Hidden Markov Model; Population synthesis; Agent-based micro-simulation transportation modeling; Multiple data sources; Scalability
Abstract :
[en] Micro-simulation travel demand and land use models require a synthetic population, which consists of a set of agents characterized by demographic and socio-economic attributes. Two main families of population synthesis techniques can be distinguished: (a) fitting methods (iterative proportional fitting, updating) and (b) combinatorial optimization methods. During the last few years, a third outperforming family of population synthesis procedures has emerged, i.e., Markov process-based methods such as Monte Carlo Markov Chain (MCMC) simulations. In this paper, an extended Hidden Markov Model (HMM)-based approach is presented, which can serve as a better alternative than the existing methods. The approach is characterized by a great flexibility and efficiency in terms of data preparation and model training. The HMM is able to reproduce the structural configuration of a given population from an unlimited number of micro-samples and a marginal distribution. Only one marginal distribution of the considered population can be used as a boundary condition to “guide” the synthesis of the whole population. Model training and testing are performed using the Survey on the Workforce of 2013 and the Belgian National Household Travel Survey of 2010. Results indicate that the HMM method captures the complete heterogeneity of the micro-data contrary to standard fitting approaches. The method provides accurate results as it is able to reproduce the marginal distributions and their corresponding multivariate joint distributions with an acceptable error rate (i.e., SRSME=0.54 for 6 synthesized attributes). Furthermore, the HMM outperforms IPF for small sample sizes, even though the amount of input data is less than that for IPF. Finally, simulations show that the HMM can merge information provided by multiple data sources to allow good population estimates.
Research center :
LEMA - Local Environment Management & Analysis Lepur : Centre de Recherche sur la Ville, le Territoire et le Milieu rural - ULiège
Disciplines :
Civil engineering Special economic topics (health, labor, transportation...)
Author, co-author :
Saadi, Ismaïl ; Université de Liège > Département ArGEnCo > Transports et mobilité
Anderson P., Farooq B., Efthymiou D., Bierlaire M. Associations generation in synthetic population for transportation applications. Transportation Research Record: Journal of the Transportation Research Board 2014, 2429:38-50. http://dx.doi.org/10.3141/2429-05.
Badsberg J.H., Malvestuto F.M. An implementation of the iterative proportional fitting procedure by propagation trees. Computational Statistics & Data Analysis 2001, 37(3):297-322. http://dx.doi.org/10.1016/S0167-9473(01)00013-5.
Balmer M., Axhausen K., Nagel K. Agent-based demand-modeling framework for large-scale microsimulations. Transportation Research Record: Journal of the Transportation Research Board 2006, 1985:125-134. http://dx.doi.org/10.3141/1985-14.
Barthelemy, J., Suesse, T., Namazi-Rad, M., 2015. Multidimensional iterative proportional fitting and alternative models.
Barthelemy J., Toint P. A stochastic and flexible activity based model for large population. application to belgium. Journal of Artificial Societies and Social Simulation 2015, 18(3):15. http://dx.doi.org/10.18564/jasss.2819.
Barthelemy J., Toint P.L. Synthetic population generation without a sample. Transportation Science 2013, 47(2):266-279. http://dx.doi.org/10.1287/trsc.1120.0408.
Beckman R.J., Baggerly K.A., McKay M.D. Creating synthetic baseline populations. Transportation Research Part A: Policy and Practice 1996, 30(6):415-429. http://dx.doi.org/10.1016/0965-8564(96)00004-3.
Bekhor S., Dobler C., Axhausen K. Integration of activity-based and agent-based models. Transportation Research Record: Journal of the Transportation Research Board 2011, 2255:38-47. http://dx.doi.org/10.3141/2255-05.
Caiola G., Reiter J.P. Random forests for generating partially synthetic, categorical data. Transactions on Data Privacy 2010, 3(1):27-42.
Denteneer D., Verbeek A. A fast algorithm for iterative proportional fitting in log-linear models. Computational Statistics & Data Analysis 1985, 3:251-264. http://dx.doi.org/10.1016/0167-9473(85)90088-X.
Duguay G., Jung W., McFadden D. SYNSAM: a methodology for synthesizing household transportation survey data 1976, Urban Travel Demand Forecasting Project, Institute of Transportation Studies.
Endo Y., Takemura A. Iterative proportional scaling via decomposable submodels for contingency tables. Computational Statistics & Data Analysis 2009, 53(4):966-978. http://dx.doi.org/10.1016/j.csda.2008.11.013.
Farooq B., Bierlaire M., Hurtubia R., Flötteröd G. Simulation based population synthesis. Transportation Research Part B: Methodological 2013, 58:243-263. http://dx.doi.org/10.1016/j.trb.2013.09.012.
Gargiulo F., Ternes S., Huet S., Deffuant G. An iterative approach for generating statistically realistic populations of households. PloS one 2010, 5(1):1-9. http://dx.doi.org/10.1371/journal.pone.0008828.
Geard N., McCaw J.M., Dorin A., Korb K.B., McVernon J. Synthetic population dynamics: a model of household demography. Journal of Artificial Societies and Social Simulation 2013, 16(1). http://dx.doi.org/10.18564/jasss.2098.
