Simulation_RFI_20210527(1) C.pdf

Request for Information – Large-scale Microsimulations with Coherent Activity RFI Number: IARPA-RFI-21-05

Agency: Office of the Director of National Intelligence Office: Intelligence Advanced Research Projects Activity – Office of Analysis

IARPA-RFI-21-05

Request for Information (RFI): Large-scale Microsimulations with Coherent Activity

IARPA is seeking information on established datasets and capabilities to create large-scale, long-duration, high-fidelity microsimulations to support human mobility research. This RFI is issued for planning purposes only, and it does not constitute a formal solicitation for proposals or suggest the procurement of any material, data sets, etc. The following sections of this announcement contain details on the specific technology areas of interest, along with instructions for the submission of responses.

Background and Scope:

The proliferation of connected devices has generated massive volumes of geo-referenced data and insights on population movement. Human mobility research to model and utilize this data requires significant ground-truth data to evaluate performance of location prediction [1], activity classification [2, 3], anomaly detection [4], transportation optimization [5], and other analytics.

To share this movement data openly for public research, it must not contain information infringing upon individual privacy. One avenue to pursue such ground-truthed, privacy-preserving data is through high-fidelity microsimulation.

To leverage simulated data as a basis for algorithm development and ensure future applicability to real world data, the simulation needs to contain realistic characteristics:

Origin-destination pairs are consistent with human activities

Demographics of the population are accurate and correlate with appropriate movement

Movement exhibits temporal variability across time of day, day of week, month of year

Recurrence of activities for an entity is coherent over time, capturing a pattern of life

Routing between locations is consistent with local road networks and transportation options

Scale of population and number trips is appropriate for a given geographic extent

The purpose of this RFI is to identify established datasets, existing data simulation capabilities and ongoing research to create simulated data with coherent activity across these types of dimensions.

Responses to this RFI should answer any or all of the following questions:

1. Does your organization currently produce this type of high-fidelity simulation commercially?

Is your organization executing research to enable such a capability?

2. What real world data is leveraged to inform the simulation and validate that it faithfully represents real world movement? What metrics are used to evaluate the overall simulation accuracy and fidelity?

3. Can you ensure any data leveraged to inform the simulations preserves individual privacy and no personally identifiable information is recoverable from the simulation itself? What measures are taken to ensure demographic biases are not incorporated into the datasets which could create unfair discrimination in algorithms leveraging this data for training and testing?

4. Are the simulations that are developed transferable to new locations or is each simulation created independently? What is your approach to scaling out to new locations? Can fully fictional locations be created and simulated to exhibit real world statistical properties?

5. What demographic attributes (e.g. age, income, ethnicity, occupation, family, etc.) are included in your simulations? What geographic and cultural diversity has been simulated?

6. What depth of recurring activity is captured for an individual (e.g. work location, child’s school, shopping preferences, etc.)?

7. What level of detail is included for locations visited? Are they random locations, general functional visits (e.g. retail, leisure, home, work), with a specific purpose (e.g. grocery store, soccer match, a friend’s house), or real world individual locations (e.g. FedEx field, IARPA

Headquarters)?

8. What single-trip attributes (e.g. mode of transportation, type of vehicle) are included in your simulation?

9. What variability is incorporated across time of day, day of week, month of year at the population level? What level of variability is modeled in an individual entity and their daily routine?

10. How large of a population can be modeled, over what geographic extent, for what temporal duration, and at what temporal resolution (e.g. locations updated by second, minute, hour?)

11. What characteristics of the simulation can be tuned or customized? What insight is available into the statistical properties underlying the activities conducted by the population?

Preparation Instructions to Respondents:

IARPA requests that submittals briefly and clearly describe the approach or capability for novel datasets, directly address any or all of the specific questions, and outline any known critical technical issues/obstacles. If appropriate, respondents may also choose to provide a non-proprietary rough order of magnitude (ROM) estimate regarding what such approaches might require in terms of funding and other resources for one or more years to support IARPA data simulation needs. This announcement contains all of the information required to submit a response. No additional forms, kits, or other materials are needed.

IARPA welcomes responses from all capable and qualified sources from within and outside of the U.S.

Because IARPA is interested in an integrated and diverse approach, responses from teams with complementary areas of expertise are encouraged.

Submissions from Federally Funded Research and Development Centers (FFRDCs) and

University Affiliated Research Centers (UARCs) are permitted but with an understanding that neither groups are able to propose against any IARPA program. Instead, any submissions from these groups should consider the technical elements described above but include reflection upon how they would support program efforts as a potential test and evaluation partner enabling

IARPA to validate different potential approaches to meet the research challenge.

Responses have the following formatting requirements:

1. A one page cover sheet that identifies the title, organization(s), respondent's technical and administrative points of contact - including names, addresses, phone and fax numbers, and email addresses of all co-authors, and clearly indicating its association with RFI-21-05;

2. A substantive, focused, one-half page executive summary;

3. Answers to the above questions including potential research approaches capable of achieving a potential program on this topic limited to 5 pages in minimum 12-point Times

New Roman font, appropriate for single-sided, single-spaced 8.5 by 11-inch paper, with

1-inch margins);

4. A list of citations (any significant claims or reports of success must be accompanied by citations);

5. Optionally, a single overview briefing chart graphically depicting the key ideas;

6. An appendix of critical reference papers or white papers (no more than 3) associated with answers or potential approaches.

