Automation for task analysis of next generation air traffic management systems |
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| Institution: | 1. Center for Air Transportation System Research, George Mason University, VA, USA;2. NASA Ames Research Center, Moffet Field, CA, USA;1. Department of Automation, Tsinghua University, Beijing 100084, China;2. Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Nanjing 210096, China;3. College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China;1. Department of Systems Engineering and Engineering Management, City University of Hong Kong, Hong Kong Special Administrative Region;2. Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA;3. Institute for Research in Technology, Comillas Pontifical University, CL Alberto Aguilera 23, 28015 Madrid, Spain;4. MIT Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02139, USA;1. Department of Geography, Ghent University, Krijgslaan 281 (S8), B-9000 Ghent, Belgium;2. Department of Applied Mathematics and Computer Science, Ghent University, Krijgslaan 281 (S9), B-9000 Ghent, Belgium;1. Asst. Prof., Department of Business Administration, Yildiz Technical University, Turkey;2. Professor, Department of Maritime Logistics, Kobe University, Japan;1. Centre Interuniversitaire de Recherche sur les Réseaux d’Entreprise, la Logistique et le Transport (CIRRELT), School of Management, Université du Québec à Montréal, Canada;2. Dipartimento di Scienze Statistiche, Sapienza Università di Roma, Italy;3. Istituto per le Applicazioni del Calcolo “Mauro Picone”, Consiglio Nazionale delle Ricerche (CNR), Italy;1. Dept. management et technologie, École des sciences de la gestion, Université du Québec à Montréal, Canada;2. Centre Interuniversitaire de Recherche sur les, Réseaux d’Entreprise, la Logistique et le Transport (CIRRELT), Canada;3. Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Italy;4. School of Engineering, Università degli Studi di Ferrara, Italy;5. Département de génie de la construction, École de technologie supérieure, Montréal, Canada |
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| Abstract: | The increasing span of control of Air Traffic Control enterprise automation (e.g. Flight Schedule Monitor, Departure Flow Management), along with lean-processes and pay-for-performance business models, has placed increased emphasis on operator training time and error rates. There are two traditional approaches to the design of human–computer interaction (HCI) to minimize training time and reduce error rates: (1) experimental user testing provides the most accurate assessment of training time and error rates, but occurs too late in the development cycle and is cost prohibitive, (2) manual review methods (e.g. cognitive walkthrough) can be used earlier in the development cycle, but suffer from poor accuracy and poor inter-rater reliability. Recent development of “affordable” human performance models provide the basis for the automation of task analysis and HCI design to obtain low cost, accurate, estimates of training time and error rates early in the development cycle.This paper describes a usability/HCI analysis tool that this intended for use by design engineers in the course of their software engineering duties. The tool computes estimates of trials-to-mastery (i.e. time to competence for training) and the probability of failure-to-complete for each task. The HCI required to complete a task on the automation under development is entered into the web-based tool via a form. Assessments of the salience of visual cues to prompt operator actions for the proposed design are used to compute training time and error rates. The web-based tool enables designers in multiple locations to review and contribute to the design. An example analysis is provided along with a discussion of the limitations of the tool and directions for future research. |
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