Synthetic population and urban mobility modeling using open and publicly available data - a Washington, DC COVID case study

Shiyang Ruan , Tunaggina Khan , Mengfei Xin , Andreas Züfle , Dieter Pfoser

Computational Urban Science ›› 2025, Vol. 5 ›› Issue (1) : 57

PDF
Computational Urban Science ›› 2025, Vol. 5 ›› Issue (1) : 57 DOI: 10.1007/s43762-025-00214-9
Original Paper
research-article

Synthetic population and urban mobility modeling using open and publicly available data - a Washington, DC COVID case study

Author information +
History +
PDF

Abstract

Human behavior changes especially when facilitated and amplified by policy changes during situations such as the COVID pandemic can have far reaching consequences for urban mobility. We have seen orphaned roads and empty metro cars during times when “rush hour” should have been the norm. To better comprehend these phenomena, this work uses Agent-based modeling (ABM), specifically the MATSim framework, in combination with a wealth of publicly available data (CENSUS and Openstreetmap - OSM) to model pre- and during COVID urban mobility for the Washington, DC metro area. The available CENSUS data combined with population generation algorithms and MATSim allows us to model a population of four million people and their daily mobility patterns on a multimodal transportation network that includes a road network, the metro system, and a number of bus services in the Washington, DC metro area. In comparing the simulation output with ground-truth flows, we show that indeed our approach is capable to accurately capturing traffic flows in this multimodal network as observed before and during the COVID pandemic. Overall, this work demonstrates that publicly available population data (CENSUS) and transportation infrastructure and POI data (OSM) can be leveraged in a powerful simulation framework to accurately model urban mobility. Example scenarios could be the evaluation of future policy proposals as well as infrastructure projects that leverage mobility patterns.

Keywords

Digital twin / Urban mobility / Microsimulation / Agent-based modeling

Cite this article

Download citation ▾
Shiyang Ruan, Tunaggina Khan, Mengfei Xin, Andreas Züfle, Dieter Pfoser. Synthetic population and urban mobility modeling using open and publicly available data - a Washington, DC COVID case study. Computational Urban Science, 2025, 5(1): 57 DOI:10.1007/s43762-025-00214-9

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Arentze T, Timmermans H, Hofman F. Creating synthetic household populations: Problems and approach. Transportation Research Record, 2007, 2014(1): 85-91.

[2]

Balac M, Ciari F, Axhausen KW. Carsharing demand estimation: Zurich, Switzerland, area case study. Transportation Research Record, 2015, 2563(1): 10-18.

[3]

Balac, M., & Hörl, S. (2021). Synthetic population for the state of California based on open-data: examples of San Francisco Bay area and San Diego County. 100th Annual Meeting of the Transportation Research Board (TRB), Jan 2021, Washington, D.C. (virtual), United States. https://hal.science/hal-03208848/.

[4]

Bhandari, P., Anastasopoulos, A., & Pfoser, D. (2024). Urban mobility assessment using llms. In Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems, SIGSPATIAL ’24 (pp. 67–79). Association for Computing Machinery.

[5]

Bohman H, Ryan J, Stjernborg V, Nilsson D. A study of changes in everyday mobility during the COVID-19 pandemic: As perceived by people living in Malmö, Sweden. Transport Policy, 2021, 106: 109-119.

[6]

Castiglione, J., Bradley, M., & Gliebe, J. (2015). Activity-based travel demand models: a primer (No. SHRP 2 Report S2-C46-RR-1).

[7]

Census Bureau, U.S. Public use microdata areas (pumas). https://www.census.gov/programs-surveys/geography/guidance/geo-areas/pumas.html. Accessed 6 June 2022.

[8]

Ciari F, Balac M, Axhausen KW. Modeling carsharing with the agent-based simulation matsim: State of the art, applications, and future developments. Transportation Research Record, 2016, 2564(1): 14-20.

[9]

Crooks AT, Wise S. Gis and agent-based models for humanitarian assistance. Computers, Environment and Urban Systems, 2013, 41: 100-111.

[10]

Das S, Boruah A, Banerjee A, Raoniar R, Nama S, Maurya AK. Impact of covid-19: A radical modal shift from public to private transport mode. Transport Policy, 2021, 109: 1-11.

