Modelling passenger flow at airport terminals : individual agent decision model for stochastic passenger behaviour

Airport system is complex. Passenger dynamics within it appear to be complicate as well. Passenger behaviours outside standard processes are regarded more significant in terms of public hazard and service rate issues. In this paper, we devised an individual agent decision model to simulate stochastic passenger behaviour in airport departure terminal. Bayesian networks are implemented into the decision making model to infer the probabilities that passengers choose to use any in-airport facilities. We aim to understand dynamics of the discretionary activities of passengers.

Stable strong order 1.0 schemes for solving stochastic ordinary differential equations

The Balanced method was introduced as a class of quasi-implicit methods, based upon the Euler-Maruyama scheme, for solving stiff stochastic differential equations. We extend the Balanced method to introduce a class of stable strong order 1. 0 numerical schemes for solving stochastic ordinary differential equations. We derive convergence results for this class of numerical schemes. We illustrate the asymptotic stability of this class of schemes is illustrated and is compared with contemporary schemes of strong order 1. 0. We present some evidence on parametric selection with respect to minimising the error convergence terms. Furthermore we provide a convergence result for general Balanced style schemes of higher orders.

Practice and Procedure: UCPR r 708 - costs order against several defendants and third party

Glenwood Homes Pty Ltd v Everhard [2008] QSC 192 involved the not uncommon situation where one costs order is made against several parties represented by a single firm of solicitors. Dutney J considered the implications when only some of the parties liable for the payment of the costs file a notice of objection to the costs statement served in respect of those costs.

Determinants of health costs due to farmers’ exposure to pesticides: an empirical analysis

Pesticide spraying by farmers has an adverse impact on their health. However, in studies to date examining farmers’ exposure to pesticides, the costs of ill health and their determinants have been based on information provided by farmers themselves. Some doubt has therefore been cast on the reliability of these estimates. In this study, we address this by conducting surveys among two groups of farmers who use pesticides on a regular basis. The first group is made up of farmers who perceive that their ill health is due to exposure to pesticides and have obtained at least some form of treatment (described in this article as the ‘general farmer group’). The second group is composed of farmers whose ill health has been diagnosed by doctors and who have been treated in hospital for exposure to pesticides (described here as the ‘hospitalised farmer group’). Cost comparisons are made between the two groups of farmers. Regression analysis of the determinants of health costs show that the most important determinants of medical costs for both samples are the defensive expenditure, the quantity of pesticides used per acre per month, frequency of pesticide use and number of pesticides used per hour per day. The results have important policy implications.

Interactions and consequences of inertia and switching costs

Purpose: Service research typically relates switching costs to customer loyalty, and portrays them as effective switching deterrents that engender harmful word-of-mouth (WOM). Rather than to customer loyalty, this paper aims to relate switching costs to consumer inertia, and show that while switching costs may result in customer retention, they can engender positive and negative WOM. This depends on whether the inertia stems from satisfaction or indifference. Design/methodology/approach: A mall-intercept survey investigated 518 customers' perceptions of their mobile phone service providers. Structural equation modelling fitted the data to the conceptual model. Findings: Switching costs deterred switching and engendered negative WOM, but only with low-inertia customers. With high-inertia customers, retention and WOM behaviours depended on whether the inertia stemmed from satisfaction or indifference. Satisfied customers with high switching costs tended to stay, gave more positive and less negative WOM. With indifferent customers, switching costs were unrelated to retention or WOM behaviours. Research limitations/implications: While they may be perceived negatively, switching costs can engender PWOM. Hence, research should not consider switching costs alone without considering the context that produces them. Practical implications: Service providers should segment their customers into low-inertia, high-inertia/satisfied and high-inertia/indifferent, and target each segment differently. By converting customers into the high-inertia/satisfied segment, service providers can make the best use of switching costs – not only in the traditional sense as a barrier to defection, but also as a way of generating positive WOM. Originality/value: This study is the first to consider the role of inertia with switching costs, positive WOM, and negative WOM. The findings suggest that past studies portraying switching costs as negative impediments that evoke only negative WOM might be misleading.

Federal Court Rules O23 r 11 - offers of compromise - offer for dismissal of claim and to forego recovery of costs.

In Uniline Australia Ltd ACN 010752057 v S Briggs Pty Ltd ACN 007415518 (No 2) [2009] FCA 920 Greenwood J considered a number of principles guiding the exercise of discretion in relation to costs, particularly when offers of compromise have been made under the formal process provided by the Federal Court Rules.

