Available via license: CC BY 4.0
Content may be subject to copyright.
RESEARCH ARTICLE
Equipping Active Travel Advocates
with Digital Mobility Data and Tools:
An Evaluation of a US Trial Program
Angela Sanguinetti1, Eli Alston-Stepnitz1, Melissa Ruhl2, Nermin
Dessouky1 and Andrea Broaddus3
1 University of California, Davis, Institute of Transportation Studies, 1605 Tilia St., Davis, CA
2 Ford Research & Advanced Engineering (now at Waymo), 3251 Hillview Ave, Palo Alto, CA
3 Ford Research & Advanced Engineering, 3251 Hillview Ave, Palo Alto, CA
Corresponding author: Angela Sanguinetti (asanguinetti@ucdavis.edu)
Shared micromobility services that oer bikes and scooters on demand are comple-
menting walking, cycling, and public transport to expand the role of active mobility
in urban transportation. However, on-demand services are often introduced at a
faster pace than streets are redesigned to protect the safety of users. The lack of
safe street infrastructure limits the potential mode shift to micromobility and the
associated benets. Active travel advocacy groups can make a critical dierence,
but they generally lack access to data resources.
In 2020, Spin, a shared micromobility company, launched an initiative called
Mobility Data for Safer Streets (MDSS). This involved equipping six advocacy
organizations in ve US cities with a suite of mobility data tools to support their
eorts to make streets safer. This research sought to understand how access to
mobility data can help active travel advocates be more eective and was assessed
via interviews with awardees throughout the 15-month program. Access to data
improved advocates’ eectiveness in working with the local government, garner-
ing community support, supporting underserved communities, and advocating safe
streets for all road users in their work. The MDSS model can be replicated to
empower advocates or other stakeholders to advance collective understanding of
active travel and inuence supportive planning and policy.
Keywords: Micromobility; Active transportation; Advocacy; Mobility data; Safety
Introduction
Active travel (AT), including walking and cycling, has long been recognized for its environ-
mental and social benefits (Lee, Sener and Jones, 2017; Smith et al., 2017). Relatively new
forms of shared and personal micromobility have expanded some of the benefits of tra-
ditional AT. On-demand services with shared docked or dockless fleets of conventional or
electric bikes or scooters and privately owned e-bikes, e-scooters, and other light electric vehi-
cles provide additional lower-emissions alternatives to private automobiles for short trips
Sanguinetti, A, et al. 2023. Equipping Active Travel Advocates
with Digital Mobility Data and Tools: An Evaluation of a US Trial
Program.
Active Travel Studies: An Interdisciplinary Journal
, 3(1):
7, 1–18. DOI: https://doi.org/10.16997/ats.1198
Sanguinetti et al: Equipping Active Travel Advocates with Digital Mobility Data and Tools2
(Abduljabbar, Liyanage and Dia, 2021), making micromobility accessible to more people (e.g.,
the elderly and those with disabilities may more easily use an electric bike or scooter than a
traditional bike or scooter) (Shaheen and Cohen, 2019; Shaheen et al., 2012; Shaheen et al.,
2014), and facilitating connections to public transport and access to jobs and other basic
needs (North American Bikeshare Association, 2020; DuPuis, Griess and Klein, 2019; Smith
and Schwieterman, 2018).
Traditional AT increased across the globe in the months following the identification of the
COVID-19 pandemic (Dingil and Esztergár-Kiss, 2021). Shared micromobility services quickly
recovered from initial setbacks due to the pandemic and have been projected to experience
increased ridership in the wake of the pandemic (Heineke, Kloss and Scurtu, 2020) and
beyond (Chang et al., 2019). Private ownership of light electric vehicles has also increased
in recent years, especially since the pandemic, in the United States (it has been popular else-
where for decades); e-bikes sold annually increased from less than 300,000 to over 1 million
between 2018 and 2021, and the boom that occurred during the pandemic has been sus-
tained for e-bikes whereas traditional bike sales have returned to prepandemic rates (Bennett
et al., 2022).
This recent increase in AT and new forms of micromobility has drawn attention to inad-
equate street facilities and created a more supportive climate for investing in safe streets
infrastructure. In the United States, institutionalized practices of transport planning, includ-
ing top-down and slow-moving processes, can tend to favor the status quo of autocentric
street engineering with low prioritization of AT facilities (Braun, Rodriguez and Gordon-
Larsen, 2019), such as sidewalks, bike lanes, and bike parking, which are now also impor-
tant to support new forms of micromobility. Though transportation planning education has
come to emphasize the needs of users of all transportation modes and public involvement
in the planning process (Combs and Pardo, 2021), these goals can be difficult to achieve
in practice (Broaddus and Cervero, 2019; Wagner, 2013). During the pandemic, cities have
experimented with new public engagement practices, managed to quickly reallocate space
previously reserved for autos, and have become more open to AT advocates (Braun, Rodriguez
and Gordon-Larsen, 2019).
AT advocates play an important role in promoting maintenance, improvements, and expan-
sions of AT infrastructure through both formal roles and informal relationships with local gov-
ernment and planning agencies (Karner et al., 2020). However, they often lack access to data
resources to support case making; they may recruit volunteers to manually collect bike and
pedestrian counts or obtain crash and injury rates from their cities (Seskin, Kite and Searfoss,
2015). With the emergence of big mobility data, increasingly used by local governments to
aid in transportation planning (Englin and Davis, 2021; Yang, Cetin and Ma, 2020), lack of
access to the same data may inhibit advocates from contributing to planning. Subscriptions
to emerging mobility data tools can be prohibitively expensive for cities (Koupal et al., 2022;
Nelson et al., 2021; Lee and Sener, 2020), let alone not-for-profit advocacy groups. Working
with emerging mobility data often requires a high level of data science skills (Nelson et al.,
2021), which may limit the degree to which advocates could use the tools even if they had
access. Use of multiple tools to complement each other and compensate for relative short-
comings has been suggested (Nelson et al., 2021), which compounds the cost and human
resource barriers.
Spin, a shared micromobility company, launched the Mobility Data for Safer Streets (MDSS)
Initiative in 2020, which awarded six AT advocacy organizations access to a suite of mobility
data tools to support their efforts to improve street safety for AT, broadly defined hence-
forth in this paper to include new forms of micromobility. The program provided advocates
with the leading emerging mobility data tools, removing the cost barrier entirely, and Spin
Sanguinetti et al: Equipping Active Travel Advocates with Digital Mobility Data and Tools 3
facilitated onboarding and support for the awardees from each of the software platform com-
panies to address the training requirements. It provided a diverse set of tools to be used in
combination, including traditional data collection tools.
Spin and Ford (Spin’s parent company at the time) enlisted researchers from the University
of California, Davis, to evaluate the program. Data for the evaluation were gathered from four
rounds of interviews with awardees over the course of the 15-month program. This research
draws on those data to understand how mobility data tools can be harnessed by AT advocates
to enable them to be more effective in their work toward safer streets and to identify some
characteristics of emerging data tools that facilitate or complicate their use among advocates.
