Predicting subway passenger flows under different traffic conditions

	Predicting subway passenger flows under different traffic conditions
	Zhang, Fan; Ling, Ximan; Huang, Zhiren; Wang, Chengcheng; Wang, Pu
刊名	PLOS ONE
	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
内容类型	期刊论文
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).