Objective-Driven Data Sharing forTransit Agencies in
Mobility Partnerships
White Paper

Acknowledgments
July 2019

As the leading public interest organization in the mobility sector, the Shared-Use Mobility Center(SUMC) has served as an advisor to cities, transportation agencies, and business leaders since 2014
with a mission of achieving a multimodal transportation system that works for all.
This report was directly supported by the Federal Transit Administration (FTA) through theInnovation and Knowledge Accelerator (IKA), a cooperative agreement between SUMC and FTA
supporting the Mobility on Demand (MOD) Sandbox program. SUMC’s IKA program supports current
and future MOD grantees and develops resources to share knowledge broadly to the transit andshared mobility community.
The content and conclusions of this report are solely those of the Shared-Use Mobility Center.
This report was written by Prashanth Gururaja and Rudy Faust, with additional editorial oversightand input from SUMC’s Sharon Feigon. The report was edited by Leslie Gray and Israel Ramirez, anddesigned by Derek Berardi.
SUMC is grateful to FTA for their support, and the MOD Sandbox agencies and other interview
subjects for their openness to share ongoing challenges and successes.

© 2019 Shared-Use Mobility Center. All Rights Reserved.

Shared-Use Mobility Center
Chicago, IL
312.448.8083
Los Angeles, CA
818.489.8651
www.sharedusemobilitycenter.org

Contents
Overview

02

Purpose03

Know What Information You Need and Why

03

What do you need to learn? What is the objective of your project?

04

What type of project is it?

04

What types of analyses are required? Why? What type of data
would be needed?

04

Concerns & Challenges to Receiving Quality
Data and Information

06

Obtaining the Data or Information You Need

08

Project-Level Data

08

Regulatory Data Environments

14

Federal Data Requirements

17

Synthesis and Next Steps

18

Decision Tree18

Conclusion

21

Overview
With advanced technologies enabling the growth of new mobility services, transit agencies and cities
have looked toward partnerships with private mobility service providers for implementing Mobility
on Demand (MOD) solutions to enhance their existing public transit systems. A primary challenge
in implementing MOD solutions has been reaching an agreement between the public and private
partners over data sharing. Drawing on lessons learned from the Federal Transit Administration’s(FTA) MOD Sandbox program and beyond, this paper aims to support the decision-making process oftransit agencies that are considering deployment of MOD or similar integrated mobility solutions inpartnership with private-sector mobility service providers.
The paper first outlines the type of information an agency might need, based on their project typeand objectives. This discussion helps guide the data request, whether it involves historical data
for MOD service planning, trip information for MOD service evaluation, and/or real-time data for
multimodal trip planning and payment apps.
The paper then discusses the challenges that local and regional agencies have faced in attempting
to obtain the data that they might need. Challenges include those from both the public and private
partners:• Traveler Privacy• Competitiveness• Public Records Laws
• Data Security• Aggregation• National Transit Database (NTD) and Performance-Based Funding• Capability Limitations
Based on observations from both inside and outside the Sandbox, a variety of possible approaches
are available to agencies to address these challenges, and subsequently obtain and analyze the datathat are necessary to meet project goals. Those approaches include project-level, regulatory, andlegislative means to overcome the specific barriers mentioned above. In certain circumstances, thetype of project will dictate which approach an agency can take, but the report also engages waysthat agencies might better lay the groundwork for better data collection and analysis, in general.
Approaches include:• Finding a mutually agreeable parameter set and aggregation level• Using third-party repositories• Working with legislatures to modernize public records laws
• Establishing open data requirements• Reaching individual Application Programming Interface (API) agreements with mobility
service providers
2

The paper includes a decision tree that assists agencies in sequential decision-making to determine
the best approach, based on their project type, project objectives, and constraints a synthesis of
the lessons learned from both the MOD Sandbox and beyond, as well as proposed next steps tocomplement the FTA’s ongoing work to establish metrics for shared mobility.

1. Purpose
One of the primary challenges of implementing MOD projects has been reaching an agreement
between public and private partners over data sharing. Specifically, transit agencies and mobility
service providers have had difficulty agreeing on the type of data to be shared and the frequency ofthat data sharing (e.g., real-time, daily, weekly, monthly). The Shared-Use Mobility Center (SUMC)
has observed that this challenge has resulted primarily from each stakeholder having particularreasons for requesting or withholding data. For example, agencies may feel entitled to data if
they are subsidizing trips. Mobility service providers, on the other hand, may be reluctant to share
granular data due to concerns about traveler privacy and releasing proprietary information.
This paper is developed to assist transit agencies in deploying MOD solutions with mobility service
providers through objective-based data acquisition and data sharing strategies that are further
informed by performance measurement methodologies that help agencies understand the impactsof their projects. While understanding that specific data requirements will vary according to thenature of a project, this document aims to provide high-level guidance about how to develop an
approach to obtaining this information.
In its best practices section, the Transportation Research Board (TRB) publication Legal
Considerations in Relationships Between Transit Agencies and Ridesourcing Service Providers proposesthat agencies understand data needs, specify data needs, maintain flexibility for data usage,
ensure data security, and consider leverage in bargaining for data rights.1 The following discussion
will expand on these concepts, identify ways that agencies might overcome barriers to a dataagreement, and chart a workflow for interested stakeholders through synthesis and a proposeddecision tree. The analyses and discussions in this document are conducted by SUMC to support
FTA’s MOD Sandbox program.2

2. Know What Information You Need and Why
In the typical project planning process, a transit agency sets goals and identifies the target
population for the service. Traditionally, fulfilling the data collection and reporting requirements for
the National Transit Database (NTD) as well as for local and regional planning processes dictate thedata needs.3

1

See pages 109-110 National Academies of Sciences, Engineering, and Medicine 2018. Legal Considerations in Relationships Between Transit Agencies
and Ridesourcing Service Providers (Legal Research Digest 53). Washington, DC: The National Academies Press. https://doi.org/10.17226/25109
2
SUMC Innovation and Knowledge Accelerator. https://sharedusemobilitycenter.org/ika/
3
National Transit Database homepage: https://www.transit.dot.gov/ntd.

