Name
Title | ||
|---|---|---|
Distance-To-Mean Continuous Conditional Random Fields: Case Study In Traffic Congestion |
Abstract | ||
|---|---|---|
Traffic prediction techniques are classified as having parametric, non-parametric, and a combination of parametric and non-parametric characteristics. The extreme learning machine (ELM) is a non-parametric technique that is commonly used to enhance traffic prediction problems. In this study, a modified probability approach, continuous conditional random fields (CCRF), is proposed and implemented with the ELM and then utilized to assess highway traffic data. The modification is conducted to improve the performance of non-parametric techniques, in this case, the ELM method. This proposed method is then called the distance-to-mean continuous conditional random fields (DM-CCRF). The experimental results show that the proposed technique suppresses the prediction error of the prediction model compared to the standard CCRF. The comparison between ELM as a baseline regressor, the standard CCRF, and the modified CCRF is displayed. The performance evaluation of the techniques is obtained by analyzing their mean absolute percentage error (MAPE) values. DM-CCRF is able to suppress the prediction model error to similar to 17.047%, which is twice as good as that of the standard CCRF method. Based on the attributes of the dataset, the DM-CCRF method is better for the prediction of highway traffic than the standard CCRF method and the baseline regressor. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3390/info10120382 | INFORMATION |
Keywords | Field | DocType |
traffic prediction, non-parametric, baseline regressor | Mean absolute percentage error,Conditional random field,Data mining,Errors-in-variables models,Extreme learning machine,Computer science,Algorithm,Nonparametric statistics,Parametric statistics,Traffic prediction,Traffic congestion | Journal |
Volume | Issue | Citations |
10 | 12 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sumarsih Condroayu Purbarani | 1 | 0 | 0.34 |
| Hadaiq Rolis Sanabila | 2 | 0 | 0.34 |
| Ari Wibisono | 3 | 15 | 5.68 |
| Noverina Alfiany | 4 | 0 | 0.34 |
| Hanif Arif Wisesa | 5 | 0 | 0.34 |
| W. Jatmiko | 6 | 105 | 23.09 |