Threshold settings for TRIP/STOP detection in GPS traces

This paper presents two methods to extract stops and trips from GPS traces: the first one focuses on periods of non-movement (stops) and the second one tries to identify the longest periods of movement (trips). A stop corresponds to a location where the individual halts with the intention to perform an activity. In order to assert the quality of both methods, the results are compared to cases where the stops and trips are known by other means. First a set of traces was used for which the stops were identified by the traveler by means of a visual tool aimed at alignment of manually reported periods in the diary to automatically recorded GPS coordinates. Second, a set of synthetic traces was used. Several quality indicators are presented; they have been evaluated using sensitivity analysis in order to determine the optimal values for the detector’s configuration settings. Person traces (as opposed to car traces) were used. Individual specific behavior seems to have a large effect on the optimal values for threshold settings used in both the TRIP and STOP detector algorithms. Accurate detection of stops and trips in GPS traces is vital to prompted recall surveys because those surveys can extend over several weeks. Inaccurate stop detection requires frequent corrections by the respondent and can cause them to quit.

[1]  Davy Janssens,et al.  Implementation Framework and Development Trajectory of FEATHERS Activity-Based Simulation Platform , 2010 .

[2]  Davy Janssens,et al.  Diary Survey Quality Assessment Using GPS Traces , 2015, ANT/SEIT.

[3]  Chiara Renso,et al.  Inferring human activities from GPS tracks , 2013, UrbComp '13.

[4]  Jiannong Cao,et al.  Effective social relationship measurement based on user trajectory analysis , 2014, J. Ambient Intell. Humaniz. Comput..

[5]  Xing Xie,et al.  Inferring social ties between users with human location history , 2014, J. Ambient Intell. Humaniz. Comput..

[6]  Kay W. Axhausen,et al.  Processing Raw Data from Global Positioning Systems without Additional Information , 2009 .

[7]  Davy Janssens,et al.  Canonic Route Splitting , 2014, ANT/SEIT.

[8]  Xing Xie,et al.  Mining user similarity based on location history , 2008, GIS '08.

[9]  Otto Anker Nielsen,et al.  Improved methods to deduct trip legs and mode from travel surveys using wearable GPS devices: A case study from the Greater Copenhagen area , 2015, Comput. Environ. Urban Syst..

[10]  Amer Shalaby,et al.  Enhanced System for Link and Mode Identification for Personal Travel Surveys Based on Global Positioning Systems , 2006 .

[11]  Zhixian Yan,et al.  Towards Semantic Trajectory Data Analysis: A Conceptual and Computational Approach , 2009, VLDB PhD Workshop.

[12]  Peter R. Stopher,et al.  Can GPS replace conventional travel surveys? Some findings , 2010 .

[13]  Davy Janssens,et al.  TRIP/STOP Detection in GPS Traces to Feed Prompted Recall Survey , 2015, ANT/SEIT.

[14]  Kees Maat,et al.  Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: A large-scale application in the Netherlands , 2009 .

[15]  Eui-Hwan Chung,et al.  A Trip Reconstruction Tool for GPS-based Personal Travel Surveys , 2005 .

[16]  Roberto Trasarti,et al.  TOSCA: two-steps clustering algorithm for personal locations detection , 2015, SIGSPATIAL/GIS.

[17]  Jianhe Du,et al.  Increasing the accuracy of trip rate information from passive multi-day GPS travel datasets: Automatic trip end identification issues , 2007 .

[18]  Vania Bogorny,et al.  A model for enriching trajectories with semantic geographical information , 2007, GIS.

[19]  Zhixian Yan,et al.  Traj-ARIMA: a spatial-time series model for network-constrained trajectory , 2010, IWCTS '10.