Development of a multivariate logistic model to predict bicycle route safety in urban areas

In response to the renewed appreciation of the benefits of bicycling to the environment and public health, public officials across the nation are working to establish new bicycle routes. During the past two decades, a number of methods have been endorsed for the selection of suitable bicycle routes. These methods are limited in that they do not explicitly address bicycle safety nor do they reflect urban conditions. The purpose of this research is to develop an objective bicycle route safety rating model based on injury severity. The model development was conducted using a logistic transformation of Jersey City\u27s bicycle crash data for the period 1997-2000. The resulting model meets a 90% confidence level by using various operational and physical factors (traffic volume, lane width, population density, highway classification, the presence of vertical. grades, one-way streets and truck routes) to predict the severity of an injury that would result from a crash that occurred at a specific location. The rating of the bicycle route\u27s safety is defined as the expected value of the predicted injury severity. This rating is founded on the premise that safe routes produce less severe accidents than unsafe routes. The contribution of this research goes beyond the model\u27s predictive capacity in comparing the safety of alternative routes. The model provides planners with an understanding, derived from objective data, of the factors that add to the route\u27s safety, the factors that reduce safety and the factors that are irrelevant. The model often confirms widely held beliefs as evidenced by the finding that highways with steep grades, truck routes and poor pavement quality create an unfavorable environment for bicyclists. Conversely, the model has found that increased volume and reduced lane width, at least in urban areas, actually reduce the likelihood of severe injury. Planners are encouraged to follow the lead of experienced bicyclists in choosing routes that travel through the urban centers as opposed to diverting bicyclists to circuitous routes on wide, low volume roads at the periphery of cities

Testing and training lifeguard visual search

Lifeguards play a crucial role in drowning prevention. However, current U.K. lifeguard qualifications are limited in training and assessing visual surveillance skills, and little is known about how lifeguards successfully detect drowning swimmers. To improve our understanding of lifeguard visual search skill, and explore the potential for improving this skill through training, this thesis had the following aims: (a) to identify whether visual skills for drowning detection improve with lifeguard experience, (b) to understand why such differences occur, and (c) design and valid a visual training intervention to improve drowning detection on the basis of these results. The first two studies investigated drowning-detection skills of participants with differing levels of lifeguard experience in a dynamic search task with simulated drownings. Lifeguards were found to detect drownings faster and more often than non-lifeguards. In three follow-up studies these results were replicated with more naturalistic stimuli. Video footage from an American wave pool was extracted, which showed genuine instances of swimmer distress. Results again demonstrated lifeguard superiority in detecting the drowning targets. Eye tracking measures, recorded on both the simulated and naturalistic clips, failed to reveal any differences between lifeguards and non-lifeguards, suggesting that superior drowning detection for lifeguards did not result from better scanning strategies per se. Following this, two cognitive mechanisms that may underlie drowning-detection skill were investigated. Lifeguard and non-lifeguard performance on Multiple Object Avoidance (MOA) and Functional Field of View (FFOV) tests was assessed. Although lifeguards had better MOA task performance compared to non-lifeguards, only the lifeguards’ accuracy at detecting the central target in the FFOV task predicted performance on a subsequent drowning detection task. It was concluded that superior drowning detection was a result of better classification recognition of drowning swimmers (which was the central task in the FFOV test). Based on these findings the final experiment explored the effectiveness of an intense classification training task to improve drowning detection. An intervention was designed that required participants to differentiate between videos of isolated drowning and non-drowning swimmers. Non-lifeguards trained in this intervention showed greater improvement on a subsequent drowning-detection task compared to untrained control participants, who completed an active-control task. The results of this thesis suggest that drowning-detection skill can be reliably assessed, and that foveal processing of drowning characteristics is key to lifeguards' superior performance. Isolating and training this key sub-skill improves drowning-detection performance and offers a method for training future lifeguards

