Key advantages and disadvantages of population synthesis approaches.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
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Several host cells' lineages are susceptible to herpesvirus infection, e.g., epithelial cells, neurons, monocytes, lymphocytes. Noteworthy, the common characteristic of Herpesviruses is to establish latency in the host cell. This lifelong infection suggests that herpesviruses have critical features to evade immunosurveillance. The knowledge about herpesvirus molecular genetics has been critical to design new therapies based on Herpes simplex vectors.
\r\n\r\n\tThis book summarizes the main aspects of Herpesviruses infection, the key molecular mechanisms associated to latency and reactivation, mechanism related to immune evasion, immunosuppression and cellular stress, the contribution of herpesvirus (Herpes simplex type 1, Human herpevirus 6) to the neurodegenerative disease and autoimmunity, development of new drugs and vaccines against Herpesviruses, and development of gene therapy against cancer based on herpes simplex vectors.
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Transportation simulation models are widely used for travel demand forecasting, testing design alternatives, or predicting travel behavior. In 1992, Axhausen and Gärling [1] developed a comprehensive review of conceptualizations and approaches of activity-based transportation models with special regard to the validity of behavioral assumptions of modeled population. In the course of their review, they concluded that individual travelers and households, rather than aggregates, should be identified and considered. Nevertheless, detailed travel records for individuals have never been easily accessible for several reasons, the most important being privacy issues and cost. Hence, individual travel diaries needed to be synthesized from travel surveys, census data, and publically available records. That process has since been known as population synthesis.
Population synthesizers initially were used as feeder data avenues to travel demand models [2]; however, recent shifts toward activity- and agent-based models brought population synthesizers to the spotlight, as they became determinants to the success or failure of any transportation model of that kind. Fitting is the core of any population synthesizer, with the main focus on fitting disaggregate sample of agents (represented by tabulated demographics of a representative sample of household and individual data) to aggregate constraints (represented by available aggregate data, such as data available from census). There are several approaches for fitting including iterative proportional fitting (IPF), iterative proportional updating (IPU), combinatorial optimization, Markov-based and fitness-based syntheses (FBS), and other emerging approaches [3]. The following sections present a critical review of each approach in the chronological order by which they were introduced to illustrate the progression and evolution of each approach, with emphasis on notable and well-established efforts.
Iterative proportional fitting has been first introduced in 1940 by Deming and Stephan [4]. Since then, it became the foundation of population synthesis for transportation models and sometimes referred to as the Fratar technique [5]. The most notable realization of the IPF technique is attributed to Beckman et al. [6] who pioneered population synthesis efforts through their development of a methodology for creating a synthetic baseline population of individuals and households for microscopic activity-based models. Their technique relied on using census data represented by a Census Standard Tape File and Public Use Microdata Sample (PUMS) for a given Public Use Microdata Area (PUMA) of 100,000 individuals with matching variables. In their case, the marginal totals of a multiway table were known, and a sample from the population which generated those totals was provided; thus, they applied the IPF technique to develop constrained maximum entropy estimates of the true proportions in the population multiway table. Their rationale was built upon the consensus that IPF estimates maintain the same odds ratios as those in the sample table in the absence of any marginal information which was their case. To validate the population synthesis method, they compared demographic characteristics of the synthetic population with those of the true population using variables not involved in the population synthesis. Despite their pioneer effort, Beckman et al. [6] did not provide an answer to the zero-cell problem in the PUMS; instead, they replaced it by 0.01 and imputed the corresponding household size. Müller and Axhausen [3] illustrated this as computing a series of tabulations
where
Almost a decade later, Arentze et al. [7] addressed one of the limitations of the IPF methods, that is, generating synthetic households when the demographic data describes population in terms of individual counts. Their solution relied on developing a two-step IPF procedure where, first, known marginal distributions of individuals are converted to marginal distributions of households of similar attributes and, second, the resulting marginal household distributions are used as constraints of a multiway household counts. Additionally, their approach aimed to assess the relevance of spatial heterogeneity across populations. The Dutch Albatross model was used as a case study and proof of concept. The validation results yielded sample biases in the synthetic population on the dimensions of socioeconomic class, the presence of children, and the availability of transport modes. However, they were able to resolve biases in over- or underrepresentation of groups that were related to age and work status by fitting the relevant tables on these dimensions.
Simultaneous to the efforts of Arentze et al. [7], Guo and Bhat [8] addressed the two main drawbacks of IPF approach, namely, the zero-cell problem and the inability to control for statistical distributions of both household- and individual-level attributes. Additionally, their study aimed to enhance the scalability and generality of the IPF method as it required code-level changes that are cumbersome and skills that are not typically found within planning agencies, who are the typical users of such approach. The algorithm developed by Guo and Bhat [8] featured generic data structures and accompanying functions to avoid the zero-cell problem, as well as revisions to the algorithm of Beckman et al. [6] to allow simultaneous control of both household- and individual-level attributes. That generic algorithm was built upon an object-oriented architecture and contained eight major steps and a recurring procedure for merging any two contingency tables with common variables. The proposed approach was used to generate synthetic population for the Dallas-Fort Worth metropolitan area in Texas, and the statistical comparison yielded results that were closer to true population than that of Beckman et al. [6]. In addition, Guo and Bhat [8] concluded that a higher percentage deviation from target size (PDTS) yielded better balance at satisfying the household- and individual-level multiway distributions than lower values of PDTS.
Srinivasan et al. [9] went a step further and attempted to fine-tune existing efforts to accommodate the household- and individual-level controls as well as assess the significance of controlling individual-level attributes. That study was performed in support of Florida Department of Transportation (FDOT) efforts to incorporate sociodemographic attributes within the Florida Standard Urban Transportation Model Structure (FSUTMS). The research was motivated by the need for reduced aggregation errors, ensuring sensitivity to demographic shifts like that of aging population, and the ability to accommodate population-specific transportation modes. That fine-tuning effort mainly aimed to address individual-level attributes of age and gender through the means of a greedy-heuristic data-fitting algorithm that was implemented in the matrix programming language GAUSS. Validation of Srinivasan et al. [9] algorithm yielded satisfactory distributions of household, size, age, gender, and employment status; however, the distributions for all other variables did not match well.
