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Biased energy efficiency perception based on instantaneous consumption displays – Indication for heuristic energy information processing
Abstract
Instantaneous consumption displays (ICDs) can be used as central information source to perceive the energy efficiency of manoeuvre-level driving. A key question is whether drivers who use ICDs can accurately derive energy efficiency differences of different driving strategies based on ICDs. There is reason to assume that drivers' consumption judgements may be biased, similar to driving-related phenomena like the time-saving bias. Therefore, the aim of the present research was to examine drivers’ accuracy in deriving average consumption from dynamic ICD sequences. Participants viewed videos of a schematic ICD in a controlled experiment where the maximum instantaneous consumption systematically varied over time. Participants (N = 55) overestimated the average consumption values. The empirical ranking of the sequences did significantly correlate with the heuristic but not with the correct efficiency ranking. The current study incorporated multilevel modelling due to the nested structure of the data. The estimation difference was greater with higher peak height and shorter peak duration. The effect of peak height on estimation difference weakened with longer peak duration. In sum, the results indicate that ICDs can create biased perceptions of energy efficiency and that drivers seem to use simplifying heuristics. Knowledge and affinity for technology interaction appear to relate to biased estimations, whereas the intensity of prior experience with consumption displays seems irrelevant. Further studies should test other interfaces with debiasing potential such as manoeuvre-based aggregation or fading-trace approaches. Moreover, studies are needed that enable modelling of the effects of more natural temporal-spatial visual attention distribution (e.g. in a driving simulator setting).
For a comprehensive understanding of user diversity, a reliable and valid assessment of stable user characteristics is essential. In the field of human-technology interaction, a plethora of personality-related constructs linked to the experience of and interaction with technical systems has been discussed. A key question for researchers in the field is thus: Which are the key personality concepts and scales for characterizing inter-individual differences in user technology interaction? Based on a literature review and citation analysis, a structured overview of frequently used technology-related personality constructs and corresponding self-report scales is provided. Changes in the popularity and content of scales and concepts that occured over time as well as overlap between constructs and scales are discussed to facilitate scale selection.
Interactive visual applications create animations that encode changes in the data. For example, cross-filtering dynamically updates linked visualizations based on the user's continuous brushing actions. The animated effects resulting from these interactions depends both on how interaction (e.g., brushing speed) controls properties of the animation such as frame rate, as well as how the data that is being explored dictates the data encoded in the animation. Past work has found that frame rate matters to general perception, however a critical question is which of these animation and data properties affects the perceptual accuracy of judgement tasks, and to what extent. Although graphical perception has been well studied for static data visualizations, it is ripe for exploration in the animated setting. We designed two animated judgment tasks of a target bar in an animated bar chart and empirically evaluate the effects of 2 animations properties - highlighting of the target bar and frame rate - as well as 3 data properties that affect the target bar's value throughout the animation. In short, we find that the rate and timing of animation changes is easier detected in larger values; that encodings such as color are easier to detect than shapes; and that timing is important - earlier changes were harder to perceive as compared to later changes in the animation. Our results are an initial step to understanding perceptual accuracy for animated data visualizations, both for presentations and ultimately as part of interactive applications.
Animated graphs that demonstrate statistical ideas and methods can both attract interest and assist understanding. In this paper we first discuss how animations can be related to some statistical topics such as iterative algorithms, random simulations, (re)sampling methods and dynamic trends, then we describe the approaches that may be used to create animations, and give an overview to the R package animation, including its design, usage and the statistical topics in the package. With the animation package, we can export the animations produced by R into a variety of formats, such as a web page, a GIF animation, a Flash movie, a PDF document, or an MP4/AVI video, so that users can publish the animations fairly easily. The design of this package is flexible enough to be readily incorporated into web applications, e.g., we can generate animations online with Rweb, which means we do not even need R to be installed locally to create animations. We will show examples of the use of animations in teaching statistics and in the presentation of statistical reports using Sweave or knitr. In fact, this paper itself was written with the knitr and animation package, and the animations are embedded in the PDF document, so that readers can watch the animations in real time when they read the paper (the Adobe Reader is required). Animations can add insight and interest to traditional static approaches to teaching statistics and reporting, making statistics a more interesting and appealing subject.
The time-saving bias describes people's tendency to misestimate the time they can save by increasing the speed in which they perform an activity such as driving or completing a task. People typically underestimate time saved when increasing from a low speed and overestimate time saved when increasing from an already high speed. We suggest that this bias is the result of people's failure to recognize the curvilinear relationship between increasing speed and reducing activity time: As initial speed rises, the same speed increases will yield smaller reductions in time. We explore a new technique to de-bias these faulty estimations: converting measurements of speed to a pace measure (e.g., minutes per fixed distance). Utilizing common driving scenarios, we show that participants who received pace data made more accurate estimations of journey duration at various speeds, time-savings at various speed increases and the required speed to complete a journey.