Literature review
Cities invest heavily in collecting data on motor vehicles (traffic volumes, safety, and
infrastructure) but allocate little for tracking similar data on AT (Nelson et al., 2021). For
example, cycling volume data tend to be limited to small areas and short time frames (Roy
et al., 2019), cycling safety incidents are grossly underreported (Winters and Branion-Calles,
2017), and cycling infrastructure changes are difficult to track because they are often incre-
mental (Nelson et al.). This dearth of data inhibits AT-supportive planning and policy (DiGioia
et al., 2017).
New forms of mobility data can help fill these gaps (Nelson et al., 2021). Lee and Sener
(2020) defined emerging AT data to include the concepts of big (Romanillos et al., 2016),
crowdsourced (Le Dantec et al., 2015; Fernandez-Heredia and Fernandez-Sanchez, 2020), and
passive data (Bonnel et al., 2015), in contrast to traditional travel survey and manual or auto-
mated bike-ped counting methods. They divided emerging data into two categories: mode-
unspecified and mode-specified. Mode-unspecified data include those gathered passively
from smartphone apps that track user location and infer travel mode, while mode-specified
data sources specifically monitor nonmotorized modes and include bicycle-tracking apps,
fitness-tracking apps, bike-share services, and user-feedback platforms.
StreetLight Data is the leading provider of mode-unspecified data. Using smartphones as
sensors, StreetLight infers travel modes and provides historical street-level traffic volumes
and speeds and origin and destination trip volumes and durations over user-specified time
frames and areas. It also provides inferred contextual data on traveler sociodemographics
and trip purpose. Data are visualized in maps and graphs. While StreetLight AT applications
are yet few (most focus on motor vehicles, e.g., modeling regional travel demand) (Englin
and Davis, 2021; Yang, Cetin and Ma, 2020), there is great potential to use this type of high-
volume and granular data to analyze AT (Nelson et al., 2021). One example of a StreetLight
AT application is an analysis of walking trips to and from light-rail stations in Sacramento,
California, that demonstrated the need for improved connectivity (McCahill, 2017).
Strava Metro, the data service associated with the Strava fitness-tracking app, is a widely
used mode-specified mobility data tool. Strava provides cycling and walking trip counts and
average times and distances over specified time frames and locations, as well as two general
trip purpose categories (commute and noncommute), and traveler gender and age group
summaries. A recent review (Lee and Sener, 2021) articulated six Strava use cases for AT analy-
sis and planning: travel pattern identification, travel demand estimation, route choice analy-
sis, infrastructure evaluation, crash exposure control, and air pollutant exposure assessment.
However, a criticism of Strava and similar crowdsourced data is that only active subscribers
(Strava users tend to be avid recreational cyclists) are reflected in the data, and thus it may
not be representative of the broader population (Asad and Le Dantec, 2017). Methods have
been developed to correct for the biases (Roy et al., 2019), but it is unclear the degree to
which practitioners are using these methods, which require calibrating the data with official
Sanguinetti et al: Equipping Active Travel Advocates with Digital Mobility Data and Tools4
counts at many locations; Englin and Davis (2021) describe several Strava AT applications by
US state departments of transportation, including some that did and some that did not use
bias-correction methods.
In addition to smartphone-enabled passive data and subscriber-based crowdsourced data,
shared mobility (including ride-hailing and shared micromobility) service providers are a
source of mode-specified big data supplied by their vehicles and users (Ciociola et al., 2017).
Cities often require these companies to share data, which they can use to ensure the compa-
nies are abiding by operating agreements and to inform transportation planning (D’Agostino,
Pellaton and Brown, 2019). This has created a need for data-sharing standards and tools that
help cities process and evaluate shared mobility data securely (ensuring the data are suf-
ficiently anonymized to protect personal privacy). Populus has emerged as a leader in this
space, providing a tool called Mobility Manager that manages and visualizes (in maps) vehicle
and trip data for shared mobility services. Data must be provided by shared mobility com-
panies, and perhaps this is why it has not been leveraged in much research (they have con-
ducted their own studies).
Lee and Sener (2020) described another mode-specified data source they termed “user-feed-
back inventories,” referring to online crowdsourcing platforms where users actively contrib-
ute data about AT-related issues such as street infrastructure, safety (crashes and near misses),
and proposed solutions. OpenStreetMaps (OSM) is a widely used user-feedback inventory of
street infrastructure data. OSM is leveraged in the urban planning tool UrbanFootprint to
supplement TIGER roads data from the US Census, particularly to incorporate more granular
and specific information on cycling and pedestrian infrastructure. UrbanFootprint contains
an extensive ready-to-use library of geospatial data, including population demographics and
land use, that draws on many sources, including the US Census.
As previously noted, AT advocates are often a driving force for progressive micromobil-
ity infrastructure and policy, yet there is a dearth of research on their needs and uses for
emerging mobility data. Asad and Le Dantec (2017) explored cycling advocates’ desired uses
for hypothetical digital mobility data tools and identified three themes: making cycling
visible through geolocation (documenting ridership and routes), making cyclists safe
through feedback loops (crowdsourcing data about routes and safety issues), and empow-
ering cyclists through communication (to other community members and decision-makers
about cyclists’ issues and needs). This research fills a gap in the literature by articulating
whether and how emerging data tools can be harnessed by AT advocates to help achieve
safer streets.
Methodology
The MDSS program awarded six nonprofit advocacy organizations around the country with
access to a set of mobility data tools to gather, analyze, and present data to enhance their
work toward making streets safer for micromobility. Advocates’ applications to the program
described how they intended to deploy these tools in support of at least one specific street
redesign project over the course of the project term. Spin provided the following examples
to guide applicants: highlight the need for a project based on an understanding of multi-
modal traffic in a neighborhood, gather critical data to make the case to a city department, or
monitor the success of an existing project to ensure the city remains committed. The program
began in January 2020 and was originally planned for the calendar year but was extended
through March 2021 because of the pandemic.
Awardees
Six advocacy groups applied to the program, and all were awarded (Table 1): Sustain Charlotte,
Bike Cleveland, Denver Streets Partnership, Walk Bike Nashville, Bike Utah (together with
Sanguinetti et al: Equipping Active Travel Advocates with Digital Mobility Data and Tools 5
Bike Walk Provo), and BikeWalk North Carolina. The organizations are all not-for-profit and
small (one to nine paid staff).
The data tools
Spin procured for each awardee several physical data collection tools and the four software-
based emerging data tools overviewed in the literature review section (StreetLight Data,
Strava Metro, Populus Mobility Manager, and UrbanFootprint). The physical data-gathering
kit included a professional-grade radar speed gun for tracking vehicle speeds, a time-lapse
camera for visual tracking of slower changes to the streetscape, and a sensor for counting
people walking and cycling on a street segment or at an intersection. Additionally, Gehl urban
design consultancy donated access to its Public Space Public Life (PSPL) survey toolkit, which
describes methods and provides online templates for observational, survey, and interview
research to study the physical and social elements of street design and travel behavior.