3

Transit agencies should think through a series of questions that link their data requests to their
MOD project’s objectives.
• What do you need to learn?
• What is the objective of your project?
• What type of project is it?
• What types of analysis are required? Why? What types of data would be needed?

What do you need to learn? What is the objective of your project?
The data needed will be determined largely by the objectives and model of their project. In orderto follow a structured approach, the agency should first determine the specific objectives of their
particular project, then determine the factors that contribute to meeting those objectives that
require information. The agency should also set a marker to identify which populations the project
intends to serve. Finally, the agency should ask itself if the project addresses operational deficiencies
and ineffectiveness in its service area, and if so, how the potential improvements will be measured.4
Once the objectives are determined, they should guide the agency’s approach to identifying thefollowing desired elements:• Service outcomes• Impact of the service• Information needed to determine whether the service outcomes are achieved• Activities, such as surveys and analyses, that are needed to determine the necessary
information• Data types and elements that need to be collected or analyzed to carry out the identified
analyses

What type of project is it?
The type of project influences the type of data an agency will need to plan, implement, and assess
the effectiveness of a project. For example, a ridesourcing partnership might require data about
trips taken, or a multimodal trip-planning app might require access to real-time vehicle availability.
Typical MOD pilot projects, such as those in the MOD Sandbox program, include first/last-mileconnections to transit, late-night service, on-demand service for people with disabilities, andmultimodal trip-planning and payment integration apps.

McCoy, K., Andrew, J., Glynn, R., & Lyons, W. (2018). Integrating Shared Mobility into Multimodal Transportation Planning: Improving Regional
Performance to Meet Public Goals (No. DOT-VNTSC-FHWA-18-13; FHWA-HEP-18-033). United States. Federal Highway Administration. Office ofPlanning, Environment, and Realty. https://rosap.ntl.bts.gov/view/dot/34689

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What types of analyses are required? Why? What type of data
would be needed?
In addition to the types of data, the timing and frequency of the data varies by need. Project dataneeds typically include those described below.
Transportation Trends – Archived, Post-Processed Data
The agency will want both its own data and, if possible, some of the historical data of demand
responsive services, such as taxi or ridesourcing services (e.g. Uber, Lyft). This historical andaggregated data ideally shows travel patterns and pick-up/drop-off locations.
Service Planning – Archived, Post-Processed Data
Here again, the agency will look at factors such as response time and service area to determine
operational characteristics of the project, such as geographic area, times of operation, andpopulation served.
Service Delivery, Effectiveness, Accountability – Trip Data
This is the type of data needed for both on-going and retrospective evaluation.
• Operations data: The purpose of these data is to understand the performance of the service
and behavior of users. These are trip-level data, analyzed in real time or periodically during
the pilot, depending on the type of partnership. Types of data can include:»

Origin-destination (O/D) pairs»

Pickup and drop-off times»

Wait times»

Travel times»

Vehicle occupancy• Accounting data: The purpose of these data is to monitor the actual cost compared tothe budget, and comprises trip and cost data analyzed post-hoc. Trip-level data include
items such as traveler fares and total cost. The program-level aggregated data can examine
parameters such as surge pricing trends from transportation network companies (TNCs),
average fares, and pooled versus non-pooled rides, i.e., passengers-per-vehicle whenapplicable.
• Auditing data: The purpose of these data is to determine if the partner is providing theservice as agreed. These include the accounting data as well as compliance data, such as
fulfillment of wheelchair requests.
Other models might require a modification of the specific data required. The items mentioned are
only examples of information that would be useful for agencies. Each agency should determine thespecific data items needed based on the objectives of the project.

5Integrated Trip-Planning and Payment: Real-Time Data
For integrated multimodal trip-planning, operators can provide data through an Application
Programming Interface (API), which allows access to a real-time feed of an operator’s trip
availability, vehicle location, cost, and other basic and pertinent data.
For transit, the General Transit Feed Specification (GTFS) is the current standard. For bikeshare, itis the General Bikeshare Feed Specification (GBFS). Both GTFS and GBFS enable a private operator
to better integrate public transit trips into their users’ trip planning. While a transit agency could
access the information on a ride, vehicle, and cost (bikeshare, carshare, TNCs, etc.), some companies
do not allow their data to be shared on the same app as a competitor. Furthermore, in the UnitedStates, there is currently no broad precedent for open APIs, where providers must make real-timeparameters available for third-party use.
Improving prediction of arrivals can be an area of data sharing as well. GTFS with real-time inputs,
such as vehicle location, exist for transit, but typically not in combination with more sophisticatedanalytics based on historical arrival and departure data to more accurately predict real-time arrivals.
Private mobility ventures are developing these analytics and computing-intensive software to
maximize utilization of their services. Although public agencies have both additional mandates forservice provisions and a longer funding horizon, both sectors might learn from each other. Whencoordinated together, real-time prediction of transit services can be improved, leading to a higherquality experience for the traveler.5

3. Concerns and Challenges to Receiving Quality Dataand Information
Through conversations and interviews with various agencies and providers in the MOD Sandbox
program and experts in the mobility ecosystem, along with supplemental research, several
challenges around data agreements were identified. These challenges exist for both the publicagency and private mobility service providers, but some are of more concern to one sector than theother. The following discussions summarize the identified challenges and some other key findings.
Privacy Concerns with Location Data
Both the public and private partners are concerned that origins and destinations for point-to-point
data might compromise privacy. Paired origin-destination data, even if anonymized, is at significant
risk of identifying individuals when combined with publicly available data sets, such as property tax
records. A recent study found that even coarse aggregation of anonymized data may put privacy atrisk.6 This emphasizes the need for security, and leads to the consideration about the accessibilityof records from public agencies.

