Payam Mokhtarian

Generating a Dynamic Synthetic Population – Using an Age-Structured Two-Sex Model for Household Dynamics

Generating a reliable computer-simulated synthetic population is necessary for knowledge processing and decision-making analysis in agent-based systems in order to measure, interpret and describe each target area and the human activity patterns within it. In this paper, both synthetic reconstruction (SR) and combinatorial optimisation (CO) techniques are discussed for generating a reliable synthetic population for a certain geographic region (in Australia) using aggregated- and disaggregated-level information available for such an area. A CO algorithm using the quadratic function of population estimators is presented in this paper in order to generate a synthetic population while considering a two-fold nested structure for the individuals and households within the target areas. The baseline population in this study is generated from the confidentialised unit record files (CURFs) and 2006 Australian census tables. The dynamics of the created population is then projected over five years using a dynamic micro-simulation model for individual- and household-level demographic transitions. This projection is then compared with the 2011 Australian census. A prediction interval is provided for the population estimates obtained by the bootstrapping method, by which the variability structure of a predictor can be replicated in a bootstrap distribution.

Scene-adaptive configuration of two cameras using the correspondence field function

In a free viewpoint video system, the scene is captured by a number of cameras and it would be desirable to optimize the configuration of cameras, such as their location or orientation, to improve the rendering quality. This paper introduces a mathematical representation of the multi-camera geometry, called the correspondence field (CF), which can be used to quantify the suitability of a camera configuration for a given arrangement of objects in the scene. The correspondence field describes the spatial topology of the intersecting rays of cameras, arranged as a number of layers or surfaces in the field of view of cameras. The paper derives the topology of CF for certain camera arrangements and analyzes the impact of changes in camera location or orientation on this topology. It demonstrates that CF can be used to find the optimum camera configuration for a given objective. It also presents simulation results of this method using our light field simulator.

Bayesian nonparametric reliability analysis for a railway system at component level

Railway system is a typical large-scale complex system with interconnected sub-systems which contain numerous components. System reliability is retained through appropriate maintenance measures and cost-effective asset management requires accurate estimation of reliability at the lowest level. However, real-life reliability data at component level of a railway system is not always available in practice, let alone complete. The component lifetime distributions from the manufacturers are often obscured and complicated by the actual usage and working environments. Reliability analysis thus calls for a suitable methodology to estimate a component lifetime under the conditions of a lack of failure data and unknown and/or mixture lifetime distributions. This paper proposes a nonparametric Bayesian approach with a Dirichlet Process Mixture Model (DPMM) to facilitate reliability analysis in a railway system. Simulation results will be given to illustrate the effectiveness of the proposed approach in lifetime estimation.

Constrained optimization of average arrival time via a probabilistic approach to transport reliability

To achieve greater transit-time reduction and improvement in reliability of transport services, there is an increasing need to assist transport planners in understanding the value of punctuality; i.e. the potential improvements, not only to service quality and the consumer but also to the actual profitability of the service. In order for this to be achieved, it is important to understand the network-specific aspects that affect both the ability to decrease transit-time, and the associated cost-benefit of doing so. In this paper, we outline a framework for evaluating the effectiveness of proposed changes to average transit-time, so as to determine the optimal choice of average arrival time subject to desired punctuality levels whilst simultaneously minimizing operational costs. We model the service transit-time variability using a truncated probability density function, and simultaneously compare the trade-off between potential gains and increased service costs, for several commonly employed cost-benefit functions of general form. We formulate this problem as a constrained optimization problem to determine the optimal choice of average transit time, so as to increase the level of service punctuality, whilst simultaneously ensuring a minimum level of cost-benefit to the service operator.

Estimating Cross-Classified Population Counts of Multidimensional Tables: An Application to Regional Australia to Obtain Pseudo-Census Counts

Abstract Estimating population counts for multidimensional tables based on a representative sample subject to known marginal population counts is not only important in survey sampling but is also an integral part of standard methods for simulating area-specific synthetic populations. In this article several estimation methods are reviewed, with particular focus on the iterative proportional fitting procedure and the maximum likelihood method. The performance of these methods is investigated in a simulation study for multidimensional tables, as previous studies are limited to 2 by 2 tables. The data are generated under random sampling but also under misspecification models, for which sample and target populations differ systematically. The empirical results show that simple adjustments can lead to more efficient estimators, but generally, at the expense of increased bias. The adjustments also generally improve coverage of the confidence intervals. The methods discussed in this article along with standard error estimators, are made freely available in the R package mipfp. As an illustration, the methods are applied to the 2011 Australian census data available for the Illawarra Region in order to obtain estimates for the desired three-way table for age by sex by family type with known marginal tables for age by sex and for family type.

An unconstrained statistical matching algorithm for combining individual and household level geo-specific census and survey data

Abstract The Population Census is an important source of statistical information in most countries that is capable of producing reliable estimates of population characteristics for small geographic areas. One limitation of a census is that there are many population characteristics that cannot be collected due to respondent burden or cost. This means that statistical agencies have to conduct population based surveys to provide social, economic and demographic characteristics for a target population which are not captured by a large-scale census. These surveys are usually capable of producing direct estimates at the national level and high level regions but often cannot produce reliable estimates for smaller areas. Due to the increasing demand for comprehensive statistical information not only at the national level but also for sub-national domains, there is a wide discussion in the literature about the use of statistical techniques that combine survey with census data to provide more detailed, finer-level estimates. Where censuses and sample surveys are based on the same reporting units, statistical matching techniques can be employed to link the records from survey and census data where exact matching of reporting units is impossible due to confidentiality restrictions. These techniques can then provide the detailed social, economic and demographic information required for small areas. An approach is developed in this paper in which a close-to-reality synthetic population of individuals and households is generated from available census tables using an iterative proportional updating (IPU) method. Statistical matching using a nearest neighbour method is then used to impute survey data to the individuals and households in the synthetic population. To evaluate this approach, 2011 Bangladesh census data is used to generate a district-specific synthetic population of individuals and households. Matching is then performed by imputing the nearest possible records among the 2011 Bangladesh Demographic and Health Survey to estimate the wealth index for each household within the synthetic population. The results show that using the method presented in this paper helps with achieving more representative estimates (comparing with direct survey estimates) particularly for areas with small sample sizes where not many population units with different socio-demographic characteristics are included.