Exploration of the Application of Collective 

Information to Transportation Data for Safety 

White Paper

FHWA Safety Program

http://safety.fhwa.dot.gov
Collective Information White Paper Final Report 

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FOREWORD

The Federal Highway Administration’s (FHWA) Highway Safety Improvement Program (HSIP) 

is a data driven program that relies on crash, roadway, and traffic data for States to conduct 

effective analyses for problem identification and evaluation. The FHWA developed the Model 

Inventory of Roadway Elements (MIRE) to provide a recommended listing and data dictionary 

of roadway and traffic data elements critical to supporting highway safety management 

programs. MIRE is intended to help support the States’ HSIPs and other safety programs.

The MIRE Management Information System (MIRE MIS) was a project to explore better means 

of collection MIRE data elements, using and integrating MIRE data, and identifying optimal data 

file structures. The resulting products include a report documenting the MIRE MIS Lead 

Agency Program, a MIRE Guidebook on the collection of MIRE, a suggested MIRE data file 

structure report, and a report on Performance Measures to Assess Quality that will assist the 

States in conducting a more effective safety program. The intent of the MIRE MIS project is the 

integration of MIRE into States’ safety management processes.

The Exploration of the Application of Collective Information to Transportation Data for Safety 

White Paper is one of the products of the MIRE MIS effort. This White Paper explores the 

relatively new technique of collective information as a means of gathering data needed for 

transportation safety. Collective information is a process of assembling or gathering information 

on a subject through the use of a large, disperse, and potentially uncontrolled group of people. 

This White Paper also examines current uses of collective information and potential next steps 

for this process to support the collection of transportation data. 

Michael S. Griffith

Director, Office of Safety Technologies

Monique R. Evans 

Director, Office of Safety Research and 

Development

 
Collective Information White Paper Final Report 

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NOTICE

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

in the interest of information exchange. The U.S. Government assumes no liability for the use of 

the information contained in this document. The U.S. Government does not endorse products or 

manufacturers. Trademarks or manufacturers’ names appear in this report only because they are 

considered essential to the objective of the document. 

QUALITY ASSURANCE STATEMENT

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

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

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

information. FHWA periodically reviews quality issues and adjusts its programs and processes 

to ensure continuous quality improvement. 
Collective Information White Paper Final Report 

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TECHNICAL DOCUMENTATION PAGE

1. Report No. 

FHWA-SA-13-006 No. 

4. Title and Subtitle

Exploration of the Application of Collective 

or Safetyf White Paper 

Information to Transportation Data 

5. Report Date

January 2013 

6. Performing Organization Code

7.Author(s)

Kim Eccles, Greg Bosworth, and Nancy Lefler 

8. Performing Organization Report No.

9. Performing Organization Name and Address

Vanasse Hangen Brustlin, Inc. (VHB)

8300 Boone Blvd., Suite 700 

Vienna, VA 22182-2626 

10. Work Unit No.

11. Contract or Grant No.DTFH61-05-

D-00024 (VHB) 

12. Sponsoring Agency Name and Address

Federal Highway Administration Office of Safety

1200 New Jersey Ave., SE

Washington, DC 20590

13. Type of Report and Period

Final Report, June 2012 - January 2013 

14. Sponsoring Agency Code

FHWA

15. Supplementary Notes

The contract managers for this report were Robert Pollack (HSA) and Dr. Carol Tan (HRDS-20). 

16. Abstract

The Federal Highway Administration (FHWA) developed MIRE, the Model Inventory of Roadway Elements, as a 

recommended listing of roadway inventory and traffic elements critical to safety management. A critical step toward the 

acceptance and implementation of MIRE is the conversion of MIRE (which is now a listing of variables) into a 

management information system (MIS). FHWA undertook the MIRE MIS project to assist States in developing and 

integrating MIRE into an MIS structure that will provide greater utility in collecting, maintaining, and using MIRE data. 

This paper was developed as part of the MIRE MIS project to explore the potential mechanisms for MIRE data collection. 

The objective of this White Paper is to explore the application of collective information in transportation data, particularly 

in the collection and management of MIRE data. Collective information is the process of assembling data or collecting 

information on a subject using a large, disperse, and potentially uncontrolled group of people. The intent of the paper is to 

introduce this concept to the transportation community and provide a starting point for discussions on the application and 

feasibility of using collective information for transportation safety data, particularly for the MIRE elements. This paper 

also discusses the potential next steps and future applications of this concept. 

17. Key Words:

Safety data, MIRE, collective data, roadway data 

collection 

18. Distribution Statement

No restrictions.

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this 

page) Unclassified

21. No. of Pages

20 

22. Price

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

Form DOT F 1700.7 (8-72) Reproduction of completed pages authorized 
Collective Information White Paper Final Report 

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Collective Information White Paper Final Report 

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TABLE OF CONTENTS

BACKGROUND ........................................................................................................................... 1

APPLICATION TO MIRE .......................................................................................................... 2

OBJECTIVE ................................................................................................................................. 4

INTENDED AUDIENCE ............................................................................................................. 4

DISCUSSION ................................................................................................................................ 5

Potential Applications for Transportation ............................................................................. 5

Types of Data Collection .......................................................................................................... 6

Data Contributor Considerations ............................................................................................ 7

Types of Contributors ............................................................................................................. 7

Motivation for Collection ........................................................................................................ 9

Training................................................................................................................................. 10

Risk and Safety of the Individuals ......................................................................................... 10

Agency Considerations ........................................................................................................... 11

Data Considerations................................................................................................................ 12

Data Accuracy ...................................................................................................................... 12

Quality Control ..................................................................................................................... 12

Analysis and Reporting ......................................................................................................... 13

Application Considerations .................................................................................................... 14

FUTURE CONSIDERATIONS/NEXT STEPS ....................................................................... 14

REFERENCES ............................................................................................................................ 16

APPENDIX A: AGENCY INTERVIEWS ............................................................................... 18

APPENDIX B: NON-TRANSPORTATION EXAMPLES OF CROWD SOURCING ....... 20
Collective Information White Paper Final Report 

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ACRONYMS

AASHTO American Association of State Highway and Transportation Officials

ADA Americans with Disabilities Act

BADCS Buckeye Asset Data Collection System

CRM Customer Relationship Management

DOT Department of Transportation 

EAM Enterprise Asset Management

FHWA Federal Highway Administration

GIS Geographic Information System 

GPS Global Positioning System 

HSIS Highway Safety Information System

HSM Highway Safety Manual

IHSDM Interactive Highway Safety Design Model 

IT Information Technology 

JMT Johnson, Mirmiran & Thompson, Inc.

