Recent advances in information technology have led to the extensive use of electronic maps in navigation tasks. An important question is how to present information about locations and directions to facilitate navigation tasks performed by different users. Extensive studies in the literature have investigated the effect of the frame of reference (i.e., North-Up and Track-Up Electronic Maps) on navigation performance but found disparate results regarding the best type of maps to optimize performance (Aretz & Wickens, 1992; Cuevas, Huthman, Knudsen, and Wei, 2001). The field dependence-independence (FDI), as one of the cognitive styles, has been found to affect an individual’s way of orienting in virtual environments but not their performance in orienting tasks (Kroutter, 2010). However, few studies have been carried out to research how users’ cognitive styles interacted with map characteristics, which in turn influenced their navigation performance. The current work investigated the effect of individual’s field dependence-independence and map’s frame of reference on user performance in orienting and navigating tasks using 2D electronic maps. The Experiments 1 indicated that field-dependent (FD) individuals showed significantly higher accuracy in orienting tasks when using the track-up maps than when using the north-up maps, whereas field-independent (FI) individuals had no difference when using either type of maps. The current study may imply that FD individuals showed worse performance than field-independent individuals when using north-up maps because an additional step of mental rotation was included when making direction judgments with north-up maps and FD individuals were more likely influenced by the external cues in mental rotations. Since track-up maps matched with the direction of travel, the mental rotation was not necessary, so that FD and FI individuals showed no significant difference in such direction judgment tasks. Experiment 2 examined the effects of field dependence and frame of reference on navigation task performance in the virtual environment. Results showed that FI individuals had better performance than FD individuals regarding the number of map references. The results also showed an interaction effect of field dependence and frame of reference on task completion time. In particular, FI individuals showed a significantly quicker time to complete the navigation task than FD individuals when using north-up maps, whereas they showed no difference in task completion time when using track-up maps. Although track-up maps led to the superior performance for FD individuals, the subjective rating of track-up maps was lower than that of north-up maps. In summary, previous empirical studies have failed to find clear evidence to support either North-up or Track-up designs. The current study proposed one possible implication of such inconsistent findings: that a user’s field dependence/ independence dimension in spatial cognition influences their performance when using different frames of reference. The mental rotation cost using a north-up display can be reduced for field-independent individuals since such users showed better performance in extracting and integrating information to maintain their mental representation of the system. This implication could be further utilized in the user-centered designs of navigation displays by considering individual differences.
Building effective Vehicular Cyber-Physical Systems (VCPS) to improve road safety is a non-trivial challenge, especially when we examine how the driver benefits from the existing and proposed technologies in the presence of Human Factors (HF) related negative factors such as information overload, confusion, and distraction. In this paper, we address a human-centric data fusion problem in VCPS. To the best of our knowledge, this work is the first to apply HF to the data fusion problem, which has both theoretical value and practical implications. In particular, we present a new architecture by defining a distinct High-Level (HL) data fusion layer with HF considerations, that is placed between the safety applications on the VCPS and the human driver. A data fusion algorithm is proposed to fuse multiple messages (based on reaction time, message type, preferred evasive actions, severity of the hazards, etc) and to maximize the total utility of the messages. The algorithm is tested with real human drivers to demonstrate the potential benefit of incorporating such human-centric fusion in existing warning systems.
While much research has investigated the predictors of operators' performance such as personality, attitudes and motivation in high-risk industries, its cognitive antecedents and boundary conditions have not been fully investigated. Based on a multilevel investigation of 312 nuclear power plant main control room operators from 50 shift teams, the present study investigated how general mental ability (GMA) at both individual and team level can influence task and safety performance. At the individual level, operators' GMA was predictive of their task and safety performance and this trend became more significant as they accumulated more experience. At the team level, we found team GMA had positive influences on all three performance criteria. However, we also found a "big-fish-little-pond" effect insofar as team GMA had a relatively smaller effect and inhibited the contribution of individual GMA to workers' extra-role behaviors (safety participation) compared to its clear beneficial influence on in-role behaviors (task performance and safety compliance). The possible mechanisms related to learning and social comparison processes are discussed.
