Traffic phenomenon search and traffic flow prediction base on data-driven
| Autor: | Huang, Wei-Xiang, 黃韋翔 |
|---|---|
| Rok vydání: | 2014 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 103 Traffic control, transportation planning, and road service are important issues in transportation management. As traffic management officers need make good decisions to keep traffic flow smooth, they often gain experiences by studying similar traffic events in historical traffic data. Among different types of traffic data, vehicle detector (VD) data are commonly used by traffic management experts for analyzing traffic events in large scale and long term traffic database. As VD data are streaming data refreshed every minute and the number of VDs on highways is large, the historic VD data are huge. An efficient search approach is thus very critical for exploring VD databases. In this thesis, we propose a Coarse-to-Fine Random Search (CFRS) method to help users retrieve similar traffic patterns in massive historical traffic data. Our CFRS approach can also be applied to traffic prediction by searching a database using the current traffic as a query pattern. The traffic data following the retrieved pattern can be used as predicted traffic due to the continuity of traffic data. In our experiment, we test our approach on the VD data collected from nearly 3000 VDs on the nine freeways in Taiwan in two years. The experimental results show that our approach can use to retrieve traffic patterns efficiently. In addition, we compared our traffic prediction method with k-nearest neighbors (KNN). Our method achieves better performance which speeds up for 14 to 89 times while maintaining the same level of accuracy of KNN. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
| Externí odkaz: |
|