, multiuse trail to answer research questions about trail user safety and social interaction. The study

concludes that e-bike riders travel approximately one to two mph faster than conventional bike riders on average, but

that individual speeds among conventional and e-bike riders are largely distributed and overwhelmingly overlap. The

study also found conventional and e-bike riders generally reduce speed in areas of potential conflict and exhibit

similar precautionary behaviors when passing other trail users and crossing trail junctions and vehicle paths.

17. Key Words

e-bike, electric bicycle, bicycle, public lands, trail,

safety, behavior, human factors

18. Distribution Statement

No restrictions. This document is available to the

public through the National Technical Information

Service, Springfield, VA 22161.

http://www.ntis.gov

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this

page) Unclassified

21. No. of Pages

23

22. Price

N/A

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized.
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Contents

INTRODUCTION ........................................................................................................................................ 5

FIELD STUDY DESIGN ............................................................................................................................. 5

Study Setting ............................................................................................................................................. 5

Participant Recruitment ............................................................................................................................ 7

Instructions to Participants ........................................................................................................................ 8

GEOLOCATION ANALYSIS ..................................................................................................................... 9

Data Preparation...................................................................................................................................... 10

Designated Segment Definition .............................................................................................................. 10

Methodology ........................................................................................................................................... 12

Results ..................................................................................................................................................... 12

PASSING EVENT DETECTION .............................................................................................................. 15

Methodology ........................................................................................................................................... 16

Results ..................................................................................................................................................... 17

REGRESSION ANALYSIS ....................................................................................................................... 18

Methodology ........................................................................................................................................... 18

Results ..................................................................................................................................................... 19

VIDEO REVIEW........................................................................................................................................ 20

Methodology ........................................................................................................................................... 20

Results ..................................................................................................................................................... 21

OPEN DATA .............................................................................................................................................. 22

CONCLUSION AND LESSONS LEARNED ........................................................................................... 22

REFERENCES ........................................................................................................................................... 23

For questions about this report, please contact: 

Seth English-Young 

Federal Highway Administration – Western Federal Lands Highway Division 

seth.english-young@dot.gov 

Jonah Chiarenza 

U.S. DOT Volpe Center – Transportation Planning Division 

jonah.chiarenza@dot.gov 

Project website: https://highways.dot.gov/federal-lands/programs-planning/studies/e-bikes
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INTRODUCTION

Electric-assist bicycles (e-bikes) are growing in popularity, and their use has increased on public lands 

managed by federal, state, and local agencies. Agencies seek to manage different uses on public lands by

evaluating their potential for positive benefit or negative impact. There is a growing body of literature on 

e-bike trends, impacts, and opportunities, particularly in the urban context and considering their use as a

transportation mode (1). However, agency evaluations of e-bike use are hindered by a dearth of scientific

research into the opportunities and challenges focused on e-bike use on public lands.

In 2020, the Federal Highway Administration (FHWA) Innovation Research Council (IRC) sponsored

research into the use of e-bikes in public lands to help address this need. Western Federal Lands Highway 

Division (WFL) provided funding to the U.S. DOT Volpe Center (Volpe) and oversaw the research 

project. FHWA and Volpe published a report documenting the state of research in November 2022 (2). 

The report is organized into four categories of consideration: Ecological/Cultural/Historical, Social, 

Safety, and Management Processes. The team collaborated with land managers and stakeholders to 

develop 60 research questions that aim to capture the universe of information needed to make informed

decisions about e-bike policies on public lands. The literature includes partial answers to many of these 

questions, but numerous research gaps remain. 

To begin addressing gaps in the literature, Volpe conducted a human factors field study to develop a 

novel, replicable study protocol and apply it to a local public land to collect primary data that addresses

two key research questions: 

1. Safety Considerations: Does rider behavior (average speed, passing other trail users, yielding to other

trail users, etc.) in a public lands setting differ between e-bike and conventional bike riders?

2. Social Considerations: Does average speed, passing behavior around other trail users, yielding

behavior related to other trail users, etc., create problems or provide benefits to others traveling by

different modes?

This report documents the protocol, analysis methods, results interpretation, and conclusions of this 

human factors study. 

