Population Synthesis Reference List: Pre-2009 Arentze, T., Timmermans, H. J. P., Hofman, F



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Lim, P. P. and D. Gargett. Population Synthesis for Travel Demand Forecasting, (2013) 14p.

Abstract - Population synthesis techniques are commonly used as viable alternatives to supplement the lack of availability and completeness of microdata collection for microsimulation modelling. The construction of a synthetic population set out in this paper is part of a broader research project that aims to develop an activity-based microsimulation model for travel demand forecasting, specifically for Australian capital cities. This paper describes the process of generating a synthetic baseline population for Sydney Greater Metropolitan (GMA) using 2006 Population Census. Microdata were created for households and individuals in Sydney GMA at Travel Zone (TZ) level and Censes Collection District (CCD) level. These synthetic data were benchmarked against aggregated census data to evaluate its representativeness.

Australasian Transport Research Forum (ATRF), 36th, 2013, Brisbane, Queensland, Australia StartDate:00000 EndDate:00000 - Australasian Transport Research Forum (ATRF), 36th, 2013, Brisbane, Queensland, Australia StartDate:00000 EndDate:00000, Cation, http://www.atrf.info/papers/2013/index.aspx



Siripirote, Treerapot, Agachai Sumalee and H. W. Ho. A Statistical Synthetic Population Calibration for Activity-Based Model with Incomplete Census Data. Journal of the Eastern Asia Society for Transportation Studies 10, (2013): pp 742-761.

Abstract - Synthetic population generator is the core component of the microsimulation in activity-based travel demand model. Typically, synthetic population is used in the way that their decisions on activity-travel pattern are simulated. Traditionally, household sample survey data is used to synthesize the population. The estimated results can be biased due to such as low-sampling size and inaccurate household sample data. To deal with this issue, a statistical maximum-likelihood method to calibrate synthetic population using the roadside observations (link counts) is proposed. Statistical performances of the proposed method are evaluated on the illustrative network and real network with census and household sample survey data. Multi-day link counts are simulated from (true) activity-based model parameters and synthetic population. Tests are carried out assuming different numbers of observations and observation variations. The results illustrate the efficiency of the model calibration based on link counts and its potential for large and complex applications. http://dx.doi.org/10.11175/easts.10.742
Zhu, Xiaoyu, Sabyasachee Mishra, Timothy F. Welch, Birat Pandey and Charles Baber. Framework for Modeling and Forecasting Population Age Distribution in Metropolitan Areas at Level of Transportation Analysis Zone, (2013) 18p.

Abstract - Recent travel demand modeling practices focus on micro, disaggregate, and activity level travel behavior and patterns. The application of such practices requires detailed population information in socio-economic and demographic data. For example, in a four-step travel demand model total household and employment at Traffic Analysis Zone (TAZ) level are sufficient for trip generation. However, in an activity based model more detailed information in the small area (TAZ), such as population by different age categories and employment type, is required to produce trip chaining and other details in the population synthesis step. Conventionally many studies have used Iterative Proportional Fitting (IPF) to generate such detailed information. But, IPF suffers from severe drawbacks and is blind to detailed synthesis of variables. In this paper, a novel approach is presented where population by age category evolves over time period using logistic regression technique. The methodology is presented in three steps: coefficient estimation, forecast and validation. First, the 1990 census data is used to model population by age group in 2000 at the TAZ level. The model result is applied to forecast 2010 data for validation. The methodology is applied to Baltimore Metropolitan Council (BMC) region and the results show that the proposed model produces and forecasts reasonably well. The experiences gained from this study are: (1) population evolution pattern in city area should be treated separately from other, e.g., Baltimore City has a special population structure from other surrounding counties; (2) this model provides a good estimation and prediction for the age group 0-24 and 35-64 and the problems occurs in 25-34 and 65+ groups, whose migration trend is not consistent over time and cannot be captured by the current parameters alone. Though in this paper population by age is considered for demonstration, the proposed methodology can be used for other variables of interest such as household type, householder’s age, employment type, occupation, etc. The proposed tool can be adapted by small and large scale planning agencies for preparing detailed socio economic and demographic input data for travel demand modeling practices.