Given the limited number of attributes that can be synthesized per agent, researchers had to further improve the IPF approach to overcome this limitation. Pritchard and Miller [10] introduced a method that implements IPF approach with sparse list-based data structure that allows more attributes per agent. Additionally, they used both the conventional Monte Carlo integerization procedure and the conditional Monte Carlo to synthesize a list of individual agents from fitted tables. Despite their thorough efforts, the study of Pritchard and Miller [10] had only a minor impact on goodness-of-fit, relative to the conventional approach.
Auld and Mohammadian [11] developed a methodology to improve the basic IPF population synthesis routine in a manner that accounts for multiple levels of analysis units—control variables, which was a limitation to the population synthesizers mentioned hereinabove. Their methodology, named multilevel control, allows population characteristics to be replicated for multilevel synthetic population with one level (such as households) serving as the base level of analysis. After a runtime of 16 hours, the proposed method was able to synthesize a 7.9 million agent population for Chicago, IL, with an improved fit of the synthesized individual-level characteristics when compared with synthesis procedures that do not account for individual-level controls. The study concluded that the improved fit comes at no cost to the fit against household-level controls. However, the developed methodology was never experimented as to synthesizing commercial- or business-related agents.
Lee and Fu [12] realized that the IPF-based population synthesis approaches, specifically the original synthetic reconstruction method [6] and the complimentary combinatorial optimization method [13], are not generally applicable to all population synthesis scenarios. Based on a comparison by Ryan et al. [14], Lee and Fu [12] concluded that combinatorial optimization method produces more accurate demographic information for populations over a small area and that the population synthesis problem should be evaluated from an optimization point of view. In addition, they explored how the estimation of a multiway demographic table can be formulated and solved as a constrained optimization problem in full consideration of both household- and individual-level attributes. Accordingly, that study tackled the inconsistency problem through an approach that is based on the minimum cross-entropy theory. The validity of that model was confirmed through a case study in Singapore, through which results from a 10,641 household study area were superior to conventional IPF approaches. However, Lee and Fu [12] did not provide a full-scale application which constrains the applicability of their model to theoretical applications only.
Zhu and Ferreira [15] were intrigued by the inability of the standard IPF algorithm to fit marginal constraints on multiple agent types simultaneously. Hence, they developed a two-stage population synthesizer that utilized IPF on the first stage and then estimated the spatial pattern of household-level attributes through a second stage IPF-based approach. Their two-stage algorithm consisted of four distinctive steps. The first step involved developing an estimate joint distribution of household- and individual-level attributes. In the second step, households and individuals were drawn from microdata samples. The third step consisted of a conventional IPF with household type and parcel capacity marginal constraints. The fourth and last step included an estimated marginal distribution of other attributes from the fitted model. To validate their approach, Zhu and Ferreira [15] generated synthetic population for Singapore. Their evaluation approach involved four comparisons, namely, fitting only for households-level constraints, fitting for both household- and individual-level constraints, allocating households to buildings while constraining building capacity, and repeating the previous comparison with income level constrained. Validation results yielded realistic spatial heterogeneity while preserving some of the joint distribution of household and locational characteristics.
Choupani and Mamdoohi [16] addressed the issue of integerization of IPF results in non-integer values instead of integers, for example, fractions of household- or individual-level attributes for zones. In doing so, they proposed a binary linear programming model for tabular rounding in which the integerized table totals and marginals perfectly fit to input data obtained from the Census Bureau. The main advantages of using tabular rounding were that it did not bias joint or marginal distributions of socioeconomic attributes of minority demographic groups and it minimized the distortion to the correlation structure of household- and individual-level non-integer tables. Furthermore, the tabular rounding approach outperformed all other eight rounding approaches. In addition, sensitivity analysis of tabular rounding demonstrated that small and large values are equally significant when it comes to integerization. Their findings were confirmed by a comprehensive literature review [17] that they performed 1 year later, which concluded that IPF is the most feasible approach for synthesizing populations for agent- and activity-based transportation models, once integer conversion and zero-cell issues were resolved. In addition, they confirmed that tabular rounding is the most efficient and feasible solution for the integerization issue.
Most recently, in an effort to further enhance the IPF approach, Otani et al. [18] identified an issue that they named the modifiable attribute cell problem (MACP) which arises from combining discrete categories of individual-level attributes or due to the contiguous nature of those attributes. The proposed solution to the MACP issue was identified as “the organized cell set” which is the best combination of categories. The procedure to identify the best organized cell set consists of five steps. The first step involves aggregation of the elemental cell set to find several cases of cell organization that generate large cells. The second step involves constructing base-year data using the conventional IPF approach. The third step focuses on forecasting using microscopic simulation. The fourth step involves identifying the statistically acceptable cell value using a Student’s t-test. The fifth and final step involves considering the case with minimum number of cells to be the best cell organization. This method is computationally complex and cannot be performed using conventional optimization algorithms. Yet, it is the sole identifiable solution to the modifiable attribute cell problem.
