Thesis (Ph.D.)--Aberdeen University, 2009. / Title from web page (viewed on Dec 1, 2009). Includes bibliographical references.

Thesis (M. S.)--Psychology, Georgia Institute of Technology, 2007. / Dr. Gregory M. Corso, Committee Chair ; Dr. Arthur D. Fisk, Committee Member ; Dr. Lawrence James, Committee Member.

Thesis (Ph. D.)--Wichita State University, 2005. / Includes bibliographical references (leaves 81-90). Also available online via the Wichita State University Library website (http://library.wichita.edu/).

<p>This paper describes the development of simulation models for a variety of traffic flow scenarios. The major goal of the models was to evaluate the effects of driver characteristics and traffic composition on traffic flow. The five scenarios modeled and their respective objectives were as follows:</p> <p>1. Vehicles switching lanes to increase speed. Objectives were thruput and number of lane switches.</p> <p> 2. Vehicles merging into an adjacent lane. Objectives were distance traveled before merging and number of collisions during lane switching.</p> <p> 3. Vehicles switching from the left or right lane into the center lane. Objectives were number of collisions and number of new misses during lane switching.</p> <p> 4. Vehicles passing on a two-lane bidirectional road. Objective was number of collisions during passing. 5. Vehicles switching from the center lane to the left or right lane to avoid an impassible obstacle. Objectives were number of collisions during lane switching and number of collisions with obstacle.</p> <p> Various driver characteristics were implemented in the models. The concept of preoccupation/attentiveness was factored into the models through the use of varied reaction times. 0ther driver characteristics were incorporated in the models via the assignment of vehicle speed. The models provided for a wide variety of driver types. Examples are as follows:</p> <p> 1. Drivers in a hurry.</p> <p> 2. Tourists or drivers unfamiliar with the area.</p> <p> 3. Law-abiding drivers.</p> <p> 4. Aggressive and passive drivers.</p> <p> 5. Young, inexperienced drivers.</p> <p> 6. Tired truck drivers.</p> <p> The driver characteristics were varied via percentage allocations entered at run-time. The traffic composition for the models consisted of automobiles and multi-axle vehicles of fixed lengths. The percentages for each vehicle type were also entered at run-time.</p> <p> The scope and level of detail for each model was delineated with assumptions. General assumptions made included the following:</p> <p> 1. An autombile is 10 feet fong, a multi-axle vehicle is 30 feet long.</p> <p> 2. The width of a lane is such that only one vehicle can be accommodated at a time.</p> <p> 3. A vehicle is considered to be entirely in one lane or another.</p> <p> 4. A vehicle switches lanes instantaneously.</p> <p> 5. The reaction time of an attentive driver is normally distributed with a mean of .5; the reaction time of a preoccupied driver is normally distibuted with a mean of .7. Three standard deviations are included to ensure complete population coverage.</p> <p> 6. A collision between two vehicles results in the termination of the vehicle causing the collision: the other vehicle continues.</p> <p> Implementation of these models was performed using the student version of the simulation language GPSS/H. The models were validated. but not verified against their real world counterparts. Test results showed that select ctiver characteristics can affect traffic flow; however, the effect of traffic composition was relatively unshown.</p> / Master of Science