Transportation Research Board 92nd Annual MeetingTransportation Research BoardWashington,DC,USA StartDate:20130113 EndDate:20130117 Sponsors:Transportation Research Board - Transportation Research Board 92nd Annual MeetingTransportation Research BoardWashington,DC,USA StartDate:20130113 EndDate:20130117 Sponsors:Transportation Research Board, Cation,


2014
Anderson, Paul, Bilal Farooq, Dimitrios Efthymiou and Michel Bierlaire. Associations Generation in Synthetic Population for Transportation Applications. A Graph-Theoretic Solution, (2014) 23p.

Abstract - The generation of synthetic populations through simulation methods is an important research topic and has a key application in transport and land-use agent-based modeling. The next step in this research area is the generation of complete synthetic households, which requires some way to associate synthetic persons with household positions. This work formulates the person to position matching problem as a bipartite graph matching and tests two different models for determining match utility using data from the 2000 Swiss Census. The functions tested are both multinomial logit models, one based on the household size attribute and the other on the household type. Synthetic persons are matched into the head position of real households, and then the remaining population is used to run a second match using a separately calibrated version of the size choice model for the spouse position. This is a long list based approach that keeps the original marginal consistent. The results show that the size choice model returns the best results for head and spouse positions, although both models provide a good match quality as measured by the distributions of individual attributes in real and matched populations as well as the distributions of unique attribute combinations. Possible extensions include matching to other household positions and evaluating the performance of these synthetic households in modeling applications.

Transportation Research Board 93rd Annual MeetingTransportation Research BoardWashington,DC StartDate:20140112 EndDate:20140116 Sponsors:Transportation Research Board - Transportation Research Board 93rd Annual MeetingTransportation Research BoardWashington,DC StartDate:20140112 EndDate:20140116 Sponsors:Transportation Research Board, Cation,


Goulias, Konstadinos G., Srinath K. Ravulaparthy, Karthik C. Konduri and Ram M. Pendyala. Using Synthetic Population Generation to Replace Sample and Expansion Weights in Household Surveys for Small Area Estimation of Population Parameters, (2014) 23p.

Abstract - In this paper the authors illustrate the use of synthetic population generation methods to replace sample weights and expansion weights in household travel surveys. The authors use a combination of exogenous (US Census) and endogenous (the survey) data as the informants and in essence transfer information from the county level sample to the tracts. The method is based on a population synthesis approach called PopGen (PopGen 1.1, 2011) and is applied to the newly collected data in the California Household Travel Survey (CHTS). An illustration of using traditional sampling and expansion weights and synthetic population generation is illustrated at the tract level. The authors show synthetic population methods are able to recreate the entire spatial distribution of households and persons in small areas, recreate the variation that is lost when sampling. This method is capable of reproducing the variation in the real population and enables transferability without having to develop complicated methods. Moreover, it fills spatial gaps in data collection, produces a large database that is ready to be used in activity microsimulation, provides as byproducts sample and expansion weights, and offers the possibility to perform resampling for model estimation. However, additional testing and experimentation is also required.

Transportation Research Board 93rd Annual MeetingTransportation Research BoardWashington,DC StartDate:20140112 EndDate:20140116 Sponsors:Transportation Research Board - Transportation Research Board 93rd Annual MeetingTransportation Research BoardWashington,DC StartDate:20140112 EndDate:20140116 Sponsors:Transportation Research Board, Cation,


Ma, L., Srinivasan, S., 2014. Synthetic Population Generation with Multilevel Controls: A Fitness-Based Synthesis Approach and Validations. Computer-Aided Civil and Infrastructure Engineering, forthcoming.
Zhu, Xiaoyu, Sabyasachee Mishra and Timothy F. Welch. Modeling and Forecasting Household Workers by Occupation in Metropolitan Areas--a Mesoscopic Framework, (2014) 15p.