Spin purchased the physical tools for the awardees, and awardees were allowed to keep
these after the program. Spin came to an agreement with each of the software tool compa-
nies to either purchase or have donated limited licenses for the awardees to access data for
the duration of the program. Software tool companies hosted an online tutorial session for
awardees and provided a contact for technical assistance to support the awardees throughout
the program.
Evaluation data collection and analysis
Evaluation methods centered on quarterly, hour-long, semistructured interviews with one or
two main points of contact for the program at each organization. Interviews were conducted
by virtual conference. These individuals were either the executive director or a program direc-
tor or coordinator.
Interviewers asked awardees to define and name specific projects for which they were using
the provided data tools (e.g., to evaluate usage of a particular bike facility or inform a particu-
lar advocacy effort). Most awardees used the tools in support of multiple projects. They asked
awardees to discuss each project in terms of goals and objectives, data collection and analysis
processes, progress, findings, dissemination methods, and outcomes. Awardees were invited
to share and discuss data visualizations. Other topics included data tool usability, the impact
of the pandemic on their use of the data tools, the history of micromobility in their cities, and
their organizational interest in emerging data.
Prior to the final round of interviews conducted in March 2021, a narrative brief was writ-
ten for each project that contained the following sections: project goals, process and tool
use, outputs and dissemination, and accomplishments and aspirations. Notes and transcripts
from the first three interview rounds were referenced to compile these briefs. One coauthor
drafted each brief; the lead author reviewed and edited all briefs.
Table 1: 2020 MDSS Initiative awardees.
Awardee Location Website
Bike Cleveland City of Cleveland, OH bikecleveland.org
Denver Streets Partnership City of Denver, CO denverstreetspartnership.org
Walk Bike Nashville City of Nashville, TN walkbikenashville.org
Bike Utah UT; City of Provo, UT bikeutah.org; linktr.ee/bikewalkprovo
Sustain Charlotte City of Charlotte, NC sustaincharlotte.org
BikeWalk North Carolina NC bikewalknc.org
Sanguinetti et al: Equipping Active Travel Advocates with Digital Mobility Data and Tools6
The project briefs formed the basis of an initial inductive analysis for this research. Taking
the project as the unit of analysis, the authors derived a set of themes to describe what the
data tools enabled advocates to do, or to do better, to increase their effectiveness in advo-
cating safer streets for AT. These themes were identified by assessing commonalities across
projects in terms of their goals, accomplishments, and aspirations.
The final round of interviews focused on the emergent themes. Interviewers inquired
whether and how access to the data tools changed the types of projects or collaborations pos-
sible in relation to each theme (i.e., anything they were able to do that they could not have
done otherwise). They also discussed types of data and visualizations they found most effec-
tive and how they would advise future MDSS program awardees with respect to each theme.
This final interview round was recorded and transcribed for content analysis.
Results
Four themes were identified to describe how access to mobility data tools can make AT advo-
cates more effective in their efforts to improve street safety. The data tools helped advocates
more effectively work with local government, garner community support, support underrep-
resented communities, and advocate safer streets for all. The following sections first discuss
general findings in relation to each theme and then describe some of the awardees’ projects
that exemplify each theme. Table 2 summarizes all the projects. A final results section dis-
cusses the challenges that the awardees experienced with the program.
Table 2: 2020 MDSS Initiative projects.
Awardee Project Description Theme Tools used
Bike
Cleveland
Memorial
Bridges Loop
Advocate for a road diet
to create a comprehensive
bike network
Advocate safer
streets for all
StreetLight,
Strava
Bike
Cleveland
Speeding on
High Injury
Network
Explore increased speeding
during the pandemic and
correlation with fatalities
Work with
government
StreetLight
Bike Utah Buffered Bike
Lanes
Demonstrate how buffered
bike lanes are being used
Work with
government
Time-lapse
camera
Bike Utah Informing
Master Plans
Provide recommendations
for new bike facilities,
highlighting the needs of
underserved communities
Work with gov-
ernment; Support
underserved
communities
StreetLight
BikeWalk
North
Carolina
Safety in the
Charlotte
Crescent
Document high AT rates in
neighborhood perceived by
the city as an “AT desert”
Support
underserved
communities
StreetLight
Denver
Streets
Partnership
Denver Shared
Streets
Evaluate use of temporary
shared streets interventions
Work with
government
EcoCounter
Sustain
Charlotte
Multimodal
Connectivity
Identify gaps in multimodal
mobility networks
Support
underserved
communities
Urban
Footprint
Sustain
Charlotte
Biketoberfest! Identify most popular bike
routes for fundraising event
Garner commu-
nity support
Strava
(Contd.)
Sanguinetti et al: Equipping Active Travel Advocates with Digital Mobility Data and Tools 7
Working with local government
According to the awardees, cities often lack AT data and make decisions about AT infrastruc-
ture based on incomplete data or in a reactive rather than proactive mode. Several awardees
described how their long-term mission is to nudge the culture of their local transportation
planning to place a higher priority on AT, which includes prioritizing AT-related data collec-
tion. Having access to data tools themselves helped them in this general mission; as one awar-
dee observed, “The way to change policy is to fundamentally change how [decision-makers]
see transportation and call into question some of the assumptions that are being made. And
you do that with data.”
Another notable outcome of advocates having access to mobility data was that their cities
became more eager to collaborate with them, changing the relationship dynamics, even to
the point where cities were reliant on advocates for supplying valuable data. One awardee
described it this way: “What [access to mobility data] has allowed me to do is to move from
being a participant in the conversation to a leader in the conversation.” Another attributed
some of this impact to other resources the program provided: “We would not have the skill
and ability to dive into this GIS data if it weren’t for the grant. The training, the information
resources, and the technical support associated with [the MDSS program] allowed us to have
a much more intellectual conversation at the state and local level.”
The nature of data that awardees were able to access with some tools was also important in
changing the dynamic of their interactions with government. Advocates reported that before
the program, they were often only able to offer small-scale quantitative data or anecdotal
evidence due to limited data resources. They relayed how bringing broad-based, quantitative
data to their efforts was useful: “Trying to tell a broader story is more important than trying to
advocate for a specific project…. I think that’s much more provocative and meaningful. It can
actually inform a lot of these more specific projects that you want to complete.”
In terms of specific projects that exemplify this theme, Bike Utah worked with multiple
Utah cities to provide recommendations for their transportation master plans. For example,
it supplied data to inform the Active Transportation Plan for the City of Springville, which is
soon to be voted on by the city council.1 It heavily used StreetLight to document where cycling
volumes were highest and compared these insights to current and planned infrastructure.
In another project, Bike Utah used the time-lapse camera to show city engineers that buff-
ered bike lanes (separated from auto lanes by a section of painted white lines) were being
used appropriately. City engineers had been under the impression that people were riding
1 https://springvilleatp.altago.site/.
