An Analysis of the Spatio-Temporal Factors Affecting Aircraft Conflicts Based on Simulation Modelling

The demand for air travel worldwide continues to grow at a rapid rate, especially in Europe and the United States. In Europe, the demand exceeded predictions with a real annual growth of 7.1% in the period 1985-1990, against a prediction of 2.4%. By the year 2010, the demand is expected to double from the 1990 level. Within the UK international scheduled passenger traffic is predicted to increase, on average, by 5.8 per cent per year between 1999 and 2003. The demand has not been matched by availability of capacity. In Western Europe many of the largest airports suffer from runway capacity constraints. Europe also suffers from an en-route airspace capacity constraint, which is determined by the workload of the air traffic controllers, i.e. the physical and mental work that controllers must undertake to safely conduct air traffic under their jurisdiction through en-route airspace. The annual cost to Europe due to air traffic inefficiency and congestion in en-route airspace is estimated to be 5 billion US Dollars, primarily due to delays caused by non-optimal route structures and reduced productivity of controllers due to equipment inefficiencies. Therefore, to in order to decrease the total delay, an increase in en-route capacity is of paramount importance. At a global scale and in the early 1980s, the International Civil Aviation Organisation (ICAO) recognised that the traditional air traffic control (ATC) systems would not cope with the growth in demand for capacity. Consequently new technologies and procedures have been proposed to enable ATC to cope with this demand, e.g. satellite-based system concept to meet the future civil aviation requirements for communication, navigation and surveillance/ air traffic management (CNS/ATM). In Europe, the organisation EUROCONTROL (established in 1960 to co-ordinate European ATM) proposed a variety of measures to increase the capacity of en-route airspace. A key change envisaged is the increasing delegation of responsibilities for control to flight crew, by the use of airborne separation assurance between aircraft, leading eventually to ?free flight? airspace. However, there are major concerns regarding the safety of operations in ?free flight? airspace. The safety of such airspace can be investigated by analysing the factors that affect conflict occurrence, i.e. a loss of the prescribed separation between two aircraft in airspace. This paper analyses the factors affecting conflict occurrence in current airspace and future free flight airspace by using a simulation model of air traffic controller workload, the RAMS model. The paper begins with a literature review of the factors that affect conflict occurrence. This is followed by a description of the RAMS model and of its use in this analysis. The airspace simulated is the Mediterranean Free Flight region, and the major attributes of this region and of the traffic demand patterns are outlined next. In particular a day?s air traffic is simulated in the two airspace scenarios, and rules for conflict detection and resolution are carefully defined. The following section outlines the framework for analysing the output from the simulations, using negative binomial (NB) and generalised negative binomial (GNB) regression, and discusses the estimation methods required. The next section presents the results of the regression analysis, taking into account the spatio-temporal nature of the data. The following section presents an analysis of the spatial and temporal pattern of conflicts in the two airspace scenarios across a day, highlighting possible metrics to indicate this. The paper concludes with future research directions based upon this analysis.

Comparative Assessment of Epidemiological Models for Analyzing and Forecasting Infectious Disease Outbreaks

Mathematical modeling offers a quantitative framework for analyzing mechanisms underlying infectious disease transmission and explaining patterns in epidemiological data. Models are also commonly applied in outbreak investigations for assessing intervention and control strategies and generating epidemic forecasts in real time. However, successful application of mathematical models depends on the ability to reliably estimate key transmission and severity parameters, which are critical for guiding public health interventions. Overall, the three studies presented provide a thorough guide for assessing and utilizing mathematical models for describing infectious disease outbreak trends. In the first study, we describe the process for analyzing identifiability of parameters of interest in mechanistic disease transmission models. In the second study, we expand this idea to simple phenomenological models and explore the idea of overdispersion in the data and how to determine an appropriate error structure within the analyses. In the third study, we use previously validated phenomenological models to generate short-term forecasts of the ongoing COVID-19 pandemic. During infectious disease epidemics, public health authorities rely on modeling results to inform intervention decisions and resource allocation. Therefore, we highlight the importance of interpreting modeling results with caution, particularly regarding theoretical aspects of mathematical models and parameter estimation methods. Further, results from modeling studies should be presented with quantified uncertainty and interpreted in terms of the assumptions and limitations of the model, methods, and data used. The methodology presented in this dissertation provides a thorough guide for conducting model-based inferences and presenting the uncertainty associated with parameter estimation results

Adopt a hypothetical pup: A count data approach to the valuation of wildlife.

The willingness to pay for a coyote conservation program is estimated using a novel payment-vehicle, based on how many coyotes respondents would be willing to sponsor. This hypothetical scenario mimics an increasingly popular type of actual market. Data from a phone survey conducted in Prince Edward Island are analyzed using count data models that consider different processes explaining zero responses and the level of positive responses. This is particularly important in the case of coyotes, often regarded as a bad. Estimates of willingness to pay per coyote around $18-$20 and annual consumer surplus per respondent of about $35-$42 are obtained.coyotes, wildlife, contingent valuation, count data, zero- inflation

Disease mapping and regression with count data in the presence of overdispersion and spatial autocorrelation: a Bayesian model averaging approach

