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Contract No. DE-AC36-08GO28308

Technical Report

NREL/TP-6A20-70331

January 2019

An Overview of Technologies for 

Individual Trip History Collection: 

Mobility Decision Science Pillar SMART 

Mobility Consortium

Ted Kwasnik, Scott P. Carmichael, and Steven Isley

National Renewable Energy Laboratory
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Contract No. DE-AC36-08GO28308

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Technical Report

NREL/TP-6A20-70331

January 2019

An Overview of Technologies for 

Individual Trip History Collection: 

Mobility Decision Science Pillar SMART 

Mobility Consortium

Ted Kwasnik, Scott P. Carmichael, and Steven Isley

National Renewable Energy Laboratory

Suggested Citation

Kwasnik, Ted, Scott P. Carmichael, and Steven Isley. 2019. An Overview of Technologies for 

Individual Trip History Collection: Mobility Decision Science Pillar SMART Mobility 

Consortium. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-70331.

https://www.nrel.gov/docs/fy19osti/70331.pdf.
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Executive Summary

The WholeTraveler Transportation Behavior Study is an initiative within the Mobility Decision 

Science pillar of the SMART (Systems and Modeling for Accelerated Research in 

Transportation) Mobility consortium. The study will survey residents in the San Francisco Bay 

Area and employ state-of-the-art multidisciplinary methodological approaches to identify and 

analyze complex changes in travel choice patterns, traveler preferences, and the traveler 

decision-making processes over various time scales, particularly with relation to emerging 

transportation technologies and services (such as electric vehicles, mobility on demand, shared

mobility options, connected and automated vehicle technology, and e-commerce). Moreover, 

stated preferences or characteristics will be correlated with observed travel behaviors to measure 

the relative importance of personality characteristics and circumstantial constraints (e.g., 

the travel modes available within one’s neighborhood) in travel choices. Innovative tools, 

capabilities, and scientific findings produced through this effort are anticipated to help improve 

future transportation and energy systems solutions by generating fundamental research and 

insights that can inform the design of pathways toward more efficient and secure transportation 

systems. 

Fine-grained location data (FGLD), which for the purpose of this study is a record of an 

individual’s location collected at a resolution sufficient to determine both the unique destinations 

a traveler visits each day and the modes used to travel between them, are a component of the 

WholeTraveler study. The methods for collecting FGLD and the means by which it is associated

with a traveler’s personal attributes have significant implications for the burden on research 

participants, the cost and complexity of the study, as well as the confidence with which 

hypotheses can be validated. This paper provides a broad overview of existing technologies for 

FGLD collection, categorizing them as either network or onboard approaches. An onboard 

system, such as smartphone or Global Positioning System (GPS) device sends signals to external 

networks to capture its own location. Conversely, network systems, such as cell towers, are 

comprised of sensing nodes with known locations that record when individually identifiable 

assets come within their range of detection. Tables ES-1 and ES-2 summarize the characteristics 

of prominent technologies within each category. Table ES-1 focuses on implications for 

researchers and categorizes technologies by their relative burden to participants and researchers, 

their potential to influence who will be willing and able to participate in the study, the risks 

associated with data collection, and the degree to which researchers will be able to communicate 

with and identify participants. Table ES-2 details key technical specifications of each technology 

including its temporal and spatial resolution, the physical environments for which it is suitable, 

and the modes it is equipped to classify. 

Our analysis indicates that the most sensible approach to FGLD collection for the purposes of 

WholeTraveler is to leverage onboard devices, and in particular the existing Moves or Google 

Maps applications for Android and iOS smartphones. About two-thirds of U.S. adults currently 

own a smartphone, and analysis of key demographics suggests a sufficiently broad pool from 

which to draw a representative study population (Smith 2015). Use of existing smartphone 

applications offloads the burden of developing and maintaining sophisticated software that 

provides a simple user experience and yields standardized travel diary data. Both Google Maps

and Moves implement background processing to classify mode, and they also allow users to 

generate export files. Instructions for how to generate and upload these files (or even automated 
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data transfer tools in the case of Moves, which supports an application programming interface 

[API] for software developers) can be integrated into a larger web-based survey. Collecting 

FGLD through Moves or Google Maps would likely need to engage users at least twice. The first 

interaction would instruct users how to install and activate location history collection through the 

Google Maps or Moves application. After a period of time in which sufficient data have been 

collected, a second interaction would provide means for participants to transmit extracts of their 

location history data to researchers.

Of these two applications, Google Maps is a strong choice because it already serves a substantial 

user base and comes with extensive user support resources. Accordingly, participants are 

expected to have at least some familiarity with Google Maps and to face low barriers to 

participation in the study. However, the robust activity tracking functionality of Moves holds 

significant value to the WholeTraveler initiative specifically. The Moves application also has the 

potential to facilitate a less burdensome participant experience through programmatic API 

data collection.

Independently developing a custom smartphone application is not recommended at this stage of 

the WholeTraveler research initiative. Such an approach is attractive in that it enables the 

coupling of data collection for both traveler location and personal characteristic data. It also 

affords the most customization, as data can be collected at intervals and in formats specified 

by the researcher. However, there are notable challenges in establishing minimal viable 

functionality, as well as in ensuring compatibility across an ever-growing combination of 

hardware devices and software versions. Moreover, mode inference is not inherent in raw data, 

so advanced machine learning, algorithmic or statistical approaches would need to be employed 

to derive mode classifications with confidence. Furthermore, behavioral barriers to user adoption 

pose unique challenges. Overall, the technical requirements and the rapid development cycles 

inherent in smartphone application development pose challenges and costs that are beyond the 

scope and primary research interests of the WholeTraveler study. 

Dedicated GPS devices, which have been widely implemented by the transportation research 

community, represent an alternative and acceptable form of FGLD collection to smartphones.

This method of FGLD collection, however, also poses unique challenges. Studies based on this 

approach typically furnish participants with car-mounted or handheld devices that passively 

collect FGLD. At the end of the study, participants return the devices via mail. Participants also 

conduct mail-in, web-based, or telephone surveys to provide supplemental information as 

necessary. While dedicated GPS devices have low barriers to use and collect data at comparable 

spatial and temporal resolutions to smartphones, these devices are less suitable for the purposes 

of WholeTraveler. To minimize study participant attrition rates, studies using dedicated GPS 

devices typically have relatively short FGLD collection periods, commonly no more than a 

week. Smartphones, on the other hand, are commonly deeply entrenched in their owners’

everyday routines and are thus less likely to be forgotten than a separate FGLD device that does 

not directly serve the need of the participant. GPS devices also do not provide FGLD with travel 

modes already classified. Furthermore, smartphone costs also do not scale in the same way as do 

GPS devices. Once a mechanism for porting smartphone derived FGLD has been developed, it 

can be made available to a vast number of users at low marginal costs. For GPS-based surveys, 

however, each new participant adds the same amount of hardware and shipping costs. 
v

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Accordingly, larger sample sizes are expected to be more readily obtained through smartphonebased applications. 

