Changes In Driving Behavior Research Articles

Identifying population segments for effective intervention design and targeting using unsupervised machine learning: an end-to-end guide

One-size-fits-all interventions that aim to change behavior are a missed opportunity to improve human health and well-being, as they do not target the different reasons that drive people's choices and behaviors. Psycho-behavioral segmentation is an approach to uncover such differences and enable the design of targeted interventions, but is rarely implemented at scale in global development. In part, this may be due to the many choices program designers and data scientists face, and the lack of available guidance through the process. Effective segmentation encompasses conceptualization and selection of the dimensions to segment on, which often requires the design of suitable qualitative and quantitative primary research. The choice of algorithm and its parameters also profoundly shape the resulting output and how useful the results are in the field. Analytical outputs are not self-explanatory and need to be subjectively evaluated and described. Finally, segments can be prioritized and targeted with matching interventions via appropriate channels. Here, we provide an end-to-end overview of all the stages from planning, designing field-based research, analyzing, and implementing a psycho-behavioral segmentation solution. We illustrate the choices and critical steps along the way, and discuss a case study of segmentation for voluntary medical male circumcision that implemented the method described here. Though our examples mostly draw on health interventions in the developing world, the principles in this approach can be used in any context where understanding human heterogeneity in driving behavior change is valuable.

GLOBAL COUNCIL ON BRAIN HEALTH LIFESTYLE RECOMMENDATIONS FOR ADULTS 50+

Lifestyle modifications are at the heart of efforts to promote healthy aging and risk reduction for cognitive decline and dementia. The Global Council on Brain Health (GCBH) is an independent collaborative of scientists, clinicians, scholars and policy experts convened by AARP to provide evidence-based advice on what people and professionals can do to maintain and improve brain health. The Council translates scientific research into actionable recommendations for the public that will help drive behavior change in individuals across communities and cultures. 2019 represents a milestone year for the Council as it prepares to address the last major lifestyle factor in our systematic review of topics based on the 2015 AARP Survey on Brain Health. This presentation will provide highlights from the multi-year effort to systematically examine lifestyle factors including topics such as physical activity, sleep, cognitively stimulating activities, and nutrition. This body of work will serve as the springboard for future Council work. Issue specialists from around the world were brought together to build consensus and issue lifestyle recommendations based upon the cumulative body of evidence. These experts have conducted research that has significantly contributed to the body of evidence linking a range of modifiable risk factors with brain health in older adults. This presentation will feature consensus points and recommendations issued by the Council. It will also showcase statements and practical tips generated by the Council that are based on the latest research advancements. Data from surveys fielded by AARP research, developed in consultation with the GCBH, will also be featured. In sum, this presentation will highlight the work of the Council at the forefront of this international effort. Particular emphasis will be paid to the scientific and policy dimensions of brain health, with an emphasis on individuals aged 50 and older.

Understanding institutional bricolage: What drives behavior change towards sustainable land use in the Eastern Amazon?

Institutional perspectives and theories have shaped how problems related to individual and collective choices in forest landscapes are perceived. This article explores the social impacts of the Rural Environmental Cadastre (CAR) in the municipality of Sao Felix do Xingu, in the state of Para, Brazilian Amazon. CAR is a tenure clarification and environmental compliance intervention to reduce deforestation and improve forest conservation in the Brazilian Amazon. With a focus on smallholders, the article provides insights on how to better understand institutional bricolage and behavior change in the context of social practices. Results suggest that CAR implementation is still too limited to adequately reflect the heterogeneity and complexity of reducing deforestation in the Eastern Amazon. The SFX case shows that land users responsible for pursuing environmental compliance normally draw on existing traditions – styles of thinking, sanctioned social relationships – to respond to changing situations. Neglecting such social processes and practices can decrease the long-term effectiveness of interventions such as CAR. This is usually a result of unforeseen interactions among activities practiced by the range of different actors in forest landscapes. Concluding remarks argue that, unless we shift our approach from focusing on changing individuals’ behavior to changing the interaction of elements that constitute social practices, we will remain blind to the conditions that actually drive behavior change towards environmental compliance and forest conservation.

Behavior change techniques in health professional training: developing a coding tool.

