Tracking and identifying areas of stress in rail commuter journeys through eye-tracking and data fusion

  1. Lookup NU author(s)
  2. Dr Amy Guo
  3. Dr Graeme Hill
  4. Dr Joan Harvey
Author(s)Guo W, Hill G, Harvey J
Editor(s)
Publication type Conference Proceedings (inc. Abstract)
Conference Name5th International Conference on Transport and Health
Conference LocationSan Jose, California, USA
Year of Conference2016
Source Publication Date
Volume3
PagesS31
Series TitleICTH Special issue, Journal of Transport & Health
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Background It is known that stressful situation can cause changes in physiological responses, such as increases in heart rate and breathing rate. This paper describes an experimental approach to monitor rail commuters’ travelling experience and reveal issues and contexts that cannot be identified through traditional market research methods, such as questionnaires and focus groups. Methods The study utilised eye tracking cameras, gyro sensor, accelerometer, heart rate monitor and GPS to capture the commuters’ eye movement and orientation, heart rate variety, elevation and location. The approach enabled the physical and non-physical stressors to be identified and context applied to changes in stress levels and causes thereof. Results A total of 15 female and 26 male commuters, aged from 20 to 60 (mean=42.8, SD=12.7), participated in the study; 41 peak-hour commuting journeys and 39 off-peak hour journeys were followed and monitored. The duration of the journeys ranged from 12 minutes to 3 hours and 51 minutes depending on the participant and journey. An interview with each participant was carried out upon completing the journeys. Information on weather, coach choice, condition of the coach, seating availability and choice, reason for journey, luggage (e.g. carrying a bike), journey’s punctuality and length of delay was recorded by researchers as well as reported by participants. Off-peak journeys were used as the baseline to filter out the typical peak-hour factors from other co-exist factors when analysing the changes in stress and anxieties. More in-depth and sophistic statistical analysis are undergoing and will be included in the presentation. Conclusions By transforming the collected second by second data into a continual proxy for the actual activity of the participant (e.g. sitting or walking) and then combining this with the journey information, it is possible to show results unveiling the hidden causes of commuting related stress and hence identify possible solutions to mitigate the stress and provide recommendations to transport operators on how the experience could be improved. The results reveal the roles various sensors have played and how they could be easily employed in future customer satisfaction studies.
PublisherElsevier
URLhttp://www.tphlink.com/icth-2016---usa.html
DOIhttp://dx.doi.org/10.1016/j.jth.2016.05.076
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