| 二维局部非负矩阵分解的路网态势算法 |
| |
| 引用本文： | 许榕,吴聪,蒋士正,陈启美. 二维局部非负矩阵分解的路网态势算法[J]. 上海交通大学学报, 2015, 49(8): 1131-1136 |
| |
| 作者姓名： | 许榕 吴聪 蒋士正 陈启美 |
| |
| 作者单位： | (南京大学 电子科学与工程学院， 南京 210046) |
| |
| 基金项目： | 国家科技重大专项(2011ZX03005 004 03)，国家自然科学基金项目(61105015)资助 |
| |
| 摘 要： | 针对路网态势评测算法存在限于断面、依赖单一指标等的不足,在解析测量指标和测量断面的相关性及局部非负矩阵分解(LNMF)算法的基础上,提出了二维局部非负矩阵分解2DLNMF算法,通过选择合适参数对路网数据进行降维处理,提取路网特征数据,从而实现路网态势评测.仿真结果表明,使用2D-LNMF算法路网态势评测结果更加准确,而在线评测准确性达到95.69%.
|
| 收稿时间： | 2014-09-01 |
Evaluation of Network-Level Traffic State Using 2D-LNMF Algorithm |
| |
| XU Rong,WU Cong,JIANG Shi Zheng,CHEN Qi Mei. Evaluation of Network-Level Traffic State Using 2D-LNMF Algorithm[J]. Journal of Shanghai Jiaotong University, 2015, 49(8): 1131-1136 |
| |
| Authors: | XU Rong WU Cong JIANG Shi Zheng CHEN Qi Mei |
| |
| Affiliation: | (School of Electronic Science and Engineering, Nanjing University， Nanjing 210046, China) |
| |
| Abstract: | Abstract： Network-level traffic state reflects the macroscopical conditions of the road network. The exesting evaluation algorithms have some shortcomings such as their applicable conditions are limited to section and they just depend on a single index. As a result, based on the analysis of the correlation between the measuring index and sections and the local non negative matrix factorization(LNMF) algorithm, the algorithm of 2D LNMF was proposed and the features of the traffic data were extracted by choosing appropriate parameters to reduce the numbers of dimensions of the road network data. The simulation results indicate that the evaluation of 2D LNMF is more accurate and its online accuracy is up to 95.69%. |
| |
| Keywords: | network-level traffic state cluster 2D-local non-negative matrix factorization (2D-LNMF) feature extraction |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
| 点击此处可从《上海交通大学学报》浏览原始摘要信息 |
|
点击此处可从《上海交通大学学报》下载免费的PDF全文 |