Awardee Project Description Theme Tools used
Sustain
Charlotte
North Davidson
Safe Streets
Analyze auto speeds to
identify and get support for
interventions for safer AT
Garner commu-
nity support
StreetLight
WalkBike
Nashville
Murfreesboro
Pike Corridor
Community active travel
needs assessment
Support
underserved
communities
Urban
Footprint
Walk Bike
Nashville
Traffic Calming
Campaign
Document suspected
speeding to advocate traffic
calming measures
Garner commu-
nity support
StreetLight
Walk Bike
Nashville
Nashville Slow
Streets
Study impact of slow streets
on auto volumes and speed
Work with
government
StreetLight
Sanguinetti et al: Equipping Active Travel Advocates with Digital Mobility Data and Tools8
inside the buffer instead of the bike lane, so the city was considering discontinuing buffered
bike lane construction. Bike Utah set up the camera at various locations and put together
videos showing that virtually all cyclists and scooter users were using the lanes appropriately.
As a result, the city recommitted to continuing to build buffered bike lanes. The awardee
observed, “In a lot of cases buffered bike lanes are like the ‘gateway drug’ to physically pro-
tected bike lanes. So if we hadn’t won this battle, it would’ve made future battles impossible—
the ultimate goal impossible.”
Denver Streets Partnership and Walk Bike Nashville used the data tools (EcoCounter and
StreetLight, respectively) to document impacts of temporary shared and slow streets inter-
ventions. Implemented in many cities across the world during the pandemic, shared streets
involved closing off parts of a street to vehicle traffic to make more safe dedicated space
for active modes, and slow streets involved reducing vehicle speeds and/or auto lanes for a
similar purpose. Largely because of the advocate’s analyses showing increased AT volumes,
Denver expanded and extended its Shared Streets program in the near term. Long term,
though many of the temporary shared streets were reverted, the city committed to some per-
manent changes and improved cycling infrastructure.2 The awardee explained, “Just simple
data on how many people walk down a street segment is incredibly valuable because there’s
such a dearth of that data. Being able to have these more objective data definitely changed
the dynamic between us and the city. We became more of a partner working on the projects.”
Garnering community support
Awardees reported that safe street redesign projects are sometimes met with resistance or
NIMBYism from community members. As active travel advocates, they aim to mitigate any
such resistance and encourage community activism by communicating the need for street
design projects to improve safety, mobility, and quality of life. They talked about trying to
educate and inspire adults and youth in their communities to be more active, take advantage
of local AT facilities, and get involved in advocacy. They sometimes also seek financial or other
types of support for street redesign projects from the community.
Awardees reported that access to mobility data made them more effective at garnering
community support in these various contexts by providing the requisite data and analytic
support for case making. They noted that broad-based quantitative data can be useful in vali-
dating community members’ personal stories and that geospatial data visualizations enabled
by some of the tools (as well as simple charts) facilitate telling clear and compelling stories
to community members. One advocate observed, “With community members it’s important
to be able to share the data in a visually accessible way. Your average person’s eyes glaze over
as soon as you start getting into numbers. So having very visually compelling information is
useful.”
The tools also provided an opportunity to engage different types of community members
in advocacy, as one awardee explained,
The way data can be helpful with community engagement is by enlisting community
members themselves in helping to collect the data. For example, people have so much
fun standing on the side of the road with the radar gun. And it engages them in start-
ing to think in a more data-centric way, having more ownership over the stories that
2 https://www.denvergov.org/Government/Agencies-Departments-Offices/Agencies-Departments-
Offices-Directory/Department-of-Transportation-and-Infrastructure/News-Outreach/News/Next-
Phase-Shared-Streets.
Sanguinetti et al: Equipping Active Travel Advocates with Digital Mobility Data and Tools 9
come out of the data because they participated in collecting it. It’s a little different
from the usual ways that people think about getting involved in initiatives, so you can
attract different types of people.
In response to community concerns about increased auto speeds during the pandemic, Walk
Bike Nashville rejuvenated their ongoing traffic calming campaign. Trafc calming refers to
interventions to slow the speeds of auto traffic in order to make streets safer for AT. Using
StreetLight, they analyzed speeds and volumes on streets with many complaints. Comparing
data from spring 2019 (pre-COVID) and spring 2020 (during the pandemic), the analysis
confirmed increased speeding. Findings were well received by the neighborhood associa-
tions because they validated their concerns; the awardee noted, “[The neighborhood asso-
ciation] loved it because it confirmed what they were seeing, so they were really excited
to have the data.” In preparation for filing a formal petition to the city in spring 2021 to
request funding for traffic calming programs, Walk Bike Nashville is compiling a list of sup-
portive neighborhood associations and asking community members to call their councilp-
erson’s and mayor’s offices.
Sustain Charlotte used Strava Metro to identify the most popular bike routes in the city
of Charlotte, which they used for their Choose Your Own Adventure–themed BiketoberFest
community ride and fundraising event. Community members were encouraged to explore
the city by bicycle and participate in a scavenger hunt. The advocate observed,
The [data tools] can be used for fun activities as well. We do events to get folks out
on bikes and maybe going places they haven’t usually gone on bikes before. Having
Strava data, we’re able to figure out, in this virtual, pandemic time, where would be
the best locations. … It’s another form of advocacy and building support for active
transportation.
Supporting underserved communities
Access to mobility data tools enabled advocates to investigate issues in areas of their cities
that are often overlooked: low-income and minority neighborhoods lacking safe AT infra-
structure. Advocates noted that poor infrastructure, despite high demand, is also related to
these neighborhoods’ lack of political power and representation in transportation planning
processes. Broad-based quantitative data, such as that provided in StreetLight, was found to
be particularly valuable to help represent these communities because it reflects the behavior
of most street users, including groups not represented at planning meetings or in data pro-
vided by subscription-based crowdsourcing tools like Strava.
In terms of specific projects with this theme, Sustain Charlotte worked with a coalition of
community leaders in neighborhoods throughout the economically and racially segregated
“Crescent” area (neighborhoods in East, North, and West Charlotte) to map travel times to
basic needs for non-auto modes in UrbanFootprint to identify gaps in mobility networks
and connectivity solutions. The project equipped community leaders with compelling data to
use in advocating their needs and highlighted common patterns across the neighborhoods,
which empowered the coalition to pool their advocacy efforts in support of shared goals.
Walk Bike Nashville used UrbanFootprint to visualize crash data (from the police depart-
ment) in relation to economic data from the US Census. The analysis revealed a strong cor-
relation between lower-income census tracts and pedestrian fatalities. They planned to use
this analysis to advocate improved AT infrastructure around Murfreesboro Pike, a multilane
arterial road in South Nashville. The advocate explained why the area is so dangerous: “We see
a lot of people biking to the bus station to get to work from South Nashville, which has no
Sanguinetti et al: Equipping Active Travel Advocates with Digital Mobility Data and Tools10
bike lanes. We also see a lot of people walking there on some of our streets with no sidewalks
in really busy traffic.”
While consulting on the Active Transportation Master Plan for the City of Provo, the Bike
Utah advocate described stumbling onto compelling equity-related insights in StreetLight
regarding where most cycling was occurring in the city:
That was actually one of the mind-blowing experiences, when it matched up almost
perfectly with where most of the Latinx folks were living, and also low-income folks.
But then I was in a meeting [with] all middle class white people … asking for recrea-
tional facilities [to be included in the master plan]…. I showed them all the [StreetLight
data] visualizations and that kind of just snowballed into me creating more visualiza-
tions [for other cities] because [I realized] there’s a case to be made beyond Provo….