The overreliance on linear methods in students' reasoning and problem solving has been documented and discussed by several scholars in the field. So far, however, there have been no attempts to assemble the evidence and to analyze it in a systematic way. This article provides an overview and a conceptual analysis of students' tendency to use linear methods beyond their applicability range. We start from an analysis of the linearity concept and its properties and representations. The main part consists of a summary and discussion of unwarranted applications of each of these properties and representations in various mathematical content domains in order to unravel similarities and differences. After that, we speculate on the factors that seem to be at the roots of the occurrence and persistence of the overreliance on linearity. Finally, we discuss educational implications and suggest perspectives for further research.
Maximum likelihood or restricted maximum likelihood (REML) estimates of the
parameters in linear mixed-effects models can be determined using the lmer
function in the lme4 package for R. As for most model-fitting functions in R,
the model is described in an lmer call by a formula, in this case including
both fixed- and random-effects terms. The formula and data together determine a
numerical representation of the model from which the profiled deviance or the
profiled REML criterion can be evaluated as a function of some of the model
parameters. The appropriate criterion is optimized, using one of the
constrained optimization functions in R, to provide the parameter estimates. We
describe the structure of the model, the steps in evaluating the profiled
deviance or REML criterion, and the structure of classes or types that
represents such a model. Sufficient detail is included to allow specialization
of these structures by users who wish to write functions to fit specialized
linear mixed models, such as models incorporating pedigrees or smoothing
splines, that are not easily expressible in the formula language used by lmer.
This paper presents a simulation study dealing with the influence of different factors on the energy consumption of an electric vehicle (EV). Due to the limited quantity of energy embedded in the battery, EVs are very sensitive to parameters which can influence their energy consumption and then can induce huge variations in their actual range. Among all these factors, driving conditions, auxiliaries' impact, driver's aggressiveness and braking energy recovery strategy are to be considered as the main factors influencing the EV energy consumption. The objective of this paper is thus to simulate and quantify the influence of each factor independently. For this, a virtual EV simulator has been created and validated through EVs experiments on a climatic 4WD chassis dyno in the frame of a project sponsored by the French ADEME and with the help of PSA, Renault and Tazzari car manufacturers. This simulator, validated thanks to a limited number of experimental results, has been then used on a very large range of operating conditions and hypotheses to extrapolate experimental results and help the analyses of influencing factors.
This paper presents information about the effects of decisions that a driver can make to influence on-road fuel economy of light-duty vehicles. These include strategic decisions (vehicle selection and maintenance), tactical decisions (route selection and vehicle load), and operational decisions (driver behavior). The results indicate that vehicle selection has by far the most dominant effect: The best vehicle currently available for sale in the U.S. is nine times more fuel efficient than the worst vehicle. Nevertheless, the remaining factors that a driver has control over can contribute, in total, to about a 45% reduction in the on-road fuel economy per driver—a magnitude well worth emphasizing. Furthermore, increased efforts should also be directed at increasing vehicle occupancy, which has dropped by 30% from 1960. That drop, by itself, increased the energy intensity of driving per occupant by about 30%.
The term 'carbon footprint' has become tremendously popular over the last few years and is now in widespread use across the media – at least in the United Kingdom. With climate change high up on the political and corporate agenda, carbon footprint calculations are in demand. Numerous approaches have been proposed to provide estimates, ranging from basic online calculators to sophisticated life-cycle-analysis or input-output based methods and tools. Despite its ubiquitous use however, there is an apparent lack of academic definitions of what exactly a 'carbon footprint' is meant to be. The scientific literature is surprisingly void of clarifications, despite the fact that countless studies in energy and ecological economics that could have claimed to measure a 'carbon footprint' have been published over decades. This commentary explores the apparent discrepancy between public and academic use of the term 'carbon footprint' and suggests a scientific definition based on commonly accepted accounting principles and modelling approaches. It addresses methodological questions such as system boundaries, completeness, comprehensiveness, units, and robustness of the indicator.
Subjects were asked to judge the mean speed of a model engine which travelled port of a fixed distance at one steady speed and the remainder at a different steady speed. Mean speed was consistently overestimated when compared with judgements of constant speed over the same distance, suggesting that the faster speed was given too much weight when an impression of mean speed was formed. This, and results reported earlier by the author, indicate that when a d river is allowed to speed up for short periods of time he may overestimate the effect of these spurts on his mean speed and travel time.