Abstract - The need for activity based models to provide micro, disaggregate simulations of travel patterns have become increasingly important to understand the complexity involved with travel behavior. Traveler occupation is one of the factors that are determinative of a trip end. To fully model how travel behavior will be influenced in the future, it is imperative to be able to estimate future occupation. The current literature does not provide suitable methods to model and forecast occupation. Two methods have primarily been used in the past to model occupation; the cohort-component method or a population synthesizing approach. The cohort-component method requires a significant amount of detailed birth, aging, death and migration information and the results obtained are at an aggregate geographic level. Such data at larger geographies (macro level) may not be suitable for advanced travel demand modeling purposes. Occupation synthesizers are used to obtain individual information at any geographic level (micro-level), but suffer from a limitation of evolution of occupation over time while considering other depend variables such as employment, and other household characteristics. In this paper, the authors propose a mesoscopic approach where occupation by employment type evolves over a time period using a logistic regression technique. Five types of occupation: management, sales, service, other and unemployed is modeled. The methodology is presented in three steps: coefficient estimation, forecast and validation. First, the occupation evolution trend from 1990 to 2000 is analyzed. The estimation result is applied to forecast 2010 and 2030 occupation composition. This evolutionary model is applied to the Baltimore Metropolitan Council (BMC) region based on 1990 and 2000 Census data then validated with 2010 Census data. The results show that the proposed model produces a forecast that reliable and accurate. The important insights gained from this study are: (1) this model provides a good estimation and forecast for management, sales and unemployment; (2) service and other occupation prove less predictable as evolution trends among these groups are not consistent over time. The proposed tool can be adapted for use by small and large scale planning agencies to prepare detailed socio-economic and demographic profiles for input data into a population synthesizer or activity based model.

Transportation Research Board 93rd Annual MeetingTransportation Research BoardWashington,DC StartDate:20140112 EndDate:20140116 Sponsors:Transportation Research Board - Transportation Research Board 93rd Annual MeetingTransportation Research BoardWashington,DC StartDate:20140112 EndDate:20140116 Sponsors:Transportation Research Board, Cation,


Zhu, Yi and Joseph Ferreira. Synthetic Population Generation at Disaggregated Spatial Scales for Land Use and Transportation Microsimulation, (2014) 16p.

Abstract - The execution of agent-based microsimulation requires an initial set of agents with detailed socioeconomic and demographic attributes to support subsequent behavioral models and market models. Data limitations and privacy reasons often restrict the scope and detail with which synthetic population can be generated by traditional population synthesis approach. To accommodate the growing requirement of microsimulation on spatial resolution and variety, it is necessary to consider new data sources that overcome the data limitations and support population synthesis at more disaggregated levels. This paper presents a two-stage population synthesis approach not only to improve the accuracy of population generation with imperfect microdata and marginal data, but also to utilize additional datasets when interpolating the spatial details of the synthetic population. A general IPF method is employed in the first stage to estimate the joint distribution of household and individual characteristics under multiple levels of constraints. Additional building information is collected from multiple sources and used to estimate spatial patterns of housing and household characteristics that are then preserved through a second IPF procedure. Preliminary tests of the proposed two-stage IPF-based approach using Singapore data yields better fitted population realizations at more fine-grained levels than a traditional one-step population synthesis method.

Transportation Research Board 93rd Annual MeetingTransportation Research BoardWashington,DC StartDate:20140112 EndDate:20140116 Sponsors:Transportation Research Board - Transportation Research Board 93rd Annual MeetingTransportation Research BoardWashington,DC StartDate:20140112 EndDate:20140116 Sponsors:Transportation Research Board, Cation,
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