Entity alignment (EA) aims to find the equivalent entity pairs between different knowledge graphs (KGs), which is crucial to promote knowledge fusion. With the wide use of temporal knowledge graphs (TKGs), time-aware EA (TEA) methods appear to enhance EA. Existing TEA models are based on Graph Neural Networks (GNN) and achieve state-of-the-art (SOTA) performance, but it is difficult to transfer them to large-scale TKGs due to the scalability issue of GNN. In this paper, we propose an effective and efficient non-neural EA framework between TKGs, namely LightTEA, which consists of four essential components: (1) Two-aspect Three-view Label Propagation, (2) Sparse Similarity with Temporal Constraints, (3) Sinkhorn Operator, and (4) Temporal Iterative Learning. All of these modules work together to improve the performance of EA while reducing the time consumption of the model. Extensive experiments on public datasets indicate that our proposed model significantly outperforms the SOTA methods for EA between TKGs, and the time consumed by LightTEA is only dozens of seconds at most, no more than 10% of the most efficient TEA method.

The vibrotactile modality has great potential for presenting takeover requests (TORs) to get distracted drivers back into the control loop. However, few studies investigate the effectiveness of directional vibrotactile TORs. Whether TORs should be directed toward the direction of hazard (stimulus-response incompatibility) or the direction of avoidance action (stimulus-response compatibility) remains inconclusive. The present study explored the impact of directional vibrotactile TORs (toward-hazard, toward-action, and non-directional) on takeover performance. The influences of TORs lead time (3 s, 4 s, 6 s, and 8 s) and non-driving related tasks (NDRTs) (playing Tetris games and monitoring the road) on the effect of directional TORs were also probed. A total of 48 participants were recruited for our simulated driving study. Results showed that when drivers were engaged in NDRTs during automated driving, directional TORs were more effective than non-directional TORs. Specifically, at the lead times of 6 s and 8 s, both toward-hazard and toward-action TORs could shorten steering response times, compared with the non-directional TORs. At the lead times of 3 s and 4 s, toward-action TORs were more beneficial, as the maximum lateral acceleration was smaller than toward-hazard and non-directional TORs. However, when drivers monitored the road during automated driving, no obvious difference existed between directional and non-directional TORs, regardless of how long the lead time was. The findings in the present study shed light on the design and implementation of the tactile takeover system for automobile designers.

Effective communication between automated vehicles and human drivers in manual driving vehicles is of great importance for traffic safety during the transition phase of automated vehicles. Gestures, which were widely used in road users’ communication, were promising in conveying the intentions of automated vehicles naturally and intuitively without extra learning costs. However, the effect of gestures in conveying the automated vehicles’ intentions on human understanding remains unknown. This study proposed the idea of adopting mechanical arms to produce gestures. An experiment based on video recordings was conducted to explore the effect of arms type (slow-waved mechanical arm (80 beats/min) vs. fast waved mechanical arm (120 beats/min) vs. human arm) and gesture type (taking the road vs. giving the road) on the participants’ objective responses and subjective opinions. A total of 30 participants were recruited as human drivers in a manual driving vehicle, who received and responded to the gestures transferred by an encountering automated vehicle. Results indicated that regardless of the gesture type, the slow-waved mechanical arm led to a longer response time (mean ± SD: 4.871 ± 0.947 s) and lower response accuracy (88.3 ± 32.4%) when compared with the human arm (response time: 4.457 ± 0.727 s, response accuracy: 95.0 ± 22.0%). It was also rated less understandable and comfortable than the human arm. Nevertheless, the fast-waved mechanical arm not only exerted as fast (4.484 ± 0.818 s) and accurate responses (98.3 ± 12.9%) as the human arm but was also rated as understandable, polite, and comfortable as the human arm. This indicated the implication of conveying gestures by utilizing the fast-waved mechanical arm (120 beats/min) to facilitate effective communication from automated vehicles to human drivers in manual driving vehicles. The present study’s findings provided reference implications for manufacturers and designers to adopt this gesture-based communication method to develop safe and user-friendly automated vehicles.