Sun, F., Pan, Y., White, J., & Dubey, A. (2016, May). Real-time and predictive analytics for smart public transportation decision support system. In
Smart Computing (SMARTCOMP), 2016 IEEE International Conference on (pp. 1-8). IEEE.
6
De Montjoye, Y. A., Hidalgo, C. A., Verleysen, M., & Blondel, V. D. (2013). Unique in the crowd: The privacy bounds of human mobility. Scientific reports,
3, 1376
5

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Competitiveness
Data generated from partnerships may also contain proprietary information of the private mobility
service provider. For example, the provider would have an interest to not share data pertaining toalgorithms, predictive features, or data analytics methods. Commercial information such as targetedmarketing, marketing strategies, and market capture are also valuable information that may be ofproprietary interest or considered to be business sensitive information. The possibility of broader
release of this information leads to the following concern over public records laws.
Public Records Laws and Related Regulations
The risks associated with privacy and competitiveness issues are largely due to public records
laws (also referred to as freedom of information laws), which allow the public to gain access togovernment records. These laws predate the modern information age, and as such, do not explicitly
address the technological advancements that have allowed for gathering and storing large amountsof granular data. Transportation authorities and transit agencies collect a large amount of personallyidentifiable information (PII), and any agency should realize and understand the risk in theirpotential exposure.7
These laws often list types of records that are exempt from public release, but exemptions fordata privacy and security vary by state and jurisdiction.8 It should be noted that non-disclosureagreements or contract provisions might not supersede laws regarding disclosure.9
Also, the service type determines what data an agency might reasonably expect. Where
partnership-based services such as subsidized first/last-mile (FLM) trips may require data about
those trips, general TNC operations might fall under regular permitting regimes, where operators
are often not required to submit data to local jurisdictions. A prominent example can be foundin San Francisco, which under California regulations does not regularly receive data from the
California Public Utilities Commission, the state-level regulating agency.10 Before implementation,agencies should refer to their local and state laws to determine how shared mobility modes are
regulated in their jurisdictions. Many states have preemptive laws on the books, which might
limit a local jurisdiction's ability to regulate or require data from certain mobility service providers
operating in its area.11

Thomas, L. W. (2017). Legal Issues Concerning Transit Agency Use of Electronic Customer Data (No. Project J-05, Topic 16-02). http://nap.
edu/24730
8“State Freedom of Information Laws,” National Freedom of Information Coalition. https://www.nfoic.org/coalitions/state-foi-resources/statefreedom-of-information-laws. Also, see National Conference of State Legislatures. Data Security Database: http://www.ncsl.org/research/telecommunications-and-information-technology/data-security-laws-state-government.aspx
9
RASIER-DC, LLC v. B & L SERVICE, 237 So. 3d 374 (Fla. Dist. Ct. App. 2018). See discussion at Chow & Mingrove “Upon Reflection, Some Trade Secrets
May Be Less Secret Than They Appear.” https://blogs.orrick.com/trade-secrets-watch/2018/01/25/upon-reflection-some-trade-secrets-may-be-lesssecret-than-they-appear/#more-2410
10“TNCs Today: A Profile of San Francisco Transportation Network Company Activity.” SUMC MOD Learning Center: https://learn.
sharedusemobilitycenter.org/overview/tncs-today-a-profile-of-san-francisco-transportation-network-company-activity-san-franciscocalifornia-2017/11
National League of Cities “City Rights in an Era of Preemption: A State-by-State Analysis.” https://www.nlc.org/resource/city-rights-in-an-era-ofpreemption-a-state-by-state-analysis
77Data Security
Whether the source is a partner or a third party, the data must be handled and stored appropriately.
The primary concern on all sides is data management and security. This could affect collection,formatting, storage, archiving, access/authorization protocols, cybersecurity, reporting (internaland external), and compatibility with existing systems. Data handling and security protocols must
be discussed thoroughly before forming partnerships and clearly defined in agreements, while
adhering to the regulations regarding data and privacy at the local, state, and federal levels.
Data Aggregation
Data could be aggregated spatially and/or temporally to address the concerns discussed above,
but the resulting resolution may be too coarse to be useful for the agency to monitor the service’s
performance relative to its metrics and objectives. Similarly, data that is not aggregated could still
be reported without a level of resolution needed to meet agency objectives.
National Transit Database (NTD) and Performance-Based Funding
Each year, NTD performance data are used to apportion FTA funds to transit agencies in urbanizedareas.12 Agencies desire accurate reporting of trips, including for example, subsidized first/last-mile
trips, to be able maximize their FTA formula funds.
Capability Constraints
Transit agencies may not have the technical expertise or infrastructure to handle, store, and analyze
large amounts of data. This may be true even for pilot projects, depending on the characteristics ofthe agency and the breadth of the pilot service.

4. Obtaining the Data or Information You Need
Based on observations from inside and outside the MOD Sandbox perspectives, a variety ofpossible approaches are available to agencies to obtain and analyze the data necessary to meetproject goals. Those approaches include project-level, regulatory, and legislative means dependingon the specific barriers that the agency faces. The type of project will also affect which approach it
should take.

Project-Level Data
Subsidized Service Projects
Depending where the agency operates, the regulatory regimes discussed below might affect theflexibility afforded to the agency as it negotiates data and other requirements for the project. Many
aspects need to be negotiated between the agency and the operator. The private and public partners
should try to build a data sharing agreement based on mutually agreeable parameters and
US Federal Transit Administration. What is the National Transit Database (NTD) Program? https://www.transit.dot.gov/ntd/what-national-transitdatabase-ntd-program.

