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.
KeywordsVehicle speed ACC state Travel identification Hot spots Logistics vehicle
This work was supported in part by the National High-tech R&D Program of China (863 Program) under Grant No. 2015AA015403, Science & Technology Pillar Program of Hubei Province under Grant No. 2014BAA146, Nature Science Foundation of Hubei Province under Grant No. 2015CFA059, Hubei Key Laboratory of Transportation Internet of Things under Grant No. 2015III015-B03 and CERNET Innovation Project under Grant No. NGII20151006.
- 1.Deng, Z., Ji, M., Chen, W.: Coupling passive GPS tracking and web-based travel surveys. J. Transp. Syst. Eng. Inf. Technol. 10(2), 178–183 (2009)Google Scholar
- 3.Zhang, B.: Research on the Simplification and Semantic Enhancement of GPS Temporal and Spatial Trajectory Data for Traffic Travel Survey. East China Normal University, Shanghai (2011)Google Scholar
- 4.Zhou, C., Frankowski, D., Ludford, P., et al.: Discovering personal gazetteers: an interactive clustering approach, pp. 266–273. ACM (2004)Google Scholar
- 5.Tietbohl, A., Bogorny, V., Kuijpers, B., et al.: A clustering-based approach for discovering interesting places in trajectories. In: SAC, pp. 863–868 (2008)Google Scholar
- 6.Zhang, J., Qiu, P., Xu, Z.: A method to identify trip based on the mobile phone positioning. J. Wuhan Univ. Technol. (Transp. Sci. Eng.) 37(5), 934–938 (2013)Google Scholar
- 7.Zou, Y., Wan, J., Xia, Y.: LBSN user movement trajectory clustering mining method based on road network. Appl. Res. Comput. 08(8), 102–110 (2013)Google Scholar
- 8.Xiao, Y., Zhang, Z., Yang, W.: Users’ mobility behaviours mining algorithm based on GPS trajectory. Comput. Appl. Softw. 32(11), 83–87 (2015)Google Scholar
- 9.Yuan, J., Zheng, Y., Xie, X.: Discovering regions of different functions in a city using human mobility and POIs. In: ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 186–194 (2012)Google Scholar
- 10.Xue, A., Zhang, R., Zheng, Y., et al.: Destination prediction by sub-trajectory synthesis and privacy protection against such prediction. In: IEEE International Conference on Data Engineering, pp. 254–265 (2013)Google Scholar
- 13.Zheng, V., Zheng, Y., Xie, X., et al.: Collaborative location and activity recommendations with GPS history data. In: Proceeding of the 19th International Conference on World Wide Web (2010)Google Scholar
- 14.Ma, S., Zheng, Y., Wolfson, O.: T-share: a large-scale dynamic taxi ridesharing service. In: IEEE International Conference on Data Engineering, pp. 410–421 (2013)Google Scholar