Alternative FGLD collection approaches outlined in Tables ES-1 and ES-2 (below) are not 

recommended primarily because of either the cost of developing a sensor network or the 

difficulty of linking location data to supplemental data sets. Bluetooth readers and radiofrequency identification (RFID) cards, for example, require the development of distributed 

sensor networks whose cost and complexity prohibit large area coverage. Also, these approaches 

lack convenience, as they may require participants to carry additional hardware (introducing 

the risk of location tracking assets being forgotten or abandoned). Likewise, network-based 

approaches, including cell towers and optical sensors are well equipped to record FGLD for 

large numbers of individuals, yet they lack the ability to interact with users in order to obtain 

key individual identifiers and attributes. 

No matter the technical means of data collection, the secure handling of FGLD is of critical 

importance. For instance, location information can be analyzed to reveal an individual's 

residence, place of work, and typical daily routine. Ensuring location data remains secure from 

the time of collection to the time the data are warehoused is a significant responsibility. 

FGLD that have been collected frequently and over long durations afford powerful insights for 

the design, operation, and planning of energy and transportation systems. One primary benefit 

of integrating these data within the WholeTraveler project will be to understand how this 

information can provide transportation managers with innovative services that save travelers 

time and money, reduce overall network congestion, and contribute to energy efficiency 

initiatives. The opportunities to improve traveler experiences with transportation and energy 

systems are only beginning to be imagined, yet personal location data clearly have an important 

role to play in these transformations. 

Table ES-1. Methods and Qualitative Attribute Assessments

Method User 

Burden

Potential 

Sampling 

Biases to 

Overcome

Privacy and

Security 

Risks

Researcher

Burden

ResearcherParticipant 

Communication

Portable 

GPS Device

High Self-selection Moderate - Costs per device 

- Mode Inference

- User engagement/ 

retention

Acceptable

Methods include 

mail-in surveys, 

web/phone 

applications, 

interviews

Vehicle 

GPS

Moderate Self-selection

Non-drivers

(young,

seniors, low 

income)

Moderate - Device cost

- User engagement

Acceptable

Methods include 

mail-in surveys, 

web/phone 

applications, 

interviews
vi

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Method User 

Burden

Potential 

Sampling 

Biases to 

Overcome

Privacy and

Security 

Risks

Researcher

Burden

ResearcherParticipant 

Communication

Existing

Smartphone 

Application

Low Self-selection

50+

No high 

school degree

Low income 

(<$30,000/yr)

Rural

Moderate to 

High

- User 

adoption/retention

- Device compatibility

- Preservation of 

battery life

Acceptable

Methods include 

mail-in surveys, 

web/phone 

applications, 

interviews

Custom

Smartphone 

Application

Moderate Self-selection

50+

No high 

school degree

Low oncome 

(<$30,000/yr)

Rural

High

(Application is 

less mature 

and the 

researcher is 

more 

accountable 

for it.)

- Software 

Development 

- Device compatibility

- Preservation of 

battery life

- Mode inference

- User adoption/ 

experience/retention

High

Continuous realtime feedback 

possible through 

application

Other methods 

include mail-in 

surveys, 

web/phone 

applications, 

interviews

Cell Tower Low 50+

No High 

School 

Degree

Low Income 

Rural

Low - User engagement

- Data acquisition

Not feasible

BlueTooth/

RFID

Low 50+

No High 

School 

Degree

Low Income 

(<$30,000/yr)

Rural

Moderate - Cost of tracking 

devices and sensing 

network

- Scaling of study 

area

- User engagement/ 

retention

Marginally 

Acceptable

Methods include 

mail-in surveys, 

web/phone 

applications, 

interviews
vii

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Method User 

Burden

Potential 

Sampling 

Biases to 

Overcome

Privacy and

Security 

Risks

Researcher

Burden

ResearcherParticipant 

Communication

License 

Plate 

Reader

Low Non-Drivers

(Young, 

Seniors, Low 

Income)

Low - User engagement Very Low

Difficult to engage 

participants 

vehicle 

registrations 
viii

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Table ES-2. Methods and Technical Attributes

Method Type Temporal 

Resolution

Spatial 

Resolution

Spatial 

Coverage

Mode Classification

Portable 

GPS Device

Onboard Adjustable—

about one

second 

intervals 

7–100 m Moderate

Most outdoor 

locations

Identifiable Modes: 

All

Accuracy: 

Moderate

Vehicle 

GPS

Device

Onboard Adjustable—

about one

second 

intervals

~ 10 m–

100 m

Moderate

Vehicleaccessible 

outdoor locations

Identifiable Modes:

Car

Accuracy:

Not Applicable

Smartphone 

Application

Onboard Adjustable—

about one

second 

intervals

8–100s m 

Reception 

Dependent

High

Most indoor and 

outdoor locations

Identifiable Modes: 

All

Accuracy: 

Moderately High

Cell Tower Network Inconsistent

Depends on 

phone use 

habits

100s m High

Range of cell 

network

Identifiable Modes: 

All

Accuracy: 

Very Low

BlueTooth Network Inconsistent

Whenever user 

is within range

10 cm Very Low

Range of beacon 

network

Identifiable Modes: 

Variable

Accuracy: 

Low (Unless network 

is mode specific)

RFID Network Inconsistent

Whenever user 

is within range

< 1 cm Very Low

Range of beacon 

network

Identifiable Modes: 

Variable

Accuracy: 

Low (Unless network 

is mode specific)

License 

Plate 

Reader

Network Inconsistent

Whenever user 

is within range

10’s m Low

Range of camera 

network

Identifiable Modes: 

Car

Accuracy: 

High
ix

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Table of Contents

Introduction ................................................................................................................................................. 1

Onboard Systems ....................................................................................................................................... 5

GPS Devices........................................................................................................................................... 5

Smartphones........................................................................................................................................... 7

Network Systems ...................................................................................................................................... 12

Cell Tower Networks ........................................................................................................................... 12

Bluetooth .............................................................................................................................................. 12

Radio Frequency Identification (RFID) ............................................................................................... 13

License Plate Readers........................................................................................................................... 14

Recommendation.................................................................................................................................. 15

Conclusion................................................................................................................................................. 17

References................................................................................................................................................. 18

Appendix.................................................................................................................................................... 21

List of Figures

Figure 1. Types of FGLD system.................................................................................................................. 4

List of Tables

Table ES-1. Methods and Qualitative Attribute Assessments...................................................................... v

Table ES-2. Methods and Technical Attributes.........................................................................................viii

Table 1. Common Location-Based Smartphone Applications.....................Error! Bookmark not defined.
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Introduction

The WholeTraveler Transportation Behavior Study is an initiative within the Mobility Decision 

Science pillar of the SMART (Systems and Modeling for Accelerated Research in 

Transportation) Mobility consortium. The study will survey residents in the San Francisco Bay 

Area and employ state-of-the-art multidisciplinary methodological approaches to identify and 

analyze complex changes in travel choice patterns, traveler preferences, and the traveler 

decision-making processes over various time scales, particularly with relation to emerging 

transportation technologies and services (such as electric vehicles, mobility on demand, shared 

mobility options, connected and automated vehicle technology, and e-commerce). Moreover, 

stated preferences or characteristics will be correlated with observed travel behaviors to measure 

the relative importance of personality characteristics and circumstantial constraints (e.g., 

the travel modes available within one’s neighborhood) in travel choices. Innovative tools, 

capabilities, and scientific findings produced through this effort are anticipated to help improve 

future transportation and energy systems solutions by generating fundamental research and 

insights that can inform the design of pathways toward more efficient and secure transportation 

systems. 