Health professional Continuing Professional Development (CPD) courses often aim to change practice; understanding which training techniques drive behavior change can help educators facilitate this. The 93-item Behaviour Change Technique Taxonomy (BCTT) describes behavior change techniques (BCTs) used in behavior change interventions but was not designed for understanding CPD; it is necessary to explore how best to use the BCTT in this context. This study aimed to explore the BCTs used by CPD course educators to change healthcare practice and to develop and pilot an e-tool, based on the BCTT, to enable course designers and educators to understand which BCTs are in their training. This understanding could lead to enhanced CPD and an experimental approach to assessing the benefits of including a variety of BCTs in CPD. Two psychologists, trained in using the BCTT, observed three postgraduate medical CPD courses. In Phase 1, the BCTT was used to code 26 hours of observations. An e-tool including observed BCTs was developed and used to code 35 hours of observations in Phase 2. Feedback was collected through short discussions with educators from each course. The tool was further refined in Phase 3. Thirty-seven BCTs were identified in Phase 1, a further four in Phase 2, and a further two in Phase 3. The final e-tool comprised 43 BCTs with examples of their use based on course observations to aid identification, since educators fed back that they would value an uncomplicated tool with practice-related examples. A coding tool to understand the active ingredients in health professional CPD could enable educators to maximize the impact of CPD on practice. Further work should explore whether educators themselves are able to use the tool to code their training interventions.

Behavioral Changes to Repeated Takeovers in Highly Automated Driving: Effects of the Takeover-Request Design and the Nondriving-Related Task Modality.

We investigated drivers' behavior and subjective experience when repeatedly taking over their vehicles' control depending on the design of the takeover request (TOR) and the modality of the nondriving-related task (NDRT). Previous research has shown that taking over vehicle control after highly automated driving provides several problems for drivers. There is evidence that the TOR design and the NDRT modality may influence takeover behavior and that driver behavior changes with more experience. Forty participants were requested to resume control of their simulated vehicle six times. The TOR design (auditory or visual-auditory) and the NDRT modality (auditory or visual) were varied. Drivers' takeover behavior, gaze patterns, and subjective workload were recorded and analyzed. Results suggest that drivers change their behavior to the repeated experience of takeover situations. An auditory TOR leads to safer takeover behavior than a visual-auditory TOR. And with an auditory TOR, the takeover behavior improves with experience. Engaging in the visually demanding NDRT leads to fewer gazes on the road than the auditory NDRT. Participants' fixation duration on the road decreased over the three takeovers with the visually demanding NDRT. The results imply that (a) drivers change their behavior to repeated takeovers, (b) auditory TOR designs might be preferable over visual-auditory TOR designs, and (c) auditory demanding NDRTs allow drivers to focus more on the driving scene. The results of the present study can be used to design TORs and determine allowed NDRTs in highly automated driving.

Assessment of the impact of selected factors forcing the driver’s energy consumption

The drivers' workplace has an impact on the safety of transport. Among the factors causing changes in driver behavior are the anthropechnical factors resulting from the actions of people in the vehicle and its surroundings, external ones resulting from the impact of weather conditions as well as the condition of the infrastructure and work resulting from the operation of the means of transport.One of such working factors is the noise which may be the source of: a drive unit, drive transmission system, suspension system, etc. The paper attempts to identify and assess the impact of this factor on the energy consumption of the driver's work.

Adaptive driver modelling in ADAS to improve user acceptance: A study using naturalistic data

Accurate understanding of driver behaviour is crucial for future Advanced Driver Assistance Systems (ADAS) and autonomous driving. For user acceptance it is important that ADAS respect individual driving styles and adapt accordingly. Using data collected during a naturalistic driving study carried out at the University of Southampton, we assess existing models of driver acceleration and speed choice during car following and when cornering. We observe that existing models of driver behaviour that specify a preferred inter-vehicle spacing in car-following situations appear to be too prescriptive, with a wide range of acceptable spacings visible in the naturalistic data. Bounds on lateral acceleration during cornering from the literature are visible in the data, but appear to be influenced by the minimum cornering radii specified in design codes for UK roadway geometry. This analysis of existing driver models is used to suggest a small set of parameters that are sufficient to characterise driver behaviour in car-following and curve driving, which may be estimated in real-time by an ADAS to adapt to changing driver behaviour. Finally, we discuss applications to adaptive ADAS with the objectives of improving road safety and promoting eco-driving, and suggest directions for future research.

