Incremental Location Matching for Human Travel Route Anomaly Detection

According to a forecast, the worldwide smartphone sales surpassed the world PC sales at the end of 2011. Smartphones are a kind of mobile handheld devices with phone capability or mobile phones with advanced features. Typical smartphone features include microbrowsers, emails, short message services, mobile games, GPS, et cetera. The feature of high mobility and small size of smartphones has created many applications that are not possible or inconvenient for PCs and servers, even notebooks. Locationbased services (LBS), one of mobile applications, have attracted great attention recently. This research proposes a location-based service, which uses location information to find travel route anomalies, a common problem of daily life. For example, an alert should be generated when a school bus misses part of a route or a pupil does not arrive at school on time. Different kinds of route anomalies are discussed, and various methods for detecting the anomalies are proposed in this chapter. The major methods use a technique of incremental location search, which finds matched routes as the search route is entered location by location. An alert is generated when no matched routes exist. Preliminary experiment results show the proposed methods are effective and easy-to-use.

[1]  Thomas W. Reps,et al.  An Incremental Algorithm for a Generalization of the Shortest-Path Problem , 1996, J. Algorithms.

[2]  David Furcy,et al.  Lifelong Planning A , 2004, Artif. Intell..

[3]  Bertrand Meyer Incremental String Matching , 1985, Inf. Process. Lett..

[4]  Holly Tootell,et al.  Location-Based Services and the Price of Security , 2006, 2006 IEEE International Symposium on Technology and Society.

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

[6]  Masao Fuketa,et al.  An incremental algorithm for string pattern matching machines , 1995, Int. J. Comput. Math..

[7]  Maiga Chang,et al.  Recommend Touring Routes to Travelers According to Their Sequential Wandering Behaviours , 2009, 2009 10th International Symposium on Pervasive Systems, Algorithms, and Networks.

[8]  Eyal de Lara,et al.  Location-Based Services , 2010, IEEE Pervasive Computing.

[9]  Byung K. Yi,et al.  Location Based Services for Mobiles :Technologies and Standards , 2008 .

[10]  Nigel Davies,et al.  Preserving Privacy in Environments with Location-Based Applications , 2003, IEEE Pervasive Comput..

[11]  Steven J. Vaughan-Nichols,et al.  Will Mobile Computing's Future Be Location, Location, Location? , 2009, Computer.

[12]  Axel Küpper Location-based Services: Fundamentals and Operation , 2005 .

[13]  Xing Xie,et al.  Smart Itinerary Recommendation Based on User-Generated GPS Trajectories , 2010, UIC.

[14]  Diane J. Cook,et al.  Human Activity Recognition and Pattern Discovery , 2010, IEEE Pervasive Computing.

[15]  Xing Xie,et al.  Learning transportation mode from raw gps data for geographic applications on the web , 2008, WWW.

[16]  Alfred V. Aho,et al.  Efficient string matching , 1975, Commun. ACM.

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

[18]  Stephan Winter,et al.  Including landmarks in routing instructions , 2010, J. Locat. Based Serv..

[19]  Yoshiharu Ishikawa,et al.  Finding Probabilistic Nearest Neighbors for Query Objects with Imprecise Locations , 2009, 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware.

[20]  Hassan A. Karimi,et al.  Location awareness through trajectory prediction , 2006, Comput. Environ. Urban Syst..

[21]  Richard E. Korf,et al.  Incremental Search Algorithms for Real-time Decision Making , 1994, AIPS.

[22]  Gad M. Landau,et al.  Incremental String Comparison , 1998, SIAM J. Comput..

[23]  Xing Xie,et al.  Mining correlation between locations using human location history , 2009, GIS.