Fuzzy logic traffic control at a road junction with time-varying flow rates

Fuzzy logic has been applied to control traffic at road junctions. A simple controller with one fixed rule-set is inadequate to minimise delays when traffic flow rate is time-varying and likely to span a wide range. To achieve better control, fuzzy rules adapted to the current traffic conditions are used.

Research and application of Building Information Modelling (BIM) in the Architecture, Engineering and Construction (AEC) industry : a review and direction for future research

With an increasing level of collaboration amongst researchers, software developers and industry practitioners in the past three decades, building information modelling (BIM) is now recognized as an emerging technological and procedural shift within the architect, engineering and construction (AEC) industry. BIM is not only considered as a way to make a profound impact on the professions of AEC, but is also regarded as an approach to assist the industry to develop new ways of thinking and practice. Despite the widespread development and recognition of BIM, a succinct and systematic review of the existing BIM research and achievement is scarce. It is also necessary to take stock on existing applications and have a fresh look at where BIM should be heading and how it can benefit from the advances being made. This paper first presents a review of BIM research and achievement in AEC industry. A number of suggestions are then made for future research in BIM. This paper maintains that the value of BIM during design and construction phases is well documented over the last decade, and new research needs to expand the level of development and analysis from design/build stage to postconstruction and facility asset management. New research in BIM could also move beyond the traditional building type to managing the broader range of facilities and built assets and providing preventative maintenance schedules for sustainable and intelligent buildings

Modelling issues on the railway resource management process using multi-agent system

Many infrastructure and necessity systems such as electricity and telecommunication in Europe and the Northern America were used to be operated as monopolies, if not state-owned. However, they have now been disintegrated into a group of smaller companies managed by different stakeholders. Railways are no exceptions. Since the early 1980s, there have been reforms in the shape of restructuring of the national railways in different parts of the world. Continuous refinements are still conducted to allow better utilisation of railway resources and quality of service. There has been a growing interest for the industry to understand the impacts of these reforms on the operation efficiency and constraints. A number of post-evaluations have been conducted by analysing the performance of the stakeholders on their profits (Crompton and Jupe 2003), quality of train service (Shaw 2001) and engineering operations (Watson 2001). Results from these studies are valuable for future improvement in the system, followed by a new cycle of post-evaluations. However, direct implementation of these changes is often costly and the consequences take a long period of time (e.g. years) to surface. With the advance of fast computing technologies, computer simulation is a cost-effective means to evaluate a hypothetical change in a system prior to actual implementation. For example, simulation suites have been developed to study a variety of traffic control strategies according to sophisticated models of train dynamics, traction and power systems (Goodman, Siu and Ho 1998, Ho and Yeung 2001). Unfortunately, under the restructured railway environment, it is by no means easy to model the complex behaviour of the stakeholders and the interactions between them. Multi-agent system (MAS) is a recently developed modelling technique which may be useful in assisting the railway industry to conduct simulations on the restructured railway system. In MAS, a real-world entity is modelled as a software agent that is autonomous, reactive to changes, able to initiate proactive actions and social communicative acts. It has been applied in the areas of supply-chain management processes (García-Flores, Wang and Goltz 2000, Jennings et al. 2000a, b) and e-commerce activities (Au, Ngai and Parameswaran 2003, Liu and You 2003), in which the objectives and behaviour of the buyers and sellers are captured by software agents. It is therefore beneficial to investigate the suitability or feasibility of applying agent modelling in railways and the extent to which it might help in developing better resource management strategies. This paper sets out to examine the benefits of using MAS to model the resource management process in railways. Section 2 first describes the business environment after the railway 2 Modelling issues on the railway resource management process using MAS reforms. Then the problems emerge from the restructuring process are identified in section 3. Section 4 describes the realisation of a MAS for railway resource management under the restructured scheme and the feasible studies expected from the model.

Load flow in electrified railway

Probabilistic load flow techniques have been adopted in AC electrified railways to study the load demand under various train service conditions. This paper highlights the differences in probabilistic load flow analysis between the usual power systems and power supply systems in AC railways; discusses the possible difficulties in problem formulation and presents the link between train movement and the corresponding power demand for load flow calculation.