Modeling and Evaluating Traffic Flow at Sag Curves When Imposing Variable Speed Limits on Connected Vehicles

Sag curves, road segments where the gradient changes from downwards to upwards, generally reduce the roadway capacity and cause congestion. This results from a change in longitudinal driving behavior when entering a sag curve as drivers tend to reduce speeds or increase headways as vehicles reach the uphill section. In this research, a control strategy is investigated through manipulating the speed of connected vehicles (CVs) in the upstream of the sag curve to avoid the formation of bottlenecks caused by the change in driver behavior. Traffic flow along a sag curve is simulated using the intelligent driver model (IDM), a time-continuous car-following model. A feedback control algorithm is developed for adjusting the approach speeds of CVs so that the throughput of the sag curve is maximized. Depending on the traffic density at the sag curve, adjustments are made for the speeds of the CVs. A simulation-based optimization method using a meta-heuristic algorithm is employed to determine the critical control parameters. Various market penetration rates for CVs are also considered in the simulations. Even at relatively low market penetration rates (e.g., 5–10%), significant improvements in travel times and throughput are observed.

P30 - Ten Communication and Delivery Methods for Teaching Kitchens that Drive Behavior Change Across the Life Cycle

Background (Background, Rationale, Prior Research, and/or Theory): Since 1995 FamilyCook Productions (FCP) has developed and evaluated hands-on cooking nutrition education across the life-cycle. FCP continually evaluates and refines it with the most responsive tactics and disseminates to audiences across 30 + states. Objective: This study seeks to fill a gap in the process of nutrition education with the development of evidenced-based Communication & Delivery Methods (CDMs) developed over 20 years of programming by FCP to drive positive and sustained healthy food behaviors. Study Design, Setting, Participants, Intervention: FCP nutrition education sessions with preschool children and parents; elementary children; middle/high school; and adults childen across the U.S. from 1995 to 2017. Outcome Measures and Analysis: Observations and analysis sessions that recorded specific CDMs that appeared to be connected to behavior change. Qualitative interviews with preschool parents and high school students that had attended FCP programming that asked about post intervention behavior change and what program elements CDMs participants felt facilitated their behavior change process. Results: Ten CDMs emerged from FCP's program development, refinement, evaluation, dissemination and interviews with program participants: challenge (push participants out of their comfort zone); peer support (create level playing field); experiencing success (weekly sense of achievement); celebratory (create enjoyable atmosphere); skill building (move from “student” to “teacher”); palate development (discover new flavors); skill reinforcement (repetition to build confidence); recipe concepts (move beyond recipe driven cooking); collaboration (value group over individual accomplishment); and home environment (address home dynamics/facilities, access to healthy food). Conclusions and Implications: These insights on effective CDM can help to shape and improve the foundation of existing and future interventions for all ages that can help nutrition education, especially theory-based and behaviorally focused nutrition education, to have high quality delivery that can lead to engaged participants during the interventions and more sustained behavior change. Funding: None.

Mind over motor mapping: Driver response to changing vehicle dynamics.

Improvements in vehicle safety require understanding of the neural systems that support the complex, dynamic task of real-world driving. We used functional near infrared spectroscopy (fNIRS) and pupilometry to quantify cortical and physiological responses during a realistic, simulated driving task in which vehicle dynamics were manipulated. Our results elucidate compensatory changes in driver behavior in response to changes in vehicle handling. We also describe associated neural and physiological responses under different levels of mental workload. The increased cortical activation we observed during the late phase of the experiment may indicate motor learning in prefrontal-parietal networks. Finally, relationships among cortical activation, steering control, and individual personality traits suggest that individual brain states and traits may be useful in predicting a driver's response to changes in vehicle dynamics. Results such as these will be useful for informing the design of automated safety systems that facilitate safe and supportive driver-car communication.

