Journal
FRONTIERS OF COMPUTER SCIENCE
Volume 6, Issue 1, Pages 111-121Publisher
HIGHER EDUCATION PRESS
DOI: 10.1007/s11704-011-1192-6
Keywords
urban traffic; GPS traces; hotspots; human mobility prediction; auto-regressive integrated moving average (ARIMA)
Categories
Funding
- Fundamental Research Funds for the Central Universities
- Zhejiang Provincial Natural Science Foundation of China [Y1090690]
- Qianjiang Talent Program [2011R10078]
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This paper investigates human mobility patterns in an urban taxi transportation system. This work focuses on predicting humanmobility fromdiscovering 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 realworld data set of 4 000 taxis' GPS traces over one year shows a prediction error of only 5.8%. We also explore the application 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 passenger, by 37.1% and 6.4%, respectively.
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