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aggregation levels, focused around what data the agency deems essential for planning andevaluation purposes. These data often include:• Trip length/distance• Origin-destination pairs: The grantees found this to be one of the most difficult negotiations.
Negotiations usually settle on which level of aggregation to use, such as ZIP Code, census
Tract, or number of decimal digits of latitude-longitude coordinates.
• Cost/fare• Customer response: Most app-based vendors gather customer satisfaction information andmay be willing to integrate specific questions for the transit agency on subsidized rides.
The frequency of the reports is another important element. While a trip planning app may requirereal-time information, service projects may only need information about trips or customer feedback
weekly or monthly.
As discussed above, data anonymization requires some care, and local agencies might want toconsider measures to assist in these efforts. The following are a few examples to demonstrate thedifferent parameters and aggregation levels used by different partnerships within and outside of the
MOD Sandbox projects.
Pierce Transit (Washington) - Once the regulatory environment is determined—again, public records
laws might preempt agreements—the agency or operator will often consider some form of nondisclosure agreement.13 The first/last-mile MOD Sandbox project agreement between Pierce Transit
and their partner Lyft involves monthly reports from Lyft to Pierce Transit with data for each trip asfollows:14• Month/Year• Day of Travel• Dispatch Method (Concierge or App-based)• Coupon Code (to indicate the zone)• Origin Census Tract and Destination Census Tract• Trip Time Period (AM Peak, Midday, PM Peak)• Trip Length Range (2-4 miles, 4-6 miles, etc.)• Trip Duration Range (5-10 minutes, 10-20 minutes, etc.)• Trip Cost, Refund, and Subsidy

13

Matthew W. Daus (2018) “Transportation Network Companies: Passenger Data Security and Privacy Issues.” Westlaw, Sharing Economy 300:100.
See also Cubic (2014) “Privacy and Data Collection in the Transportation Industry”
14
Correspondence with Pierce Transit. For more on the project, see: https://www.piercetransit.org/limited-access-connections/9Lyft and Pierce Transit are working under the presumption that these data could be released in a
public records request, and the parameters and aggregation levels are determined accordingly.
City of Arlington (Texas) - The partnership between the City of Arlington, Texas
and Via yielded a trove of publicly available documents from the issuance ofthe request for proposal through implementation. The partners are making
efforts to anonymize the data, as discussed below. SUMC’s analysis of theirproject shows how the City was able to work towards mutually agreeable
terms through an iterative procurement process.15
These data will include, on a periodic basis:• Individual ride data (anonymized): requested origin, requested destination, number ofpassengers, time and length of ride, fare paid
• Aggregated service data (for a given period): completed rides, active drivers, driver hours,
utilization (rides per vehicle per hour), average trip duration (minutes)• Performance standards (for a given period): average estimated time of arrival (ETA) to pick-up,% of on-time rides, % completed rides, rider satisfaction metrics• Historical trends (over longer periods): overall ride volume/growth, top requested origins anddestinations, demand 'heat maps'
These data shall be made available in formatted numerical and graphical reports. For the avoidanceof doubt, the information above constitutes proprietary trade secrets of Contractor, and shall be
subject to the confidentiality obligations set forth in the Contract.
MBTA (Boston, Massachusetts) - The Massachusetts Bay Transportation
Authority’s RIDE On-Demand program for people with disabilities formed
agreements with Uber and Lyft that are similar to City of Arlington’s
agreement with Via, although it requires the monthly data to be reported on a
ZIP code-level resolution.16
LA Metro (California) - In Los Angeles County, the Los Angeles County
Metropolitan Transportation Authority has signed an agreement with Via
to provide first/last-mile service in three stations around the county, as
part of the agency’s MOD Sandbox project. The service provides rides totransit stations from “virtual” bus stops located near the trip requester
as determined by Via’s routing algorithm. The data sharing agreement
between the two is comprehensive, with a detailed list of parameters forindividual trips and weekly aggregations of app, call center, vehicle, and ridership information.

“The City of Arlington, TX and Via MOD P3 RFP & Contract Process.” SUMC MOD Learning Center: https://learn.sharedusemobilitycenter.org/
casestudy/new-the-city-of-arlington-tx-and-via-mod-p3-rfp-contract-process/16“Lyft and Uber Agreements, MBTA, Boston, Massachusetts, 2016.” SUMC MOD Learning Center: https://learn.sharedusemobilitycenter.org/
overview/lyft-and-uber-agreements-mbta-boston-2016/15

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Among the many trip-level parameters listed in the agreement between LA Metro and Via are:17• Requested pick-up and drop-off locations – latitude and longitude to three decimal places• Actual wait times before pick-up, and actual pick-up and drop-off date and time
• Whether an accessible vehicle ride was requested and provided• Whether there was more than one passenger in the car• Trip cost charged to paying passenger• Ride rating awarded by passenger (1-5 starts)
These examples highlight that different agencies are willing to agree to different parameters andaggregation levels of the data produced by their partnership. These depend on the projecttype, the public disclosure laws in the agency’s
jurisdiction, the agency’s capacity to manage
the data, the urgency to introduce service in thetarget areas and populations, and the partner's
Agencies should understandbusiness interests. Thus, a single best practicehas yet to emerge and may not even be useful.
what potential providers’
Agencies should understand what potential
approaches to data sharing
providers’ approaches to data sharing are andare and aim to select a partner
aim to select a partner with whom they canwith whom they can find afind a mutually agreeable parameter set andaggregation level for data sharing.
mutually agreeable parameter

set and aggregation level fordata sharing.

Data Repositories and Collaborations

Another approach to sharing data from mobility
service providers while avoiding some of theaforementioned challenges is for the publicagency to never hold the mobility service
provider's disaggregated data. Instead, such data would be held by the provider or a trusted
third-party repository under strong legal, cybersecurity, privacy, and technical protections suchas encryption, anonymization, data lakes, etc. Those types of repositories would also require aclear access agreement, through which the public agency would be guaranteed access to “views”
of the data at the levels needed for accomplishing its work such as service planning, performancemeasurement, annual reporting, etc.

One third-party model is currently being developed at the University of Washington
Transportation Data Collaborative (TDC).18 This model combines an understanding of how tobalance the needs of public and private stakeholders with the institutional heft and technicalcapability of a major research institution. Capability includes both the technical know-how and thesheer storage and computing power needed to handle massive and ever-growing data flows.
17

Los Angeles County Metropolitan Transit authority. “Contract for Federal First and Last Mile Mobility On-Demand Services,” FAIN No. CA-2017-018,
Exhibit A – Statement of Work. December 18, 201818
University of Washington Transportation Data Collaborative: https://www.uwtdc.org.