FIELD STUDY DESIGN 

Study Setting

Volpe conducted the field study on the Battle Road Trail between Meriam’s Corner and Fiske Hill Loop

at Minute Man National Historical Park in Concord, MA. Figure 1 shows a photograph of the setting of 

the trail, and the route is mapped in Figure 2. This segment of the Battle Road Trail is an unpaved, 

multiuse path that caters to park visitors of all ages for walking, running, and bicycling. The trail is not an 

accessible route of travel for people with limited mobility and recreational horseback riding is prohibited 

throughout the park. The National Park Service allows park superintendents to manage the use of bicycles 

and e-bikes (4). Park rules at Minute Man permit bicycles, including pedal-assist e-bikes, on all park 

roads and trails (5). 
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Figure 1: Battle Road Trail at Minute Man National Historical Park (3). 

This segment of the Battle Road Trail exhibits varied conditions, including a number of winding turns, 

narrow and wide sections, elevation changes, and a mix of crushed stone, dirt, and gravel surfaces. The 

total length of the trail segment that participants rode is approximately 4 miles in each direction, has 380-

feet net elevation gain, and has a maximum slope of six percent. Portions of the Battle Road Trail cross 

roadways and driveways and intersect with other trail segments; these locations required participants to

negotiate interactions with other trail and road users, including people driving vehicles. These conditions 

allowed the study team to investigate context-specific behavior among participants. 

Figure 2: Battle Road Trail field study route. 
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Participant Recruitment

The study team recruited participants between April and September 2022, with participants completing 

their rides on a rolling basis. The study team placed posters in bike shops throughout the greater Boston 

area and distributed a weblink via bicycling-related social media and newsletters. 

The study team vetted adult cyclists between 18 and 65 years of age, aiming to recruit roughly equal sized 

groups of conventional bike and e-bike riders. Participants needed to use their own bicycles and be 

capable of riding on an unpaved, multiuse trail with grade changes, obstacles, and other trail users. The 

study team implemented these participant requirements to attract riders that would exhibit behavior 

typical of local trail users who are comfortable with riding on this kind of facility. The study team 

avoided novice riders and riders without their own bicycle, as they would likely not represent the typical 

behavior of experienced riders – the type of riders who generally frequent this trail.

This study recruited a total of 37 riders, including 19 conventional bike riders and 18 e-bike riders. See 

Table 1 for a matrix of all participants. Some of the 37 riders were ultimately excluded from the analysis. 

Three of the recruited participants did not complete the study within the allotted time. Eight other 

participants did not record suitable GPS tracks due to equipment issues and/or user error. In total, the 

study team included 26 participants’ data in the analysis. See Figure 3 for a summary of these 26 

participants by sex and bicycle type.

Human factors studies rely on relatively small numbers of voluntary participants. The study team 

acknowledges that the 26 participants whose data were analyzed for this study do not necessarily 

constitute a statistically representative sample of all bike riders. Indeed, different contexts would likely 

attract different “typical” trail users. The limited number of participants in this study, the requirements for 

participating, and recruitment methods likely influenced group composition and may have introduced 

some unknown biases. 

Figure 3: Distribution of 26 study participants with suitable data for analysis, grouped by selfreported sex and bicycle type.
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Table 1. Participant Characteristics. 

# Age 

(yrs)

Sex Bicycle Type Class

(E-Bikes

Only) 

Number 

of GPS 

Pings 

(Raw)

Number of 

GPS Pings 

(After 

Filtering) 

