Technical report : acoustic analysis of the natural environment

This technical report is concerned with one aspect of environmental monitoring—the detection and analysis of acoustic events in sound recordings of the environment. Sound recordings offer ecologists the potential advantages of cheaper and increased sampling. An acoustic event detection algorithm is introduced that outputs a compact rectangular marquee description of each event. It can disentangle superimposed events, which are a common occurrence during morning and evening choruses. Next, three uses to which acoustic event detection can be put are illustrated. These tasks have been selected because they illustrate quite different modes of analysis: (1) the detection of diffuse events caused by wind and rain, which are a frequent contaminant of recordings of the terrestrial environment; (2) the detection of bird calls using the spatial distribution of their component events; and (3) the preparation of acoustic maps for whole ecosystem analysis. This last task utilises the temporal distribution of events over a daily, monthly or yearly cycle.

Developing creativity and innovation in management education : an artful event for transformative learning

This paper argues that management education needs to consider a trend in learning design which advances more creative learning through an alliance with art-based pedagogical processes. A shift is required from skills training to facilitating transformational learning through experiences that expand human potential, facilitated by artistic processes. In this paper the authors discuss the necessity for creativity and innovation in the workplace and the need to develop better leaders and managers. The inclusion of arts-based processes enhances artful behaviour, aesthetics and creativity within management and organisational behaviour, generating important implications for business innovation. This creative learning focus stems from an analysis of an arts-based intervention for management development. Entitled Management Jazz the program was conducted over three years at a large Australian University. The paper reviews some of the salient literature in the field. It considers four stages of the learning process: capacity, artful event, increased capability, and application/action to produce product. One illustrative example of an arts-based learning process is provided from the Management Jazz program. Research findings indicate that artful learning opportunities enhance capacity for awareness of creativity in one’s self and in others. This capacity correlates positively with a perception that engaging in artful learning enhances the capability of managers in changing collaborative relationships and habitat constraint. The authors conclude that it is through engagement and creative alliance with the arts that management education can explore and discover artful approaches to building creativity and innovation. The illustration presented in this paper will be delivered as a brief workshop at the Fourth Art of Management Conference. The process of bricolage and articles at hand will be used to explore creative constraints and prototypes while generating group collaboration. The mini-workshop will conclude with discussion of the arts-based process and capability enhancement outcomes.

