Granule mining and its application for network traffic characterization

Decision table and decision rules play an important role in rough set based data analysis, which compress databases into granules and describe the associations between granules. Granule mining was also proposed to interpret decision rules in terms of association rules and multi-tier structure. In this paper, we further extend granule mining to describe the relationships between granules not only by traditional support and confidence, but by diversity and condition diversity as well. Diversity measures how diverse of a granule associated with the other ganules, it provides a kind of novel knowledge in databases. Some experiments are conducted to test the proposed new concepts for describing the characteristics of a real network traffic data collection. The results show that the proposed concepts are promising.

A safety adapted car following model for traffic safety studies

Traffic safety studies demand more than what current micro-simulation models can provide as they presume that all drivers exhibit safe behaviors. All the microscopic traffic simulation models include a car following model. This paper highlights the limitations of the Gipps car following model ability to emulate driver behavior for safety study purposes. A safety adapted car following model based on the Gipps car following model is proposed to simulate unsafe vehicle movements, with safety indicators below critical thresholds. The modifications are based on the observations of driver behavior in real data and also psychophysical notions. NGSIM vehicle trajectory data is used to evaluate the new model and short following headways and Time To Collision are employed to assess critical safety events within traffic flow. Risky events are extracted from available NGSIM data to evaluate the modified model against them. The results from simulation tests illustrate that the proposed model can predict the safety metrics better than the generic Gipps model. The outcome of this paper can potentially facilitate assessing and predicting traffic safety using microscopic simulation.

Exploring perceived legitimacy of traffic law enforcement

The purpose of traffic law enforcement is to encourage compliant driver behaviour. That is, the threat of an undesirable sanction encourages drivers to comply with traffic laws. However, not all traffic law violations are considered equal. For example, while drink driving is generally seen as socially unacceptable, behaviours such as speeding are arguably less so, and speed enforcement is often portrayed in the popular media as a means of “revenue raising”. The perceived legitimacy of traffic law enforcement has received limited research attention to date. Perceived legitimacy of traffic law enforcement may influence (or be influenced by) attitudes toward illegal driving behaviours, and both of these factors are likely to influence on-road driving behaviour. This study aimed to explore attitudes toward a number of illegal driving behaviours and traffic law enforcement approaches that typically target these behaviours using self-reported data from a large sample of drivers. The results of this research can be used to inform further research in this area, as well as the content of public education and advertising campaigns designed to influence attitudes toward illegal driving behaviours and perceived legitimacy of traffic law enforcement.

Novel methods and the maximum likelihood estimation technique for estimating traffic critical gap

Most unsignalised intersection capacity calculation procedures are based on gap acceptance models. Accuracy of critical gap estimation affects accuracy of capacity and delay estimation. Several methods have been published to estimate drivers’ sample mean critical gap, the Maximum Likelihood Estimation (MLE) technique regarded as the most accurate. This study assesses three novel methods; Average Central Gap (ACG) method, Strength Weighted Central Gap method (SWCG), and Mode Central Gap method (MCG), against MLE for their fidelity in rendering true sample mean critical gaps. A Monte Carlo event based simulation model was used to draw the maximum rejected gap and accepted gap for each of a sample of 300 drivers across 32 simulation runs. Simulation mean critical gap is varied between 3s and 8s, while offered gap rate is varied between 0.05veh/s and 0.55veh/s. This study affirms that MLE provides a close to perfect fit to simulation mean critical gaps across a broad range of conditions. The MCG method also provides an almost perfect fit and has superior computational simplicity and efficiency to the MLE. The SWCG method performs robustly under high flows; however, poorly under low to moderate flows. Further research is recommended using field traffic data, under a variety of minor stream and major stream flow conditions for a variety of minor stream movement types, to compare critical gap estimates using MLE against MCG. Should the MCG method prove as robust as MLE, serious consideration should be given to its adoption to estimate critical gap parameters in guidelines.

