Name
Abstract | ||
|---|---|---|
Sensing of urban bus travelling data based on data collection from the Internet enables researchers from different regions to focus on the same study objects and promote the development of corresponding theories and technology. In this study, seven processes running on two servers were employed to retrieve real-time bus travelling data from the official bus information inquiry website of Suzhou, China. For data acquisition, a frequent problem encountered is the missing of bus travelling data caused by GPS signal fluctuation and network failure. In order to recover the missing data, we proposed Knearest-neighbor linear regression (KNN-LR) for data interpolation. Leave-one-out cross-validation is adapted to assess the performance of the proposed approach. It is demonstrated that the proposed KNN-LR method is robust and suitable for recovering missing data of bus trajectories in practice. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/MSN.2016.062 | 2016 12th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) |
Keywords | Field | DocType |
Missing data recovery,Data sensing,Urban public transportation,Linear regressive | Data mining,Data collection,Computer science,Interpolation,Server,Data acquisition,Public transport,Global Positioning System,Missing data,The Internet | Conference |
ISBN | Citations | PageRank |
978-1-5090-5697-2 | 0 | 0.34 |
References | Authors | |
12 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| ChangFei Tong | 1 | 88 | 4.58 |
| Yu Xu | 2 | 67 | 8.47 |
| Jun Li | 3 | 0 | 0.34 |
| Ying-Ying Xu | 4 | 19 | 2.63 |
| Huaizhong Li | 5 | 177 | 18.16 |