[As a result, the city] changed their request for proposals that they were putting out
to consultancies [to work on the master plan], pretty drastically, to be more about
utilitarian cycling.
Advocating safer streets for all
Awardees used mobility data tools to analyze multiple travel modes, including AT, automo-
biles, and public transport. They took advantage of the breadth of available data to highlight
how street redesign projects that facilitate AT can promote safety for all modes. Advocates
stressed the value of data related to safety (examples they gave were vehicle speeds and
crashes) as being a uniting force to garner support for projects:
A lot of times when we communicate around a project it isn’t just about making the
roadway safer for people biking and walking, but it’s about making it safer for every-
body…. People can argue all day, “I don’t want a bike facility on this roadway because
I want to be able to park” or “I want to be able to get through as fast as I can”; but it’s
tough for people to argue against safety. Leading with, “This isn’t just about bikes, this
is about safety,” it’s tough for people to say no.
For example, Bike Cleveland used multiple data tools in its advocacy for a comprehensive
network of AT facilities around the city. Specifically, it hopes to connect existing infrastruc-
ture in different areas (the Lorain Avenue Cycle Track and the protected bike lane on Detroit
Superior Bridge) by adding protected bike lanes to the connecting roads (Huron Road and
Ontario Street) via road diets. A road diet is a street design intervention involving the removal
of an auto lane to make room for protected bike lanes. To help make the case, Bike Cleveland
used data from StreetLight and Strava in conjunction with crash data from the local depart-
ment of transportation to highlight how the streets in focus are unsafe for cyclists.
Strava route data revealed that on a particular stretch of Ontario Street heading into down-
town, cyclists were avoiding the motor vehicle and bicycle shared lane and instead traveling
on the sidewalk. In Cleveland, as in many US cities, cycling on the sidewalk is not legal, as it
can make the sidewalk less safe for pedestrians. The advocate explained, “People are actually
foregoing the most direct route and hopping up on the sidewalk, which technically is illegal…
But they’re doing it because they don’t feel comfortable mixing with four lanes of traffic all
heading into downtown.”
Further, Bike Cleveland used Strava and StreetLight to illustrate high AT volumes and
relatively low auto volumes on those roads, prepandemic, indicating a road diet could be
achieved without major negative impact on auto traffic and with improved safety for all. The
advocate summarized, “The data can help confirm a lot of the ideas that we have, and show
Sanguinetti et al: Equipping Active Travel Advocates with Digital Mobility Data and Tools 11
that on some roadways [auto] traffic volumes are really low…. You can remove a lane of traffic
to create a safer place for other users.”
Another aspect of this theme is the equitable prioritization of safety for all micromobility
users regardless of travel purpose (utilitarian or recreational). As previously mentioned, Bike
Utah used mobility data to demonstrate that there was more utilitarian cycling in the cit-
ies than recreational, correcting a common misperception that the reverse is true. BikeWalk
North Carolina was particularly passionate about not only dispelling that myth but also
emphasizing that trip purpose should not matter when it comes to road safety. The advocate
observed that the needs of recreational travelers are often not considered mainstream traffic
engineering concerns and compared this to the idea that one should only fasten one’s seat-
belt when driving for utilitarian purposes.
Challenges
Challenges for the MDSS program included issues related to the data tools, the pandemic and
other natural disasters, and difficulty achieving concrete outcomes within the 15-month pro-
gram. Regarding the latter, advocates’ analyses, however compelling, did not often result in
near-term concrete impacts on street design and policy within the program term. Advocates
explained how infrastructure projects can be slow to manifest, even once they have approv-
als and funding. Add to that the pandemic, which shifted cities’ priorities and pushed back
project timelines, and a devastating tornado in Nashville that demolished part of the street
that was a focus of Walk Bike Nashville’s proposed project.
Shelter-in-place directives were in place during much of the program, precluding staff and
volunteers from deploying the physical data collection tools and implementing the Gehl sur-
vey. Additionally, travel behavior data collected during this time would not be representative
of typical conditions. As a result, however, advocates had more time than expected to engage
with the software-based tools. Most awardees had to pivot from their original plans in some
ways, though some found opportunities to use the data tools to measure pandemic-related
changes in travel behavior.
Challenges with the data tools included usability and human resource issues, and the two
were sometimes related. The learning curve for some of the tools (StreetLight in particular)
was steep. Some awardees enlisted new staff or student interns with GIS expertise from local
colleges, and some pursued additional (including in-person) tutorials beyond the onboarding
that was provided. Another barrier to using cycling ridership data from the software tools was
a lack of official data for calibration purposes, and awardees were limited in collecting their
own counts since they were given only one bike-ped sensor (in addition to the pandemic-
related data validity issue).
Awardees did not use all the provided tools. Gehl PSPL and Populus were particularly under-
utilized. A major reason advocates did not use Gehl PSPL was because it required more staff to
implement data collection than they had available. Advocates were interested in Populus, but
because they only had access to data from vehicles operated by Spin, and not the other shared
micromobility operators in their respective cities, they felt it would provide an incomplete
picture and thus findings might not be compelling.
Discussion
This research explored whether and how access to emerging mobility data tools can help AT
advocates be more effective in their efforts to improve street safety. Consistent with prior
suggestions (Nelson et al., 2021), the resources required to use the tools (i.e., skill level and
time investment) created challenges for awardees, but nearly all were able to overcome the
barriers with a modest amount of program support (i.e., tutorials for the tools and a support
Sanguinetti et al: Equipping Active Travel Advocates with Digital Mobility Data and Tools12
contact at each company). Advocates demonstrated a wide array of use cases for the tools and
predominately used the emerging data tools (more than the physical data tools they were
given). The pandemic and shelter-in-place directives contributed to this outcome because
advocates had more time at their desks and computers to learn the software-based tools and
they were unable to gather valid data with the physical tools for many of their planned pro-
jects since travel behavior was drastically altered.
This research identified four themes to describe how AT advocates can use emerging mobil-
ity data tools. Regarding the first two themes, access to data tools improved awardees’ effec-
tiveness in working with local government (to influence civil and traffic engineering projects
or legislative efforts) and garnering community support (e.g., encouraging cycling and activ-
ism through informal and organized social events), which are central missions of AT advo-
cates (Asad and Le Dantec, 2017). These findings are consistent with the concept of digitally
supported advocacy, whereby computing technologies enhance existing advocacy strategies
as opposed to transforming the nature of practices (Asad and Le Dantec, 2017).
Consistent with prior research (DiGioia et al., 2017; Nelson et al., 2021; Roy et al., 2019;
Winters and Branion-Calles, 2017), awardees reported that their cities and states do not prior-
itize collecting AT data. Until institutional changes result in higher prioritization of AT within
transportation planning, advocates empowered with mobility data can “speak the language”
of city engineers and planners to help fill the gap; this shared language can positively change
the dynamics between advocates and local government. In some cases, cities sought out the
advocates and enlisted them to collect or supply data. This exemplifies the concept of digi-
tally supported enhanced advocacy, when advocates take on a service provider role to fill gaps
where public institutions lack capacity (Asad and Le Dantec, 2017).