Driver’s under-arousal occurred in automated driving systems (ADS) impairs takeover safety. This study aims to determine electrodermal activity (EDA) features’ importance for driver’s arousal quantification. A car-following simulator study was conducted with participants concurrently executing four levels of cognitive tasks, triggering four levels of arousal. Participants’ skin conductance (SC) data were collected and decomposed into tonic (skin conductance level, SCL) and phasic (skin conductance response, SCR) components. Seventeen features extracted from SC, SCL and SCR were compared. As a result, SCR-relevant features showed higher significance and larger effect size than SC and SCL features in response to cognitive load, which suggests the phasic component dominates changes in EDA under varying cognitive load. Moreover, the SCR rate TTP.nSCRs, identified by$0.03 ~\mu \text{S}$thresholds, attained the largest effect size among all features for driver’s arousal measurement. A varying time windows (TW) analysis showed that TTP.nSCRs was the most suggested arousal metric when TW was over 20 s, whereas the sum of SCRs amplitudes TTP.AmpSum was preferred when TW was less than 20 s. For driver’s arousal quantification with multi-features, the top five suggested features were TTP.nSCRs, SC_Rate5, CDA.SCR (or CDA.ISCR), CDA.AmpSum, and TTP.AmpSum. Although male drivers showed higher values of EDA features than female drivers, the sensitivity of the proposed EDA features stands across gender and individuals. This study promotes an improved understanding of EDA changes in human cognitive process. The sensitive EDA features proposed could be used from uni- or multi-modalities in driver state management and takeover-safety prediction for ADS.

Most existing driver models focus on predicting driving performance in normal and near-collision situations without considering the impact of collision warning parameters on driver behavior. This study develops a cognitive computational driver model based on the Queueing Network-Model Human Processor (QN-MHP) to quantify the effects of key warning parameters (i.e., warning lead time, warning reliability, and speech warning style) on driver performance in warning responses, in connected vehicle systems (CVSs). The model was validated by comparing its predictions of driver response time, response type, and braking and steering performance with data from thirty-two drivers collected in an experimental study. Once the route choice mechanism had been implemented, the driver model was found to explain the cognitive mechanism underlying how drivers process warnings in CVSs. Indeed, the validation results showed that the model was able to capture major changes in patterns of the experimental data, with R-squared values of 0.88 for warning response time, 0.69 and 0.65 for decision making in response type for the initial trial and across trials, 0.85 for braking performance, and 0.83 for steering performance. The model can be applied to optimize the interface design of CVSs based on driver needs.

Warning systems play a crucial role in the takeover of conditional automated driving. However, the widely used single-stage warning systems in takeover had inevitable and critical issues in situation awareness (SA), driving stress, and takeover performance. As such, two-stage warning systems might be an optimal solution to alleviate these problems. On this basis, this study investigated the effect of warning types (single-stage vs two-stage warning systems) and non-driving related tasks (NDRTs) (playing Tetris game vs monitoring automated systems) on takeover. A total of 32 participants were recruited to join our driving-simulated study. These participants responded to different types of takeover warning systems upon receipt while engaging in NDRTs. Simultaneously, the SA, physiology stress, takeover performance, and acceptance data of the participants were recorded. Results showed that the drivers exhibited higher SA, lower physiology stress, better takeover performance, and higher acceptance ratings in the two-stage warning systems than in the single-stage warning systems. In conclusion, two-stage warning systems are promising in improving takeover safety based on connected vehicle technologies in the future. These findings can provide some guidelines for designers and engineers when applying the warning systems in automated driving.

With the development of cutting-edge technology in the area of driving performance, driver warning systems based on head up displays (HUD) are considered to have the potential to improve driving safety in the future. The location of HUD warning graphics is a vital component to ensure that drivers obtain information the first time and avoid cognitive tunneling when coming across hazards; however, few studies have critically examined this. The present study investigated the advantages of HUD in presenting warning graphics in comparison with traditional head down display (HDD) in vehicles, and further explored the effect of HUD location based on comprehensive indicators, including behavior performance, eye movement data, and subjective assessment. The results revealed that compared with HDD, presenting warning graphics to drivers on HUD could significantly improve driving performance and eye movement patterns, and HUD was the preferential mode for drivers. Results also demonstrated that presenting HUD warning graphics at a location of 8°below the sight line was associated with the worst results in driving performance, eye movement patterns and subjective assessment. Other locations of HUD presentation were not associated with any significant differences for most indicators. These findings have some reference implications for automobile designers as they construct and implement HUD warning systems.

