Dataset Information

Predicting subway passenger flows under different traffic conditions.

ABSTRACT: 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 smartcard 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.

SUBMITTER: Ling X

PROVIDER: S-EPMC6110475 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Predicting subway passenger flows under different traffic conditions.

Ling Ximan X Huang Zhiren Z Wang Chengcheng C Zhang Fan F Wang Pu P

PloS one 20180827 8

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 smartcard 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 ano ...[more]

PMID: 30148888

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Project description:BACKGROUND:Several mechanistic models aim to explain the diversification of the multitude of endemic species on Madagascar. The island's biogeographic history probably offered numerous opportunities for secondary contact and subsequent hybridization. Existing diversification models do not consider a possible role of these processes. One key question for a better understanding of their potential importance is how they are influenced by different environmental settings. Here, we characterized a contact zone between two species of mouse lemurs, Microcebus griseorufus and M. murinus, in dry spiny bush and mesic gallery forest that border each other sharply without intermediate habitats between them. We performed population genetic analyses based on mtDNA sequences and nine nuclear microsatellites and compared the results to a known hybrid zone of the same species in a nearby wide gradient from dry spiny bush over transitional forest to humid littoral forest. RESULTS:In the spiny-gallery system, Microcebus griseorufus is restricted to the spiny bush; Microcebus murinus occurs in gallery forest and locally invades the dryer habitat of its congener. We found evidence for bidirectional introgressive hybridization, which is closely linked to increased spatial overlap within the spiny bush. Within 159 individuals, we observed 18 hybrids with mitochondrial haplotypes of both species. Analyses of simulated microsatellite data indicate that we identified hybrids with great accuracy and that we probably underestimated their true number. We discuss short-term climatic fluctuations as potential trigger for the dynamic of invasion and subsequent hybridization. In the gradient hybrid zone in turn, long-term aridification could have favored unidirectional nuclear introgression from Microcebus griseorufus into M. murinus in transitional forest. CONCLUSIONS:Madagascar's southeastern transitional zone harbors two very different hybrid zones of mouse lemurs in different environmental settings. This sheds light on the multitude of opportunities for the formation of hybrid zones and indicates an important influence of environmental factors on secondary contact and hybridization. Our findings suggest that hybridization could enhance the adaptability of mouse lemurs without necessarily leading to a loss of distinctiveness. They point to a potential role of hybridization in Madagascar's diversification history that requires further investigation.

Dataset Information

Predicting subway passenger flows under different traffic conditions.

Publications

Predicting subway passenger flows under different traffic conditions.

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