Dynamic cost indexing

This paper describes the development of a generic tool for dynamic cost indexing (DCI), which encompasses the ability to manage flight delay costs on a dynamic basis, trading accelerated fuel burn against ‘cost of time’. Many airlines have significant barriers to identifying which costs should be included in ‘cost of time’ calculations and how to quantify them. The need is highlighted to integrate historical passenger delay and policy data with real-time passenger connections data. The absence of industry standards for defining and interfacing necessary tools is recognised. Delay recovery decision windows and ATC cooperation are key constraints. DCI tools could also be used in the pre-departure phase, and may offer environmental decision support functionality: which could be used as a differentiating technology required for access to designated, future ‘green’ airspace. Short-term opportunities for saving fuel and/or reducing emissions are also identified.

Dynamic cost indexing: cost estimations and building the prototype

The design of a decision-support prototype tool for managing flight delay costs in the pre-departure and airborne phases of a flight is described. The tool trades accelerated fuel burn and emissions charges against 'cost of time'. Costs for all major 'cost of time' components, by three cost scenarios, twelve aircraft types and by magnitude of delay are derived. Short-term opportunities for saving fuel and/or reducing environmental impacts are identified. A shift in ATM from managing delay minutes to delay cost is also supported.

Forecasting overseas visitors into the United Kingdom using continuous time and autoregressive fractional integrated moving average models with discrete data

This paper applies Gaussian estimation methods to continuous time models for modelling overseas visitors into the UK. The use of continuous time modelling is widely used in economics and finance but not in tourism forecasting. Using monthly data for 1986–2010, various continuous time models are estimated and compared to autoregressive integrated moving average (ARIMA) and autoregressive fractionally integrated moving average (ARFIMA) models. Dynamic forecasts are obtained over different periods. The empirical results show that the ARIMA model performs very well, but that the constant elasticity of variance (CEV) continuous time model has the lowest root mean squared error (RMSE) over a short period.

A review of dynamic Bayesian network techniques with applications in healthcare risk modelling

Coping with an ageing population is a major concern for healthcare organisations around the world. The average cost of hospital care is higher than social care for older and terminally ill patients. Moreover, the average cost of social care increases with the age of the patient. Therefore, it is important to make efficient and fair capacity planning which also incorporates patient centred outcomes. Predictive models can provide predictions which their accuracy can be understood and quantified. Predictive modelling can help patients and carers to get the appropriate support services, and allow clinical decision-makers to improve care quality and reduce the cost of inappropriate hospital and Accident and Emergency admissions. The aim of this study is to provide a review of modelling techniques and frameworks for predictive risk modelling of patients in hospital, based on routinely collected data such as the Hospital Episode Statistics database. A number of sub-problems can be considered such as Length-of-Stay and End-of-Life predictive modelling. The methodologies in the literature are mainly focused on addressing the problems using regression methods and Markov models, and the majority lack generalisability. In some cases, the robustness, accuracy and re-usability of predictive risk models have been shown to be improved using Machine Learning methods. Dynamic Bayesian Network techniques can represent complex correlations models and include small probabilities into the solution. The main focus of this study is to provide a review of major time-varying Dynamic Bayesian Network techniques with applications in healthcare predictive risk modelling.