Developed and validated the Need for Cognition Scale (NCS). In Study 1, a pool of items was administered to 96 faculty members (high-need-for-cognition group) and assembly line workers (low-need-for-cognition group). Ambiguity, irrelevance, and internal consistency were used to select items for subsequent studies. Factor analysis yielded one major factor. In Study 2, the NCS and the Group Embedded Figures Test were administered to 419 undergraduates to validate the factor structure and to determine whether the NCS tapped a construct distinct from test anxiety and cognitive style. The factor structure was replicated, and responses to the NCS were weakly related to cognitive style and unrelated to test anxiety. In Study 3, 104 undergraduates completed the NCS, the Marlowe-Crowne Social Desirability Scale, and a dogmatism scale. Results indicate that need for cognition was related weakly and negatively to being closeminded, unrelated to social desirability, and positively correlated with general intelligence. Study 4 (97 undergraduates) furnished evidence of the predictive validity of the NCS. (32 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
The design of effective energy interfaces for electric vehicles needs an integrated perspective on the technical and psychological factors that together establish real-world vehicle energy efficiency. The objective of the present research was to provide a transdisciplinary synthesis of key factors for the design of energy interfaces for battery electric vehicles (BEVs) that effectively support drivers in their eco-driving efforts. While previous research tends to concentrate on the (visual) representation of common energy efficiency measures, we focus on the design of action-integrated metrics and indicators for vehicle energy efficiency that account for the perceptual capacities and bounded rationality of drivers. Based on this rationale, we propose energy interface examples for the most basic driving maneuvers (acceleration, constant driving, deceleration) and discuss challenges and opportunities of these design solutions.
Vehicle automation offers promise for improving safe transportation, access to mobility, and quality of life. However, at least in the early stages of automation, human drivers remain an integral component of the system and their acceptance and use of the automated technology needs to be much better understood. One factor that has emerged as a strong influence on the acceptance and use of automated technology is trust. We used regression analyses to address two research questions. The first was: What factors are associated with the extent to which individuals report trust in automated technology after a simulated automated drive experience? The second research question was: How are trust in automated technology, control preferences (both preference for control specifically related to driving and more general preference for control), and experience with technology associated with objective measures intended to capture acceptance of automated technology? With regard to the first research question, we found that driving-specific control preferences were significantly related to reported trust. Specifically, after experiencing a simulated drive that required switching between manual and automated modes, the extent to which individuals reported that they trusted the automated technology was significantly higher among those who also reported being comfortable with other drivers behind the wheel. While specific results were mixed with regard to the second research question, we did find evidence that trust in automated driving, at least as reported after a simulated experience with the technology, was an important component of acceptance of the technology.
The estimation of power in two-level models used to analyze data that are hierarchically structured is particularly complex because the outcome contains variance at two levels that is regressed on predictors at two levels. Methods for the estimation of power in two-level models have been based on formulas and Monte Carlo simulation. We provide a hands-on tutorial illustrating how a priori and post hoc power analyses for the most frequently used two-level models are conducted. We describe how a population model for the power analysis can be specified by using standardized input parameters and how the power analysis is implemented in SIMR, a very flexible power estimation method based on Monte Carlo simulation. Finally, we provide case-sensitive rules of thumb for deriving sufficient sample sizes as well as minimum detectable effect sizes that yield a power ≥ .80 for the effects and input parameters most frequently analyzed by psychologists. For medium variance components, the results indicate that with lower level (L1) sample sizes up to 30 and higher level (L2) sample sizes up to 200, medium and large fixed effects can be detected. However, small L2 direct- or cross-level interaction effects cannot be detected with up to 200 clusters. The tutorial and guidelines should be of help to researchers dealing with multilevel study designs such as individuals clustered within groups or repeated measurements clustered within individuals.
Bar charts are among the most commonly used visualization graphs. Their main goal is to communicate quantities that can be visually compared. Since they are easy to produce and interpret, they are found in any situation where quantitative data needs to be conveyed (websites, newspapers, etc.). However, depending on the layout, the perceived values can vary substantially. For instance, previous research has shown that the positioning of bars (e.g. stacked vs separate) may influence the accuracy in bar ratio length estimation. Other works have studied the effects of embellishments on the perception of encoded quantities. However, to the best of the authors’ knowledge, the effect of perceptual elements used to reinforce the quantity depicted within the bars, such as contrast and inner lines, has not been studied in depth.
In this research we present a study that analyzes the effect of several internal contrast and framing enhancements with respect to the use of basic solid bars. Our results show that the addition of minimal visual elements that are easy to implement with current technology can help users to better recognize the amounts depicted by the bar charts.