The iterative proportional updating approach is a heuristic approach that was developed by Ye et al. [19] to address the drawbacks of the IPF approach. Specifically, the IPU approach addresses the issue of control for individual-level attributes and joint distributions of personal characteristics. The IPU algorithm matches both household- and individual-level attributes in a computationally efficient manner by iteratively adjusting and reallocating weights among households of a specific type until both household- and individual-level attributes are matched. Another advantage of the IPU approach is its practicality from the implementation and computational points of view. Eq. (2) represents the mathematical optimization problem as addressed by the IPU approach. In addition, the IPU approach has been generally described in 23 computational steps that can be easily coded in most, if not all, programming languages:
Subject to
where
Furthermore, Ye et al. [19] proposed an alternative method to address the zero-cell problem that undermined the IPF practicality. Their method is based on borrowing the prior information for the zero cells from PUMS data for the entire region, where zero cells are not likely to exist as long as the control variables of interest and their categories are defined appropriately. However, that method has the inherent risk of overrepresenting the demographic group of interest. Despite their attempt to overcome the zero-cell problem, the researchers could not overcome the zero-marginal problem that may result due to nonexistence of a certain attribute in households of a certain geographic area, for example, having no low-income households in a certain census block or tract. Furthermore, a review by Müller and Axhausen [3] pointed to the lack of a theoretical proof of convergence.
The combinatorial optimization approach was materialized by Abraham et al. [20] and is a versatile approach capable of matching targets at multiple agent levels for both household- and individual-level attributes. A combinatorial optimization approach is generally simpler and more direct than IPF. Mostly, it starts by the creation of a trial population from the disaggregate sample data, and then the overall level of fit is assessed across all marginal targets. Units from the trial population are swapped with units chosen from the disaggregate samples, and when the measure of fit improves, the swap is made. This is implemented through a proprietary computer program that first identifies a list of units whose aggregate attribute values match a pre-specified set of corresponding target values and then iteratively performs one of three operations, namely, adding a unit from the sample to the list, subtracting a unit, or swapping a unit between the sample and the previously identified list. That process is performed on a zone-by-zone level with equal probability of the three actions (i.e., add, subtract, or swap) being considered. The developed algorithm was applied to California and Oregon to synthesize populations for their models. The California application served the California Statewide Travel Demand Model including short- and long-distance travel considering personal and commercial vehicles. The Oregon application served the Oregon Statewide Integrated Model, which included employment synthesis for 34 industries. Both model applications resulted in a near-perfect fit for synthesized populations. Generally, the population synthesis procedure using combinatorial optimization has proven to be fast and flexible with the possibility for application to both households and employment scenarios. However, this algorithm can be further improved by using multicore and parallel computing techniques.
As demonstrated, hereinabove, IPF, IPU, and combinatorial optimization approaches rely on cloning attributes that were captured in microdata. In addition, they all share key drawbacks including (a) fitting of a contingency table while ignoring other solutions matching the available data; (b) loss of heterogeneity that has been captured in the microdata due to cloning rather than true population synthesis; (c) dependency on the accuracy of captured data to determine the cloning weights which may replicate inherent inaccuracies; and (d) limited scalability, in terms of the number of attributes of synthesized agents. Hence, Markov process-based approaches were developed to overcome such drawbacks and to offer an approach that truly synthesizes populations instead of cloning them.
The earliest notable effort in this direction was pioneered in 2013 by Farooq et al. [21] who developed a Markov chain Monte Carlo (MCMC) simulation-based approach for synthesizing populations. The proposed approach is a computer-based simulation technique that can be used to simulate a dependent sequence of random draws from complicated stochastic models. To synthesize populations that approach uses three sources of data, namely, (a) zoning systems such as census blocks, census tracts, counties, and states; (b) sample of individuals such as the North American PUMS and the European Sample of Anonymized Records (SARs); and (c) cross-classification tables for socioeconomics and demographics like income by age at a certain zoning level. Assuming that in a given spatial region at any point in time there exists a true population, the MCMC simulation-based approach synthesizes that population by drawing the individual attributes from their uniquely joint distribution using the available partial views while ensuring that the empirical distribution in the synthetic population is as close as possible to the unique actual distribution of that population. The proposed approach was applied to the Swiss census data, and results were compared against those developed by a conventional IPF approach. Eq. (3) illustrates the standardized root mean square error (SRMSE)-based goodness-of-fit tests that were performed on each case, and results indicated that MCMC simulation-based synthesis outperformed IPF synthesis while featuring a higher level of heterogeneity:
where
Two years later, in 2015, Casati et al. [22] proposed an extension of the MCMC simulation-based approach to simultaneously combine both individual- and household-level attributes in a process that was named hierarchical MCMC. Furthermore, generalized raking was introduced as a technique to fit the simulated synthetic population to actual observed control totals. The hierarchical MCMC is a combination of two methods: (a) an extension of the original MCMC method that allows producing hierarchies of persons grouped into households and (b) a post-processing method to satisfy known control totals on both the individual- and household-level. That extension aimed to synthesize populations with a hierarchical structure that is based upon ordering the agents living in the same household according to their household roles. The general formulation of the extension is based upon the definition of three groups of agent types (viz., owners, intermediate, and others) running Gibbs sampling on the three groups and merging subpopulations. The proposed approach was applied to the 2008 household interview travel survey of Singapore. The application resulted in realistic synthetic populations, and SRMSE-based test confirmed the goodness-of-fit of synthesized populations and their generated hierarchical structures.
Saadi et al. [23] proposed an integrated MCMC approach and profiling-based methods to capture the behavioral complexity and heterogeneity of synthesized agents. This approach used two types of datasets, namely, (a) aggregated sociodemographic and transportation-related variables derived from household travel surveys and (b) individual activity-travel diaries collected from travel diary surveys. The integrated approach consists of six steps that run on those two data types. The first step involves performing a MCMC simulation on the sociodemographic dataset. The second step concerns synthesizing population by a Gibbs sampling procedure. The third step selects sociodemographics to compare behaviors in the activity-travel patterns. The fourth step uses results from the previous two steps to cluster synthesized populations according to sociodemographics and related activity sequences. The fifth step utilizes multiple sequence alignments to estimate hidden Markov model (HMM). The final step characterizes clusters including mixed socioeconomic effects. The integrated approach was applied to the 2010 Belgian household daily travel survey. Results indicated that the integrated approach effectively captured the behavioral heterogeneity of travelers. In addition, comparisons against IPF and IPU approaches demonstrated that the proposed integrated approach is adequately adapted to meeting the demand for large-scale microsimulation scenarios of urban transportation systems.