11

TDC is working to develop the policy environment, governance model, and security protocols
so that they can serve as a public/private data management turnkey solution for agencies facing
constraints related to data disclosure or capability to perform large-scale analysis. In the case of
Washington State, the TDC aims to use research exemptions in the State’s public records laws toprotect sensitive data from disclosure.
However, the TDC is experiencing challenges. First, the legal constraints of Washington’s publicrecords laws require that the setup of this type of institution be vetted through the state attorney
general’s office, which has added significant time to launch due to availability of resources. The TDC
is also required to have an agreement with the private mobility service provider prior to generationof the data.19
Business interests are also a factor. Unless there is a requirement to report granular data, there
will not be much incentive for a private mobility service provider to participate in a third-party
repository. Submitting data requires resources from the business end, but without financial or otherincentives, there is a limited business case to prioritize it. Further still, the University of Washington
also requires that businesses pay to use the TDC, not just the public-sector clients, creating an
additional barrier for its use. These fees may be marginal for a global company, such as a major TNC,
but can be a significant burden for smaller providers.
A similar effort is being conducted by Shared Streets, supported by National Association of County
Transportation Officials (NACTO) and the Open Transport Partnership (a Bloomberg Philanthropiesfunded initiative). Shared Streets aggregates general operational data from TNCs and otheranonymized transportation data to include in a repository for analyzing and understandingtransportation behavior in cities. Shared Streets is a partner on pilot partnerships including the
Department of For-Hire Vehicles and the Department of Transportation in Washington, DC
to anonymize and aggregate taxi and TNC trip activity, and with the Seattle Department ofTransportation and Ford to map and regulate their curb use. Other partnerships are planned aroundtraffic speeds and incidents, construction, and closure data.20 However, as of this writing, Shared
Streets has not been used to share data in a partnership service, such as a subsidized first/last-mile
service, but this kind of tool presents an opportunity for transit agencies to better understand travel
patterns for planning and implementing new projects. It remains to be seen whether the tool can orwill be used effectively for operational data of a specific service partnership.
The National Renewable Energy Laboratory’s Transportation Secure Data Center is a partnership
between the U.S. Department of Transportation and the U.S. Department of Energy that contains
archived data that may be useful for planning.21
While the formation of these third-party repositories could possibly be a longer-term solution, theincentives between public and private sectors are not aligned enough to offer a reliable solution.
While a research exemption to public record laws is available in the state of Washington, it

Interview with University of Washington
Shared Streets. http://sharedstreets.io/
21
Transportation Secure Data Center home: https://www.nrel.gov/transportation/secure-transportation-data/tsdc-about.html
19
20

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is not entirely resolved that a publicly funded university would not be subject to public disclosure
laws, and this could vary by jurisdiction. Furthermore, agencies should consider whether theappropriate level of detail would be available, and if the resources dedicated by the mobility
service provider through an arrangement with such an entity would meet their objectives. If
constraints related to public records disclosures or agency capability are impeding progress
to obtaining the data that they need, agencies should explore using a third-party repository,
provided that the information that will be made available is supportive of agency objectives.
The examples highlighted, along with the emergence of mobility data management companies,
show that a third-party solution may be more useful in the project planning stage, where general
travel patterns are of interest, as opposed to the operations stage, where more granular trip
information is of interest.
While there are benefits to agencies not directly holding and managing the data, the data
themselves might be of significant value to generate revenue or exchange for private sector data.
The Transit Cooperative Research Program has initiated a research effort to assist public transit
agencies on how to better understand the potential uses and value of data generated by public
transportation and to be prepared to enter into agreements with entities to trade, share, and selldata.23
Integrated Trip-Planning Apps
Transit agencies are increasingly interested in becoming mobility managers for their regions,and some have embarked on developing multimodal trip-planning apps, where travelers cantheoretically plan, book, and pay for multimodal trips that consider all participating mobility
service providers operating in their area. Agencies have taken different
approaches to achieve this goal.
TriMet (Portland, Oregon) - MOD Sandbox grantee TriMet is developing
an open-source trip-planning app that aims to integrate bike sharing
and TNCs into its public transit trip-planning options. The agency needed to balance the concernsof Uber and their proprietary, real-time feed, with the needs of the TriMet app to provide
accurate information. The terms of use for major TNCs’ APIs preclude the appearance of thatdata on the same screen with a competing service, even though these same data are available
freely on Uber’s own app. This presents a barrier to true mobility integration. However, for the
MOD Sandbox project, TriMet and Uber agreed that TriMet could use Uber’s fare and arrival
time estimates in their trip-planning app.23 However, if a competing TNC, such as Lyft, were to
be included on the same screen within the app, say as a side-by-side comparison, then only thearrival time information could be used. TriMet also agreed to consider these data confidential
information and protect it from public disclosure, including notifying Uber of public records
requests involving the data. As of March 2019, TriMet had not reached a similar agreement with
Lyft, and Lyft was not included in the public demo release of TriMet’s trip planner.24
22

Transit Cooperative Research Program J-11/Task 31. http://apps.trb.org/cmsfeed/TRBNetProjectDisplay.asp?ProjectID=4466
23
Uber-TriMet API agreement for MOD Sandbox project, February 2019.
24
TriMet MOD Open Trip Planner Demo. https://betaplanner.trimet.org

