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| Content Provider | Springer Nature Link |
|---|---|
| Author | Li, Xiaolong Pan, Gang Wu, Zhaohui Qi, Guande Li, Shijian Zhang, Daqing Zhang, Wangsheng Wang, Zonghui |
| Copyright Year | 2011 |
| Abstract | 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. |
| Starting Page | 111 |
| Ending Page | 121 |
| Page Count | 11 |
| File Format | |
| ISSN | 16737350 |
| Journal | Frontiers of Computer Science in China |
| Volume Number | 6 |
| Issue Number | 1 |
| e-ISSN | 16737466 |
| Language | English |
| Publisher | SP Higher Education Press |
| Publisher Date | 2012-01-27 |
| Publisher Institution | Chinese Universities |
| Publisher Place | Heidelberg |
| Access Restriction | Subscribed |
| Subject Keyword | urban traffic GPS traces hotspots human mobility prediction auto-regressive integrated moving average (ARIMA) Computer Science |
| Content Type | Text |
| Resource Type | Article |
| Subject | Theoretical Computer Science Computer Science |
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