Full circle on costs

This article considers the implications of the decision in Paroz v Clifford Gouldson Lawyers [2012] QDC 151, which examined provisions of the Legal Profession Act 2007 (Qld) dealing with costs disclosure and assessment, and also considered associated provisions of the Uniform Civil Procedure Rules 1999 (Qld).

Court overrides costs assessment limits

This article considers the implications for Queensland practitioners of the decision of the New South Wales Court of Appeal in Branson v Tucker [2012] NSWCA 310. That decision involved the question whether the court retained a jurisdiction to examine the reasonableness of costs charged by a barrister, who had entered a costs agreement with solicitors, in circumstances where where had been no application under the Legal Profession Act 2004 (NSW) for an assessment of the costs the subject of the bill and it was no longer possible for such an application to be made.

Adaptive stepsize based on control theory for stochastic differential equations

The numerical solution of stochastic differential equations (SDEs) has been focused recently on the development of numerical methods with good stability and order properties. These numerical implementations have been made with fixed stepsize, but there are many situations when a fixed stepsize is not appropriate. In the numerical solution of ordinary differential equations, much work has been carried out on developing robust implementation techniques using variable stepsize. It has been necessary, in the deterministic case, to consider the "best" choice for an initial stepsize, as well as developing effective strategies for stepsize control-the same, of course, must be carried out in the stochastic case. In this paper, proportional integral (PI) control is applied to a variable stepsize implementation of an embedded pair of stochastic Runge-Kutta methods used to obtain numerical solutions of nonstiff SDEs. For stiff SDEs, the embedded pair of the balanced Milstein and balanced implicit method is implemented in variable stepsize mode using a predictive controller for the stepsize change. The extension of these stepsize controllers from a digital filter theory point of view via PI with derivative (PID) control will also be implemented. The implementations show the improvement in efficiency that can be attained when using these control theory approaches compared with the regular stepsize change strategy.

Numerical simulation of stochastic ordinary differential equations in biomathematical modelling

In this work we discuss the effects of white and coloured noise perturbations on the parameters of a mathematical model of bacteriophage infection introduced by Beretta and Kuang in [Math. Biosc. 149 (1998) 57]. We numerically simulate the strong solutions of the resulting systems of stochastic ordinary differential equations (SDEs), with respect to the global error, by means of numerical methods of both Euler-Taylor expansion and stochastic Runge-Kutta type.

Numerical methods for strong solutions of stochastic differential equations : an overview

This paper gives a review of recent progress in the design of numerical methods for computing the trajectories (sample paths) of solutions to stochastic differential equations. We give a brief survey of the area focusing on a number of application areas where approximations to strong solutions are important, with a particular focus on computational biology applications, and give the necessary analytical tools for understanding some of the important concepts associated with stochastic processes. We present the stochastic Taylor series expansion as the fundamental mechanism for constructing effective numerical methods, give general results that relate local and global order of convergence and mention the Magnus expansion as a mechanism for designing methods that preserve the underlying structure of the problem. We also present various classes of explicit and implicit methods for strong solutions, based on the underlying structure of the problem. Finally, we discuss implementation issues relating to maintaining the Brownian path, efficient simulation of stochastic integrals and variable-step-size implementations based on various types of control.

High strong order explicit Runge-Kutta methods for stochastic ordinary differential equations

The pioneering work of Runge and Kutta a hundred years ago has ultimately led to suites of sophisticated numerical methods suitable for solving complex systems of deterministic ordinary differential equations. However, in many modelling situations, the appropriate representation is a stochastic differential equation and here numerical methods are much less sophisticated. In this paper a very general class of stochastic Runge-Kutta methods is presented and much more efficient classes of explicit methods than previous extant methods are constructed. In particular, a method of strong order 2 with a deterministic component based on the classical Runge-Kutta method is constructed and some numerical results are presented to demonstrate the efficacy of this approach.

Order conditions of stochastic Runge-Kutta methods by B-series

In this paper, general order conditions and a global convergence proof are given for stochastic Runge Kutta methods applied to stochastic ordinary differential equations ( SODEs) of Stratonovich type. This work generalizes the ideas of B-series as applied to deterministic ordinary differential equations (ODEs) to the stochastic case and allows a completely general formalism for constructing high order stochastic methods, either explicit or implicit. Some numerical results will be given to illustrate this theory.

Numerical solutions of stochastic differential equations – implementation and stability issues

Stochastic differential equations (SDEs) arise fi om physical systems where the parameters describing the system can only be estimated or are subject to noise. There has been much work done recently on developing numerical methods for solving SDEs. This paper will focus on stability issues and variable stepsize implementation techniques for numerically solving SDEs effectively.