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Thesis (PhD)--University of Stellenbosch, 2003. / ENGLISH ABSTRACT: The problem of how the mind relates to the brain stands as one of the greatest challenges today. The materialistic worldview and pragmatic approach to social problems are both being transformed by discoveries of how human experience and culture arise in cerebral activity. Even so, this effort, spearheaded by neuroscience, has seen the important and contentious issue of driver behaviour somehow been left behind. From an extensive literature study, it can be concluded that gross disregard of the neural underpinnings of such behaviour tied to a behaviouristic approach is endemic to the field. Numerous qualitative psychological models (each associated with debates about their validity) and Artificial Intelligence models, which effectively only imitate robots 'impaired' to display some humanlike characteristics, were come across. Although neural networks are derived from current knowledge of computation within the brain and deployed in industry, human driver behaviour modelling is not benefiting from this revolution in humanlike information processing. To date, very little has been done to determine what makes road users speed, drive while drunk, overtake, or yield at crossroads. As the central nervous system is the human measuring device in and of the world and thus key affector of human behaviour, it is ofutrnost importance to invest resources in 'inoculating' the field of driver behaviour modelling onto a robust basis provided by neuroscience. Being a human driver incorporates a broad complement of interrelated brain systems to perform driving tasks (psychological functions) at hand, such as lane keeping, speed choice, risk perception, and obstacle avoidance. The proper level of analysis of such a psychological function is the level at which that function is represented in the brain. Providing a theoretical model of human behaviour, based on biological facts of the brain as a whole, is surely a challenge for decades to come, but the field of driver behaviour should be part of such an effort. Collaboration is needed among investigators from the fields of neuroscience, psychology, mathematics, computer science, and engineering to further driver behaviour modelling. It is uncommon that professionals from these fields have a thorough understanding of the other fields involved, but the author, not pretending to be an expert, argues that such a union of fields will be of significant value not only to transportation, but all behavioural sciences. The wealth of to-date knowledge amassed in neuroscience lies ready to be tapped by researchers interested in explaining human driver behaviour. To this end, the use of modem brain-imaging techniques will be invaluable in pinning down the neural correlates of particular driving subtasks, bearing in mind the extent of structural impacts on the brain of each individual, brought about by a lifetime of interaction with the environment. Thus, based on the findings of this literature study, the author proposes that supplementary work be conducted by a multi-disciplinary team to roll-out an experiment to study the nature of environmental stimuli as instigators of aggression and road rage, by drawing on knowledge about brain imaging and (amygdala) activation. / AFRIKAANSE OPSOMMING: Die vraagstuk hoe die verstand [denke] met die brein in verband staan, is een van die grootste uitdagings tans. Die materialistiese wêreldbeskouing sowel as die pragmatiese benadering van maatskaplike probleme word verander deur ontdekkings aangaande die wyse waarop menslike ervaring en kultuur hul in serebrale aktiwiteit voordoen. Desondanks is in hierdie poging, met die neurowetenskap aan die spits, die belangrike en omstrede kwessie van bestuurdersgedrag om een of ander rede agterweë gelaat. Uit 'n uitgebreide literatuurstudie kan afgelei word dat grootskaalse verontagsaming van die neurale basis van sodanige gedrag gekoppel aan 'n behavioristiese benadering endemies is aan die gebied. Talle kwalitatiewe sielkundige modelle en kunsmatige intelligensiemodelle is teëgekom, elk gepaard met debatte oor die geldigheid daarvan. Hoewel neurale netwerkmodelle gebaseer word op huidige kennis van verwerking binne die brein en ontplooi word in die industrie, trek menslike bestuurdersgedragmodellering nie voordeel uit hierdie revolusie in neurale inligtingsverwerking nie. Tot op hede is baie min gedoen om vas te stel waarom padgebruikers jaag, dronkbestuur, verbysteek of by kruispaaie toegee. Aangesien die sentrale senuweestelsel die menslike meettoestel in en van die wêreld is en dus die sleutelbeïnvloeder van menslike gedrag is, is dit van die uiterste belang om middele te investeer in die fundering van die gebied van bestuurdersgedragmodellering op 'n stewige basis daargestel deur die neurowetenskappe. Om'n menslike bestuurder te wees behels 'n omvattende komplement van verbandhoudende breinstelsels om bestuurstake (sielkundige funksies) te verrig, soos spoedkeuse, risikowaarneming en die vermyding van obstruksies. Die gepaste ontledingsvlak van so 'n sielkundige funksie is die vlak waarop daardie funksie in die brein verteenwoordig word. Die daarstelling van 'n teoretiese model van menslike gedrag, gebaseer op biologiese feite van die brein in die geheel, is gewis nog vir komende dekades 'n uitdaging, maar die gebied van bestuurdersgedrag moet deel uitmaak van so 'n pogmg. Samewerking is nodig tussen navorsers uit die neurowetenskappe, sielkunde, wiskunde, rekenaarwetenskap en ingenieurswese om bestuurdersgedragmodellering te bevorder. Dit is ongewoon dat vakkundiges uit hierdie velde 'n deeglike begrip het van die ander gebiede wat betrokke is, maar die outeur, sonder om voor te gee dat hy 'n deskundige is, betoog dat so 'n samesnoering van vakgebiede van betekenisvolle waarde sal wees, nie net vir die vervoerwese nie, maar ook vir al die gedragwetenskappe. Die omvang van die jongste kennis wat in die neurowetenskappe vergaar is, lê gereed om deur navorsers benut te word wat belang stel in die verklaring van menslike bestuurdersgedrag. Met dié doel sal die gebruik van moderne breinskanderingstegnieke van onskatbare waarde wees om die neurale korrelate van bepaalde bestuursubtake vas te pen, gedagtig aan die omvang van strukturele impakte op die brein van elke indiwidu teweeggebring deur 'n leeftyd van interaksie met die omgewing. Daarom, gebaseer op die bevindinge van hierdie literatuurstudie, stel die outeur voor dat addisionele werk gedoen word deur 'n multi-dissiplinêre span ten einde 'n eksperiment uit te voer, waarin die aard van stimuli uit die omgewing, wat lei tot padwoede, bestudeer kan word, met inagneming van die beskikbare kennis oor breinskandering en (amygdala) -aktiveringpatrone.

Road safety concerns are a legitimate concern when promoting increased bicycle use. Currently, the share of bicyclist traffic fatalities and injuries is not proportional to its mode share, and presents both a public health concern and a disincentive to people taking up or continuing to bicycle for transportation. Bicycling is not an inherently dangerous activity; automobile drivers pose the most risk of harm in crashes with bicyclists. Despite that, drivers' attitudes and behaviors toward bicyclists have not enjoyed much systematic study, particularly in the United States. This research explored the dimensions of drivers' attitudes toward bicyclists, including implicit bias and social attitudes, and examined the relationships between these attitudes and drivers' self-reported behaviors. The online survey included a cognitive test of respondents' implicit preference between drivers and bicyclists. The research questions are detailed in the introduction (Chapter 1), followed by a review of selected literature (Chapter 2) and detailed methodology (Chapter 3). The first set of results (Chapter 4) explores the potential usefulness of the implicit method and the attitude measures developed for this research, and presents an analysis of drivers' attitudes and what predicts more positive attitudes toward bicyclists. The second set of results (Chapter 5) extends the analysis to drivers' self-report behaviors, and how demographics, individual travel behavior, attitudes, and the built environment predict drivers' behaviors related to bicyclist safety. The dissertation concludes with a discussion of the contribution to the literature on driver attitudes and behaviors, and the implications for both practice and research.

Thesis (D.P.H.)--University of Michigan, 1996.

Thesis (Ph. D.)--University of Adelaide, 2004. / Title from title screen. Description based on contents viewed Apr. 21, 2005. Includes bibliographical references.