Analysis of distrubances in large interconnected power systems

World economies increasingly demand reliable and economical power supply and distribution. To achieve this aim the majority of power systems are becoming interconnected, with several power utilities supplying the one large network. One problem that occurs in a large interconnected power system is the regular occurrence of system disturbances which can result in the creation of intra-area oscillating modes. These modes can be regarded as the transient responses of the power system to excitation, which are generally characterised as decaying sinusoids. For a power system operating ideally these transient responses would ideally would have a “ring-down” time of 10-15 seconds. Sometimes equipment failures disturb the ideal operation of power systems and oscillating modes with ring-down times greater than 15 seconds arise. The larger settling times associated with such “poorly damped” modes cause substantial power flows between generation nodes, resulting in significant physical stresses on the power distribution system. If these modes are not just poorly damped but “negatively damped”, catastrophic failures of the system can occur. To ensure system stability and security of large power systems, the potentially dangerous oscillating modes generated from disturbances (such as equipment failure) must be quickly identified. The power utility must then apply appropriate damping control strategies. In power system monitoring there exist two facets of critical interest. The first is the estimation of modal parameters for a power system in normal, stable, operation. The second is the rapid detection of any substantial changes to this normal, stable operation (because of equipment breakdown for example). Most work to date has concentrated on the first of these two facets, i.e. on modal parameter estimation. Numerous modal parameter estimation techniques have been proposed and implemented, but all have limitations [1-13]. One of the key limitations of all existing parameter estimation methods is the fact that they require very long data records to provide accurate parameter estimates. This is a particularly significant problem after a sudden detrimental change in damping. One simply cannot afford to wait long enough to collect the large amounts of data required for existing parameter estimators. Motivated by this gap in the current body of knowledge and practice, the research reported in this thesis focuses heavily on rapid detection of changes (i.e. on the second facet mentioned above). This thesis reports on a number of new algorithms which can rapidly flag whether or not there has been a detrimental change to a stable operating system. It will be seen that the new algorithms enable sudden modal changes to be detected within quite short time frames (typically about 1 minute), using data from power systems in normal operation. The new methods reported in this thesis are summarised below. The Energy Based Detector (EBD): The rationale for this method is that the modal disturbance energy is greater for lightly damped modes than it is for heavily damped modes (because the latter decay more rapidly). Sudden changes in modal energy, then, imply sudden changes in modal damping. Because the method relies on data from power systems in normal operation, the modal disturbances are random. Accordingly, the disturbance energy is modelled as a random process (with the parameters of the model being determined from the power system under consideration). A threshold is then set based on the statistical model. The energy method is very simple to implement and is computationally efficient. It is, however, only able to determine whether or not a sudden modal deterioration has occurred; it cannot identify which mode has deteriorated. For this reason the method is particularly well suited to smaller interconnected power systems that involve only a single mode. Optimal Individual Mode Detector (OIMD): As discussed in the previous paragraph, the energy detector can only determine whether or not a change has occurred; it cannot flag which mode is responsible for the deterioration. The OIMD seeks to address this shortcoming. It uses optimal detection theory to test for sudden changes in individual modes. In practice, one can have an OIMD operating for all modes within a system, so that changes in any of the modes can be detected. Like the energy detector, the OIMD is based on a statistical model and a subsequently derived threshold test. The Kalman Innovation Detector (KID): This detector is an alternative to the OIMD. Unlike the OIMD, however, it does not explicitly monitor individual modes. Rather it relies on a key property of a Kalman filter, namely that the Kalman innovation (the difference between the estimated and observed outputs) is white as long as the Kalman filter model is valid. A Kalman filter model is set to represent a particular power system. If some event in the power system (such as equipment failure) causes a sudden change to the power system, the Kalman model will no longer be valid and the innovation will no longer be white. Furthermore, if there is a detrimental system change, the innovation spectrum will display strong peaks in the spectrum at frequency locations associated with changes. Hence the innovation spectrum can be monitored to both set-off an “alarm” when a change occurs and to identify which modal frequency has given rise to the change. The threshold for alarming is based on the simple Chi-Squared PDF for a normalised white noise spectrum [14, 15]. While the method can identify the mode which has deteriorated, it does not necessarily indicate whether there has been a frequency or damping change. The PPM discussed next can monitor frequency changes and so can provide some discrimination in this regard. The Polynomial Phase Method (PPM): In [16] the cubic phase (CP) function was introduced as a tool for revealing frequency related spectral changes. This thesis extends the cubic phase function to a generalised class of polynomial phase functions which can reveal frequency related spectral changes in power systems. A statistical analysis of the technique is performed. When applied to power system analysis, the PPM can provide knowledge of sudden shifts in frequency through both the new frequency estimate and the polynomial phase coefficient information. This knowledge can be then cross-referenced with other detection methods to provide improved detection benchmarks.

Application of K-tree to document clustering

Digital collections are growing exponentially in size as the information age takes a firm grip on all aspects of society. As a result Information Retrieval (IR) has become an increasingly important area of research. It promises to provide new and more effective ways for users to find information relevant to their search intentions. Document clustering is one of the many tools in the IR toolbox and is far from being perfected. It groups documents that share common features. This grouping allows a user to quickly identify relevant information. If these groups are misleading then valuable information can accidentally be ignored. There- fore, the study and analysis of the quality of document clustering is important. With more and more digital information available, the performance of these algorithms is also of interest. An algorithm with a time complexity of O(n2) can quickly become impractical when clustering a corpus containing millions of documents. Therefore, the investigation of algorithms and data structures to perform clustering in an efficient manner is vital to its success as an IR tool. Document classification is another tool frequently used in the IR field. It predicts categories of new documents based on an existing database of (doc- ument, category) pairs. Support Vector Machines (SVM) have been found to be effective when classifying text documents. As the algorithms for classifica- tion are both efficient and of high quality, the largest gains can be made from improvements to representation. Document representations are vital for both clustering and classification. Representations exploit the content and structure of documents. Dimensionality reduction can improve the effectiveness of existing representations in terms of quality and run-time performance. Research into these areas is another way to improve the efficiency and quality of clustering and classification results. Evaluating document clustering is a difficult task. Intrinsic measures of quality such as distortion only indicate how well an algorithm minimised a sim- ilarity function in a particular vector space. Intrinsic comparisons are inherently limited by the given representation and are not comparable between different representations. Extrinsic measures of quality compare a clustering solution to a “ground truth” solution. This allows comparison between different approaches. As the “ground truth” is created by humans it can suffer from the fact that not every human interprets a topic in the same manner. Whether a document belongs to a particular topic or not can be subjective.

