Rozprawy doktorskie na temat „Driver behaviour models”

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2014.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
117
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 173-180).
The last decade has seen an explosion in the availability of data. In this thesis, we propose new techniques to leverage these data to tractably model uncertainty and behavior. Specifically, this thesis consists of three parts: In the first part, we propose a novel schema for utilizing data to design uncertainty sets for robust optimization using hypothesis testing. The approach is flexible and widely applicable, and robust optimization problems built from our new data driven sets are computationally tractable, both theoretically and practically. Optimal solutions to these problems enjoy a strong, finite-sample probabilistic guarantee. Computational evidence from classical applications of robust optimization { queuing and portfolio management { confirm that our new data-driven sets significantly outperform traditional robust optimization techniques whenever data is available. In the second part, we examine in detail an application of the above technique to the unit commitment problem. Unit commitment is a large-scale, multistage optimization problem under uncertainty that is critical to power system operations. Using real data from the New England market, we illustrate how our proposed data-driven uncertainty sets can be used to build high-fidelity models of the demand for electricity, and that the resulting large-scale, mixed-integer adaptive optimization problems can be solved efficiently. With respect to this second contribution, we propose new data-driven solution techniques for this class of problems inspired by ideas from machine learning. Extensive historical back-testing confirms that our proposed approach generates high quality solutions that compare with state-of-the-art methods. In the third part, we focus on behavioral modeling. Utility maximization (single agent case) and equilibrium modeling (multi-agent case) are by far the most common behavioral models in operations research. By combining ideas from inverse optimization with the theory of variational inequalities, we develop an efficient, data-driven technique for estimating the primitives of these models. Our approach supports both parametric and nonparametric estimation through kernel learning. We prove that our estimators enjoy a strong generalization guarantee even when the model is misspecified. Finally, we present computational evidence from applications in economics and transportation science illustrating the effectiveness of our approach and its scalability to large-scale instances.
by Vishal Gupta.
Ph. D.

When further demands are placed on emissions and performance of cars, trucks and busses, the vehicle manufacturers are looking to have cheap ways to evaluate their products for specific customers' needs. Using simulation tools to quickly compare use cases instead of manually recording data is a possible way forward. However, existing traffic simulation tools do not provide enough detail in each vehicle for the driving to represent real life driving patterns with regards to road features. For the purpose of this thesis data has been recorded by having different people drive a specific route featuring highway driving, traffic lights and many curves. Using this data, models have then been estimated that describe how human drivers adjust their speed through curves, how long braking distances typically are with respect to the driving speed, and the varying deceleration during braking sequences. An additional model has also been created that produces a speed variation when driving on highways. In the end all models are implemented in Matlab using a traffic control interface to interact with the traffic simulation tool SUMO. The results of this work are promising with the improved simulation being able to replicate the most significant characteristics seen from human drivers when approaching curves, traffic lights and intersections.

Young drivers have markedly higher motor vehicle crash-risk and crash involvement rates than do older drivers, and are severely over-represented in both morbidity and mortality rates related to vehicle crashes. Underpinning these findings is the tendency for young drivers to engage in more risk-taking whilst driving than older drivers. The current research focuses on unsafe driving behaviours. Unsafe driving behaviours satisfy Arnett’s (1992) three criteria of recklessness: they lack mainstream social approval and may even involve violations of the law; they carry strong connotations of negative consequences by placing drivers and/or their passengers at risk of morbidity, mortality, and other negative outcomes; and, by definition, they involve deliberate deviations from safe driving. Examples of these behaviours include speeding, tailgating, driving whilst using a mobile phone, driving whilst under the influence of alcohol and other psychoactive substances, and driving whilst tired or fatigued, all of which compromise both driving performance and driving safety. These are also more common amongst younger than older drivers. The current dissertation describes an application of Mischel and Shoda’s (1995) Cognitive-Affective Personality System (CAPS) to unsafe driving behaviours. CAPS is a meta-theoretical framework that fuses research from cognitive-social theory, as well as research on connectionism and activation. CAPS posits that personality consists of a mental representation comprised of a stable system of processes or dispositions, called Cognitive-Affective Units (CAUs), which mediate the relationship between features of the situation and subsequent behaviours. The CAPS model is distinctive in that it allows researchers to evaluate multiple, relevant predictors within a comprehensive, general framework and allows for the assessment of their inter-relations.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Applied Psychology
Griffith Health
Full Text

Modelling of biochemical systems has received considerable attention over the last decade from bioengineering, biochemistry, computer science, and mathematics. This thesis investigates the applications of computational techniques to computational systems biology, for the construction of biochemical models in terms of topology and kinetic rates. Due to the complexity of biochemical systems, it is natural to construct models representing the biochemical systems incrementally in a piecewise manner. Syntax and semantics of two patterns are defined for the instantiation of components which are extendable, reusable and fundamental building blocks for models composition. We propose and implement a set of genetic operators and composition rules to tackle issues of piecewise composing models from scratch. Quantitative Petri nets are evolved by the genetic operators, and evolutionary process of modelling are guided by the composition rules. Metaheuristic algorithms are widely applied in BioModel Engineering to support intelligent and heuristic analysis of biochemical systems in terms of structure and kinetic rates. We illustrate parameters of biochemical models based on Biochemical Systems Theory, and then the topology and kinetic rates of the models are manipulated by employing evolution strategy and simulated annealing respectively. A new hybrid modelling framework is proposed and implemented for the models construction. Two heuristic algorithms are performed on two embedded layers in the hybrid framework: an outer layer for topology mutation and an inner layer for rates optimization. Moreover, variants of the hybrid piecewise modelling framework are investigated. Regarding flexibility of these variants, various combinations of evolutionary operators, evaluation criteria and design principles can be taken into account. We examine performance of five sets of the variants on specific aspects of modelling. The comparison of variants is not to explicitly show that one variant clearly outperforms the others, but it provides an indication of considering important features for various aspects of the modelling. Because of the very heavy computational demands, the process of modelling is paralleled by employing a grid environment, GridGain. Application of the GridGain and heuristic algorithms to analyze biological processes can support modelling of biochemical systems in a computational manner, which can also benefit mathematical modelling in computer science and bioengineering. We apply our proposed modelling framework to model biochemical systems in a hybrid piecewise manner. Modelling variants of the framework are comparatively studied on specific aims of modelling. Simulation results show that our modelling framework can compose synthetic models exhibiting similar species behaviour, generate models with alternative topologies and obtain general knowledge about key modelling features.

Vehicle activated signs (VAS) display a warning message when drivers exceed a particular threshold. VAS are often installed on local roads to display a warning message depending on the speed of the approaching vehicles. VAS are usually powered by electricity; however, battery and solar powered VAS are also commonplace. This thesis investigated devel-opment of an automatic trigger speed of vehicle activated signs in order to influence driver behaviour, the effect of which has been measured in terms of reduced mean speed and low standard deviation. A comprehen-sive understanding of the effectiveness of the trigger speed of the VAS on driver behaviour was established by systematically collecting data. Specif-ically, data on time of day, speed, length and direction of the vehicle have been collected for the purpose, using Doppler radar installed at the road. A data driven calibration method for the radar used in the experiment has also been developed and evaluated. Results indicate that trigger speed of the VAS had variable effect on driv-ers’ speed at different sites and at different times of the day. It is evident that the optimal trigger speed should be set near the 85th percentile speed, to be able to lower the standard deviation. In the case of battery and solar powered VAS, trigger speeds between the 50th and 85th per-centile offered the best compromise between safety and power consump-tion. Results also indicate that different classes of vehicles report differ-ences in mean speed and standard deviation; on a highway, the mean speed of cars differs slightly from the mean speed of trucks, whereas a significant difference was observed between the classes of vehicles on lo-cal roads. A differential trigger speed was therefore investigated for the sake of completion. A data driven approach using Random forest was found to be appropriate in predicting trigger speeds respective to types of vehicles and traffic conditions. The fact that the predicted trigger speed was found to be consistently around the 85th percentile speed justifies the choice of the automatic model.

This research effort aims to create a new car-following model that accounts for the effects of emotion on driver behavior. This research effort is divided into eight research milestones: (1) the development of a segmentation and clustering algorithm to perform new investigations into driver behavior; (2) the finding that driver behavior is different between drivers, between car-following periods, and within a car-following period; (3) the finding that there are patterns in the distribution of driving behaviors; (4) the finding that driving states can result in different driving actions and that the same driving action can be the result of multiple driving states; (5) the finding that the performance of car-following models can be improved by calibration to state-action clusters; (6) the development of a psychophysiological driving simulator study; (7) the finding that the distribution of driving behavior is affected by emotional states; and (8) the development of a car-following model that incorporates the influence of emotions.
Ph. D.

The work undertaken in this research aims to develop a mathematical model which can replicate the behaviour of a racing driver controlling a vehicle at its handling limit. Most of the models proposed in the literature assume a perfect driver. A formulation taking human limitations into account would serve as a design and simulation tool for the automotive sector. A nonlinear vehicle model with five degrees of freedom under the action of external disturbances controlled by a Linear Quadratic Regulator (LQR) is first proposed to assess the validity of state variances as stability metrics. Comparison to existing stability and controllability criteria indicates that this novel metric can provide meaningful insights into vehicle performance. The LQR however, fails to stabilise the vehicle as tyres saturate. The formulation is extended to improve its robustness. Full nonlinear optimisation with direct transcription is used to derive a controller that can stabilise a vehicle at the handling limit under the action of disturbances. The careful choice of discretisation method and track description allow for reduced computing times. The performance of the controller is assessed using two vehicle configurations, Understeered and Oversteered, in scenarios characterised by increasing levels of non- linearity and geometrical complexity. All tests confirm that vehicles can be stabilised at the handling limit. Parameter studies are also carried out to reveal key aspects of the driving strategy. The driver model is validated against Driver In The Loop simulations for simple and complex manoeuvres. The analysis of experimental data led to the proposal of a novel driving strategy. Driver randomness is modelled as an external disturbance in the driver Neuromuscular System. The statistics of states and controls are found to be in good agreement. The prediction capabilities of the controller can be considered satisfactory.

