An online learning framework for predicting the taxi stand's profitability

Taxi services play a central role in the mobility dynamics of major urban areas. Advanced communication devices such as GPS (Global Positioning System) and GSM (Global System for Mobile Communications) made it possible to monitor the drivers' activities in real-time. This paper presents an online learning approach to predict profitability in taxi stands. This approach consists of classifying each stand based according to the type of services that are being requested (for instance, short and long trips). This classification is achieved by maintaining a time-evolving histogram to approximate local probability density functions (p.d.f.) in service revenues. The future values of this histogram are estimated using time series analysis methods assuming that a non-homogeneous Poisson process is in place. Finally, the method's outputs were combined using a voting ensemble scheme based on a sliding window of historical data. Experimental tests were conducted using online data transmitted by 441 vehicles of a fleet running in the city of Porto, Portugal. The results demonstrated that the proposed framework can provide an effective insight on the characterization of taxi stand profitability.

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

[2]  Favyen Bastani,et al.  Towards Reducing Taxicab Cruising Time Using Spatio-Temporal Profitability Maps , 2011, SSTD.

[3]  Jane Yung-jen Hsu,et al.  Context-aware taxi demand hotspots prediction , 2010, Int. J. Bus. Intell. Data Min..

[4]  Michel Ferreira,et al.  On Predicting the Taxi-Passenger Demand: A Real-Time Approach , 2013, EPIA.

[5]  Daqing Zhang,et al.  From taxi GPS traces to social and community dynamics , 2013, ACM Comput. Surv..

[6]  Schaller Consulting,et al.  The New York City Taxicab Fact Book , 2006 .

[7]  Zhaohui Wu,et al.  Prediction of urban human mobility using large-scale taxi traces and its applications , 2012, Frontiers of Computer Science.

[8]  Thomas Pogge,et al.  From New York City , 2003, Science.

[9]  João Gama,et al.  Predicting Taxi–Passenger Demand Using Streaming Data , 2013, IEEE Transactions on Intelligent Transportation Systems.

[10]  Jie Cao,et al.  Pick-Up Tree Based Route Recommendation from Taxi Trajectories , 2012, WAIM.

[11]  João Gama,et al.  Discretization from data streams: applications to histograms and data mining , 2006, SAC.

[12]  Qingquan Li,et al.  Mining time-dependent attractive areas and movement patterns from taxi trajectory data , 2009, 2009 17th International Conference on Geoinformatics.

[13]  Dongjoo Park,et al.  Dynamic multi-interval bus travel time prediction using bus transit data , 2010 .

[14]  Rob J Hyndman,et al.  Automatic Time Series Forecasting: The forecast Package for R , 2008 .