Realizing the advantages of Markov process-based approaches, Saadi et al. [24] developed an extended HMM-based approach which promised better alternatives than the existing ones. More specifically, the proposed HMM-based approach promised great flexibility and efficiency in terms of data preparation and model training while being able to reproduce the structural configuration of a given population from an unlimited number of micro samples and a marginal distribution. The HMM-based approach considers population synthesis as a variant of the standard decoding problem, at which the state sequences are supposed to be unknown. Accordingly, the maximum likelihood estimators related to the transition states were determined through the Viterbi algorithm. An important advantage of the HMM-based approach is its ability to handle both continuous and discrete variables, which addresses the inherent issue of loss of information due to aggregation of continuous variables like age. Also, the proposed HMM-based approach satisfies the need to discretize continuous variables to meet the fundamental limitation of Markov process to discrete states. The statistical and machine Learning Toolbox of MATLAB was used to generate sequences from an estimated HMM that were applied to the 2013 Belgian National household travel survey. Three simulations were run to illustrate the HMM-based approach. The first simulation tested the combined effects of scalability and dimensionality. The second simulation compared the HMM-based approach against IPF, and the third demonstrated the advantage of the HMM-based approach over IPF using various samples. Simulation results indicated that the proposed HMM-based approach provided accurate results due to its ability to reproduce the marginal distributions and their corresponding multivariate joint distributions with an acceptable error. Furthermore, the HMM-based approach outperformed IPF for small sample sizes while using smaller amount of input data than IPF. In addition, simulation results demonstrated that the HMM-based approach can integrate information provided by several data sources to allow good estimates of synthesized population.
To address the inability of the IPF approach to deal with multilevel controls, Ma and Srinivasan [25] developed the fitness-based synthesis approach that directly generates a list of households to match several multilevel controls without the need for determining a joint multiway distribution. The FBS approach generally involves selecting a set of households from the seed data, like PUMS, such that tract-level controls are satisfied. The FBS approach starts with an initial set of households that can either be a null set or a random sample from the seed data. Then, the population of each census tract is synthesized in an iterative fashion, with one household being either added or removed from the current list in each iteration. Count tables, defined in terms of control attributes, are used to track the number of households of each type that have already been included. The FBS approach implements an adding or removing procedure, while swapping is not considered. The main criteria in the FBS approach is the reduced sum of squared error for addition
Subject to
where
Three applications of the FBS approach were performed to demonstrate the feasibility of incorporating many controls at multiple levels in the synthesis and increased accuracy of synthesized population. The three applications were performed using the 2000 Census data for 12 census tracts in Florida. The first application involved population synthesis using the IPF approach with only household-level controls. The second application involved population synthesis using the proposed FBS approach with few household- and individual-level controls. The third application also involved population synthesis using the FBS approach but with significantly larger number of controls. Validation for the three applications was performed by comparing the mean absolute error against 22 artificial census tracts that were created by randomly selecting subsets of households from the 2000 PUMS. Validation results demonstrated that FBS outperformed IPF and demonstrated efficiency and scalability. In addition, FBS did not require many iterations as it required only one to three times the number of households to be synthesized. In addition, the proposed FBS approach addresses the notorious IPF issues of zero-cell problems, computational resources (memory), and non-integers cell value in the joint-distribution tables.
Hafezi and Habib [26] refined the FBS approach, and the refined FBS population synthesizer was examined by three models. The first model used household-level control tables. The second model used individual- and household-level control tables, and the third model used weighting individual-and household-level control tables. The models were applied to the province of Nova Scotia in Atlantic, Canada, using the 2006 Canadian Census and Public Use Microdata File (PUMF). The refined approach was implemented using the sparse matrix technique package in MATLAB that is based on high-level matrix programming for numerical computation. The three models were validated by error percentages and goodness-of-fit evaluation. Validation results indicated that the refined FBS approach can efficiently obtain a satisfactory result using both individual- and household-level control tables. However, higher homogeneity was achieved within the third model.
Other emerging approaches have been developed in an attempt to replace the IPF approach or to overcome one or more of its drawbacks. Emerging approaches include Bayesian network, annealing algorithm, linear programming, heuristic-based, copula-based, and entropy maximization approaches. The following paragraphs introduce each of the emerging approaches.
The Bayesian network approach was developed by Sun and Erath [27] in 2015. The proposed Bayesian network approach is a probabilistic population synthesizer that is intended as an alternative to approximate the inherent joint distribution in a more efficient manner. Using a graphical model, the proposed Bayesian network approach encodes probabilistic relationships, like causality or dependence, among a set of variables. The advantages of Bayesian network models lie in their ability to learn the structure of population systems, particularly when the number of attributes of interest is large using limited amounts of microdata. The Bayesian network approach was founded on the inference of the joint distribution—that is, perceiving the population synthesis problem as an inference of a multivariate probability distribution of demographic and socioeconomic household- and individual-level attributes. Like the Markov process-based approaches, the Bayesian network approach does not require marginals as input. In addition, it does not require any conditionals since structure learning and parameter estimation are inherently integrated in the learning model. The performance of the proposed Bayesian network approach was demonstrated through an application to the 2010 household interview travel survey of Singapore. The Bayesian network approach demonstrated good performance as illustrated by low SRMSE values. It also demonstrated good heterogeneity in synthetic population when the size of PUMS is less than 70% of the full population.