7. Identify any risks of unfair bias and discrimination that can occur through the data itself or through the human bias within the workforce programming. Identify risk mitigations such as removal of any personal characteristics that cannot be objectively justified for use, with particular care over protected characteristics to ensure the outputs are free from unfair bias and prejudice, whether conscious or unconscious.

Submission Instructions to Respondents:

Responses to this RFI are due no later than 5 p.m., Eastern Time, on 11 June, 2021. All submissions must be electronically submitted to dni-iarpa-rfi-21-05@iarpa.gov as a PDF document. Inquiries to this RFI must be submitted to dni-iarpa-rfi-21-05@iarpa.gov. Do not send questions with proprietary content. No telephone inquiries will be accepted.

Disclaimers and Important Notes:

This is an RFI issued solely for information and planning purposes and does not constitute a solicitation. Respondents are advised that IARPA is under no obligation to acknowledge receipt of the information received or provide feedback to respondents with respect to any information submitted under this RFI.

Responses to this notice are not offers and cannot be accepted by the Government to form a binding contract. Respondents are solely responsible for all expenses associated with responding to this RFI. IARPA will not provide reimbursement for costs incurred in responding to this RFI. It is the respondent's responsibility to ensure that the submitted material has been approved for public release by the information owner.

The Government does not intend to award a contract on the basis of this RFI or to otherwise pay for the information solicited, nor is the Government obligated to issue a solicitation based on responses received. Neither proprietary nor classified concepts or information should be included in the submittal. However, should a respondent wish to submit classified concepts or information, prior coordination must be made with the IARPA Chief of Security. Email the

Primary Point of Contact with a request for coordination with the IARPA Chief of Security.

Input on technical aspects of the responses may be solicited by IARPA from non-Government consultants/experts who are bound by appropriate non-disclosure requirements. Submissions may be reviewed and followed up on by an assigned technical contractor supporting the designated IARPA POC.

Several key laws, enacted over the past three decades, provide general privacy and confidentiality requirements that either directly or indirectly affect all government agencies.

These include The Privacy Act of 1974, The Computer Security Act of 1987, Health Insurance

Portability and Accountability Act of 1996 (HIPAA), US Patriot Act of 2001, and The Confidential

Information Protection and Statistical Efficiency Act of 2002. Under federal law, protected characteristics include race, color, national origin, religion, gender (including pregnancy), disability, age (if the employee is at least 40 years old), and citizenship status. Processing any of these data sets could inadvertently cause discrimination or bias, of these protected characteristics.

Federal laws and regulations that mandate protections for the privacy of citizens are applicable to the use of geospatial data. The Office of Management and Budget (OMB) states in “Circular

A-16 Revised: Coordination of Geographic Information and Related Spatial Data Activities” that geographic and spatial data must not compromise the privacy and the security of personal data about citizens.

Contracting Office Address:

Office of the Director of National Intelligence Intelligence Advanced Research Projects Activity Washington, District of Columbia 20511 United States

Primary Point of Contact:

Jack Cooper

Program Manager - Office of Analysis Research

Intelligence Advanced Research Projects Activity dni-iarpa-rfi-21-05@iarpa.gov

Citations:

1. Vaibhav Kulkarni and Benoît Garbinato. 2019. 20 Years of Mobility Modeling &

Prediction: Trends, Shortcomings & Perspectives. In Proceedings of the 27th ACM

SIGSPATIAL International Conference on Advances in Geographic Information Systems

(SIGSPATIAL '19). Association for Computing Machinery, New York, NY, USA, 492–

495. DOI:https://doi.org/10.1145/3347146.3359110

2. K. Zheng, Y. Zheng, N. J. Yuan and S. Shang, "On discovery of gathering patterns from trajectories," 2013 IEEE 29th International Conference on Data Engineering (ICDE), 2013, pp. 242-253, doi: 10.1109/ICDE.2013.6544829.

3. M. Yue, Y. Li, H. Yang, R. Ahuja, Y. Chiang and C. Shahabi, "DETECT: Deep Trajectory

Clustering for Mobility-Behavior Analysis," 2019 IEEE International Conference on Big

Data (Big Data), 2019, pp. 988-997, doi: 10.1109/BigData47090.2019.9006561.

4. Zhang, Daqing, et al. "iBAT: detecting anomalous taxi trajectories from GPS traces."

Proceedings of the 13th international conference on Ubiquitous computing. 2011.

5. Chenfeng Xiong, Mehrdad Shahabi, Jun Zhao, Yafeng Yin, Xuesong Zhou, Lei Zhang, An Integrated and Personalized Traveler Information and Incentive Scheme for Energy

Efficient Mobility Systems, Transportation Research Procedia, Volume 38, 2019, pp.160-

179, https://doi.org/10.1016/j.trpro.2019.05.010