Risk factor analysis and spatiotemporal CART model of cryptosporidiosis in Queensland, Australia

Background: It remains unclear whether it is possible to develop a spatiotemporal epidemic prediction model for cryptosporidiosis disease. This paper examined the impact of social economic and weather factors on cryptosporidiosis and explored the possibility of developing such a model using social economic and weather data in Queensland, Australia. ----- ----- Methods: Data on weather variables, notified cryptosporidiosis cases and social economic factors in Queensland were supplied by the Australian Bureau of Meteorology, Queensland Department of Health, and Australian Bureau of Statistics, respectively. Three-stage spatiotemporal classification and regression tree (CART) models were developed to examine the association between social economic and weather factors and monthly incidence of cryptosporidiosis in Queensland, Australia. The spatiotemporal CART model was used for predicting the outbreak of cryptosporidiosis in Queensland, Australia. ----- ----- Results: The results of the classification tree model (with incidence rates defined as binary presence/absence) showed that there was an 87% chance of an occurrence of cryptosporidiosis in a local government area (LGA) if the socio-economic index for the area (SEIFA) exceeded 1021, while the results of regression tree model (based on non-zero incidence rates) show when SEIFA was between 892 and 945, and temperature exceeded 32°C, the relative risk (RR) of cryptosporidiosis was 3.9 (mean morbidity: 390.6/100,000, standard deviation (SD): 310.5), compared to monthly average incidence of cryptosporidiosis. When SEIFA was less than 892 the RR of cryptosporidiosis was 4.3 (mean morbidity: 426.8/100,000, SD: 319.2). A prediction map for the cryptosporidiosis outbreak was made according to the outputs of spatiotemporal CART models. ----- ----- Conclusions: The results of this study suggest that spatiotemporal CART models based on social economic and weather variables can be used for predicting the outbreak of cryptosporidiosis in Queensland, Australia.

Technical report : acoustic analysis of the natural environment

This technical report is concerned with one aspect of environmental monitoring—the detection and analysis of acoustic events in sound recordings of the environment. Sound recordings offer ecologists the advantage of cheaper and increased sampling but make available so much data that automated analysis becomes essential. The report describes a number of tools for automated analysis of recordings, including noise removal from spectrograms, acoustic event detection, event pattern recognition, spectral peak tracking, syntactic pattern recognition applied to call syllables, and oscillation detection. These algorithms are applied to a number of animal call recognition tasks, chosen because they illustrate quite different modes of analysis: (1) the detection of diffuse events caused by wind and rain, which are frequent contaminants of recordings of the terrestrial environment; (2) the detection of bird and calls; and (3) the preparation of acoustic maps for whole ecosystem analysis. This last task utilises the temporal distribution of events over a daily, monthly or yearly cycle.

Procuring Sydney’s Olympic Stadium : a world class event?

This paper describes and analyses the procurement processes employed in delivering the Sydney Olympic Stadium – arguably the most significant stadia project in the region today. This current high profile project is discussed in terms of a case study into the procurement processes used. Interviews, personal site visits and questionnaires were used to obtain information on the procurement processes used and comments on their application to the project. The alternative procurement process used on this project—Design and Construction within a Build, Own, Operate and Transfer (BOOT) project—is likely to impact on the construction industry as a whole. Already other projects and sectors are following this lead. Based on a series of on-site interviews and questionnaires, a series of benefits and drawbacks to this procurement strategy are provided.The Olympic Stadium project has also been further analysed during construction through a Degree of Interaction framework to determine anticipated project success. This analysis investigates project interaction and user satisfaction to provide a comparable rating. A series of questionnaires were used to collect data to calculate the Degree of Interaction and User Satisfaction ratings.