This paper applies the generalised linear model for modelling geographical variation to esophageal cancer incidence data in the Caspian region of Iran. The data have a complex and hierarchical structure that makes them suitable for hierarchical analysis using Bayesian techniques, but with care required to deal with problems arising from counts of events observed in small geographical areas when overdispersion and residual spatial autocorrelation are present. These considerations lead to nine regression models derived from using three probability distributions for count data: Poisson, generalised Poisson and negative binomial, and three different autocorrelation structures. We employ the framework of Bayesian variable selection and a Gibbs sampling based technique to identify significant cancer risk factors. The framework deals with situations where the number of possible models based on different combinations of candidate explanatory variables is large enough such that calculation of posterior probabilities for all models is difficult or infeasible. The evidence from applying the modelling methodology suggests that modelling strategies based on the use of generalised Poisson and negative binomial with spatial autocorrelation work well and provide a robust basis for inference

SAFETY-BASED GUIDELINES FOR LEFT-TURN PHASING DECISIONS WITH NEGATIVE BINOMIAL REGRESSION

The efficient and safe movement of traffic at signalized intersections is the primary objective of any signal phasing and timing plan. Accommodation of left turns is more critical due to the higher need for balancing operations and safety. The objective of this study is to develop models to estimate the safety impacts of the use of left-turn phasing schemes. The models are based on data from 200 intersections in urban areas in Kentucky. For each intersection, approaches with a left-turn lane were isolated and considered with their opposing through approach in order to examine the left-turn related crashes. This combination of movements is considered to be one of the most dangerous in terms of intersection safety. Hourly traffic volumes and crash data were used in the modeling approach along with the geometry of the intersection. The models allow for the determination of the most effective type of left-turn signalization based on the specific characteristics of an intersection approach. The accompanying nomographs provide an improvement over the existing methods and warrants and allow for a systematic and quick evaluation of the left-turn phase to be selected. The models utilize the most common variables that are already known during the design phase and can be used to determine whether a permitted or protected-only phase will suit the intersection when considering safety performance

Count Data Regression Analysis: Concepts, Overdispersion Detection, Zero-inflation Identification, and Applications with R

In this paper is proposed a straightforward model selection approach that indicates the most suitable count regression model based on relevant data characteristics. The proposed selection approach includes four of the most popular count regression models (i.e. Poisson, negative binomial, and respective zero-inflated frameworks). Moreover, it addresses two of the most relevant problems commonly found in real-world count datasets, namely overdispersion and zero-inflation. The entire selection approach may be performed using the programme language R, being all commands used throughout the paper availabe for practical purposes. It is worth mentioning that counting regression models are still not widespread within the social sciences

Decoding Success with Zero-Inflated and Hurdle Models: Unveiling the Winning Strategies in Portuguese Public Procurement Activity - Evidence from Portugal

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsPublic procurement plays a vital role in promoting economic development, employment, innovation, and sustainability within the private and public sector. Traditionally, public procurement has been primarily based on lowest-price bids, but recent shifts have emphasized the need for additional evaluation criteria, such as quality, resources capacity, financial stability, experience in the market, innovation, sustainability, and contribution to the society. Therefore, this study aims to predict the success of Portuguese companies in public tender procedures and identify the key factors influencing their success. Additionally, it examines the factors of influence within each contract type to uncover potential variations across different types of contracts. The study makes use of public procurement data from the BASE portal in Portugal, along with business information of the corresponding participants in public procurement, obtained from the global database ORBIS. The combination of these two datasets forms the comprehensive dataset used for analysis in this study. To measure companies’ success in these procedures, the number of public tenders won by each company per year is predicted using count data regression methodologies, considering the discrete nature of the response variable. Advanced models, such as Zero-Inflated and Hurdle models, are employed to effectively handle excess zero values and improve prediction accuracy. The model’s evaluation indicates that these models outperform traditional models in addressing the overdispersion and high variance observed in the data. The results allow to identify and quantify the key factors that significantly influence the success of companies in the public tender procedure within each contract type. Overall, it shows that companies’ size and experience in the public tender’s activity are one of the key factors in the success of winning public tenders. Moreover, the results also shown that the relevance and impact of the different factors studied, which also includes, resources capacity, market experience, profitability and financial stability can vary across contract types. This comprehensive understanding of the determinants of success in public tenders provides valuable insights for companies, enabling them to tailor their strategies and improve their competitiveness in the market. These insights also provide benefits for public authorities by helping to elaborate more effectively the public procurement award criteria and develop targeted policies that support the growth and sustainability of companies facing challenges in the market. These efforts foster a competitive market environment that encourages innovation, economic development, and fair distribution of opportunities

“What attracts knowledge workers? The role of space, social connections, institutions, jobs and amenities”

The aim of the present paper is to identify the determinants of the geographical mobility of skilled individuals, such as inventors, across European regions. Their mobility contributes to the geographical diffusion of knowledge and reshapes the geography of talent. We test whether geography, amenities, job opportunities and social proximity between inventors’ communities, and the so-called National System of Innovation, drive in- and out-flows of inventors between pairs of regions. We use a control function approach to address the endogenous nature of social proximity, and zero-inflated negative binomial models to accommodate our estimations to the count nature of the dependent variable and the high number of zeros it contains. Our results highlight the importance of physical proximity in driving the mobility patterns of inventors. However, job opportunities, social and institutional relations, and technological and cultural proximity also play key roles in mediating this phenomenon.inventors’ mobility, gravity model, amenities, job opportunities, social and institutional proximities, zero-inflated negative binomial, European regions. JEL classification: C8, J61, O31, O33, R0