Reinvigorating the domestic violence sector : systemically addressing conflict, power and practitioner turnover

The purpose of this exploratory Australian study was to consider methods of retaining skilled and experienced staff within the domestic violence sector. The antecedents that might influence turnover of practitioners were investigated and analysed. Antecedents broadly included the work-related factors, organisational factors and professional factors. The changing nature of the domestic violence sector was also examined, in particular, feminist identity and feminist practice frameworks. It became evident, however, that the primary reasons for the turnover of study participants can be described as parallel power processes. The concept of parallel power processes as developed through this research aims to capture how workplace behaviours can strongly mirror, or parallel, behaviours used by domestic violence perpetrators. As such, it appears that some domestic violence practitioners are experiencing their own abusive relationship, not within the confines of their home, but within their workplace. Additionally, parallel power processes are compounded by ineffective conflict management processes within the workplace. These concepts directly contribute to practitioners leaving their workplace and, sometimes, the sector. This qualitative study utilised a feminist research epistemology and focused strongly on practitioners' stories. Interviews were undertaken with fifteen domestic violence practitioners from three services within South-East Queensland, Australia. Two sets of semi-structured interviews provided in-depth information based on practitioners‘ experiences of working within this specialised sector. Analysis was conducted using a thematic analytical frame, drawing attention to the key themes as mentioned above. From these findings, it is suggested that in order to retain practitioners, domestic violence services must identify and address parallel power processes through effective conflict management processes. In an operational sense, it is recommended that education and training be undertaken within all staffing levels, in particular management committees. Lastly, it is recommended that the sector itself places greater attention on the re-invigoration of the feminist principles and philosophy that has traditionally guided the sector.

Integrated health prediction of bridge systems using dynamic object oriented Bayesian networks (DOOBNS)

The serviceability and safety of bridges are crucial to people’s daily lives and to the national economy. Every effort should be taken to make sure that bridges function safely and properly as any damage or fault during the service life can lead to transport paralysis, catastrophic loss of property or even casualties. Nonetheless, aggressive environmental conditions, ever-increasing and changing traffic loads and aging can all contribute to bridge deterioration. With often constrained budget, it is of significance to identify bridges and bridge elements that should be given higher priority for maintenance, rehabilitation or replacement, and to select optimal strategy. Bridge health prediction is an essential underpinning science to bridge maintenance optimization, since the effectiveness of optimal maintenance decision is largely dependent on the forecasting accuracy of bridge health performance. The current approaches for bridge health prediction can be categorised into two groups: condition ratings based and structural reliability based. A comprehensive literature review has revealed the following limitations of the current modelling approaches: (1) it is not evident in literature to date that any integrated approaches exist for modelling both serviceability and safety aspects so that both performance criteria can be evaluated coherently; (2) complex system modelling approaches have not been successfully applied to bridge deterioration modelling though a bridge is a complex system composed of many inter-related bridge elements; (3) multiple bridge deterioration factors, such as deterioration dependencies among different bridge elements, observed information, maintenance actions and environmental effects have not been considered jointly; (4) the existing approaches are lacking in Bayesian updating ability to incorporate a variety of event information; (5) the assumption of series and/or parallel relationship for bridge level reliability is always held in all structural reliability estimation of bridge systems. To address the deficiencies listed above, this research proposes three novel models based on the Dynamic Object Oriented Bayesian Networks (DOOBNs) approach. Model I aims to address bridge deterioration in serviceability using condition ratings as the health index. The bridge deterioration is represented in a hierarchical relationship, in accordance with the physical structure, so that the contribution of each bridge element to bridge deterioration can be tracked. A discrete-time Markov process is employed to model deterioration of bridge elements over time. In Model II, bridge deterioration in terms of safety is addressed. The structural reliability of bridge systems is estimated from bridge elements to the entire bridge. By means of conditional probability tables (CPTs), not only series-parallel relationship but also complex probabilistic relationship in bridge systems can be effectively modelled. The structural reliability of each bridge element is evaluated from its limit state functions, considering the probability distributions of resistance and applied load. Both Models I and II are designed in three steps: modelling consideration, DOOBN development and parameters estimation. Model III integrates Models I and II to address bridge health performance in both serviceability and safety aspects jointly. The modelling of bridge ratings is modified so that every basic modelling unit denotes one physical bridge element. According to the specific materials used, the integration of condition ratings and structural reliability is implemented through critical failure modes. Three case studies have been conducted to validate the proposed models, respectively. Carefully selected data and knowledge from bridge experts, the National Bridge Inventory (NBI) and existing literature were utilised for model validation. In addition, event information was generated using simulation to demonstrate the Bayesian updating ability of the proposed models. The prediction results of condition ratings and structural reliability were presented and interpreted for basic bridge elements and the whole bridge system. The results obtained from Model II were compared with the ones obtained from traditional structural reliability methods. Overall, the prediction results demonstrate the feasibility of the proposed modelling approach for bridge health prediction and underpin the assertion that the three models can be used separately or integrated and are more effective than the current bridge deterioration modelling approaches. The primary contribution of this work is to enhance the knowledge in the field of bridge health prediction, where more comprehensive health performance in both serviceability and safety aspects are addressed jointly. The proposed models, characterised by probabilistic representation of bridge deterioration in hierarchical ways, demonstrated the effectiveness and pledge of DOOBNs approach to bridge health management. Additionally, the proposed models have significant potential for bridge maintenance optimization. Working together with advanced monitoring and inspection techniques, and a comprehensive bridge inventory, the proposed models can be used by bridge practitioners to achieve increased serviceability and safety as well as maintenance cost effectiveness.

