Routing Service Quality -- Local Driver Behavior Versus Routing Services

Mobile location-based services is a very successful class of services that are being used frequently by users with GPS-enabled mobile devices such as smartphones. This paper presents a study of how to exploit GPS trajectory data, which is available in increasing volumes, for the assessment of the quality of one kind of location-based service, namely routing services. Specifically, the paper presents a framework that enables the comparison of the routes provided by routing services with the actual driving behaviors of local drivers. Comparisons include route length, travel time, and also route popularity, which are enabled by common driving behaviors found in available trajectory data. The ability to evaluate the quality of routing services enables service providers to improve the quality of their services and enables users to identify the services that best serve their needs. The paper covers experiments with real vehicle trajectory data and an existing online navigation service. It is found that the availability of information about previous trips enables better prediction of route travel time and makes it possible to provide the users with more popular routes than does a conventional navigation service.

[1]  Helmut Alt,et al.  Matching Polygonal Curves with Respect to the Fréchet Distance , 2001, STACS.

[2]  Seung-won Hwang,et al.  Supporting Pattern-Matching Queries over Trajectories on Road Networks , 2011, IEEE Transactions on Knowledge and Data Engineering.

[3]  Yannis Manolopoulos,et al.  Trajectory Similarity Search in Spatial Networks , 2006, 2006 10th International Database Engineering and Applications Symposium (IDEAS'06).

[4]  Xing Xie,et al.  Reducing Uncertainty of Low-Sampling-Rate Trajectories , 2012, 2012 IEEE 28th International Conference on Data Engineering.

[5]  Lei Chen,et al.  Robust and fast similarity search for moving object trajectories , 2005, SIGMOD '05.

[6]  Jianwen Su,et al.  One Way Distance: For Shape Based Similarity Search of Moving Object Trajectories , 2008, GeoInformatica.

[7]  Yannis Manolopoulos,et al.  Searching for similar trajectories in spatial networks , 2009, J. Syst. Softw..

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

[9]  Shazia Wasim Sadiq,et al.  An Effectiveness Study on Trajectory Similarity Measures , 2013, ADC.

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

[11]  Donald J. Berndt,et al.  Using Dynamic Time Warping to Find Patterns in Time Series , 1994, KDD Workshop.

[12]  Mi-Yen Yeh,et al.  Discovering personalized routes from trajectories , 2011, LBSN '11.

[13]  Seung-won Hwang,et al.  TPM: supporting pattern matching queries for road-network trajectory data , 2011, EDBT/ICDT '11.

[14]  Christos Faloutsos,et al.  FTW: fast similarity search under the time warping distance , 2005, PODS.

[15]  Ada Wai-Chee Fu,et al.  Efficient time series matching by wavelets , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).

[16]  Thomas Brinkhoff,et al.  A Framework for Generating Network-Based Moving Objects , 2002, GeoInformatica.

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

[18]  Mario A. Nascimento,et al.  A Trajectory Cleaning Framework for Trajectory Clustering , 2012 .

[19]  Hui Ding,et al.  Querying and mining of time series data: experimental comparison of representations and distance measures , 2008, Proc. VLDB Endow..

[20]  Fosca Giannotti,et al.  Mining mobility user profiles for car pooling , 2011, KDD.

[21]  Dimitrios Gunopulos,et al.  Discovering similar multidimensional trajectories , 2002, Proceedings 18th International Conference on Data Engineering.

[22]  Clu-istos Foutsos,et al.  Fast subsequence matching in time-series databases , 1994, SIGMOD '94.

[23]  Nikos Pelekis,et al.  Mining Trajectory Databases via a Suite of Distance Operators , 2007, 2007 IEEE 23rd International Conference on Data Engineering Workshop.