The human factor: Classification of European community-based behaviour change initiatives

Behaviour, practices and culture constitute a powerful human factor in the energy system; in particular the interactions between technologies, practices and norms lock individuals in to certain patterns of (often inefficient) energy use. Consequently, behaviour change has gained traction amongst policymakers as a key area of intervention given the impact energy-related behaviours have on climate change. Given the increasing emphasis within policy perspectives in the European Union, it is surprising that a gap in understanding of the success factors of behaviour change initiatives remains. This paper addresses this gap by identifying and characterising behaviour change initiatives across five European countries (the UK, Ireland, France, Italy, and Spain). The paper provides insights into the success factors and commonly encountered barriers to behaviour change initiatives. Initiatives are classified into 6 broad categories (community-based interventions; information and awareness based interventions; eco-districts; show-case events; energy switching; and smart-technology focused interventions). The results suggest that there are significant knowledge gaps between what is known to work to engage individuals in behavioural change and what is currently being applied in practice. An over-reliance on education and awareness-raising projects is evident, illustrating that such projects do not sufficiently aim for sustained behavioural changes. A dearth of projects incorporating fiscal measures, regulations or legislation to drive behaviour change reflects reluctance on behalf of decision-makers to engage widely with diverse approaches to foster lifestyle change. This paper contributes understandings of the different models and delivery tools employed to change energy-related behaviours; insights into the critical success factors that underpin best practice and the barriers to action; and a ‘what works in practice’ overview of the meaningful approaches to change behaviour.

Conceptual Model for an Intelligent Persuasive Driver Assistant

Traffic congestion is a serious issue for large cities. This is especially critical for cities that has insufficient mass transit system like Bangkok. Although transportation infrastructure projects and rail mass transit lines are being implemented, these efforts require major financial investment and take a long time to complete. This work proposes to help reduce traffic problems through influencing a change in driver behavior. In this initial stage, a model for an intelligent persuasive driver assistant is conceptualized as a voice-interactive smart assistant on a smartphone. The system uses information about the driver, his physical state, vehicle performance information, and geolocation information to form persuasive strategies to influence driver behavior and to adapt user interfaces and interactions to reduce driver distraction. Integrating these components together is expected to provide improved assistance in driving tasks and affect driving behavior changes. Keywords: intelligent driver assistant, navigation, smart assistant, persuasive technology

How Placemaking and Positive Incentives Can Enhance Urban Walkability and Revolutionize the Citizens’ Experience of Streets as Public Spaces ( symposia )

Background Cities increasingly celebrate their public spaces incl. streets through placemaking as one tool to create awareness on their societal value. Strong partnerships are required from grassroots to governments in promoting safe, walkable and accessible public spaces. Events from Nairobi and Cape Town will be illustrated. For too long – strategies such as charging, restrictions and regulation – were used to “force” a shift to sustainable mobility. It is time to adopt a ‘reward rather than punishment’ approach. The EMPOWER project looks at positive behaviour change incentives that guide and enable, rather than eliminate or restrict mobility choice. Aims To promote walkability and vibrant streets – incl. examples from the Global South; To illustrate ways on activating streets through a variety of low-cost interventions including pop-up activities; To advocate for rewarding change via positive incentives such as information points, discounts, community support and games, and; To generate partnerships and exchange among audience. Method Speakers will present on Case Studies that come from developed and developing contexts in order to compare and assess walkability incentives. Examples: Nairobi: Nairobi’s Placemaking week happened in November 2016 which led to a revolution of experiencing the city differently. The event leveraged on the expertise of stakeholders from government, civil society and academia. The week was initiated by the Nairobi City Council with technical support from UN-Habitat; Cape Town: Open Streets Cape Town was founded in 2012 and enjoys the full support of the City. OSCT has its roots in street action and research, and has grown as a dynamic movement of street-minded citizens. OSCT challenges the paradigm of urban mobility via campaigns, temporary interventions, dialogues and walks to spark public debate, and drive behaviour change around the role of streets; Europe: The activities of the EMPOWER project started in May 2015 to gather evidence on the power of positive incentives and social innovation to promote Sustainable Transport. Using smartphones, new mobility services are designed to deliver personalised incentives to the public. Examples will be shared where positive incentives delivered on enhanced walkability. Conclusion The session will share successful projects and promote partnerships for future collaborations.