[11]

de Dios Ortúzar, J. & Willumsen, L. G. (2011). Modelling transport. John wiley & sons.

[12]

Dingel JI, Neiman B. How many jobs can be done at home?. Journal of Public Economics, 2020, 189: 104235.

[13]

Federal Highway Administration, Department of Transpoation. National household travel survey (rts). https://https://nhts.ornl.gov/. Accessed 6 June 2024.

[14]

Gkountouna, O., Pfoser, D., & Züfle, A. (2020). Traffic flow estimation using probe vehicle data. In 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA) (pp. 579–588). IEEE.

[15]

Guo Z, Liu X. How artificial intelligence cooperating with agent-based modeling for urban studies: A systematic review. Transactions in GIS, 2024, 28(3): 654-674.

[16]

Gurcan, O. Llm-augmented agent-based modelling for social simulations: Challenges and opportunities (2024). arXiv:2405.06700.

[17]

Harris, M. A., & Branion-Calles, M. (2021). Changes in commute mode attributed to COVID-19 risk in Canadian national survey data. Findings (Sydney (N.S.W.). https://doi.org/10.32866/001c.19088.

[18]

Hörl S, Balac M. Synthetic population and travel demand for Paris and Île-de-France based on open and publicly available data. Transportation Research Part C: Emerging Technologies, 2021, 130. 103291

[19]

Horni, A., Nagel, K., & Axhausen, K. (2016). The Multi-Agent Transport Simulation MATSim. Ubiquity Press.

[20]

Hsu, S.-L., Tung, E., Krumm, J., Shahabi, C., & Shafique, K. (2024). Trajgpt: Controlled synthetic trajectory generation using a multitask transformer-based spatiotemporal model. In Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems, SIGSPATIAL ’24 (pp. 362–371). Association for Computing Machinery.

[21]

Huang X, Li Z, Jiang Y, Li X, Porter D. Twitter reveals human mobility dynamics during the covid-19 pandemic. PLoS One, 2020, 15(11): 1-21.

[22]

Javadinasr, M., Magassy, T. B., Rahimi, E., Davatgari, A., Salon, D., Bhagat-Conway, M. W., Chauhan, R. S., Pendyala, R. M., Derrible, S., Khoeini, S., et al. (2021). The enduring effects of covid-19 on travel behavior in the united states: A panel study on observed and expected changes in telecommuting, mode choice, online shopping and air travel. arXiv preprint arXiv:2109.07988.

[23]

Jenelius E, Cebecauer M. Impacts of COVID-19 on public transport ridership in Sweden: Analysis of ticket validations, sales and passenger counts. Transportation Research Interdisciplinary Perspectives, 2020, 8. 100242

[24]

Jiang B, Cheng T, Tsou M-H, Zhu D, Ye X. Advancing translational human dynamics research: Bridging space, mind, and computational urban science in the era of geoai. Computational Urban Science, 2025, 5(1): 12.

[25]

Jiang N, Crooks AT, Kavak H, Burger A, Kennedy WG. A method to create a synthetic population with social networks for geographically-explicit agent-based models. Computational Urban Science, 2022, 2(1): 1-18.

[26]

Kang Y, Gao S, Liang Y, Li M, Rao J, Kruse J. Multiscale dynamic human mobility flow dataset in the US during the covid-19 epidemic. Scientific Data, 2020, 7(1): 1-13.

[27]

Klein, B., LaRock, T., McCabe, S., Torres, L., Friedland, L., Privitera, F., Lake, B., Kraemer, M. U., Brownstein, J. S., Lazer, D., et al. (2020). Reshaping a nation: Mobility, commuting, and contact patterns during the covid-19 outbreak. Northeastern University-Network Science Institute Report.

[28]

Kraemer MUG, Sadilek A, Zhang Q, Marchal NA, Tuli G, Cohn EL, Hswen Y, Perkins TA, Smith DL, Reiner RC, Brownstein JS. Mapping global variation in human mobility. Nature Human Behaviour, 2020, 4(8): 800-810.