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Other Agencies (Valley Metro, DART, VTrans) - Other agencies in the MOD
Sandbox who are creating integrated trip-planning apps have been in similar
conversations with the major TNCs about use of their API data. Valley Metro(Phoenix, Arizona) is working to integrate Lyft rides into its integrated trip planning and paymentapp. It has delegated agreements to use TNC API data to its app developer, Routematch, in part toavoid possessing the data, which might then be disclosed in a public records request.
Dallas Area Rapid Transit (DART) in Texas has been negotiating with the major TNCs and otherproviders for first/last-mile discoverability in their trip-planning app, similar to TriMet. In DART's
microtransit service zones, DART and Uber have negotiated an agreement to mutually discoveron-demand shared rides through both DART’s microtransit service (GoLink) and UberPool on eachother’s apps, with the UberPool rides being subject to the GoLink fare structure. As a first step,
UberPool trips are discoverable through the DART GoPass app and can be booked through a deep
link out to the Uber app.25
While individual agencies that are considering integrated trip-planning apps can attempt oneoff agreements with TNCs, this appears to be an inefficient approach for the industry in the longterm. As these efforts unfold, the contrasting approaches that TriMet, Valley Metro, and DART
are taking—relying on a commercial agreement or a direct contract/agreement with a TNC—will
provide insight toward which approach may be more viable and produce a more successful service
and which areas of policy need addressing if communities indeed see integrated trip-planning as
a worthwhile goal. The difficulties arise naturally from a lack of a requirements or incentives forproviders to make these data available, either to the government or the public. The MOD Sandbox
program presents an opportunity to evaluate these approaches.
However, bike share operators and smaller mobility service providers havebeen more open with others’ use of their API data. These services can help
accelerate integrated multimodal trip planning in areas where they play abigger role. For example, the Vermont Agency of Transportation (VTrans) isdeveloping an open-source trip planner, Go! Vermont (https://plan.govermont.
org/), and using the GTFS-Flex data standard to integrate flexible transportation providers acrossthe state. Private mobility partners, such as demand-response transportation and carpool providers,have agreed to integrate their data using this standard. This creates opportunities in rural areas,
where TNCs may not be a significant provider of trips.

Regulatory Data Environments
Local governments are typically constrained by state regulations, especially public records laws, thatfactor in many of the approaches discussed earlier. Also, while most states (41 as of this writing) havesome form of preemption law related to ridesourcing services, interpretation of those laws for data
might vary.26 Through their legislative process, the local agency could work with the state legislature
DART hires Uber to give Dallas-area customers free, discounted rides. https://www.dallasnews.com/business/technology/2019/03/12/dart-hiresuber-give-dallas-area-customers-free-discounted-rides
26
For FOIA/FOIL laws see National Freedom of Information. See National League of Cities (2018), “City Rights in an Era of Preemption: A State-byState Analysis” at https://www.nlc.org/resource/city-rights-in-an-era-of-preemption-a-state-by-state-analysis
25

14

to strengthen public records laws to prevent disclosure of travel data. This typically happens throughexempting such data from disclosure by defining certain parameters as personally identifiable
information (PII), to protect privacy, or as trade secrets, to protect proprietary information. What thismeans in practice is that agencies have to choose between seeking exemptions—working with thestates to change the law—or seeking innovative options such as finding a third-party data repository.
Exemptions in public records laws are being used a data protection tool by the joint first/last-mile
MOD Sandbox projects undertaken by LA Metro in Los Angeles County and Sound Transit/KingCounty Metro in the Seattle area.27
LA Metro (California) - LA Metro’s agreement with Via, mentioned earlier, has specific provisions
around which data constitute trade secrets, and that as a result, those data will not be disclosed
to the public under the California Public Records Act. Furthermore, the agreement reinforces theCalifornia Streets and Highways Code provision that any information that identifies or describes a
person, including travel pattern data, is PII and exempt from public disclosure.
Sound Transit and King County Metro (Washington) - The State of Washington
statute, however, does not include an exemption for location data. It isinterpreted to exempt social security numbers, driver’s license numbers, and other “highly sensitive”
information as personally identifiable information, potentially forcing the Seattle project to disclose
location data generated by their MOD service. However, integration of the service with ORCA, theregional transit fare card, has provided a path for exempting location data, due to provisions in thestatute relating to the use of fare media.
Transit agencies have also played a proactive role in helping tomodernize state laws to exempt sensitive data from public access.
TriMet (Portland, Oregon) - In 2014, TriMet was developing an electronic fare collection system, Hop
FastPass (currently operating), that would generate large amounts of travel pattern data. Yet, there
were no protections against disclosure of such data from public records requests. TriMet supported
the Oregon legislature to update laws to exempt travel pattern data in possession of transit
agencies, citing the potential of use by stalkers and domestic violence offenders.28 The legislation
listed PII “collected as part of an electronic fare collection system of a mass transit system” asexempt from disclosure, and classified location and other travel information from these systems
as PII. The legislation permitted disclosure of “public records that have attributes of anonymitythat are sufficient, or that are aggregated into groupings that are broad enough, to ensure that
persons cannot be identified by disclosure of the public records.”29 As of a discussion with TriMet
in December 2017, no requests had been made that would prompt TriMet to determine whether
certain travel data would not be exempt.
DART (Dallas, Texas) - In 2015, during the Texas Legislative Session, the North Texas Toll Authority
sought to protect toll customers’ data, including location data gathered at toll gantries, fromdisclosure.
28

27
Sourced from interviews for Sandbox: California Streets and Highway Code, Section 31490 and Revised Code of Washington 42.56.330
Oregon State Legislature, Legislative Information for House Bill 4086. Protect the Privacy of Public Transit Riders. https://olis.leg.state.or.us/
liz/2014R1/Downloads/CommitteeMeetingDocument/35111
29
Oregon Revised Statutes Chapter 192, Section 192.345(38). https://www.oregonlegislature.gov/bills_laws/ors/ors192.html

15

DART requested that the bill’s authors modify the legislation to include the same
protections for transit users. Upon DART’s request, protections were added fortransit customer’s electronic data collected through DART’s mobile application(GoPass), and it was further modified to protect customer data collected by all
applicable transit systems in Texas. Location is protected since it is part of thetransaction activity.30

Agencies can positively
influence themodernization of publicrecords laws to protect
sensitive travel data...
and still retain datainternally for evaluation,planning, and monitoring
the performance of theirservices.

In both the Oregon and Texas examples, the updates tothe public records legislation were uncontroversial andwere easily passed in their legislatures.