This paper is primarily concerned with assessing the modern technologies suitable for recording 

fine-grained location data (FGLD), which represent a travel history of sufficient spatial and 

temporal resolution to determine the unique destinations a traveler visits and the modes used to 

travel between them. Detailed route locations and trajectories may be beneficial to mode 

classification efforts and technologies will accordingly be assessed by their spatial and temporal 

resolution. Moreover, this paper will discuss the suitability of technologies to the priorities of the

WholeTraveler research initiative, including low participant burden and the ability to effectively

associate FGLD with supplemental traveler profiles. Since the information in this report is 

expected to be of interest to researchers similarly exploring the societal implications of 

transportation decisions, we also propose categorization of the modern FGLD technologies to 

frame distinct approaches to FGLD collection.

Furthermore, while this report focuses on automated FGLD solutions, the use of self-reported 

travel diaries as a primary source of data bears discussion. Researchers have employed such 

methods extensively for decades, and their known limitations include participant burden, cost, 

accuracy, and the inability to record precise fluctuations in speed and trajectory during trips (Jan, 

Horowitz, and Peng 2000; Raza et al. 2015; Vij and Shankari 2015). Houston, Luong, and 

Boarnet (2014) suggest self-reported diaries typically fail to record between 10% and 80% of 

trips, and a summary of eight transportation studies engaging 1,900 households or more (Bricka 

and Bhat 2006a) suggests that underreporting rates tend toward about 30% of trips on average. 

Short trips (often within a chain of trips) and those occurring later in the day are also most likely 

to be neglected in trip accounting (Bricka and Bhat 2006a; Wolf et al. 2004). Though selfreported travel diaries are not of primary interest for this study, they may be indispensable for 

collecting mobility related information from individuals for time periods prior to participation in 

a study. Historical information regarding duration of car ownership, transit usage, and frequently 

visited locations adds context and richness to personalized analyses, even if it is subject to 

human reporting error.
2

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Priorities for WholeTraveler

The technologies that underlie FGLD collection yield distinct implications for the success of 

WholeTraveler in capturing high-quality records of origin and destination pairs and mode 

choices that can be associated with traveler attitudes and preferences. This paper assesses the 

suitability of each technology according to the following attributes: 

Technical Considerations

• Temporal Resolution: Technologies are assessed according to the frequency at which 

spatial coordinates are collected. Ideal methods collect data frequently enough to reveal all 

origin-destination pairs throughout a day. Acceleration and velocity estimates may also be 

necessary for fine-grained travel mode inference.

• Spatial Resolution: An ideal source collects spatial coordinates at fine enough resolution to 

reveal all origin-destination pairs. This research effort does not require a detailed 

understanding of the exact route taken during trips, so long as travel mode can be acquired or 

imputed through alternative methods.

• Spatial Coverage: An ideal FGLD approach facilitates data collection in all urban and rural 

locations outside of buildings.

• Mode Classification: An ideal FGLD approach facilitates data collection across walking, 

biking, public transit, and automotive modes. The ability to distinguish between driving 

alone, driving with others, and using ride-sharing or car-sharing/rental services is also 

of interest.

Other Considerations

1. Participant Burden: An ideal FGLD collection process would be entirely passive, 

requiring no user intervention beyond opt-in consent. It would pose no costs to 

participants, financially or otherwise. In practice, however, participants will likely need 

to be prompted for information at various stages of the study. Additionally, the study may 

require participants to carry a device (e.g., a smartphone). Further, participation in the 

study could result in participants incurring costs related to smartphone data plan usage or 

battery drainage. A user experience designed to minimize unnecessary hassles or 

annoyances will be critical in retaining participants throughout the entire duration of the 

study. For instance, an FGLD solution that frequently prompts the participant to enter 

their transportation mode would be expected to have a higher attrition rate than a solution 

that can discern the user’s transportation mode passively.

2. Sampling Bias: The decision to collect FGLD through a particular technology may 

inherently bias the study population. Vehicle-mounted Global Positioning System (GPS)

devices, for example, will exclude those who do not drive or otherwise have access to a 

vehicle. This demographic segment includes the young, physically disabled, very old, and 

low-income populations. While sampling biases can never be eliminated, the selection of 

this study’s FGLD collection method should include the most equitable representation of 

age, race, ethnicity, gender, income, and education levels possible. Recruitment outreach 

efforts may be necessary to include demographic groups that may otherwise be 

inadequately represented in the study's sample population. Unavoidable sampling biases 
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should be accounted for by employing appropriate bias reduction techniques (e.g., 

weighting the survey sample).

3. Privacy and Security Risks: Maintaining control and security over all location data 

throughout all phases of data collection and analysis is a critical responsibility for 

researchers. An optimal FGLD collection method will allow data to be encrypted 

at the time of collection and remain encrypted until analysis requires decryption.

4. Researcher-Participant Engagement: The study requires the ability to link FGLD with 

survey data collected from a given participant. The technology used to collect FGLD has 

notable implications for the ability of researchers to regularly engage with study 

participants, and individual identifiers of some form are necessary to facilitate this 

interaction. An ideal solution would provide opportunities for researchers to prompt users 

for feedback at any point throughout the study. Further, the solution should allow these 

interactions to occur in response to changes in the participant’s location.

5. Researcher Burden: Each technology poses unique challenges to researchers in study 

design and implementation. An ideal solution would furnish researchers with the data 

needed to answer the essential research question at minimal costs in terms of time and 

money. For the purposes of this study, existing high-quality FGLD methodologies and 

technologies that enable researchers to focus on analysis rather than collection will be 

prioritized over the development of innovative approaches. 

6. Third Parties: Each technology will be evaluated in terms of the ability for researcher 

and participant burden to be mitigated through the involvement of third parties. 

Involvement may take the form of partnerships with similarly aligned entities, including 

state, federal, and regional planning agencies that are conducting their own FGLD 

collection efforts. Researchers may also be able to use FGLD streams already being 

collected by users, such as the location history on smartphone devices, or locations 

captured by popular activity tracking apps.