Methods and techniques for analyzing human factors facets on drivers

Mención Internacional en el título de doctorWith millions of cars moving daily, driving is the most performed activity worldwide. Unfortunately, according to the World Health Organization (WHO), every year, around 1.35 million people worldwide die from road traffic accidents and, in addition, between 20 and 50 million people are injured, placing road traffic accidents as the second leading cause of death among people between the ages of 5 and 29. According to WHO, human errors, such as speeding, driving under the influence of drugs, fatigue, or distractions at the wheel, are the underlying cause of most road accidents. Global reports on road safety such as "Road safety in the European Union. Trends, statistics, and main challenges" prepared by the European Commission in 2018 presented a statistical analysis that related road accident mortality rates and periods segmented by hours and days of the week. This report revealed that the highest incidence of mortality occurs regularly in the afternoons during working days, coinciding with the period when the volume of traffic increases and when any human error is much more likely to cause a traffic accident. Accordingly, mitigating human errors in driving is a challenge, and there is currently a growing trend in the proposal for technological solutions intended to integrate driver information into advanced driving systems to improve driver performance and ergonomics. The study of human factors in the field of driving is a multidisciplinary field in which several areas of knowledge converge, among which stand out psychology, physiology, instrumentation, signal treatment, machine learning, the integration of information and communication technologies (ICTs), and the design of human-machine communication interfaces. The main objective of this thesis is to exploit knowledge related to the different facets of human factors in the field of driving. Specific objectives include identifying tasks related to driving, the detection of unfavorable cognitive states in the driver, such as stress, and, transversely, the proposal for an architecture for the integration and coordination of driver monitoring systems with other active safety systems. It should be noted that the specific objectives address the critical aspects in each of the issues to be addressed. Identifying driving-related tasks is one of the primary aspects of the conceptual framework of driver modeling. Identifying maneuvers that a driver performs requires training beforehand a model with examples of each maneuver to be identified. To this end, a methodology was established to form a data set in which a relationship is established between the handling of the driving controls (steering wheel, pedals, gear lever, and turn indicators) and a series of adequately identified maneuvers. This methodology consisted of designing different driving scenarios in a realistic driving simulator for each type of maneuver, including stop, overtaking, turns, and specific maneuvers such as U-turn and three-point turn. From the perspective of detecting unfavorable cognitive states in the driver, stress can damage cognitive faculties, causing failures in the decision-making process. Physiological signals such as measurements derived from the heart rhythm or the change of electrical properties of the skin are reliable indicators when assessing whether a person is going through an episode of acute stress. However, the detection of stress patterns is still an open problem. Despite advances in sensor design for the non-invasive collection of physiological signals, certain factors prevent reaching models capable of detecting stress patterns in any subject. This thesis addresses two aspects of stress detection: the collection of physiological values during stress elicitation through laboratory techniques such as the Stroop effect and driving tests; and the detection of stress by designing a process flow based on unsupervised learning techniques, delving into the problems associated with the variability of intra- and inter-individual physiological measures that prevent the achievement of generalist models. Finally, in addition to developing models that address the different aspects of monitoring, the orchestration of monitoring systems and active safety systems is a transversal and essential aspect in improving safety, ergonomics, and driving experience. Both from the perspective of integration into test platforms and integration into final systems, the problem of deploying multiple active safety systems lies in the adoption of monolithic models where the system-specific functionality is run in isolation, without considering aspects such as cooperation and interoperability with other safety systems. This thesis addresses the problem of the development of more complex systems where monitoring systems condition the operability of multiple active safety systems. To this end, a mediation architecture is proposed to coordinate the reception and delivery of data flows generated by the various systems involved, including external sensors (lasers, external cameras), cabin sensors (cameras, smartwatches), detection models, deliberative models, delivery systems and machine-human communication interfaces. Ontology-based data modeling plays a crucial role in structuring all this information and consolidating the semantic representation of the driving scene, thus allowing the development of models based on data fusion.I would like to thank the Ministry of Economy and Competitiveness for granting me the predoctoral fellowship BES-2016-078143 corresponding to the project TRA2015-63708-R, which provided me the opportunity of conducting all my Ph. D activities, including completing an international internship.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: José María Armingol Moreno.- Secretario: Felipe Jiménez Alonso.- Vocal: Luis Mart

Analyzing human factors in road accidents: TRACE WP5 Summary Report

The main objectives of TRACE WP5 'Human factors' deliverables are: i) To support a better standardization of accident analysis in Europe on a scientific background, ii) To provide operational models and methodological classification grids dealing with 'human factors' aspects involved in road accidents, iii) To promote a comprehensive analysis of the involvement of human beings, going further than the usual 'user-orientated causal analysis' often limited at establishing the driver 'at fault' and without searching for the background reasons of the problems met par road users. Such objectives involve analyzing accidents as the symptom of the difficulties met by drivers in certain driving situations, and as a revelatory of their needs in help. Two questions have to be asked in order to progress in the understanding of accident causation: 1) What are precisely and operationally the human failures in accidents? But also: 2) What are the reasons for these human failures? Keeping in mind that these reasons are of multiple natures and combine most of the time to produce the final event. By so doing, the definition of typical scenarios of 'human error' production can open to the definition of more appropriate countermeasures, well fitted to human needs

A Comparative Study of Middle School Deaf Students’ Perceptions towards Vocational Internships According to Their Gender, Grade Level and Family Income at The Special Education School of Qujing, China

The purpose of this study was to identify the demographic factors of the deaf students, to determine the deaf students’ perceptions towards vocational internships, and to compare the deaf students’ perceptions towards vocational internships at the Special Education School of Qujing according to gender, grade level and family income in 2015. A total of 147 deaf students (106 male and 41 female), from grade level 7 to vocational high school completed the survey. Statistical measures employed included frequency and percentage, mean and standard deviation, one-way ANOVA and independent samples t-test. The result of this study has indicated that gender difference was not a significant issue to impact students’ perceptions, yet the researcher discovered that students from different grade levels and different extents of family income had significant perception differences.Specifically, students from a higher grade level had higher perceptions than those from lower grade levels. In terms of family income, students from families whose monthly income was lower or included 1000 RMB had lower perceptions than other students. Recommendations for directors, teachers, the school, the students and future researchers are provided

The historical development and basis of human factors guidelines for automated systems in aeronautical operations

In order to derive general design guidelines for automated systems a study was conducted on the utilization and acceptance of existing automated systems as currently employed in several commercial fields. Four principal study area were investigated by means of structured interviews, and in some cases questionnaires. The study areas were aviation, a both scheduled airline and general commercial aviation; process control and factory applications; office automation; and automation in the power industry. The results of over eighty structured interviews were analyzed and responses categoried as various human factors issues for use by both designers and users of automated equipment. These guidelines address such items as general physical features of automated equipment; personnel orientation, acceptance, and training; and both personnel and system reliability