The simulated annealing (SA) algorithm was developed by Kim and Lee [28] to synthesize populations for activity-based models. The proposed SA algorithm is built upon the concepts of thermodynamics and metallurgy and was first introduced as a generic heuristic method for discrete optimization. The Metropolis-Hastings Algorithm was employed to solve the inherent problems of hill climbing and cooling schedule when applying SA to population synthesis. The proposed algorithm consists of seven steps. The first step concerns setting the maximum number of iterations. The second step sets up the total amount of columns and rows in the population and enters observed values of sample distribution. The third step sets up the before-distribution, which is composed by random numbers, while satisfying the total amount of restrictive conditions. The fourth step sets up the after-distribution, which is also composed by random numbers that satisfy total amount restrictive conditions. The fifth step involves calculation of absolute error on the before−/after-distributions as well as observed data. The sixth step involves calculation of selection probability. The seventh and final step iterates steps 4 through 6 and ends the calculations when the absolute error (calculated in the fifth step) has the smallest value or satisfies ending conditions. The SA algorithm was implemented using the household travel diary survey from the Korean National Statistics Office. Results from the implementation indicated the need for further verification of the accuracy of this algorithm.
The linear programming (LP) approach was developed by Vovsha et al. [29] to synthesize populations as part of an activity-based model developed for the Maricopa Association of Governments. The LP approach is an analytical method that balances a list or sample of household weights to meet the controls imposed at some spatial level, typically, for each traffic analysis zone (TAZ). Features of the LP approach include (a) the general formulation of convergence of the balancing procedure with imperfect controls, (b) optimized discretization of weights while preserving the best possible match to the controls, and (c) ability to set controls at multiple spatial levels. In addition, the proposed LP approach featured an innovative discretizing method applied for the household weights and integrated with the balancing procedure. While validation of the proposed LP approach is questionable, it still demonstrates reasonable accommodation to various fine-resolution spatial levels that are much needed by newer-generation activity- and agent-based models.
The heuristic-based approach was developed by Zhuge et al. [30] to address two IPF limitations that received less attention from earlier studies. The first limitation stems from the existence of various solutions for one target marginal distribution. The second limitation stems from the optimization nature of population synthesis with the objective function being minimizing the mean absolute percentage error (MAPE) of control variables. The proposed heuristic-based approach consists of 11 steps arranged in three parts. The first part, including steps 1 and 2, is used to generate the initial household weights. The second part, including steps 3 through 11, adjusts the household weights until a stop criterion is met. The third part, including steps 10 and 11, calculates the adjustment steps and adjustment range, which are two fundamental parameters of the approach. The 2007 household travel survey data from Baoding, China, were used as a case study. Results indicated that heuristic-based approach cannot perform as well as IPF-based on comparing MAPE values for both approaches.
Most recently, the copula-based approach was proposed by Kao et al. [31] to address previously identified limitations of IPF approach. Copulas are joint probability distributions with uniform marginal, which are a relatively new statistical tool. Hence, the copula-based approach was designed to preserve marginal distributions and dependence structure between variables. The proposed method was tested for the state of Iowa, and the results were compared with the IPF approach using mean, median, and correlation matrices. The synthesized households resulted in the same local statistics at each block group, but having similar intervariable correlations as described in the PUMS suggests the applicability of the copula-based approach.
Another recent effort to develop an alternative to IPF approaches resulted in the development of entropy maximization-based population synthesizer by Paul et al. [32] which handles multiple geographies and avoids algorithmic errors. The entropy maximization approach was developed as part of the Oregon Department of Transportation (ODOT) effort to utilize an open-source population synthesis platform. The approach consists mainly of two algorithms. The first algorithm, namely, list balancing, finds weights that match the given marginal control distributions. The second algorithm, namely, integerizing, implements a LP-based procedure to covert fractional weights to integers. The proposed entropy maximization-based approach was implemented in Python and made heavy use of the Pandas and NumPy libraries, which allow for vectorization of operations to reduce overall runtime. Validation results against those of IPF approach were promising and demonstrated reasonable match to controls.
This study presented a critical, comprehensive literature review of population synthesizers starting from the early efforts through the most recent approaches. The review and synthesis indicated that, despite its identified limitations and drawbacks, IPF approach is the most feasible and widely used population synthesizer. All other studies and efforts used it as a reference for comparison and produced similar or slightly improved results. Evidently, IPF has its drawbacks and limitations. Yet reviewed literature indicates that there is no single approach that can result in an efficient and accurate population synthesizer. However, an integration of robust methods appears as the most promising approach, like the effort of Fournier et al. [33] where the limitations of IPF are resolved by combining five methods into an integral framework for population synthesis. Table 1, in the
Approach | Advantage(s) | Disadvantage(s) |
---|---|---|
Iterative proportional fitting (IPF) | Synthesized estimates maintain the same odds ratios as those in the sample table Most studied and improved approach with more than 20 years of continuous refinements Widely available with ready-to-use implementations in several computer programming languages | Does not provide an answer to the zero-cell problem in the public use microdata sample (PUMS) Unable to control for statistical distributions of both household- and individual-level attributes Limited number of attributes that can be synthesized per agent |
Iterative proportional updating (IPU) | Addresses the issue of control for individual-level attributes and joint distributions of personal characteristics Computationally efficient Described in 23 computational steps that can be easily coded in most programming languages | Cannot overcome the zero-marginal problem that may result due to nonexistence of a certain attribute in the households of a certain geographic area |
Combinatorial optimization | Generally simpler and more direct than IPF Fast and flexible with the possibility for application to both households and employment scenarios | Implementation is limited to a proprietary computer program Resource-demanding and needs multicore, parallel computers |
Markov process-based approach | Truly synthesizes populations instead of cloning them Meets the demand for large-scale microsimulation scenarios Can handle both continuous and discrete variables | Requires extensive knowledge of computer programming Difficult to trace errors Refinement for specific scenarios or locations requires substantial redevelopment of the computer algorithm |
Fitness-based synthesis (FBS) | No need for determining a joint multiway distribution Addresses the notorious IPF issues of zero-cell problems | Requires extensive knowledge of the sparse matrix technique package in MATLAB that is based on high-level matrix programming for numerical computation |
Emerging approaches | Scalable and adaptive Addresses all disadvantages of IPF approach | Requires advanced expertise in Python and makes heavy use of the Pandas and NumPy libraries Limited successful applications compared to IPF |
Key advantages and disadvantages of population synthesis approaches.
