Detecting and visualizing cohesive activity-travel patterns: A network analysis approach

摘要: Abstract This article presents a network analytical framework to detect individual-based activity-travel patterns (ATPs) in space and time. Compared many existing classification methods (e.g., hot-spot detection, sequential alignment method), the method substantiates social meanings underlying interconnectedness similarities of people's activity trajectories better integrates spatial interaction (colocation or distance-decay) temporal connections (concurrence sequence) daily lives measure similarity. approach enables us variant community structures, with individuals same interacting relatively more than belonging different communities, by decomposing complex into meaningful events activities, trips, tours, subsequences). We also demonstrate practicality scientific merit analysis case study household travel behavior Charlotte, North Carolina. Results derived from disaggregated survey data establish effectiveness flexibility cohesive communities ATPs narratives everyday-life events. suggests that has great potential classify large datasets other space-time discover policy-sensitive activity, trip, tour help develop policy planning alternatives for sustainable mobility.