Location Information Collection Systems

The field of possible FGLD collection technologies is broad enough to benefit from a taxonomy. 

We have chosen to distinguish between onboard and network locational systems (Figure 1). An 

onboard system sends signals to one or more external networks to capture its own location. 

Examples of such systems include GPS devices that triangulate location from communication 

with satellites, and smartphones that interact with GPS, wireless, and Bluetooth networks to 

position themselves. Conversely, network systems are comprised of sensing nodes with known 

locations that record when individually identifiable assets come within their range. Network 

systems include cell towers, Bluetooth beacons, and license plate readers. Network and onboard 

systems can be coupled such that the individual assets a network interacts with are aware of their 

own location (e.g., as is the case with cell towers and smartphones).
4

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Figure 1. Types of FGLD system

As illustrated in Figure 1, the onboard and network system approaches yield distinct forms 

of raw locational data. Onboard systems can continuously track an individual's location, thus

providing detailed trajectories. Onboard systems may also directly interact with participants to 

classify modes and trips, and to collect supplemental traveler attribute information. Network 

systems, conversely, record only the intermittent times at which devices interact with them, for 

example, when an individual comes within range of a Bluetooth beacon or license plate reader, 

or when he or she places a call over a cellular network. The passive nature of individual 

interactions with network systems poses minimal burdens to participants, many of whom are 

unaware these data are being generated. However, such systems do not typically afford 

opportunities to obtain consent from users or mechanisms to further engage with them for more 

information. Location data obtained from third parties are typically anonymized, and thus

correlations between travel patterns and traveler characteristics would need to be made for 

populations aggregated by geographic boundaries, such as Zip codes or census statistical areas.
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Onboard Systems

GPS Devices

The Global Positioning System (GPS) is a network of 24 satellites that orbit the Earth. It was 

developed by the U.S. Department of Defense and is now freely available for the public to use.1

GPS receivers on the Earth’s surface process signals from this network and triangulate their 

latitude and longitude after having established connection to four or more satellites. GPS 

receivers are typically either sold as lightweight handheld battery-powered devices or as devices 

that plug into a passenger vehicle’s on-board diagnostic (OBD) port. GPS trackers have been 

used extensively to inform transportation studies since the 1990s (Shen and Stopher 2014). The 

2010–2012 California Household Travel Survey is one of the largest U.S. travel surveys

conducted using the technology. It engaged 5,715 households with either wearable or vehiclemounted GPS devices (NuStat Research Solutions 2013).

Technical Considerations

The spatial accuracy of GPS positioning varies according to the quality of the receiving device, 

the number of satellites with which it can communicate, atmospheric conditions, and interference 

on the ground (e.g., from deep canyons or clusters of tall buildings). Conventional GPS device 

positioning is typically accurate to within 7–100 meters (Houston, Luong, and Boarnet 2014). 

Vehicle- mounted devices typically offer slightly less accurate location readings because of 

signal blocking from the metal in the car body and potentially from window tinting (GPS 

Technologies 2017). Similarly, the spatial coverage is limited to outdoor locations with 

reception to satellites (and vehicle accessibility for OBD devices). The temporal resolution of 

conventional receivers is typically sufficient to record multiple position readings per second, 

though higher sampling frequencies do come at the cost of reduced battery life. 

Mode classification is not innate to GPS-derived location data, yet post-processing techniques 

to infer this information are advancing. Zhu et al. (2016) recently demonstrated machine learning 

algorithms to distinguish between walking, cycling, bus, subway, and driving modes with greater 

than 90% accuracy. 

Research Implications

A primary burden posed to participants by dedicated GPS-devices is the need for participants to 

remember to carry and charge the device. Numerous studies have shown that owners of GPSbased travel diaries can fail to record trips, especially those occurring later in the day, likely

because they forget to carry the devices (Houston, Luong, and Boarnet 2014; Montini et al. 2015;

Wolf et al. 2004). Bricka and Bhat (2006b) report that of those who elect to participate in GPS 

studies, those most likely to underreport trips are households that own three or more vehicles, 

households with incomes under $50,000, and those 25 years old and younger. 

Beyond reading use instructions, charging device batteries, and shipping devices back to 

researchers at the end of the survey (possibly requiring a trip to a shipment location), GPS data 

collection requires little effort on a daily basis. Some sampling bias may be introduced among 

older demographics that face physical challenges, skepticism, or difficulties learning new 

 1

“What is GPS?” Garmin, http://www8.garmin.com/aboutGPS/. 
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technologies (Smith 2014). Researchers may consider making dedicated support staff available 

via email, phone, or in-person communication to mitigate such risks. 

Regarding privacy, GPS-derived location data hold notable privacy implications. Supreme Court 

Justice Sotomayor has stated, “GPS monitoring generates a precise, comprehensive record of a 

person’s public movements that reflects a wealth of detail about her familial, political, 

professional, religious, and sexual associations” (New York Times 2012). Accordingly, such 

information should be handled with utmost care. While the risk of cyberattack on dedicated GPS 

devices is limited in that they are not inherently connected to the internet, once data are

warehoused for post-processing and analysis, industry best practices should be implemented 

to restrict access to and anonymize data.

Regarding researcher burden, undertaking a large-scale FGLD collection effort primarily 

through GPS devices is a significant challenge. The 2012 California Travel Survey, for example, 

cost about $10 million over two years (NuStat Research Solutions 2013). Costs scale 

proportionally with the size of the study population; as more participants are brought onboard, 

more devices will need to be acquired, mailed, and retrieved. The same GPS can be used 

consecutively by multiple participants, though this adds to the duration of the data collection 

phase of the research initiative.

Development of a separate platform to solicit auxiliary surveys and diaries also adds to project 

complexity. When using dedicated GPS devices to obtain FGLD, researchers commonly 

supplement GPS data collection with written mail-in surveys, computer-assisted telephone 

interviewing (CATI), or web-based surveys (NuStat Research Solutions 2013).

Travel surveys already being conducted by state, regional, and national transportation planning 

agencies provide opportunities to collaborate with third parties to derive new GPS-derived data

sets. Such partnerships naturally reduce the control over the manner in which FGLD are

collected, as well as the types and number of supplemental questions to which participants

respond. However, long-term partnerships provide greater access to funding and opportunities to 

reduce researcher burdens that may help facilitate longitudinal studies over many years. The 

Transportation Secure Data Center2 at the National Renewable Energy Laboratory (NREL) hosts 

such data from previous transportation studies, using anonymization and controlled access 

techniques that extract additional value from these publicly funded studies in a way that 

preserves the privacy of study participants. 

