Name
Abstract | ||
|---|---|---|
With the influence of extreme weather, road surface temperature (RST), which threatens the safety of people's travel, has attracted more and more attention to the government and citizens. However, traditional methods are hard to meet real-time requirements in forecasting RST. In order to improve the predictive accuracy of RST and meet the real-time requirement, this paper compares three different machine learning algorithms including, Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF) and Gradient Boosting Regression Tree (GBRT). Using the RST data and BJ-RUC (Beijing-rapidly update cycle) data during November 2012 and June 2015, the performance of three models is evaluated. The experimental results show that GBRT performs the best and its MSE is 6.7853. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3265689.3265712 | PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON CROWD SCIENCE AND ENGINEERING (ICCSE 2018) |
Keywords | DocType | Citations |
Random Forest, Gradient Boosting Regression Tree, LASSO, road surface temperature | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bo Liu | 1 | 521 | 84.67 |
| Libin Shen | 2 | 744 | 46.42 |
| Huanling You | 3 | 1 | 0.70 |
| Yan Dong | 4 | 78 | 9.26 |
| Jianqiang Li | 5 | 47 | 4.44 |
| Yong Li | 6 | 0 | 1.01 |
| Jianlei Lang | 7 | 1 | 1.72 |
| Rentao Gu | 8 | 25 | 8.24 |