Characterizing the signal pattern of a four-cylinder diesel engine using acoustic emission and vibration analysis

Condition monitoring of diesel engines can prevent unpredicted engine failures and the associated consequence. This paper presents an experimental study of the signal characteristics of a 4-cylinder diesel engine under various loading conditions. Acoustic emission, vibration and in-cylinder pressure signals were employed to study the effectiveness of these techniques for condition monitoring and identifying symptoms of incipient failures. An event driven synchronous averaging technique was employed to average the quasi-periodic diesel engine signal in the time domain to eliminate or minimize the effect of engine speed and amplitude variations on the analysis of condition monitoring signal. It was shown that acoustic emission (AE) is a better technique than vibration method for condition monitor of diesel engines due to its ability to produce high quality signals (i.e., excellent signal to noise ratio) in a noisy diesel engine environment. It was found that the peak amplitude of AE RMS signals correlating to the impact-like combustion related events decreases in general due to a more stable mechanical process of the engine as the loading increases. A small shift in the exhaust valve closing time was observed as the engine load increases which indicates a prolong combustion process in the cylinder (to produce more power). On the contrary, peak amplitudes of the AE RMS attributing to fuel injection increase as the loading increases. This can be explained by the increase fuel friction caused by the increase volume flow rate during the injection. Multiple AE pulses during the combustion process were identified in the study, which were generated by the piston rocking motion and the interaction between the piston and the cylinder wall. The piston rocking motion is caused by the non-uniform pressure distribution acting on the piston head as a result of the non-linear combustion process of the engine. The rocking motion ceased when the pressure in the cylinder chamber stabilized.

Parametric differences between a real-world distributed denial-of-service attack and a flash event

Distributed Denial-of-Service (DDoS) attacks continue to be one of the most pernicious threats to the delivery of services over the Internet. Not only are DDoS attacks present in many guises, they are also continuously evolving as new vulnerabilities are exploited. Hence accurate detection of these attacks still remains a challenging problem and a necessity for ensuring high-end network security. An intrinsic challenge in addressing this problem is to effectively distinguish these Denial-of-Service attacks from similar looking Flash Events (FEs) created by legitimate clients. A considerable overlap between the general characteristics of FEs and DDoS attacks makes it difficult to precisely separate these two classes of Internet activity. In this paper we propose parameters which can be used to explicitly distinguish FEs from DDoS attacks and analyse two real-world publicly available datasets to validate our proposal. Our analysis shows that even though FEs appear very similar to DDoS attacks, there are several subtle dissimilarities which can be exploited to separate these two classes of events.

Aerial image analysis using spiking neural networks with application to power line corridor monitoring

Trees, shrubs and other vegetation are of continued importance to the environment and our daily life. They provide shade around our roads and houses, offer a habitat for birds and wildlife, and absorb air pollutants. However, vegetation touching power lines is a risk to public safety and the environment, and one of the main causes of power supply problems. Vegetation management, which includes tree trimming and vegetation control, is a significant cost component of the maintenance of electrical infrastructure. For example, Ergon Energy, the Australia’s largest geographic footprint energy distributor, currently spends over $80 million a year inspecting and managing vegetation that encroach on power line assets. Currently, most vegetation management programs for distribution systems are calendar-based ground patrol. However, calendar-based inspection by linesman is labour-intensive, time consuming and expensive. It also results in some zones being trimmed more frequently than needed and others not cut often enough. Moreover, it’s seldom practicable to measure all the plants around power line corridors by field methods. Remote sensing data captured from airborne sensors has great potential in assisting vegetation management in power line corridors. This thesis presented a comprehensive study on using spiking neural networks in a specific image analysis application: power line corridor monitoring. Theoretically, the thesis focuses on a biologically inspired spiking cortical model: pulse coupled neural network (PCNN). The original PCNN model was simplified in order to better analyze the pulse dynamics and control the performance. Some new and effective algorithms were developed based on the proposed spiking cortical model for object detection, image segmentation and invariant feature extraction. The developed algorithms were evaluated in a number of experiments using real image data collected from our flight trails. The experimental results demonstrated the effectiveness and advantages of spiking neural networks in image processing tasks. Operationally, the knowledge gained from this research project offers a good reference to our industry partner (i.e. Ergon Energy) and other energy utilities who wants to improve their vegetation management activities. The novel approaches described in this thesis showed the potential of using the cutting edge sensor technologies and intelligent computing techniques in improve power line corridor monitoring. The lessons learnt from this project are also expected to increase the confidence of energy companies to move from traditional vegetation management strategy to a more automated, accurate and cost-effective solution using aerial remote sensing techniques.

