基于出行方式及语义轨迹的位置预测模型
|
摘要
现有位置预测方法的研究多集中于对轨迹数据的挖掘和分析,而在如何通过轨迹数据中含有的信息内容以及外源数据以提高位置预测精确度方面的研究尚不深入,有很大研究空间.提出了一种挖掘语义轨迹信息并结合出行方式的未来位置预测模型,该模型首先可实现根据语义轨迹进行相似用户挖掘,并结合个人语义轨迹和相似用户位置轨迹得到频繁模式集合,最后结合2个集合对目标轨迹得到未来位置预测候选集;然后可实现对未来出行方式进行识别,同时结合历史出行方式和位置轨迹数据,建立Markov模型对未来位置进行预测得到候选集,最后结合前一部分的候选集得到最终未来位置结果.此模型不仅能结合语义轨迹挖掘相似用户的行为活动,还可同时融合出行方式的外源数据克服位置轨迹的局限性.实验验证表明:该模型能对日常生活中的轨迹位置数据进行预测并达到86%的精确度,同时在不同的频繁模式支持度下,其精确度都比未结合出行方式模型时平均高出5%,因此本模型对位置预测结果的提高具有有效性.
The research of existing location prediction technologies focuses on the mining and analysis of trajectory data, but there still exists space for research that how to improve the location prediction result with mining the information contained in trajectory data and exogenous data. In this paper, we propose a new location prediction model of mining the semantic trajectory and the transportation mode. On one hand, this model firstly mines the similar users according to the semantic trajectory, then establishes the frequent pattern set combined with the individual semantic trajectory and location trajectory of similar users, and finally obtains the candidate future location prediction set; On the other hand, it recognizes the future transportation mode, then combines the history transportation mode and historical location trajectory to predict the future location set with building Markov model. Finally the prediction result will be obtained with these two candidate sets. This method not only uses the semantic trajectory to mine the behavior of similar users, but also combines the transportation mode to overcome the limitation of location trajectory. The experimental result shows that the accuracy of this model can reach 86%, and 5% higher than that of the unmatched travel model under different frequent pattern support with the daily trajectory data. Therefore, it is effective to improve the location prediction result with this model.
The research of existing location prediction technologies focuses on the mining and analysis of trajectory data, but there still exists space for research that how to improve the location prediction result with mining the information contained in trajectory data and exogenous data. In this paper, we propose a new location prediction model of mining the semantic trajectory and the transportation mode. On one hand, this model firstly mines the similar users according to the semantic trajectory, then establishes the frequent pattern set combined with the individual semantic trajectory and location trajectory of similar users, and finally obtains the candidate future location prediction set; On the other hand, it recognizes the future transportation mode, then combines the history transportation mode and historical location trajectory to predict the future location set with building Markov model. Finally the prediction result will be obtained with these two candidate sets. This method not only uses the semantic trajectory to mine the behavior of similar users, but also combines the transportation mode to overcome the limitation of location trajectory. The experimental result shows that the accuracy of this model can reach 86%, and 5% higher than that of the unmatched travel model under different frequent pattern support with the daily trajectory data. Therefore, it is effective to improve the location prediction result with this model.
引文
[1]Chen Meng,Liu Yang,Yu Xiaohui.NLPMM:A next location predictor with Markov modeling[C]//LNCS 8444:Proc of the 6th Int Conf on Cooperative Information Systems.Berlin:Springer,2014:186-197
[2]Yang Jie,Xu Jian,Xu Ming,et al.Predicting next location using a variable order Markov model[C]//Proc of the 5th ACM SIGSPATIAL Int Workshop on GeoStreaming.New York:ACM,2014:37-42
[3]Monreale A,Pinelli F,Trasarti R,et al.Wherenext:Alocation predictor on trajectory pattern mining[C]//Proc of the 15th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining.New York:ACM,2009:637-646
[4]Yu Ruiyun,Xia Xingyou,Liao Shiyang,et al.A location prediction algorithm with daily routines in location-based participatory sensing systems[J].International Journal of Distributed Sensor Networks,2015,2015:Article No.6
