中国科学院深圳先进技术研究院机构知识库(SIAT OpenIR): Predicting subway passenger flows under different traffic conditions
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Predicting subway passenger flows under different traffic conditions
Ling, Ximan; Huang, Zhiren; Wang, Chengcheng; Zhang, Fan; Wang, Pu
2018
Source PublicationPLOS ONE
Subtype期刊论文
AbstractPassenger 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.
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Indexed BySCI
Language英语
Department其他
Document Type期刊论文
Identifierhttp://ir.siat.ac.cn/handle/172644/14853
Collection其他
Recommended Citation
GB/T 7714
Ling, Ximan,Huang, Zhiren,Wang, Chengcheng,et al. Predicting subway passenger flows under different traffic conditions[J]. PLOS ONE,2018.
APA Ling, Ximan,Huang, Zhiren,Wang, Chengcheng,Zhang, Fan,&Wang, Pu.(2018).Predicting subway passenger flows under different traffic conditions.PLOS ONE.
MLA Ling, Ximan,et al."Predicting subway passenger flows under different traffic conditions".PLOS ONE (2018).
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