Policy analysis of disaster health management in China

Humankind has been dealing with all kinds of disasters since the dawn of time. The risk and impact of disasters producing mass casualties worldwide is increasing, due partly to global warming as well as to increased population growth, increased density and the aging population. China, as a country with a large population, vast territory, and complex climatic and geographical conditions, has been plagued by all kinds of disasters. Disaster health management has traditionally been a relatively arcane discipline within public health. However, SARS, Avian Influenza, and earthquakes and floods, along with the need to be better prepared for the Olympic Games in China has brought disasters, their management and their potential for large scale health consequences on populations to the attention of the public, the government and the international community alike. As a result significant improvements were made to the disaster management policy framework, as well as changes to systems and structures to incorporate an improved disaster management focus. This involved the upgrade of the Centres for Disease Control and Prevention (CDC) throughout China to monitor and better control the health consequences particularly of infectious disease outbreaks. However, as can be seen in the Southern China Snow Storm and Wenchuan Earthquake in 2008, there remains a lack of integrated disaster management and efficient medical rescue, which has been costly in terms of economics and health for China. In the context of a very large and complex country, there is a need to better understand whether these changes have resulted in effective management of the health impacts of such incidents. To date, the health consequences of disasters, particularly in China, have not been a major focus of study. The main aim of this study is to analyse and evaluate disaster health management policy in China and in particular, its ability to effectively manage the health consequences of disasters. Flood has been selected for this study as it is a common and significant disaster type in China and throughout the world. This information will then be used to guide conceptual understanding of the health consequences of floods. A secondary aim of the study is to compare disaster health management in China and Australia as these countries differ in their length of experience in having a formalised policy response. The final aim of the study is to determine the extent to which Walt and Gilson’s (1994) model of policy explains how disaster management policy in China was developed and implemented after SARS in 2003 to the present day. This study has utilised a case study methodology. A document analysis and literature search of Chinese and English sources was undertaken to analyse and produce a chronology of disaster health management policy in China. Additionally, three detailed case studies of flood health management in China were undertaken along with three case studies in Australia in order to examine the policy response and any health consequences stemming from the floods. A total of 30 key international disaster health management experts were surveyed to identify fundamental elements and principles of a successful policy framework for disaster health management. Key policy ingredients were identified from the literature, the case-studies and the survey of experts. Walt and Gilson (1994)’s policy model that focuses on the actors, content, context and process of policy was found to be a useful model for analysing disaster health management policy development and implementation in China. This thesis is divided into four parts. Part 1 is a brief overview of the issues and context to set the scene. Part 2 examines the conceptual and operational context including the international literature, government documents and the operational environment for disaster health management in China. Part 3 examines primary sources of information to inform the analysis. This involves two key studies: • A comparative analysis of the management of floods in China and Australia • A survey of international experts in the field of disaster management so as to inform the evaluation of the policy framework in existence in China and the criteria upon which the expression of that policy could be evaluated Part 4 describes the key outcomes of this research which include: • A conceptual framework for describing the health consequences of floods • A conceptual framework for disaster health management • An evaluation of the disaster health management policy and its implementation in China. The research outcomes clearly identified that the most significant improvements are to be derived from improvements in the generic management of disasters, rather than the health aspects alone. Thus, the key findings and recommendations tend to focus on generic issues. The key findings of this research include the following: • The health consequences of floods may be described in terms of time as ‘immediate’, ‘medium term’ and ‘long term’ and also in relation to causation as ‘direct’ and ‘indirect’ consequences of the flood. These two aspects form a matrix which in turn guides management responses. • Disaster health management in China requires a more comprehensive response throughout the cycle of prevention, preparedness, response and recovery but it also requires a more concentrated effort on policy implementation to ensure the translation of the policy framework into effective incident management. • The policy framework in China is largely of international standard with a sound legislative base. In addition the development of the Centres for Disease Control and Prevention has provided the basis for a systematic approach to health consequence management. However, the key weaknesses in the current system include: o The lack of a key central structure to provide the infrastructure with vital support for policy development, implementation and evaluation. o The lack of well-prepared local response teams similar to local government based volunteer groups in Australia. • The system lacks structures to coordinate government action at the local level. The result of this is a poorly coordinated local response and lack of clarity regarding the point at which escalation of the response to higher levels of government is advisable. These result in higher levels of risk and negative health impacts. The key recommendations arising from this study are: 1. Disaster health management policy in China should be enhanced by incorporating disaster management considerations into policy development, and by requiring a disaster management risk analysis and disaster management impact statement for development proposals. 2. China should transform existing organizations to establish a central organisation similar to the Federal Emergency Management Agency (FEMA) in the USA or the Emergency Management Australia (EMA) in Australia. This organization would be responsible for leading nationwide preparedness through planning, standards development, education and incident evaluation and to provide operational support to the national and local government bodies in the event of a major incident. 3. China should review national and local plans to reflect consistency in planning, and to emphasize the advantages of the integrated planning process. 4. Enhance community resilience through community education and the development of a local volunteer organization. China should develop a national strategy which sets direction and standards in regard to education and training, and requires system testing through exercises. Other initiatives may include the development of a local volunteer capability with appropriate training to assist professional response agencies such as police and fire services in a major incident. An existing organisation such as the Communist Party may be an appropriate structure to provide this response in a cost effective manner. 5. Continue development of professional emergency services, particularly ambulance, to ensure an effective infrastructure is in place to support the emergency response in disasters. 6. Funding for disaster health management should be enhanced, not only from government, but also from other sources such as donations and insurance. It is necessary to provide a more transparent mechanism to ensure the funding is disseminated according to the needs of the people affected. 7. Emphasis should be placed on prevention and preparedness, especially on effective disaster warnings. 8. China should develop local disaster health management infrastructure utilising existing resources wherever possible. Strategies for enhancing local infrastructure could include the identification of local resources (including military resources) which could be made available to support disaster responses. It should develop operational procedures to access those resources. Implementation of these recommendations should better position China to reduce the significant health consequences experienced each year from major incidents such as floods and to provide an increased level of confidence to the community about the country’s capacity to manage such events.