Summary

Dedicated GPS devices provide an acceptable means of gathering FGLD for the WholeTraveler 

research effort. Data can be collected at more than sufficient temporal and spatial resolutions, 

and over large enough areas to capture travel patterns. While mode classification is not inherent 

in data collection or viable through device notifications, plentiful literature exists for those 

willing to invest time in developing post-processing schemes.

The greatest limitation inherent in dedicated GPS device data collection is participant burden, 

particularly in terms of the need to carry an additional device but also the need to supply 

 2 Transportation Secure Data Center, NREL, https://www.nrel.gov/transportation/secure-transportation-data/.
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additional information through an alternate data collection platform. Costs also scale 

proportionally to the number of participants engaged in the study, inhibiting 

widespread deployment.

Smartphones

Over two-thirds of adults in the United States owned smartphones as of 2015 (Smith 2015), 

and such widespread adoption has spurred a suite of location-aware applications related to 

navigation, ridesharing, fitness, activity, and destination reviews. These applications have 

become essential everyday tools, and they collect rich data sets pertaining to travel histories. 

Numerous researchers have also developed custom applications for smartphones to collect 

FGLD and make personal travel patterns more visible to travelers (Bie et al. 2012; Cottrill et al.

2013; Froehlich et al. 2009; Holleis et al. 2012; Jariyasunant et al. 2015; Meloni et al. 2014;

Jylhä et al. 2013; Schrammel, Busch, and Tscheligi 2013; Shankari et al. 2014).

Technical Considerations

Smartphones collect location through a suite of instruments, including GPS, accelerometer, 

Bluetooth, and wireless sensors. While the spatial accuracy of GPS sensors are equivalent 

to those within dedicated GPS devices, in areas of poor GPS reception, smartphones can 

use Bluetooth and wireless signals to position themselves. The received signal strength 

indication (RSSI) methodology for wireless signals is accurate to within tens of meters, and 

matching wireless fingerprints of known locations further refines accuracy to within a few 

meters (Lawson 2012; Lee et al. 2016; Swangmuang and Krishnamurthy 2008). Moreover, 

smartphones are capable of multiple readings per second. The spatial coverage of GPS-equipped 

smartphones includes nearly all indoor and outdoor locations. 

Detailed GPS traces in coordination with precise tilt and positioning attributes provided by 

accelerometers provide information that facilitates mode inference. Reddy et al. (2010) report 

that the use of GPS and accelerometers together can distinguish between stillness, walking, 

running, biking, and motorized transport with an accuracy of over 93%, and that eliminating 

either sensor would decrease accuracy between 10% and 20%. Users can also be prompted 

through notifications to ground truth predicted mode classifications, though this increase in 

classification accuracy comes at cost of increased user burden and may be dangerous for users 

(e.g., by prompting drivers for mode classification while they are at a stop light) (Vij and 

Shankari 2015; Reddy et al. 2010).

Research Implications

The burden for participants to provide smartphone derived FGLD includes smartphone 

ownership, battery drainage, and data usage fees (Shen and Stopher 2014). The installation 

process of a new application, no matter how simple, is likely to dissuade users from completing 

the study and introduces a sampling bias toward more technologically savvy users. While users 

are less likely to forget a smartphone than a dedicated GPS device, users will also need to 

remember to make sure a smartphone application is running and configured appropriately 

throughout the study duration to avoid underreporting trips.

Reliance on smartphone ownership is expected to introduce some sampling bias. The Pew 

Research Group reports that there is no difference among racial or ethnic groups in smartphone
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ownership but that ownership rates are as low as 30% for those 65 years and older and those 

without a high school degree (Smith 2015). Those earning less than $30,000 a year and those 

living in rural areas are also more likely not to own a smartphone than more affluent and urban 

participants, though adoption rates in these groups still exceed 50% (Smith 2015). Sampling bias 

can be diminished through dedicated efforts to solicit participation among underrespresented 

groups, or by following the model of the RAND American Life panel which provides devices to 

those who could not otherwise participate in the study (RAND Corporation 2017).

Collecting FGLD from smartphone applications poses greater privacy and security concerns than

from dedicated GPS devices because poor programming, unanticipated cyberattacks, inadequate 

encryption schemes, and the possibility of exposed passwords introduce additional 

vulnerabilities to the data collection pipeline. Moreover, beyond exposing location data, another 

significant concern with smartphones is the disclosure of user credentials that may be necessary 

to access applications that collect FGLD. Some security comes from hosting apps on the Android 

and iOS app stores which enforce basic integrity requirements. Moreover, third party user 

account services like Oauth leverage existing user credentials on popular site like Google or 

Facebook, and in doing so incorporate latest industry best practices.

A clear advantage of smartphones is that they facilitate rich researcher engagement with 

participants and the ability to couple data collection efforts for both location information and 

traveler characteristics. For example, notifications prompting users to answer questions about 

trip purpose and travel attitudes can be either scheduled for certain times of day or triggered by 

changes in one’s location. Commonly, after a user has remained at a location for a notable 

amount of time, smartphone application developers will tag the local area with a “geofence.” 

Entering or exiting a geofence serves as the basis for identifying distinct trips, initiating location 

collection algorithms, and providing information to users.

The burden for researchers depends largely on whether a third-party application is used to 

collect FGLD or the researcher decides to build a custom application. Custom development 

requires a handle on effective user experience strategies, deep technical knowledge, and an 

understanding of compatibility across hardware, software, and data protocols. There exist 

software development kits such as HyperTrack3 that reduce the burden of undertaking the 

development of a location-tracking smartphone application. This particular software 

(HyperTrack) allows for raw data error correction, real-time tracking, and trip purpose 

classification (but not inherent mode classification as of the time of publication). 

Fortunately, many third-party applications already adopted by users passively collect trip 

information and provide access to detailed trip histories. Table 1 (below) organizes these apps by 

vendor and category, and also summarize key app limitations for the purposes of WholeTraveler. 

Researchers may consider partnering with these providers, or more simply, inviting new users to 

use the applications and then periodically send extracts of their trip logs.

The Google Maps application, which is available for both iOS and Android devices, has a

Timeline feature through which a full export of trip histories (inclusive of route and mode) may 

be downloaded as a raw data file. While the process can be completed in little more time than it 

 3 Developer homepage, HyperTrack, https://developer.hypertrack.com/.
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takes to download the file, users must have an account with Google, be signed into this account

on the device, and activate the location history for the Google Maps application before the FGLD 

application will record travel histories. Participants must also have access to both a smartphone 

and an internet-connected desktop to retrieve FGLD via Google Maps. Access to a desktop is not 

expected to be a significant barrier to participation, as a recent study (Smith 2015) found that

only 7% of smartphone users have a smartphone as their only form of internet connection. It is 

still noteworthy, however, that the rates of smartphone dependence for internet are nearly twice 

as high for blacks, Hispanics, those between 18 and 29 years old, and those with incomes less 

than $30,000 annually (Smith 2015). For these participants, special accommodations (such as 

researcher-provided devices) may be necessary to overcome the burden of accessing publicly 

available computers. Overall, use of Google Maps will introduce some selection toward more 

affluent participants who already own and operate multiple internet-connected devices.