LIDAR Light Detection and Ranging 

MIRE Model Inventory of Roadway Elements 

MIS Management Information System

MUTCD Manual on Uniform Traffic Control Devices 

NCHRP National Cooperative Highway Research Program

NHTS National Household Travel Survey 

PTA Parent Teacher Association

RITA Research and Innovative Technology Administration

SFCTA San Francisco County Transportation Authority 

SMS Short Message Service
Collective Information White Paper Final Report 

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BACKGROUND

The need for roadway data to support safety decisions is increasing due to the development of a 

new generation of safety analysis tools and methods. These include the 2010 Highway Safety 

Manual (HSM) (1), the Interactive Highway Safety Design Model (IHSDM) (2), SafetyAnalyst

(3), and the American Association of State Highway and Transportation Officials (AASHTO) 

National Cooperative Highway Research Program (NCHRP) Series 500 Data and Analysis 

Guide (4), which all require crash, roadway, and traffic data to achieve the most accurate results. 

State and local Departments of Transportation (DOTs) need more detailed roadway and traffic 

data to support data driven decision-making that will have the greatest impact in safety on our 

nation’s roadways. 

Transportation agencies responsible for the collection, analysis, management, and 

interoperability of highway safety data have expressed that one of the largest hurdles to having a 

comprehensive data management information system for highway safety is the lack of roadway 

inventory data, particularly for local roads. More specifically, agencies identified a lack of 

resources (both personnel and monetary) to collect roadway data for local roads, or to collect 

detailed roadway data elements for specific types of locations such as intersections, ramps, 

curves, or pedestrian facilities. Similarly, the data are also difficult to maintain or update as 

agencies make changes to these facilities. 

One potential solution is collective information. Collective information is the process of 

assembling data or collecting information on a subject using a large, disperse, and potentially 

uncontrolled group of people. Collective data is an emerging concept for highway safety data, 

although there have been some very powerful examples of success in other transportation fields. 

Agencies have the potential to significantly advance their data collection efforts with minimal 

investments by using applications that leverage data-enabled phones or a standard web browser 

for the collection, identification, or classification of data by the public. 

The process of collecting the data could be described as crowdsourcing, but the results would be 

classified as collective information. The popular website Wikipedia is an excellent example of 

collective information. Wikipedia is a web-based, collaborative encyclopedia. Users of the 

website collectively populate the information in the online encyclopedia. This is an organic 

process as the users independently elect to populate the information, guided by some loose 

principles and forms of oversight. Most of the production of the information is done 

anonymously and essentially independent of one another (5). However, collectively the 

information presents a nearly complete description of many topics.

Collective information is starting to emerge as a method to collect data in the transportation 

world. Currently, two of the largest and most well-known examples of collective data 

applications in transportation are the City of Boston’s Citizens Connect and Street Bump 

applications. The Citizens Connect application (described in more detail in Appendix A) allows 

residents and visitors in Boston to collect information on potholes, faulty street lights, and other 
Collective Information White Paper Final Report 

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public works issues. Citizens report these issues to the City using their smart phones and 

populate a database that the City uses to prioritize public works efforts. A more detailed 

application by the City of Boston is the Street Bump application which uses a mobile device’s 

accelerometer, or motion sensor, and global positioning system (GPS) to detect a distinct jolt 

when a motorist drives over a pothole or sunken manhole cover and pinpoint its location. The 

users of this application are collectively developing a pavements condition database for the City 

through their daily travel. While these applications utilize the general public, transportation 

agencies could rely on anyone from contractors, maintenance crews, volunteer organizations, to 

the general public for information. 

Many of the other notable applications of this concept in transportation involve more planning 

level data to include applications that track travel behaviors for use in these models. Chapter 26 

of The On-line Travel Survey Manual identifies five such examples, the most notable of which is 

San Francisco County’s CycleTracks application (6). CycleTracks, a tool developed by the San 

Francisco County Transportation Authority (SFCTA), uses smart phone’s GPS support to record 

citizen’s bicycle trips for use in a route choice model.

Why are agencies using these collective data applications? One of the primary reasons is the 

applications present a cost-effective way to collect transportation data that agencies need for 

decision-making. The examples to date are only a handful; however, these pioneers provide a 

glimpse of the potential of the application this technology. The prevalence of smart phones and 

other mobile devices makes this type of data collection feasible. Distributed data collection 

applications may provide a tremendous opportunity for transportation agencies to costeffectively collect the data needed to improve the transportation system or to develop a database 

of existing infrastructure or conditions. 

APPLICATION TO MIRE

MIRE, the Model Inventory of Roadway Elements, is a recommended listing of roadway 

inventory and traffic elements critical to safety management. There are a total of 202 elements 

that comprise the MIRE listing. These elements are divided among three broad categories: 

roadway segments, roadway alignment, and roadway junctions. Examples of the MIRE data 

elements include: 

• Roadway classification. 

• Paved surface characteristics. 

• Number and type of travel lanes. 

• Shoulder, median, and roadside descriptors. 

• Curve and grade information. 

• Traffic control devices. 

• Intersection features. 
Collective Information White Paper Final Report 

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• Interchange and ramp descriptors. 

• Pedestrian and bicyclist facilities. 

• Traffic volumes. 

MIRE is intended as a guideline to help transportation agencies improve their roadway and 

traffic data inventories. It provides a basis for a standard of what agencies can consider a 

good/robust data inventory, and helps them move towards the use of performance measures. 

Most States lack the critical elements in the MIRE listing on all public roads and also the funding 

to collect this information using traditional methods (7). More information on MIRE is available 

on the FHWA website, http://safety.fhwa.dot.gov/rsdp/. 