Kyoto University (京都大学)
0048
新制・課程博士
博士(エネルギー科学)
甲第10332号
エネ博第68号
新制||エネ||20(附属図書館)
UT51-2003-H753
京都大学大学院エネルギー科学研究科エネルギー基礎科学専攻
(主査)教授 前川 孝, 教授 近藤 克己, 助教授 浜口 智志
学位規則第4条第1項該当

Driver distraction contributes to approximately 43% of motor-vehicle crashes and 27% of near-crashes. Rapidly developing in-vehicle technology and electronic devices place additional demands on drivers, which might lead to distraction and diminished capacity to perform driving tasks. This situation threatens safe driving. Technology that can detect and mitigate distraction by alerting drivers could play a central role in maintaining safety. Correctly identifying driver distraction in real time is a critical challenge in developing distraction mitigation systems, and this function has not been well developed. Moreover, the greatest benefit may be from real-time distraction detection in advance of dangerous breakdowns in driver performance. Based on driver performance, two types of distraction - visual and cognitive - are identified. These types of distraction have very different effects on visual behavior and driving performance; therefore, they require different algorithms for detection. Distraction detection algorithms typically rely on either eye measures or driver performance measures because the effect of distraction on the coordination of measures has not been established. Combining both eye glance and vehicle data could enhance the ability of algorithms to detect and differentiate visual and cognitive distraction. The goal of this research is to examine whether poor coordination between visual behavior and vehicle control can identify diminished attention to driving in advance of breakdowns in lane keeping. The primary hypothesis of this dissertation is that detection of changes in eye-steering relationship caused by distraction could provide a prospective indication of vehicle state changes. Three specific aims are pursued to test this hypothesis. The first aim examines the effect of distracting activity on eye and steering movements to assess the degree to which the correlation parameters are indicative of distraction. The second aim applies a control-theoretic system identification approach to the eye movement and steering data to distinguish between distracted and non-distracted conditions. The third aim examines whether changes of eye-steering coordination associated with distraction provide a prospective indication of breakdowns in driver performance, i.e., lane departures. Together, the three aims show how that a combination of visual and steering behavior, i.e., eye-steering model, can differentiate between non-distracted and distracted state. This model revealed sensitivity to distraction associated with off-road glances. The models derived for different drivers have similar structure and fit to data from other drivers reasonably well. In addition, the differences in model order and model coefficients indicate the variability in driving behavior: some people generate more complex behavior than others. As was expected, eye-steering correlation on straight roads is not as strong as observed on curvy roads. However, eye-steering correlation measured through correlation coefficient and time delay between two movements is sensitive to different types of distraction. Time delay mediates changes in lane position and the eye-steering system predicts breakdowns in lane keeping. This dissertation contributes to developing a distraction detection system that integrates visual and steering behavior. More broadly, these results suggest that integrating eye and steering data can be helpful in detecting and mitigating impairments beyond distraction, such as those associated with alcohol, fatigue, and aging.

The driver-specific data from a naturalistic driving study provides car-following events in real-world driving situations, while additionally providing a wealth of information about the participating drivers. Reducing a naturalistic database into finite car-following events requires significant data reduction, validation, and calibration, often using manual procedures. The data collection performed herein included: the identification of commuting routes used by multiple drivers, the extraction of data along those routes, the identification of potential car-following events from the dataset, the visual validation of each car-following event, and the extraction of pertinent information from the database during each event identified. This thesis applies the developed process to generate car-following events from the 100-Car Study database, and applies the dataset to analyze four car-following models. The Gipps model was found to perform best for drivers with greater amounts of data in congested driving conditions, while the Rakha-Pasumarthy-Adjerid (RPA) model was best for drivers in uncongested conditions. The Gipps model was found to generate the lowest error value in aggregate, with the RPA model error 21 percent greater, and the Gaxis-Herman-Rothery model (GHR) and the Intelligent Driver Model (IDM) errors 143 percent and 86 percent greater, respectively. Additionally, the RPA model provides the flexibility for a driver to change vehicles without the need to recalibrate parameter values for that driver, and can also capture changes in roadway surface type and condition. With the error values close between the RPA and Gipps models, the additional advantages of the RPA model make it the recommended choice for simulation.
Master of Science

The objective of this thesis is to analyse driving behaviour and to characterize the effectsof an efficient way of driving, termed eco-driving, that enables the driver to reduce fuelconsumption and CO2emissions.The approach used to assess driving style is a collection of data from a CAN bus of acar equipped with OBD-II (on-board diagnostic) system. The driving experiment wasperformed for nine drivers who drove in a normal way or regular driving style and onedriver was an eco-driver who drove in an economical driving style. The driving routewas approximately 18.7 kms (which took between 25 to 30 minutes) in Halmstad city,Sweden.The drivers are compared using a statistical analysis of the driving parameters such as,speed, accelerator (gas pedal) and brake pressure, which are obtained from CAN busdata. A hierarchical clustering algorithm also used to classify the drivers based on theaverage result of the signals.In the results, a driving difference between the eco-driver and the normal drivers is visi-ble, most of the normal drivers have more or less similar behaviour. The average speed ofthe eco-driver lower than the normal drivers and the accelerator (gas pedal) result is alsoshown less usage by the eco-driver than the normal drivers. On the other hand, the eco-driver has braked more often than the normal drivers, but gently. Nevertheless, differenttraffic conditions during the experiment obstructs comparisons between the drivers.

The research and development of embodied agents with advanced relational capabilities is constantly evolving. In recent years, the development of behavioural signal generation models to be integrated in social robots and virtual characters, is moving from rule-based to data-driven approaches, requiring appropriate and reliable evaluation techniques. This work proposes a novel machine learning approach for the evaluation of speech-to-gestures models that is independent from the audio source. This approach enables the measurement of the quality of gestures produced by these models and provides a benchmark for their evaluation. Results show that the proposed approach is consistent with evaluations made through user studies and, furthermore, that its use allows for a reliable comparison of speech-to-gestures state-of-the-art models.

Software systems will eventually contribute to their own maintenance using implementations of self-awareness. Understanding how to specify, model, and implement software with a sense of self is a daunting problem. This research draws inspiration from the automatic functioning of a gimbal---a self-righting mechanical device that supports an object and maintains the orientation of this object with respect to gravity independently of its immediate operating environment. A software gimbal exhibits a self-righting feature that provisions software with two auxiliary mechanisms: a historical mechanism and a reflective mechanism. The historical mechanism consists of behavior classifiers trained on statistical models of data that are collected from executions of the program that exhibit known behaviors of the program. The reflective mechanism uses the historical mechanism to assess an ongoing or selected execution. This dissertation presents techniques for the identification and modeling of program execution features as statistical models. It further demonstrates how statistical machine-learning techniques can be used to manipulate these models and to construct behavior classifiers that can automatically detect and label known program behaviors and detect new unknown behaviors. The thesis is that statistical summaries of data collected from a software program's executions can model and predict external behaviors of the program. This dissertation presents three control-flow features and one value-flow feature of program executions that can be modeled as stochastic processes exhibiting the Markov property. A technique for building automated behavior classifiers from these models is detailed. Empirical studies demonstrating the efficacy of this approach are presented. The use of these techniques in example software engineering applications in the categories of software testing and failure detection are described.

There is a growing societal need to address the increasing prevalence of behavioral health issues, such as obesity, alcohol or drug use, and general lack of treatment adherence for a variety of health problems. The statistics, worldwide and in the USA, are daunting. Excessive alcohol use is the third leading preventable cause of death in the United States (with 79,000 deaths annually), and is responsible for a wide range of health and social problems. On the positive side though, these behavioral health issues (and associated possible diseases) can often be prevented with relatively simple lifestyle changes, such as losing weight with a diet and/or physical exercise, or learning how to reduce alcohol consumption. Medicine has therefore started to move toward finding ways of preventively promoting wellness, rather than solely treating already established illness. Evidence-based patient-centered Brief Motivational Interviewing (BMI) interven- tions have been found particularly effective in helping people find intrinsic motivation to change problem behaviors after short counseling sessions, and to maintain healthy lifestyles over the long-term. Lack of locally available personnel well-trained in BMI, however, often limits access to successful interventions for people in need. To fill this accessibility gap, Computer-Based Interventions (CBIs) have started to emerge. Success of the CBIs, however, critically relies on insuring engagement and retention of CBI users so that they remain motivated to use these systems and come back to use them over the long term as necessary. Because of their text-only interfaces, current CBIs can therefore only express limited empathy and rapport, which are the most important factors of health interventions. Fortunately, in the last decade, computer science research has progressed in the design of simulated human characters with anthropomorphic communicative abilities. Virtual characters interact using humans’ innate communication modalities, such as facial expressions, body language, speech, and natural language understanding. By advancing research in Artificial Intelligence (AI), we can improve the ability of artificial agents to help us solve CBI problems. To facilitate successful communication and social interaction between artificial agents and human partners, it is essential that aspects of human social behavior, especially empathy and rapport, be considered when designing human-computer interfaces. Hence, the goal of the present dissertation is to provide a computational model of rapport to enhance an artificial agent’s social behavior, and to provide an experimental tool for the psychological theories shaping the model. Parts of this thesis were already published in [LYL+12, AYL12, AL13, ALYR13, LAYR13, YALR13, ALY14].

There are decades of research suggesting that drivers can compensate for distraction through behavioural adaptations such as selecting lower driving speeds, increasing road environment monitoring, decreasing lane changes, and avoiding overtaking. This research has confirmed the value of a new model for behavioural adaptation in distracted driving in explaining these phenomena. As such, the objective of this research was to characterize and evaluate the behavioural adaptation of mobile phone distracted drivers. A wide range of findings were generated, e.g. that behavioural adaptations are initiated by drivers and are not a de facto consequence of mobile phone use.