This study aims to investigate the usability of a head-up display (HUD) in presenting warning messages during driving and create a new and effective vehicle early warning system for drivers. Two experiments were conducted. In Experiment 1, 36 drivers were randomly assigned to a group using HUD and a control group. The simulated driving performance of the two groups was compared to determine if the HUD graphic early warning system facilitates driving safety. Results revealed that the HUD-using group demonstrated better driving performance than the control group in terms of collision, mean deceleration, accelerator release reaction time, brake reaction time, reduced velocity, reduced energy, steering reaction time, mean reaction time, and minimum reaction time. We investigated the influence of the presentation mode of warning messages on simulated driving performance in Experiment 2. Forty-eight drivers were randomly assigned to an HUD warning group, an audio warning group, and an audiovisual group that integrated HUD and audio warning. The drivers in the HUD warning group performed better than those in the two other groups in terms of mean deceleration. The audiovisual group that integrated HUD and audio warning showed an advantage in reduced velocity. The findings indicated that HUD technology has the potential to promote safe driving by improving the early warning system.

Automated vehicles offer the possibility of significantly increasing traffic safety, mobility, and driver comfort, and reducing congestion and fuel emissions. Current automation technology, however, remains imperfect, and in certain situations, automation will still require the driver to suspend non-driving tasks and take back control of the automated vehicle in a limited period of time. During automated driving, drivers engaged in non-driving tasks (e.g., reading, taking a nap) may not perceive the visual or auditory take-over request in a timely nor accurate manner. Therefore, it is necessary to explore the potential of tactile warning further. This study investigates the effects of vibration patterns of take-over requests (six vibration patterns with different orders of the vibration location) and various realistic non-driving tasks (six non-driving tasks: reading, typing, watching videos, playing games, taking a nap, and monitoring the driving scenario on the driving simulator) on driver take-over behavior, and driver trust and acceptance of automated vehicles. Across all non-driving tasks, the fastest response time was observed with Vibration Pattern 5 (order of the vibration location: back–back–seat–seat). The shortest response time and largest minimum time-to-collision (TTC) also were observed when drivers took back control of the vehicle after monitoring the driving scenario. No interaction effects between vibration patterns and non-driving tasks were observed. Potential applications of the results of designing take-over requests in automated vehicles are discussed.

Automated vehicles have received great attention, since they offer the possibility of significantly increasing traffic safety, mobility, and driver comfort. Current automation technology is still imperfect; therefore, there will still be situations in which the automation will not be able to handle and will request the driver to suspend nondriving tasks and take over control of the automated vehicle in a limited period of time. Accordingly, it is necessary to understand the effects of the lead time of take-over request as well as nondriving tasks on driver take-over. The present driving simulator experiment studied the effects of lead time and various realistic nondriving tasks on take-over behavior and driver acceptance to the automated vehicle. Results suggested optimal driver take-over performance when the lead time of the take-over request was 10-60 s for general nondriving tasks. Specifically, a take-over request with lead time at 10-60 s led to lower crash rate, greater minimum time-to-collision, and lower lateral acceleration. However, a longer lead time (e.g., 15-60 s) was necessary to achieve optimal driver acceptance even though drivers could successfully take over control with shorter lead time (e.g., 10 s). In addition, driver take over performance was significantly influenced by nondriving tasks. When more sensory modalities were occupied or when the cognitive load was very low, driver take-over performance was impaired, especially when the take-over request was too late (e.g., lead time was 3 s). Potential applications of the results in designing of take-over request in automated vehicles were further discussed.

Transportation cyberphysical systems (CPS) aim to improve driving safety by informing drivers of hazards with warnings in advance. The understanding of human responses to speech warnings is essential in the design of transportation CPS to eliminate hazards and accidents. To date, many works have addressed diverse warning characteristics with experimental approaches. However, the computational model to quantify the effects of warning characteristics on human performance in responses to speech warnings is still missing. Mathematical equations were built to model the effects of lead time, loudness, and signal word choices on human perceptual, cognitive, and motor activities involved in speech warning responses. Different levels of lead time, levels of loudness, and signal word choices served as inputs in the model to predict human error rate and reaction time of speech warning responses. The model was validated with drivers' crash rates and reaction times to speech warnings of upcoming hazards in driving assistant systems in two empirical studies. Results showed a good prediction of human performance in responding to the speech warnings compared with the empirical data. The application of the model to identify optimal parameter settings in the design of speech warnings in order to achieve greater safety benefits is later discussed.

