Travel time prediction on signalised urban arterials by applying SARIMA modelling on Bluetooth data

Travel time prediction has long been the topic of transportation research. But most relevant prediction models in the literature are limited to motorways. Travel time prediction on arterial networks is challenging due to involving traffic signals and significant variability of individual vehicle travel time. The limited availability of traffic data from arterial networks makes travel time prediction even more challenging. Recently, there has been significant interest of exploiting Bluetooth data for travel time estimation. This research analysed the real travel time data collected by the Brisbane City Council using the Bluetooth technology on arterials. Databases, including experienced average daily travel time are created and classified for approximately 8 months. Thereafter, based on data characteristics, Seasonal Auto Regressive Integrated Moving Average (SARIMA) modelling is applied on the database for short-term travel time prediction. The SARMIA model not only takes the previous continuous lags into account, but also uses the values from the same time of previous days for travel time prediction. This is carried out by defining a seasonality coefficient which improves the accuracy of travel time prediction in linear models. The accuracy, robustness and transferability of the model are evaluated through comparing the real and predicted values on three sites within Brisbane network. The results contain the detailed validation for different prediction horizons (5 min to 90 minutes). The model performance is evaluated mainly on congested periods and compared to the naive technique of considering the historical average.

Modelling busway station dwell time using smart cards

Dwell time at the busway station has a significant effect on bus capacity and delay. Dwell time has conventionally been estimated using models developed on the basis of field survey data. However field survey is resource and cost intensive, so dwell time estimation based on limited observations can be somewhat inaccurate. Most public transport systems are now equipped with Automatic Passenger Count (APC) and/or Automatic Fare Collection (AFC) systems. AFC in particular reduces on-board ticketing time, driver’s work load and ultimately reduces bus dwell time. AFC systems can record all passenger transactions providing transit agencies with access to vast quantities of data. AFC data provides transaction timestamps, however this information differs from dwell time because passengers may tag on or tag off at times other than when doors open and close. This research effort contended that models could be developed to reliably estimate dwell time distributions when measured distributions of transaction times are known. Development of the models required calibration and validation using field survey data of actual dwell times, and an appreciation of another component of transaction time being bus time in queue. This research develops models for a peak period and off peak period at a busway station on the South East Busway (SEB) in Brisbane, Australia.

Measurement, modelling and capacity analysis for novel, fixed multi-user single-antenna MIMO-OFDM system, in rural environments

High-speed broadband internet access is widely recognised as a catalyst to social and economic development. However, the provision of broadband Internet services with the existing solutions to rural population, scattered over an extensive geographical area, remains both an economic and technical challenge. As a feasible solution, the Commonwealth Scientific and Industrial Research Organization (CSIRO) proposed a highly spectrally efficient, innovative and cost-effective fixed wireless broadband access technology, which uses analogue TV frequency spectrum and Multi-User MIMO (MUMIMO) technology with Orthogonal-Frequency-Division-Multiplexing (OFDM). MIMO systems have emerged as a promising solution for the increasing demand of higher data rates, better quality of service, and higher network capacity. However, the performance of MIMO systems can be significantly affected by different types of propagation environments e.g., indoor, outdoor urban, or outdoor rural and operating frequencies. For instance, large spectral efficiencies associated with MIMO systems, which assume a rich scattering environment in urban environments, may not be valid for all propagation environments, such as outdoor rural environments, due to the presence of less scatterer densities. Since this is the first time a MU-MIMO-OFDM fixed broadband wireless access solution is deployed in a rural environment, questions from both theoretical and practical standpoints arise; For example, what capacity gains are available for the proposed solution under realistic rural propagation conditions?. Currently, no comprehensive channel measurement and capacity analysis results are available for MU-MIMO-OFDM fixed broadband wireless access systems which employ large scale multiple antennas at the Access Point (AP) and analogue TV frequency spectrum in rural environments. Moreover, according to the literature, no deterministic MU-MIMO channel models exist that define rural wireless channels by accounting for terrain effects. This thesis fills the aforementioned knowledge gaps with channel measurements, channel modeling and comprehensive capacity analysis for MU-MIMO-OFDM fixed wireless broadband access systems in rural environments. For the first time, channel measurements were conducted in a rural farmland near Smithton, Tasmania using CSIRO's broadband wireless access solution. A novel deterministic MU-MIMO-OFDM channel model, which can be used for accurate performance prediction of rural MUMIMO channels with dominant Line-of-Sight (LoS) paths, was developed under this research. Results show that the proposed solution can achieve 43.7 bits/s/Hz at a Signal-to- Noise Ratio (SNR) of 20 dB in rural environments. Based on channel measurement results, this thesis verifies that the deterministic channel model accurately predicts channel capacity in rural environments with a Root Mean Square (RMS) error of 0.18 bits/s/Hz. Moreover, this study presents a comprehensive capacity analysis of rural MU-MIMOOFDM channels using experimental, simulated and theoretical models. Based on the validated deterministic model, further investigations on channel capacity and the eects of capacity variation, with different user distribution angles (θ) around the AP, were analysed. For instance, when SNR = 20dB, the capacity increases from 15.5 bits/s/Hz to 43.7 bits/s/Hz as θ increases from 10° to 360°. Strategies to mitigate these capacity degradation effects are also presented by employing a suitable user grouping method. Outcomes of this thesis have already been used by CSIRO scientists to determine optimum user distribution angles around the AP, and are of great significance for researchers and MU-MUMO-OFDM system developers to understand the advantages and potential capacity gains of MU-MIMO systems in rural environments. Also, results of this study are useful to further improve the performance of MU-MIMO-OFDM systems in rural environments. Ultimately, this knowledge contribution will be useful in delivering efficient, cost-effective high-speed wireless broadband systems that are tailor-made for rural environments, thus, improving the quality of life and economic prosperity of rural populations.