Understanding the mechanism of traffic hysteresis and traffic oscillations through the change in task difficulty level

This paper provides a detailed understanding of the mechanism of traffic hysteresis and traffic oscillations from the driver behavior perspective. Microscopic evaluation of trajectories inside seven selected oscillations is performed to obtain a comprehensive picture of these puzzling phenomena. A new method based on driver's task difficulty (TD) profile is proposed to capture changes in driver behavior in response to the disturbance caused by traffic oscillations. A close connection between the TD profile and evolution (such as formation and growth) of the stop-and-go traffic oscillations is found. Furthermore, driver behaviors inside the oscillations are identified based on driver's TD profile, and their connection with hysteresis magnitudes is established. Finally, a generalized linear model suggests that variables related to traffic flow and driver characteristics are significant predictors of hysteresis magnitude. One noteworthy finding is that, the bigger the difference between the average TD levels between deceleration and acceleration phases of a vehicle trajectory, the larger the hysteresis magnitude becomes.

Negotiating the Edge: The Rationalization of Sexual Risk Taking Among Western Male Sex Tourists to Thailand

Every year thousands of Western men travel to Thailand as sex tourists to participate in paid-for sex. Although many of these men will use condoms to protect themselves against sexually transmitted infections (STIs), others will not, despite the risks. By applying Steven Lyng’s (1990) concept of edgework to data collected from 14 face-to-face interviews with male sex tourists in Pattaya, Thailand, and 1,237 online discussion board posts, this article explores the ways in which these men understood and sought to rationalize the sexual risks they took. We argue that notions of likelihood of infection and significance of consequence underpin these behaviors, and we identify the existence of understandings of sexual risk that reject mainstream safer-sex messages and frame condomless sex as a broadly safe activity for heterosexual men. The article concludes by summarizing the difficulties inherent in driving behavior change among this group of men, for whom sexual risks appear to be easily rationalized away as either inconsequential or irrelevant.

A new method for evaluating driver behavior and interventions for passive railway level crossings with pneumatic tubes

ABSTRACTThe recent development of new technologies has attracted the interest of the rail industry to increase driver awareness at railway-level crossings. Such interventions have been predominantly tested in driving simulators, but driver behavior at level crossings is still poorly understood due to the limited number, focus, and period of time of studies conducted. This highlights the lack of manageable methods for studying driver behavior on a long-term basis in naturalistic conditions, particularly for passive-level crossings. The authors propose a new, practical, and affordable application of pneumatic tubes to evaluate over the long term the compliance of drivers at level crossings and their speed during the approach of level crossing with low road-traffic volume. The authors tested this methodology at two railway-level crossings in the Brisbane region and recorded more than 10,000 vehicle movements for each site over a 6-month data collection. The authors showed that this methodology detected differences in driver compliance at the two crossings that had different sighting characteristics. The authors also showed by sensitivity analysis that compliance estimates from this approach were within a ±6% range, which would be sufficient for detecting significant changes in driver behavior with new interventions for level crossings.

At-Risk Driving Behavior in Drivers with Diabetes: A Neuroergonomics Approach

This pilot study tackles the overarching need for driver-state detection through real-world measurements of driver behavior and physiology in at-risk drivers with type 1 diabetes mellitus (DM). 35 drivers (19 DM, 14 comparison) participated. Real-time glucose levels were measured over four weeks with continuous glucose monitor (CGM) wearable sensors. Contemporaneous real-world driving performance and behavior were measured with in-vehicle video and electronic sensor instrumentation packages. Results showed clear links between at-risk glucose levels (particularly hypoglycemia) and changes in driver performance and behavior. DM participants often drove during at-risk glucose levels (low and high) and showed cognitive impairments in key domains for driving, which are likely linked to frequent hypoglycemia. The finding of increased driving risk in DM participants was mirrored in state records of crashes and traffic citations. Combining sensor data and phenotypes of driver behavior can inform patients, caregivers, safety interventions, policy, and design of supportive in-vehicle technology that is responsive to driver state.

