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- W655918383 abstract "Identifying activities from continuous GPS trajectories is an essentially preliminary step in obtaining PT (Person Trip) data and activity-based traffic demand forecasting. In this research, a two-step methodology for identifying activity stop is proposed utilizing an improved variation of DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm and SVMs (Support Vector Machines) method. In the first step, improved DBSCAN algorithm is used to identify stop point and move point; in the second step, SVMs is used to distinguish activity stop and non-activity stop from stop points in the first step. In order to precisely adapt DBSCAN algorithm to the feature of GPS points, two constraints, time sequence constraint and direction change constraint are applied as improvements to DBSCAN (this improved variant of DBSCAN is named as ConstDBSCAN). Stop duration, mean distance to centroid in a cluster of points at stopping location, and minimum distance to home, workplace or nearest station are extracted as the three major features utilized in the SVMs method. This methodology was tested by GPS data collected by mobile phone in Nagoya area, Japan. ConstDBSCAN algorithm achieves an accuracy of 90% in identifying the stop positions in the first step and the SVMs method achieves an accuracy of 96% in distinguishing activity stops and non-activity stops in the second step." @default.
- W655918383 created "2016-06-24" @default.
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- W655918383 date "2015-01-01" @default.
- W655918383 modified "2023-09-24" @default.
- W655918383 title "Activity Stop and Non-Activity Stop Identification in GPS Trajectories Utilizing Density-Based Clustering Method and Support Vector Machines" @default.
- W655918383 hasPublicationYear "2015" @default.
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