A critical step toward acceptance and implementation of MIRE is the conversion of MIRE 

(which is now a listing of variables) into a management information system (MIS). The Federal 

Highway Administration (FHWA) has undertaken the MIRE MIS project to assist States in 

developing and integrating the MIRE into an MIS structure that will provide greater utility in 

collecting, maintaining, and using MIRE data. The proposed MIS design will include the 

exploration, development, and documentation of the following: 

• Mechanisms for data collection.

• An efficient process for data handling and storage. 

• Details of data file structure.

• Methods to assure (1) the integration of MIRE data with crash data and other data types, 

and (2) that access to these data can be accomplished through the MIRE MIS. 

• The identification of performance metrics to assess and assure MIRE data quality and 

MIS performance.

The collective data concept falls under the first objective of the MIRE MIS project – to explore 

the mechanisms for MIRE data collection. A distributed data collection application, suitable for 

use on a mobile device such as an iPhone, iPad, Android phone or tablet, or a Windows-based 

phone, may provide an opportunity for an agency to cost-effectively collect many of the MIRE 

elements. The intent of this application would be to provide the public a way to assist in the 

collection of MIRE elements, in turn reducing costs of the DOT’s data collection effort and

allowing more funds to go towards safety improvement projects.

The data collected with this application would focus around traffic data elements and would 

integrate the functionality of these connected devices such as GPS, data connectivity, camera, 

video, accelerometer, and keypad data entry. If DOT’s could harness just a portion of the mass 

of people that have connected smart phone devices, it would reduce their effort in collecting 

traffic safety data.

One of the largest challenges for State DOTs is collecting data on local roads. Collective data 

could provide a mechanism to cost-effectively populate local road or local intersection 

information. 
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OBJECTIVE

Collective data is an emerging concept in information technology (IT) and largely a new concept 

to transportation agencies. There are many issues that would need to be addressed before 

transportation agencies could envision the potential benefits of this approach. The objective of 

this White Paper is to explore the application of collective information in transportation data, 

particularly in the collection and management of MIRE data elements. The intent of this paper is 

to introduce this concept to the transportation community and provide a starting point for 

discussions on the application and feasibility of using collective information for transportation 

safety data, particularly for the MIRE elements. As previously identified, the Travel Survey 

Manual discusses the use of these applications for collecting travel behavior.

This paper presents the opinions and ideas of the authors. The information in this paper was 

developed by the authors in part based on information on transportation agencies that are 

currently using collective data applications. This includes reviews and in some cases interviews 

on the following applications: 

• Citizens Connect App – Boston, Massachusetts. 

• Street Bump – Boston, Massachusetts. 

• CycleTracks – San Francisco, California. 

• Spot Reporters – Baltimore, Maryland; Boston, Massachusetts; Brookline, 

Massachusetts; and Grand Rapids, Michigan. 

• Buckeye Asset Data Collection System (BADCS) – Ohio DOT. 

The detailed findings of these interviews are included in Appendix A. Note: this is not an 

exhaustive list of all the existing data collection applications; rather those that fit within the 

context of collective data and that the project team identified as being relevant and applicable to 

the collection of MIRE data.

INTENDED AUDIENCE

The intended audience for this paper is public agencies, transportation professionals, IT 

professionals, and researchers who are interested in finding ways to advance the collection and 

use of transportation data for use in highway safety analysis. There are potential opportunities 

for collective data applications in almost every field of transportation. The primary function for 

collective data applications that the authors describe in this paper are for use by public agencies, 

particularly local agencies, to cost-effectively collect roadway attribute data in support of their 

highway safety efforts.
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DISCUSSION

Potential Applications for Transportation 

The data needed to safely and efficiently operate the nation’s transportation systems are immense 

and include data on: 

• Demand, such as traffic volumes. 

• System performance, such as queue lengths or crashes. 

• Operating parameters, such as speed limits. 

• Operating characteristics, such as travel times. 

• Facilities, such as asset inventories.

Several of these types of data could benefit from collective information. Specific to the MIRE 

listing, potential applications include: 

• Intersection inventory. This could include number of legs, intersection control (e.g., 

signalized, stop-controlled, yield-controlled), presence of turn lanes, left turn phasing, 

number of traffic control devices, etc. 

• Signal inventory. This could include traditional red/yellow/green signals, pedestrian 

signals, and flashing beacons. The data collector could enter information on the presence 

of the signals or outages. 

• Lighting. This could include the location and types of lighting. 

• Pedestrian facilities inventory. This could include the presence and condition of 

sidewalks, crosswalks, pedestrian signals, push buttons, wayfinding signs, warning signs, 

etc.

• Bicycle facility inventory. This could include the presence and condition of bicycle 

paths, bicycle lanes, wayfinding signs, warning signs, etc. 

• User reported walkability index. This could provide an opportunity for local citizens to 

rate facilities using existing walkability index assessments.

• Pavement surface characteristics. This could include the pavement condition and specific 

locations of deterioration. 

• Sign inventory. This could include the presence, condition, and information conveyed 

for regulatory and warning signs.

• Curve and grade inventory. This could include the location and degree of curvature for 

horizontal curves or ramps. For vertical curves, it could include presence and grade. For 

both horizontal and vertical curves it could also include the presence and conditions of 

warning signs or the presence of features within the curve, such as driveways, that may 

impact safety.
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The concept could also extend beyond the MIRE listing. Agencies could use these applications 

to collect other data to improve their transportation system including:

• Transit performance. This could include passenger reported arrival and departure times, 

and origin and destination information for the overall trip, not just the transit portions. 

• Transit facilities. This could include the presence and condition of amenities such as 

benches and shelters at transit stops.

• Operating speed inventory. This could include passively reported operating speeds on 

roadways in a jurisdiction at peak and non-peak times.

The type of application used and the information collected would vary based on the needs of the 

agency. An agency may use one application to collect data across several categories. For 

example, an agency could use one application to collect inventory elements such as intersection, 

signal, sign, and pedestrian facility elements. Or, an agency could use an application to only 

collect elements of interest to one user group. 