The key lesson is that agencies can positively influence
the modernization of public records laws to protect
sensitive travel data from public disclosure and stillretain data internally for evaluation, planning, andmonitoring the performance of their services. The
original laws were written before advancements intechnology enabled the rapid collection of real-time,
granular, and voluminous personal travel data. The
advancement in fare collection technology for traditional
fixed-route services is enough of a reason to better
protect personal data, and considerations for pointto-point travel data should also be incorporated. In
the interest of transparency about how public funds
are spent on transportation, the public deserves tounderstand the data that serve as the basis of anagency’s policy and service decisions. However, the public’s access to individuals’ travel patterns
has not been shown to be in the public interest, and protecting the privacy of individuals has drawn
broad political support. Agencies can work with legislatures to include location data as well as othernecessary elements to be classified as PII.
To overcome the lack of availability of API data for integrated trip-planning, transit agencies,
together with states or cities, can establish permitting systems to require providers to open up basic
data parameters needed for developing trip-planning apps. Many of these parameters are alreadyavailable through providers’ own apps. These requirements could enable side-by-side comparisonof trip itineries in coordination with transit, or otherwise make providers' APIs publicly accessible.
The Finland Transport Codes, effective in 2018, present an example of how a country the size ofmany US regions has required open data and interoperability from public and private transportation
providers with the goal of seamless multimodal travel and payment.31 The City of Arlington, Virginia
requires micromobility operators in that city to provide publicly accessible APIs regarding theTexas Transportation Code Chapter 451, Section 451.061. https://statutes.capitol.texas.gov/Docs/TN/htm/TN.451.htm#451.061. Also sourced
from correspondence with T. Plesko, DART.
31“National Mobility as a Service (MaaS) Policy, Finland, 2018.” SUMC MOD Learning Center: https://learn.sharedusemobilitycenter.org/overview/
national-mobility-as-a-service-maas-policy-finland-2018/30

16

location of micromobility devices (bicycles and scooters available for rental. As the MOD Sandbox
trip-planning projects and the regulatory environments like Finland and Arlington, Virginia mature,understanding the effects of these approaches will help to further refine this recommendation.
Some cities require data sharing from all mobility service providers operating in their jurisdictions.
For example, Chicago requires data from TNCs regarding vehicles, drivers, trip origins anddestinations, and crashes.32
Such requirements can be important tools for cities to evaluate the impacts of TNCs and develop
policies accordingly regarding congestion, equity, safety, or other goals. Similarly, some cities,
including Los Angeles, require micromobility operators to use the Mobility Data Specification(MDS), a common standard for reporting trip, cost, and other vehicle and system data to the city tohelp the city actively manage the providers and the public right-of-way. However, these industrywide requirements are not necessarily suitable for the project-level data sharing needs discussed inthis paper, which emerge from the particular objectives of specific MOD partnerships.33

Federal Data Requirements
The lack of clarity regarding data requirements for shared mobility partnerships—as acknowledged
by a recent US General Accountability Office (GAO) report to the US Senate Committee on Banking,Housing, and Urban Affairs—results in some confusion over reporting requirements to the National
Transit Database (NTD).
Recipients and beneficiaries (i.e. transit agencies) of FTA grants are required to report certain trip
data (depending on mode) to the NTD. These data, in tandem with those needed for planning,
shapes how local agencies view their core data-gathering responsibilities. While no datarequirements exist for shared mobility trips, the GAO report shows that the FTA is moving towards
expanding NTD reporting requirements.34
The federal government could also consider requiring
partnerships have to clearly defined data management
strategies to be eligible for federal funding. This could
place a mutual responsibility between the public andprivate sector participants to develop a strategy andinfrastructure for data sharing that could then benefit
other partnerships.

The federal government
could consider requiring
partnerships to have clearly
defined data management
strategies to be eligible forfederal funding.

32
National Mobility as a Service (MaaS) Policy, Finland, 2018.” SUMC MOD Learning Center: https://learn.sharedusemobilitycenter.org/overview/
national-mobility-as-a-service-maas-policy-finland-2018/ “Shared Mobility Devices – Transportation.” And, Arlington, VA city website: https://transportation.arlingtonva.us/sharedmobility-device-api/
33
Institute for Transportation and Development Policy. “Ride Fair: A Policy Framework for Managing Transportation Network Companies” https://www.itdp.org/wp-content/uploads/2019/03/2019.03.13.TNC-Policy.V9.pdf. “Mobility Data Specification: A Data Standard for Shared Mobility Providers, Los Angeles, California, 2018.” SUMC MOD Learning Center: https://learn.sharedusemobilitycenter.org/overview/data-standard-for-shared-mobility-providers-los-angeles-california-2018/34
See pages 24; 25, footnote 44, re: reporting; and 37; US Government Accountability Office, 2018, “Public Transit Partnerships: Additional Information Needed to Clarify Data Reporting and Share Best Practices.” https://www.gao.gov/assets/700/693518.pdf.

17

5. Synthesis and Next Steps
Based on their project objectives, project type, and metrics for performance, agencies can create astrategy for establishing a data agreement with their partner. This strategy first requires:• Defining project objectives clearly• Developing the performance indicators to measure the performance of the project and its
outcomes• Creating a list of data elements truly needed for measuring the outcomes of the project,considering potential constraints around aggregation levels and reporting frequency

Decision TreeThen, agencies will need to decide from various pathways to form the structure and details of theirdata agreement. The decision tree shown on the next page is intended to guide agencies that areconsidering mobility partnerships in a systematic way.
The decision tree sequentially directs the agency through a set of issues, with consideration of
benefits and tradeoffs to inform important choices, such as:• How to determine potential partners among institutions and private mobility service
providers• What is the best strategy for data handling and management, balancing considerations such as
storage, sanitization, authorization, and security, including protocols for using third-party datarepositories• Identifying which policies might serve as barriers to collecting, using, and safeguarding data.
Once identified, the agency can participate, as one of many stakeholders, in the legislative
process to modernize those policies.
For example, if the agreement parameter and aggregation level are not initially achievable, thedecision tree prompts the agency to examine whether its public records laws are the reason for
this, and if so, encourages the agency to work with lawmakers to modernize those laws. If thisis not feasible, then a third-party repository is suggested. Following this process will support theestablishment of data agreements that respect individual privacy and proprietary information whileforming integrated and multimodal service partnerships that serve the public’s best interest.