Probabilistic load flow calculation for AC traction supplies with AT feeding

Power load flow analysis is essential for system planning, operation, development and maintenance. Its application on railway supply system is no exception. Railway power supplies system distinguishes itself in terms of load pattern and mobility, as well as feeding system structure. An attempt has been made to apply probability load flow (PLF) techniques on electrified railways in order to examine the loading on the feeding substations and the voltage profiles of the trains. This study is to formulate a simple and reliable model to support the necessary calculations for probability load flow analysis in railway systems with autotransformer (AT) feeding system, and describe the development of a software suite to realise the computation.

Markov modelling of the IEEE 802.11 DCF for real-time applications with periodic traffic

Popular wireless network standards, such as IEEE 802.11/15/16, are increasingly adopted in real-time control systems. However, they are not designed for real-time applications. Therefore, the performance of such wireless networks needs to be carefully evaluated before the systems are implemented and deployed. While efforts have been made to model general wireless networks with completely random traffic generation, there is a lack of theoretical investigations into the modelling of wireless networks with periodic real-time traffic. Considering the widely used IEEE 802.11 standard, with the focus on its distributed coordination function (DCF), for soft-real-time control applications, this paper develops an analytical Markov model to quantitatively evaluate the network quality-of-service (QoS) performance in periodic real-time traffic environments. Performance indices to be evaluated include throughput capacity, transmission delay and packet loss ratio, which are crucial for real-time QoS guarantee in real-time control applications. They are derived under the critical real-time traffic condition, which is formally defined in this paper to characterize the marginal satisfaction of real-time performance constraints.

Fair flow control of ABR service by per-VC virtual queuing

In this paper, a rate-based flow control scheme based upon per-VC virtual queuing is proposed for the Available Bit Rate (ABR) service in ATM. In this scheme, each VC in a shared buffer is assigned a virtual queue, which is a counter. To achieve a specific kind of fairness, an appropriate scheduler is applied to the virtual queues. Each VC's bottleneck rate (fair share) is derived from its virtual cell departure rate. This approach of deriving a VC's fair share is simple and accurate. By controlling each VC with respect to its virtual queue and queue build-up in the shared buffer, network congestion is avoided. The principle of the control scheme is first illustrated by max–min flow control, which is realised by scheduling the virtual queues in round-robin. Further application of the control scheme is demonstrated with the achievement of weighted fairness through weighted round robin scheduling. Simulation results show that with a simple computation, the proposed scheme achieves the desired fairness exactly and controls network congestion effectively.

A two-Dimensional finite volume method for variable density flow and solute transport through saturated-unsaturated media

Extensive groundwater withdrawal has resulted in a severe seawater intrusion problem in the Gooburrum aquifers at Bundaberg, Queensland, Australia. Better management strategies can be implemented by understanding the seawater intrusion processes in those aquifers. To study the seawater intrusion process in the region, a two-dimensional density-dependent, saturated and unsaturated flow and transport computational model is used. The model consists of a coupled system of two non-linear partial differential equations. The first equation describes the flow of a variable-density fluid, and the second equation describes the transport of dissolved salt. A two-dimensional control volume finite element model is developed for simulating the seawater intrusion into the heterogeneous aquifer system at Gooburrum. The simulation results provide a realistic mechanism by which to study the convoluted transport phenomena evolving in this complex heterogeneous coastal aquifer.

Modelling complex resource requirements in Business Process Management Systems

Real-world business processes are resource-intensive. In work environments human resources usually multitask, both human and non-human resources are typically shared between tasks, and multiple resources are sometimes necessary to undertake a single task. However, current Business Process Management Systems focus on task-resource allocation in terms of individual human resources only and lack support for a full spectrum of resource classes (e.g., human or non-human, application or non-application, individual or teamwork, schedulable or unschedulable) that could contribute to tasks within a business process. In this paper we develop a conceptual data model of resources that takes into account the various resource classes and their interactions. The resulting conceptual resource model is validated using a real-life healthcare scenario.