Once one agency develops an application, it may be able to be adapted to be beneficial to other 

agencies. The United States DOT Research and Innovative Technology Administration (RITA) 

tested San Francisco’s CycleTracks application in Austin, Texas with positive results (8). 

Types of Data Collection 

The authors envision two types of data collection: mobile collection and remote collection. 

Mobile collection would occur in the field through the use of mobile devices such as smart 

phones or tablets. The user of the application would use GPS or maps to locate the data. The 

location of the person collecting the data would be important for the actual collection.

CycleTracks is a good example of mobile collection. The GPS in the mobile devices of the data 

collectors tracks their location throughout their route. 

The second type of data collection is remote collection. This type of collection could be done 

anywhere. The location of the person collecting the data is not important to the actual collection. 

The actual data collection is better described as data capturing. The agency in need of data 

would collect a large amount of data in the field, likely using a video camera or other device 

(e.g., light detection and ranging [LIDAR]). The remote data collector would review the video 

and process the captured data. For example, an agency might post a video of a corridor. Data 

collectors could process that video to develop a street sign condition inventory. 

A good example of remote collection is a recent effort undertaken as part of the FHWA MIRE 

MIS project to develop an intersection inventory for New Hampshire DOT. Data entry clerks 

populated an intersection inventory using video logs and publically available photos in Google 

Maps Street ViewTM and Microsoft's Bing® Maps Bird’s Eye. Remote data collectors, hundreds 

of miles away from the actual intersections in New Hampshire, reviewed the images and 

characterized attributes of each intersection in a database. Although in this example the data 

collectors were compensated, crowdsourcing could be used for similar efforts. 
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Data Contributor Considerations 

For the purpose of this paper, the term contributor refers to a user of the application who would 

collect the data. These contributors are also likely users of the data in the more global sense in 

that they are users of the facility or users of the collected data. There are several types of 

potential contributors. They include: public contributor, qualified contributor, opportunistic 

contributor, and directed contributors, and are discussed in more detail in the following section.

Types of Contributors 

The authors envision several types of contributors, or collective data imputers. The first type is 

public contributor. This type of contributor is used for applications that are open to any 

interested users. Public contributors are appropriate in the following circumstances:

• A large or representative sample of people is needed to accurately reflect operating

conditions or to provide enough coverage for the application to reflect the transportation 

environment. 

• Detailed training, beyond what can be conveyed directly by the application, is not 

needed.

• No equipment is needed, beyond the capabilities of the mobile device, to collect and 

transmit the information. 

• The data are not needed urgently. 

The previously described CycleTracks is an example of an application with public contributors. 

SFCTA made their application open to anyone who wanted to download it, and required the 

contributors to register using some basic contact information. SFCTA did not place any 

restrictions on who could register. This provided an opportunity for a diverse sample of 

bicyclists around the jurisdiction. Similarly, Boston’s Street Bump and Citizen Connect 

applications all use public contributors. 

The second type is qualified contributor. Civic organizations, Parent Teacher Association 

(PTA) members, or neighborhood watch members are examples of potential qualified 

contributors. Agencies would specifically identify these groups for the effort and would receive 

some form of training. Qualified contributors are appropriate in the following circumstances: 

• Training, beyond what can be conveyed directly by the application, is needed. 

• The needed reliability of the data is greater than what can be collected by public 

contributors.

• The collected data has some direct value to the qualified group. 

An example of the type of application that may use qualified contributors includes school zone 

inventories or pedestrian facilities inventories. 

The third type is opportunistic contributor. This could include agency employees whose 

primary responsibility is not data collection but their role with the agency provides an 

opportunity to collect data. Examples of this type of contributor include downtown 
Collective Information White Paper Final Report 

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ambassadors, maintenance personnel, personnel responsible for collecting other data, such as 

traffic counters, or parking enforcement. Opportunistic contributors are appropriate in the 

following circumstances:

• The data collection is not time sensitive, since the collection of data by these contributors 

occurs only when the opportunity is present. 

• Some training or equipment may be needed. 

• The data elements to be collected are proximate to the general duties of the contributor, 

more likely in an urban environment. 

An example is the Ohio BADCS. BADCS is a web-based application to view video of Ohio 

DOT’s roadways to collect large-scale asset inventories. It is available to anyone within the 

DOT, although primarily used by Districts 1 and 2 (9). 

An example of the type of application that may use opportunistic contributors includes signal 

inventories or Americans with Disabilities Act (ADA) compliancy in a central business district.

The fourth type is directed contributors. These are contributors that are specifically directed to 

collect data. This includes contractors retained to collect data, summer interns, or maintenance 

staff directed to collect data while performing their normal duties. In most cases, these 

contributors would be compensated for collecting the data. Directed contributors are appropriate 

in the following circumstances:

• The data are needed urgently, or must be collected over a short period of time. 

• Some training, equipment, or specialized expertise is needed.

• The reliability of the data is very important.

ADAMobile developed by Johnson, Mirmiran & Thompson (JMT), Inc., is an example of an 

application collected by directed contributors (10). The Delaware DOT retained JMT to collect 

pedestrian facilities inventory data including ADA compliance data in the field and provide realtime updates to the office. JMT collected the data over several months with a high level of 

reliability.

The authors also envision other contributor groups forming beyond these four categories as the 

need arises. 

An agency could construct an application for use by any of the four categories. The application 

could also recognize the type of contributor. Agencies could use this information to qualify the 

reliability of the data. For example, an agency could develop an intersection inventory as a 

combined effort of public contributors, qualified contributors from a local civic group, and 

directed contributors as part of a summer traffic counting program. The agency would 

characterize the reliability of the data collected for each intersection by the contributor group that 

collected it. Data from the public contributors would receive the lowest quality. In the case of 

redundant data (e.g., data for the same intersection collected by the local civic group and the 
Collective Information White Paper Final Report 

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summer traffic counting program), the agency would use the data collected by the higher 

contributor group. 

