Queuing network modeling of age differences in driver mental workload and performance

Wu, Changxu; Liu, Yili

doi:10.1037/e577562012-005

Cited by 3 publications

(4 citation statements)

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“…QN family of models were used to model driving performance including lateral movement and steering (Cao & Liu, 2014; Feng, 2015) and NDRT performance (Fuller et al, 2012; Wu & Liu, 2006) (Table 2). Most studies validated model’s performance with human-subject data (Bi et al, 2013a, 2013b; Zhao et al, 2011).…”

Section: Resultsmentioning

confidence: 99%

“…There are some unique characteristics of QN-MHP observed from the studies. First, the model was used to quantify cognitive workload (i.e., NASA-TLX dimensions) and situation awareness (based on Situation Awareness Global Assessment Technique (SAGAT)) (Bi et al, 2015; Feng et al, 2017; Feng et al, 2014; Jeong & Liu, 2017b, 2018; Rehman et al, 2019; Wu & Liu, 2006; Wu et al, 2012). Second, QN-MHP was used in many studies to measure visual attention allocation (Feng, 2015; Fuller, 2010; Jeong, 2018; Lim et al, 2010; Sanghavi, 2020; Tsimhoni, 2004), which indicates the capability of QN-MHP to model human perception as compared to GOMS and ACT-R.…”

Section: Resultsmentioning

confidence: 99%

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A Review of Human Performance Models for Prediction of Driver Behavior and Interactions With In-Vehicle Technology

Park

Zahabi

2022

Hum Factors

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Objective This study investigated the use of human performance modeling (HPM) approach for prediction of driver behavior and interactions with in-vehicle technology. Background HPM has been applied in numerous human factors domains such as surface transportation as it can quantify and predict human performance; however, there has been no integrated literature review for predicting driver behavior and interactions with in-vehicle technology in terms of the characteristics of methods used and variables explored. Method A systematic literature review was conducted using Compendex, Web of Science, and Google Scholar. As a result, 100 studies met the inclusion criteria and were reviewed by the authors. Model characteristics and variables were summarized to identify the research gaps and to provide a lookup table to select an appropriate method. Results The findings provided information on how to select an appropriate HPM based on a combination of independent and dependent variables. The review also summarized the characteristics, limitations, applications, modeling tools, and theoretical bases of the major HPMs. Conclusion The study provided a summary of state-of-the-art on the use of HPM to model driver behavior and use of in-vehicle technology. We provided a table that can assist researchers to find an appropriate modeling approach based on the study independent and dependent variables. Application The findings of this study can facilitate the use of HPM in surface transportation and reduce the learning time for researchers especially those with limited modeling background.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

A Review of Human Performance Models for Prediction of Driver Behavior and Interactions With In-Vehicle Technology

Park

Zahabi

2022

Hum Factors

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“…into the simulation and empirical validations of the system. Previous published work of QN-MHP has considered an aging factor (variable A in Equations 2-7 in Wu & Liu, 2006b, 2006c) as one of the major factors in predicting driver workload.…”

Section: Discussionmentioning

confidence: 99%

“…Besides modeling human performance in these tasks, QN-MHP is also used to account for the indexes of mental workload: subjective workload rating measured by NASA-TLX (Wu & Liu, 2006b, 2006c and the amplitude and latency of P300 component measured by the event related brain potential (ERP) techniques (Wu & Liu, 2006a).…”

Section: Queuing Network Modeling Of Human Performance and Mental Wormentioning

confidence: 99%

Development of an Adaptive Workload Management System using the Queueing Network-Model Human Processor

Tsimhoni

Liu

2007

Proceedings of the Human Factors and Ergonomics Society Annual

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Drivers overloaded with information from in-vehicle systems significantly increase the chance of vehicle collisions. Developing adaptive workload management systems (AWMS) to dynamically control the rate of messages from these in-vehicle systems is one of the solutions to this problem. However, existing AWMS do not use driver models to estimate workload, and only suppress or redirect messages without changing the rate of messages from the in-vehicle systems. In this work, we propose a prototype of a new adaptive workload management system, the Queuing Network-Model Human Processor (QN-MHP) AWMS, which includes a model of driver workload based on the queueing network theory of human performance that estimates driver workload in different driving situations and a message controller that dynamically controls the rate of messages presented to drivers. Corresponding experimental studies were conducted to validate the potential effectiveness of this system in reducing driver workload and improving driver performance.

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State of science: mental workload in ergonomics

et al. 2014

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Mental workload (MWL) is one of the most widely used concepts in ergonomics and human factors and represents a topic of increasing importance. Since modern technology in many working environments imposes ever more cognitive demands upon operators while physical demands diminish, understanding how MWL impinges on performance is increasingly critical. Yet, MWL is also one of the most nebulous concepts, with numerous definitions and dimensions associated with it. Moreover, MWL research has had a tendency to focus on complex, often safety-critical systems (e.g. transport, process control). Here we provide a general overview of the current state of affairs regarding the understanding, measurement and application of MWL in the design of complex systems over the last three decades. We conclude by discussing contemporary challenges for applied research, such as the interaction between cognitive workload and physical workload, and the quantification of workload 'redlines' which specify when operators are approaching or exceeding their performance tolerances.

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Queuing network modeling of age differences in driver mental workload and performance

Cited by 3 publications

References 0 publications

A Review of Human Performance Models for Prediction of Driver Behavior and Interactions With In-Vehicle Technology

A Review of Human Performance Models for Prediction of Driver Behavior and Interactions With In-Vehicle Technology

Development of an Adaptive Workload Management System using the Queueing Network-Model Human Processor

State of science: mental workload in ergonomics

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