No description available.

No description available.

Graduation date: 1976

In the past, the planning of airports has largely been an intuitive process, leading to an often serious misallocation of resources. It is the contention of this thesis that the adoption of a more economic and integrated method of evaluating alternative airport plans could eliminate some of this mis-investment, and that the groundwork for such an evaluation process has already been done in the field of port planning. The evaluation method proposed is the use of a mathematical model of the airport's operation and of the benefit and cost interrelationships arising from the activities performed. The model can then be used to simulate the value of the benefits and costs of a number of possible alternative plans. It is the purpose of this thesis to discuss the applicability of the port model as a tool for airport planning and to point out the ease with which it could be applied both from the point of view of modifications and data requirements and availability. As background to the evaluation process, Chapter 2 presents some general theory and problems of economic evaluation and of the measurement of benefits and costs. Chapter 3 presents a description of planning processes currently being used by the Department of Transport in planning Canada's airports and points out some of the flaws in this approach. Chapter 4 then describes the type of port model now developed in so far as it can be used to determine interrelationships between investment, cost to ships of using the port, cost of port operation, and net community benefits. The calculations derived from the application of the model can then be used to determine the net present value of the benefit and cost streams arising from alternative ways of achieving a given level of port output, and thus to select the best possible combination of facilities. Chapter 5 then points out the similarities and differences between port and airport operation and hence the applicability of and the modifications required in the application of the port model to airport planning situations. The remainder of the chapter delineates the type of data required to construct and use an airport model and the availability of this data to the airport planners. Finally, Chapter 6 summarizes the findings and concludes that, while it has its limitations as a terminal model, as a representation of airport operation and as an evaluation process, the port model can be adapted relatively easily to airport planning to provide a more integrated, more economic approach to the evaluation of alternative airport plans. / Business, Sauder School of / Graduate

Planners have generally failed to prevent the urban strife (including civil disorders, housing shortages, visual blight and rising pollution which characterizes so many North American cities. While they cannot necessarily be blamed for these occurrences, they cannot entirely be exhonerated. Planning techniques for guiding and controlling urban development have not kept pace with the rapid growth of our modern cities. Certain techniques have been borrowed from other fields, notably simulation modelling, but their use has frequently been hampered by a lack of data and by the high cost of implementation, furthermore, these techniques have generally failed to filter down to the Profession at large, with the result that they have largely remained the preserve of the technical expert who may not be in the mainstream of broader planning principles. The sophisticated nature of these techniques has promoted their isolation from the day-to-day planning processes. One alternative to a rigourous computer simulation is to employ a gaming simulation. The latter may permit a considerable simplication of the model by allowing the players (in this case, planners) to become ' simulation actors' who emulate the behavior of various interest groups or institutions in response to carefully selected rewards. This format has the advantage of precipitating the direct involvement of planners in the model and of facilitating their understanding of problems through the process of abstracting from reality. Such an abstraction is often conducive to the achieving of an overview; this may permit planners to be less distracted by the routine problems of planning administration, which are short term in nature, and to redirect their focus to longer term considerations. The purpose of this Study is to develop a gaining simulation framework for the analysis of planning problems which are not readily amenable to many quantitative techniques and for the evaluation of alternative planning strategies. This framework or tool is capable of incorporating a series of very simple interrelationships into a recursive process which will ultimately generate the implications of various decision alternatives and which will permit planners to identify optimum strategies. The framework incorporates the simulational features of a 'gaming simulation' and the strategy evaluating features of 'game theory'. The former have generally constituted abstractions from reality which were merely assertions in mathematical form but which were not particularly useful for either rigourous analysis or accurate forecasting. The lack of mathematical rigour in their structures has tended to inhibit their use for any but educational purposes, notably prediction and research. The latter have been confined to the identification of optimum strategies in only the most simple exchanges, which cannot generally be related to the complexities of the real world. This Study represents a step towards combining these two approaches. The gaming simulation framework, when 'primed' with appropriate data, will generate optimum strategies which may be followed by the participants. Its mathematical structure constitutes an amalgam of Markov processes, network analysis and Eayesian decision analysis. This technique is primarily designed to be used in the day-to-day planning process in large cities rather than in the cloistered research context, although it may later prove to have even wider applications. The null hypothesis is presented in the Study which states that the framework is not capable of generating an optimal solution. It was then refuted using probability theory to demonstrate that an -optimal solution was attainable. The use of the framework in the planning context was then illustrated by applying it to the specific public/private negotiations preceding major urban landmark developments in Canada. / Applied Science, Faculty of / Community and Regional Planning (SCARP), School of / Graduate

