Logistics Vehicle Travel Preference of Interest Points Based on Speed and Accessory State

In a crowded city, directions and speed of vehicles are usually changed arbitrarily. Analyzing travel preferences of vehicle has become a focus of research as it helps to classify region of interest in city and can be used in personalized recommendation and many other areas of application. In this paper, a travel identification method based on vehicle speed and Accessory (ACC) State is proposed. Continuously classifying and merging the trajectory points in GPS data stream, the travel activities of vehicle is extracted. It can provide a basis of data for the research on hot spots and support the research and application of vehicle trajectory data mining in areas of intelligent transportation and logistics.

[1]  Xing Xie,et al.  Collaborative location and activity recommendations with GPS history data , 2010, WWW '10.

[2]  Yu Zheng,et al.  T-share: A large-scale dynamic taxi ridesharing service , 2013, 2013 IEEE 29th International Conference on Data Engineering (ICDE).

[3]  Xia Ying.  LBSN user movement trajectory clustering mining method based on road network , 2013 .

[4]  Peter R. Stopher,et al.  Search for a global positioning system device to measure person travel , 2008 .

[5]  Xing Xie,et al.  T-Drive: Enhancing Driving Directions with Taxi Drivers' Intelligence , 2013, IEEE Transactions on Knowledge and Data Engineering.

[6]  Vania Bogorny,et al.  A clustering-based approach for discovering interesting places in trajectories , 2008, SAC '08.

[7]  Yuan Tian,et al.  Understanding intra-urban trip patterns from taxi trajectory data , 2012, J. Geogr. Syst..

[8]  Xing Xie,et al.  Learning travel recommendations from user-generated GPS traces , 2011, TIST.

[9]  Shashi Shekhar,et al.  Discovering personal gazetteers: an interactive clustering approach , 2004, GIS '04.

[10]  Xing Xie,et al.  Destination prediction by sub-trajectory synthesis and privacy protection against such prediction , 2013, 2013 IEEE 29th International Conference on Data Engineering (ICDE).

[11]  Nicholas Jing Yuan,et al.  T-Finder: A Recommender System for Finding Passengers and Vacant Taxis , 2013, IEEE Transactions on Knowledge and Data Engineering.

[12]  Ji Min-hea.  Coupling Passive GPS Tracking and Web-based Travel Surveys , 2010 .

[13]  Xing Xie,et al.  Discovering regions of different functions in a city using human mobility and POIs , 2012, KDD.