[5]Horvitz E,Krumm J.Some help on the way:Opportunistic routing under uncertainty[C]//Proc of the 3rd ACM Conf on Ubiquitous Computing.New York:ACM,2012:371-380
[6]Ziebart B D,Maas A L,Dey A K,et al.Navigate like a cabbie:Probabilistic reasoning from observed context-aware behavior[C]//Proc of the 10th Int Conf on Ubiquitous Computing.New York:ACM,2008:322-331
[7]Krumm J,Horvitz E.Predestination:Where do you want to go today?[J].Computer,2007,40(4):105-107
[8]Gogate V,Dechter R,Bidyuk B.Modeling transportation routines using hybrid dynamic mixed networks[C]//Proc of the 21st Conf on Uncertainty in Artificial Intelligence.Washington:UAI,2005:217-224
[9]Zheng Yu,Zhang Lizhu,Ma Zhengxin,et al.Recommending friends and locations based on individual location history[J].ACM Transactions on the Web,2011,5(1):Article No.1921596
[10]Chen C J,Pai T W,Lin S S,et al.Application of PrefixSpan algorithms for disease pattern analysis[C]//Proc of the IEEE 2016Int Computer Symp.Piscataway,NJ:IEEE,2016:274-278
[11]Ying J C,Lee W C,Weng T C,et al.Semantic trajectory mining for location prediction[C]//Proc of the 19th ACMSIGSPATIAL Int Conf on Advances in Geographic Information Systems.New York:ACM,2011:34-43
[12]Ying J C,Lu H C,Lee W C,et al.Mining user similarity from semantic trajectories[C]//Proc of the 2nd ACM Int Workshop on Location Based Social Networks.New York:ACM,2010:19-26
[13]Zheng Yu,Liu Like,Wang Longhao,et al.Learning transportation modes from raw GPS data for geographica application on the Web[C]//Proc of the 17th Int Conf on World Wild Web.New York:ACM,2008:247-256
[14]Gambs S,Killijian M O,Prado C,et al.Next place prediction using mobility Markov chains[C]//Proc of the 1st Workshop on Measurement,Privacy,and Mobility.New York:ACM,2012:Article No.3
[2]Yang Jie,Xu Jian,Xu Ming,et al.Predicting next location using a variable order Markov model[C]//Proc of the 5th ACM SIGSPATIAL Int Workshop on GeoStreaming.New York:ACM,2014:37-42
[3]Monreale A,Pinelli F,Trasarti R,et al.Wherenext:Alocation predictor on trajectory pattern mining[C]//Proc of the 15th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining.New York:ACM,2009:637-646
[4]Yu Ruiyun,Xia Xingyou,Liao Shiyang,et al.A location prediction algorithm with daily routines in location-based participatory sensing systems[J].International Journal of Distributed Sensor Networks,2015,2015:Article No.6
[5]Horvitz E,Krumm J.Some help on the way:Opportunistic routing under uncertainty[C]//Proc of the 3rd ACM Conf on Ubiquitous Computing.New York:ACM,2012:371-380
[6]Ziebart B D,Maas A L,Dey A K,et al.Navigate like a cabbie:Probabilistic reasoning from observed context-aware behavior[C]//Proc of the 10th Int Conf on Ubiquitous Computing.New York:ACM,2008:322-331
[7]Krumm J,Horvitz E.Predestination:Where do you want to go today?[J].Computer,2007,40(4):105-107
[8]Gogate V,Dechter R,Bidyuk B.Modeling transportation routines using hybrid dynamic mixed networks[C]//Proc of the 21st Conf on Uncertainty in Artificial Intelligence.Washington:UAI,2005:217-224
[9]Zheng Yu,Zhang Lizhu,Ma Zhengxin,et al.Recommending friends and locations based on individual location history[J].ACM Transactions on the Web,2011,5(1):Article No.1921596
[10]Chen C J,Pai T W,Lin S S,et al.Application of PrefixSpan algorithms for disease pattern analysis[C]//Proc of the IEEE 2016Int Computer Symp.Piscataway,NJ:IEEE,2016:274-278
[11]Ying J C,Lee W C,Weng T C,et al.Semantic trajectory mining for location prediction[C]//Proc of the 19th ACMSIGSPATIAL Int Conf on Advances in Geographic Information Systems.New York:ACM,2011:34-43
[12]Ying J C,Lu H C,Lee W C,et al.Mining user similarity from semantic trajectories[C]//Proc of the 2nd ACM Int Workshop on Location Based Social Networks.New York:ACM,2010:19-26
[13]Zheng Yu,Liu Like,Wang Longhao,et al.Learning transportation modes from raw GPS data for geographica application on the Web[C]//Proc of the 17th Int Conf on World Wild Web.New York:ACM,2008:247-256
[14]Gambs S,Killijian M O,Prado C,et al.Next place prediction using mobility Markov chains[C]//Proc of the 1st Workshop on Measurement,Privacy,and Mobility.New York:ACM,2012:Article No.3
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |