Airports in their urban settings : towards a conceptual model of interfaces in the Australian context

In Australia, airports have emerged as important sub-regional activity centres and now pose challenges for both airport operation and planning in the surrounding urban and regional environment. The changing nature of airports in their metropolitan context and the emergence of new pressures and problems require the introduction of a fresh conceptual framework to assist the better understanding of these complex roles and spatial interactions. The approach draws upon the meta-concept of interfaces of an ‘airport metropolis’ as an organising device consisting of four main domains: economic development, land use,infrastructure, and governance. The paper uses the framework to further discuss airport and regional interactions and highlights the use of sustainability criteria to operationalise the model. The approach aims to move research and practice beyond the traditionally compartmentalised analysis of airport issues and policy-making by highlighting interdependencies between airports and regions.

Stochastic modelling of financial time series with memory and multifractal scaling

Financial processes may possess long memory and their probability densities may display heavy tails. Many models have been developed to deal with this tail behaviour, which reflects the jumps in the sample paths. On the other hand, the presence of long memory, which contradicts the efficient market hypothesis, is still an issue for further debates. These difficulties present challenges with the problems of memory detection and modelling the co-presence of long memory and heavy tails. This PhD project aims to respond to these challenges. The first part aims to detect memory in a large number of financial time series on stock prices and exchange rates using their scaling properties. Since financial time series often exhibit stochastic trends, a common form of nonstationarity, strong trends in the data can lead to false detection of memory. We will take advantage of a technique known as multifractal detrended fluctuation analysis (MF-DFA) that can systematically eliminate trends of different orders. This method is based on the identification of scaling of the q-th-order moments and is a generalisation of the standard detrended fluctuation analysis (DFA) which uses only the second moment; that is, q = 2. We also consider the rescaled range R/S analysis and the periodogram method to detect memory in financial time series and compare their results with the MF-DFA. An interesting finding is that short memory is detected for stock prices of the American Stock Exchange (AMEX) and long memory is found present in the time series of two exchange rates, namely the French franc and the Deutsche mark. Electricity price series of the five states of Australia are also found to possess long memory. For these electricity price series, heavy tails are also pronounced in their probability densities. The second part of the thesis develops models to represent short-memory and longmemory financial processes as detected in Part I. These models take the form of continuous-time AR(∞) -type equations whose kernel is the Laplace transform of a finite Borel measure. By imposing appropriate conditions on this measure, short memory or long memory in the dynamics of the solution will result. A specific form of the models, which has a good MA(∞) -type representation, is presented for the short memory case. Parameter estimation of this type of models is performed via least squares, and the models are applied to the stock prices in the AMEX, which have been established in Part I to possess short memory. By selecting the kernel in the continuous-time AR(∞) -type equations to have the form of Riemann-Liouville fractional derivative, we obtain a fractional stochastic differential equation driven by Brownian motion. This type of equations is used to represent financial processes with long memory, whose dynamics is described by the fractional derivative in the equation. These models are estimated via quasi-likelihood, namely via a continuoustime version of the Gauss-Whittle method. The models are applied to the exchange rates and the electricity prices of Part I with the aim of confirming their possible long-range dependence established by MF-DFA. The third part of the thesis provides an application of the results established in Parts I and II to characterise and classify financial markets. We will pay attention to the New York Stock Exchange (NYSE), the American Stock Exchange (AMEX), the NASDAQ Stock Exchange (NASDAQ) and the Toronto Stock Exchange (TSX). The parameters from MF-DFA and those of the short-memory AR(∞) -type models will be employed in this classification. We propose the Fisher discriminant algorithm to find a classifier in the two and three-dimensional spaces of data sets and then provide cross-validation to verify discriminant accuracies. This classification is useful for understanding and predicting the behaviour of different processes within the same market. The fourth part of the thesis investigates the heavy-tailed behaviour of financial processes which may also possess long memory. We consider fractional stochastic differential equations driven by stable noise to model financial processes such as electricity prices. The long memory of electricity prices is represented by a fractional derivative, while the stable noise input models their non-Gaussianity via the tails of their probability density. A method using the empirical densities and MF-DFA will be provided to estimate all the parameters of the model and simulate sample paths of the equation. The method is then applied to analyse daily spot prices for five states of Australia. Comparison with the results obtained from the R/S analysis, periodogram method and MF-DFA are provided. The results from fractional SDEs agree with those from MF-DFA, which are based on multifractal scaling, while those from the periodograms, which are based on the second order, seem to underestimate the long memory dynamics of the process. This highlights the need and usefulness of fractal methods in modelling non-Gaussian financial processes with long memory.

Condition-based prognosis of machine health

Modern machines are complex and often required to operate long hours to achieve production targets. The ability to detect symptoms of failure, hence, forecasting the remaining useful life of the machine is vital to prevent catastrophic failures. This is essential to reducing maintenance cost, operation downtime and safety hazard. Recent advances in condition monitoring technologies have given rise to a number of prognosis models that attempt to forecast machinery health based on either condition data or reliability data. In practice, failure condition trending data are seldom kept by industries and data that ended with a suspension are sometimes treated as failure data. This paper presents a novel approach of incorporating historical failure data and suspended condition trending data in the prognostic model. The proposed model consists of a FFNN whose training targets are asset survival probabilities estimated using a variation of Kaplan-Meier estimator and degradation-based failure PDF estimator. The output survival probabilities collectively form an estimated survival curve. The viability of the model was tested using a set of industry vibration data.

The distribution of the sample minimum-variance frontier

In this paper, we present a finite sample analysis of the sample minimum-variance frontier under the assumption that the returns are independent and multivariate normally distributed. We show that the sample minimum-variance frontier is a highly biased estimator of the population frontier, and we propose an improved estimator of the population frontier. In addition, we provide the exact distribution of the out-of-sample mean and variance of sample minimum-variance portfolios. This allows us to understand the impact of estimation error on the performance of in-sample optimal portfolios. Key Words: minimum-variance frontier; efficiency set constants; finite sample distribution

Beyond airport enclaves : insights for overcoming turf wars

Airports, over time, have emerged as separate independent entities often described as ‘enclaves’. As such airports regularly planned and implemented developments within their boundaries with limited inclusion of local actors in decision making processes. Urban encroachment on airport boundaries has increasingly focused the planning interests of airports to consider what their neighbouring cities are doing. Likewise city planners are progressively more interested in the development activities of airports. Despite shared interests in what happens on the either side of the fence line, relationships between airports and their neighbouring cities have often been strained, if not, at times, hostile. A number of strategies and conceptualisations for the co-existence of urban and airport environs have been put forward. However, these models are likely to have a limited effect unless they can be implemented to maximise opportunities for both cities and airports, and at the same time not confound their long-term interests. The isolation of airport planning from local and regional planning agencies, and the resulting power struggles are not new. Under current conditions the need to ‘bridge the gap’ between airports and their urban surrounds has become an increasing, yet under explored imperative. This paper examines the decision making arena for airport-region development to define the barriers, enablers, tensions and puzzles for the governance of airport-region development, from a cross-country perspective. Findings suggest that while there are many embedded rule structures that foster airport-region tensions, there are nonetheless a number of pathways for moving airports beyond decision making enclaves, to more integrated mechanisms for city and regional planning. In providing preliminary answers for overcoming the barriers, tensions and intractable issues of mutually agreeable airport and city development, the research makes a primary contribution to the ground level governance of collaborative planning. This research also serves as a launching point for future, more detailed research into the areas of airport-region decision making and collaborative planning for airport-regions. This work was carried out through the Airport Metropolis Research Project under the Australian Research Council’s Linkage Projects funding scheme (LP0775225).

The applicability of biased estimation in model and model order selection

Biased estimation has the advantage of reducing the mean squared error (MSE) of an estimator. The question of interest is how biased estimation affects model selection. In this paper, we introduce biased estimation to a range of model selection criteria. Specifically, we analyze the performance of the minimum description length (MDL) criterion based on biased and unbiased estimation and compare it against modern model selection criteria such as Kay's conditional model order estimator (CME), the bootstrap and the more recently proposed hook-and-loop resampling based model selection. The advantages and limitations of the considered techniques are discussed. The results indicate that, in some cases, biased estimators can slightly improve the selection of the correct model. We also give an example for which the CME with an unbiased estimator fails, but could regain its power when a biased estimator is used.

Unmanned Aerial Vehicles UAVs attitude, height, motion estimation and control using visual systems

This paper presents an implementation of an aircraft pose and motion estimator using visual systems as the principal sensor for controlling an Unmanned Aerial Vehicle (UAV) or as a redundant system for an Inertial Measure Unit (IMU) and gyros sensors. First, we explore the applications of the unified theory for central catadioptric cameras for attitude and heading estimation, explaining how the skyline is projected on the catadioptric image and how it is segmented and used to calculate the UAV’s attitude. Then we use appearance images to obtain a visual compass, and we calculate the relative rotation and heading of the aerial vehicle. Additionally, we show the use of a stereo system to calculate the aircraft height and to measure the UAV’s motion. Finally, we present a visual tracking system based on Fuzzy controllers working in both a UAV and a camera pan and tilt platform. Every part is tested using the UAV COLIBRI platform to validate the different approaches, which include comparison of the estimated data with the inertial values measured onboard the helicopter platform and the validation of the tracking schemes on real flights.