About 17% of the worldwide CO2-emissions can be ascribed to road transportation. Using information systems (IS)-enabled feedback has shown to be very efficient in promoting a less fuel-consuming driving style. Today, in-car IS that provide feedback on driving behavior are in the midst of a fundamental change. Increasing digitalization of in-car IS enables virtually any kind of feedback. Still, we see a gap in the empirical evidence on how to leverage this potential, raising questions on future HCI-based feedback design. To address this knowledge gap, we designed an eco-driving feedback IS and, building upon construal level theory, hypothesize that abstract feedback is more effective in reducing fuel consumption than concrete feedback. Deployed in a large field experiment with 56 participants covering over 297,000km, we provide first empirical evidence that supports this hypothesis. Despite its limitations, this research may have general implications for the design of real-time feedback.
Successful coping with technology is relevant for mastering daily life. Based on related conceptions, we propose affinity for technology interaction (ATI), defined as the tendency to actively engage in intensive technology interaction, as a key personal resource for coping with technology. We present the 9-item ATI scale, an economical unidimensional scale that assesses ATI as an interaction style rooted in the construct need for cognition (NFC). Results of multiple studies (n > 1500) showed that the scale achieves good to excellent reliability, exhibits expected moderate to high correlations with geekism, technology enthusiasm, NFC, self-reported success in technical problem-solving and technical system learning success, and also with usage of technical systems. Further, correlations of ATI with the Big Five personality dimensions were weak at most. Based on the results, the ATI scale appears to be a promising tool for research applications such as the characterization of user diversity in system usability tests and the construction of general models of user-technology interaction.
The adoption of a vehicle-to-infrastructure (V2I) cooperative crossing collision prevention system (CCPS) has been recognized as an option for reducing accident risk at intersections. However, the effects of driver characteristics on the general design of CCPSs have remained relatively un-examined. This study was conducted based on a field operational test (FOT) conducted in 2014 that included 64 monitored drivers receiving input from a CCPS and a driving style questionnaire (DSQ) that was administered to all 64 monitored drivers. To provide a better understanding of the various reactions of each driver with different driving styles, we employed both a logistic regression model and a time-series cross-sectional Prais-Winsten regression model with panel-corrected standard errors (PCSEs). The impacts of the driver characteristics and driving styles on a surrogate safety measure, namely, time-to-collision (TTC) risk, were modeled. The results revealed that both CCPS information displays and alarms significantly affect the TTC, whereas limited differences were observed when the serial correlations were removed during the FOT. For drivers younger than 65, only drivers who exhibited a natural worrier driving style (one of the eight DSQ classifications) did not benefit from the CCPS; thus, safety at intersections can be improved in practice with a higher CCPS penetration ratio. However, the evaluated CCPS had a limited effect on elderly drivers due to the significant negative effects of most driving styles on the TTC risk; thus, CCPSs should be specifically designed for elderly drivers with various driving styles.
Hybrid electric vehicles (HEVs) have the potential to accomplish high energy efficiency (i.e., low fuel consumption) given that drivers apply effective ecodriving control strategies (i.e., ecodriving behavior). However, HEVs have a relatively complex powertrain and therefore require a considerable knowledge acquisition process to enable optimal ecodriving behavior. The objective of the present research was to examine the acquisition of ecodriving strategy knowledge in HEV drivers who are successful in achieving a relatively high energy efficiency. To this end, we recruited 39 HEV drivers with above-average fuel efficiencies and collected interview data on the ecodriving strategy acquisition process. Drivers reported the acquisition of different types of knowledge as important for ecodriving, namely specific strategy knowledge and general technical system knowledge. They acquired this knowledge both with system-interaction (e.g., actively testing specific strategies, continuous monitoring of energy consumption) and without system-interaction (e.g., internet forums, consulting experts). This learning process took drivers on average 6.4 months or 10062 km. The results show the high diversity of the means that HEV drivers use to develop their ecodriving knowledge and the considerable time it takes HEV drivers to develop their ecodriving strategies.
This driving simulator study extended knowledge on user experience using a strategy to mitigate distraction resulting from the use of in-vehicle information systems. It examined the impact of system restrictions on users’ needs, emotions, and consequences of users’ experience in terms of psychological reactance. In a repeated measures design, we asked 53 participants to perform secondary tasks with an in-vehicle information system while driving. Three versions of the system varied with respect to the number of operable functionalities. The more functionalities that were disabled while driving, the more negatively users rated the systems. Multilevel regression analyses of at least n = 155 data points revealed that drivers’ need fulfilment predicted their emotions. Reactance depended on users’ need fulfilment and emotions. Experienced autonomy mediated the relation between functional limitations and reactance. When developing interactive systems, one should focus on needs and be aware of potential unwanted consequences such as psychological reactance.
Practitioner Summary
This driving simulator study highlights the importance of considering need fulfilment and users’ emotions when developing an interactive system that provides high user experience. System restrictions could have negative consequences as users might show psychological reactance.