Almost three-decade old, yet the IPF approach is still being used in state-of-the-art simulation platforms like MATSim. Given that IPF is the most studied approach and the fact that none of the alternatives provided an out-of-the-box solution, IPF is preferred approach by modelers and practitioners. This conclusion is confirmed by the findings of Saadi et al. [34], who investigated the influence of scalability on the accuracy of different population synthesizers using both fitting- and generation-based approaches. Their results revealed that simulation-based approaches are more stable than IPF approaches when the number of attributes increases; however, IPF approaches are less sensitive to changes in sample size.
Overall, this study provides a critical review and comprehensive synthesis of population synthesis approaches that can serve as a valuable reference to future efforts focusing on population synthesis for activity- and agent-based transportation models.
This study was performed in support of the project “Technology Influence on Travel Demand and Behaviors” that was sponsored by the US Department of Transportation Office of the Assistant Secretary for Research and Technology (OST-R) through the Southeastern Transportation Research, Innovation, Development, and Education (STRIDE) Center under Contract No. 69A3551747104 with matching funds from the Alabama Department of Transportation (ALDOT).
Health psychology explores different ways in the pursuit of getting people to embrace health promotion, illness prevention and health maintenance. As a speciality, health psychology examines how biological, psychological and social factors influence people’s behaviour about their health status. The aim of this chapter is to examine possible contributory connections between bio-psychosocial factors and health at the population level. The book explores bio-psychosocial model which can help individuals to develop and maintain healthy lifestyles so as to promote good health and prevent illness. Friedman and Adler [1] noted that the original bio-psychosocial model shaped not only research and theory on health but also the development of health psychology.
Kazarian and Evans [2] suggest that people commonly think about health in terms of an absence of (1) objective signs that the body is not functioning properly and (2) subjective symptoms of disease or injury, such as pain or nausea. World Health Organization defined health as ‘a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity’ (WHO, 1946 cited in [3]:4). Some health psychologists defined health as a positive state of physical, mental and social well-being not simply the absence of injury or disease that varies over time along a continuum [4]. At the wellness end of the continuum, health is the dominant state. At the other end of the continuum, the dominant state is illness or injury, in which destructive processes produce characteristic signs, symptoms or disabilities [4]. For further detail, see Figure 1.
Health (source: adopted from Sarafino [
Health psychology is a speciality within the discipline of psychology concerned with individual behaviours and lifestyles affecting physical health. The discipline strives to enhance health, prevent and treat disease, identify risk factors and improve the healthcare system public opinion regarding health issues [5]. Matarazzo in 1980 (as cited in [3]:4) offered a definition of health psychology which has become widely accepted:
Sarafino ([4]:11) mentioned the following goals of health psychology as to:
Promote and maintain health
Prevent and treat illness
Identify the causes and diagnosis correlates of health, illness and related dysfunction
Analyse and improve healthcare systems and health policy
The recognition of health psychology as a designated field is widely acknowledged. The relationship between mind and body and the effect of one upon the other has always been a controversial topic amongst philosophers, psychologists and physiologists. Within psychology, the development of the study of psychosomatic disorders owes much to Freud [3]. It has been observed in the recent studies that more deaths are caused now by heart disease, cancer and strokes which are by-product of changes in lifestyles in the twentieth century. Psychologists can be instrumental in investigating and influencing lifestyles and behaviours which are conducive or detrimental to good health [3].
Health behaviour is part of maintaining a healthy lifestyle and avoiding ill health. These are known as protective health behaviours. Health protective behaviours include the following categories:
Environmental hazard avoidance—avoiding areas of pollution or crime.
Harmful substance avoidance—not smoking or drinking alcohol.
Health practices—sleeping enough, eating sensibly and so forth.
Preventive health care—dental check-ups and smear tests.
Safety practices—repairing things, keeping first aid kits and emergency telephone numbers handy.
Although most of us are familiar with the need to engage in these health behaviours, only a few of us actually do so, and that is what we need to work on to remind people of adopting a better health lifestyles. Many other researchers such as Berg (1976 as cited in Pitts, 1998) asserted that most people are aware of which health behaviours should be engaged in; however, they frequently do not do so, and they instead do engage in activities which they know to be harmful to their health. It is this cantankerousness which psychologists have spent a great deal of time examining. The dilemma for health psychologists is to explain why some or many people do not do what they know is in their own best interest to do and why some people are more amenable to the adoption of healthy habits than others.
This chapter is therefore in support of a consistent focus on the role of knowledge in informing people of the risks to themselves that certain behaviours can engender. Pitts [3] reported studies that examining a range of issues relevant to health such as smoking, drug-taking, medical checks and adopting safer sex have fairly consistently shown that knowledge, by itself, does not lead to behaviour change. The only question left to ask is: So what is required, other than knowledge, to persuade people to look after their health? This question is the guiding principle to understand the role of health psychology in persuading people to look after their health informed by bio-psychosocial model.
It is generally recognized that there are two models of health, namely, biomedical and bio-psychosocial models. Biomedical model focuses on treatment and elimination of symptoms, while bio-psychosocial model focuses on individual’s perception of their symptoms and how they and their families respond to symptoms they are experiencing [6]. Also Deacon [7] asserts that under the biomedical model, illnesses were understood as having physiological aetiologies that were diagnosed through distinct biochemical markers and were to be treated through physical interventions. This chapter however is primarily focusing only on the bio-psychosocial models of health. Its founder, Engel [8], discovered that bio-psychosocial model represents the contribution of biological, psychological and social factors in determining health. Table 1 shows the differences between the two models.