Daily numerical data entry is subject to human errors, and errors in numerical data can cause serious losses in health care, safety and finance. Difficulty in detecting errors by human operators in numerical data entry necessitates an early error detection/prediction mechanism to proactively prevent severe accidents. To explore the possibility of using multi-channel electroencephalography (EEG) collected before movements/reactions to detect/predict human errors, linear discriminant analysis (LDA) classifier was utilised to predict numerical typing errors before their occurrence in numerical typing. Single trial EEG data were collected from seven participants during numerical hear-and-type tasks and three temporal features were extracted from six EEG sites in a 150-ms time window. The sensitivity of LDA classifier was revealed by adjusting the critical ratio of two Mahalanobis distances as a classification criterion. On average, the LDA classifier was able to detect 74.34% of numerical typing errors in advance with only 34.46% false alarms, resulting in a sensitivity of 1.05. A cost analysis also showed that using the LDA classifier would be beneficial as long as the penalty is at least 15 times the cost of inspection when the error rate is 5%. LDA demonstrated its realistic potential in detecting/predicting relatively few errors in numerical data without heavy pre-processing. This is one step towards predicting and preventing human errors in perceptual-motor tasks before their occurrence.

Numerical typing errors can lead to serious consequences, but various causes of human errors and the lack of contextual clues in numerical typing make their prediction difficult. Human behavior modeling can predict the general tendency in making errors, while data mining can recognize neurophysiological feedback in detecting cognitive abnormality on a trial-by-trial basis. This study suggests integrating human behavior modeling and data mining to predict human errors because it utilizes both 1) top-down inference to transform interactions between task characteristics and conditions into a general inclination of an average operator to make errors and 2) bottom-up analysis in parsing psychophysiological measurements into an individual's likelihood of making errors on a trial-by-trial basis. Real-time electroencephalograph (EEG) features collected in a numerical typing experiment and modeling features produced by an enhanced human behavior model (queuing network model human processor) were combined to improve error classification performance by a linear discriminant analysis (LDA) classifier. Integrating EEG and modeling features improved the results of LDA classification by 28.3% in keenness (d') and by 10.7% in the area under ROC curve (AUC) from that of using EEG only; it also outperformed the other three benchmarking scenarios: using behaviors only, using apparent task features, and using task features plus trial information. The AUC was significantly increased from using EEG along only if EEG + Model features were used.

Traffic control agencies (TCAs), including police officers, firefighters, or other traffic law enforcement officers, can override automatic traffic signal control and manually control the traffic at an intersection. TCA-based traffic signal control is crucial to mitigate nonrecurrent traffic congestion caused by planned and unplanned events. Understanding and predicting TCA behaviors is significant to optimize event traffic management and operations. In this paper, we propose a pressure-based human behavior model to mimic TCA's decision-making behavior. The model calculates TCA's pressure based on two attributes: vehicle and pedestrian queue dynamics and the red time duration for each phase. When TCA's pressure on each phase meet certain criteria and the minimal green is satisfied, TCA will terminate the current phase and switch to another phase. In order to study TCA behavior systematically, we first build a manual signal control simulator based on a microscopic traffic simulation tool. Supported by the manual control simulator, a series of human subject experiments have been conducted with real-world TCAs. Experiment data are divided into training data and test data. The proposed behavior model is then calibrated by training data, and the model is validated by both offline segment-based phase and duration prediction and online VISSIM-based simulation. Further, we test the model with videotaped TCA behavior data at a real-world intersection. Both validation results support the effectiveness of proposed behavior model.

