期刊文献+

移动数据的交通出行方式识别方法 被引量:9

Research of the identification methods for transportation modes based on mobile data
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摘要 识别用户出行的交通方式,对理解用户移动性、交通状况的分析和预测、社会活动模式挖掘等方面起着非常关键的作用。随着无线网络技术的快速发展,越来越多的传感器被用于收集移动数据,如何通过收集的信息准确地识别用户不同的交通出行方式,近年来得到了广泛的研究。针对已有的从不同角度识别交通方式的方法,首先介绍了每种方法的具体内容及应用,然后对不同方法进行分类研究,并重点分析了各类方法的特点,分析几种不同方法在不同条件下的识别精确度,最后,给出了交通方式识别方法的进一步研究方向。 Identification of different transportation modes in the process of user travel plays an important role in understanding individuals' mobility,analyzing and forecasting traffic conditions and mining social activity pattern.With the rapid development of wireless network technology,more and more sensors are used to collect mobile data.Specially,how to accurately identify user's different transportation modes from the collected data has been extensively researched in recent years. In addition,the methods of identification proposed from different points of view to solve the problem were studied in this paper. Each method and its application was introduced in detail and then classified and researched,respectively. The focus of analysis is put on the characteristics of each method. The levels of the recognition accuracy of different methods under different conditions were analyzed in table form. Finally,the research directions of the identification methods for transportation modes were further discussed.
出处 《智能系统学报》 CSCD 北大核心 2014年第5期536-543,共8页 CAAI Transactions on Intelligent Systems
基金 国家自然科学基金资助项目(61262089 61262087) 新疆教育厅高校教师科研计划重点资助项目(XJEDU2012I09) 新疆大学博士毕业生科研启动基金资助项目(BS110127)
关键词 交通出行方式识别 用户行为 移动数据 无线网络技术 传感器 identification of transportation modes user behavior mobile data wireless network technology sensors
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参考文献43

  • 1Miao Lin, Hsu Wenjing. Mining GPS data for mobility patterns: A survey.http://www.sciencedirect.com/science/article/pii/S1574119213000825.
  • 2谢幸,郑宇.基于地理信息的用户行为理解[J].计算机学会通讯,2008,4(10):45-51.
  • 3张治华.基于GPS轨迹的出行信息提取研究.上海:华东师范大学,2010.
  • 4Yang Jun. Toward physical activity diary: motion recognition using simple acceleration features with mobile phones[C]//Proceedings of the 1st international workshop on Interactive multimedia for consumer electronics. Beijing: ACM Press, 2009:1-10.
  • 5Ravi N, Dandekar N, Mysore P, et al. Activity recognition from accelerometer data[C]//Proceedings of the17th Conference on Innovative Applications of Artificial Intelligence. Pittsburgh: AAAI Press, 2005:1541-1546.
  • 6Wang S, Chen C, Ma J. Accelerometer based transportation mode recognition on mobile phones[C]//Wearable Computing Systems (APWCS),2010 Asia-Pacific Conference on.Shenzhen,China, 2010:44-46.
  • 7Wang H, Calabrese F, Di Lorenzo G, et al. Transportation mode inference from anonymized and aggregated mobile phone call detail records[C]//Intelligent Transportation Systems (ITSC), 2010 13th International IEEE Conference on. Piscataway: IEEE, 2010:318-323.
  • 8Xu Dafeng, Song Guojie, Gao Peng, et al. Transportation modes identification from mobile phone data using probabilistic models[C]//Proceedings of the 7th International Conference on Advanced Data Mining and Applications. Beijing: China, 2011:359-371.
  • 9Muller IAH. Practical Activity Recognition using GSM Data[C]//proceedings of the 5th International Semantic Web Conference (ISWC).Athens,2006:1-8.
  • 10Anderson I, Muller H. Exploring GSM data in pervasive environments[J]. International Journal of Pervasive Computing and Communications, 2008, 4(1): 8-25.

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