Focal area | Biomedical model | Bio-psychosocial model |
---|---|---|
What causes illness? | Biological factors (chemical imbalances, bacteria, viruses and genetic predisposition) | Biological (virus), psychological (beliefs, behaviour) and social (unemployment) |
Who is responsible for illness? | Individuals are regarded as victims of some external force causing internal changes. Because illness is seen as a result of biological changes beyond their control, individuals are not seen as responsible for their illness | Individuals should be held responsible for his/her health and illness |
How should illness be treated? | Through vaccination, surgery, chemotherapy and radiotherapy, all of which aim to change the physical state of the body | The whole person should be treated, e.g. behaviour change, change in beliefs and coping strategies and compliance with medical recommendations |
Who is responsible for treatment? | The responsibility for treatment rests with the medical profession | The focus is the whole person to be treated not just their physical illness; the patient is therefore responsible for their treatment (e.g. taking the medication or changing their behaviour) |
What is the relationship between health and illness? | Health and illness are seen as qualitatively different—you are either healthy or ill—there is no continuum between the two | Health and illness exist on a continuum. Individuals progress along this continuum from health to illness and back again |
What is the relationship between the mind and the body? | The mind and body function independently of each other. In other words, the mind and body are separate entities | The focus is on an interaction between the mind and the body. The mind and body interact |
What is the role of psychology in health and illness? | Illness may have psychological consequences, but not psychological causes (e.g. cancer may cause unhappiness, but mood is not seen as related to either the onset or progression of the cancer) | Psychological factors not only as possible consequences of illness but as contributing to it at all stages along the continuum from healthy to being ill |
Comparing biomedical and bio-psychosocial models of health.
Within health psychology one model that has enjoyed considerable popularity is the ‘stress-diathesis’ model (Steptoe cited in [3]) which is currently called bio-psychosocial model. This model was first described by G.L. Engel in 1977. It emphasizes the interactive effect of environment and individual vulnerability (genetic and psychological characteristics) factors upon health [3]. According to bio-psychosocial model, psychological, physical and social threats present demands upon an individual’s resources and capacity for coping which give rise to physiological reactions involving the autonomic nervous system (ANS) and endocrine and immune system of the body.
The effects include both short-term and long-term components, and these may have consequences on health depending upon the individual’s predisposition or vulnerability to adverse effects. Vulnerable individuals develop chronic allostatic reactions such as reduced immunocompetence or exaggerated sympathetic activation of the ANS or increased secretion of adrenal hormones. Physiological reactions of these types have been implicated in the development of many disease states, including cancers, cardiovascular diseases and other non-communicable diseases susceptibility to infections [3]. The following section presents the strengths and critical views of bio-psychosocial model.
Bio-psychosocial model benefits the patients and healthcare system as revealed by research [8, 9, 10, 11, 12, 13, 14]:
Guiding application of medical knowledge to the needs of each patient.
Improved patient satisfaction, better adherence to prescriptions, more maintained behaviour change, better physical and psychological health and less of a tendency to initiate malpractice litigations.
Development and application of techniques to reduce health risk behaviour.
Reduce multiple visits and admission into hospitals.
Individuals with health challenges are acknowledged to be active participants in the recovery process and good health, rather than mere passive victims.
Increase efficiency of care by reducing unnecessary prescription of drugs (i.e. diabetes and other chronic conditions).
Development of psychological techniques in the strengthening of immune reaction to illness.
Bio-psychosocial model can be used as a predictor of pain and other psychosocial problems resulting into development appropriate prevention and intervention strategies.
Improvement of communication between health staff and the patients.
Development and introduction of programmes of life quality improvement for chronic patients, physically disabled individuals and the elderly patients.
A significant influence on contemporary understanding of mental health difficulties.
Development and application of psychosocial support for the terminally ill patients and their families.
A list of critical views of bio-psychosocial model has been noted in literature [9, 10, 12, 15, 16, 17, 18] as follows:
Time-consuming and expensive apply.
It requires more information be gathered during the assessment about an individual’s socioeconomic status, culture, religion, as well as psychological factors that might affect the individual’s condition.
There is a lack of theoretical basis of bio-psychosocial model and scientific evidence to support the model.
The complex relations between causes and effects of biological, psychological and social factors to influence the state of health and or occurrence of diseases.
The holistic nature of the bio-psychosocial model makes it a luxury many healthcare systems in resource-poor settings cannot afford.
Insufficient training opportunities or financial resources available to support the existence of multidisciplinary teams consisting of psychiatrists, clinical psychologists, mental health nurses and social welfare workers to allow for a full understanding of the biological, psychological and social factors involved in individual’s condition.
The model’s failure to provide straightforward guidelines for clinical treatment or rules for prioritization in clinical practice.
Medical students receive very limited amount of content in psychosocial subjects compared to biomedical-oriented courses.
The focus of this chapter was mainly on integrating bio-psychosocial model in public health discipline. Authors like Nadir et al. [12] found that bio-psychosocial model has been a mainstay in the ideal practice of modern medicine. It is attributed to improve patient care, compliance and satisfaction and to reduce physician-patient conflict. Both strengths and critical views of bio-psychosocial model were presented in the chapter. Even though it appears that patients and healthcare system are likely to benefit from the utilization of bio-psychosocial model, further research is still needed to determine whether or not bio-psychosocial model is a workable model in healthcare system to benefit all patients. In particular, more knowledge about how psychosocial factors can influence health and disease remain unclear to most public health professionals.
"Open access contributes to scientific excellence and integrity. It opens up research results to wider analysis. It allows research results to be reused for new discoveries. And it enables the multi-disciplinary research that is needed to solve global 21st century problems. Open access connects science with society. It allows the public to engage with research. To go behind the headlines. And look at the scientific evidence. And it enables policy makers to draw on innovative solutions to societal challenges".
\n\nCarlos Moedas, the European Commissioner for Research Science and Innovation at the STM Annual Frankfurt Conference, October 2016.
