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Title: Enhanced information design for high speed train displays
Author: Naweed, Anjum
ISNI:       0000 0004 2720 4978
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2010
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The primary goals of the empirical work outlined in this thesis were to explore the utility of preview and predictive information in train driving and develop a substantive simulated task environment (micro-world) for enhancing the systematic study of rail human factors issues. To achieve these goals, the specific domain features and informational requirements of the domain were required. The first phase of this research was to derive a multi-layer task and domain analysis framework that could extract the implicit and intuitive knowledge of the task from train drivers and subject matter experts. This produced the data required for the micro-world designing process, and additionally, identified two train driving task types (traditional, modern). The second phase used these data and a comprehensive evaluation of existing train information displays to design and develop the train micro-world ATREIDES. ATREIDES was employed in the third phase of this research to systematically study the design and utility of preview and predictive information. The features studied included information for braking, pursuit enhancing movement zones, dynamic preview, and estimated time of arrival (ETA) data. Having established The empirical studies followed an iterative process that compared the design of these features according to each task type, serving to answer the overarching research question, how can drivers be given the best information? A within-individual design was used to conduct this research. In general, the results indicated very little utility for exclusive enhanced information under the traditional train-driving task. However, under certain features, performance under the pursuit task was enhanced dramatically. These findings are discussed in relation to the two train driving tasks, and more generally, applied to collision avoidance analogues.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available