3D visualisation of groundwater/surface water systems as a planning and communication tool

Groundwater is increasingly recognised as an important yet vulnerable natural resource, and a key consideration in water cycle management. However, communication of sub-surface water system behaviour, as an important part of encouraging better water management, is visually difficult. Modern 3D visualisation techniques can be used to effectively communicate these complex behaviours to engage and inform community stakeholders. Most software developed for this purpose is expensive and requires specialist skills. The Groundwater Visualisation System (GVS) developed by QUT integrates a wide range of surface and sub-surface data, to produce a 3D visualisation of the behaviour, structure and connectivity of groundwater/surface water systems. Surface data (elevation, surface water, land use, vegetation and geology) and data collected from boreholes (bore locations and subsurface geology) are combined to visualise the nature, structure and connectivity of groundwater/surface water systems. Time-series data (water levels, groundwater quality, rainfall, stream flow and groundwater abstraction) is displayed as an animation within the 3D framework, or graphically, to show water system condition changes over time. GVS delivers an interactive, stand-alone 3D Visualisation product that can be used in a standard PC environment. No specialised training or modelling skills are required. The software has been used extensively in the SEQ region to inform and engage both water managers and the community alike. Examples will be given of GVS visualisations developed in areas where there have been community concerns around groundwater over-use and contamination.

Beyond statistical procedures for predictive modelling: Data mining algorithms and support for university research at QUT

In a seminal data mining article, Leo Breiman [1] argued that to develop effective predictive classification and regression models, we need to move away from the sole dependency on statistical algorithms and embrace a wider toolkit of modeling algorithms that include data mining procedures. Nevertheless, many researchers still rely solely on statistical procedures when undertaking data modeling tasks; the sole reliance on these procedures has lead to the development of irrelevant theory and questionable research conclusions ([1], p.199). We will outline initiatives that the HPC & Research Support group is undertaking to engage researchers with data mining tools and techniques; including a new range of seminars, workshops, and one-on-one consultations covering data mining algorithms, the relationship between data mining and the research cycle, and limitations and problems with these new algorithms. Organisational limitations and restrictions to these initiatives are also discussed.

Patterns, heuristics for architectural design support: making use of evolutionary modelling in design

Software used by architectural and industrial designers – has moved from becoming a tool for drafting, towards use in verification, simulation, project management and project sharing remotely. In more advanced models, parameters for the designed object can be adjusted so a family of variations can be produced rapidly. With advances in computer aided design technology, numerous design options can now be generated and analyzed in real time. However the use of digital tools to support design as an activity is still at an early stage and has largely been limited in functionality with regard to the design process. To date, major CAD vendors have not developed an integrated tool that is able to both leverage specialized design knowledge from various discipline domains (known as expert knowledge systems) and support the creation of design alternatives that satisfy different forms of constraints. We propose that evolutionary computing and machine learning be linked with parametric design techniques to record and respond to a designer’s own way of working and design history. It is expected that this will lead to results that impact on future work on design support systems-(ergonomics and interface) as well as implicit constraint and problem definition for problems that are difficult to quantify.

Derivation of motor vehicle tailpipe particle emission factors suitable for modelling urban fleet emissions and air quality assessments