Motivation for Collection

What is the incentive of the general public to collect data? Jonathan Zittrain, a social theorist 

and professor of internet law at Harvard Law School, has provided some input on the subject 

(11). Mr. Zittrain theorizes that the motivation in related crowdsourcing examples includes a 

general desire to help others but could also include recognition, reward, and entertainment. One 

notable example is that the problems page on the popular website Wikipedia has more visitors to 

the page looking to solve problems than the number of visitors there to report problems (12). 

Other interesting (non-transportation) examples on people contributing for the greater good 

include XPrize, Facewatch ID, and Vertices, among several others. Appendix B provides more 

detail on these non-transportation examples.

The motivation for data collection could differ by the type of contributor and the type of data 

being collected. Public contributors and qualified contributors may be motivated by altruistic 

motives of contributing to the greater good; for example, collecting data that will be used to 

improve school zones. This is particularly true for qualified contributors as their existing 

affiliation with a community group, such as the PTA, signals their willingness to contribute to 

the community. 

These same contributors may also be self-motivated or vested in the results. In the CycleTracks 

example, contributors were cyclists motivated to participate because the data were ultimately 

used to improve bicycle facilities. Contributors could also be motivated by the application itself. 

Using the CycleTracks example again, the application included some added functionality, 

beyond what was needed for the data collection, as a benefit to the contributors of the app. In 

addition to recording and sending GPS data to SFCTA servers, contributors could also see their 

recorded routes mapped with their distance and speed by trip. This added functionality was a 

motivation to use the application. 

An application could also motivate contributors to use the app by being fun to use, or by 

introducing some elements of gaming. An application could include a scoring system so that 

contributors could be ranked against one another. This concept could be used in a sign 

inventory. Contributors would receive points both for the number of signs collected and the 

various categories of signs or diversity of collected signs. Each new type of sign would be like 

obtaining a tool or a randomized treasure in common adventure or role playing computer games. 

This concept could encourage contributors to collect data in a more distributed fashion. This 

may appeal to passengers on road trips. It may also appeal to players of the popular geocaching 

games or similar.

For directed contributors, and to some degree for opportunistic contributors, the motivation is 

compensation or negative consequences for not completing their assigned duties. Agencies 

could also use compensation to motivate public or qualified contributors. Unlike directed 
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contributors, all contributors would not be compensated. Instead, the agency could introduce 

some randomization of compensation. For example, contributors could be randomly selected to 

receive gift cards. The data collected could serve as a contributor’s entry into the random 

drawing. That is, the more data a contributor collects, the more entries they have in the drawing. 

Similarly, compensation could also be incorporated into the previously described gaming 

approach. 

Regardless of the type of contributor, the overwhelming take-away is that the contributors must 

understand the benefit of the effort to which they are contributing. Therefore, any successful 

application must identify the expected contribution to improved safety and communicate that to 

the contributors as part of the application. 

Training 

Most applications will require some form of training for contributors. This training could be 

very basic, such as information on how to use the application, or more detailed training such as 

classifying highway signs or assessing the condition of a crosswalk. The amount of training 

required would be dependent on the application. Agencies should consider the amount of 

training required when selecting the targeted contributor. 

Agencies could deliver training through traditional methods for qualified, opportunistic, and 

directed contributors. However, for public contributors, web or application based training is 

more appropriate. 

Several web-based collective data applications exist that could provide useful examples of 

training for classification data. The website Zooniverse uses public contributors to identify and 

classify galaxies. The contributors have to work through a self-paced training guide in which 

they essentially learn to classify galaxies by working example problems with some sample data

(13). An agency could use this concept in the envisioned collective data applications. An 

agency could train contributors by working through the collection of some sample data on the 

device.

Risk and Safety of the Individuals 

Any application used for collective data needs to prevent or discourage contributors from 

entering data while driving or from collecting data at a potentially hazardous location such as in 

the roadway. Agencies should consider this in the data input needs for specific types of 

locations. For example, an application should not require or allow the collection of video data on 

an Interstate.

There is always some risk that someone will use a mobile device in a situation that introduces a 

hazard to themselves or others such as driving while talking on the mobile device, or walking 

along a hazardous roadway. The application should launch with a disclaimer that warns the user 

of the hazards of using the application and instructions on appropriate use of the application. 

Specifically any active data collection should be limited to passengers in a vehicle or pedestrians 
Collective Information White Paper Final Report 

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safely out of the roadway. (A driver’s device could collect passive data such as pavement 

condition.) As part of the registration process for the application, the user could agree to the 

conditions of appropriate use. 

Agency Considerations 

As previously described, the primary function for collective data applications is for use by public 

agencies to collect transportation data in support of their highway safety efforts. Costeffectiveness is one of the primary motivations for agencies to seek this type of application. 

However, there are other agency considerations including the following:

• Need for Access to a Larger or More Representative Data Set. Collective data 

applications may appeal to a broader base of contributors than traditional survey or data 

collection methods as smart phones are almost ubiquitous. This is important if the 

collected data needs to reflect the diversity of the contributors or be conducted in diverse 

locations throughout a jurisdiction. SFCTA compared the demographics of their 

contributors to the National Household Travel Survey (NHTS) respondents and found 

their CycleTracks contributor to be a more diverse and representative sample of their 

jurisdiction. According to a 2012 study by the Pew Internet & American Life Project, 

nearly half of Americans (45 percent) use a smart phone or other mobile device, and the 

number is growing (14). 

• Agency Size. The size of the agency and jurisdiction it manages would impact several of 

the considerations. Collective data efforts that would rely on a “community” of people 

would be most effective for local jurisdictions rather than statewide initiatives.

• Ability to Administer the Application. Agencies need to have the ability to oversee the 

collective data application. A contractor could develop or manage the application; 

however, the responsible agency has to be able to clearly identify their data needs, 

requirements, and intended use for the data. 

• Ability to Manage the Data. Agencies would need the technology and knowledge on 

how to store and manage the additional data. This may require additional IT investments 

and training for the data managers.

• Vision. Although the process of collecting the data will be more cost-effective than the 

traditional methods, the initial development of the data collection application will be a 

cost or time investment. This cost may be larger than the initial start-up costs for some 

traditional methods. Therefore, the agency needs to have a clear vision of how they will 

use the data so they can develop the application appropriately. An agency should also 

develop the application with some flexibility as their needs may evolve. 