",metaTitle:"About Open Access",metaDescription:"Open access contributes to scientific excellence and integrity. It opens up research results to wider analysis. It allows research results to be reused for new discoveries. And it enables the multi-disciplinary research that is needed to solve global 21st century problems. Open access connects science with society. It allows the public to engage with research. To go behind the headlines. And look at the scientific evidence. And it enables policy makers to draw on innovative solutions to societal challenges.\n\nCarlos Moedas, the European Commissioner for Research Science and Innovation at the STM Annual Frankfurt Conference, October 2016.",metaKeywords:null,canonicalURL:"about-open-access",contentRaw:'[{"type":"htmlEditorComponent","content":"The Open Access publishing movement started in the early 2000s when academic leaders from around the world participated in the formation of the Budapest Initiative. They developed recommendations for an Open Access publishing process, “which has worked for the past decade to provide the public with unrestricted, free access to scholarly research—much of which is publicly funded. Making the research publicly available to everyone—free of charge and without most copyright and licensing restrictions—will accelerate scientific research efforts and allow authors to reach a larger number of readers” (reference: http://www.budapestopenaccessinitiative.org)
\\n\\nIntechOpen’s co-founders, both scientists themselves, created the company while undertaking research in robotics at Vienna University. Their goal was to spread research freely “for scientists, by scientists’ to the rest of the world via the Open Access publishing model. The company soon became a signatory of the Budapest Initiative, which currently has more than 1000 supporting organizations worldwide, ranging from universities to funders.
\\n\\nAt IntechOpen today, we are still as committed to working with organizations and people who care about scientific discovery, to putting the academic needs of the scientific community first, and to providing an Open Access environment where scientists can maximize their contribution to scientific advancement. By opening up access to the world’s scientific research articles and book chapters, we aim to facilitate greater opportunity for collaboration, scientific discovery and progress. We subscribe wholeheartedly to the Open Access definition:
\\n\\n“By “open access” to [peer-reviewed research literature], we mean its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself. The only constraint on reproduction and distribution, and the only role for copyright in this domain, should be to give authors control over the integrity of their work and the right to be properly acknowledged and cited” (reference: http://www.budapestopenaccessinitiative.org)
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\\n\\nLicense
\\n\\nBook chapters published in edited volumes are distributed under the Creative Commons Attribution 3.0 Unported License (CC BY 3.0). IntechOpen upholds a very flexible Copyright Policy. There is no copyright transfer to the publisher and Authors retain exclusive copyright to their work. All Monographs/Compacts are distributed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Read more
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\\n\\nAll scientific works are Peer Reviewed prior to publishing. Read more
\\n\\nOA Publishing Fees
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\\n\\nDigital Archiving Policy
\\n\\nIntechOpen is committed to ensuring the long-term preservation and the availability of all scholarly research we publish. We employ a variety of means to enable us to deliver on our commitments to the scientific community. Apart from preservation by the Croatian National Library (for publications prior to April 18, 2018) and the British Library (for publications after April 18, 2018), our entire catalogue is preserved in the CLOCKSS archive.
\\n"}]'},components:[{type:"htmlEditorComponent",content:'The Open Access publishing movement started in the early 2000s when academic leaders from around the world participated in the formation of the Budapest Initiative. They developed recommendations for an Open Access publishing process, “which has worked for the past decade to provide the public with unrestricted, free access to scholarly research—much of which is publicly funded. Making the research publicly available to everyone—free of charge and without most copyright and licensing restrictions—will accelerate scientific research efforts and allow authors to reach a larger number of readers” (reference: http://www.budapestopenaccessinitiative.org)
\n\nIntechOpen’s co-founders, both scientists themselves, created the company while undertaking research in robotics at Vienna University. Their goal was to spread research freely “for scientists, by scientists’ to the rest of the world via the Open Access publishing model. The company soon became a signatory of the Budapest Initiative, which currently has more than 1000 supporting organizations worldwide, ranging from universities to funders.
\n\nAt IntechOpen today, we are still as committed to working with organizations and people who care about scientific discovery, to putting the academic needs of the scientific community first, and to providing an Open Access environment where scientists can maximize their contribution to scientific advancement. By opening up access to the world’s scientific research articles and book chapters, we aim to facilitate greater opportunity for collaboration, scientific discovery and progress. We subscribe wholeheartedly to the Open Access definition:
\n\n“By “open access” to [peer-reviewed research literature], we mean its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself. The only constraint on reproduction and distribution, and the only role for copyright in this domain, should be to give authors control over the integrity of their work and the right to be properly acknowledged and cited” (reference: http://www.budapestopenaccessinitiative.org)
\n\nOAI-PMH
\n\nAs a firm believer in the wider dissemination of knowledge, IntechOpen supports the Open Access Initiative Protocol for Metadata Harvesting (OAI-PMH Version 2.0). Read more
\n\nLicense
\n\nBook chapters published in edited volumes are distributed under the Creative Commons Attribution 3.0 Unported License (CC BY 3.0). IntechOpen upholds a very flexible Copyright Policy. There is no copyright transfer to the publisher and Authors retain exclusive copyright to their work. All Monographs/Compacts are distributed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Read more
\n\nPeer Review Policies
\n\nAll scientific works are Peer Reviewed prior to publishing. Read more
\n\nOA Publishing Fees
\n\nThe Open Access publishing model employed by IntechOpen eliminates subscription charges and pay-per-view fees, enabling readers to access research at no cost. In order to sustain operations and keep our publications freely accessible we levy an Open Access Publishing Fee for manuscripts, which helps us cover the costs of editorial work and the production of books. Read more
\n\nDigital Archiving Policy
\n\nIntechOpen is committed to ensuring the long-term preservation and the availability of all scholarly research we publish. We employ a variety of means to enable us to deliver on our commitments to the scientific community. Apart from preservation by the Croatian National Library (for publications prior to April 18, 2018) and the British Library (for publications after April 18, 2018), our entire catalogue is preserved in the CLOCKSS archive.
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