Notes

1 65 Male Conventional n/a 37,465 36,861

2 33 Female Conventional n/a 25,652 24,581

3 34 Male Conventional n/a 20,929 392

4 61 Male Electric Class 3 660 502

5 36 Female Electric Class 1 38,019 36,535

6 37 Female Conventional n/a 426 224

7 42 Male Conventional n/a 22,156 19,680

8 27 Female Conventional n/a 16,372 13,562

9 37 Male Conventional n/a 1,858 0 GPS recording error

10 56 Male Electric Class 3 71 0 GPS recording error

11 25 Male Electric Class 3 0 0 Study not completed

12 33 Female Electric Class 2 34,116 29,918

13 37 Male Electric Class 2 0 0 Study not completed

14 37 Male Electric Class 1 39,085 18,864

15 33 Male Electric Class 2 18,215 17,942

16 36 Male Electric Class 1 4,217 3,287

17 52 Female Conventional n/a 0 0 GPS recording error

18 56 Male Conventional n/a 0 0 GPS recording error

19 35 Female Electric Class 2 17,670 17,254

20 57 Male Conventional n/a 0 0 GPS recording error

21 38 Female Electric Class 1 16,716 15,898

22 35 Female Conventional n/a 0 0 GPS recording error

23 34 Male Conventional n/a 22,422 21,734

24 65 Male Conventional n/a 50,742 30,251

25 51 Female Electric Class 3 20,240 19,788

26 40 Male Conventional n/a 29,900 29,254

27 41 Female Conventional n/a 0 0 GPS recording error

28 27 Male Conventional n/a 4,564 3,521

29 35 Male Conventional n/a 0 0 Study not completed

30 47 Male Electric Class 2 0 0 GPS recording error

31 65 Male Electric Class 3 1,780 1,064

32 63 Female Conventional n/a 36,242 34,977

33 31 Male Conventional n/a 16,393 16,209

34 59 Female Electric Class 1 7,358 7,211

35 29 Male Electric Class 2 3,426 2,286

36 40 Male Electric Class 2 4,901 3,532

37 42 Female Electric Class 2 5,543 3,036

Instructions to Participants

Participants received a GoPro Max 360-degree video camera and bicycle handlebar mount, completed an 

informed consent form, and provided voluntary demographic, bicycle type, and riding history 

information. The study team trained participants to install and operate the camera themselves. Each 

participant was guided through a practice ride using the camera with video and audio recording enabled 

and was given nine days to complete the route on their own. The study team then reviewed the route with 

the participant and answered any clarifying questions. 
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The study team did not provide details about the study’s research questions to avoid influencing 

participant behavior during their ride. Participants were asked to ride the trail as they would under normal 

circumstances. 

Figure 4 shows two still views from the GoPro 360-degree video, illustrating the capabilities of the video 

to reveal a forward view that simulates the rider’s vantage point (top), or to reveal a wider view of the 

rider and their surrounding area (bottom). A presentation video (https://vimeo.com/741907069) illustrates 

how the 360-degree video affords observation of different aspects of the scene around the rider, which 

was important for the study team’s analysis.

Figure 4: Still images from a demonstration 360-degree video recording.

GEOLOCATION ANALYSIS 

The primary goal of this study was to identify whether speed and behavioral differences exist between 

conventional and e-bike riders on unpaved, multiuse trails of this type. Rather than evaluate average 

speed and behavior across the entire study corridor, Volpe designed this field study to conduct specific 

analyses within key contexts that repeated along the trail. This method sought to increase the precision of 

the findings and the value for land managers seeking to apply the findings to their own trail networks. The 

study team identified five different contexts – designated segment types – where increased rider speed and 

certain rider behaviors would increase the risk of conflicts or crashes, and/or be considered impolite or 

disrespectful to other trail users. 

These categories are:

• Blind Turns: Changes in trail direction where vegetation or other obstacles visually obscure oncoming trail users.

• Narrow: Areas where vegetation, trees, rocks, and other natural or manufactured obstacles like 

boardwalks constrict the trail width.

• Trail Hazards: Locations where vegetation, trees, rocks, and other natural or manufactured 

obstacles appear erratically within the trail, requiring trail users to navigate around or over them.

• Trail Junctions: Locations where the trail branches or intersects to connect with other trail spurs.

• Vehicle Conflict Points: Locations where the trail crosses designated vehicle paths, such as 

roadways and driveways.

Trail obstacles and narrowed segments reduce space for maneuvering, visual obstructions shorten reaction 

time, and merge/crossing locations are more likely than other segments to introduce conflicts. Focusing 

data collection and analysis on this subset of designated segments means the conclusions of this study 

should better reflect conditions where safety risks are higher and where riders would ideally exhibit more 

cautious and courteous behavior. If, for example, these data were to reveal a predominant trend among 

riders of one bicycle type that did not appear among riders of another bicycle type, then it would be more 

useful for land managers making decisions about trails with similar, higher-risk segments than data 
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showing only average speeds across the entire corridor. Inversely, if all riders exhibit similar speeds and 

behaviors, that suggests it is acceptable to manage both bike types similarly. 

Data Preparation 

The study team analyzed rider speed using a dataset of GoPro-recorded GPS pings. The full dataset 

included approximately ten GPS pings per second for each participant’s recording, for a total of 408,363 

pings across the 26 included participants. Each ping provided information on a rider’s location

coordinates and speed. The data from the 360-degree video files included sensor data encoded using the 

GoPro Metadata Format (6). The research team used open-source tools to extract the GPS track into data 

tables for analysis (7). 