The number of electric vehicles in service throughout the world has increased from a few thousand in 2009 to some 740,000 in December 2014. These vehicles are often seen as a means of reducing climate and health damaging emissions, and their development is directly supported by some countries and endorsed by the EU. Aside from questions of rebound effects, embedded emissions and cleanness of electricity generation, there are unanswered questions about the energy performance of such cars under a range of driving conditions, and the results of existing studies are not easily interpretable by policymakers and drivers. This study uses the results of extensive dynamometer tests on eight commonly sold electric vehicles. It develops a multivariate model, with regression coefficients around 0.97, to map power demand and energy consumption for all likely combinations of speed and acceleration, producing accessible, easily interpretable displays. While electric vehicles are frequently marketed on the basis of their high acceleration, an important finding is that episodes of modest to high acceleration severely compromise their range and energy efficiency, regardless of speed. This also raises questions as to how well such vehicles perform in the erratic driving conditions of urban traffic.
Electric cars (EVs) are a promising alternative to combustion engine cars to lower emissions and fossil fuel dependencies. On the downside, in comparison to internal combustion engine cars (ICE), the user experience (UX) of EVs is seriously compromised due to shorter and more varied driving range depending on driving style other context of use. A further complication is that recovering from unexpectedly low battery levels is tedious due to long charging times. This causes range stress among drivers and research has highlighted a need to improve the information and tools available in order for drivers to better understand range-influencing factors and estimations, leading to increased reliability, and trust in the information. This currently leads to poor UX that may shadow all the benefits and other important environmental and experiential qualities of electric cars. In this chapter, we will provide an introduction to the subject and go through some of our studies and key lessons that have emerged from our research. In particular, we have come to the realisation that we need to energy-empower electric car drivers in order for them to be able to conceptualise how energy is intertwined with their actions and behaviour while driving. This is important, as current tools fail to provide such empowerment, causing unnecessary surprises and worries among the drivers who call the standard tool available in the electric car for the ‘guess-o-meter’. Through our designs and discussion we demonstrate how some aspects might be addressed to energy empower electric car drivers.
Objective: The objective of the present research was to understand drivers’ interaction patterns with hybrid electric vehicles’ (HEV) eco-features (electric propulsion, regenerative braking, neutral mode) and their relationship to fuel efficiency and driver characteristics (technical system knowledge, ecodriving motivation).
Background: Ecodriving (driving behaviors performed to achieve higher fuel efficiency) has the potential to reduce CO2 emissions caused by road vehicles. Ecodriving in HEVs is particularly challenging due to the systems’ dynamic energy flows. As a result, drivers are likely to show diverse ecodriving behaviors, depending on factors like knowledge and motivation. The eco-features represent an interface for the control of the systems’ energy flows.
Method: A sample of 121 HEV drivers who had constantly logged their fuel consumption prior to the study participated in an online questionnaire.
Results: Drivers’ interaction patterns with the eco-features were related to fuel efficiency. A common factor was identified in an exploratory factor analysis, characterizing the intensity of actively dealing with electric energy, which was also related to fuel efficiency. Driver characteristics were not related to this factor yet they were significant predictors of fuel efficiency.
Conclusion: From the perspective of user-energy interaction, the relationship of the aggregated factor to fuel efficiency emphasizes the central role of drivers’ perception of and interaction with energy conversions in determining HEV ecodriving success.
Application: To arrive at an in-depth understanding of drivers’ ecodriving behaviors that can guide interface design, future research should be concerned with the psychological processes that underlie drivers’ interaction patterns with eco-features.
Tools for performing model selection and model averaging. Automated model selection through subsetting the maximum model, with optional constraints for model inclusion. Model parameter and prediction averaging based on model weights derived from information criteria (AICc and alike) or custom model weighting schemes.
[Please do not request the full text - it is an R package. The up-to-date manual is available from CRAN].
The term ‘bounded rationality’ is used to designate rational choice that takes into account the cognitive limitations of the decision-maker — limitations of both knowledge and computational capacity. Bounded rationality is a central theme in the behavioural approach to economics, which is deeply concerned with the ways in which the actual decision–making process influences the decisions that are reached.
Animated transitions are an important part of graphical user interface design practice. They can help to guide users' attention and highlight changes in the interface. However, there is only little empirical research on how such animated transitions influence the users' perception of an interface. We therefore aim to investigate how different animation principles for animated transitions in mobile applications influence perceived user experience. In this late breaking work we present the results of a pilot study in which we tested the suitability of different animated transitions for our main study. Moreover, we outline a tentative design and procedure of the main study. We plan to recruit over 250 participants that will rate different styles of animated transitions in regard to perceived pragmatic and hedonic quality. Our findings are expected to provide insights in users' perception of animation styles and therefore to have implications for graphical user interface design practice.