[29]

Lim, P. P. (2020). Population synthesis for travel demand modelling in Australian capital cities. PhD dissertation, The University of Queensland.

[30]

Liu T, Yang J, Yin Y. Toward LLM-agent-based modeling of transportation systems: A conceptual framework. Artificial Intelligence for Transportation, 2025, 1: 100001.

[31]

Lopez, P. A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., & Wießner, E. (2018). Microscopic traffic simulation using sumo. In 2018 21st international conference on intelligent transportation systems (ITSC) (pp. 2575–2582). IEEE.

[32]

Lu X, Bengtsson L, Holme P. Predictability of population displacement after the 2010 Haiti earthquake. Proceedings of the National Academy of Sciences, 2012, 109(29): 11576-11581.

[33]

Lu Y, Aleta A, Du C, Shi L, Moreno Y. Llms and generative agent-based models for complex systems research. Physics of Life Reviews, 2024, 51: 283-293.

[34]

Maryland State Department of Education (2020). State superintendent salmon announces temporary closure of maryland public schools. maryland.gov.

[35]

Mbuya, J. K., Pfoser, D., & Anastasopoulos, A. (2024). Trajectory anomaly detection with language models. In Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems, SIGSPATIAL ’24 (pp. 208–219). Association for Computing Machinery.

[36]

Meckler, L. (2020). Seven states, d.c. order all schools closed in effort to prevent spread of covid-19. The Washington Post.

[37]

Mobility Analytics Research Group (MARG). Popgen: Synthetic population generator. http://www.mobilityanalytics.org/popgen.html. Accessed 6 June 2022.

[38]

Moreland A, Herlihy C, Tynan MA, Sunshine G, McCord RF, Hilton C, Poovey J, Werner AK, Jones CD, Fulmer EB, Gundlapalli AV, Strosnider H, Potvien A, García MC, Honeycutt S, Baldwin G. Timing of state and territorial covid-19 stay-at-home orders and changes in population movement – United States, March 1–May 31, 2020. MMWR. Morbidity And Mortality Weekly Report, 2020, 69: 1198-1203.

[39]

Natanson, H. (2020). Virginia closes schools for the year, sending districts and families scrambling. The Washington Post.

[40]

Ozaydin, O., & Ulengin, F. (2020). Impacts of COVID-19 on the Transport Sector and Measures as Well as Recommendations of Policies and Future Research: A Report on Turkey. Available at SSRN: http://dx.doi.org/10.2139/ssrn.3686628.

[41]

Perez L, Dragicevic S. An agent-based approach for modeling dynamics of contagious disease spread. International Journal of Health Geographics, 2009, 8: 1-17.

[42]

Politis I, Georgiadis G, Nikolaidou A, Kopsacheilis A, Fyrogenis I, Sdoukopoulos A, Verani E, Papadopoulos E. Mapping travel behavior changes during the covid-19 lock-down: A socioeconomic analysis in Greece. European Transport Research Review, 2021, 13: 1-19.

[43]

RECKER, J. (2020). Dc public schools are closed for the rest of the school year. Washingtonian.

[44]

Rich J, Mulalic I. Generating synthetic baseline populations from register data. Transportation Research Part A: Policy And Practice, 2012, 46: 467.

[45]

SafeGraph. Covid19-commerce-pattern. https://www.safegraph.com/data-examples/covid19-commerce-pattern. Accessed 6 June 2022.

[46]

SafeGraph. Store visit attribution: Importance, methods, & where to get data. https://www.safegraph.com/guides/visit-attribution. Accessed 21 June 2022.

[47]

Sallard, A., Balać, M., & Hörl, S. (2020). A synthetic population for the greater São Paulo metropolitan region. ETH Zurich. https://doi.org/10.3929/ETHZ-B-000429951.

[48]

Schweizer J, Poliziani C, Rupi F, Morgano D, Magi M. Building a large-scale micro-simulation transport scenario using big data. ISPRS International Journal of Geo-Information, 2021, 10(3): 165.

[49]

Smolak K, Rohm W, Knop K, Siła-Nowicka K. Population mobility modelling for mobility data simulation. Computers, Environment and Urban Systems, 2020, 84: 101526.