Some of the extracted GPS pings were located outside of the study area. Figure 5 illustrates the variability 

in GPS data quality. Different colors represent distinct participants. While some of this can be explained 

by participants using the equipment beyond the Battle Road Trail corridor (black line), other erroneous 

data appeared due to poor-quality GPS reception. Erroneous pings often occurred during the first several 

minutes after the device was turned on, potentially due to the GPS device not yet having acquired a signal 

from enough satellites to accurately determine its position or time. 

The study team conducted initial data cleaning by filtering out pings with timestamps outside of the data 

collection period, often occurring at the beginning of recording, and removing pings located further than 

15 meters from the trail. The resulting dataset retained 82% of all pings.

Figure 5: Variability in GPS ping quality, with different colored points representing pings from 

distinct participants.

Designated Segment Definition

To extract pings within designated segment types, the study team created a set of spatial polygons 

representing each segment of interest and identified all GPS pings that fell within each polygon’s area. 

Polygons were created programmatically from an annotated list of video time stamps based on a reference 
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rider’s video and GPS pings. To develop this list, a study team member rode the route and documented 

when designated segments occurred. The final input dataset contained columns for the segment type, 

video file name, the video time stamp at which the individual entered the segment, and the time stamp at 

which the individual exited the segment; this data was cross referenced with the individual’s GPS pings to 

extract just the pings corresponding to a segment of interest along the trail (“target pings”). The study 

team produced spatial polygons from the target pings in the following steps, illustrated in Figure 6:

1. Snap the target pings to the nearest point on a line feature class representing the trail

2. Generate a buffer (radius of 40 feet) around the route line 

3. Generate a buffer (radius of 50 feet) around the snapped target pings 

4. Dissolve the boundaries between the buffers to create a single polygon 

5. Use the dissolved polygon to clip the intersecting portion of the buffered route line 

Figure 6: Five-step process for creating spatial polygons from target pings for designated segments. 

This process yields smooth polygons that cleanly illustrate segment locations and limit opportunities for 

pings to land outside of the polygon perimeter before the participant exits the segment. The script that 

uses polygons to extract pings detects each time a participant enters or exits a polygon. The buffer radii 

were selected through experimentation to yield smooth polygons large enough to extract GPS pings 

falling on either side of the route linear feature while minimizing overlap between polygons of the same 

segment type. Figure 7 presents a snapshot of some of the resulting polygons along a short segment of the 

trail. 

Figure 7: Snapshot of resulting polygons from the reference participant.
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Separately, the study team generated polygons for the primary uphill and downhill segments of the trail 

based on terrain data. The team obtained a digital elevation model with one-meter grid cell size from the 

Massachusetts Bureau of Geographic Information (8). Figure 8 shows the elevation along the route along 

with the six numbered areas designated “uphill” and “downhill” depending on participant direction of 

travel. 

The study team anticipated that uphill and downhill segment analysis would reveal potential errors in the 

speed analyses or source data, as e-bike riders were generally expected to travel uphill at a higher speed 

than conventional bike riders. As shown in the results section below, the data confirm this hypothesis and 

support the validity of the study methods and underlying data. 

Figure 8: Elevation along the field study route from west to east (Meriam’s Corner to Fiske Hill 

Loop). 

The study team recorded each time a participant entered or exited a polygon of interest. This created a list 

of “events” with each event representing a participant’s traversal of a polygon. More than one event could

exist per participant, per polygon (for example, a participant traversed it traveling eastbound, then turned 

around and traversed it again westbound). The direction of travel was also captured, as some polygons 

were only designated for a particular direction of travel (e.g., elevation segments that change from uphill 

to downhill or vice versa). The study team completed a spatial join between the polygons and GPS pings 

and used the pings’ speed attribute to calculate an average speed for each event. Participant characteristics

– such as bike type – were merged into the dataset to facilitate further analysis. 

Methodology 

The study team sought to identify any statistical significance in differences in average speeds between 

electric and conventional bike riders within designated segment types. For an initial analysis, the team 

performed a series of Student’s t-tests. Equal variance t-tests were conducted for individual segment types 

to compare average speeds of conventional and e-bike riders, with the null hypothesis that there was no 

difference in average speed between the two groups. E-bike and conventional bike rider average speeds 

were treated as independent samples of different sizes but with identical variances, based on initial data 

exploration. 

