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篇目详细内容 |
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【篇名】 |
Prediction of urban human mobility using large-scale taxi traces and its applications |
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【刊名】 |
Frontiers of Computer Science |
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【刊名缩写】 |
Front. Comput. Sci. |
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【ISSN】 |
2095-2228 |
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【EISSN】 |
2095-2236 |
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【DOI】 |
10.1007/s11704-011-1192-6 |
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【出版社】 |
Higher Education Press and Springer-Verlag Berlin
Heidelberg |
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【出版年】 |
2012 |
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【卷期】 |
6
卷1期 |
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【页码】 |
111-121
页,共
11
页 |
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【作者】 |
Xiaolong LI;
Gang PAN;
Zhaohui WU;
Guande QI;
Shijian LI;
Daqing ZHANG;
Wangsheng ZHANG;
Zonghui WANG;
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【关键词】 |
urban traffic; GPS traces; hotspots; human mobility prediction; auto-regressive integrated moving average (ARIMA) |
【摘要】 |
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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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