Scheduling techniques to optimise rail operations

In Australia, railway systems play a vital role in transporting the sugarcane crop from farms to mills. The sugarcane transport system is very complex and uses daily schedules, consisting of a set of locomotives runs, to satisfy the requirements of the mill and harvesters. The total cost of sugarcane transport operations is very high; over 35% of the total cost of sugarcane production in Australia is incurred in cane transport. Efficient schedules for sugarcane transport can reduce the cost and limit the negative effects that this system can have on the raw sugar production system. There are several benefits to formulating the train scheduling problem as a blocking parallel-machine job shop scheduling (BPMJSS) problem, namely to prevent two trains passing in one section at the same time; to keep the train activities (operations) in sequence during each run (trip) by applying precedence constraints; to pass the trains on one section in the correct order (priorities of passing trains) by applying disjunctive constraints; and, to ease passing trains by solving rail conflicts by applying blocking constraints and Parallel Machine Scheduling. Therefore, the sugarcane rail operations are formulated as BPMJSS problem. A mixed integer programming and constraint programming approaches are used to describe the BPMJSS problem. The model is solved by the integration of constraint programming, mixed integer programming and search techniques. The optimality performance is tested by Optimization Programming Language (OPL) and CPLEX software on small and large size instances based on specific criteria. A real life problem is used to verify and validate the approach. Constructive heuristics and new metaheuristics including simulated annealing and tabu search are proposed to solve this complex and NP-hard scheduling problem and produce a more efficient scheduling system. Innovative hybrid and hyper metaheuristic techniques are developed and coded using C# language to improve the solutions quality and CPU time. Hybrid techniques depend on integrating heuristic and metaheuristic techniques consecutively, while hyper techniques are the complete integration between different metaheuristic techniques, heuristic techniques, or both.

Coping with aggressive driving : driver accounts of how they manage themselves and others to reduce conflict on the road

Dealing with the aggression of other drivers on the road is an important skill given that driving is a common activity for adults in highly motorised countries. Even though incidents of extreme aggression on the road (such as assault) are reportedly rare, milder forms, some of them dangerous (such as tailgating or deliberately following too closely) are apparently common, and may be increasing. At the very least, this is likely to render the driving environment more stressful, and at worst elevates the risk of crashing by increasing both the level of risky driving behaviours and the likelihood of responses that escalate the situation. Thus the need for drivers to manage incidents of conflict is likely to become increasingly important. However, little research examines how drivers manage their own or others’ aggressive driving behaviour. Recently greater attention has been paid to driver cognitions, especially the attributions that drivers make about other drivers, that then might influence their own driving responses, particularly aggressive or risky ones. The study reported below was the first in a larger exploration of aggressive driving that focussed on driver cognitions, emotions and underlying motivations for aggressive behaviours on the road. Qualitative, in-depth interviews of drivers (n = 30, aged 18-49 years) were subjected to thematic analysis to investigate driver experiences with aggressive driving. Two main themes were identified from these accounts: driver management of self; and driver attempts to influence or manage other drivers. This paper describes the subthemes falling under the management of self main theme. These subthemes were labelled ‘being magnanimous’, ‘chilling out’, ‘slowing down’, and ‘apology/acknowledgment’. ‘Being magnanimous’ referred to situations where the respondent perceived him/herself to be a recipient of another’s aggressive driving and made a deliberate choice not to respond. However, a characteristic of this sub-theme was that this choice was underpinned by the adoption of morally superior stance, or sense of magnanimity. ‘Chilling out’ was a more general response to both the milder aggressive behaviours of other drivers and the general frustrations of driving. ‘Slowing down’ referred to reducing one’s speed in response to the perceived aggressive driving, often tailgating, of another. This subtheme appeared to consist of two separate underlying motivations. One of these was a genuine concern for one’s own safety while the other was more aimed at “getting back” at the other driver. ‘Apology’ referred to how drivers modified their more negative reactions and responses when another driver made gestures that acknowledged their having made a mistake, indicated an apology, or acknowledged the recipient driver. These sub-themes are discussed in relation to their implications for understanding aggressive driving and intervening to reduce it.

