Finding time period-based most frequent path in big trajectory data

The rise of GPS-equipped mobile devices has led to the emergence of big trajectory data. In this paper, we study a new path finding query which finds the most frequent path (MFP) during user-specified time periods in large-scale historical trajectory data. We refer to this query as time period-based MFP (TPMFP). Specifically, given a time period T, a source v_s and a destination v_d, TPMFP searches the MFP from v_s to v_d during T. Though there exist several proposals on defining MFP, they only consider a fixed time period. Most importantly, we find that none of them can well reflect people's common sense notion which can be described by three key properties, namely suffix-optimal (i.e., any suffix of an MFP is also an MFP), length-insensitive (i.e., MFP should not favor shorter or longer paths), and bottleneck-free (i.e., MFP should not contain infrequent edges). The TPMFP with the above properties will reveal not only common routing preferences of the past travelers, but also take the time effectiveness into consideration. Therefore, our first task is to give a TPMFP definition that satisfies the above three properties. Then, given the comprehensive TPMFP definition, our next task is to find TPMFP over huge amount of trajectory data efficiently. Particularly, we propose efficient search algorithms together with novel indexes to speed up the processing of TPMFP. To demonstrate both the effectiveness and the efficiency of our approach, we conduct extensive experiments using a real dataset containing over 11 million trajectories.

[1]  Xing Xie,et al.  T-drive: driving directions based on taxi trajectories , 2010, GIS '10.

[2]  Chengyang Zhang,et al.  Map-matching for low-sampling-rate GPS trajectories , 2009, GIS.

[3]  Antonin Guttman,et al.  R-trees: a dynamic index structure for spatial searching , 1984, SIGMOD '84.

[4]  Yufei Tao,et al.  MV3R-Tree: A Spatio-Temporal Access Method for Timestamp and Interval Queries , 2001, VLDB.

[5]  Xing Xie,et al.  Mining interesting locations and travel sequences from GPS trajectories , 2009, WWW '09.

[6]  Mario A. Nascimento,et al.  Towards historical R-trees , 1998, SAC '98.

[7]  Guangzhong Sun,et al.  Driving with knowledge from the physical world , 2011, KDD.

[8]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[9]  Elias Frentzos,et al.  Indexing Objects Moving on Fixed Networks , 2003, SSTD.

[10]  Jignesh M. Patel,et al.  Indexing Large Trajectory Data Sets With SETI , 2003, CIDR.

[11]  Ralf Hartmut Güting,et al.  Indexing the Trajectories of Moving Objects in Networks* , 2004, GeoInformatica.

[12]  Yu Zheng,et al.  Constructing popular routes from uncertain trajectories , 2012, KDD.

[13]  S. Pallottino,et al.  Shortest Path Algorithms in Transportation models: classical and innovative aspects , 1997 .

[14]  Samuel Madden,et al.  TrajStore: An adaptive storage system for very large trajectory data sets , 2010, 2010 IEEE 26th International Conference on Data Engineering (ICDE 2010).

[15]  Xing Xie,et al.  Where to find my next passenger , 2011, UbiComp '11.

[16]  Jae-Gil Lee,et al.  Trajectory clustering: a partition-and-group framework , 2007, SIGMOD '07.

[17]  Heng Tao Shen,et al.  Discovering popular routes from trajectories , 2011, 2011 IEEE 27th International Conference on Data Engineering.

[18]  Ariel Orda,et al.  Shortest-path and minimum-delay algorithms in networks with time-dependent edge-length , 1990, JACM.

[19]  Jae-Gil Lee,et al.  Traffic Density-Based Discovery of Hot Routes in Road Networks , 2007, SSTD.

[20]  Jiawei Han,et al.  Adaptive Fastest Path Computation on a Road Network: A Traffic Mining Approach , 2007, VLDB.

[21]  Yang Du,et al.  Finding Fastest Paths on A Road Network with Speed Patterns , 2006, 22nd International Conference on Data Engineering (ICDE'06).

[22]  Jiawei Han,et al.  Swarm: Mining Relaxed Temporal Moving Object Clusters , 2010, Proc. VLDB Endow..

[23]  Jeffrey Xu Yu,et al.  Finding time-dependent shortest paths over large graphs , 2008, EDBT '08.

[24]  Dino Pedreschi,et al.  Trajectory pattern mining , 2007, KDD '07.

[25]  Padhraic Smyth,et al.  Trajectory clustering with mixtures of regression models , 1999, KDD '99.

[26]  Xing Xie,et al.  Urban computing with taxicabs , 2011, UbiComp '11.

[27]  Verena Kantere,et al.  On-line discovery of hot motion paths , 2008, EDBT '08.

[28]  T. Lindvall ON A ROUTING PROBLEM , 2004, Probability in the Engineering and Informational Sciences.

[29]  Dieter Pfoser,et al.  Novel Approaches in Query Processing for Moving Object Trajectories , 2000, VLDB 2000.

[30]  Timos K. Sellis,et al.  Spatio-temporal indexing for large multimedia applications , 1996, Proceedings of the Third IEEE International Conference on Multimedia Computing and Systems.

[31]  Jae-Gil Lee,et al.  TraClass: trajectory classification using hierarchical region-based and trajectory-based clustering , 2008, Proc. VLDB Endow..

[32]  George Kollios,et al.  Mining, indexing, and querying historical spatiotemporal data , 2004, KDD.