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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Ph.D. Thesis Defense Announcement
Cyclist Stress and Biometric Sensing in Naturalistic Settings
by
April Gadsby
Advisor(s):
Dr. James Tsai (CEE) & Dr. Kari Watkins (CEE)
Committee Members:
Dr. Michael Rodgers (CEE), Dr. Christopher Le Dantec (Interactive Computing)
Dr. Daniel Spieler (Psych), Dr. Marjan Hagenzieker (TU Delft - Transport & Planning)
Date & Time: May 10, 2021 | 9:00 AM EST
Location: https://bluejeans.com/568881535
Cycling is gaining traction in the United States as a mode of transportation due to its plethora of benefits. However, cycling still makes up a very low percentage of modal share. One major hurdle to increased cycling modal share is that people feel cycling is unsafe and stressful. Many studies have considered cyclists’ stress, but these studies have not allowed participants to self-define their stressors during a cycling experience. This dissertation fills this gap by combining in-ride, open-ended surveys/interviews with naturalistic cycling methods. Cyclists wore eye tracking glasses and rode instrumented bicycles equipped with GPS and LiDAR to allow researchers to gain a deeper knowledge of their surroundings and reaction to them.
This dissertation uses different combinations of sensors and survey techniques to explore cyclists’ stress and demonstrate the value of these methods. The first study uses in-ride surveys and instrumented bicycle data to explore the top causes of cyclists’ stress in an emerging and an established cycling city. The second study uses eye tracking glasses and survey techniques to better understand cyclists’ gaze behavior with varying stress, complexity, and stated skill. The last study uses eye tracking and survey techniques as well but uses them to give practical guidance for cyclist-focused pavement asset management. Various data analysis methods are used to assess these data individually and in combination including thematic analysis, GPS analysis, exploratory eye tracking measures, frame-by-frame video analysis, descriptive, and inferential statistics.
These studies demonstrate that cyclists prefer separated infrastructure with smooth pavements. Although there were some differences by location or rider characteristics, the preferences for separated, smooth facilities are largely universal among cyclists. Although what caused cyclists stress was fairly consistent, gaze behavior did change with stated skill in unexpected ways demonstrating that researchers cannot assume cyclists’ gaze behavior will match what is known about drivers’ gaze behavior. These findings can contribute to bike infrastructure design and maintenance and the methods have opened the door to plenty of opportunity for future research into cyclist and other road user behavior.