• Existing Applications. Once an agency has established a clear vision, they should do 

some research to determine if another agency has already developed a similar application. 

This additional research could save valuable time and resources in the long-run by 

building off of existing applications rather than developing one from scratch.

• Pilot the Effort. As with any large data collection effort, the agency should consider a 

pilot effort before launching a large scale effort. This is related to the previous point 
Collective Information White Paper Final Report 

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about flexibility in the application. It allows for the agency to identify and resolve 

problems or additional needs before wide-scale use.

• Secondary Benefits of Data. An agency may find that there are some secondary 

benefits of the application of the collected data. For example, inventory data collected 

for safety analysis may also help with asset management. 

• Secondary Benefits of Process. Agencies that use collective data applications will gain 

experience, knowledge, and comfort/familiarity with collective data and mobile 

technologies. Agencies may view data collection and the actual data in a more dynamic, 

connected context. This process will fundamentally change how they view both the data 

and the process of data collection by introducing the agency to other possibilities and 

potentially generating ideas for future applications. This could be future crowdsourcing 

applications or future uses for mobile technologies in general.

Data Considerations 

Data Accuracy

Accuracy concerns will certainly limit the potential applications, but in many cases, there can be 

tremendous value in collecting data through collective data applications, realizing that the 

accuracy of the data may be in question. Agencies might decide to use these applications as an 

interim measure, with a willingness not to allow the perfect to stand in the way of the good. 

For the types of applications envisioned, data accuracy could include location accuracy (e.g., is 

the horizontal curve correctly placed), accuracy in classification of elements (e.g., is the sign a 

standard Manual of Uniform Traffic Control [MUTCD] R1-1 STOP sign (15)), accuracy in 

characterization of elements (e.g., does the intersection have a protected left turn on the 

northbound approach or a protected-permitted left turn), and accuracy in measurement (e.g., 

does the ramp actually have a 10% grade). Before using collective data for their efforts, the 

agency should realize that the accuracy of the data has some limitations when collected through 

this method. 

Location accuracy for most data elements would be limited to the location accuracy of the 

mobile device or the contributor’s ability to locate the position of the element or asset on a map. 

The application could increase the location accuracy by providing two measures of location. 

That is, the contributor’s position as understood by the mobile device could be identified on a 

map and the contributor would be asked to verify that the position reflects their understanding of 

their location.

Classification accuracy and characterization accuracy would be dependent on the contributor’s 

ability to understand the training or direction provided and apply that to their characterization or 

classification of elements. Therefore, the skill of the contributor is critical.

Quality Control 

The type of contributors affects the expected quality and therefore the quality control required. 

Directed contributors should provide higher quality data and require less quality control. The 
Collective Information White Paper Final Report 

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application could also be more complex and the data collected could be more in-depth. Public 

contributors are at the other extreme. There would be an increased priority to validate and 

normalize the data that are collected by these contributors. 

An agency can employ several methods to increase the accuracy or at least understand the 

accuracy of the data through a quality control process. As previously discussed, there may be 

value to collecting the information, realizing the accuracy of the information may be in question. 

However, the agency needs to have an understanding of the accuracy of the data.

The first method is to provide some ground truthing. That is, a sample of data would be 

collected by, or on behalf of, the agency with more traditional data collection methods. The 

agency would consider these data the ground truth data. The agency would then compare the 

ground truth data to data collected through the application to determine the relative accuracy of 

the application data. This could be done for a sample of data (e.g., one percent of all 

observations) or in a structured fashion to provide a fixed reference point in the collected data 

that contributors could use to conduct their own quality control (e.g., one sign in every mile of 

segment is characterized).

The second method is through the use of redundancy. That is, multiple contributors collect 

information on the same element. The values of multiple contributors are compared for similar 

responses. In this case, the majority prevails. Galaxy Zoo, part of the previously described 

Zooniverse, uses this process to control the quality of their data, noting, “Having multiple 

classifications of the same object is important, as it allows us to assess how reliable each one is. 

For some projects, we may only need a few thousand galaxies but want to be sure they're all 

spirals. No problem – just use those that 100% of classifiers agree on. For other projects we 

might want larger numbers of galaxies, so might use those that a majority say are spiral. (16)” 

The third method, ratings, incorporates elements of the first two methods. With this ratings 

method, contributors receive a rating on the quality of their data entry. This rating could be 

assigned by comparing the contributor’s individual collected data to the ground truthing data or 

to the data collected by other contributors. This rating would be similar to the seller ratings used 

in the popular online auction site, EBay. Similarly, the rating could be applied to individual 

elements instead of individual contributors. Agencies could tag those elements that show low 

consistency in contributed data based on the redundancy checks with a rating. For example, 75 

percent of contributors agree on the left turn protection at this intersection. Agencies may decide 

that some elements or specific locations need to be collected with traditional methods to

supplement the collected data.

Analysis and Reporting 

Analysis and reporting are important data considerations for any data collection effort. Specific 

to collective data, the application should provide feedback to the contributors. This is important 

for at least two reasons. The first is related to quality control and providing feedback to the 

contributors. The second is related to the motivation to the contributors. As previously 
Collective Information White Paper Final Report 

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described, the desire to contribute to the greater cause will be one of the primary motivations for 

the contributors using the application. Information as simple as summary statistics on the data 

collection effort can help to provide the necessary feedback to the contributors to keep them 

engaged in the effort. 

Application Considerations

Just over five years ago the only feasible solutions for customized field data collection was 

through the use of hand-held data collectors, common with surveyors, or some type of 

ruggedized or semi-ruggedized laptop. If photographs or geo-location were required, the data 

collection team needed to bring a camera and/or a GPS device in the field. Connecting all these 

devices reliably in the field was sometimes a challenge. There were options of integrated 

hardware, but the cost of these solutions limited the use to professional services firms that could 

afford them, in turn limiting who could collect these data. 

Today, there is a vast array of platforms available to agencies to collect data. The majority of 

these devices are consumer level devices. Connectivity and processing power on the mobile 

devices continues to improve. Built-in features on devices such as cameras, video recorders, 

GPS, and accelerometers enhance the ability of data collection.