Access to mobility data tools supported community engagement, education, and fund-
raising—the foundational elements of advocacy (Asad and Le Dantec, 2017). The data tools
enabled advocates to investigate community concerns and to put individual experiences in
a wider context, creating stories that could be both personal and reflective of wider trends
(Asad and Le Dantec, 2017). The tools also facilitated a new form of community participa-
tion—engaging community members in data collection and analysis.
The other two themes, supporting underserved communities and advocating safer streets
for all, may be historically less central or less universal (or at least more difficult to achieve)
among AT advocacy missions (Ciociola et al., 2017). These are examples of data-driven advo-
cacy, whereby access to data makes it possible to address issues and advance concerns to
local officials, leading to new practices designed to influence social or political change (Asad
and Le Dantec, 2017). Access to the data tools helped advocates represent and empower
underserved communities by revealing high levels of AT and inadequate infrastructure in
low-income and minority neighborhoods. They were able to assess and sometimes challenge
local governments’ assumptions, which helped strengthen decision-making and provide bet-
ter outcomes for all stakeholders. Projects with this theme were particularly compelling, thus
another paper delves deeper into the use of emerging data to advocate equitable safe streets
(Sanguinetti and Alston-Stepnitz, 2023).
StreetLight was the most popular and most used tool despite having the steepest learn-
ing curve. Awardees pointed to the usefulness of its geographically broad-based, mul-
timodal, quantitative, and historical data in supporting the four themes described above.
Prior research suggests mobility data tools are more powerful in combination (Nelson et al.,
2021), but employing multiples of the provided tools in a single project was rare. However,
most advocates used more than one tool during the program across their different projects,
consistent with the prior suggestion that multiple tools are desirable to meet different user
requirements and goals (Asad and Le Dantec, 2017).
Sanguinetti et al: Equipping Active Travel Advocates with Digital Mobility Data and Tools 13
Limitations and future research
Limitations of the present research include the small sample size of active travel advocates
and the chance that they are not necessarily representative of all such organizations in the
United States and beyond. In fact, it is possible that the deficits awardees felt in terms of the
human resources to get the most out of the data tools may be an even bigger hurdle for simi-
lar organizations that did not apply to the program. The suite of data tools provided, though
diverse, also lacked the capacity to collect some types of emerging mobility data, such as data
explicitly reflecting safety (e.g., crashes) and public attitudes (Nelson et al., 2021; Lee and
Sener, 2020). Future research with more advocates that includes tools with different types of
emerging data would help create a more complete account of the full potential for promot-
ing safer streets by equipping advocates with data tools.
This research lacks methodological detail regarding how advocates used the data tools
because evaluators’ time with the awardees was limited and they were not provided with
access to the data or tools. This limits the usefulness of the project descriptions as guidance
for prospective data tool users. Further research should map the tools and data types they
provide to more specific use cases, such as those articulated in Lee and Sener (2021) for Strava
(e.g., travel pattern identification, travel demand estimation, route choice analysis, and infra-
structure evaluation) and/or use cases based on targeted street design interventions to help
guide future users with similar aims. There is also more to consider regarding usability and
user experience with the specific tools.
This project did not estimate the monetary value of the data tools provided to the advo-
cates. As previously mentioned, licenses for some emerging data software tools can be very
expensive (Koupal et al., 2022; Nelson et al., 2021; Lee and Sener, 2020; it should be noted
that Strava made their Metro platform free halfway into the MDSS program term). In this
pilot program, the project sponsors bore the cost. As these were private companies, this could
have presented a source of bias; however, this independent program evaluation study did
not find that data analysis efforts or outcomes favored the project sponsors. Future simi-
lar efforts should attempt to estimate program value and explore best practices for funding
mechanisms, e.g., government grant programs that could support active travel data collection
efforts and eligibility for non-profit advocacy groups to have access.
The future of the MDSS Initiative
A recently completed subsequent round of the MDSS program involved awardees across
five municipalities: Arlington County, Virginia; City of Omaha, Nebraska; City of Baltimore,
Maryland; City of San Jose, California; and City of Seattle, Washington. This round offered
only software platforms (no physical tools), including several repeats from MDSS 2020 (i.e.,
StreetLight Data, Urban Footprint, and Populus) as well as several new tools (i.e., Numina,
Kurb.io, and Ford Safety Insights) with additional types of data. Unfortunately, two of these
(Kurb and Safety Insights) were discontinued shortly after program launch, so awardees did
not get a chance to use them. The rapidly changing market for these tools is an important
consideration for future rounds and similar programs.
Several other changes were made for the second round based on lessons learned from the
first year. Rather than providing all tools to all teams, awardees received access to two or three
tools they specifically proposed to use. This was based on the findings that no team in the
2020 program used all the provided tools; in fact, there were some tools that were not used
by any team. The idea for the 2021 program was that the applicants would be more likely to
use all the provided tools if they had fewer and were able to choose the tools. Analyses of this
round are ongoing, but it is safe to foreshadow that it was not always the case that applicants
used all provided tools for several reasons: some tools were discontinued; for another tool,
Sanguinetti et al: Equipping Active Travel Advocates with Digital Mobility Data and Tools14
there were political issues and bureaucratic hurdles that made implementation difficult; and
the teams did not always fully understand the capabilities of tools at the time of selection.
Based on the findings that most teams expressed a need for more technical assistance
with tool use and data analysis and had difficulty achieving concrete impacts on street infra-
structure or policy, Spin required 2021 program applicants to include partnerships involving
universities and/or local government stakeholders for generally more robust analytical capa-
bilities and to bring timely findings to bear on current policy-making agendas, respectively.
Program applications were mainly led by universities with city and/or nonprofit advocate
partners; no selected teams were led by advocacy organizations. This very different context
promises to yield additional lessons learned and contribute to a richer understanding of best
practices for different mobility data user groups. It will also be particularly interesting to see
how local governments, as partners in these efforts instead of audiences to convince, will
be able to leverage findings. Their involvement suggests investment in AT in contrast to the
institutionalized autocentric approaches many advocates in the 2020 program confronted as
a barrier.
Conclusion
Smartphones and shared mobility services offer a new wealth of mobility data that cities are
only beginning to leverage to better understand pedestrian and micromobility use patterns
and identify safety issues. As cities and mobility operators discuss how to use these emerg-
ing data, active travel advocates are likely to be left out of the conversation because they
lack access. Moreover, applications to understand active travel patterns and needs may go
underutilized in transportation planning departments’ adoption and use of emerging data
tools since mainstream planning practices prioritize auto traffic, including in investments
in data.
These considerations inspired Spin, a shared micromobility company, to launch the Mobility
Data for Safer Streets (MDSS) Initiative to put emerging data tools in the hands of advo-
cates who are often a driving force for improving pedestrian and micromobility infrastruc-
ture. For the six participating active travel advocacy organizations in the inaugural round
of the program, access to emerging data tools increased their effectiveness in working with
local government, garnering community support, supporting underserved communities, and
advocating safer streets for all road users by supporting existing advocacy strategies, which
also led to enhanced and new practices.