Table 1. Common Location-Based Smartphone Applications

a application programming interface

Genre App Android iOS User Access to 

FGLD

Limitations

Maps and 

Navigation

Google Maps Yes Yes Export file Google account sign in;

Enabling location history

Maps and 

Navigation

Apple Maps No Yes View only Point locations only, 

no trip details

Maps and 

Navigation

Waze Yes Yes Export file Car trips when location 

services enabled only

Transportation Uber Yes Yes View/ API Rideshare trips only

Transportation Lyft Yes Yes View / API Rideshare trips only

Activity Tracker Moves Yes Yes Export file/APIa Account sign in;

Enabling location history

City Guide FourSquare Yes Yes Export file/API Point locations only, 

no trip details

Fitness RunKeeper Yes Yes Export file Pedestrian trips only, 

manual trip start/stop

Fitness Strava Yes Yes Export file Pedestrian/Bike Trips Only

Manual Trip Start/Stop

Fitness MapMyRide Yes Yes Export file Pedestrian/Bike trips only, 

Manual trip start/stop
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Apple Maps on iOS devices with location history enabled similarly record location data, but they 

are not available for export to an external file. Information about the route and mode of trips 

between frequently visited locations is also not retained.

Moves, an activity tracking application, is compatible with iOS and Android smartphones and 

makes location histories available to users for download. The application passively captures all 

the destinations one visits throughout the day and classifies the modes for the trips that connect 

them. It also provides relevant fitness metrics, including steps taken and calories burned. For 

researchers, Moves is advantageous because it provides an API through which participants can 

consent to grant researchers access to their location history with just a few button clicks. The 

most notable burden for participants for this application will likely be new user installation 

and registration. 

Foursquare is social application through which users record visits to and review popular 

locations, and sometimes receive special discounts. As with Moves, location histories are 

available for download to users as an external file or to developers through an API. The 

application does not capture trips between destinations, which poses challenges to mode 

inference. Also, the app requires manually “checking in” to destinations, further posing 

barriers to usability over long study durations. 

Fitness trackers, including RunKeeper, Strava, and MapMyride likewise can capture FGLD and 

fitness metrics and can make personal data readily available to users. While these are intended 

for activities such as running and biking, they could be used by participants to track commutes as 

well. However, this approach would be burdensome for users who would need to manually start, 

stop, and tag each trip. New user installation and registration also pose user challenges.

Rideshare applications, including Uber and Lyft make trip histories available to users through 

web dashboards. Moreover, these vendors operate API services that make this data accessible 

programmatically. As of the time of publication, the data returned from these vendors included

each trip’s origin, destination and duration; additionally, Uber provides distance, and Lyft 

provides fare information. Once user credentials are secured, these major rideshare providers 

afford low burden access to rich rideshare datasets.

Beyond the third-party applications already discussed, commercial location data aggregators, 

such as Uber Retail Media4 and PlaceIQ5 sell location information collected from smartphones. 

However, this information is anonymized, which eliminates the possibility of directly engaging 

with users for supplemental information.

 4 Vista homepage, Vista by UberMedia, https://uber-retail.com/. 5 PlaceIQ homepage, PlaceIQ, http://placeiq.com/.
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Summary

Moves and Google Maps provide the most promising means of FGLD collection through 

smartphones. They provide researchers access to robust destination and trip route and mode 

information in a way that requires minimal user intervention. Google Maps has a more 

substantial user base than Moves, though participants could be required to download and use 

either application as a precondition to involvement in the study. The advantage of Google Maps

is the extensive existing user base, while the advantage of Moves is the API through which the 

burden of users porting data to researchers can be greatly reduced. 

Custom smartphone application development is not recommended, and an application that 

satisfies just the basic FGLD requirements would largely duplicate existing functionality in thirdparty applications. This approach may be considered if additional functionality is deemed 

necessary, in which case the use of existing frameworks are recommended, as is keeping the 

scope bounded to that of minimal functionality.
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Network Systems

Cell Tower Networks

A common network-based approach to interpolating individual travel patterns is to use 

maintenance records created as cell towers record completed calls made by mobile phones (not 

strictly smartphones). These call detail records (CDRs), are collected by telecommunication 

companies, contain timestamps as well as triangulated coordinates.

Technical Considerations

Call detail records are spatially accurate to within a few hundred meters (Toole et al. 2015), and 

their temporal resolution is variable, which is reflective of the intermittent nature of user cellular 

activity. Accordingly, a CDR may indicate the coordinate of a daily destination or it may reflect 

a traveler’s location while traveling. Post-processing algorithms use timestamps to help 

refine data points and make assumptions about a user’s true travel history throughout a day. 

Probabilistic models, reflecting the likelihood of a user taking a certain mode between two 

destinations, have also been the most promising means of mode classification (Toole et al 2015).

The spatial coverage of cell towers permits FGLD collection throughout most urban areas, 

though breaks in cellular activity will result in incomplete trip logs.

Research Implications

CDRs pose low participant burden and privacy concerns relative to GPS and smartphone 

derived coordinates. Call Detail Records are foremost less accurate, but also importantly are 

commonly provided in aggregated and anonymized formats by third-party data aggregators, such 

as AirSage6 (Calabrese et al. 2013). The potential sampling biases are low as well. The least 

represented groups, including those with less than a high school education, those over 65 years

of age, those earning less than $30,00 per year, and those living in rural areas all have mobile 

phone adoption rates in excess of 80% (and the majority of these phones are smartphones) (Pew 

Research Center 2017).

The burden for researchers is high in that sophisticated statistical models typically are required 

to infer travel diaries from sparse low-quality data. Additionally, the anonymous nature of CDRs

means that engagement with individual participants is not feasible. Instead, location data trends 

need to be aggregated by Zip code or U.S. census block group to make correlations with 

aggregate demographic attributes. 

Summary

The poor resolution of CDRs and the inability to associate them with individuals render this 

approach unsuitable to the goals of WholeTraveler. 

Bluetooth 

Bluetooth-enabled devices, such as smartphones, wireless controllers, car stereos, and laptops, 

periodically broadcast a unique identifier code, known as a media access control (MAC) address. 

Leveraging this identifier, networks of Bluetooth sensors can triangulate the position of a device 

 6 AirSage homepage, AirSage, https://airsage.com/.
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and record changes in its location over time. Use of these technologies for marketing and 

advertising is growing, and there have been some promising demonstrations within 

transportation research, including Barceló et al. (2010), who used the technology to record traffic 

flows along a highway in Spain.