Results

Figure 9 presents a box and whisker plot of average e-bike and conventional bike speeds, by segment 

category. Each point in the plot represents the average speed for each “event” (a single traversal of a 
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polygon by a participant). For each category presented, the median of the events’ average speeds for ebikes is higher than the median for conventional bikes; however, there is substantial overlap and 

variability in speeds between e-bike and conventional bike riders in almost every category. The exception 

to this overlap is the “uphill” category. For uphill segments, e-bike riders exhibited consistently higher 

speeds than conventional bike riders with less overlap in speeds, as expected by the study team. 

Figure 9: Average speeds (points) and distribution for e-bikes and conventional bikes, by segment 

type. 

Table 2 presents the t-test results by segment category. The difference in mean speeds for e-bikes and 

conventional bikes is statistically significant at the 5% level for blind turns, narrow segments, trail 

hazards, trail junctions, and uphill segments.
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Table 2: Number of observations and average speed (miles per hour) by segment type and bike 

type.

Segment Type Conventional Bike Electric Bike P-Value

n mean n mean

Blind Turn 144 8.94 120 9.82 0.008*

Downhill 48 9.92 42 10.40 0.460

Narrow 138 7.31 119 8.34 0.007*

Trail Hazards 126 9.38 100 10.57 0.004*

Trail Junction 75 9.63 68 11.04 0.011*

Uphill 41 7.65 40 9.49 0.0003*

Vehicle Conflict Point 104 8.07 81 8.83 0.111 

* Statistically significant at 5% level

Conclusions from Figure 9 and Table 2 are twofold. First, for most segment types, the median e-bike rider 

speed is roughly one mile per hour faster than the median conventional bike rider speed. Second, the 

distributions of conventional and e-bike rider speeds overwhelmingly overlap with one another. Both 

electric and conventional bike rider speeds have a large distribution across each of the designated segment 

types, including many similar extremes at the high and low ends of the speed spectrum represented. 

Figure 10 further illustrates the second part of this conclusion. This histogram bins the participant speed 

pings that are located within the designated segments, divided by bike type. Each bin’s height represents 

the fraction of all pings within a bike type that appear in that bin. The chart also shows a distribution 

curve fit to the data. This curve illustrates the large distribution of speeds, and the high volume of overlap 

between speeds, for e-bike and conventional bike riders. Conventional bike rider speeds are more highly 

concentrated, with the modal bin around 8 mph, while e-bike riders speeds are slightly more distributed

with a mode around 9 mph.

Figure 10: Relative distribution of speeds for all participants across all designated segments.

The uphill and downhill segment data illustrate the least and greatest overlap in speed distribution, 

respectively. Figure 11 illustrates that uphill conventional bike rider speed density concentrating primarily 

between 6 mph and 8 mph, while uphill e-bike rider speed density concentrates primarily between 9 mph 

and 11 mph – representing the greatest distinction between rider speeds by bike type. Figure 12 illustrates 
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that downhill conventional and e-bike rider speed densities are far less distinct from one another. The 

distribution curves peak at similar speeds, around 9 mph, and the shallow curve shapes reflect more even

distribution of speeds across the spectrum. 

Figure 11: Relative distribution of speed events for all participants across uphill segments.

Figure 12: Relative distribution of speed events for all participants across downhill segments.

PASSING EVENT DETECTION

In addition to segments designated by geography and trail characteristics, the study team sought to 

investigate rider speed when passing other trail users. Mitigating conflicts between users such as near 

misses, startling encounters, and crashes are of particular importance to land managers and trail users 

alike. Crashes involving multiple trail users are undoubtedly the events people want most to avoid, but 

perception of trail users being impolite or disrespectful can be damaging to a visitor’s experience and are 

likely to be much more common than crashes (9). Passing speed is a useful proxy for investigating the 

likelihood of potential conflicts; in the urban context, lower cyclist speeds have been shown to correlate 

with lower conflict likelihood (10). 
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To isolate passing behavior in the video data, the study team leveraged open-source computer vision 

libraries. These libraries allowed the study team to use artificial intelligence to detect when passing events 

occurred in the dataset. This innovation saved hundreds of hours of manual analysis of the over 40 hours 

of video in the participant dataset. The computer models accurately detected, coded, and output passing 

event data in a matter of days with minimal human oversight. 

