Location
Hilton Waikoloa Village, Hawaii
Event Website
http://hicss.hawaii.edu/
Start Date
1-3-2018
End Date
1-6-2018
Description
Existing studies normally focus on extracting temporal or periodical patterns of people’s daily travel for location based services. However, people’s characteristics and preference are actually paid much more attention by business. Therefore, how to capture characteristics from their daily travel patterns, is an interesting question. In order to address the research question, we first develop two basic measures in terms of repetitiveness of travel and then two advanced measures, to capture people’s activity of daily travel, and the colorfulness of lifestyle, respectively. Incorporating historical trajectories, with real-time positions from a location-based social network (LBSN), i.e. Foursquare, we conduct statistical analysis for people’s travel patterns in US cities. Finally, we illustrate people’s profiles of travel patterns and lifestyles. Results show that people’s preference can be inferred from the developed activity and colorfulness measures. Those findings demonstrate that proposed measures are supposed to be effectively adopted for researchers on travel pattern analysis and preference analysis, and further give suggestions to individuals for location-based decision making.
Does Daily Travel Pattern Disclose People’s Preference?
Hilton Waikoloa Village, Hawaii
Existing studies normally focus on extracting temporal or periodical patterns of people’s daily travel for location based services. However, people’s characteristics and preference are actually paid much more attention by business. Therefore, how to capture characteristics from their daily travel patterns, is an interesting question. In order to address the research question, we first develop two basic measures in terms of repetitiveness of travel and then two advanced measures, to capture people’s activity of daily travel, and the colorfulness of lifestyle, respectively. Incorporating historical trajectories, with real-time positions from a location-based social network (LBSN), i.e. Foursquare, we conduct statistical analysis for people’s travel patterns in US cities. Finally, we illustrate people’s profiles of travel patterns and lifestyles. Results show that people’s preference can be inferred from the developed activity and colorfulness measures. Those findings demonstrate that proposed measures are supposed to be effectively adopted for researchers on travel pattern analysis and preference analysis, and further give suggestions to individuals for location-based decision making.
https://aisel.aisnet.org/hicss-51/cl/it_enabled_collaboration/5