There are many options for developing a data collection application for a laptop or a data 

collector, but for a smart phone, presently there are only two viable approaches for data 

collection – web based applications or native apps such as what you would find on the iTunes 

App Store or the Google Apps Marketplace. There are pros and cons for both options. 

The potential benefit of developing an application on a web platform is that you can develop it 

once and it is usable on any device. There are many browsers and mobile development tool sets

that make this mobile web development more streamlined, but the challenge is that the variety of 

browsers and hardware platforms will typically require a lot of compatibility testing. Native 

apps can leverage the built-in functionality of the device, but may require specialized 

development resources. Additionally, an agency will need to either select a platform to support, 

such as the iPhone, Android, or Windows, or invest in developing the application on multiple 

platforms to get as much penetration as possible. 

FUTURE CONSIDERATIONS/NEXT STEPS

This paper introduces the concept of collective information to the transportation safety 

community. It is a starting point for discussions on the application and feasibility of using 

collective information for transportation safety data. This paper focuses on the use of these 

applications for transportation agencies in collection of MIRE data. The authors envision other 

uses beyond what is discussed here. For example, could collective data be used to supplement 

the data in the Highway Safety Information System (HSIS) database? What if researchers who 

use the HSIS data amend the database with additional data they collected as part of their research 

studies? In addition, there were aspects of collective information that are not addressed in this 
Collective Information White Paper Final Report 

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White Paper such as how the applications are marketed to the public to solicit participation in the 

collection. 

A logical next step for this concept is the development of a prototype. The prototype would 

provide an opportunity to explore some issues about the potential application of this concept. 

The prototype would not cover all situations and types of collection information for 

transportation data collection applications. Specifically, a good starting point would be a 

prototype for an intersection inventory. Many agencies are now aware of the importance and 

value of an intersection inventory. The prototype would demonstrate the concept while 

identifying additional issues or questions that need to be addressed. 

Based on the initial exploration detailed in this paper, collective data collection applications may 

provide a tremendous opportunity for transportation agencies to cost-effectively collect the data 

agencies need for data driven decision-making to improve safety on the nation’s roadways and 

should be explored further. 
Collective Information White Paper Final Report 

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REFERENCES

1. American Associate of State Highway and Transportation Officials. Highway Safety Manual. 

Available online at: http://www.highwaysafetymanual.org/. Accessed September 2012. 

2. Federal Highway Administration. Interactive Highway Safety Design Model. Available 

online at: 

http://www.fhwa.dot.gov/research/tfhrc/projects/safety/comprehensive/ihsdm/index.cfm. 

Accessed September 2012.

3. American Associate of State Highway and Transportation Officials. SafetyAnalyst. 

http://www.safetyanalyst.org/. Accessed September 2012.

4. Council, Forrest, et al. NCHRP Report 500: Guidance for Implementation of the AASHTO 

Strategic Highway Safety Plan, Volume 21: Safety Data and Analysis in Developing 

Emphasis Area Plans. Transportation Research Board, Washington D.C., 2008. Available 

online at: http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_rpt_500v21.pdf . Accessed 

September 2012. 

5. Wikipedia. Available online at: http://www.wikipedia.org/. Accessed October, 2012. 

6. The On-line Travel Survey Manual: A Dynamic Document for Transportation Professionals. 

Chapter 26.0 Using Smartphones for Travel Behavior Studies. Available online at: 

http://www.travelsurveymanual.org/. Accessed September 2012. 

7. Lefler, Nancy X., F. Council, H. McGee, D. Harkey, D. Carter, M. Daul. Model Inventory of 

Roadway Elements - MIRE, Version 1.0, Final Report. FHWA-SA-10-018. Federal Highway 

Administration, Washington, D.C., June 2010. 

8. Hudson, Joan G., et al. Using Smartphones to Collect Bicycle Travel Data in Texas. UTCM 

11-35-69. Department of Transportation, Research and Innovative Technology 

Administration, Washington, DC, August 2012. 

9. Federal Highway Administration. A Look at Ohio DOT's Asset Management Applications. 

GIS in Transportation Summer 2010 Newsletter. Available online at: 

http://www.gis.fhwa.dot.gov/documents/Newsletter_Summer2010.htm. Accessed October 

2012. 

10. Johnson, Mirmiran & Thompson, Inc. ADAMobile Solution for Pedestrian Inventory & 

ADA Compliance. Available on at: http://www.jmt.com/project-portfolio/adamobilesolution-for-pedestrian-inventory-ada-compliance/, Accessed October 2012. 

11. Zittrain, Jonathan. The Future of the Internet and How to Stop it. March, 2009. 

12. Zittrain, Jonathan: The Web as Random Acts of Kindness. TED Talks. Available at 

http://www.ted.com/talks/jonathan_zittrain_the_web_is_a_random_act_of_kindness.html. 

Accessed September 2012.

13. Zooniverse. Available online at: https://www.zooniverse.org/. Accessed October 2012. 

14. Smartphone Ownership Update: September 2012, Pew Internet and American Life Project. 

Available online at: http://pewinternet.org/Reports/2012/Smartphone-Update-Sept2012.aspx. Accessed October 2012. 
Collective Information White Paper Final Report 

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15. Federal Highway Administration. Manual on Uniform Traffic Control Devices. Available 

on at: http://mutcd.fhwa.dot.gov/. Accessed October 2012. 

16. Galaxy Zoo documentation. Available online at: http://www.galaxyzoo.org/#/story. 

Accessed September 2012. 
Collective Information White Paper Final Report 

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APPENDIX A: AGENCY INTERVIEWS

Example A 

Name: Citizens Connect App

Location: Boston, Massachusetts 

Link: http://www.cityofboston.gov/doit/apps/citizensconnect.asp

Overview: This simple to use, award winning smart phone app enables residents and visitors of 

the City of Boston to send issues and problems directly from their iPhones or Android-based 

devices to the city’s work order system. It makes services easily available to constituents, 

cutting down on the amount of time that elapses between when an issue is reported and 

sequentially resolved, reducing the City’s cost to manage requests & transactions. Initially 

created to report potholes in the city, citizens can now report issues such as clogged storm drains, 

excessive jackhammer noise, illegal trash dumping, and faulty street lights, complete with 

pictures. Reported cases then go directly into the city’s work order queue for resolution, and 

users are informed how quickly the case will be closed. When cases are resolved the date, time 

of the resolution is listed, providing users with the sense that the city is on the job. Within the 

application, users are able to publicly share reports they have made, see reports others have 

shared, and text or use Twitter to publish efforts they have made to improve their neighborhoods 

making it also a social networking app. 