18

Decision Tree
The orange circles indicate the starting and ending points for the decision process, beginning withthe objectives agencies must determine before a data agreement can be reached. The green boxes
represent actions and decisions on a project level, and the blue boxes represent decisions on apolicy level.

START WITH
PROJECTOBJECTIVES

Policy Decision
Can you establish
API requirements?

Multimodal
trip-planning app:
Trip Discovery,
Booking,
Payment Integration

Project Action
Establish
individual API
Agreements

NO

YES

MOD Service:
Planning,
Operations,
Accounting,
Auditing

Usage Data andTraveler Feedback

Project Action

Develop metrics.
Policy Decision
Are your publicrecord laws thereason?

Project Action

Policy Decision
Can you workwith lawmakers
to modernize
these laws?

YES

Create or revise
data needs.

NO

Project Decision
Can you reach a mutuallyagreeable data parameter
set and aggregation levelwith your partner?

FORM
YOUR DATAAGREEMENT

NO
NO

YES

Project DecisionNO

Project Decision
YES

YES

Do you have thecapability to
manage the data?

Are your data needs
truly driven by yourobjectives?

YES

NO

Project Decision
YES

NO

19

Can you work withthird-party
repositories?

For each major decision presented in the decision tree, a set of advantages and disadvantages arelisted in the table below to help an agency weigh their options. It is important to understand that
there is no right answer, and the agency must manage tradeoffs based on their specific and desired
values, capabilities, outcomes, and project objectives.
Advantages and Disadvantages of Decisions

Project Type

Decision

Mutually agreeableparameter set andaggregation level

Modernize publicrecords laws

MOD
Service

Manage datain-house

Use third-party
repositories

Advantages

Disadvantages• Direct negotiation with
provider to address concernsand data delivery format• Data resolution may be toocoarse• Addresses specific needs for
project and methods for data
ownership and handling• Lack of standard requires
one-off agreements for every
project, including extensions• Differences may not bereconcilable in the end• Clarity on what data areprotected• May be a longer-term
solution• Establishes bounds for
future projects/agreements• Requires time and• Data ownership sits withagency; can query as needed• Data handling expertise
required• Additional benefits foragency; can use for planning• IT infrastructure may beneeded• Possible monetization in the
future• More burdensome asservices grow beyond pilot
stage• Possible protection for
personally identifiable
information from public
disclosure• Data resolution may be toocoarse• Warehousing, management,
and/or analysis of raw data, as
neededresources for legislative
outreach and crossdepartmental coordination• Requires additional work tocheck against public records
laws• Data ownership sits outside
agency• Legal and governanceframework still not mature• All providers will be seen by
travelersMultimodal
Trip-PlanningApp

Establish API
requirements for mobility • Avoids separate or differingagreements for individual
service providers• Lack of standards for mosttrip discovery and payment
APIs

providers• Requires legislative or
regulatory action• Work with providers who
are ready to integrate• Lack of uniform standard
for API connections

Individual API agreements • Avoids legislative process20• Might not include providers
with large market share

Conclusion and Next Steps
One of the primary challenges of implementing MOD projects has been reaching an agreement
between public and private partners over data sharing. SUMC has observed that this challenge has
resulted primarily from each stakeholder having particular reasons for requesting or withholding
data—privacy concerns, competitiveness, proprietary information, public records laws, and data
security being among the most prominent reasons. Through the analyses and discussions in thisdocument, the following noteworthy conclusions were reached:• Agencies should understand what potential providers’ approaches to data sharing are andaim to select a partner with whom they can find a mutually agreeable parameter set andaggregation level for data sharing.
• If constraints related to public records disclosures or agency capability are impeding progress
to obtaining the data that they need, agencies should explore using a third-party repository,
provided that the information that will be made available is supportive of agency objective.
• Transit agencies and supporting organizations can proactively influence the modernization of
public records laws to protect sensitive travel data from public disclosure and still retain datainternally for evaluation, planning, and monitoring the performance of their services.
• To overcome the lack of availability of API data for multimodal trip planning, transit agencies,
together with states or cities, can establish requirements to open up basic data parameters
needed for developing trip-planning apps.
• The federal government could consider requiring partnerships to have clearly defined data
management strategies to be eligible for federal funding.
• Regardless of their geographic location, size, coverage area, customers, or culture, transit
agencies and their partners would benefit from following a structured approach while forming
any partnerships to integrate their services. This structured approach would include clearly
defining the objectives and associated performance indicators, identifying the required data
and data constraints, identifying the partners and establishing agreements where the required
data will be available, strategizing on data management, managing associated tradeoffs, andworking on regulatory barriers that could provide opportunities for better service for the
public.
The MOD Sandbox projects are in their early states of implementation, which will be followedby complete performance evaluation. The Sandbox initiative is a platform for testing different
data arrangements and a convening platform to share lessons on this issue and work toward bestpractices moving forward.

21

This paper complements the FTA’s development of the Mobility Performance Metrics (MPM),
which aim to identify new supplemental performance indicators for integrated mobility systems
and services. The MPM efforts introduce supplemental performance indicators and assess theirfeasibility for measuring various metrics that are typically not covered within the traditional
performance measurement approaches. Furthermore, the MPM effort attempts to identify gaps
and redundancies between the proposed metrics and goals of MOD and other mobility integration
projects, and evaluate broader policy implications of operationalizing those metrics to measure theperformance of integrated mobility services. This effort will shape the longer-term approach for theindustry to frame the data sharing issue in the appropriate broader context of agency goals, project
objectives, and emerging measurement methods.

22

A stand-alone Executive Summary of this paper can be found atwww.sharedusemobilitycenter.org/publications

312.448.8083
www.sharedusemobilitycenter.org