Name
Abstract | ||
|---|---|---|
With the rapid development of mobile internet and wireless network technologies, more and more people use the mobile app to call a taxicab to pick them up. Therefore, understanding the passengers' travel demand becomes crucial to improve the utilization of the taxicabs and reduce their cost. In this paper, based on spatio-temporal clustering, we propose a demand hotspots prediction framework to generate recommendation for taxi drivers. Specially, an adaptive prediction approach is presented to demand hotspots and their hotness, and then combing the driver's location and the hotness, top candidates are recommended and visually presented to drivers. Based on the dataset provided by CAR INC., the experiment shows that our approach gains a significant improvement in hotspots prediction and recommendation, with 15.21% improvement on average f-measure for prediction and 79.6% hit ratio for recommendation. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/SCC.2016.51 | 2016 IEEE International Conference on Services Computing (SCC) |
Keywords | Field | DocType |
spatio-temporal cluster,passenger demand hotspot,demand prediction,hotspot recommendation | Data mining,Computer science,Operations research | Conference |
ISSN | ISBN | Citations |
2474-8137 | 978-1-5090-2629-6 | 8 |
PageRank | References | Authors |
0.57 | 14 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kai Zhang | 1 | 21 | 7.60 |
| Zhiyong Feng | 2 | 794 | 167.21 |
| Shizhan Chen | 3 | 67 | 22.79 |
| Keman Huang | 4 | 242 | 22.29 |
| Guiling Wang | 5 | 832 | 52.06 |