Age-related declines can compromise older adults' abilities to drive safely. Researchers have promoted self-regulation as a positive approach that can balance the needs for mobility and safety. The lack of a theoretically-informed model of driving self-regulation inhibits our ability to understand and describe such complex process. This thesis contributes to the theoretical understanding of older drivers' self-regulation by examining the applicability of the Precaution Adoption Process Model in categorising older adults into different stages of behaviour change while capturing a wide range of interpersonal, intrapersonal, and environmental factors that influence self-regulatory driving behaviour.

In the present work a simulation model for examining the fundamental dynamic behaviour of a servo driven powertrain is developed. This powertrain consists of a permanent magnet synchronous motor, a cycloidal gearbox and a torque motor to apply a load. On basis of this model the selection of components for the design of a test rig is possible. This leads to the constructive draft of the test rig. In order to model the system, the fundamentals give a brief overview of the components incorporated in the test rig system. With ais of the specified task the simulation purpose is defined and the modelling process enabled. The subsequent system analysis is performed intensively to decompose the system into subsystems, which are then investigated to find the optimal modelling approach for the given simulation task. Particular emphasis is put on the investigation of the cycloidal gearbox subsystem and it shows, that approaches for modelling the dynamic behaviour of the gearbox as a whole have only been published partially. Therefore, the available modelling approaches are analysed and suitable models are developed as conceptual models. Those will be formalised and implemented in Matlab/Simulink. The model is verified and simulation experiments are performed, that help in the selection of suitable test rig components. On basis of a flexible test rig, finally the constructive draft is presented.

Young novice drivers - that is, drivers aged 16-25 years who are relatively inexperienced in driving on the road and have a novice (Learner, Provisional) driver's licence - have been overrepresented in car crash, injury and fatality statistics around the world for decades. There are numerous persistent characteristics evident in young novice driver crashes, fatalities and offences, including variables relating to the young driver themselves, broader social influences which include their passengers, the car they drive, and when and how they drive, and their risky driving behaviour in particular. Moreover, there are a range of psychosocial factors influencing the behaviour of young novice drivers, including the social influences of parents and peers, and person-related factors such as age-related factors, attitudes, and sensation seeking. Historically, a range of approaches have been developed to manage the risky driving behaviour of young novice drivers. Traditional measures predominantly relying upon education have had limited success in regulating the risky driving behaviour of the young novice driver. In contrast, interventions such as graduated driver licensing (GDL) which acknowledges young novice drivers' limitations - principally pertaining to their chronological and developmental age, and their driving inexperience - have shown to be effective in ameliorating this pervasive public health problem. In practice, GDL is a risk management tool that is designed to reduce driving at risky times (e.g., at night) or in risky driving conditions (e.g., with passengers), while still enabling novice drivers to obtain experience. In this regard, the GDL program in Queensland, Australia, was considerably enhanced in July 2007, and major additions to the program include mandated Learner practice of 100 hours recorded in a logbook, and passenger limits during night driving in the Provisional phase. Road safety researchers have also continued to consider the influential role played by the young driver's psychosocial characteristics, including psychological traits and states. In addition, whilst the majority of road safety user research is epidemiological in nature, contemporary road safety research is increasingly applying psychological and criminological theories. Importantly, such theories not only can guide young novice driver research, they can also inform the development and evaluation of countermeasures targeting their risky driving behaviour. The research is thus designed to explore the self-reported behaviours - and the personal, psychosocial, and structural influences upon the behaviours - of young novice drivers This thesis incorporates three stages of predominantly quantitative research to undertake a comprehensive investigation of the risky driving behaviour of young novices. Risky driving behaviour increases the likelihood of the young novice driver being involved in a crash which may harm themselves or other road users, and deliberate risky driving such as driving in excess of the posted speed limits is the focus of the program of research. The extant literature examining the nature of the risky behaviour of the young novice driver - and the contributing factors for this behaviour - while comprehensive, has not led to the development of a reliable instrument designed specifically to measure the risky behaviour of the young novice driver. Therefore the development and application of such a tool (the Behaviour of Young Novice Drivers Scale, or BYNDS) was foremost in the program of research. In addition to describing the driving behaviours of the young novice, a central theme of this program of research was identifying, describing, and quantifying personal, behavioural, and environmental influences upon young novice driver risky behaviour. Accordingly the 11 papers developed from the three stages of research which comprise this thesis are framed within Bandura's reciprocal determinism model which explicitly considers the reciprocal relationship between the environment, the person, and their behaviour. Stage One comprised the foundation research and operationalised quantitative and qualitative methodologies to finalise the instrument used in Stages Two and Three. The first part of Stage One involved an online survey which was completed by 761 young novice drivers who attended tertiary education institutions across Queensland. A reliable instrument for measuring the risky driving behaviour of young novices was developed (the BYNDS) and is currently being operationalised in young novice driver research in progress at the Centre for Injury Research and Prevention in Philadelphia, USA. In addition, regression analyses revealed that psychological distress influenced risky driving behaviour, and the differential influence of depression, anxiety, sensitivity to punishments and rewards, and sensation seeking propensity were explored. Path model analyses revealed that punishment sensitivity was mediated by anxiety and depression; and the influence of depression, anxiety, reward sensitivity and sensation seeking propensity were moderated by the gender of the driver. Specifically, for males, sensation seeking propensity, depression, and reward sensitivity were predictive of self-reported risky driving, whilst for females anxiety was also influential. In the second part of Stage One, 21 young novice drivers participated in individual and small group interviews. The normative influences of parents, peers, and the Police were explicated. Content analysis supported four themes of influence through punishments, rewards, and the behaviours and attitudes of parents and friends. The Police were also influential upon the risky driving behaviour of young novices. The findings of both parts of Stage One informed the research of Stage Two. Stage Two was a comprehensive investigation of the pre-Licence and Learner experiences, attitudes, and behaviours, of young novice drivers. In this stage, 1170 young novice drivers from across Queensland completed an online or paper survey exploring their experiences, behaviours and attitudes as a pre- and Learner driver. The majority of novices did not drive before they were licensed (pre-Licence driving) or as an unsupervised Learner, submitted accurate logbooks, intended to follow the road rules as a Provisional driver, and reported practicing predominantly at the end of the Learner period. The experience of Learners in the enhanced-GDL program were also examined and compared to those of Learner drivers who progressed through the former-GDL program (data collected previously by Bates, Watson, & King, 2009a). Importantly, current-GDL Learners reported significantly more driving practice and a longer Learner period, less difficulty obtaining practice, and less offence detection and crash involvement than Learners in the former-GDL program. The findings of Stage Two informed the research of Stage Three. Stage Three was a comprehensive exploration of the driving experiences, attitudes and behaviours of young novice drivers during their first six months of Provisional 1 licensure. In this stage, 390 of the 1170 young novice drivers from Stage Two completed another survey, and data collected during Stages Two and Three allowed a longitudinal investigation of self-reported risky driving behaviours, such as GDL-specific and general road rule compliance; risky behaviour such as pre-Licence driving, crash involvement and offence detection; and vehicle ownership, paying attention to Police presence, and punishment avoidance. Whilst the majority of Learner and Provisional drivers reported compliance with GDL-specific and general road rules, 33% of Learners and 50% of Provisional drivers reported speeding by 10-20 km/hr at least occasionally. Twelve percent of Learner drivers reported pre-Licence driving, and these drivers were significantly more risky as Learner and Provisional drivers. Ten percent of males and females reported being involved in a crash, and 10% of females and 18% of males had been detected for an offence, within the first six months of independent driving. Additionally, 75% of young novice drivers reported owning their own car within six months of gaining their Provisional driver's licence. Vehicle owners reported significantly shorter Learner periods and more risky driving exposure as a Provisional driver. Paying attention to Police presence on the roads appeared normative for young novice drivers: 91% of Learners and 72% of Provisional drivers reported paying attention. Provisional drivers also reported they actively avoided the Police: 25% of males and 13% of females; 23% of rural drivers and 15% of urban drivers. Stage Three also allowed the refinement of the risky behaviour measurement tool (BYNDS) created in Stage One; the original reliable 44-item instrument was refined to a similarly reliable 36-item instrument. A longitudinal exploration of the influence of anxiety, depression, sensation seeking propensity and reward sensitivity upon the risky behaviour of the Provisional driver was also undertaken using data collected in Stages Two and Three. Consistent with the research of Stage One, structural equation modeling revealed anxiety, reward sensitivity and sensation seeking propensity predicted self-reported risky driving behaviour. Again, gender was a moderator, with only reward sensitivity predicting risky driving for males. A measurement model of Akers' social learning theory (SLT) was developed containing six subscales operationalising the four constructs of differential association, imitation, personal attitudes, and differential reinforcement, and the influence of parents and peers was captured within the items in a number of these constructs. Analyses exploring the nature and extent of the psychosocial influences of personal characteristics (step 1), Akers' SLT (step 2), and elements of the prototype/willingness model (PWM) (step 3) upon self-reported speeding by the Provisional driver in a hierarchical multiple regression model found the following significant predictors: gender (male), car ownership (own car), reward sensitivity (greater sensitivity), depression (greater depression), personal attitudes (more risky attitudes), and speeding (more speeding) as a Learner. The research findings have considerable implications for road safety researchers, policy-makers, mental health professionals and medical practitioners alike. A broad range of issues need to be considered when developing, implementing and evaluating interventions for both the intentional and unintentional risky driving behaviours of interest. While a variety of interventions have been historically utilised, including education, enforcement, rehabilitation and incentives, caution is warranted. A multi-faceted approach to improving novice road safety is more likely to be effective, and new and existing countermeasures should capitalise on the potential of parents, peers and Police to be a positive influence upon the risky behaviour of young novice drivers. However, the efficacy of some interventions remains undetermined at this time. Notwithstanding this caveat, countermeasures such as augmenting and strengthening Queensland's GDL program and targeting parents and adolescents particularly warrant further attention. The findings of the research program suggest that Queensland's current-GDL can be strengthened by increasing compliance of young novice drivers with existing conditions and restrictions. The rates of speeding reported by the young Learner driver are particularly alarming for a number of reasons. The Learner is inexperienced in driving, and travelling in excess of speed limits places them at greater risk as they are also inexperienced in detecting and responding appropriately to driving hazards. In addition, the Learner period should provide the foundation for a safe lifetime driving career, enabling the development and reinforcement of non-risky driving habits. Learners who sped reported speeding by greater margins, and at greater frequencies, when they were able to drive independently. Other strategies could also be considered to enhance Queensland's GDL program, addressing both the pre-Licence adolescent and their parents. Options that warrant further investigation to determine their likely effectiveness include screening and treatment of novice drivers by mental health professionals and/or medical practitioners; and general social skills training. Considering the self-reported pre-licence driving of the young novice driver, targeted education of parents may need to occur before their child obtains a Learner licence. It is noteworthy that those participants who reported risky driving during the Learner phase also were more likely to report risky driving behaviour during the Provisional phase; therefore it appears vital that the development of safe driving habits is encouraged from the beginning of the novice period. General education of parents and young novice drivers should inform them of the considerably-increased likelihood of risky driving behaviour, crashes and offences associated with having unlimited access to a vehicle in the early stages of intermediate licensure. Importantly, parents frequently purchase the car that is used by the Provisional driver, who typically lives at home with their parents, and therefore parents are ideally positioned to monitor the journeys of their young novice driver during this early stage of independent driving. Parents are pivotal in the development of their driving child: they are models who are imitated and are sources of attitudes, expectancies, rewards and punishments; and they provide the most driving instruction for the Learner. High rates of self-reported speeding by Learners suggests that GDL programs specifically consider the nature of supervision during the Learner period, encouraging supervisors to be vigilant to compliance with general and GDL-specific road rules, and especially driving in excess of speed limit. Attitudes towards driving are formed before the adolescent reaches the age when they can be legally licensed. Young novice drivers with risky personal attitudes towards driving reported more risky driving behaviour, suggesting that countermeasures should target such attitudes and that such interventions might be implemented before the adolescent is licensed. The risky behaviours and attitudes of friends were also found to be influential, and given that young novice drivers tend to carry their friends as their passengers, a group intervention such as provided in a school class context may prove more effective. Social skills interventions that encourage the novice to resist the negative influences of their friends and their peer passengers, and to not imitate the risky driving behaviour of their friends, may also be effective. The punishments and rewards anticipated from and administered by friends were also found to influence the self-reported risky behaviour of the young novice driver; therefore young persons could be encouraged to sanction the risky, and to reward the non-risky, driving of their novice friends. Adolescent health programs and related initiatives need to more specifically consider the risks associated with driving. Young novice drivers are also adolescents, a developmental period associated with depression and anxiety. Depression, anxiety, and sensation seeking propensity were found to be predictive of risky driving; therefore interventions targeting psychological distress, whilst discouraging the expression of sensation seeking propensity whilst driving, warrant development and trialing. In addition, given that reward sensitivity was also predictive, a scheme which rewards novice drivers for safe driving behaviour - rather than rewarding the novice through emotional and instrumental rewards for risky driving behaviour - requires further investigation. The Police were also influential in the risky driving behaviour of young novices. Young novice drivers who had been detected for an offence, and then avoided punishment, reacted differentially, with some drivers appearing to become less risky after the encounter, whilst for others their risky behaviour appeared to be reinforced and therefore was more likely to be performed again. Such drivers saw t

