A goal programming model for crew duties generation

An Erratum has been published for this article in Journal of Multi‐Criteria Decision Analysis 10(5) 2001, 285. This paper proposes a model for the generation of daily work duties of airside crew (being bus drivers) at the Hong Kong International Airport. The results can be adopted as a good crew schedule, in the sense that it is both feasible, satisfying requirements of various work conditions, and ‘optimal’ in minimizing overtime shifts. It is formulated as a goal programme, specifically designed to cater for the manpower planning issues to handle frequent changes of flight schedules by flexibility in work patterns of driver duties. Illustrative results from an actual case study are given. Copyright © 2001 John Wiley & Sons, Ltd.     

Parametrized preference structures and some geometrical interpretation

Order structures such as linear orders, semiorders and interval orders are often used to model preferences in decision-making problems. In this paper we introduce a family of preference structures where the mutual indifference threshold belongs to a specific family parametrized by extended reals α. This family includes interval orders (α=1), tangent circle orders (α=0) and a new preference structure called ‘diamond order’ (α=−∞). All these preference relations present an asymmetric part which is shown to be always quasi-transitive and to be transitive for α > 1. Diamond orders present ‘forbidden configurations’ which can occur in the case of tangent circle orders. © 1997 John Wiley & Sons, Ltd.     

Towards statistical multicriteria decision modelling: a first approach

Many real life situations result from decisions taken by a very large number of decision makers. Among them, we may cite road traffic congestion, crowding during shopping, equity market behaviour, distribution of holiday destinations, etc. Furthermore, these decisions often depend on the optimisation of several conflicting criteria. In this paper, we introduce a new multicriteria tool based on Markov chains to model and manage these macroscopic phenomena. Finally, the road traffic congestion problem will be considered to illustrate the applicability of our approach. Copyright © 2003 John Wiley & Sons, Ltd.     

New measures of political attitudes in Northern Ireland: a social identity perspective

Twenty‐eight measures of political attitudes were validated on a sample of 388 undergraduate students from Northern Ireland. Confirmatory factor analysis showed the scales to be unidimensional, discriminantly valid, with generally excellent reliabilities. The pattern of intergroup differentiation between Catholics and Protestants conformed to Social Identity Theory, with maximum differentiation on important issues, Catholics adopting a social change ideology and Protestants defending the status quo. Catholics and Protestants resolved their respective group associations with violence by condemning both it and terrorism, and also reported interdenominational friendships. The utility of these new measures of political attitudes in terms of measuring changes due to political initiatives, cross‐community reconciliation programmes and in assessing changes in attitudes as a result of integrated or segregated denominational schooling within the Province is outlined. Copyright © 2000 John Wiley & Sons, Ltd.     

Reactions to daily events as a function of familiarity with an environment

Undergraduate participants provided measures of their psychological well‐being and described the positive and negative events that occurred each day, once during the first and second semesters of an academic year. For four of the five measures of daily well‐being, reactivity to negative events decreased from the first to second semester, whereas for four of the five measures of well‐being, reactivity to positive events did not change over the year. These results suggest that familiarity with an environment moderates reactivity to negative daily events. As people become more familiar with an environment, negative events may elicit smaller decreases in well‐being. In contrast, increases in well‐being elicited by positive events appear to be unrelated to familiarity with the environment. More broadly, these differences suggest that the reactivity to positive and negative events reflect the operation of different processes. Copyright © 2007 John Wiley & Sons, Ltd.     

A Note on Comparing the Estimates of Models for Cluster-Correlated or Longitudinal Data with Binary or Ordinal Outcomes

When using linear models for cluster-correlated or longitudinal data, a common modeling practice is to begin by fitting a relatively simple model and then to increase the model complexity in steps. New predictors might be added to the model, or a more complex covariance structure might be specified for the observations. When fitting models for binary or ordered-categorical outcomes, however, comparisons between such models are impeded by the implicit rescaling of the model estimates that takes place with the inclusion of new predictors and/or random effects. This paper presents an approach for putting the estimates on a common scale to facilitate relative comparisons between models fit to binary or ordinal outcomes. The approach is developed for both population-average and unit-specific models.     

A Latent Gaussian process model for analysing intensive longitudinal data

Intensive longitudinal studies are becoming progressively more prevalent across many social science areas, and especially in psychology. New technologies such as smart-phones, fitness trackers, and the Internet of Things make it much easier than in the past to collect data for intensive longitudinal studies, providing an opportunity to look deep into the underlying characteristics of individuals under a high temporal resolution. In this paper we introduce a new modelling framework for latent curve analysis that is more suitable for the analysis of intensive longitudinal data than existing latent curve models. Specifically, through the modelling of an individual-specific continuous-time latent process, some unique features of intensive longitudinal data are better captured, including intensive measurements in time and unequally spaced time points of observations. Technically, the continuous-time latent process is modelled by a Gaussian process model. This model can be regarded as a semi-parametric extension of the classical latent curve models and falls under the framework of structural equation modelling. Procedures for parameter estimation and statistical inference are provided under an empirical Bayes framework and evaluated by simulation studies. We illustrate the use of the proposed model though the analysis of an ecological momentary assessment data set.     

A comparison of computational driver models using naturalistic and test-track data from cyclist-overtaking manoeuvres

The improvement of advanced driver assistance systems (ADAS) and their safety assessment rely on the understanding of scenario-dependent driving behaviours, such as steering to avoid collisions.This study compares driver models that predict when a driver starts steering away to overtake a cyclist on rural roads. The comparison is among four models: a threshold model, an accumulator model, and two models inspired by a proportional-integral and proportional-integral-derivative controller. These models were tested and cross-applied using two different datasets: one from a naturalistic driving (ND) study and one from a test-track (TT) experiment. Two perceptual variables, expansion rate (the horizontal angular expansion rate of the image of the lead road user on the driver’s retina) and inverse tau (the ratio between the image’s expansion rate and its horizontal optical size), were tested as input to the models. A linear cost function is proposed that can obtain the optimal parameters of the models by computationally efficient linear programming.The results show that the models based on inverse tau fitted the data better than the models that included expansion rate. In general, the models fitted the ND data reasonably well, but not as well the TT data. For the ND data, the models including an accumulative component outperformed the threshold model. For the TT data, due to the poorer fit of the models, more analysis is required to determine the merit of the models. The models fitted to TT data captured the overall pattern of steering onsets in the ND data rather well, but with a persistent bias, probably due to the drivers employing a more cautious strategy in TT.The models compared in this paper may support the virtual safety assessment of ADAS so that driver behaviour may be considered in the design and evaluation of new safety systems.