Deriving Attributes of Walking Behavior Using GPS-Based Travel Survey and Fuzzy Logic: A Case Study in Lyon, France

Walking plays an essential role in both daily mobility and human health. Unfortunately, measuring walking behavior in self-reported surveys is usually subject to lack of accuracy due to the limit of human memory, the habit of not only rounding travel time but also neglecting short and unimportant trip legs. To cope with such a problem, the use of the Global Positioning System (GPS) is promising. This study aimed at developing an inference model to derive travel modes from 406-day GPS data of 90 respondents living in Lyon city and its suburbs (France). Based on mode detection results, aspects of walking (i.e., purposes, length, duration, speed, the relation between walking and home, the relation between walking and other modes) were estimated. For detecting modes well, particularly walking, a fuzzy logic-based algorithm using average speed, the 95th percentile of speed, and the 95th percentile of acceleration were created. Rules of signal loss were proposed to identify metro segments, while respondents’ reports on the frequency of cycling were utilized to address the confusion between walking and cycling better. As for findings, 951 walking stages were found. Because all of the walking patterns, estimated from prediction results of travel modes, were compatible with previous studies’ findings, the mode detection method adopted in this study has performed satisfactorily, particularly concerning imputing walking. The proposed method may enable better use of GPS to conduct further analyses of walking behaviors thanks to deriving walking segments adequately from spatio-temporal data.