This dissertation contains work that addresses both theoretical and numerical aspects of reduced order models (ROMs). In an under-resolved regime, the classical Galerkin reduced order model (G-ROM) fails to yield accurate approximations. Thus, we propose a new ROM, the data-driven variational multiscale ROM (DD-VMS-ROM) built by adding a closure term to the G-ROM, aiming to increase the numerical accuracy of the ROM approximation without decreasing the computational efficiency. The closure term is constructed based on the variational multiscale framework. To model the closure term, we use data-driven modeling. In other words, by using the available data, we find ROM operators that approximate the closure term. To present the closure term's effect on the ROMs, we numerically compare the DD-VMS-ROM with other standard ROMs. In numerical experiments, we show that the DD-VMS-ROM is significantly more accurate than the standard ROMs. Furthermore, to understand the closure term's physical role, we present a theoretical and numerical investigation of the closure term's role in long-time integration. We theoretically prove and numerically show that there is energy exchange from the most energetic modes to the least energetic modes in closure terms in a long time averaging. One of the promising contributions of this dissertation is providing the numerical analysis of the data-driven closure model, which has not been studied before. At both the theoretical and the numerical levels, we investigate what conditions guarantee that the small difference between the data-driven closure model and the full order model (FOM) closure term implies that the approximated solution is close to the FOM solution. In other words, we perform theoretical and numerical investigations to show that the data-driven model is verifiable. Apart from studying the ROM closure problem, we also investigate the setting in which the G-ROM converges optimality. We explore the ROM error bounds' optimality by considering the difference quotients (DQs). We theoretically prove and numerically illustrate that both the ROM projection error and the ROM error are suboptimal without the DQs, and optimal if the DQs are used.
Doctor of Philosophy
In many realistic applications, obtaining an accurate approximation to a given problem can require a tremendous number of degrees of freedom. Solving these large systems of equations can take days or even weeks on standard computational platforms. Thus, lower-dimensional models, i.e., reduced order models (ROMs), are often used instead. The ROMs are computationally efficient and accurate when the underlying system has dominant and recurrent spatial structures. Our contribution to reduced order modeling is adding a data-driven correction term, which carries important information and yields better ROM approximations. This dissertation's theoretical and numerical results show that the new ROM equipped with a closure term yields more accurate approximations than the standard ROM.

Currently, the development of high-integrity embedded component-based software systems is not supported by well-integrated means allowing for quality evaluation and design support within a development process. Quality, especially dependability, is very important for such systems. The CHESS (Composition with Guarantees for High-integrity Embedded Software Components Assembly) project aims at providing a new systems development methodology to capture extra-functional concerns and extend Model Driven Engineering industrial practices and technology approaches to specifically address the architectural structure, the interactions and the behavior of system components while guaranteeing their correctness and the level of service at run time. The CHESS methodology is expected to be supported by a tool-set which consists of a set of plug-ins integrated within the Eclipse IDE. In the framework of the CHESS project, this thesis addresses the lack of well integrated means concerning quality evaluation and proposes an integrated framework to evaluate the dependability of high-integrity embedded systems. After a survey of various failure behavior analysis techniques, a specific technique, called Failure Propagation and Transformation Calculus (FPTC), is selected and a plug-in, called CHESS-FPTC, is developed within the CHESS tool-set. FPTC technique allows users to calculate the failure behavior of the system from the failure behavior of its building components. Therefore, to fully support FPTC, CHESS-FPTC plug-in allows users to model the failure behavior of the building components, perform the analysis automatically and get the analysis results back into their initial models. A case study about AAL2 Signaling Protocol is presented to illustrate and evaluate the CHESS-FPTC framework.
CHESS Project - http://chess-project.ning.com/

Efficiency and innovative problem solving are contradictory requirements for productdevelopment (PD), and both requirements must be satisfied in companies that strive to remainor to become competitive. Efficiency is strongly related to ”doing things right”, whereasinnovative problem solving and creativity is focused on ”doing the right things”.Engineering design, which is a sub-process within PD, can be viewed as problem solving or adecision-making process. New technologies in computer science and new software tools openthe way to new approaches for the solution of mechanical problems. Product datamanagement (PDM) technology and tools can enable concurrent engineering (CE) bymanaging the formal product data, the relations between the individual data objects, and theirrelation to the PD process. Many engineering activities deal with the relation betweenbehavior and shape. Modern CAD systems are highly productive tools for conceptembodiment and detailing. The finite element (FE) method is a general tool used to study thephysical behavior of objects with arbitrary shapes. Since a modern CAD technology enablesdesign modification and change, it can support the innovative dimension of engineering aswell as the verification of physical properties and behavior. Concepts and detailed solutionshave traditionally been evaluated and verified with physical testing. Numerical modeling andsimulation is in many cases a far more time efficient method than testing to verify theproperties of an artifact. Numerical modeling can also support the innovative dimension ofproblem solving by enabling parameter studies and observations of real and syntheticbehavior. Simulation-driven design is defined as a design process where decisions related tothe behavior and performance of the artifact are significantly supported by computer-basedproduct modeling and simulation.A framework for product modeling, that is based on a modern CAD system with fullyintegrated FE modeling and simulation functionality provides the engineer with tools capableof supporting a number of engineering steps in all life-cycle phases of a product. Such aconceptual framework, that is based on a moderately coupled approach to integratecommercial PDM, CAD, and FE software, is presented. An object model and a supportingmodular modeling methodology are also presented. Two industrial cases are used to illustratethe possibilities and some of the opportunities given by simulation-driven design with thepresented methodology and framework.
QC 20100810