Technical Considerations

Meshes of Bluetooth sensors can triangulate a Bluetooth-enabled device to a spatial resolution 

within 10 cm (Lawson 2012) and at a temporal resolution on the order of ten readings per second

(Barceló et al. 2010). The spatial coverage of this approach is limited by the number 

and distribution of mesh nodes, each having a range of about 70 meters.7

Strategies for dense urban Bluetooth network design have been proposed (Isukapati, Barlow, and 

Smith 2014), though real world applications are typically contained to indoor locations, such as 

shopping malls. Reddy et al. (2010) suggest that this technology is not suitable for mode 

classification because its coverage does not extend outdoors; some modes can be inferred from 

their proximity to network nodes, but confidence and validation opportunities related to such 

data are low. 

Research Implications 

As passive data collection systems, the participant burden through network-based Bluetooth 

signals is low. The resolution and access mechanisms also present privacy concerns similar to 

those of smartphone-derived FGLD. Researcher engagement with participants would likely 

require users to either register the MAC address of their smartphone or be provided a dedicated 

Bluetooth device for location tracking. With either approach, a separate web application, 

smartphone app or survey device would need to be developed and linked to the Bluetooth 

identifier for the purposes of WholeTraveler.

While developing the infrastructure for a Bluetooth data collection network is conceivable at 

small scales, the cost and timeframe associated with implementing a system at the size necessary 

for the WholeTraveler project poses insurmountable researcher burdens. Costs scale both with 

network coverage and user participation (assuming dedicated Bluetooth devices with known 

MAC addresses need to be provided to users to facilitate FGLD collection). Moreover, third 

parties that have experience in Bluetooth network development typically collect data for 

commercial enterprises that have not historically made such data publicly available. 

Summary 

The use of Bluetooth technology is not suitable to the WholeTraveler research initiative.

Radio Frequency Identification (RFID)

Radio-frequency identification (RFID) is the technology behind many access cards, including 

ones swiped at kiosks to gain access within college or corporate campuses, or at ski resorts. 

Public transportation and highway toll collection agencies also commonly implement this

technology to collect fares from regular commuters. 

 7 “Beacons: Everything You Need to Know,” Pointr Labs, http://www.pointrlabs.com/blog/beacons-everythingyou-need-to-know/.
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Technical Considerations

RFID affords very precise spatial (<1 cm) and temporal resolutions. However, coverage and 

mode classification capabilities, much like Bluetooth networks, are limited by the number and 

distribution of network nodes. Accordingly, this technology would be very good at capturing 

entry to and egress from public transit infrastructure or toll roads. Oberli, Torres-Torriti, and 

Landau (2010) found “Commercial off-the-shelf RFID technologies can effectively be used to 

recognize individual passengers as they board and alight buses in public transportation systems.” 

Outside these systems, however, the technology gives little insight into traveler movements. 

Research Implications

As with Bluetooth-based networks, the cost and timeframe associated with developing the 

infrastructure for a robust RFID data collection system exceeds the scope of the WholeTraveler 

project. The technology would be better suited to transportation studies of public transit or 

highway usage.

Summary 

The use of RFID technology is not suitable to the WholeTraveler project.

License Plate Readers 

Distributed optical surveillance systems in urban environments coupled with machine learning 

technologies provide additional mechanisms for obtaining FGLD. Since the 1990s, researchers 

have demonstrated the use of automated license plate recognition (ALPR) scanning systems to 

conduct origin-destination pattern analyses (Shuldiner, D’Agostino, and Woodson 1996). Many 

state transportation agencies rely on such technologies for tolling, and use among police agencies 

is common.

Technical Considerations

The resolution and coverage of FGLD derived from ALPR systems is variable and dependent on 

the number and distribution of nodes (nodes can notably be fixed cameras or mounted to mobile 

assets such as police cars) and user’s intermittent interaction with them and mode classification

is inherently limited to passenger vehicles. Distributed surveillance systems aided through 

artificial intelligence could theoretically interpret images to identify travelers and classify any 

mode; however, the development of such sophisticated systems pose very high privacy and

security concerns, and the systems are thus well outside the scope of the WholeTraveler project.

Research Implications

As passive and unobtrusive data collection systems, optical sensor signals present a low

participant burden. The privacy risks are highly variable, depending on the distribution, location,

and quality of sending nodes. The low FGLD quality of ALPR systems, which stems from the 

intermittent nature of user interaction with nodes, might seem to suggest the risks are lower than

those of high-resolution GPS or smartphone devices. However, additional information captured 

in images pose notable privacy and security concerns that post-processing tasks (obscuring all 

but license plate information) may help mitigate.

Traveler mailing addresses could conceivably be inferred from license plate readings of ALPR 

systems and be used to deliver mailed invitations for engagement in the WholeTraveler study. 
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In practice, however, the channels through which these data would be obtained are heavily 

restricted. Moreover, engagement through mail communications is not expected to result in 

high participation rates or to easily enable study sizes to scale as rapidly as would web-based 

approaches.

Summary 

The use of ALPR technologies is not suitable to the WholeTraveler project.

Recommendation

Smartphone Application

Leveraging an existing smartphone application is the most promising means of FGLD collection. 

Smartphones themselves afford the greatest coverage and high spatial and temporal resolutions.

More importantly, existing applications meet the needs of researchers and participants are 

already deriving personal benefit from them. Google Maps and Moves notably provide 

navigation, activity tracking, and health metrics services.

For researchers, the use of existing applications offloads the deeply technical components of 

FGLD collection (including user experience, battery life, and cross-platform compatibility)

that would otherwise pose considerable costs. Also, costs do not dramatically scale with growing 

population sizes. Once instructions (or possibly automated tools) have been established to guide 

participants through installation, use, and data retrieval, the cost of adding more participants does

not increase dramatically. Other methods require more physical devices or network infrastructure 

to be developed to bring more participants into the study group and lead to escalations in overall 

project cost and complexity. 

Development of a custom smartphone application to collect FGLD should be undertaken with 

caution and an awareness of the human factors inherent in software development. Recent 

evidence suggests that (1) smartphone user’s interest in new applications has been saturated and 

(2) user fatigue stemming from anxiety over privacy, information overload, and frustrating user 

experiences dissuades users from installing new applications.8 Moreover, costs and efforts spent 

customizing an application may also distract research efforts away from more pertinent 

objectives. Existing applications are successful in part because they have undergone many stages 

of iterative development, and the WholeTraveler project timeline does not lend itself to this 

development approach.

Google Maps and Moves already capture standardized data with modes classified with more than 

sufficient spatial and temporal resolution to answer the research questions of the WholeTraveler 

initiative. These entities also have their own incentives to fix bugs and continually improve 

offerings. 

The choice of an application does have direct implications for the success of WholeTraveler, yet 

little published literature exists from which to compare Google Maps, Moves, and other 

applications that collect FGLD. Accordingly, a preliminary study to assess willingness to install 

the application, the user experience, and the ease with which users can export copies of their data 

 8 “How App Fatigue is Taking a Toll on Smartphone Owners,” Barb Darrow, Fortune, 

http://fortune.com/2016/08/16/app-fatigue-is-taking-a-toll-on-smartphone-owners/.
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across representative demographics is recommended to make an informed decision about which 

application will yield the greatest chance of success.

