Predicting subway passenger flows under different traffic conditions
文献类型：期刊论文
| 作者 | Zhang, Fan; Ling, Ximan; Huang, Zhiren; Wang, Chengcheng; Wang, Pu |
| 刊名 | PLOS ONE
 |
| 出版日期 | 2018 |
| 文献子类 | 期刊论文 |
| 英文摘要 | Passenger flow prediction is important for the operation, management, efficiency, and reliability of urban rail transit (subway) system. Here, we employ the large-scale subway smart-card data of Shenzhen, a major city of China, to predict dynamical passenger flows in the subway network. Four classical predictive models: historical average model, multilayer perceptron neural network model, support vector regression model, and gradient boosted regression trees model, were analyzed. Ordinary and anomalous traffic conditions were identified for each subway station by using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The prediction accuracy of each predictive model was analyzed under ordinary and anomalous traffic conditions to explore the high-performance condition (ordinary traffic condition or anomalous traffic condition) of different predictive models. In addition, we studied how long in advance that passenger flows can be accurately predicted by each predictive model. Our finding highlights the importance of selecting proper models to improve the accuracy of passenger flow prediction, and that inherent patterns of passenger flows are more prominently influencing the accuracy of prediction. |
| URL标识 | 查看原文 |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/14853]  |
| 专题 | 深圳先进技术研究院_其他 |
| 推荐引用方式 GB/T 7714 | Zhang, Fan,Ling, Ximan,Huang, Zhiren,et al. Predicting subway passenger flows under different traffic conditions[J]. PLOS ONE,2018. |
| APA | Zhang, Fan,Ling, Ximan,Huang, Zhiren,Wang, Chengcheng,&Wang, Pu.(2018).Predicting subway passenger flows under different traffic conditions.PLOS ONE. |
| MLA | Zhang, Fan,et al."Predicting subway passenger flows under different traffic conditions".PLOS ONE (2018). |
入库方式： OAI收割
来源：深圳先进技术研究院
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。