This paper presents a new computational framework for early detection of driver distractions (map viewing) using brain activity measured by electroencephalographic (EEG) signals. Compared with most studies in the literature, which are mainly focused on the classification of distracted and nondistracted periods, this study proposes a new framework to prospectively predict the start and end of a distraction period, defined by map viewing. The proposed prediction algorithm was tested on a data set of continuous EEG signals recorded from 24 subjects. During the EEG recordings, the subjects were asked to drive from an initial position to a destination using a city map in a simulated driving environment. The overall accuracy values for the prediction of the start and the end of map viewing were 81% and 70%, respectively. The experimental results demonstrated that the proposed algorithm can predict the start and end of map viewing with relatively high accuracy and can be generalized to individual subjects. The outcome of this study has a high potential to improve the design of future intelligent navigation systems. Prediction of the start of map viewing can be used to provide route information based on a driver's needs and consequently avoid map-viewing activities. Prediction of the end of map viewing can be used to provide warnings for potential long map-viewing durations. Further development of the proposed framework and its applications in driver-distraction predictions are also discussed.

Navigating a car in an unfamiliar country becomes one of the major concerns with driving safety. Existing studies mainly used survey, focus group and statistical analysis to study this problem. Although the navigation system (e.g. GPS) gains an advantage in providing navigation assistances, paper maps and particularly internet maps are one of major ways for navigating in an unfamiliar area. This study is one of a few experimental studies which addressed a typical multitasking driving behaviour (driving and navigation task) in a cross-culture context. Twenty-four native American-English speakers navigated a driving simulator in urban environments which involved three formats of language settings of the street signs (English, Chinese or no street signs) and two types of map orientation consistency (driving from south to north vs. driving from north to south with a north-up map). It was found that female drivers made more wrong turns only with Chinese street signs but not in the other two conditions compared to male drivers. This indicated that female drivers actually behaved differently from male drivers in an unfamiliar driving environment with unfamiliar street names language. Both male and female drivers benefited from English street signs and reported higher driver workload with Chinese street signs. Interestingly, the average glance duration of maps with Chinese street signs was significantly less than that with English street signs, indicating that even though Chinese language belongs to ideograph with graphical information, its graphical information was not that helpful in assisting navigation task. In addition, female drivers had more instances of collisions with other vehicles, a longer distance of deviation from central line position, higher driver workload and a longer time period of map glance duration. For the main effect of map consistency, drivers made more wrong turns and perceived higher driving workload when they drove with inconsistent maps. Further implications of the current study in transportation safety of globalisation were also discussed, including improvement of street sign infrastructures and optimal ways of using and designing internet maps for drivers navigating in an unfamiliar country.

Vehicular Cyber-Physical Systems (VCPS) provide human drivers with various services related to road safety, and on-road infotainments. Since a service (message) delivery includes service transmission, service display and driver processing, many challenges arise due to limited network resources, possible pre-emption and contention between services for the display and non-negligible driver processing delay. In this paper, we address a new Driver-centric Service Delivery Problem (DSDP) from a cross-disciplinary resource allocation standpoint. Our goal is to deliver a number of services to a set of intended drivers in a given time period so as to maximize the system-wide performance in terms of total utility income (TUI) to drivers. We show that DSDP differs from all existing problems and is NP-Complete. A number of efficient heuristics are proposed to address several issues, including wireless transmission failure as well as distributed implementation of the multi-sender systems. Utilizing real traces collected from taxis in the city of Shanghai, we also present a case study in a more realistic scenario and conduct comprehensive simulations providing numerical results.

Speeding is one of the most prevalent contributing factors in traffic crashes. The prediction of speeding is important to reduce excessive speeds and prevent speeding-related traffic accidents and injuries. Speeding (either intentional or unintentional) is a consequence of inappropriate speed control. This paper extends a previous mathematical model of driver speed control to provide quantitative predictions of intentional and unintentional speeding. These predictions consist of the time at which the driver exceeds the speed limit and the magnitude of speeding. Based on these modeling predictions, this paper develops an intelligent speeding prediction system (ISPS) to prevent the occurrence of speeding. An experimental study using a driving simulator is conducted to evaluate the ISPS. We find no significant difference between modeled predictions and experimental results in terms of the time and magnitude of intentional speeding. In addition, the ISPS can successfully predict the majority of unintentional speeding instances, with only a small portion of unnecessary speeding warnings. Applications of the ISPS to reduce driving speed and prevent the real-time occurrence of speeding and speeding-related traffic accidents are discussed.