[50]

Snowdon, J., Gkountouna, O., Züfle, A., & Pfoser, D. (2018). Spatiotemporal traffic volume estimation model based on gps samples. In Proceedings of the Fifth International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data (pp. 1–6).

[51]

Solomou, S., & Sengupta, U. (2024). Simulating complex urban behaviours with ai: Incorporating improved intelligent agents in urban simulation models. Urban Planning, 10.

[52]

Müller, J.; Straub, M.; Richter, G.; Rudloff, C. (2022). Integration of Different Mobility Behaviors and Intermodal Trips in MATSim. Sustainability, 14, 428. https://doi.org/10.3390/su14010428.

[53]

Truong R, Gkountouna O, Pfoser D, Züfle A. Towards a better understanding of public transportation traffic: A case study of the Washington, DC metro. Urban Science, 2018, 2(3): 65.

[54]

United States Office of Management and Budget (OMB). North american industry classification system - naics. https://www.census.gov/naics/. Accessed 21 June 2022.

[55]

U.S. Census Bureau. American community survey, 2019 american community survey 5-year estimates, table s0101. https://data.census.gov/cedsci/table?q=ACSST5Y2019.S0101. Accessed 6 June 2022.

[56]

U.S. Census Bureau, Center for Economic Studies. Lehd origin-destination employment statistics (lodes). US Census Bureau Center for Economic Studies Publications and reports page,https://lehd.ces.census.gov/data/. Accessed 6 June 2022.

[57]

Wang D, He BY, Gao J, Chow JY, Ozbay K, Iyer S. Impact of COVID-19 behavioral inertia on reopening strategies for New York City transit. International Journal of Transportation Science and Technology, 2021, 10(2): 197-211.

[58]

Wang, J., Xiong, J., Yang, K., Peng, S., & Xu, Q. (2010). Use of GIS and agent-based modeling to simulate the spread of influenza. In 2010 18th International Conference on Geoinformatics (pp. 1–6).

[59]

Wang S, Huang X, Liu P, Zhang M, Biljecki F, Hu T, Fu X, Liu L, Liu X, Wang R, Huang Y, Yan J, Jiang J, Chukwu M, Reza Naghedi S, Hemmati M, Shao Y, Jia N, Xiao Z. et al.. Mapping the landscape and roadmap of geospatial artificial intelligence (GeoAI) in quantitative human geography: An extensive systematic review. International Journal of Applied Earth Observation and Geoinformation, 2024, 128. 103734

[60]

Washington Metropolitan Area Transit Authority (WMATA). Rail ridership data viewer. https://www.wmata.com/initiatives/ridership-portal/Metrorail-Ridership-Summary.cfm. Accessed 6 Mar 2025.

[61]

Washington Metropolitan Area Transit Authority (WMATA). Transit feed. https://transitfeeds.com/feeds. Accessed 6 June 2022.

[62]

Wise S, Crooks A, Batty MNamazi-Rad M-R, Padgham L, Perez P, Nagel K, Bazzan A. Transportation in agent-based urban modelling. Agent Based Modelling of Urban Systems, 2017Springer International Publishing129-148.

[63]

Yigitcanlar T, Hossain ST, Shaamala A, Ye X. Quantum AI urbanism: Redefining the future of artificial intelligence in cities. Journal of Urban Technology, 2025, 32(3): 213-226.

[64]

Zhai W, Ye X. Moving towards climate-resilient mobility: Challenges and emerging trends. Travel Behaviour and Society, 2025, 39: 100971.

[65]

Zhang, L., Pfoser, D., & Züfle, A. (2020). Station-to-user transfer learning: Towards explainable user clustering through latent trip signatures using tidal-regularized non-negative matrix factorization. In Proceedings of the 28th International Conference on Advances in Geographic Information Systems (pp. 303–313).

[66]

Zhou Y, Liu XC, Chen B, Grubesic T, Wei R, Wallace D. A data-driven framework for agent-based modeling of vehicular travel using publicly available data. Computers, Environment and Urban Systems, 2024, 110: 102095.

RIGHTS & PERMISSIONS

The Author(s)

AI Summary AI Mindmap
PDF

99

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/