Identifying golden routes in tourist areas based on AMP collectors

Abstract

This study uses anonymous media access control address packet (AMP) collector data obtained from Wi-Fi signals to identify golden routes, i.e., routes that are most frequently followed in tourist areas. The rise of radiofrequency scanner technology has led to its potential application in the observation of people movements. This study analysed the travelling behaviour of tourists in the Higashiyama area (Kyoto, Japan) using digital footprint data collected by 20 AMP sensors. K-means clustering analysis was performed to identify the trajectory of tourists. Then, sequential pattern mining was used to extract the frequent sequence of destinations visited by tourists. As a result, we characterised the smart device users into four groups: same-day visitors, overnight visitors, commuters, and residents. Moreover, it was found that the most frequent trip patterns of tourists matched our expectations, and we conclude that the proposed method can identify golden routes.