Methodology 

Figure 13. Data processing pipeline for the computer vision model.

The data processing pipeline used to detect these events is depicted in Figure 13. First, for the data 

gathered from the GoPro Max 360 cameras to be used in an AI-powered passing event detection tool, the 

360-degree frames from the footage captured on each participant’s ride must be converted into a single 

stitched image. Conducting this conversion allows for object detection across a single frame of footage at 

a time. The collection of libraries used to handle the 360-degree camera footage is FFmpeg (11). 

After the video frames are formatted, a computer vision model is used to detect bicycles and persons

present in the video. Pretrained models were selected because the objects being classified are wellrepresented in existing datasets, and the ability of existing models to detect these objects has already been 

robustly tested. The selected models required use of a computer equipped with a graphical processing unit 

(GPU). 

The object detection and depth estimation models utilized in this study analysis were Meta AI’s 

DEtection TRansformer (DETR) model, and Intel’s Dense Prediction Transformer model (12, 13). The 

object detection model was set to only detect persons and bicycles. As the program parses through video 

files, it detects when persons and bicycles are in the field of view of the camera and draws a bounding 

mask around each object in the video. Figure 14 shows a sample video frame with detected objects 

labeled. As can be seen in the image, the model detects the rider who is recording data. A filter is applied 

to remove the subject rider from analysis. When a “bicycle” object is identified, that event is labeled 

“cyclist” and when only a “person” object is identified, that event is labeled “pedestrian.” To reduce 

potential false positive detections, an “event” is only recorded when an object is detected for five 

consecutive frames (approximately 1/6 of a second) of video. 
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Figure 14. Sample video frame depicting object detection results. Note: this frame is from a video 

recorded outside of the study area by a member of the research team used for evaluation of the 

computer vision model. 

Results

In addition to the detected objects and their locations within each frame, the computer vision pipeline 

outputs a table of all detected passing events. Across all video footage collected by participants (which 

includes some footage where the corresponding GPS data was discarded by quality filters), 18,602 events 

were detected. A significant number of events (6,843 events) lasted less than one second. For events that 

lasted longer than one second, and where trail-aligned GPS data were available, the corresponding pings 

between the start and end of the passing event were tagged as “passing”, which was then used in the 

regression analysis below. 

Figure 15 shows passing events by length and rider type for participants passing pedestrians, and Figure 

16 shows the length of time and rider type for participants passing other bicycle riders. Overall, e-bike 

riders and conventional bike riders exhibit similar distribution of passing event duration, and similar 

shares of passing events longer than 2 seconds (26.3% for e-bike riders, compared to 27.1% for 

conventional bike riders). This suggests that e-bike riders and conventional bike riders similar maintain 

awareness of other trail users and adjust their speed in a similar manner when passing both pedestrians 

and other cyclists. 
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Figure 15: Length of passing events for participants passing pedestrians, by participant bicycle 

type.

Figure 16: Length of passing events for participants passing other bicyclists, by participant bicycle 

type.

REGRESSION ANALYSIS

Methodology 

The participant speed data collected for this study presented an opportunity to apply regression analysis, 

which provides a different view of participant speed and behavior to the geolocation analysis described 

above. The study team fit a linear mixed effects model to the dataset of individual GPS pings, controlling 

for participant characteristics while accounting for the non-independence of GPS pings collected from the 

same individual. Several models were considered, including simple linear models with and without 

variable interactions. Candidate predictors of average speed included bike type, participant age (mean 

centered), sex, and a set of binary variables indicating whether the GPS ping was located within a 

particular segment type (e.g., uphill, narrow, vehicle conflict point, etc.,) or occurred during a passing 

event. The study team calculated the Akaike Information Criterion (AIC) and compared all assessed 

models to identify the best fit model. 
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Results

Equation 1 describes the selected linear mixed effects model, which was fit on 407,299 observations 

across 25 participants. Participant 31 was excluded as none of their cleaned pings fell within a geographic 

segment of interest.

yyii = ββ0 + ββxxii + zzi + εi (1)

In this model, yyii is the speed observed at ping i. Then xxii is a vector of information for ping i, specifically 

the corresponding participant’s bike type (1 if electric), sex (1 if male), mean-centered age, and the ping’s 

location within segments of interest (1 if in segment type). Each segment type was included as a separate 

predictor. The vector zzii contains binary variables for individual participants to account for random 

effects, where an element in the vector is 1 if ping i belongs to that participant and 0 otherwise. This 

vector captures the variability of individual participants’ behavior and focuses the model interpretation on 

the fixed effects of interest. ββ is the vector of estimated parameters for each predictor in XXii. ββ0 is an 

intercept, and εi is an error term.