At the unmarked roadway, pedestrians cross the road with changing speeds and directions that result in curved paths and high chances of road accidents. However, few computational models have been built to address the mechanisms underlying the curved paths in crossing unmarked roadways. To better understand pedestrian behaviors and finally facilitate their safety, this paper modeled pedestrian paths at the unmarked roadway as a result of the decision-making process in which pedestrians try to minimize discomfort by weighing perceived risk (PR) with efficiency. PR is assumed to come from vehicles and specific positions on the road. Efficiency is modeled by the deviation from destination. The modeling mechanisms are consistent with existing theories, enhancing the understanding of pedestrian crossing behavior mechanisms at the unmarked roadway rather than treating the crossing process as a black box. The observed 135 pedestrian paths at two unmarked roadways in the real world were compared with the model's predictions. The potential applications of the model in exploring pedestrian position distribution at a crossing site and improving pedestrian presentation in existing driving simulators and intelligent transportation systems are discussed, as well as its limitations.

The quantitative prediction and understanding of a driver's speed control is an essential component in preventing speeding and designing of vehicle systems. Driver speed control is a complex behavior of longitudinal vehicle control consisting of speed perception, decision making, motor control, vehicle dynamics modeling, and individual driver differences. However, there are few existing models that can integrate all of these aspects in a cohesive manner. To address this problem, this paper introduces a mathematical model for a driver's speed control with analytical solutions based on human cognitive mechanisms in driving. This model includes an integrated queuing network-model human processor structure and the rule-based decision field theory. This new model consequently can predict several aspects of driver speed control behavior at the same time, such as driving speed, throttle/brake pedal angle, acceleration, and the frequency of speedometer inspection. A laboratory session involving a driving simulator is conducted to validate the current model. The model accounted for over 99% of the experimental speed of the average driver, and over 95% of the experimental speed for the majority of individual drivers.

Automatic machines are increasingly being used to help drivers automatically complete tasks; however, the high error rate of automatic machines limits how they might reduce driver task load. Therefore, allocating tasks between human and machine becomes an important question in system design. Existing methods of task allocation do not consider several natural characteristics of human-machine systems simultaneously, including speed-error tradeoff, cognitive modeling of workload, multicriteria decision modeling, dynamic allocation, and global optimum. In this paper, a queueing model-based intelligent task allocator (QM-ITA) that covers the criteria above and optimally allocates tasks between a human operator and an automatic machine is developed. The optimal task allocation algorithm is described in four scenarios that demonstrate how QM-ITA is able to minimize the workload of human operator, minimize system error rate, propose a maximum acceptable error rate of an automatic machine, determine if an automatic machine is necessary for a system, and suggest a maximum acceptable task arrival rate. Further development of the model and the prospects for future research are also discussed.

This paper studies the applications of data mining techniques in early detection of numerical typing errors by human operators through a quantitative analysis of multichannel electroencephalogram (EEG) recordings. Three feature extraction techniques were developed to capture temporal, morphological, and time-frequency (wavelet) characteristics of EEG data. Two most commonly used data mining techniques, namely, linear discriminant analysis (LDA) and support vector machine (SVM), were employed to classify EEG samples associated with correct and erroneous keystrokes. The leave-one-error-pattern-out and leave-one-subject-out cross-validation methods were designed to evaluate the in- and cross-subject classification performances, respectively. For the in-subject classification, the best testing performance had a sensitivity of 62.20% and a specificity of 51.68%, which were achieved by SVM using morphological features. For the cross-subject classification, the best testing performance was achieved by LDA using temporal features, based on which it had a sensitivity of 68.72% and a specificity of 49.45%. In addition, the receiver operating characteristic (ROC) analysis revealed that the averaged values of the area under ROC curves of LDA and SVM for the in- and cross-subject classifications were both greater than 0.60 using the EEG 300 ms prior to the keystrokes. The classification results of this study indicated that the EEG patterns of erroneous keystrokes might be different from those of the correct ones. As a result, it may be possible to predict erroneous keystrokes prior to error occurrence. The classification problem addressed in this study is extremely challenging due to the very limited number of erroneous keystrokes made by each subject and the complex spatiotemporal characteristics of the EEG data. However, the outcome of this study is quite encouraging, and it is promising to develop a prospective early detection system for erroneous keystrokes based on brain-wave signals.

