Developing Driving Behaviour Models Incorporating the Effects of Stress

Driving is a complex task and several factors influence drivers’ decisions and performance including traffic conditions, attributes of vehicles, network and environmental characteristics, and last but not least characteristics of the drivers themselves. in an effort to better explain and represent driving behaviour, several driving behaviour models have been suggested over the years. In the existing literature, there are two main streams of driving behaviour models that can be found. The first is approaching driving behaviour from a human factors and cognitive perspective while the second is engineering-based. Driving behaviour models of the latter category are mathematical representations of drivers’ behaviour at the individual level, mostly focussing on acceleration/deceleration, lane-change and gap-acceptance decisions. Many of these factors are captured by existing driving behaviour models used in microscopic simulation tools. However, while the vast majority of existing models is approximating driving behaviour, primarily focusing on the effects of traffic conditions, little attention has been given to the impact of drivers’ characteristics. The aim of the current thesis is to investigate the effects of stress on driving behaviour and quantify its impact using an econometric modelling framework. This main research question emerged as a result of a widely acknowledged research gap in existing engineering-based driving behaviour models related to the incorporation of human factors and drivers’ characteristics within the model specification. The research was based on data collected using the University of Leeds Driving Simulator. Two main scenarios were presented to participants, while they were also deliberately subjected to stress induced by time pressure and various scenarios. At the same time, stress levels were measured via physiological indicators. Sociodemographic and trait data was also collected in the form of surveys. The data has been initially analysed for each main scenario and several statistics are extracted. The results show a clear effect of time pressure in favour of speeding, however relations related to physiological responses are not always clear. Moreover, two driving behaviour models are developed, a gap-acceptance and a car-following model. In the former model, increase in physiological responses is related to higher probability of accepting a gap and time pressure has a positive effect of gap-acceptance probability as well. In the car-following model, stress is associated with increased acceleration and potentially a more aggressive driving style. The aforementioned analysis is based on data collected in a driving simulator. Given the potential differences in driving behaviour between real and simulated driving, the transferability of a model based on the latter data to field traffic setting is also investigated. Results indicate significant differences in parameters estimated from a video and the simulator dataset, however these differences can be significantly reduced after applying parameter updating techniques. The findings in this thesis show that stress and drivers’ characteristics can influence driving behaviour and thus should be considered in the driving behaviour models for microscopic simulation applications. However, for real life applications, it is suggested that the extent of these effects should be treated with caution and ideally rescaled based on real traffic observations

Aerospace Medicine and Biology. A continuing bibliography with indexes

This bibliography lists 244 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1981. Aerospace medicine and aerobiology topics are included. Listings for physiological factors, astronaut performance, control theory, artificial intelligence, and cybernetics are included

Work Zone Safety Analysis, Investigating Benefits from Accelerated Bridge Construction (ABC) on Roadway Safety

The attributes of work zones have significant impacts on the risk of crash occurrence. Therefore, identifying the factors associated with crash severity and frequency in work zone locations is of important value to roadway safety. In addition, the significant loss of workers’ lives and injuries resulting from work zone crashes indicates the emergent need for a comprehensive and in-depth investigation of work zone crash mechanisms. The cost of work zone crashes is another issue that should be taken into account as work zone crashes impose millions of dollars on society each year. Applying innovative construction methods like Accelerated Bridge Construction (ABC) dramatically decreases on-site construction duration and thus improves roadway safety. This safe and cost-effective procedure for building new bridges or replacing/rehabilitating existing bridges in just a few weeks instead of months or years may prevent crashes and avoid injuries as a result of work zone presence. The application of machine learning techniques in traffic safety studies has seen explosive growth in recent years. Compared to statistical methods, MLs are more accurate prediction models due to their ability to deal with more complex functions. To this end, this study focuses on three major areas: crash severity at construction work zones with worker presence, crash frequency at bridge locations, and assessment of the associated costs to calculate the contribution of safety to the benefit-cost ratio of ABC as compared to conventional methods. Some key findings of this study can be highlighted as in-depth investigation of contributing factors in conjunction with the results from statistical and machine learning models, which can provide a more comprehensive interpretation of crash severity/frequency outcomes. The demonstration of work zone crashes needs to be modeled separately by time of day for severity analysis with a high level of confidence. Investigation of the contributing factors revealed the nonlinear relationship between crash severity/frequency and contributing factors. Finally, the results showed that the safety benefits from a case study in Florida consisted of 43% of the total ABC implementation cost. This indicates that the safety benefits of ABC implementation consist of a considerable portion of its benefit-cost ratio

Cognitive correlates of sleepiness and sleep disruption in everyday domestic settings

Sleepiness and sleep disruption caused by cohabitees could have deleterious cognitive consequences in everyday life. Research in this area is scarce, thus cognitive correlates of varying degrees of sub-optimal sleep patterns in five groups of healthy adults in domestic settings were studied. The groups studied included adults living with healthy partners, adults living with partners with a chronic, sleep-disrupting illness (Parkinson's disease), and mothers of young children

Transfers and path choice in urban public transport systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 2008.Includes bibliographical references (p. 285-294).Transfers are endemic in public transport systems. Empirical evidence shows that a large portion of public transport journeys involve at least one change of vehicles, and that the transfer experience significantly affects the travelers' satisfaction with the public transport service, and whether they view public transport as an effective option. Despite their importance, however, transfers have long been overlooked by decision-makers, transportation planners, and analysts. Transfer-related research, practice, and investments are rare compared with many other aspects of transportation planning, probably because (1) the underlying transfer behavior is too complex; (2) the analysis methods are too primitive; and (3) the applications are not straightforward. This dissertation focuses on these issues and contributes to current literature in three aspects: methodology development, behavior exploration, and applications in practice. In this research, I adopt a path-choice approach based on travelers' revealed preference to measure the disutility associated with transfer, or the so-called transfer penalty. I am able to quantify transfer experience in a variety of situations in great spatial detail, and reduce the external "noises" that might contaminate the model estimation. I then apply the method to two public transport networks: a relative small and simple rail network (subway and commuter rail) in Boston and a large and complex network (Underground) in London. Both networks offer a large variability of transfer environment and transfer activities. Estimation results show high system-wide transfer penalties in both studies, indicating that transfer experience can have a very negative impact on the performance and competitiveness of public transport. They also suggest that the system-average value has limited applications in planning and operation because the transfer penalty varies greatly across station and movement. Such variation is largely caused by different transfer environments, not by different personal characteristics, attitudes, preferences, or perceptions, at least in the two investigated networks.The two applications to the London Underground network illustrate that the lack of careful consideration of transfer effect can lead to inaccurate passenger flow estimation as well as less credible project evaluation and investment justification. The results further confirm the potential, as well as the importance, of transfer planning in major multimodal public transport networks.by Zhan Guo.Ph.D