Redefine time series models for transportation planning use

Time series models are used to model, simulate, and forecast the behaviour of a phenomenon over time based on data recorded over consistent intervals. The digital era has resulted in data being captured and archived in unprecedented amounts, such that vast amounts of information are available for analysis. Feature-rich time-series datasets are one of the data sets that have become available due to the expanding trend of data collection technologies worldwide. With the application of time series analysis to support financial and managerial decision-making, the development and advancement of time series models in the transportation domain are unavoidable. As a result, this thesis redefines time series models for transportation planning use with the following three aims: (1) To combine parametric and bootstrapping techniques within time series models; (2) to develop a time series model capable of modelling both temporal and spatial dependencies in time-series data; and (3) to leverage the hierarchical Bayesian modelling paradigm to accommodate flexible representations of heterogeneity in data. The first main chapter introduces an ensemble of ARIMA models. It compares its performance against conventional ARIMA (a parametric method) and LSTM models (a non-parametric method) for short-term traffic volume prediction. The second main chapter introduces a copula time series model that describes correlations between variables through time and space. Temporal correlations are modelled by an ARMA-GARCH model which enables a modeller to describe heteroscedastic data. The copula model has a flexible correlation structure and is used to model spatial correlations with the ability to model nonlinear, tailed and asymmetric correlations. The third main chapter provides a Bayesian modelling framework to raise awareness about using hierarchical Bayesian approaches for transport time series data. In addition, this chapter presents a Bayesian copula model. The combination of the two models provides a fully Bayesian approach to modelling both temporal and spatial correlations. Compared with frequentist models, the proposed modelling structures can incorporate prior knowledge. In the fourth main chapter, the fully Bayesian model is used to investigate mobility patterns before, during and after the COVID-19 pandemic using social media data. A more focused analysis is conducted on the mobility patterns of Twitter users from different zones and land use types

Beyond a Company of Soldiers: Exploring Phenotypic Integration across the Multivariate Human Growth and Development Phenotype

Traditional studies exploring the interrelationships between growth and development traits have lacked the data necessary to fully describe the multivariate growth and development phenotype and the statistical methodology to quantify the complex interrelationships between varied trait types. Subsequently, human growth and development are often defined by a series of contrasts via the juxtaposition of seemingly disjoint processes in skeletal diaphyseal growth, skeletal ossification and fusion, and development of the dentition. In conjunction with robust data sources from the Subadult Virtual Anthropology Databases (SVAD), this work introduces a Mixed Discrete-Continuous Gaussian copula to explore the multivariate human growth and development phenotype. A copula is a probabilistic function that explicitly models the interrelationships between traits and describes the joint structure of the multivariate relationships.Fifty-four growth traits are collected from the United States sample in SVAD (n = 1,316). These traits include 18 measurements associated with diaphyseal dimensions collected from six long bones, 20 scores of both epiphyseal fusion and primary ossification centers, and 16 scores of dental development across the left-sided mandibular and maxillary dentition. All data are collected from computed tomography (CT) images and includes demographic information such as an individual’s chronological age and biological sex. The joint probability distribution of the 54 growth traits and the underlying dependency structure are fit to a Mixed Discrete-Continuous Gaussian copula using the gradient-based Markov Chain Monte Carlo algorithm known as Hamiltonian Monte Carlo within the Stan probabilistic programming environment. Six total copula models are fit: the first model utilizes the full dataset, the next three models use subsets of the full dataset representing the individual developmental stages of infancy, childhood, and juvenile/adolescence, and the last two models use subset of the full dataset representing biological males and females.Results from the full model show that relationships are strongest within each growth module. Further, traits that develop across similar developmental windows show stronger positive correlations as compared to traits that grow and develop during separate periods. These relationships are similar between males and females suggesting that, independent of age, multivariate growth and development processes are the same across the sexes. When considering developmental stages, the results show that the multivariate phenotype presents with different relationships between variables across ontogeny with the strongest relationships between growth and development modules tied to active growth and development periods. Importantly, the skeletal growth, skeletal development, and dental development modules can be further divided into additional units that themselves have various levels of dependence.The copula demonstrates that the relationships between broad growth modules cannot be summarized via a few pairwise correlations taken at one point during ontogeny. Instead, analyses should be conducted with as much trait information as possible and at various points throughout ontogeny. In the future, copulas could also be extended to additional applications in biological anthropology including research in bioarchaeology and paleoanthropology, method formation in forensic anthropology, and the estimation and imputation of missing data. In sum, the Mixed Discrete-Continuous Gaussian copula provides the most comprehensive analysis to date of the multivariate human growth and development phenotype and lays the groundwork for future research into the growing, developing, multivariate human

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

Socio-Environmental Vulnerability Assessment for Sustainable Management

This Special Issue explores the cross-disciplinary approaches, methodologies, and applications of socio-environmental vulnerability assessment that can be incorporated into sustainable management. The volume comprises 20 different points of view, which cover environmental protection and development, urban planning, geography, public policymaking, participation processes, and other cross-disciplinary fields. The articles collected in this volume come from all over the world and present the current state of the world’s environmental and social systems at a local, regional, and national level. New approaches and analytical tools for the assessment of environmental and social systems are studied. The practical implementation of sustainable development as well as progressive environmental and development policymaking are discussed. Finally, the authors deliberate about the perspectives of social–environmental systems in a rapidly changing world

Spatial Copula Model for Imputing Traffic Flow Data from Remote Microwave Sensors

Issues of missing data have become increasingly serious with the rapid increase in usage of traffic sensors. Analyses of the Beijing ring expressway have showed that up to 50% of microwave sensors pose missing values. The imputation of missing traffic data must be urgently solved although a precise solution that cannot be easily achieved due to the significant number of missing portions. In this study, copula-based models are proposed for the spatial interpolation of traffic flow from remote traffic microwave sensors. Most existing interpolation methods only rely on covariance functions to depict spatial correlation and are unsuitable for coping with anomalies due to Gaussian consumption. Copula theory overcomes this issue and provides a connection between the correlation function and the marginal distribution function of traffic flow. To validate copula-based models, a comparison with three kriging methods is conducted. Results indicate that copula-based models outperform kriging methods, especially on roads with irregular traffic patterns. Copula-based models demonstrate significant potential to impute missing data in large-scale transportation networks

Dipterocarps protected by Jering local wisdom in Jering Menduyung Nature Recreational Park, Bangka Island, Indonesia

Apart of the oil palm plantation expansion, the Jering Menduyung Nature Recreational Park has relatively diverse plants. The 3,538 ha park is located at the north west of Bangka Island, Indonesia. The minimum species-area curve was 0.82 ha which is just below Dalil conservation forest that is 1.2 ha, but it is much higher than measurements of several secondary forests in the Island that are 0.2 ha. The plot is inhabited by more than 50 plant species. Of 22 tree species, there are 40 individual poles with the average diameter of 15.3 cm, and 64 individual trees with the average diameter of 48.9 cm. The density of Dipterocarpus grandiflorus (Blanco) Blanco or kruing, is 20.7 individual/ha with the diameter ranges of 12.1 – 212.7 cm or with the average diameter of 69.0 cm. The relatively intact park is supported by the local wisdom of Jering tribe, one of indigenous tribes in the island. People has regulated in cutting trees especially in the cape. The conservation agency designates the park as one of the kruing propagules sources in the province. The growing oil palm plantation and the less adoption of local wisdom among the youth is a challenge to forest conservation in the province where tin mining activities have been the economic driver for decades. More socialization from the conservation agency and the involvement of university students in raising environmental awareness is important to be done