A spectral power analysis of driving behavior changes during the transition from nondistraction to distraction

ABSTRACTObjective: This article investigated and compared frequency domain and time domain characteristics of drivers' behaviors before and after the start of distracted driving.Method: Data from an existing naturalistic driving study were used. Fast Fourier transform (FFT) was applied for the frequency domain analysis to explore drivers' behavior pattern changes between nondistracted (prestarting of visual–manual task) and distracted (poststarting of visual–manual task) driving periods. Average relative spectral power in a low frequency range (0–0.5 Hz) and the standard deviation in a 10-s time window of vehicle control variables (i.e., lane offset, yaw rate, and acceleration) were calculated and further compared. Sensitivity analyses were also applied to examine the reliability of the time and frequency domain analyses.Results: Results of the mixed model analyses from the time and frequency domain analyses all showed significant degradation in lateral control performance after engaging in visual–manual tasks while driving. Results of the sensitivity analyses suggested that the frequency domain analysis was less sensitive to the frequency bandwidth, whereas the time domain analysis was more sensitive to the time intervals selected for variation calculations. Different time interval selections can result in significantly different standard deviation values, whereas average spectral power analysis on yaw rate in both low and high frequency bandwidths showed consistent results, that higher variation values were observed during distracted driving when compared to nondistracted driving.Conclusions: This study suggests that driver state detection needs to consider the behavior changes during the prestarting periods, instead of only focusing on periods with physical presence of distraction, such as cell phone use. Lateral control measures can be a better indicator of distraction detection than longitudinal controls. In addition, frequency domain analyses proved to be a more robust and consistent method in assessing driving performance compared to time domain analyses.

Factors affecting perceived change in physical activity in pregnancy

Objectivereduction of physical activity (PA) during pregnancy is common but undesirable, as it is associated with negative outcomes, including excessive gestational weight gain. Our objective was to explore changes in five types of activity that occurred during pregnancy and the behavioural determinants of the reported changes in PA. Designwe performed a secondary analysis of a cross sectional survey that was constructed using the ASE-Model – an approach to identifying the factors that drive behaviour change that focuses on Attitude, Social influence, and self-Efficacy. Participants455 healthy pregnant women of all gestational ages, receiving prenatal care from midwifery practices in the Netherlands. Findingsmore than half of our respondents reported a reduction in their PA during pregnancy. The largest reduction occurred in sports and brief rigorous activities, but other types of PA were reduced as well. Reduction of PA was more likely in women who considered themselves as active before pregnancy, women who experienced pregnancy-related barriers, women who were advised to reduce their PA, and multiparous women. Fewer than 5% increased their PA. Motivation to engage in PA was positively associated with enjoying PA. Key conclusions and implications for practiceall pregnant women should be informed about the positive effects of staying active and should be encouraged to engage in, or to continue, moderately intensive activities like walking, biking or swimming. Our findings concerning the predictors of PA reduction can be used to develop an evidence-based intervention aimed at encouraging healthy PA during pregnancy.

The combined effects of driver attitudes and in-vehicle feedback on fuel economy

This paper presents results from a study of driver feedback, driver attitudes, and the adoption of ecodriving behaviors. The study ran for one year; each driver was engaged in the experiment for four weeks. Narrowly defined, ecodriving represents the set of behaviors that a driver can use to minimize the energy use of a trip after the trip has begun. The general ecodriving behaviors are moderating acceleration, top speed, and braking. Ecodriving has long been recognized as a potential source of reductions in transportation energy use, with reduction estimates ranging widely from less than 5% to over 20% depending on context. In-vehicle feedback that provides drivers with salient information suited to their personal goals may be one way to motivate ecodriving. Although many studies have tested unique feedback designs, little research has been conducted into the cognitive precursors to driver behavior change that may underlie the adoption or rejection of ecodriving practices, and therefore underlie the effectiveness of any feedback design. This study examines both precursor cognitive factors and driver behavior changes with the introduction of energy feedback, using a framework hypothesizing that attitudes, social norms, perceived control, and goals influence behavior and behavior change. The study finds that the introduction of a feedback interface can both activate these cognitive factors and result in behavior change. Furthermore, the study finds that there was an overall 4.4% reduction in fuel consumption due entirely to one group that showed increases in their knowledge of fuel economy and reported high levels of technical proficiency during the experiment. Statistically significant relationships are found in this group between the magnitude of cognitive change and the magnitude of behavior change – supporting the theoretical framework. The second group made no improvement and may have been confused by the feedback. The effect of baseline (pre-feedback) performance of the drivers indicates drivers that already have highly efficient driving styles do not benefit much from feedback.