Computational analysis of laminated glass panels under blast loads: A comparison of two dimensional and three dimensional modelling approaches

This paper illustrates the use of finite element (FE) technique to investigate the behaviour of laminated glass (LG) panels under blast loads. Two and three dimensional (2D and 3D) modelling approaches available in LS-DYNA FE code to model LG panels are presented. Results from the FE analysis for mid-span deflection and principal stresses compared well with those from large deflection plate theory. The FE models are further validated using the results from a free field blast test on a LG panel. It is evident that both 2D and 3D LG models predict the experimental results with reasonable accuracy. The 3D LG models give slightly more accurate results but require considerably more computational time compared to the 2D LG models.

Optimal catalyst thickness in titanium dioxide fixed film reactors: Mathematical modelling and experimental validation

The use of immobilised TiO2 for the purification of polluted water streams introduces the necessity to evaluate the effect of mechanisms such as the transport of pollutants from the bulk of the liquid to the catalyst surface and the transport phenomena inside the porous film. Experimental results of the effects of film thickness on the observed reaction rate for both liquid-side and support-side illumination are here compared with the predictions of a one-dimensional mathematical model of the porous photocatalytic slab. Good agreement was observed between the experimentally obtained photodegradation of phenol and its by-products, and the corresponding model predictions. The results have confirmed that an optimal catalyst thickness exists and, for the films employed here, is 5 μm. Furthermore, the modelling results have highlighted the fact that porosity, together with the intrinsic reaction kinetics are the parameters controlling the photocatalytic activity of the film. The former by influencing transport phenomena and light absorption characteristics, the latter by naturally dictating the rate of reaction.

MODAM: A MODular Agent-based Modelling framework

Designing the smart grid requires combining varied models. As their number increases, so does the complexity of the software. Having a well thought architecture for the software then becomes crucial. This paper presents MODAM, a framework designed to combine agent-based models in a flexible and extensible manner, using well known software engineering design solutions (OSGi specification [1] and Eclipse plugins [2]). Details on how to build a modular agent-based model for the smart grid are given in this paper, illustrated by an example for a small network.

Spatio-temporal modelling of ultrafine particle number concentration

This thesis developed semi-parametric regression models for estimating the spatio-temporal distribution of outdoor airborne ultrafine particle number concentration (PNC). The models developed incorporate multivariate penalised splines and random walks and autoregressive errors in order to estimate non-linear functions of space, time and other covariates. The models were applied to data from the "Ultrafine Particles from Traffic Emissions and Child" project in Brisbane, Australia, and to longitudinal measurements of air quality in Helsinki, Finland. The spline and random walk aspects of the models reveal how the daily trend in PNC changes over the year in Helsinki and the similarities and differences in the daily and weekly trends across multiple primary schools in Brisbane. Midday peaks in PNC in Brisbane locations are attributed to new particle formation events at the Port of Brisbane and Brisbane Airport.