Link analysis (LA) is one of most widely used methods in user interface design to arrange control elements on user interfaces. However, traditional LA method is insufficient for evaluating transitional cost associated with accessibility (the easiness for the operator to reach certain control element on the interface) and the link table commonly used contains no directional information for assessing difficulty. To address these two problems, an improved LA method based on a modified link table and a branch-and-bound algorithm is proposed in this study. A case study on a simplified control interface of a boiling water reactor (BWR) in a real-world nuclear control system was exemplified to elucidate the improved method and an experiment was conducted to validate the effectiveness of the method in improving users’ performance time. The results showed that the total completion time (CT) and the completion time of accessibility-associated operations were significantly shorter on the interface modified by the improved method than by the traditional LA method, while the difference of the completion time of proximity-associated operations between the two interfaces was non-significant. Therefore, although the traditional LA method can significantly ameliorate the random interface by optimising the proximity between control elements, the improved method can further improve the CT by optimally trading off the accessibility and proximity. The method can be applied to the interface which requires physical movements between the user and the interface and within the interface, especially touch screen and control panels.

The risk of vehicle collisions significantly increases when drivers are overloaded with information from in-vehicle systems. One of the solutions to this problem is developing adaptive workload management systems (AWMSs) to dynamically control the rate of messages from these in-vehicle systems. However, existing AWMSs do not use a model of the driver cognitive system to estimate workload and only suppress or redirect in-vehicle system messages, without changing their rate based on driver workload. In this paper, we propose a prototype of a new queueing network-model human processor AWMS (QN-MHP AWMS), which includes a queueing network model of driver workload that estimates the driver workload in several driving situations and a message controller that determines the optimal delay times between messages and dynamically controls the rate of messages presented to drivers. Given the task information of a secondary task, the QN-MHP AWMS adapted the rate of messages to the driving conditions (i.e., speeds and curvatures) and driver characteristics (i.e., age). A corresponding experimental study was conducted to validate the potential effectiveness of this system in reducing driver workload and improving driver performance. Further development of the QN-MHP AWMS, including its use in in-vehicle system design and possible implementation in vehicles, is discussed.

Transcription typing is one of the basic and common activities in human-machine interaction and 34 transcription typing phenomena have been discovered involving many aspects of human performance including interkey time, typing units and spans, typing errors, concurrent task performance, eye movements, and skill effects. Based on the queuing network theory of human performance [Liu 1996; 1997] and current discoveries in cognitive and neural science, this article extends and applies the Queuing Network-Model Human Processor (QN-MHP [Liu et al. 2006]) to model 32 transcription typing phenomena. The queuing network model of transcription typing offers new insights into the mechanisms of cognition and human-computer interaction. Its value in proactive ergonomics design of user interfaces is illustrated and discussed.

Modeling and predicting of mental workload are among the most important issues in studying human performance in complex systems. Ample research has shown that the amplitude of the P300 component of event-related potential (ERP) is an effective real-time index of mental workload, yet no computational model exists that is able to account for the change of P300 amplitude in dual-task conditions compared with that in single-task situations. We describe the successful extension and application of a new computational modeling approach in modeling P300 and mental workload—a queuing network approach based on the queuing network theory of human performance and neuroscience discoveries. Based on the neurophysiological mechanisms underlying the generation of P300, the current modeling approach accurately accounts for P300 amplitude both in temporal and intensity dimensions. This approach not only has a basis in its biological plausibility but also has the ability to model and predict workload in real time and can be applied to other applied domains. Further model developments in simulating other dimensions of mental workload and its potential applications in adaptive system design are discussed.

Drivers overloaded with information significantly increase the chance of vehicle collisions. Driver workload, which is a multidimensional variable, is measured by both performance-based and subjective measurements and affected by driver age differences. Few existing computational models are able to cover these major properties of driver workload or simulate subjective mental workload and human performance at the same time. We describe a new computational approach in modeling driver performance and workload-a queuing network approach based on the queuing network theory of human performance and neuroscience discoveries. This modeling approach not only successfully models the mental workload measured by the six National Aeronautic and Space Administration Task Load Index workload scales in terms of subnetwork utilization but also simulates the driving performance, reflecting mental workload from both subjective- and performance-based measurements. In addition, it models age differences in workload and performance and allows us to visualize driver mental workload in real time. Further usage and implementation of the model in designing intelligent and adaptive in-vehicle systems are discussed.