The MDSS Initiative model—granting data resources to advocates—can be replicated in
other mobility contexts and with other industries entirely. Particularly when progressive
change can be difficult to achieve politically, this collaborative data-based model of influ-
encing decision-making can invite new participants into the conversation and empower
those participants with the technical resources to advance collective understanding. Data
can empower advocates to tell a compelling story to busy decision-makers who are often
inundated with information and perspectives.
Acknowledgements
This research was made possible by the important work of individuals who participated in the
MDSS program. The researchers are grateful for their time and insights. The authors would
also like to thank Cindy Ross for her vital project management support.
Funding Information
Funding for this research came from Spin and Ford Motor Company (Spin’s parent company
during the 2020 MDSS program).
Sanguinetti et al: Equipping Active Travel Advocates with Digital Mobility Data and Tools 15
Competing Interests
The authors have no competing interests to declare.
Author Contributions
The authors confirm they contributed to the paper in the following ways: study conception
and design: A. Sanguinetti, A. Broaddus, E. Alston-Stepnitz, N. Dessouky; data collection: A.
Sanguinetti, E. Alston-Stepnitz, N. Dessouky; data analysis: A. Sanguinetti, E. Alston-Stepnitz,
N. Dessouky; interpretation of results: A. Sanguinetti, M. Ruhl, A. Broaddus; literature review:
A. Sanguinetti, M. Ruhl, A. Broaddus. All authors reviewed the results and approved the final
version of the manuscript.
References
Abduljabbar, R. L., Liyanage, S. and Dia, H. (2021). The role of micro-mobility in shaping
sustainable cities: a systematic literature review. Transportation Research Part D: Transport
and Environment, 92, 102734. DOI: https://doi.org/10.1016/j.trd.2021.102734
Asad, M. and Le Dantec, C. A. (2017). Tap the “make this public” button: a design-based
inquiry into issue advocacy and digital civics. In: Proceedings of the 2017 CHI confer-
ence on human factors in computing systems, CHI 2017. Denver, CO. 6–11 May 2017.
New York: Association for Computing Machinery, 6304–6316. DOI: https://doi.
org/10.1145/3025453.3026034
Bennett, C., MacArthur, J., Cherry, C. R. and Jones, L. R. (2022). Using e-bike purchase
incentive programs to expand the market–North American trends and recommended prac-
tices. A white paper from the Transportation Research and Education Center.Available
from https://trid.trb.org/view/1956108 [Accessed 21 January 2023].
Bonnel, P., Hombourger, E., Olteanu-Raimond, A. M. and Smoreda, Z. (2015). Passive mobile
phone dataset to construct origin-destination matrix: potentials and limitations. Transpor-
tation Research Procedia, 11, 381–398. DOI: https://doi.org/10.1016/j.trpro.2015.12.032
Braun, L.M., Rodriguez, D. A. and Gordon-Larsen, P. (2019). Social (in)equity in access
to cycling infrastructure: cross-sectional associations between bike lanes and area-level
sociodemographic characteristics in 22 large US cities. Journal of Transport Geography, 80,
102544. DOI: https://doi.org/10.1016/j.jtrangeo.2019.102544
Broaddus, A. and Cervero, R. (2019). Transportation planning. In: Leigh, N.G., French, S.P.,
Guhathakurta, S. and Stiftel, B. (Eds.) The Routledge handbook of international planning edu-
cation. New York: Routledge, 253–264. DOI: https://doi.org/10.4324/9781315661063-22
Chang, A. Y., Miranda-Moreno, L., Clewlow, R. and Sun, L. (2019). Trend or fad? Decipher-
ing the enablers of micromobility in the US. A report of SAE International. Available from
https://www.researchgate.net/publication/335028238_Trend_or_Fad_Deciphering_
the_Enablers_of_Micromobility_in_the_US [Accessed 21 January 2023].
Ciociola, A., Cocca, M., Giordano, D., Mellia, M., Morichetta, A., Putina, A. and Salutari,
F. (2017). UMAP: Urban mobility analysis platform to harvest car sharing data. In: 2017
IEEE SmartWorld, ubiquitous intelligence & computing, advanced & trusted computed, scal-
able computing & communications, cloud & big data computing, internet of people and
smart city innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). San Fran-
cisco, CA. 4–8 August 2017. Piscataway, NJ: IEEE, 1–8. DOI: https://doi.org/10.1109/UIC-
ATC.2017.8397566
Combs, T.S. and Pardo, C. F. (2021). Shifting streets COVID-19 mobility data: findings from
a global dataset and a research agenda for transport planning and policy. Transporta-
tion Research Interdisciplinary Perspectives, 9, 100322. DOI: https://doi.org/10.1016/j.
trip.2021.100322
Sanguinetti et al: Equipping Active Travel Advocates with Digital Mobility Data and Tools16
D’Agostino, M., Pellaton, P. and Brown, A. (2019). Mobility data sharing: challenges and
policy recommendations. An issue paper from the University of California Davis Policy
Institute for Energy, Environment, and the Economy. Available from https://escholarship.
org/uc/item/4gw8g9ms [Accessed 21 January 2023].
DiGioia, J., Watkins, K. E., Xu, Y., Rodgers, M. and Guensler, R. (2017). Safety impacts
of bicycle infrastructure: a critical review. Journal of Safety Research, 61, 105–119. DOI:
https://doi.org/10.1016/j.jsr.2017.02.015
Dingil, A.E. and Esztergár-Kiss, D. (2021). The influence of the Covid-19 pandemic on
mobility patterns: the first wave’s results. Transportation Letters, 13 (5–6), 434–446. DOI:
https://doi.org/10.1080/19427867.2021.1901011
DuPuis, N., Griess, J. and Klein, C. (2019). Micromobility in cities: a history and policy
overview. A report of the National League of Cities. Available from https://trid.trb.org/
view/1603755 [Accessed 21 January 2023].
Englin, E. and Davis, S. (2021). Crowdsourced data and safety at state dots and local Partners:
Case Studies of Select Transportation Agencies (No. DOT-VNTSC-FHWA-21-11). Cambridge,
MA: US Department of Transportation, Federal Highway Administration. Available from
https://rosap.ntl.bts.gov/view/dot/59221 [Accessed 23 January 2023].
Fernandez-Heredia, A. and Fernandez-Sanchez, G. (2020). Processes of civic participation
in the implementation of sustainable urban mobility systems. Case Studies on Transport
Policy, 8 (2), 471–483. DOI: https://doi.org/10.1016/j.cstp.2019.10.011
Heineke, K., Kloss, B. and Scurtu, D. (2020). The future of micromobility: ridership and
revenue after a crisis. McKinsey Center for Future Mobility. Available from https://www.
mckinsey.com/industries/automotive-and-assembly/our-insights/the-future-of-micro-
mobility-ridership-and-revenue-after-a-crisis [Accessed 23 January 2023].