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
The agile methods of software development have emerged as an alternative to traditional approaches, to spend less time on documentation and more time with solving problems interactively and iteratively. In this context, the development approach Domain Driven Design (DDD) is a way of developing software in which the application design process is guided by a domain model. In conjunction with the DDD development approach, the existence of tests during the implementation of an application is required to ensure quality. Objective: This work aims to study the agile development approach Domain-Driven Design (DDD) and the software test techniques Test-Driven Development (TDD) and Behaviour-Driven Development (BDD). A case study was built to exemplify the integration of each test technique with DDD. The case study was developed with the support of the Apache Isis framework, from a well-defined domain model. Methodology: Based on the literature, the concepts of the DDD development approach and of the TDD and BDD test techniques, that provided the comparison between the techniques, and later, the implementation to illustrate this research, were extracted. The Apache Isis framework was used in this work because it allows develop DDD applications quickly. During the development of the sample system of this work, the possibility of creating an automatic generator tests and scenarios for BDD was noted. Results: A case study was created using the concepts of DDD and tests, with TDD and BDD. Furthermore, a prototype of tests and scenarios generator for software projects that use DDD, through the Apache Isis framework and tests using BDD, was developed. Conclusions: The combination of test techniques mentioned with DDD aim to boosting the development of applications, since DDD is not associate with any test technique. Writing scenarios with ubiquitous language is a great advantage to integrate DDD and BDD, because it allows a clear understanding for all involved in the project. Moreover, the creation of automatic generator speeds up the testing phase, and can detect errors that might go unnoticed or only be found as the project evolved.
Os métodos ágeis de desenvolvimento de software surgiram como uma alternativa às abordagens tradicionais, com o intuito de despender menos tempo com documentação e mais com a resolução de problemas de forma interativa e iterativa. Neste contexto, a abordagem de desenvolvimento Domain-Driven Design (DDD) representa uma forma de desenvolver software em que o processo de design de uma aplicação é guiado pelo modelo de domínio. Em conjunto com a abordagem de desenvolvimento DDD, a existência de testes durante a implementação de uma aplicação é necessária para garantir a qualidade. Objetivo: Este trabalho teve como objetivo o estudo da abordagem ágil de desenvolvimento Domain-Driven Design (DDD) e as técnicas de teste de software Test-Driven Development (TDD) e Behaviour-Driven Development (BDD). Um estudo de caso foi construído para exemplificar a integração de cada técnica de teste com o DDD. O estudo de caso foi desenvolvido por meio do framework Apache Isis, a partir de um modelo de domínio bem definido. Metodologia: Com base na literatura foram extraídos os conceitos da abordagem de desenvolvimento DDD e das técnicas de testes TDD e BDD que proporcionaram a comparação entre as técnicas e posterior implementação para exemplificar a pesquisa. O framework Apache Isis foi utilizado neste trabalho porque permite desenvolver aplicações com DDD de forma rápida. No decorrer do desenvolvimento do sistema exemplo deste trabalho, notou-se a possibilidade da criação de um gerador automático de testes e cenários para BDD. Resultados: Foi criado um estudo de caso empregando os conceitos de DDD e testes com o TDD e com o BDD. Além disso, foi desenvolvido um protótipo de gerador de testes e cenários para projetos de software que empregam DDD, por meio do framework Apache Isis e testes utilizando BDD. Conclusões: A junção das técnicas de testes citadas com o DDD visa potencializar o desenvolvimento de aplicações, uma vez que o DDD não aborda nenhuma técnica de teste. A escrita de cenários em linguagem ubíqua é um grande diferencial ao integrar DDD e BDD, pois permite um claro entendimento a todos os envolvidos do projeto. Ademais, a criação do gerador automático agiliza a fase de testes, sendo possível detectar erros que poderiam não ser notados ou apenas serem encontrados com a evolução do projeto.

This paper aims to show that controlled hydraulic drives, when properly considered and approached, are a technology that allows for a systematic and safe system design with regard to performance and energy efficiency. Controlled hydraulic drives are predestinated to be an indispensable alternative to electromechanical drives for many fields of application, especially against the background of Industry 4.0. But hydraulic drives will only be able to play this role if they see themselves as a part of mechatronics, speak the language of mechatronics and recognize the increasing importance of electric drives as part of the hydraulic toolbox as a chance.

Des nombreux utilisateurs des systèmes multi-agents (SMA) sont très souvent découragés de modéliser et simuler dans les plates-formes actuelles SMA. Plus précisément, modéliser la dynamique d'un système (en particulier les comportements de l'agent) est très souvent vu comme un défi pour les utilisateurs de SMA. Dans le domaine des systèmes socio-écologiques (SES), cet inconvénient est plus souvent observé une fois que les experts de domaine en SES sont rarement des programmeurs. De plus, la majorité des plateformes SMA n'a pas été conçue en prenant en considérant le fait que les experts de domaines ne sont pas des programmeurs. On constate que la majeure partie des outils MAS ne sont pas dédiés à SES, ou qu'ils ne possèdent pas un formalisme compréhensible pour représenter les comportements de SMA. En outre, comme ces outils sont dépendant des plateformes, un modèle réalisé dans une plateforme SMA ne peut pas être correctement utilisé dans une autre plate-forme en raison de l'incompatibilité entre ces plateformes SMA. Afin de surpasser ces limitations, nous proposons un langage dédié au domaine SES pour décrire les comportements des agents réactifs, quelle que soit la plate-forme utilisée pour la simulation des SMA. Pour ce faire, nous avons appliqué l’approche de l’ingénierie dirigée par les modèles (IDM), une approche qui fournit des outils pour développer des langages dédiés à partir d'un méta-modèle (syntaxe abstraite), des éditeurs textuels avec coloration syntaxique (pour la syntaxe concrète) et des outils des générateurs de code (pour la génération de source code à partir d'un modèle). En conséquence, nous avons mis en œuvre un langage et un éditeur de texte qui permet à des experts du domaine SES de décrire les comportements de trois manières différentes qui sont fermées à leur expression naturelle : sous forme d'équations quand ils sont familiers avec celles-ci, en tant que séquence d'activités proche du langage naturel ou comme un diagramme d'activité pour représenter les décisions et une séquence de comportements en utilisant un formalisme graphique. Pour montrer la généralité, nous avons également développé des générateurs de code ciblant deux plates-formes différentes SMA (Cormas et Netlogo). Nous avons testé les générateurs de code en mettant en œuvre deux modèles SES avec le langage dédié développé. Le code généré obtenu a été généré pour les deux plates-formes SMA Cormas et NetLogo, et simulé avec succès dans un des deux plateformes. Nous avons conclu que l'approche IDM fournit des outils adéquats à développer des langages dédiés et des générateurs de code pour faciliter la modélisation et la simulation SMA par des non-programmeurs. En ce qui concerne le langage développé, bien que l’aspect comportemental de la simulation MAS fasse partie de la complexité de la modélisation en SMA, il y a encore d'autres aspects essentiels du modèle et de la simulation de SMA qui sont encore à être explorés, tels que l'initialisation et les points de vue sur un le monde simulé d’un modèle
Many users of multi-agent systems (MAS) are very commonly discouraged to model and simulate using current MAS platforms. More specifically, modeling the dynamics of a system (in particular the agent's behaviors) is very often a challenge to users of MAS. That issue is more often observed in the domain of socio-ecological systems (SES), because SES domain experts are rarely programmers. Indeed,the majority of MAS platforms were not conceived taking into consideration domain-experts that are non-programmers. Most of the current MAS tools are not dedicated to SES, or they do not possess an easily understandable formalism to represent behaviors of agents. Moreover, because it is platform-dependent, a model realized in a MAS platform cannot be properly used in another platform due to incompatibility between MAS platforms. To overcome these limitations, we propose a domain-specific language (DSL) to describe the behaviors of reactive agents, regardless of the MAS platform used for simulation. To achieve that, we applied model-driven engineering (MDE), an approach that provides tools to develop DSLs from a meta-model (abstract syntax), textual editors with syntax highlighting (for the concrete syntax) and code generation capabilities (for source-code generation of a model). As a result, we implemented a language and a textual editor that allows SES domain experts to describe behaviors in three different ways that are closed to their natural expression: as equations when they are familiar to those, as a sequence of activities close to natural language or as an activity diagram to represent decisions and a sequence of behaviors using a graphic formalism. To show the generality we also developed code generators targeting two different MAS platforms (Cormas and Netlogo). We tested the code generators by implementing two SES models with the developed DSL. The generated code was targeted for both MAS platforms (Cormas and Netlogo), and successfully simulated in one of them.We conclude that the MDE approach provides adequate tools to develop DSL and code generators to facilitate MAS modeling and simulation by non-programmers. Concerning the developed DSL, although the behavioral aspect of MAS simulation is part of the complexity of modeling in MAS, there are still other essential aspects of model and simulation of MAS that are yet to be explored, such as model's initialization and points of view on the model's simulated world

Dissertação para obtenção do Grau de Mestre em Engenharia Informática
Due to the limited existence of dedicated robot programming solutions for children (as well as scientific studies), this work presents the design and implementation of a visual domain specific language (DSL), using the Model-Driven Development approach(MDD), for programming robotics and automaton systems with the goal to increase productivity and simplify the software development process. The target audience for this DSL is mostly children with ages starting from 8 years old. Our work implied to use the typical Software Language Engineering life cycle, starting by an elaborate study of the user’s profile, based on work in cognitive sciences, and a Domain analysis. Several visual design paradigms were considered during the design phase of our DSL, and we have focused our studies on the Behavior Trees paradigm, a paradigm intensively used in the gaming industry. Intuitive, simplicity and a small learning curve were the three main concerns considered during the design and development phases. To help validating the DSL and the proposed approach, we used a concrete robotic product for children built with the Open Source Arduino platform as target domain. The last part of this work was dedicated to study the adequacy of the language design choices, compared to other solutions (including commercial technologies), to the target users with different ages and different cognitive-development stages. We have also studied the benefits of the chosen paradigm to domain experts’ proficient on robot programming in different paradigms to determine the possibility to generalize the solution to different user profiles.

This study examined variation in ectoparasite density and grooming behavior of naturally occurring white-tailed deer (Odocoileus virginianus) in southwest Alabama. Stimulus-driven grooming as well as the intraspecific body size and vigilance principles of programmed grooming were tested. During the rut, males had a higher average tick (Ixodidae) density than females and exhibited complete separation of tick parasitism between non-rutting and rutting periods, supporting the vigilance principle. Stimulus-driven grooming was supported, as both fawns and yearlings had significantly higher fly (Hippoboscidae) and combined fly/tick densities than adults, and fawns oral groomed at a significantly higher rate than adults, even in the absence of allogrooming. Programmed and stimulus-driven grooming of deer examined in this study were not mutually exclusive but ectoparasite dependent.

This thesis highlights the limitations of the existing car following models to emulate driver behaviour for safety study purposes. It also compares the capabilities of the mainstream car following models emulating driver behaviour precise parameters such as headways and Time to Collisions. The comparison evaluates the robustness of each car following model for safety metric reproductions. A new car following model, based on the personal space concept and fish school model is proposed to simulate more precise traffic metrics. This new model is capable of reflecting changes in the headway distribution after imposing the speed limit form VSL systems. This research facilitates assessing Intelligent Transportation Systems on motorways, using microscopic simulation.

