Taxi-RS: Taxi-Hunting Recommendation System Based on Taxi GPS Data

Recommender systems are constructed to search the content of interest from overloaded information by acquiring useful knowledge from massive and complex data. Since the amount of information and the complexity of the data structure grow, it has become a more interesting and challenging topic to find an efficient way to process, model, and analyze the information. Due to the Global Positioning System (GPS) data recording the taxi's driving time and location, the GPS-equipped taxi can be regarded as the detector of an urban transport system. This paper proposes a Taxi-hunting Recommendation System (Taxi-RS) processing the large-scale taxi trajectory data, in order to provide passengers with a waiting time to get a taxi ride in a particular location. We formulated the data offline processing system based on HotSpotScan and Preference Trajectory Scan algorithms. We also proposed a new data structure for frequent trajectory graph. Finally, we provided an optimized online querying subsystem to calculate the probability and the waiting time of getting a taxi. Taxi-RS is built based on the real-world trajectory data set generated by 12 000 taxis in one month. Under the condition of guaranteeing the accuracy, the experimental results show that our system can provide more accurate waiting time in a given location compared with a naïve algorithm.

[1]  Victor C. S. Lee,et al.  Distance indexing on road networks , 2006, VLDB.

[2]  Yu Zheng,et al.  Computing with Spatial Trajectories , 2011, Computing with Spatial Trajectories.

[3]  Ramayya Krishnan,et al.  Calibrating Large Scale Vehicle Trajectory Data , 2012, 2012 IEEE 13th International Conference on Mobile Data Management.

[4]  Christian S. Jensen,et al.  Efficient tracking of moving objects with precision guarantees , 2004, The First Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, 2004. MOBIQUITOUS 2004..

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

[6]  Xing Xie,et al.  Understanding transportation modes based on GPS data for web applications , 2010, TWEB.

[7]  Wei-Ying Ma,et al.  Understanding mobility based on GPS data , 2008, UbiComp.

[8]  Ee-Peng Lim,et al.  Analyzing feature trajectories for event detection , 2007, SIGIR.

[9]  Cyrus Shahabi,et al.  A Road Network Embedding Technique for K-Nearest Neighbor Search in Moving Object Databases , 2002, GIS '02.

[10]  Christian S. Jensen,et al.  Techniques for efficient road-network-based tracking of moving objects , 2005, IEEE Transactions on Knowledge and Data Engineering.

[11]  J. Bobadilla,et al.  Recommender systems survey , 2013, Knowl. Based Syst..

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

[13]  Qiming Chen,et al.  PrefixSpan,: mining sequential patterns efficiently by prefix-projected pattern growth , 2001, Proceedings 17th International Conference on Data Engineering.

[14]  Siyuan Liu,et al.  HUNTS: A Trajectory Recommendation System for Effective and Efficient Hunting of Taxi Passengers , 2013, 2013 IEEE 14th International Conference on Mobile Data Management.

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

[16]  Xing Xie,et al.  T-Drive: Enhancing Driving Directions with Taxi Drivers' Intelligence , 2013, IEEE Transactions on Knowledge and Data Engineering.

[17]  Hanan Samet,et al.  The Quadtree and Related Hierarchical Data Structures , 1984, CSUR.

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

[19]  Jing Yuan,et al.  An efficient pre-computation technique for approximation KNN search in road networks , 2009, LBSN '09.

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

[21]  J. Greenfeld.  MATCHING GPS OBSERVATIONS TO LOCATIONS ON A DIGITAL MAP , 2002 .