Proteomics science & society : the role of knowledge translation in moving towards clinical applications

Background: Outside the mass-spectrometer, proteomics research does not take place in a vacuum. It is affected by policies on funding and research infrastructure. Proteomics research both impacts and is impacted by potential clinical applications. It provides new techniques & clinically relevant findings, but the possibilities for such innovations (and thus the perception of the potential for the field by funders) are also impacted by regulatory practices and the readiness of the health sector to incorporate proteomics-related tools & findings. Key to this process is how knowledge is translated. Methods: We present preliminary results from a multi-year social science project, funded by the Canadian Institutes of Health Research, on the processes and motivations for knowledge translation in the health sciences. The proteomics case within this wider study uses qualitative methods to examine the interplay between proteomics science and regulatory and policy makers regarding clinical applications of proteomics. Results: Adopting an interactive format to encourage conference attendees’ feedback, our poster focuses on deficits in effective knowledge translation strategies from the laboratory to policy, clinical, & regulatory arenas. An analysis of the interviews conducted to date suggests five significant choke points: the changing priorities of funding agencies; the complexity of proteomics research; the organisation of proteomics research; the relationship of proteomics to genomics and other omics sciences; and conflict over the appropriate role of standardisation. Conclusion: We suggest that engagement with aspects of knowledge translation, such as those mentioned above, is crucially important for the eventual clinical application ofproteomics science on any meaningful scale.

A new approach for trip generation estimation for use in traffic impact assessments

Findings from an online survey conducted by Queensland University of Technology (QUT) shows that Australia is suffering from a lack of data reflecting trip generation for use in Traffic Impact Assessments (TIAs). Current independent variables for trip generation estimation are not able to create robust outcomes as well. It is also challenging to account for the impact of the new development on public and active transport as well as the effect of trip chaining behaviour in Australian TIA studies. With this background in mind, research is being implemented by QUT to find a new approach developing a combined model of trip generation and mode choice with consideration of trip chaining effects. It is expected that the model will provide transferable outcomes as it is developed based on socio-demographic parameters. Child Care Centres within the Brisbane area have been nominated for model development. At the time, the project is in the data collection phase. Findings from the pilot survey associated with capturing trip chaining and mode choice information reveal that applying questionnaire is able to capture required information in an acceptable level. The result also reveals that several centres within an area should be surveyed in order to provide sufficient data for trip chaining and modal split analysis.

Integrating driving and traffic simulators to study railway level crossing safety interventions : a methodology

Safety at Railway Level Crossings (RLXs) is an important issue within the Australian transport system. Crashes at RLXs involving road vehicles in Australia are estimated to cost $10 million each year. Such crashes are mainly due to human factors; unintentional errors contribute to 46% of all fatal collisions and are far more common than deliberate violations. This suggests that innovative intervention targeting drivers are particularly promising to improve RLX safety. In recent years there has been a rapid development of a variety of affordable technologies which can be used to increase driver’s risk awareness around crossings. To date, no research has evaluated the potential effects of such technologies at RLXs in terms of safety, traffic and acceptance of the technology. Integrating driving and traffic simulations is a safe and affordable approach for evaluating these effects. This methodology will be implemented in a driving simulator, where we recreated realistic driving scenario with typical road environments and realistic traffic. This paper presents a methodology for evaluating comprehensively potential benefits and negative effects of such interventions: this methodology evaluates driver awareness at RLXs , driver distraction and workload when using the technology . Subjective assessment on perceived usefulness and ease of use of the technology is obtained from standard questionnaires. Driving simulation will provide a model of driving behaviour at RLXs which will be used to estimate the effects of such new technology on a road network featuring RLX for different market penetrations using a traffic simulation. This methodology can assist in evaluating future safety interventions at RLXs.

