Abstract

This paper investigates human mobility patterns in an urban taxi transportation system. This work focuses on predicting human mobility from discovering patterns of in the number of passenger pick-ups quantity (PUQ) from urban hotspots. This paper proposes an improved ARIMA based prediction method to forecast the spatial-temporal variation of passengers in a hotspot. Evaluation with a large-scale real- world data set of 4 000 taxis� GPS traces over one year shows a prediction error of only 5.8%. We also explore the applica- tion of the prediction approach to help drivers find their next passengers. The simulation results using historical real-world data demonstrate that, with our guidance, drivers can reduce the time taken and distance travelled, to find their next pas- senger, by 37.1% and 6.4%, respectively.

Description