This study develops and tests a new multivariate distribution model for the estimation of advertising vehicle exposure. The new multivariate distribution model is developed as three versions (i.e., one which doesn't adjust negative probabilities, and the others which adjust negative probabilities in unvariate distributions). In addition, eight other media exposure models are evaluated against a database of 440 tabulated schedules constructed from 2003 comScore network data. The types of models tested include: three univariate models -- the Binomial Distribution Model (BIN), the Beta Binomial Distribution Model (BBD), and the Hofmans Beta Binomial Distribution Model (HBBD); three multivariate models -- the Dirichlet Multinomial Distribution Model (DMD), the Canonical Expansion Model (CANEX), and the Conditional Beta Distribution Model (CBD); and one aggregation model -- the Morgensztern Sequential Aggregation Model (MSAD). All of the models tested are based on probability distributions. Some models are a combination of probability distributions and ad hoc methods. In addition, the approximation model of the MBD called the Hyper Beta Distribution Model (HBD), is described and tested. The accuracy of the eleven models is assessed via two evaluation criteria of model performance -- the Average Percentage Error in Reach (AER) and the Average Percentage Error in Exposure Distribution (APE). All models are compared according to their relative overall accuracy as assessed by the two error measures. The proposed new multivariate model -- the Multivariate Beta Binomial Distribution Model (MBD) -- was generally more accurate than the other models for the estimation of reach. For the estimation of the exposure distribution, the model proved more accurate than the Binomial Distribution Model (BIN), the Beta Binomial Distribution Model (BBD), the Hofmans Beta Binomial Distribution Model (HBBD), and the Dirichlet Multinomial Distribution Model (DMD), but less accurate than the Canonical Expansion Model (CANEX), the Conditional Beta Distribution Model (CBD), the Morgensztern Sequential Aggregation Model (MSAD), and Hyper Beta Distribution Model (HBD). This study provides the foundation for further improvement of the model, along with recommendations for further investigation, since the theoretical potential for the performance of the model is high. / text

Designing systems with the ability to make optimal decisions under uncertainty is one of the goals of artificial intelligence. However, in many applications the design of optimal planners is complicated due to imprecise inputs and uncertain outputs resulting from stochastic dynamics. Partially Observable Markov Decision Processes (POMDPs) provide a rich mathematical framework to model these kinds of problems. However, the high computational demand of solution methods for POMDPs is a drawback for applying them in practice. / In this thesis, we present a two-fold approach for improving the tractability of POMDP planning. First, we focus on designing good heuristics for POMDP approximation algorithms. We aim to scale up the efficiency of a class of POMDP approximations called point-based planning methods by designing a good planning space. We study the effect of three properties of reachable belief state points that may influence the performance of point-based approximation methods. Second, we investigate approaches to designing good controllers using an alternative representation of systems with partial observability called Predictive State Representation (PSR). This part of the thesis advocates the usefulness and practicality of PSRs in planning under uncertainty. We also attempt to move some useful characteristics of the PSR model, which has a predictive view of the world, to the POMDP model, which has a probabilistic view of the hidden states of the world. We propose a planning algorithm motivated by the connections between the two models.

No description available.

Thesis. 1979. Ph.D.--Massachusetts Institute of Technology. Alfred P. Sloan School of Management. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND DEWEY. / Bibliography: leaves 152-154. / Ph.D.

This thesis focuses on a new graphon-based approach for fitting models to large networks and establishes a general framework for incorporating nodal attributes to modeling. The scale of network data nowadays, renders classical network modeling and inference inappropriate. Novel modeling strategies are required as well as estimation methods. Depending on whether the model structure is specified a priori or solely determined from data, existing models for networks can be classified as parametric and non-parametric. Compared to the former, a non-parametric model often allows for an easier and more straightforward estimation procedure of the network structure. On the other hand, the connectivities and dynamics of networks fitted by non-parametric models can be quite difficult to interpret, as compared to parametric models. In this thesis, we first propose a computational estimation procedure for a class of parametric models that are among the most widely used models for networks, built upon tools from non-parametric models with practical innovations that make it efficient and capable of scaling to large networks. Extensions of this base method are then considered in two directions. Inspired by a popular network sampling method, we further propose an estimation algorithm using sampled data, in order to circumvent the practical obstacle that the entire network data is hard to obtain and analyze. The base algorithm is also generalized to consider the case of complex network structure where nodal attributes are involved. Two general frameworks of a non-parametric model are proposed in order to incorporate nodal impact, one with a hierarchical structure, and the other employs similarity measures. Several simulation studies are carried out to illustrate the improved performance of our proposed methods over existing algorithms. The proposed methods are also applied to several real data sets, including Slashdot online social networks and in-school friendship networks from the National Longitudinal Study of Adolescent to Adult Health (AddHealth Study). An array of graphical visualizations and quantitative diagnostic tools, which are specifically designed for the evaluation of goodness of fit for network models, are developed and illustrated with these data sets. Some observations of using these tools via our algorithms are also examined and discussed.