Karner, A., London, J., Rowangould, D. and Manaugh, K. (2020). From transportation
equity to transportation justice: within, through, and beyond the state. Journal of Plan-
ning Literature, 35 (4), 440–459. DOI: https://doi.org/10.1177/0885412220927691
Koupal, J. W., DenBleyker, A., Manne, G., Batista, M. H. and Schmitt, T. (2022). Capa-
bilities and limitations of telematics for vehicle emissions inventories. Transportation
Research Record,2676 (3), 49–57. DOI: https://doi.org/10.1177/03611981211049109
Le Dantec, C. A., Asad, M., Misra, A. and Watkins, K. E. (2015). Planning with crowd-
sourced data: rhetoric and representation in transportation planning. In: Proceedings
of the 18th ACM conference on computer supported cooperative work & social computing,
CSCW 2015. Vancouver, Canada. 14–18 March 2015. New York: Association for Computing
Machinery, 1717–1727. DOI: https://doi.org/10.1145/2675133.2675212
Lee, K. and Sener, I. N. (2020). Emerging data for pedestrian and bicycle monitoring: sources
and applications. Transportation Research Interdisciplinary Perspectives, 4, 100095. DOI:
https://doi.org/10.1016/j.trip.2020.100095
Lee, K. and Sener, I. N. (2021). Strava Metro data for bicycle monitoring: a literature review.
Transport Reviews, 41(1), 27–47. DOI: https://doi.org/10.1080/01441647.2020.179
8558
Lee, R. J., Sener, I. N. and Jones, S. N. (2017). Understanding the role of equity in active
transportation planning in the United States. Transport Reviews, 37 (2), 211–226. DOI:
https://doi.org/10.1080/01441647.2016.1239660
McCahill, C. (2017). Improving last-mile connections to transit: an exploration of data and
analysis tools. Conference paper from CNU 25: The annual conference of Congress for
the New Urbanism. Available from http://dev.cnu.org.697elmp01.blackmesh.com/sites/
default/files/2017_NewUrbanResearch_ImprovingLastMileConnectionstoTransit_McCa-
hill.pdf [Accessed 23 January 2023].
Sanguinetti et al: Equipping Active Travel Advocates with Digital Mobility Data and Tools 17
Nelson, T., Ferster, C., Laberee, K., Fuller, D. and Winters, M. (2021). Crowdsourced data
for bicycling research and practice. Transport Reviews, 41 (1), 97–114. DOI: https://doi.org
/10.1080/01441647.2020.1806943
North American Bikeshare Association (NABSA). (2020). 1st annual micromobility state
of the industry report. Available from https://nabsa.net/wp-content/uploads/2020/12/
NABSA-2020-State-of-the-Industry-Report.pdf [Accessed Jul. 30, 2021].
Romanillos, G., Zaltz Austwick, M., Ettema, D. and De Kruijf, J. (2016). Big data and
cycling. Transport Reviews, 36 (1), 114–133. DOI: https://doi.org/10.1080/01441647.20
15.1084067
Roy, A., Nelson, T.A., Fotheringham, A.S. and Winters, M. (2019). Correcting bias in
crowdsourced data to map bicycle ridership of all bicyclists. Urban Science, 3 (2), 62. DOI:
https://doi.org/10.3390/urbansci3020062
Sanguinetti, A. and Alston-Stepnitz, E. (2023). Using emerging mobility data to advo-
cate equitable micromobility infrastructure in underserved communities. Transportation
Research Part D: Transport and Environment, 117, 103613. DOI: https://doi.org/10.1016/j.
trd.2023.103613
Seskin, S., Kite, H. and Searfoss, L. (2015). Evaluating complete streets projects: a guide for
practitioners. Available from https://trid.trb.org/view/1351126 [Accessed 23 January 2023]
Shaheen, S. and Cohen, A. (2019). Shared micromoblity policy toolkit: docked and dockless
bike and scooter sharing. Available from https://escholarship.org/content/qt00k897b5/
qt00k897b5.pdf [Accessed 23 January 2023].
Shaheen, S. A., Martin, E. W., Cohen, A. P., Chan, N. D. and Pogodzinski, M. (2014). Public
bikesharing in North America during a period of rapid expansion: understanding business
models, industry trends & user impacts, MTI Report 12-29. San José, CA: Mineta Transpor-
tation Institute Publications. Available from https://scholarworks.sjsu.edu/mti_publica-
tions/71/ [Accessed 23 January 2023].
Shaheen, S. A., Martin, E.W., Cohen, A. P. and Finson, R. S. (2012). Public bikesharing in
North America: early operator and user understanding (No. CA-MTI-12-1029). San José, CA:
Mineta Transportation Institute. Available from https://transweb.sjsu.edu/sites/default/
files/1029-public-bikesharing-understanding-early-operators-users.pdf [Accessed 23 Jan-
uary 2023]
Smith, C. S. and Schwieterman, J. P. (2018). E-scooter scenarios: evaluating the potential
mobility benets of shared dockless scooters in Chicago. Available from https://trid.trb.org/
view/1577726 [Accessed 23 January 2023]
Smith, M., Hosking, J., Woodward, A., Witten, K., MacMillan, A., Field, A., Baas, P. and
Mackie, H. (2017). Systematic literature review of built environment effects on physi-
cal activity and active transport—an update and new findings on health equity. Interna-
tional Journal of Behavioral Nutrition and Physical Activity, 14(1), 1–27. DOI: https://doi.
org/10.1186/s12966-017-0613-9
Wagner, J. (2013). Measuring performance of public engagement in transportation plan-
ning: three best principles. Transportation Research Record, 2397 (1), 38–44. DOI: https://
doi.org/10.3141/2397-05
Winters, M. and Branion-Calles, M. (2017). Cycling safety: quantifying the under report-
ing of cycling incidents in Vancouver, British Columbia. Journal of Transport & Health, 7,
48–53. DOI: https://doi.org/10.1016/j.jth.2017.02.010
Yang, H., Cetin, M. and Ma, Q. (2020). Guidelines for using StreetLight data for planning tasks
(No. FHWA/VTRC 20-R23). Charlottesville, VA: Virginia Transportation Research Council
(VTRC). Available from https://rosap.ntl.bts.gov/view/dot/55501 [Accessed 23 January
2023].
Sanguinetti et al: Equipping Active Travel Advocates with Digital Mobility Data and Tools18
How to cite this article: Sanguinetti, A, Alston-Stepnitz, E, Ruhl, M, Dessouky, N and Broaddus, A.
2023. Equipping Active Travel Advocates with Digital Mobility Data and Tools: An Evaluation of a
US Trial Program.
Active Travel Studies: An Interdisciplinary Journal
, 3(1): 7, 1–18. DOI: https://doi.
org/10.16997/ats.1198
Submitted: 19 October 2021 Accepted: 14 November 2022 Published: 09 March 2023
Copyright: © 2023 The Author(s). This is an open-access article distributed under the terms of the
Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use,
distribution, and reproduction in any medium, provided the original author and source are credited.
See http://creativecommons.org/licenses/by/4.0/.
Active Travel Studies: An Interdisciplinary Journal
is a peer-reviewed
open access journal published by University of Westminster Press.