Hybrid electric vehicles (HEVs) can help to reduce transport emissions; however, user behaviour has a significant effect on the energy savings actually achieved in everyday usage. The present research aimed to advance understanding of HEV drivers' ecodriving strategies, and the challenges for optimal user-energy interaction. We conducted interviews with 39 HEV drivers who achieved above-average fuel efficiencies. Regression analyses showed that technical system knowledge and ecodriving motivation were both important predictors for ecodriving efficiency. Qualitative data analyses showed that drivers used a plethora of ecodriving strategies and had diverse conceptualisations of HEV energy efficiency regarding aspects such as the efficiency of actively utilizing electric energy or the efficiency of different acceleration strategies. Drivers also reported several false beliefs regarding HEV energy efficiency that could impair ecodriving efforts. Results indicate that ecodriving support systems should facilitate anticipatory driving and help users locate and maintain drivetrain states of maximum efficiency.
The time saving bias predicts that the time saved when increasing speed from a high speed is overestimated, and underestimated when increasing speed from a slow speed. In a questionnaire, time saving judgements were investigated when information of estimated time to arrival was provided. In an active driving task, an alternative meter indicating the inverted speed was used to debias judgements. The simulated task was to first drive a distance at a given speed, and then drive the same distance again at the speed the driver judged was required to gain exactly 3 min in travel time compared with the first drive. A control group performed the same task with a speedometer and saved less than the targeted 3 min when increasing speed from a high speed, and more than 3 min when increasing from a low speed. Participants in the alternative meter condition were closer to the target. The two studies corroborate a time saving bias and show that biased intuitive judgements can be debiased by displaying the inverted speed.
The judgments with which this chapter is concerned are common in daily life. We often have the occasion to evaluate the pleasantness or awfulness of incidents in people's lives; most of us have opinions about what it is like to be old, or physically handicapped, or to live in California; and all of us spontaneously assign grades to events and situations and store evaluations of how good or bad they were, in the form of likes and dislikes. These judgments and feelings have also been produced in the laboratory, with the usual combination of artificiality and improved precision. The goal of this chapter is to review the evidence for a unifying principle, which accounts for four major conclusions, derived from distinct research paradigms. The first of these conclusions has been supported in many studies of choice. (i) The carriers of value in both risky and riskless choices are gains and losses. The same final state of wealth or endowment is valued differently, depending on its relation to the original state from which it has been reached (Kahneman and Tversky, 1979). In studies of the endowment effect, for example, the outcomes of owning or not owning a particular decorated mug are represented, depending on the current reference point, as getting a mug or giving up a mug (Kahneman, Knetsch and Thaler, 1991; Thaler, 1980; Tverksy and Kahneman, 1991). The following sets of conclusions will be documented in the present chapter: (ii) Global judgments of fictitious episodes or lives are very sensitive to trends of improvement or deterioration and are radically insensitive to duration. (iii) Retrospective evaluations of affective episodes are strongly influenced by the affect experienced at singular moments, notably the moment at which affect was most extreme and the final moment. They show little or no sensitivity to duration. (iv) Forecasts of the long term effects of circumstances on subjective happiness tend to neglect the likelihood of adaptation, and therefore to exaggerate long-term benefits and costs of life changes.
Eco-driving has the potential to reduce fuel consumption and therefore emissions considerably. Previous research suggests that drivers have a certain level of eco-driving knowledge and skills, which they refrain from practising in their everyday lives. At the same time misconceptions and ambiguous messages from eco-driving support systems can confuse and demotivate. This research aimed to identify the mental models of eco-driving that regular drivers have. A driving simulator experiment with a varied road layout comprising urban and motorway sections was designed. The study used simple driving task instructions to investigate changes in the participants’ behaviour and thoughts in three conditions. Sixteen drivers were asked to ‘Drive normally’, ‘Drive safely’ or ‘Drive fuel-efficiently’. Behavioural measures, think aloud protocols and interviews were compared and analysed. The emphasis of this study was on eco-driving relevant indicators such as accelerating, braking, coasting and car-following. The results show that the participants do have mental models of eco-driving, which they did not use in the Baseline drive, when they were instructed to ‘Drive normally’. Misconceptions about speed and travel time provide the potential for more effective communication with the driver about the momentary efficient speed as well as resulting time losses and fuel savings. In addition, in-vehicle guidance can increase driving safety compared to practicing eco-driving without them.
New technologies have led to an increasingly sedentary lifestyle. Sedentary behaviour is characterised by physical inactivity and is associated with several health risks. This excessive sitting does not only take place in the office or at home, but also during daily commute. Therefore, BMW AG developed an active seating system for the back seat of a car, consisting of sensors in the back rest that register upper body movements of the passenger, with which the passenger controls a game. This study evaluated three different aspects of active seating compared to other tasks (reading, working on laptop, and gaming on tablet). First, discomfort and comfort perception were measured in a 30-minute driving test. Discomfort was very low for all activities and participants felt significantly more challenged, more fit and more refreshed during active seating. Second, heart rate was measured, indicating a light intensity, but nevertheless non-sedentary, activity. Third, average and variability in activity of six postural muscles was measured by electromyography (EMG), showing a higher muscle activity and higher muscle variability for active seating compared to other activities. Active seating might stimulate movements, thereby increasing comfort and well-being.