Table 3 presents the parameter estimates, confidence intervals and p-values. Controlling for other factors, 

the average speed for all riders is 7.91 mph. Variables included in the final model are listed under the 

“predictors” heading. Variables associated with a significant speed increase are e-bikes (+2.19 mph), male 

sex (+2.51 mph), downhill riding (+0.38 mph), and trail junctions (+0.29 mph). Variables associated with 

a significant speed decrease are uphill riding (−1.05 mph), narrow segments (−3.33 mph), vehicle conflict 

points (−2.14 mph), walk bike signs (−2.92 mph), and passing maneuvers (−1.13 mph). These results 

generally show the speed changes of riders are as expected under specific conditions. Sex, not bike type, 

predicts the largest increase in speed – an interesting result considering the policy implications of this 

research (no public land would consider prohibiting riders based on sex). The results show that riders of 

both bike types reduce speed when passing other trail users. While e-bike riders reduce speed slightly 

more in absolute terms than conventional cyclists, this may be because they are generally traveling faster 

in the first place. 
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Table 3: Regression Results

Estimates

(Speed, in mph)

Confidence Interval P-Value

Predictors

(Intercept) 7.91 6.52 – 9.29 <0.001

bike type [electric] 2.19 0.77 – 3.61 0.002

sex [male] 2.51 1.08 – 3.93 0.001

age centered -0.01 -0.07 – 0.05 0.694

Uphill -1.05 -1.08 – -1.03 <0.001

Downhill 0.38 0.36 – 0.41 <0.001

Blind Turn 0.02 -0.01 – 0.06 0.174

Narrow -3.33 -3.36 – -3.30 <0.001

Trail Hazards -0.33 -0.36 – -0.30 <0.001

Trail Junction 0.29 0.23 – 0.35 <0.001

Vehicle Conflict Point -2.14 -2.18 – -2.10 <0.001

Walk Bike Sign -2.92 -3.07 – -2.78 <0.001

Passing [1] -1.13 -1.17 – -1.10 <0.001

Bike type [electric] *

Passing [1] -0.38 -0.43 – -0.33 <0.001

Random Effects

σ2 9.65

τ00 ParticipantID 3.11

ICC 0.24

Num Participants 25

Num Observations 407,299

Marginal R2 / Conditional R2 0.242 / 0.427

VIDEO REVIEW

The study team relied on manual video review to analyze study participants’ glance behavior at potential 

conflict points with motorists or other trail users. Glance behavior refers to the eye movement and/or 

physical movement of a rider’s head and gaze toward the potential source of conflict; for example, 

glancing to the left and right before crossing a road or driveway, or glancing towards a trail spur that joins 

the Battle Road Trail. Riders that do not appear to glance toward potential sources of conflict may be 

more likely to come into conflict with other trail and road users. 

Methodology 

Glance analysis is common in human factors studies. While it is often conducted with sensitive 

equipment and/or in a controlled simulation lab setting to track participant eye movements, field studies 

require a modified approach using equipment that is capable of being used during different weather 

conditions. Therefore, a dedicated mobile eye tracker could not be used. The videos collected by the 

handlebar mounted GoPro Max camera required manual video review to observe rider behavior and 

determine glancing behavior. 
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This glance analysis task leveraged the output from the geolocation analysis to precisely identify 

segments of video for manual review. This greatly reduced the volume of footage and amount of time 

needed for the study team to perform reviews. The geolocation analysis logged the timestamps for each 

participant as they entered and exited the polygons labeled “trail junction” or “vehicle conflict point.” 

These timestamps were converted into start and stop times that the study team used to scrub through 

frames to the segments of interest. 

Because the GoPro cameras produced 360-degree video, the reviewer was able to set the view to show a 

scene revealing the participant’s head and eyes, the trail in front of the rider, and surrounding trail users. 