Tinamous and moa flock together : mitochondrial genome sequence analysis reveals independent losses of flight among ratites

Ratites are large, flightless birds and include the ostrich, rheas, kiwi, emu, and cassowaries, along with extinct members, such as moa and elephant birds. Previous phylogenetic analyses of complete mitochondrial genome sequences have reinforced the traditional belief that ratites are monophyletic and tinamous are their sister group. However, in these studies ratite monophyly was enforced in the analyses that modeled rate heterogeneity among variable sites. Relaxing this topological constraint results in strong support for the tinamous (which fly) nesting within ratites. Furthermore, upon reducing base compositional bias and partitioning models of sequence evolution among protein codon positions and RNA structures, the tinamou–moa clade grouped with kiwi, emu, and cassowaries to the exclusion of the successively more divergent rheas and ostrich. These relationships are consistent with recent results from a large nuclear data set, whereas our strongly supported finding of a tinamou–moa grouping further resolves palaeognath phylogeny. We infer flight to have been lost among ratites multiple times in temporally close association with the Cretaceous–Tertiary extinction event. This circumvents requirements for transient microcontinents and island chains to explain discordance between ratite phylogeny and patterns of continental breakup. Ostriches may have dispersed to Africa from Eurasia, putting in question the status of ratites as an iconic Gondwanan relict taxon. [Base composition; flightless; Gondwana; mitochondrial genome; Palaeognathae; phylogeny; ratites.]

Root cause analysis with enriched process logs

In the field of process mining, the use of event logs for the purpose of root cause analysis is increasingly studied. In such an analysis, the availability of attributes/features that may explain the root cause of some phenomena is crucial. Currently, the process of obtaining these attributes from raw event logs is performed more or less on a case-by-case basis: there is still a lack of generalized systematic approach that captures this process. This paper proposes a systematic approach to enrich and transform event logs in order to obtain the required attributes for root cause analysis using classical data mining techniques, the classification techniques. This approach is formalized and its applicability has been validated using both self-generated and publicly-available logs.

Effects of Mobile Phone Distraction at the Onset of Amber Light: Analysis of Driving Simulator Data

Distraction whilst driving on an approach to a signalized intersection is particularly dangerous, as potential vehicular conflicts and resulting angle collisions tend to be severe. This study examines the decisions of distracted drivers during the onset of amber lights. Driving simulator data were obtained from a sample of 58 drivers under baseline and handheld mobile phone conditions at the University of IOWA - National Advanced Driving Simulator. Explanatory variables include age, gender, cell phone use, distance to stop-line, and speed. An iterative combination of decision tree and logistic regression analyses are employed to identify main effects, non-linearities, and interactions effects. Results show that novice (16-17 years) and younger (18-25 years) drivers’ had heightened amber light running risk while distracted by cell phone, and speed and distance thresholds yielded significant interaction effects. Driver experience captured by age has a multiplicative effect with distraction, making the combined effect of being inexperienced and distracted particularly risky. Solutions are needed to combat the use of mobile phones whilst driving.