Hermes K., Poulsen M. A review of current methods to generate synthetic spatial microdata using reweighting and future directions. Computers, Environment and Urban Systems 2012, 36(4):281-290. http://dx.doi.org/10.1016/j.compenvurbsys.2012.03.005.
Jiroušek R., Přeᅭcil S. On the effective implementation of the iterative proportional fitting procedure. Computational Statistics & Data Analysis 1995, 19(2):177-189. http://dx.doi.org/10.1016/0167-9473(93)E0055-9.
Knudsen D.C., Fotheringham A.S. Matrix comparison, goodness-of-fit, and spatial interaction modeling. International Regional Science Review 1986, 10(2):127-147. http://dx.doi.org/10.1177/016001768601000203.
Lenormand M., Deffuant G. Generating a synthetic population of individuals in households: sample-free vs. sample-based methods. Journal of Artificial Societies and Social Simulation 2012, 16(4). http://dx.doi.org/10.18564/jasss.2319.
Müller K., Axhausen K.W. Population synthesis for microsimulation: State of the art 2010, Eth zürich, institut für verkehrsplanung, transporttechnik, strassen-und eisenbahnbau (ivt).
Namazi-Rad M.-R., Mokhtarian P., Perez P. Generating a dynamic synthetic population-using an age-structured two-sex model for household dynamics. PloS one 2014, 9(4):1-16. http://dx.doi.org/10.1371/journal.pone.0094761.
Pritchard D.R., Miller E.J. Advances in population synthesis: fitting many attributes per agent and fitting to household and person margins simultaneously. Transportation 2012, 39(3):685-704. http://dx.doi.org/10.1007/s11116-011-9367-4.
Rich J., Mulalic I. Generating synthetic baseline populations from register data. Transportation Research Part A: Policy and Practice 2012, 46(3):467-479. http://dx.doi.org/10.1016/j.tra.2011.11.002.
Rieser M., Nagel K., Beuck U., Balmer M., Rümenapp J. Agent-oriented coupling of activity-based demand generation with multiagent traffic simulation. Transportation Research Record: Journal of the Transportation Research Board 2007, 2021:10-17. http://dx.doi.org/10.3141/2021-02.
Saadi I., Mustafa A., Teller J., Cools M. An integrated framework for forecasting travel behavior using markov chain monte carlo simulation and profile hidden markov models. Proceedings of the 95th Annual Meeting of the Transportation Research Board 2016, Transportation Research Board of the National Academies, Washington, D.C.
Sun L., Erath A. A bayesian network approach for population synthesis. Transportation Research Part C: Emerging Technologies 2015, 61:49-62. http://dx.doi.org/10.1016/j.trc.2015.10.010.
Tirumalachetty S., Kockelman K.M., Nichols B.G. Forecasting greenhouse gas emissions from urban regions: microsimulation of land use and transport patterns in austin, texas. Journal of Transport Geography 2013, 33:220-229. http://dx.doi.org/10.1016/j.jtrangeo.2013.08.002.
Visser I., Speekenbrink M. depmixs4: an r package for hidden markov models. Journal of Statistical Software 2010, 36(7):1-21.
Voas D., Williamson P. An evaluation of the combinatorial optimisation approach to the creation of synthetic microdata. International Journal of Population Geography 2000, 6(5):349-366.
Vovsha P., Hicks J.E., Paul B.M., Livshits V., Maneva P., Jeon K. New features of population synthesis. Proceedings of the 94th Annual Meeting of the Transportation Research Board 2015, Transportation Research Board of the National Academies, Washington, D.C.
Waddell P. Urbansim: modeling urban development for land use, transportation, and environmental planning. Journal of the American Planning Association 2002, 68(3):297-314. http://dx.doi.org/10.1080/01944360208976274.
Williamson P. An evaluation of two synthetic small-area microdata simulation methodologies: Synthetic reconstruction and combinatorial optimisation. Understanding Population Trends and Processes 2013, 19-47. Springer Netherlands. http://dx.doi.org/10.1007/978-94-007-4623-7_3.
Williamson P., Birkin M., Rees P.H. The estimation of population microdata by using data from small area statistics and samples of anonymised records. Environment and Planning A 1998, 30(5):785-816. http://dx.doi.org/10.1068/a300785.
Yang B., Janssens D., Ruan D., Cools M., Bellemans T., Wets G. A data imputation method with support vector machines for activity-based transportation models. Advances in Intelligent and Soft Computing 2012, 249-257. http://dx.doi.org/10.1007/978-3-642-25664-6_29.
Yasmin F., Morency C., Roorda M.J. Assessment of spatial transferability of an activity-based model, tasha. Transportation Research Part A: Policy and Practice 2015, 78:200-213. http://dx.doi.org/10.1016/j.tra.2015.05.008.
Ye X., Konduri K.C., Pendyala R.M., Sana B., Waddell P. Methodology to match distributions of both household and person attributes in generation of synthetic populations. Proceedings of the 88th Annual Meeting of the Transportation Research Board 2009, Transportation Research Board of the National Academies, Washington, D.C.