A methodology to develop an urban transport disadvantage framework : the case of Brisbane, Australia

Most individuals travel in order to participate in a network of activities which are important for attaining a good standard of living. Because such activities are commonly widely dispersed and not located locally, regular access to a vehicle is important to avoid exclusion. However, planning transport system provisions that can engage members of society in an acceptable degree of activity participation remains a great challenge. The main challenges in most cities of the world are due to significant population growth and rapid urbanisation which produces increased demand for transport. Keeping pace with these challenges in most urban areas is difficult due to the widening gap between supply and demand for transport systems which places the urban population at a transport disadvantage. The key element in mitigating the issue of urban transport disadvantage is to accurately identify the urban transport disadvantaged. Although wide-ranging variables and multi-dimensional methods have been used to identify this group, variables are commonly selected using ad-hoc techniques and unsound methods. This poses questions of whether the current variables used are accurately linked with urban transport disadvantage, and the effectiveness of the current policies. To fill these gaps, the research conducted for this thesis develops an operational urban transport disadvantage framework (UTDAF) based on key statistical urban transport disadvantage variables to accurately identify the urban transport disadvantaged. The thesis develops a methodology based on qualitative and quantitative statistical approaches to develop an urban transport disadvantage framework designed to accurately identify urban transport disadvantage. The reliability and the applicability of the methodology developed is the prime concern rather than the accuracy of the estimations. Relevant concepts that impact on urban transport disadvantage identification and measurement and a wide range of urban transport disadvantage variables were identified through a review of the existing literature. Based on the reviews, a conceptual urban transport disadvantage framework was developed based on the causal theory. Variables identified during the literature review were selected and consolidated based on the recommendations of international and local experts during the Delphi study. Following the literature review, the conceptual urban transport disadvantage framework was statistically assessed to identify key variables. Using the statistical outputs, the key variables were weighted and aggregated to form the UTDAF. Before the variable's weights were finalised, they were adjusted based on results of correlation analysis between elements forming the framework to improve the framework's accuracy. The UTDAF was then applied to three contextual conditions to determine the framework's effectiveness in identifying urban transport disadvantage. The development of the framework is likely to be a robust application measure for policy makers to justify infrastructure investments and to generate awareness about the issue of urban transport disadvantage.

Towards modelling web search behaviour : integrating users’ cognitive styles

With the rapid growth of information on the Web, the study of information searching has let to an increased interest. Information behaviour (IB) researchers and information systems (IS) developers are continuously exploring user - Web search interactions to understand and to help users to provide assistance with their information searching. In attempting to develop models of IB, several studies have identified various factors that govern user's information searching and information retrieval (IR), such as age, gender, prior knowledge and task complexity. However, how users' contextual factors, such as cognitive styles, affect Web search interactions has not been clearly explained by the current models of Web Searching and IR. This study explores the influence of users' cognitive styles on their Web search behaviour. The main goal of the study is to enhance Web search models with a better understanding of how these cognitive styles affect Web searching. Modelling Web search behaviour with a greater understanding of user's cognitive styles can help information science researchers and IS designers to bridge the semantic gap between the user and the IS. To achieve the aims of the study, a user study with 50 participants was conducted. The study adopted a mixed method approach incorporating several data collection strategies to gather a range of qualitative and quantitative data. The study utilised pre-search and post-search questionnaires to collect the participants' demographic information and their level of satisfaction about the search interactions. Riding's (1991) Cognitive Style Analysis (CSA) test was used to assess the participants' cognitive styles. Participants completed three predesigned search tasks and the whole user - web search interactions, including thinkaloud, were captured using a monitoring program. Data analysis involved several qualitative and quantitative techniques: the quantitative data gave raise to detailed findings about users' Web searching and cognitive styles, the qualitative data enriched the findings with illustrative examples. The study results provide valuable insights into Web searching behaviour among different cognitive style users. The findings of the study extend our understanding of Web search behaviour and how users search information on the Web. Three key study findings emerged: • Users' Web search behaviour was demonstrated through information searching strategies, Web navigation styles, query reformulation behaviour and information processing approaches while performing Web searches. The manner in which these Web search patterns were demonstrated varied among the users with different cognitive style groups. • Users' cognitive styles influenced their information searching strategies, query reformulation behaviour, Web navigational styles and information processing approaches. Users with particular cognitive styles followed certain Web search patterns. • Fundamental relationships were evident between users' cognitive styles and their Web search behaviours; and these relationships can be illustrated through modelling Web search behaviour. Two models that depict the associations between Web search interactions, user characteristics and users' cognitive styles were developed. These models provide a greater understanding of Web search behaviour from the user perspective, particularly how users' cognitive styles influence their Web search behaviour. The significance of this research is twofold: it will provide insights for information science researchers, information system designers, academics, educators, trainers and librarians who want to better understand how users with different cognitive styles perform information searching on the Web; at the same time, it will provide assistance and support to the users. The major outcomes of this study are 1) a comprehensive analysis of how users search the Web; 2) extensive discussion on the implications of the models developed in this study for future work; and 3) a theoretical framework to bridge high-level search models and cognitive models.

