From data to knowledge to action: A taxi business intelligence system

Taxis play an important role in offering comfortable and flexible service within Singapore's public transport system. Due to the inherent randomness in taxi service system, many taxi companies still rely on the drivers' experience to seek passengers. Today, Singapore's five taxi companies now use some form of wireless and GPS (Global Position System) satellite to track taxis traveling in urban area. GPS-equipped taxis can be viewed as ubiquitous mobile sensors which enable us to collect large amounts of location traces of individuals or objects. In this paper, we first investigate the characteristics of travel behavior of urban population. Next, a taxi business intelligence system is proposed to explore the massive transportation data based on spatial-temporal data mining techniques. Furthermore, various taxi business models are created to make comprehensive analysis on taxi business problems. Finally, the value of the taxi business intelligence system is demonstrated by applying it to some real-world scenarios. Results show that this system can significantly improve the quality of taxi services.

[1]  Y. Murata,et al.  Waiting / cruising location recommendation for efficient taxi business , .

[2]  J. Mennis,et al.  Mining Association Rules in Spatio-Temporal Data , 2003 .

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

[4]  R. Jayakrishnan,et al.  Effect of Taxi Information System on Efficiency and Quality of Taxi Services , 2005 .

[5]  Maguelonne Teisseire,et al.  Mining spatio-temporal data , 2006, Journal of Intelligent Information Systems.

[6]  Yilin Zhao,et al.  Mobile phone location determination and its impact on intelligent transportation systems , 2000, IEEE Trans. Intell. Transp. Syst..

[7]  Zhiheng Li,et al.  Integrated Traffic Management Platform Design Based on GIS-T , 2006, 2006 6th International Conference on ITS Telecommunications.

[8]  Jinxing Hu,et al.  Dynamic modeling of urban population travel behavior based on data fusion of mobile phone positioning data and FCD , 2009, 2009 17th International Conference on Geoinformatics.

[9]  Ashish Agarwal,et al.  A Comparison of Weekend and Weekday Travel Behavior Characteristics in Urban Areas , 2004 .

[10]  R. Geetha,et al.  A SURVEY OF SPATIAL , TEMPORAL AND SPATIO-TEMPORAL DATA MINING , 2011 .

[11]  Gyung-Leen Park,et al.  Analysis of the Passenger Pick-Up Pattern for Taxi Location Recommendation , 2008, 2008 Fourth International Conference on Networked Computing and Advanced Information Management.

[12]  Lionel M. Ni,et al.  SEER: Metropolitan-Scale Traffic Perception Based on Lossy Sensory Data , 2009, IEEE INFOCOM 2009.

[13]  P. Haluzová,et al.  Effective Data Mining for a Transportation Information System , 2008 .

[14]  Der-Horng Lee,et al.  A Collaborative Multiagent Taxi-Dispatch System , 2010, IEEE Transactions on Automation Science and Engineering.

[15]  John Shawe-Taylor,et al.  Data mining, data fusion and information management , 2006 .

[16]  Junghoon Lee.  Analysis on the Waiting Time of Empty Taxis for the Taxi Telematics System , 2008, 2008 Third International Conference on Convergence and Hybrid Information Technology.

[17]  Qingbiao Meng,et al.  A Novel Taxi Dispatch System Integrating a Multi-Customer Strategy and Genetic Network Programming , 2010, J. Adv. Comput. Intell. Intell. Informatics.

[18]  Junghoon Lee.  Traveling Pattern Analysis for the Design of Location-Dependent Contents Based on the Taxi Telematics System , 2008 .

[19]  Haifeng Li,et al.  Fusion-based recommender system , 2010, 2010 13th International Conference on Information Fusion.

[20]  Ziqi Liao,et al.  Real-time taxi dispatching using Global Positioning Systems , 2003, CACM.

[21]  Rajesh Krishna Balan,et al.  Real-time trip information service for a large taxi fleet , 2011, MobiSys '11.

[22]  Jing Dai,et al.  Spatial-Temporal Data Mining in Tra c Incident Detection , 2006 .

[23]  Shih-Fen Cheng,et al.  A service choice model for optimizing taxi service delivery , 2009, 2009 12th International IEEE Conference on Intelligent Transportation Systems.

[24]  Jan Fabian Ehmke,et al.  Data allocation and application for time-dependent vehicle routing in city logistics , 2010 .

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

[26]  Rajesh Krishna Balan,et al.  Spatio-Temporal Efficiency in a Taxi Dispatch System , 2008 .

[27]  Hui Xiong,et al.  A taxi business intelligence system , 2011, KDD.

[28]  Xiaogang Jin,et al.  Study on spatial and temporal mobility pattern of urban taxi services , 2010, 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering.