Abstract
Agent-based modeling (ABM) is a wide-spread technique that can be utilized as an artificial laboratory for in-silico experiments of real-case studies of different domains such as mobility. To initialize agent/environment attributes and their relationships, disaggregated (individual level) micro-data is required as an input. However, having such data is not often possible due to several reasons such as privacy concerns. To bridge the gap, generating realistic synthetic data (from census/survey data) becomes an initial and essential step of agent-based modeling. In this piece of research, we employ the mobility profiles of the Swiss population for generating synthetic populations along with their mobility activities. To validate the synthetic data, an agent-based model, which is already calibrated to the empirical data, is re-run with a sample and the generated synthetic data. Accumulated decisions of agents in both cases are compared. In addition, marginal frequencies of control attributes are benchmarked. The first obtained results demonstrate that increasing size of the generated population decreases the difference between simulation results of the synthesized data and the real data.
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References
J.P. Antonini, G. Vuidel, O. Klein, Generating a located synthetic population of individuals, households, and dwellings (Tech. rep, LISER, 2017)
ARE/BfS: Verkehrsverhalten der Bevölkerung Ergebnisse des Mikrozensus Mobilität und Verkehr 2015. Federal Office for Spatial Development and Swiss Federal Statistical Office (2017)
T. Arentze, H. Timmermans, Albatross: a learning based transportation oriented simulation system. Citeseer (2000)
A. Bektas, R. Schumann, How to optimize gower distance weights for the k-medoids clustering algorithm to obtain mobility profiles of the swiss population, to be published and represented in the Swiss Conference on Data Science (SDS) Conference - June 2019
D. Casati, K. Müller, P.J. Fourie, A. Erath, K.W. Axhausen, Synthetic population generation by combining a hierarchical, simulation-based approach with reweighting by generalized raking. Transportation Research Record 2493(1), 107–116 (2015)
W.E. Deming, F.F. Stephan, On a least squares adjustment of a sampled frequency table when the expected marginal totals are known. The Annals of Mathematical Statistics 11(4), 427–444 (1940)
B. Farooq, M. Bierlaire, R. Hurtubia, G. Flötteröd, Simulation based population synthesis. Transportation Research Part B: Methodological 58, 243–263 (2013)
M. Frick, Generating synthetic populations using ipf and monte carlo techniques: Some new results. [Arbeitsbericht Verkehrs-und Raumplanung] 225 (2004)
K. Harland, A. Heppenstall, D. Smith, M.H. Birkin, Creating realistic synthetic populations at varying spatial scales: A comparative critique of population synthesis techniques. Journal of Artificial Societies and Social Simulation 15(1), (2012)
N. Huynh, M.R. Namazi-Rad, P. Perez, M. Berryman, Q. Chen, J. Barthelemy, Generating a synthetic population in support of agent-based modeling of transportation in sydney (12 2013). 10.13140/2.1.5100.8968
S. Jain, N. Ronald, S. Winter, Creating a synthetic population: A comparison of tools. In: Proceedings of the 3rd Conference Transportation Reserch Group, Kolkata, India. pp. 17–20 (2015)
B. Jeong, W. Lee, D.S. Kim, H. Shin, Copula-based approach to synthetic population generation. PloS one 11(8), e0159496 (2016)
K. Nguyen, R. Schumann, On developing a more comprehensive decision-making architecture for empirical social research: Lesson from agent-based simulation of mobility demands in switzerland, to be published and represented in the Multi-Agent-Based Simulation (MABS) workshop - May 2019
A.R. Pinjari, C.R. Bhat et al., Activity-based travel demand analysis. A Handbook of Transport Economics 10, 213–248 (2011)
P. Salvini, E.J. Miller, Ilute: An operational prototype of a comprehensive microsimulation model of urban systems. Networks and spatial economics 5(2), 217–234 (2005)
S. Srinivasan, L. Ma, Synthetic population generation: A heuristic data-fitting approach and validations. In: 12th International Conference on Travel Behaviour Research (IATBR), Jaipur (2009)
The Competence Center for Research in Energy, Society and Transition - CREST: Swiss Household Energy Demand Survey (SHEDS) (2018), uRL: https://www.sccer-crest.ch/research/swiss-household-energy-demand-survey-sheds/. Last visited on 2019/04/21
P. Williamson, M. Birkin, P.H. Rees, The estimation of population microdata by using data from small area statistics and samples of anonymised records. Environment and Planning A 30(5), 785–816 (1998)
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Bektas, A., Schumann, R. (2021). Using Mobility Profiles for Synthetic Population Generation. In: Ahrweiler, P., Neumann, M. (eds) Advances in Social Simulation. ESSA 2019. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-61503-1_20
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DOI: https://doi.org/10.1007/978-3-030-61503-1_20
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