Formation Control for Virtual Coupling Trains With Parametric Uncertainty and Unknown Disturbances

Wang, Xi; Hu, Mingxing; Wang, Hongwei; Dong, Hairong; Ying, Zhipeng

doi:10.1109/tcsii.2023.3262511

Cited by 9 publications

(4 citation statements)

References 17 publications

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“…Following the same steps as above, we can construct three spatio-temporal modules of closeness, period, and trend in Figure 4. After processing through the model, the outputs of the three modules are ( 4) k closeness X  , ( 4) k period X  , and…”

Section: Fusion Of Spatio-temporal Modulesmentioning

confidence: 99%

“…The time granularity is set to 30 min, resulting in a total of 16 stations and 30,816 time segments. The shape of the dataset is (30,816,2,4,4), where "2" represents inbound and outbound stations, and the last two digits indicate the 4 × 4 station distribution network. Additionally, external data including holiday and daily meteorological information for each time segment are also added.…”

Section: Datasetsmentioning

confidence: 99%

“…Data from the last 28 days are retained for testing purposes, while the rest of the data were employed for training. Therefore, the test set size is (1344,2,4,4) and the training set size is (23,952,2,4,4), with 10% of the data in the training set being allocated for validation.…”

Section: Datasetsmentioning

confidence: 99%

“…Under such conditions, passenger flow prediction has been one of the pivotal technologies for ensuring the uninterrupted operations and optimal planning of rail transit systems [1]. Highly accurate short-term traffic forecasting can aid operators in optimizing resource utilization, including proactively managing passenger flow [2], and modifying train schedules well in advance [3,4]. Moreover, it can help anticipate station congestion, effectively ensuring the safety of passengers and safeguarding their assets [5].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Prediction of Inbound and Outbound Passenger Flow in Urban Rail Transit Based on Spatio-Temporal Attention Residual Network

Yang,

Dong,

Yang

et al. 2023

Applied Sciences

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Passenger flow prediction is a critical approach to ensure the effective functioning of urban rail transit. However, there are few studies that combine multiple influencing factors for short-term passenger flow prediction. It is also a challenge to accurately predict passenger flow at all stations in the line at the same time. To overcome the above limitations, a deep learning-based method named ST-RANet is proposed, which consists of three spatio-temporal modules and one external module. The model is capable of predicting inbound and outbound passenger flow for all stations within the network simultaneously. We model the spatio-temporal data in terms of three temporal characteristics, including closeness, period, and trend. For each characteristic, we construct a spatio-temporal module that innovatively integrates the attention mechanisms into the middle of residual units and convolutional neural networks (CNNs) to extract and learn spatio-temporal features. Subsequently, the results of the three modules are integrated using a parameter matrix method, which allows for dynamic aggregation based on data. The integration results are further combined with external factors, such as holidays and meteorological information, to obtain passenger flow prediction values for each station. The proposed model is validated using real data from Beijing Subway, and optimized parameters are applied for 30-min granularity passenger flow predictions. Comparing the performance against 5 baseline models and verifying with data from multiple lines, the results indicate that the proposed ST-RANet model shows the best results. It is demonstrated that the method proposed in this paper has high prediction accuracy and good applicability.

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Section: Fusion Of Spatio-temporal Modulesmentioning

confidence: 99%