Background, aim, and scope Urban motor vehicle fleets are a major source of particulate matter pollution, especially of ultrafine particles (diameters < 0.1 µm), and exposure to particulate matter has known serious health effects. A considerable body of literature is available on vehicle particle emission factors derived using a wide range of different measurement methods for different particle sizes, conducted in different parts of the world. Therefore the choice as to which are the most suitable particle emission factors to use in transport modelling and health impact assessments presented as a very difficult task. The aim of this study was to derive a comprehensive set of tailpipe particle emission factors for different vehicle and road type combinations, covering the full size range of particles emitted, which are suitable for modelling urban fleet emissions. Materials and methods A large body of data available in the international literature on particle emission factors for motor vehicles derived from measurement studies was compiled and subjected to advanced statistical analysis, to determine the most suitable emission factors to use in modelling urban fleet emissions. Results This analysis resulted in the development of five statistical models which explained 86%, 93%, 87%, 65% and 47% of the variation in published emission factors for particle number, particle volume, PM1, PM2.5 and PM10 respectively. A sixth model for total particle mass was proposed but no significant explanatory variables were identified in the analysis. From the outputs of these statistical models, the most suitable particle emission factors were selected. This selection was based on examination of the statistical robustness of the statistical model outputs, including consideration of conservative average particle emission factors with the lowest standard errors, narrowest 95% confidence intervals and largest sample sizes, and the explanatory model variables, which were Vehicle Type (all particle metrics), Instrumentation (particle number and PM2.5), Road Type (PM10) and Size Range Measured and Speed Limit on the Road (particle volume). Discussion A multiplicity of factors need to be considered in determining emission factors that are suitable for modelling motor vehicle emissions, and this study derived a set of average emission factors suitable for quantifying motor vehicle tailpipe particle emissions in developed countries. Conclusions The comprehensive set of tailpipe particle emission factors presented in this study for different vehicle and road type combinations enable the full size range of particles generated by fleets to be quantified, including ultrafine particles (measured in terms of particle number). These emission factors have particular application for regions which may have a lack of funding to undertake measurements, or insufficient measurement data upon which to derive emission factors for their region. Recommendations and perspectives In urban areas motor vehicles continue to be a major source of particulate matter pollution and of ultrafine particles. It is critical that in order to manage this major pollution source methods are available to quantify the full size range of particles emitted for traffic modelling and health impact assessments.

Development of a composite line source emission model for traffic interrupted microenvironments and its application in particle number emissions at a bus station

A composite line source emission (CLSE) model was developed to specifically quantify exposure levels and describe the spatial variability of vehicle emissions in traffic interrupted microenvironments. This model took into account the complexity of vehicle movements in the queue, as well as different emission rates relevant to various driving conditions (cruise, decelerate, idle and accelerate), and it utilised multi-representative segments to capture the accurate emission distribution for real vehicle flow. Hence, this model was able to quickly quantify the time spent in each segment within the considered zone, as well as the composition and position of the requisite segments based on the vehicle fleet information, which not only helped to quantify the enhanced emissions at critical locations, but it also helped to define the emission source distribution of the disrupted steady flow for further dispersion modelling. The model then was applied to estimate particle number emissions at a bi-directional bus station used by diesel and compressed natural gas fuelled buses. It was found that the acceleration distance was of critical importance when estimating particle number emission, since the highest emissions occurred in sections where most of the buses were accelerating and no significant increases were observed at locations where they idled. It was also shown that emissions at the front end of the platform were 43 times greater than at the rear of the platform. Although the CLSE model is intended to be applied in traffic management and transport analysis systems for the evaluation of exposure, as well as the simulation of vehicle emissions in traffic interrupted microenvironments, the bus station model can also be used for the input of initial source definitions in future dispersion models.

Multi-criteria ranking and receptor modelling of airborne fine particles at three sites in the Pearl River Delta region of China

The multi-criteria decision making methods, Preference METHods for Enrichment Evaluation (PROMETHEE) and Graphical Analysis for Interactive Assistance (GAIA), and the two-way Positive Matrix Factorization (PMF) receptor model were applied to airborne fine particle compositional data collected at three sites in Hong Kong during two monitoring campaigns held from November 2000 to October 2001 and November 2004 to October 2005. PROMETHEE/GAIA indicated that the three sites were worse during the later monitoring campaign, and that the order of the air quality at the sites during each campaign was: rural site > urban site > roadside site. The PMF analysis on the other hand, identified 6 common sources at all of the sites (diesel vehicle, fresh sea salt, secondary sulphate, soil, aged sea salt and oil combustion) which accounted for approximately 68.8 ± 8.7% of the fine particle mass at the sites. In addition, road dust, gasoline vehicle, biomass burning, secondary nitrate, and metal processing were identified at some of the sites. Secondary sulphate was found to be the highest contributor to the fine particle mass at the rural and urban sites with vehicle emission as a high contributor to the roadside site. The PMF results are broadly similar to those obtained in a previous analysis by PCA/APCS. However, the PMF analysis resolved more factors at each site than the PCA/APCS. In addition, the study demonstrated that combined results from multi-criteria decision making analysis and receptor modelling can provide more detailed information that can be used to formulate the scientific basis for mitigating air pollution in the region.