This thesis describes a research study relating naturalistic Global Positioning System (GPS) driving data with long-term traffic safety performance for two classes of roadways. These two classes are multilane arterial streets and limited access highways. GPS driving data used for this study was collected from 33 volunteer drivers from July 2012 to March 2013. The GPS devices used were custom GPS data loggers capable of recording speed, position, and other attributes at an average rate of 2.5 hertz. Linear Referencing in ESRI ArcMAP was performed to assign spatial and other roadway attributes to each GPS data point collected. GPS data was filtered to exclude data with high horizontal dilution of precision (HDOP), incorrect heading attributes or other GPS communication errors. For analysis of arterial roadways, the Two-Fluid model parameters were chosen as the measure for long-term traffic safety analysis. The Two-Fluid model was selected based on previous research which showed correlation between the Two-Fluid model parameters n and Tm and total crash rate along arterial roadways. Linearly referenced GPS data was utilized to obtain the total travel time and stop time for several half-mile long trips along two arterial roadways, Grand Avenue and California Boulevard, in San Luis Obispo. Regression between log transformed values of these variables (total travel time and stop time) were used to derive the parameters n and Tm. To estimate stop time for each trip, a vehicle “stop” was defined when the device was traveling at less than 2 miles per hour. Results showed that Grand Avenue had a higher value for n and a lower value for Tm, which suggests that Grand Avenue may have worse long-term safety performance as characterized by long-term crash rates. However, this was not verified with crash data due to incomplete crash data in the TIMS database. Analysis of arterial roadways concluded by verifying GPS data collected in the California Boulevard study with sample data collected utilizing a traditional “car chase” methodology, which showed that no significant difference in the two data sources existed when trips included noticeable stop times. For analysis of highways the derived measurement of vehicle jerk, or rate of change of acceleration, was calculated to explore its relationship with long-term traffic safety performance of highway segments. The decision to use jerk comes from previous research which utilized high magnitude jerk events as crash surrogate, or near-crash events. Instead of using jerk for near-crash analysis, the measurement of jerk was utilized to determine the percentage of GPS data observed below a certain negative jerk threshold for several highway segments. These segments were ¼-mile and ½-mile long. The preliminary exploration was conducted with 39 ¼-mile long segments of US Highway 101 within the city limits of San Luis Obispo. First, Pearson’s correlation coefficients were estimated for rate of ‘high’ jerk occurrences on these highway segments (with definitions of ‘high’ depending on varying jerk thresholds) and an estimate of crash rates based on long-term historical crash data. The trends in the correlation coefficients as the thresholds were varied led to conducting further analysis based on a jerk threshold of -2 ft./sec3 for the ¼-mile segment analysis and -1 ft./sec3 for the ¼-mile segment analysis. Through a negative binomial regression model, it was shown that utilizing the derived jerk percentage measure showed a significant correlation with the total number of historical crashes observed along US Highway 101. Analysis also showed that other characteristics of the roadway, including presences of a curve, presence of weaving (indicated by the presence of auxiliary lanes), and average daily traffic (ADT) did not have a significant correlation with observed crashes. Similar analysis was repeated for 19 ½-mile long segments in the same study area, and it was found the percentage of high negative jerk metric was again significant with historical crashes. The ½-mile negative binomial regression for the presence of curve was also a significant variable; however the standard error for this determination was very high due to a low sample size of analysis segments that did not contain curves. Results of this research show the potential benefit that naturalistic GPS driving data can provide for long-term traffic safety analysis, even if data is unaccompanied with any additional data (such as live video feed) collected with expensive vehicle instrumentation. The methodologies of this study are repeatable with many GPS devices found in certain consumer electronics, including many newer smartphones.

The relational identity threat response model describes how workplace relationships experiencing relational identity threat predict discretionary workplace behavior (i.e. momentary organizational voice, momentary behavioral engagement, and momentary supervisor-directed deviance). More specifically, the model utilizes an experience sampling methodology to capture the ebb and flow or momentary changes within supervisor-subordinate relationships with a focus on potential negative shifts in a subordinate’s relational identity with his/her supervisor. By examining momentary behavior, meaning actions subordinates take within a short period of time such as the last few hours, I draw attention to the often overlooked effects of dynamic workplace relationships, arguing that the ebb and flow within supervisor-subordinate interactions play a critical role in subordinate choice as to the amount of momentary organizational voice, momentary behavioral engagement, and momentary supervisor-directed deviance to offer. That is, I utilize recent developments within social identity theory (Brewer & Gardner, 1996; Sluss & Ashforth, 2007) to examine how subordinates reconcile relational identity threat, or potential shifts in the nature of ‘who we are’ (Sluss & Ashforth, 2007). Further, the data support the argument that momentary relational voice, which is communication to the supervisor focused on improving the workplace relationship or relational functioning, partially mediates relational identity threat’s consequences for positive discretionary workplace behavior (i.e. momentary organizational voice, momentary behavioral engagement). Additionally, I draw attention to self-compassion (i.e. how a subordinate treats him or herself [Neff, 2003a]), which moderates subordinate responses to relational identity threat within a supervisor-subordinate relationship.

Rapid developments in vehicle technology, such as in-car systems, have provided opportunities to encourage drivers to drive in a more fuel-efficient and safe manner. This thesis focused on the design, development and evaluation of a new in-car system to improve eco-safe driving, with the aim of reducing the impact of motorised vehicles on both human health and the environment. This multidisciplinary research contributes to enhancing our understanding of the requirements of drivers in relation to in-car systems and provides a foundation for the future design and development of innovative and accepted in-car systems to improve fuel efficiency and road safety.

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CAPEs
A etapa de pré-processamento em um projeto de descoberta do conhecimento é custosa, em geral, consome cerca de 50 a 80% do tempo total de um projeto. É nesta etapa que um banco de dados relacional é transformado para aplicação de um algoritmo de mineração de dados. A transformação dos dados nesta etapa é uma tarefa complexa, uma vez que exige uma forte integração entre projetistas de banco de dados e especialistas do domínio da aplicação. Os frameworks que buscam sistematizar a etapa de transformação dos dados encontrados na literatura apresentam limitações significativas quando aplicados a soluções comportamentais, como Credit Behavior Scoring. Estas soluções visam a auxiliar as instituições financeiras a decidirem sobre a concessão de crédito aos consumidores com base no risco das solicitações. Este trabalho propõe um framework baseado no Desenvolvimento Dirigido por Modelos para sistematizar esta etapa em soluções de Credit Behavior Scoring. Ele é composto por um meta-modelo que mapeia os conceitos do domínio e um conjunto de regras de transformações. As três principais contribuições do framework proposto são: 1) aumentar o poder discriminatório da solução, através da construção de novas variáveis que maximizam o conteúdo estatístico da informação do domínio; 2) reduzir o tempo da transformação dos dados através da geração automática de código e 3) permitir que profissionais e pesquisadores de Inteligência Artificial e Estatística realizem a transformação dos dados sem o auxílio de especialistas de Banco de Dados. Para validar o framework proposto, dois estudos comparativos foram realizados. Primeiro, um estudo comparando o desempenho entre os principais frameworks existentes na literatura e o framework proposto foi realizado em duas bases de dados. Uma base de dados de um conhecido benchmark de uma competição internacional organizada pela PKDD, e outra obtida de uma das maiores empresas de varejo do Brasil, que possui seu próprio cartão de crédito. Os frameworks RelAggs e Validação de Múltiplas Visões Baseado em Correção foram escolhidos como representantes das abordagens proposicional e mineração de dados relacional, respectivamente. A comparação foi realizada através do processo de validação cruzada estratificada, para definir os intervalos de confiança para a avaliação de desempenho. Os resultados mostram que o framework proposto proporciona um desempenho equivalente ou superior aos principais framework existentes, medido pela área sob a curva ROC, utilizando uma rede neural MultiLayer Perceptron, K vizinho mais próximos e Random Forest como classificadores, com um nível de confiança de 95%. O segundo estudo verificou a redução de tempo proporcionada pelo framework durante a transformação dos dados. Para isso, sete times compostos por estudantes de uma universidade brasileira mensuraram o tempo desta atividade com e sem o framework proposto. O teste pareado Wilcoxon Signed-Rank mostrou que o framework proposto reduz o tempo de transformação com um nível de confiança de 95%.
The pre-processing stage in knowledge discovery projects is costly, generally taking between 50 and 80% of total project time. It is in this stage that data in a relational database are transformed for applying a data mining technique. This stage is a complex task that demands from database designers a strong interaction with experts who have a broad knowledge about the application domain. The frameworks that aim to systemize the data transformation stage have significant limitations when applied to behavior solutions such as the Credit Behavior Scoring solutions. Their goal is help financial institutions to decide whether to grant credit to consumers based on the credit risk of their requests. This work proposes a framework based on the Model Driven Development to systemize this stage in Credit Behavioral Scoring solutions. It is composed by a meta-model which maps the domain concepts and a set of transformation rules. This work has three main contributions: 1) improving the discriminant power of data mining techniques by means of the construction of new input variables, which embed new knowledge for the technique; 2) reducing the time of data transformation using automatic code generation and 3) allowing artificial intelligence and statistics modelers to perform the data transformation without the help of database experts. In order to validate the proposed framework, two comparative studies were conducted. First, a comparative study of performance between the main existing frameworks found in literature and the proposed framework applied to two databases was performed. One database from a known benchmark of an international competition organized by PKDD, and another one obtained from one of the biggest retail companies from Brazil, that has its own private label credit card. The RelAggs and Correlation-based Multiple View Validation frameworks were chosen as representatives of the propositional and relational data mining approaches, respectively. The comparison was carried out through by a 10-fold stratified cross-validation process with ten stratified parts in order to define the confidence intervals. The results show that the proposed framework delivers a performance equivalent or superior to those of existing frameworks, for the evaluation of performance measured by the area under the ROC curve, using a Multilayer Perceptron neural network, k-nearest neighbors and Random Forest as classifiers, with a confidence level of 95%. The second comparative study verified the reduction of time required for data transformation using the proposed framework. For this, seven teams composed by students from a Brazilian university measured the runtime of this stage with and without the proposed framework. The paired Wilcoxon Signed-Rank’s Test showed that the proposed framework reduces the time of data transformation with a confidence level of 95%.