Mathematical modelling of soft callus formation in early murine bone repair

Fracture healing is a complicated coupling of many processes. Yet despite the apparent complexity, fracture repair is usually effective. There is, however, no comprehensive mathematical model addressing the multiple interactions of cells, cytokines and oxygen that includes extra-cellular matrix production and that results in the formation of the early stage soft callus. This thesis develops a one dimensional continuum transport model in the context of early fracture healing. Although fracture healing is a complex interplay of many local factors, critical components are identified and used to construct an hypothesis about regulation of the evolution of early callus formation. Multiple cell lines, cellular differentiation, oxygen levels and cytokine concentrations are examined as factors affecting this model of early bone repair. The model presumes diffusive and chemotactic cell migration mechanisms. It is proposed that the initial signalling regime and oxygen availability arising as consequences of bone fracture, are sufficient to determine the quantity and quality of early soft callus formation. Readily available software and purpose written algorithms have been used to obtain numerical solutions representative of various initial conditions. These numerical distributions of cellular populations reflect available histology obtained from murine osteotomies. The behaviour of the numerical system in response to differing initial conditions can be described by alternative in vivo healing pathways. An experimental basis, as illustrated in murine fracture histology, has been utilised to validate the mathematical model outcomes. The model developed in this thesis has potential for future extension, to incorporate processes leading to woven bone deposition, while maintaining the characteristics that regulate early callus formation.

Fractal dimension of dried foods : a correlation between microstructure and porosity

The quality of dried food is affected by a number of factors including quality of raw material, initial microstructure, and drying conditions. The structure of the food materials goes through deformations due to the simultaneous effect of heat and mass transfer during the drying process. Shrinkage and changes in porosity, microstructure and appearance are some of the most remarkable features that directly influence overall product quality. Porosity and microstructure are the important material properties in relation to the quality attributes of dried foods. Fractal dimension (FD) is a quantitative approach of measuring surface, pore characteristics, and microstructural changes [1]. However, in the field of fractal analysis, there is a lack of research in developing relationship between porosity, shrinkage and microstructure of different solid food materials in different drying process and conditions [2-4]. Establishing a correlation between microstructure and porosity through fractal dimension during convective drying is the main objective of this work.

A Hybrid Queue-based Bayesian Network Framework for passenger facilitation modelling

This paper presents a novel framework for the modelling of passenger facilitation in a complex environment. The research is motivated by the challenges in the airport complex system, where there are multiple stakeholders, differing operational objectives and complex interactions and interdependencies between different parts of the airport system. Traditional methods for airport terminal modelling do not explicitly address the need for understanding causal relationships in a dynamic environment. Additionally, existing Bayesian Network (BN) models, which provide a means for capturing causal relationships, only present a static snapshot of a system. A method to integrate a BN complex systems model with stochastic queuing theory is developed based on the properties of the Poisson and Exponential distributions. The resultant Hybrid Queue-based Bayesian Network (HQBN) framework enables the simulation of arbitrary factors, their relationships, and their effects on passenger flow and vice versa. A case study implementation of the framework is demonstrated on the inbound passenger facilitation process at Brisbane International Airport. The predicted outputs of the model, in terms of cumulative passenger flow at intermediary and end points in the inbound process, are found to have an $R^2$ goodness of fit of 0.9994 and 0.9982 respectively over a 10 hour test period. The utility of the framework is demonstrated on a number of usage scenarios including real time monitoring and `what-if' analysis. This framework provides the ability to analyse and simulate a dynamic complex system, and can be applied to other socio-technical systems such as hospitals.