Copyright © 2014 Elsevier Ltd and The Ergonomics Society. All rights reserved.
Electric vehicles are currently a held of research with many challenges that raise the interest of many researchers. The major challenge is about the autonomy of electric vehicles, which is limited as compared to that of conventional vehicles, and thus the drivers' anxiety about reaching or not the desired destination is very important. In this paper, we propose an experimental study to understand the energy consumption of electric vehicles, and we investigate some factors that have an important impact on their autonomy, such as the route type, the driving style and the ambient temperature. This study can be very useful in a further step to conceive a driving assistance system that indicates in real time the remaining energy and gives online instructions to reach the next charging station or the final destination. The analysis reported in this paper is based on real-world data collected using a full electric car with different driving conditions.
This study investigates the impact of driver behaviour/driving-style on the energy consumption, state-of-charge (SOC) usage and range, of all-electric vehicles (EVs). Results from many driving cycles using a sole driver, along with those from a pre-defined ~40 km route encompassing both urban and rural roads in Sheffield (UK) with various drivers, are given and analysed. The platform for the study is an all electric-drive Smart Fortwo ED, supplied using Zebra battery technology. Measurements of real-time quantities such as wheel speed and SOC over a number of driving trials show that energy consumption is significantly affected by driving style, and that through basic statistical analysis of acceleration profiles, for instance, a metric for assessing 'good driving practice' can be obtained. It is ultimately shown that the difference between driving in a moderate manner, and more aggressively, can make ~30% difference in energy consumption - amounting to 30 g/km of CO2 (equivalent) over the driving duty considered in this case. The results also highlight the substantial savings that can be accrued by appropriate traffic management in congested areas, by allowing the driver to minimise periods of repeated acceleration/deceleration and allow longer periods of steady-speed motion. Although a pure EV platform is used to focus the study, ultimately the results are more widely applicable to plug-in hybrid counterparts.
The introduction of information technology based on digital computers for the design of man-machine interface systems has led to a requirement for consistent models of human performance in routine task environments and during unfamiliar task conditions. A discussion is presented of the requirement for different types of models for representing performance at the skill-, rule-, and knowledge-based levels, together with a review of the different levels in terms of signals, signs, and symbols. Particular attention is paid to the different possible ways of representing system properties which underlie knowledge-based performance and which can be characterised at several levels of abstraction-from the representation of physical form, through functional representation, to representation in terms of intention or purpose. Furthermore, the role of qualitative and quantitative models in the design and evaluation of interface systems is mentioned, and the need to consider such distinctions carefully is discussed.
Fechner's proposal of measuring sensation using discrimination thresholds has been criticized by a number of authors. It can be argued, however, that the historical connections between the experimental study of sensory thresholds and the problem of measuring sensation is fortuitous. In fact, Fechner's scheme of psychophysics can be generalized in a straightforward manner to arbitrary discrimination indices. This raises immediately some interesting questions such as: What conditions must satisfy two discrimination indices to yield identical scales? These and related questions are investigated from a theoretical and experimental standpoint. The paper also contains a discussion of the usual questions of existence, uniqueness, and construction of solutions to Fechner's Problem.
The effects of training in fuel-efficient driving for bus drivers in a city environment were evaluated. Three dependent variables, hypothetically associated with such training, were used; fuel and accident data from the bus company, and driver acceleration behavior from five buses, over time periods of several years. Effects of temperature and number of passengers on fuel consumption were held constant. Fuelling and acceleration data yielded fairly similar results. It was found that, although the effects on these variables during training were very strong (as found in a previous study), these did not transfer well into the drivers’ working situation. Overall, the effect was about two percent fuel consumption reduction as a mean over 12 months after training. No effect was found for accidents, although a two percent reduction would not have been detectable. In a second phase of the study, 28 buses were equipped with Econen feedback equipment, which give an indication on how much fuel is used concurrently, resulting in a further reduction of consumption of about two percent.
This paper explores a heuristic-representativeness-according to which the subjective probability of an event, or a sample, is determined by the degree to which it: (i) is similar in essential characteristics to its parent population; and (ii) reflects the salient features of the process by which it is generated. This heuristic is explicated in a series of empirical examples demonstrating predictable and systematic errors in the evaluation of un- certain events. In particular, since sample size does not represent any property of the population, it is expected to have little or no effect on judgment of likelihood. This prediction is confirmed in studies showing that subjective sampling distributions and posterior probability judgments are determined by the most salient characteristic of the sample (e.g., proportion, mean) without regard to the size of the sample. The present heuristic approach is contrasted with the normative (Bayesian) approach to the analysis of the judgment of uncertainty.