Whitetopping has recently been generating considerable interest and greater acceptance as an approach to asphalt pavement rehabilitation. A number of thin whitetopping (TWT) and ultra-thin-whitetopping (UTW) pavement test sections have been constructed during the past 10 years, and the pavements have demonstrated considerable advantages as a rehabilitation technique. In 1996 the Colorado Department of Transportation (CDOT) sponsored a research project to develop a mechanistic design procedure for TWT pavements. Construction Technology Laboratories, Inc., installed the instrumentation, conducted the load testing on the instrumented test section, performed a theoretical analysis, and developed a TWT design procedure for CDOT. Many variables were considered in the construction of the test sections, including concrete overlay thickness, slab dimension, existing asphalt layer thickness, different asphalt surface preparation techniques, and the use of dowel bars and tie bars. Based on the original design procedure development, there are several observations and conclusions regarding the use of TWT pavements for rehabilitation that should be examined more extensively with a supplemental investigation. The items include subgrade support conditions, required thickness of asphalt beneath the concrete layer, and effects of variable joint spacings. New TWT pavement test sections were constructed during 2001 in conjunction with a TWT project constructed by CDOT on SH 121 near Denver, Colorado. This provided an opportunity to instrument and load best additional TWT test sections and use the data to calibrate and verify the existing observations and design procedure. Therefore, the objective of this project is to instrument, load test, and monitor the new and original TWT test section performances to supplement and confirm the results of the 1996 study.
Objective: Drinking and driving is a primary cause of traffic fatalities and it has been suggested that binge drinkers comprise a major portion of those drivers involved in drinking and driving accidents. Although several experimental studies have investigated the driving behavior of binge drinkers (particularly college students and/or young adults) under the influence of alcohol, few studies have focused on a comparison of sober driving behavior of the general population between binge and non-binge drinkers with a consideration of drivers' income levels. In addition, these studies have not taken other potentially influential factors into account such as socio economic status. Methods: A driving simulator study was conducted with a 2 × 2 factorial design (binge vs. non-binge drinker; low vs. high income). Sixty-two participants who were not under the influence of alcohol or drugs were asked to operate a driving simulator following traffic rules. Multiple aspects of participants' driving behaviors were measured in a sober driving situation. To control the potential effects of confounding factors, factors (e.g., age, gender, etc.) that were significantly correlated to the driving behavior were all entered into the multivariate analysis of variance (MANOVA) as covariates. Results: Significant interaction effects were found between effects of binge drinking and income levels. Analyses indicated that binge drinkers—independent of their income levels—exhibited more speeding exceedances and longer speeding duration than those of non-binge drinkers with a high income. Individuals characterized as non-binge drinkers with a low income also exhibited more speeding behaviors. Conclusion: Cognitive deficits and problems in vehicle control resulting from chronic alcohol consumption may impact binge drinkers' abilities to perform adequately, even in a sober driving situation. In addition, non-binge drinkers with a low income were more prone to make unsafe choices compared to non-binge drinkers with a high income. Further implications of the results in transportation safety and alcohol addiction were also discussed.
Linear Discriminant Analysis (LDA) was utilized to detect numerical typing errors in the context of daily data input. Single EEG trial data from 6 subjects were analyzed and a 67% detection rate was demonstrated by Fisher LDA classifier with an optimal Mahalanobis distance ratio setting. Sensitivity analysis showed that Fisher LDA classifier detected the errors in terms of 9-digit numbers by 62.19% on average, in comparison with 3.33% and 47.22% using the prior model and the chance model. This is one step towards predicting human errors in perceptual-motor tasks before their occurrence; future work would focus on benchmarking to improve current method toward an online and robust classifier.
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