Traffic-related fine and ultrafine particle exposures of professional drivers and illness : An opportunity to better link exposure science and epidemiology to address an occupational hazard?

Exposures to traffic-related air pollution (TRAP) can be particularly high in transport microenvironments (i.e. in and around vehicles) despite the short durations typically spent there. There is a mounting body of evidence that suggests that this is especially true for fine (b2.5 μm) and ultrafine (b100 nm, UF) particles. Professional drivers, who spend extended periods of time in transport microenvironments due to their job, may incur exposures markedly higher than already elevated non-occupational exposures. Numerous epidemiological studies have shown a raised incidence of adverse health outcomes among professional drivers, and exposure to TRAP has been suggested as one of the possible causal factors. Despite this, data describing the range and determinants of occupational exposures to fine and UF particles are largely conspicuous in their absence. Such information could strengthen attempts to define the aetiology of professional drivers' illnesses as it relates to traffic combustion-derived particles. In this article, we suggest that the drivers' occupational fine and UF particle exposures are an exemplar case where opportunities exist to better link exposure science and epidemiology in addressing questions of causality. The nature of the hazard is first introduced, followed by an overview of the health effects attributable to exposures typical of transport microenvironments. Basic determinants of exposure and reduction strategies are also described, and finally the state of knowledge is briefly summarised along with an outline of the main unanswered questions in the topic area.

Evaluation of the GHR car following model for traffic safety studies

Traffic safety studies mandate more than what existing micro-simulation models can offer as they postulate that every driver exhibits a safe behaviour. All the microscopic traffic simulation models are consisting of a car-following model and the Gazis–Herman–Rothery (GHR) car-following model is a widely used model. This paper highlights the limitations of the GHR car-following model capability to model longitudinal driving behaviour for safety study purposes. This study reviews and compares different version of the GHR model. To empower the GHR model on precise metrics reproduction a new set of car-following model parameters is offered to simulate unsafe vehicle conflicts. NGSIM vehicle trajectory data is used to evaluate the new model and short following headways and Time to Collision are employed to assess critical safety events within traffic flow. Risky events are extracted from available NGSIM data to evaluate the modified model against the generic versions of the GHR model. The results from simulation tests illustrate that the proposed model does predict the safety metrics better than the generic GHR model. Additionally it can potentially facilitate assessing and predicting traffic facilities’ safety using microscopic simulation. The new model can predict Near-miss rear-end crashes.

Microscopic traffic hysteresis in traffic oscillations : a behavioral perspective

This paper studies traffic hysteresis arising in traffic oscillations from a behavioral perspective. It is found that the occurrence and type of traffic hysteresis is closely correlated with driver behavior when experiencing traffic oscillations and with the time driver reaction begins relative to the starting deceleration wave. Statistical results suggest that driver behavior is different depending on its position along the oscillation. This suggests that different car-following models should be used inside the different stages of an oscillation in order to replicate realistic congestion features.

Measurements of particle emissions from nanotechnology processes, with assessment of measuring techniques and workplace controls

The overall aim of this project was to contribute to existing knowledge regarding methods for measuring characteristics of airborne nanoparticles and controlling occupational exposure to airborne nanoparticles, and to gather data on nanoparticle emission and transport in various workplaces. The scope of this study involved investigating the characteristics and behaviour of particles arising from the operation of six nanotechnology processes, subdivided into nine processes for measurement purposes. It did not include the toxicological evaluation of the aerosol and therefore, no direct conclusion was made regarding the health effects of exposure to these particles. Our research included real-time measurement of sub, and supermicrometre particle number and mass concentration, count median diameter, and alveolar deposited surface area using condensation particle counters, an optical particle counter, DustTrak photometer, scanning mobility particle sizer, and nanoparticle surface area monitor, respectively. Off-line particle analysis included scanning and transmission electron microscopy, energy-dispersive x-ray spectrometry, and thermal optical analysis of elemental carbon. Sources of fibrous and non-fibrous particles were included.