Example B 

Name: Street Bump

Location: Boston, Massachusetts 

Link: http://www.cityofboston.gov/DoIT/apps/streetbump.asp, 

http://www.cityofboston.gov/news/uploads/46245_32_21_59.pdf

Overview: Street Bump is a mobile app that helps identify potholes by recording "bump" data, 

providing the City with real-time information to help improve roads. This app’s roots came from 

Citizen Connect. The City was looking for a way to find potholes before they were reported. 

Initially the information that came from the smart phone devices had many false positives. The 

City chose to set up a contest to create an algorithm to analyze the information to get more 

accurate results. From this contest there were three winners; these algorithms are in place now. 

The app uses a mobile device’s accelerometer, or motion sensor, and GPS to detect the tell-tale 

jolt when a motorist drives over a pothole or sunken manhole cover and pinpoint where it is. By 

collating the data from drivers and matching it against known speed humps, level crossings and 

roadworks, sections of road in need of maintenance can easily be identified.
Collective Information White Paper Final Report 

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Example C 

Name: CycleTracks

Location: San Francisco, California 

Link: http://www.sfcta.org/content/category/4/67/145/, 

http://www.sfcta.org/images/stories/IT/SFCHAMP/PDFs/BikeRouteChoiceModel.pdf, 

http://itunes.apple.com/us/app/cycletracks/id338203326?mt=8

Overview: CycleTracks, a tool developed by the San Francisco County Transportation Authority, 

uses your smartphone’s GPS support to record your bicycle trips, display maps of your rides, and 

help transportation planners make San Francisco a better place to bike. At the end of each trip, 

real-time data representing the trip purpose, route, and the date and time are sent to the 

Transportation Authority. Planners use the data to improve the bicycle-use component of their 

computer model and better predict where cyclists will ride and how land development and 

transportation infrastructure will affect cycling. Users get to see maps and statistics of their 

rides.

RITA further explored the CycleTracks application in a 2012 study which tested the application 

in Austin, Texas (8). The study found that despite challenges, the amount of information that 

could be collected by CycleTracks exceeded what would be able to be collected using other data 

collection methodologies.

Example D

Name: Spot Reporters 

Location: Baltimore, Maryland; Boston, Massachusetts; Brookline, Massachusetts; and Grand 

Rapids, Michigan 

Link: http://www.connectedbits.com/

Overview: Spot Reporters was designed to integrate seamlessly with existing customer 

relationship management (CRM) systems, enterprise asset management (EAM) systems, and 

geographic information systems (GIS). It enables anyone with a mobile phone to report issues 

they encounter by sending photos, locations, and descriptions directly to Spot Reporters app 

server. Smartphone users can download apps or use Web, Twitter, or short message service 

(SMS) clients to report. Four cities have started to use the program: Baltimore, Maryland; 

Boston, Massachusetts; Brookline, Massachusetts; and Grand Rapids, Michigan. 
Collective Information White Paper Final Report 

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APPENDIX B: NON-TRANSPORTATION EXAMPLES OF CROWD SOURCING

Some interesting examples of crowd sourcing/data collection that could provide some insight 

into potential applications for highway safety data include the following: 

• XPrize.org – http://www.xprize.org/. Crowd sourcing innovative solutions to world 

challenges. 

• Amazon Mechanical Turk – https://www.mturk.com/mturk/welcome. A crowdsourcing 

internet marketplace that enables what are described as requesters to identify human 

workers (referred to as providers) willing to perform tasks that computers are currently 

unable to do. For example, the workers might classify pictures. The workers are 

compensated at a very low level. This is referred to as Artificial Artificial Intelligence. 

(The duplication of the word “artificial” is intended.) This is an ideal solution for simple 

repetitive tasks and may have a use in transportation safety data for classifying collected 

elements.

• Facewatch ID – http://facewatch.co.uk/cms/. This is a crime prevention application in 

the United Kingdom. The public watches feeds from the vast number of closed circuit 

televisions in the UK.

• Zooniverse – https://www.zooniverse.org/projects. Zooniverse is a citizen science web 

portal that currently hosts eight collective data applications which allows users to 

participate in scientific research. As of March 2012, the project has over 600,000 human 

volunteers, collectively referred to as Zooites. Two examples of Zooniverse projects 

include: 

o Galaxy Zoo – http://www.galaxyzoo.org. Launched in 2007, Galaxy Zoo has 

over 250,000 people helping with the classification of galaxies, providing more 

than 60,000,000 classifications. This work is entirely uncompensated. 

o Ancient Lives – http://ancientlives.org/. Users transcribe ancient Egyptian 

papyruses. The papyri belong to the Egypt Exploration Society and their texts will 

eventually be published and numbered in Society's Greco-Roman Memoirs series.

• Vertices – http://www2.vertices.com/home/. This firm specializes in community 

participatory mapping and has a few examples on their website on projects they have 

done with various communities, capturing things from walking routes to healthy 

restaurants. In these examples the community collected data using a variety of 

participants, from school kids to an identified task force. Additionally they have an

interactive mapping tool called Mappler (http://www.mappler.com/) which provides the 

ability for users to go out and collect a variety of information and interactively map the 

information. One example they have is creating a road safety assessment.
For More Information:

Visit: http://safety.fhwa.dot.gov/rsdp

FHWA, Office of Safety

Robert Pollack Carol Tan, PhD

Robert.Pollack@dot.gov Carol.Tan@dot.gov

202-366-5019 202-493-3315 

FHWA-SA-13-006