It is well accepted by now that most change initiatives are unsuccessful even though more organisations are experiencing change as they fight to retain and improve their competitiveness in the market place. It is against this background of change failure that researchers have looked for new strategies to improve change outcomes. Theoretical models conceptualising the dynamic change process advise on better change strategies, but little empirical evidence has demonstrated that these models are effective in improving change implementation outcomes. Theoretical models were also developed to counter change resistance, but little emphasis has been placed on employee change readiness. Some empirical research on employee change readiness explores employees? perception of organisational readiness, but no empirical research has explored employee readiness from a psychological perspective. That is, how to create change readiness in employees. This thesis has contributed to both the theoretical and empirical understanding of the change readiness model. Firstly, the theoretical readiness for change model Armenakis et al.?s (1993, 2002) was extended by the inclusion of the ?understanding of the change? driver. Secondly, this change readiness model was empirically tested on two distinct organisational changes: organisational restructure and IT change. The extended model is also examined for two change stages of the dynamic process to identify which readiness drivers should be prioritised by change agents. Two online questionnaires were administered eight months apart assessing the responses to three change stages (planning, implementation and post-implementation) of employees ? supervisors and subordinates - of a flat structured organisation in the human resource industry. At the two measurement points 189 and 141 employees returned completed surveys. Six employee readiness drivers were operationalised and regressed against behavioural change intention. The quantitative findings using regression models across two change types and longitudinally did not identify a specific change pattern. However, all six readiness drivers including the ?understanding of the change? driver were influential on employees? behavioural change intention. Furthermore, statistical differences between supervisors and subordinates were identified in the organisational restructure change. The quantitative findings using a triangulation approach with qualitative date including data from two unstructured interviews and employee comments further validated the quantitative findings. The thematic analysis of the employee comments enhanced the findings and identified employee specific concerns including information dissemination of the changes and a level of uncertainty. The findings supported Armenakis et al.?s (1993, 2002) theoretical contribution that change readiness drivers are an important part of the organisational change process explaining why employee do and do not change. The empirical application of readiness change driver evaluation during the dynamic change is supported as it permits change agents to directly monitor employees? readiness perception of a specific change target. This valuable information finds practical utilisation for change agents in providing targeted guidance and support for employees thus facilitating a greater likelihood of a positive change outcome. Implications of these findings and future research opportunities are discussed.

For many companies today, it is highly valuable to collect and analyse data in order to support decision making and functions of various sorts. However, this kind of data-driven approach is seldomly applied to software testing and there is often a lack of verification that the testing performed is relevant to how the system under test is used. Therefore, the aim of this thesis is to investigate the possibility of introducing a data-driven approach to test automation by extracting user behaviour data and curating it to form input for testing. A prestudy was initially conducted in order to collect and assess different data sources for augmenting the testing. After suitable data sources were identified, the required data, including data about user activity in the system, was extracted. This data was then processed and three prototypes where built on top of this data. The first prototype augments the model-based testing by automatically creating models of the most common user behaviour by utilising data mining algorithms. The second prototype tests the most frequent occurring client actions. The last prototype visualises which features of the system are not covered by automated regression testing. The data extracted and analysed in this thesis facilitates the understanding of the behaviour of the users in the system under test. The three prototypes implemented with this data as their foundation can be used to assist other testing methods by visualising test coverage and executing regression tests.

The major problem addressed in this thesis is the definition of a new SPLE methodology that covers both phases of the SPLE process and focuses on the derivation of behavioral models of SPL products. In Chapter 2 three research areas scope context of this thesis: Software Product Lines, Business Processes, and Model-Driven Engineering. Throughout Chapter 3, we propose a new SPLE methodology that focuses on the derivation of product behavior. We first describe the main flow of the methodology, and then detail the individual steps. In chapter 4 we propose a new domain specific language called CBPF created for modeling composable business process fragments. A model driven approach is followed for creating CBPF: definition of the abstract syntax, graphical concrete syntax and translational semantics. In Chapter 5 we propose several types of verifications that can be applied to business processfragments to determine their "correctness". For structural verification we definine a set of fragment consistency rules that should be valid for every business process fragment created with CBPF. To check behavioral correctness we first transform the business process fragment into an equivalent HCPN. We can then check generic properties but also define aset of fragment specific properties. In chapter 6 we exemplify the proposed SPL methodology by applying it to a case study from the crisis management system domain. We also propose a tool suite that supports our methodology. Chapter 7 describes possible improvements and extensions to the contributions of this thesis. We conclude the thesis in Chapter 8 and draw some conclusions.

Humans can induce contemporary evolution through harvesting wild animals. This thesis provides evidence of this process for recreational fishing at surprisingly small geographical scales. Marine fish populations are characterized by high variability in heritable individual life‐history traits which are exposed to biased fishing mortality. The life histories strategies of actual populations, which collectively reduce adult body size, mirror such biased mortality in populations exposed to limited gene flow. Because the relationship between life history and behaviour, it is very likely that fishing also induces adaptive responses in behavioural traits. Due the potential negative effects of fishing selection the productivity and quality of the fishery, this thesis recommend considering the evolutionary impacts of fishing in the management plans of species exploited by the recreational fishing
El humans poden produir evolució contemporània a través de la recol∙lecció de animals salvatges. Aquesta tesis proporciona evidencies d’aquest procés per la pesca recreativa a una sorprenent escala espacial petita. Les poblacions marines de peixos tenen una alta variabilitat individual en caràcters heretables de la història exposada a una mortalitat selectiva per pesca. Les estratègies vitals de les poblacions actuals de peixos costaners, les quals conjuntament produeixin una reducció en la mida del individu, són el reflexa d’aquesta pesca selectiva en poblacions amb baixa connectivitat. Degut a la correlació entre història vital i comportament, la pesca pot induir també respostes adaptatives en cert caràcters comportamentals. Considerant els efectes negatius de la selecció per pesca en la productivitat i la qualitat de les pesqueries, aquesta tesis recomana considerar els possibles efectes evolutius produïts per la pesca en les estratègies de gestió d’espècies explotades per la pesca recreativa.
El hombre puede producir evolución contemporánea por la recolección de animales salvajes. La presente tesis proporciona evidencias de este proceso por la pesca recreativa en una sorprendente escala espacial pequeña. Las poblaciones de peces marinos presentan una alta variabilidad en caracteres heredables de la historia vital expuesta a mortalidad selectiva por pesca. Las estrategias vitales de las poblaciones actuales de los peces costeros, las cuales conjuntamente producen una reducción en el tamaño del individuo, son el reflejo de esta mortalidad selectiva en poblaciones con baja conectividad. Debido a la relación entre historia vital y comportamiento, la pesca puede también inducir cambios adaptativos en ciertos rasgos de comportamiento. Considerando los efectos negativos de la selección por pesca en al productividad y calidad de la pesquería, esta tesis recomienda considerara los posibles efectos evolutivos inducidos por la pesca en la estrategias de gestión de especies explotadas por la pesca recreativa.

his Disertation thesis involves the creating of the simulation model of the C axis drive over mentioned machine and them verification on the prototype of this machine. C axis is controlled with position feedback. Simulation model was created before the realisation of the machine prototype for the preliminary identification of the dynamic behaviours in the working cycles and them opportunity of the realisation this conception. C axis is constructed with worm gear and is controlled with help of Master-Slave drive. This torque drive eliminates the production backlash in the worm gear. The multifunction turning center, where is used this C axis, is determinate for heavy duty roughing cutting of the forged peaces, where is problem with dynamic stability of the cutting process. Simulation model includes the problems with multi-body mass system, friction on the worm gear, self locking, damping on the worm gear and the optimization of the parametrs for many regulators. Simulation model was verified on the prototype of the machine. Achieved results bring the new knowledge, which are used for simulation complicated machine nodes and this knowledge is used for research and developing of the similar mechatronics system.

Variable Speed Limits (VSL) are often used to improve traffic conditions on congested motorways. VSL can be implemented as mandatory or advisory. The objective of the thesis isto study in detail the effectiveness of VSL. The focus is on both, design parameters and conditions under which VSL are most effective. The MCS system on the E4 motorway inStockholm is used as a case study. The evaluation was conducted using empirical methods (including aggregate data from microwave sensors and other sources, and disaggregate data from a mobile study), and microscopic traffic simulation. The empirical analysis is based on before and after VSL data, including evaluation of individual measures of performance, and multivariate analysis in the form of the fundamental diagram, and speed-density relationships. The results from the empirical study are mixed with an indication that driver behavior has a strong impact on the effectiveness of the system. The microscopic traffic simulation analysis included the development of a platform for testing VSL and more generally motorway control strategies. The simulation platform was calibrated and validated with the empirical data and includes in addition to VSL, and Automatic Incident Detection (AID) system, the ALINEA ramp metering algorithm. The test-platform allows the testing of different control strategies and various combinations of control strategies, under different scenarios and in a controlled environment. The results from the simulation study indicate that driver compliance is an important factor and VSL performance quickly deteriorates as compliance rate drops. Hence, VSL should be implemented as mandatory instead of advisory. In addition, mandatory VSL can be effective both, under incident and moderately congested conditions. A combined VSL and ramp metering strategy can be most effective in reducing travel time, improving traffic conditions on the motorway. Furthermore, the results indicate that such a strategy also has the least impact on the flows entering the motorway from the ramps.
QC20100630