The major categories of models of the past two decades are reviewed in order to pinpoint their strengths - and perhaps their weaknesses - in that framework. This review includes such models as McKnight & Adams' task analysis, Kidd & Laughery's early behavioral computer simulations, the linear control models (such as McRuer & Weir's), as well as some more recent concepts such as Naeaetaenen & Summala's, Wilde's and Fuller's risk coping models which already carry some cognitive weight. Having proposed my answers to these questions an attempt is made to formulate an alternative approach, based on production systems as developed by J. R. Anderson.
The use of both linear and generalized linear mixed-effects models (LMMs and GLMMs) has become popular not only in social and medical sciences, but also in biological sciences, especially in the field of ecology and evolution. Information criteria, such as Akaike Information Criterion (AIC), are usually presented as model comparison tools for mixed-effects models. The presentation of variance explained' (R2) as a relevant summarizing statistic of mixed-effects models, however, is rare, even though R2 is routinely reported for linear models (LMs) and also generalized linear models (GLMs). R2 has the extremely useful property of providing an absolute value for the goodness-of-fit of a model, which cannot be given by the information criteria. As a summary statistic that describes the amount of variance explained, R2 can also be a quantity of biological interest. One reason for the under-appreciation of R2 for mixed-effects models lies in the fact that R2 can be defined in a number of ways. Furthermore, most definitions of R2 for mixed-effects have theoretical problems (e.g. decreased or negative R2 values in larger models) and/or their use is hindered by practical difficulties (e.g. implementation). Here, we make a case for the importance of reporting R2 for mixed-effects models. We first provide the common definitions of R2 for LMs and GLMs and discuss the key problems associated with calculating R2 for mixed-effects models. We then recommend a general and simple method for calculating two types of R2 (marginal and conditional R2) for both LMMs and GLMMs, which are less susceptible to common problems. This method is illustrated by examples and can be widely employed by researchers in any fields of research, regardless of software packages used for fitting mixed-effects models. The proposed method has the potential to facilitate the presentation of R2 for a wide range of circumstances.
People generally overestimate the time they can save when increasing from a relatively high driving speed. Previous research suggested that people follow a Proportion Heuristic, calculating the time saved as the proportion of speed increase from the new higher speed. The present study suggests that drivers use another heuristic - the Percentage Heuristic - to calculate how much time they save by increasing speed. In the percentage heuristic, the initial (rather than higher) speed is used as the denominator. Using a discriminating set of questions, we classified participants' responses as normative (correct answer), as following the proportion or percentage heuristic, or some other strategy. We found that participants used the percentage heuristic more often, perhaps because it predicts linearly increasing values of time saved when increasing speed. In addition, we found that participants high in need for cognition (NFC) gave correct answers more often than low NFC participants who relied more on heuristics.
Participants made decisions between two road improvements to increase mean speed. Time saved when speed increased from a higher driving speed was overestimated in relation to time saved from increases from lower speeds. In Study 2, participants matched pairs of speed increases so that they would give the same time saving and repeated the bias. The increase in risk of an accident with person injury was underestimated and the increase in risk of a fatal accident grossly underestimated when speed increased. The increase of stopping distance when speed increased was systematically underestimated. In Study 3, the tasks and results of Study 2 were repeated with engineering students. When forming opinions about speed limits and traffic planning, drivers, the public, politicians and others who do not collect the proper facts are liable to the same biases as those demonstrated in the present study. Copyright © 2008 John Wiley & Sons, Ltd.
The subject of graphical methods for data analysis and for data presentation needs a scientific foundation. In this article we take a few steps in the direction of establishing such a foundation. Our approach is based on graphical perception—the visual decoding of information encoded on graphs—and it includes both theory and experimentation to test the theory. The theory deals with a small but important piece of the whole process of graphical perception. The first part is an identification of a set of elementary perceptual tasks that are carried out when people extract quantitative information from graphs. The second part is an ordering of the tasks on the basis of how accurately people perform them. Elements of the theory are tested by experimentation in which subjects record their judgments of the quantitative information on graphs. The experiments validate these elements but also suggest that the set of elementary tasks should be expanded. The theory provides a guideline for graph construction: Graphs should employ elementary tasks as high in the ordering as possible. This principle is applied to a variety of graphs, including bar charts, divided bar charts, pie charts, and statistical maps with shading. The conclusion is that radical surgery on these popular graphs is needed, and as replacements we offer alternative graphical forms—dot charts, dot charts with grouping, and framed-rectangle charts.