Spectroscopic correlation analysis of NMR-based metabonomics in exercise science

Spectroscopic studies of complex clinical fluids have led to the application of a more holistic approach to their chemical analysis becoming more popular and widely employed. The efficient and effective interpretation of multidimensional spectroscopic data relies on many chemometric techniques and one such group of tools is represented by so-called correlation analysis methods. Typical of these techniques are two-dimensional correlation analysis and statistical total correlation spectroscopy (STOCSY). Whilst the former has largely been applied to optical spectroscopic analysis, STOCSY was developed and has been applied almost exclusively to NMR metabonomic studies. Using a 1H NMR study of human blood plasma, from subjects recovering from exhaustive exercise trials, the basic concepts and applications of these techniques are examined. Typical information from their application to NMR-based metabonomics is presented and their value in aiding interpretation of NMR data obtained from biological systems is illustrated. Major energy metabolites are identified in the NMR spectra and the dynamics of their appearance and removal from plasma during exercise recovery are illustrated and discussed. The complementary nature of two-dimensional correlation analysis and statistical total correlation spectroscopy are highlighted.

Rating for sustainable subdivision neighbourhood design (RSSND) : case of Bangkok metropolitan region (BMR), Thailand an eco-efficiency modelling approach

Bangkok Metropolitan Region (BMR) is the centre for various major activities in Thailand including political, industry, agriculture, and commerce. Consequently, the BMR is the highest and most densely populated area in Thailand. Thus, the demand for houses in the BMR is also the largest, especially in subdivision developments. For these reasons, the subdivision development in the BMR has increased substantially in the past 20 years and generated large numbers of subdivision developments (AREA, 2009; Kridakorn Na Ayutthaya & Tochaiwat, 2010). However, this dramatic growth of subdivision development has caused several problems including unsustainable development, especially for subdivision neighbourhoods, in the BMR. There have been rating tools that encourage the sustainability of neighbourhood design in subdivision development, but they still have practical problems. Such rating tools do not cover the scale of the development entirely; and they concentrate more on the social and environmental conservation aspects, which have not been totally accepted by the developers (Boonprakub, 2011; Tongcumpou & Harvey, 1994). These factors strongly confirm the need for an appropriate rating tool for sustainable subdivision neighbourhood design in the BMR. To improve level of acceptance from all stakeholders in subdivision developments industry, the new rating tool should be developed based on an approach that unites the social, environmental, and economic approaches, such as eco-efficiency principle. Eco-efficiency is the sustainability indicator introduced by the World Business Council for Sustainable Development (WBCSD) since 1992. The eco-efficiency is defined as the ratio of the product or service value according to its environmental impact (Lehni & Pepper, 2000; Sorvari et al., 2009). Eco-efficiency indicator is concerned to the business, while simultaneously, is concerned with to social and the environment impact. This study aims to develop a new rating tool named "Rating for sustainable subdivision neighbourhood design (RSSND)". The RSSND methodology is developed by a combination of literature reviews, field surveys, the eco-efficiency model development, trial-and-error technique, and the tool validation process. All required data has been collected by the field surveys from July to November 2010. The ecoefficiency model is a combination of three different mathematical models; the neighbourhood property price (NPP) model, the neighbourhood development cost (NDC) model, and the neighbourhood occupancy cost (NOC) model which are attributable to the neighbourhood subdivision design. The NPP model is formulated by hedonic price model approach, while the NDC model and NOC model are formulated by the multiple regression analysis approach. The trial-and-error technique is adopted for simplifying the complex mathematic eco-efficiency model to a user-friendly rating tool format. Credibility of the RSSND has been validated by using both rated and non-rated of eight subdivisions. It is expected to meet the requirements of all stakeholders which support the social activities of the residents, maintain the environmental condition of the development and surrounding areas, and meet the economic requirements of the developers.

Parametric modelling for the efficient design of daylight strategies with complex geometries

Daylight devices are important components of any climate responsive façade system. But, the evolution of parametric CAD systems and digital fabrication has had an impact on architectural form so that regular forms are shifting to complex geometries. Architectural and engineering integration of daylight devices in envelopes with complex geometries is a challenge in terms of design and performance evaluation. The purpose of this paper is to assess daylight performance of a building with a climatic responsive envelope with complex geometry that integrates shading devices in the façade. The case study is based on the Esplanade buildings in Singapore. Climate-based day-light metrics such as Daylight Availability and Useful Daylight Illuminance are used. DIVA (daylight simulation), and Grasshopper (parametric analysis) plug-ins for Rhinoceros have been employed to examine the range of performance possibilities. Parameters such as dimension, inclination of the device, projected shadows and shape have been changed in order to maximize daylight availability and Useful Daylight Illuminance while minimizing glare probability. While orientation did not have a great impact on the results, aperture of the shading devices did, showing that shading devices with a projection of 1.75 m to 2.00 m performed best, achieving target lighting levels without issues of glare.