The ability of a piezoelectric transducer in energy conversion is rapidly expanding in several applications. Some of the industrial applications for which a high power ultrasound transducer can be used are surface cleaning, water treatment, plastic welding and food sterilization. Also, a high power ultrasound transducer plays a great role in biomedical applications such as diagnostic and therapeutic applications. An ultrasound transducer is usually applied to convert electrical energy to mechanical energy and vice versa. In some high power ultrasound system, ultrasound transducers are applied as a transmitter, as a receiver or both. As a transmitter, it converts electrical energy to mechanical energy while a receiver converts mechanical energy to electrical energy as a sensor for control system. Once a piezoelectric transducer is excited by electrical signal, piezoelectric material starts to vibrate and generates ultrasound waves. A portion of the ultrasound waves which passes through the medium will be sensed by the receiver and converted to electrical energy. To drive an ultrasound transducer, an excitation signal should be properly designed otherwise undesired signal (low quality) can deteriorate the performance of the transducer (energy conversion) and increase power consumption in the system. For instance, some portion of generated power may be delivered in unwanted frequency which is not acceptable for some applications especially for biomedical applications. To achieve better performance of the transducer, along with the quality of the excitation signal, the characteristics of the high power ultrasound transducer should be taken into consideration as well. In this regard, several simulation and experimental tests are carried out in this research to model high power ultrasound transducers and systems. During these experiments, high power ultrasound transducers are excited by several excitation signals with different amplitudes and frequencies, using a network analyser, a signal generator, a high power amplifier and a multilevel converter. Also, to analyse the behaviour of the ultrasound system, the voltage ratio of the system is measured in different tests. The voltage across transmitter is measured as an input voltage then divided by the output voltage which is measured across receiver. The results of the transducer characteristics and the ultrasound system behaviour are discussed in chapter 4 and 5 of this thesis. Each piezoelectric transducer has several resonance frequencies in which its impedance has lower magnitude as compared to non-resonance frequencies. Among these resonance frequencies, just at one of those frequencies, the magnitude of the impedance is minimum. This resonance frequency is known as the main resonance frequency of the transducer. To attain higher efficiency and deliver more power to the ultrasound system, the transducer is usually excited at the main resonance frequency. Therefore, it is important to find out this frequency and other resonance frequencies. Hereof, a frequency detection method is proposed in this research which is discussed in chapter 2. An extended electrical model of the ultrasound transducer with multiple resonance frequencies consists of several RLC legs in parallel with a capacitor. Each RLC leg represents one of the resonance frequencies of the ultrasound transducer. At resonance frequency the inductor reactance and capacitor reactance cancel out each other and the resistor of this leg represents power conversion of the system at that frequency. This concept is shown in simulation and test results presented in chapter 4. To excite a high power ultrasound transducer, a high power signal is required. Multilevel converters are usually applied to generate a high power signal but the drawback of this signal is low quality in comparison with a sinusoidal signal. In some applications like ultrasound, it is extensively important to generate a high quality signal. Several control and modulation techniques are introduced in different papers to control the output voltage of the multilevel converters. One of those techniques is harmonic elimination technique. In this technique, switching angles are chosen in such way to reduce harmonic contents in the output side. It is undeniable that increasing the number of the switching angles results in more harmonic reduction. But to have more switching angles, more output voltage levels are required which increase the number of components and cost of the converter. To improve the quality of the output voltage signal with no more components, a new harmonic elimination technique is proposed in this research. Based on this new technique, more variables (DC voltage levels and switching angles) are chosen to eliminate more low order harmonics compared to conventional harmonic elimination techniques. In conventional harmonic elimination method, DC voltage levels are same and only switching angles are calculated to eliminate harmonics. Therefore, the number of eliminated harmonic is limited by the number of switching cycles. In the proposed modulation technique, the switching angles and the DC voltage levels are calculated off-line to eliminate more harmonics. Therefore, the DC voltage levels are not equal and should be regulated. To achieve this aim, a DC/DC converter is applied to adjust the DC link voltages with several capacitors. The effect of the new harmonic elimination technique on the output quality of several single phase multilevel converters is explained in chapter 3 and 6 of this thesis. According to the electrical model of high power ultrasound transducer, this device can be modelled as parallel combinations of RLC legs with a main capacitor. The impedance diagram of the transducer in frequency domain shows it has capacitive characteristics in almost all frequencies. Therefore, using a voltage source converter to drive a high power ultrasound transducer can create significant leakage current through the transducer. It happens due to significant voltage stress (dv/dt) across the transducer. To remedy this problem, LC filters are applied in some applications. For some applications such as ultrasound, using a LC filter can deteriorate the performance of the transducer by changing its characteristics and displacing the resonance frequency of the transducer. For such a case a current source converter could be a suitable choice to overcome this problem. In this regard, a current source converter is implemented and applied to excite the high power ultrasound transducer. To control the output current and voltage, a hysteresis control and unipolar modulation are used respectively. The results of this test are explained in chapter 7.

В дисертації розвинуто методичні засади формування системи організаційного розвитку та обгрунтовано концептуальні засади класифікації його моделей; визначено сутність коопетиції, здійснено кластеризацію теорії коопетиції за напрямками досліджень та хронологією становлення, виявлено найбільш ймовірні сфери формування стратегічних коопетиційних альянсів; запропоновано теоретико-методологічні засади визначення ключових факторів успіху підприємств – галузевих лідерів та отримано концептуальне пояснення причин і наслідків формування конкурентного тиску та ключових драйверів коопетиційної взаємодії; розроблено методологію галузевої селекції потенціальних учасників коопетиційної взаємодії, а також методичний інструментарій вибору потенційного коопетиційного партнера, методологічні основи та методичний інструментарій оцінювання ступеня готовності підприємства до впровадження стратегії коопетиції як проактивної стратегії організаційного розвитку.
The disseration is aimed to develop theoretical basis and methodology for forming coopetition model of the organizational development in the context of strategic management. In the thesis, the methodology and methodical guidelines for evaluation of the readiness of an enterprise to implement coopetition model are performed that based on integral indicator assessment which generalizes the features of industry dynamics, market heterogeneity, the degree of cooperation inside of an enterprise and competition level. At the same time, the understanding of coopetition is presented as a system of paradoxical multilevel interactions between proactive economic entities that choose the partner consciously despite the previous experience or duration of the interactions towards new value creation and new competences generation. The classification of the coopetition is developed by restructuring the features, types and number of participants of the interaction, meanwhile, the organizational development models are classified by including the coopetition as an atypical and yet normal attribute of organizational dynamic. Therefore, the organizational models are classified by the following approaches: situational, selection, dialectic, theological, institutional, phenomena, game theory, revolutionary, cyclic and wave approach, and structural functionalism. The organizational development is interpreted as a system of coopetitive organizational and economic interrelations that are formed owing to dialectic interactions of planned and spontaneous interventions into organizational processes and structures, that develop co-evolutionarily reproducing mutual influence of the transformations in the business environment and strategic management concepts. As a results of overview of previous findings and comparative analysis the system of key features of organizational development is complemented by the following: coevolution of the strategic and organizational adaptations (or mutations), multifunctional leadership, the existence of systematic paradox of organizational development (proactivity and adaptivity co-existence), and coopetitive features of organizational development. In the thesis, the bibliometric analysis was conducted to reveal the clusters of the main academic outcomes in the sphere of coopetition research, the chronology analysis, global trends and the most possible trajectories of the future development of coopetition research were presented. The hypothesis about the existence of certain drivers of competitive, coopetitive and cooperative behavior in the market was formulated and proved by DEA which implies the determination of the efficiency of transformation of the inputs of an enterprise into outputs, and which gives the opportunity to reveal the industrial specifics of forming the competitive pressure, key drivers of competitive and coopetitive interactions. The air crafting industry became the pilot for the research to check the hypothesis. It is proved that the competitive pressure from B2B customers (airlines) arise and shifted the focus of competitive interactions between main players towards cost-effective decisions to the detriment of human-friendly decisions. Coopetition as an opposite to rent-seeking behaviour may offer many options that will bring benefits in terms of resources economization, as well as safety and security benefits. The methodology of the preliminary selection of the potential partners was performed in the thesis. The offered methodology is based on evaluation of three criteria: market share, intentions to competences development and ability to cooperate. These criteria allowed to form the profiles of the enterprises (outsider, laggard, business shark, lone ranger, scout, benefactor, innovator and technological leader) and their possible interferences (coopetition, intense competition, light competition, the cooperation of strong ones, cooperation of weak ones, neutrality and opportunism). The matrix of interference presented in the thesis is an effective tool for decisionmaking about the choice of strategic partner for coopetition. The methodical toolbox is developed as a two-steps approach which implies the microeconomic stability evaluation and cooperation stability assessment. The microeconomic stability hexagon is based on the evaluation of the dispersion of revenue and margin, research and development costs, sales and administration costs, long-term debts (quantitative data), meanwhile the cooperation stability hexagon is based on assessment of cooperative interactions in a sphere of supply, research and production (combination of quantitative and qualitative data). After revealing the most relevant potential partner for further coopetition there is a need to evaluate the business-patterns and strategic ploys in accordance complementarity of the further structuring business-processes. CANVAS modelling as a methodical approach was re-structured and re-grouped towards the search of homogeneity and /or heterogeneity of the patterns, and new zones of interactions with the potential partner, which can be done by the modification of neural network analysis (as it was performed in the thesis). The CANVAS-model is complemented by new blocks of the analysis: competitors-opportunists and competitors-voluntars (neologism). In the thesis the qualitative data research was performed (the combination of the expert evaluations and empiric research). The survey data were collected and processed to reveal the main antecedents and laws of coopetition appearance in such industries of Ukraine as: air crafting, car manufacturing, chemistry, energy and machine-building industries. It was revealed the link between the readiness to implement the coopetition strategy and life cycle of the organization, between readiness and internality – externality, and reactivity of the TOP-management of the enterprises. It is proved the coopetition is related to the competences-oriented strategy and is the opposite to the risk-avoiding and rent-seeking behavior. The hypothesis about the interrelations between the readiness to implement coopetition and the organizational characteristics was proved. Thus, the industrial specifics were revealed as a result of data processing: there is a link between the readiness and life cycle of the organization, internality or externality of TOP-management and activity degree in a process of decision-making. The criterions of the partner choice were detailed for the industries of Ukraine, and the link between opportunism and readiness to implement the coopetition was identified. In the thesis, it is offered to use the term "organizational viscosity" as a characteristic of an organizational system that affects the speed of evolution towards coopetition as a new paradigm, and which is related to decentralization and speed of decision-making and dynamics of organizational configurations.