The reviewer confirmed that a trail junction or road crossing appeared at the timestamp, then observed 

whether the rider glanced to the left or right for opposing traffic. 

Results

Results are shown in Table 4 and Figure 17. Within the sample, conventional bike riders glanced toward 

potential conflict points more frequently than e-bike riders, both at trail junctions (58 percent versus 45 

percent) and at road and driveway crossings (69 percent versus 64 percent). The study team speculated 

that the capacity for more rapid acceleration may contribute to the lower glance rate among e-bike riders 

but do not have objective conclusions. Data in the analysis demonstrate similar attentiveness between ebike and conventional bike riders, such as their speed reduction while passing other trail users, so there is 

no clear explanation from these data for this divergent glance behavior frequency. 

Table 4. Glance Behavior Results.

Category Conventional: 

Events Observed

Conventional: 

Glance 

Occurred

E-Bike: Events 

Observed

E-Bike: Glance 

Occurred

Road/Driveway 

Crossing

70 48 (69%) 47 30 (64%) 

Trail Junction 50 29 (58%) 22 10 (45%) 

Figure 17. Participant glance behavior frequency at road crossing and trail junction potential 

conflict points.

Glance behavior analysis is a proxy for evaluating rider intent and risk-taking at conflict points. It is 

especially useful as a comparative analysis between riders on different types of bikes (e-bikes and 

conventional bikes). However, it is difficult to ascertain whether riders adequately assessed potential 
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conflicts before proceeding through road crossings and trail junctions, as this is an inherently subjective 

judgement. Some riders may also have had sufficient peripheral vision and/or used other auditory and 

visual clues to assess potential conflicts without turning their heads. 

OPEN DATA

The study team is committed to sharing the data collected and methods used in this study for reuse. GPS 

pings used in the regression analysis, which incorporate the geolocation and passing event tags, are 

available online at https://data.transportation.gov/Bicycles-and-Pedestrians/E-Bike-Field-StudyData/xdkm-ken4. The raw video files are not posted due to the potential for exposure of study 

participants’ personally identifiable information and file size limitations. Source code developed for the 

analysis is available online at https://github.com/VolpeUSDOT/E-Bike_PublicAccess. The repository 

contains code necessary to replicate the results presented in this paper with the data posted online, as well 

as code used in preparing the data (video extraction, data cleaning, and passing event detection) which 

may be useful for researchers interested in performing similar analyses using GoPro data. 

CONCLUSION AND LESSONS LEARNED

In locations identified as higher risk for potential conflicts along an unpaved, multiuse trail: 

1. Analyses show that e-bike riders travel slightly faster on average than conventional bike riders.

a. T-tests show approximately one mile per hour increase in median speed for e-bike riders. 

b. Regression analysis indicates that e-bike usage predicts an average 2.19 mph increase in 

rider speed. However, this was not the predictor which had the greatest effect on speed; 

male sex predicts a greater increase in speed (average 2.51 mph increase). 

2. Distributions of e-bike and conventional bike rider speeds overwhelmingly overlap with one 

another. 

a. Both electric and conventional bike rider speeds have a large distribution across each of 

the study’s designated segment types. 

b. Both electric and conventional bike riders exhibit similar extremes at the high and low 

ends of the speed spectrum. 

3. Conventional and e-bike rider behavior is similar at locations with higher risk of conflict.

a. Regression analysis shows that both e-bike and conventional bike riders reduce speeds 1) 

at vehicle conflict points, 2) in narrow sections of trail, and 3) when passing other trail 

users.

b. Video data show e-bike and conventional bike riders both exhibit moderate precaution by 

glancing to the side at trail junctions and vehicle crossing locations, though e-bike riders 

glance slightly less frequently than conventional bike riders. 

Future studies may wish to purposefully recruit novice riders or participants riding unfamiliar bicycles to 

study their behavior in a public lands context where rental services or tour operators provide bicycles to 

visitors. Future research may also consider how unpaved trail surfaces are affected by e-bike usage. Also 

for future studies, modifications to equipment or participant instructions could enable higher GPS and 

video data quality, and allow usage of additional data (e.g., accelerometer readings). Modifications to 

consider include mounting instructions for the GoPro device (i.e., the side which should face forward) to 

ensure directional consistency throughout a ride and across participants, and instructions to participants to 

turn on their camera a certain number of minutes before beginning their ride to allow the device to obtain 

an accurate GPS signal. 
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