Dedicated GPS Devices

There is significant precedent for using dedicated GPS devices in transportation studies, and it 

should be noted that the devices do pose a viable alternative to smartphone applications. GPS 

devices provide high-quality passively collected data. They are lightweight and portable. And, 

they are unlikely to induce sampling biases if distributed effectively. 

GPS devices are not as well suited to the objectives of WholeTraveler as smartphone 

applications primarily because they are burdensome to participants who must remember to 

charge and carry GPS devices with them. Participants must also spend time mailing these 

devices back to researchers at the conclusion of the study. For researchers, as noted previously, 

study costs escalate proportionally to the study size making large-scale data collection 

unfeasible, given budget and time constraints. Furthermore, the raw data GPS traces provided

require data processing techniques to reduce errors, classify mode, distinguish trips, and make 

associations with known locations. 

Other Alternatives

Network FGLD systems are collectively unsuitable for the WholeTraveler project because of 

the cost and complexity of distributing sensors over a wide enough area to capture the daily 

travel patterns of large populations. Moreover, legal and related data access constraints prohibit 

direct communication with travelers to understand key characteristics, attitudes, and behaviors.
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Conclusion

Detailed travel histories, correlated with socioeconomic, life trajectory and other contextual 

information, provide a rich dataset for energy and transportation analysis. Collecting quality data

of this kind is the first step in establishing current baselines for mode shares and transportation 

related energy impacts, assessing the energy and mobility impacts of emerging transportation 

mega-trends (electric vehicles, ridesharing services, connected and autonomous vehicles), and 

revealing the traveler preferences that will drive future travel decisions. Innovative analytic tools

that can compare and forecast mobility trends are anticipated to improve traveler experiences, 

better elucidate the impact of emerging and transformative transportation technologies and 

services, and inform the design of pathways towards more efficient and secure transportation 

systems. 

This analysis of the technical aspects and research implications of existing locational

technologies suggests that smartphone applications provide the most promising means of finegrained location data collection. Leverage existing applications, moreover, affords a more 

practical solution rather than independently building a data collection application. Deploying 

dedicated GPS devices provides a less scalable, but compelling alternative as a data collection 

approach. The cost and complexity of building network based FGLD collection systems, and 

their limited opportunities for direct communication and identification of participants render 

them unsuitable for the purposes of WholeTraveler.

As location data and other big data resources becoming increasing embedded in the design, 

operation and maintenance of complex transportation and energy systems, future research into 

collection methods that increase the resolution and quality of the data and allow for more 

sophisticated traveler interaction will help to promote more robust scientific understandings. 
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This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Appendix. Summary Tables

The following tables summarize the key attributes of FGLD technologies described in this report. 

Table A-1. Methods and Qualitative Attribute Assessments

Method User 

Burden

Potential 

Sampling 

Biases to 

Overcome

Privacy and 

Security 

Risks

Researcher

Burden

ResearcherParticipant 

Engagement

Portable 

GPS Device

High Self-selection Moderate - Costs per device 

- Mode Inference

- User engagement/ 

retention

Acceptable

Methods include 

mail-in surveys, 

web/phone 

applications, 

interviews

Vehicle GPS Moderate Self-selection

Non-drivers

(young,

seniors, low 

income)

Moderate - Device cost

- User engagement

Acceptable

Methods include 

mail-in surveys, 

web/phone 

applications, 

interviews

Existing

Smartphone 

Application

Low Self-selection

50+

No high school 

degree

Low income 

(<$30,000/yr)

Rural

Moderate to 

High

- User 

adoption/retention

- Device compatibility

- Preservation of 

battery life

Acceptable

Methods include 

mail-in surveys, 

web/phone 

applications, 

interviews

Custom

Smartphone 

Application

Moderate Self-selection

50+

No high school 

degree

Low oncome 

(<$30,000/yr)

Rural

High

(Application is 

less mature 

and the 

researcher is 

more 

accountable 

for it.)

- Device compatibility

- Preservation of 

battery life

- Mode inference

- User adoption/ 

experience/retention

High

Continuous realtime feedback 

possible through 

application

Other methods 

include mail-in 

surveys, 

web/phone 

applications, 

interviews

Cell Tower Low 50+ Low - User engagement Not feasible
22

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Method User 

Burden

Potential 

Sampling 

Biases to 

Overcome

Privacy and 

Security 

Risks

Researcher

Burden

ResearcherParticipant 

Engagement

Less than 

High School 

Degree

Low Income 

Rural

BlueTooth/

RFID

Low 50+

Less than 

High School 

Degree

Low Income 

(<$30,000/yr)

Rural

Moderate - Cost of tracking 

devices and sensing 

network

- Scaling of study 

area

- User engagement/ 

retention

Marginally 

Acceptable

Methods include 

mail-in surveys, 

web/phone 

applications, 

interviews

License 

Plate 

Reader

Low Non-Drivers

(Young, 

Seniors, Low 

Income)

Low - User engagement Very Low

Difficult to 

engage 

participants 

vehicle 

registrations 
23

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table A-2. Methods and Technical Attributes

Method Type Temporal 

Resolution

Spatial 

Resolution

Spatial 

Coverage

Mode Classification

Portable 

GPS Device

Onboard Adjustable—

about one 

second 

intervals 

7–100 m Moderate

Most outdoor 

locations

Identifiable Modes: 

All

Accuracy: 

Moderate

Vehicle 

GPS

Device

Onboard Adjustable—

about one 

second 

intervals

~ 10 m–

100 m

Moderate

Vehicleaccessible 

outdoor locations

Identifiable Modes:

Car

Accuracy:

Not Applicable

Smartphone 

Application

Onboard Adjustable—

about one 

second 

intervals

8–100s m 

Reception 

Dependent

High

Most indoor and 

outdoor locations

Identifiable Modes: 

All

Accuracy: 

Moderately High

Cell Tower Network Inconsistent

Depends on 

phone use 

habits

100s m High

Range of cell 

network

Identifiable Modes: 

All

Accuracy: 

Very Low

BlueTooth Network Inconsistent

Whenever user 

is within range

10 cm Very Low

Range of beacon 

network

Identifiable Modes: 

Variable

Accuracy: 

Low (Unless network 

is mode specific)

RFID Network Inconsistent

Whenever user 

is within range

< 1 cm Very Low

Range of beacon 

network

Identifiable Modes: 

Variable

Accuracy: 

Low (Unless network 

is mode specific)

License 

Plate 

Reader

Network Inconsistent

Whenever user 

is within range

10’s m Low

Range of camera 

network

Identifiable Modes: 

Car

Accuracy: 

High