Load–frequency control : a GA-based multi-agent reinforcement learning

The load–frequency control (LFC) problem has been one of the major subjects in a power system. In practice, LFC systems use proportional–integral (PI) controllers. However since these controllers are designed using a linear model, the non-linearities of the system are not accounted for and they are incapable of gaining good dynamical performance for a wide range of operating conditions in a multi-area power system. A strategy for solving this problem because of the distributed nature of a multi-area power system is presented by using a multi-agent reinforcement learning (MARL) approach. It consists of two agents in each power area; the estimator agent provides the area control error (ACE) signal based on the frequency bias estimation and the controller agent uses reinforcement learning to control the power system in which genetic algorithm optimisation is used to tune its parameters. This method does not depend on any knowledge of the system and it admits considerable flexibility in defining the control objective. Also, by finding the ACE signal based on the frequency bias estimation the LFC performance is improved and by using the MARL parallel, computation is realised, leading to a high degree of scalability. Here, to illustrate the accuracy of the proposed approach, a three-area power system example is given with two scenarios.

Investigating the patterns and determinants of life satisfaction in Germany following reunification

This paper investigates the patterns and determinants of life satisfaction in Germany following reunification. We implement a new fixed-effect estimator for ordinal life satisfaction in the German Socio-Economic Panel and find negative effects on life satisfaction from being recently fired, losing a spouse through either death or separation, and time spent in hospital, while we find strong positive effects from income and marriage. Using a new causal decomposition technique, we find that East Germans experienced a continued improvement in life satisfaction to which increased household incomes contributed around 12 percent. Most of the improvement is explained by better average circumstances, such as greater political freedom. For West Germans, we find little change in average life satisfaction over this period.

The causal effect of income on health: Evidence from German reunification

We investigate whether therewas a causal effect of income changes on the health satisfaction of East and West Germans in the years following reunification. Our data source is the German Socio-Economic Panel (GSOEP) between 1984 and 2002, and we fit a recently proposed fixed-effects ordinal estimator to our health measures and use a causal decomposition technique to account for panel attrition.We find evidence of a significant positive effect of income changes on health satisfaction, but the quantitative size of this effect is small. This is the case with respect to current income and a measure of ‘permanent’ income.

Bootstrap approaches for estimation and confidence intervals of long term memory processes

In this work, we investigate an alternative bootstrap approach based on a result of Ramsey [F.L. Ramsey, Characterization of the partial autocorrelation function, Ann. Statist. 2 (1974), pp. 1296-1301] and on the Durbin-Levinson algorithm to obtain a surrogate series from linear Gaussian processes with long range dependence. We compare this bootstrap method with other existing procedures in a wide Monte Carlo experiment by estimating, parametrically and semi-parametrically, the memory parameter d. We consider Gaussian and non-Gaussian processes to prove the robustness of the method to deviations from normality. The approach is also useful to estimate confidence intervals for the memory parameter d by improving the coverage level of the interval.

Cities in the airports 'shadow' : underlying interests and discretionary power in airport-region development

The previously distinct boundary between airports and their cities has become increasingly blurred as new interests and actors are identified as important stakeholders in the decision making process. As a consequence airport entities are more than ever seeking an integrated existence with their surrounding regions. While current planning strategies provide insights on how to improve and leverage land use planning in and around airports, emerging challenges for implementing and protecting these planning ideals stem from the governance shadows of development decisions. The thesis of this paper is that improving the identification, articulation and consideration of city and airport interests in the development approval process (between planning and implementation) can help avoid outcomes that hinder the ability of cities and their airports to meet their separate/mutual long-term objectives. By applying a network governance perspective to the pilot case study of Brisbane, analysis of overlapping and competing actor interests show how different governance arrangements facilitate (or impede) decision making that protects sustainable ‘airport region’ development. ---------- Contributions are made to airport and city development decision makers through the identification and analysis of effective and ineffective decision making pathways, and to governance literature by way of forwarding empirically derived frameworks for showing how actors protect their interests in the ‘crowded decision making domain’ of airport region development. This work was carried out through the Airport Metropolis Research Project under the Australian Research Council’s Linkage Projects funding scheme (LP0775225).