An Interpretable Station Delay Prediction Model Based on Graph Community Neural Network and Time-Series Fuzzy Decision Tree

Zhang, Dalin; Xu, Yi; Peng, Yunjuan; Du, Chenyue; Wang, Nan; Tang, Mincong; Lu, Lixin; Liu, Jiqiang

doi:10.1109/tfuzz.2022.3181453

Cited by 16 publications

(9 citation statements)

References 31 publications

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“…The data provided by different platforms have different attributes. The periodicity and spatio-temporal correlation of time series data have been considered in the field of intelligent transportation system [30,31] and medical imaging [32], and there have been a large number of deep learning application cases. In the future, we will further improve the deep learning framework to consider the periodicity and spatio-temporal correlation of internet financial data.…”

Section: Discussionmentioning

confidence: 99%

Internet Financial Credit Risk Assessment with Sliding Window and Attention Mechanism LSTM Model

Zhang

Lü

et al. 2023

Teh. vjesn.

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With the accelerated pace of market-oriented reform, Internet finance has gained a broad and healthy development environment. Existing studies lack consideration of time trends in financial risk, and treating all features equally may lead to inaccurate predictions. To address the above problems, we propose an LSTM model based on sliding window and attention mechanism. The model uses sliding windows to enable the model to effectively exploit the contextual relevance of loan data. And we introduce the attention mechanism into the model, which enables the model to focus on important information. The result on the Lending Club public desensitization dataset shows that our model outperforms ARIMA, SVM, ANN, LSTM, and GRU models.

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Section: Discussionmentioning

confidence: 99%

Internet Financial Credit Risk Assessment with Sliding Window and Attention Mechanism LSTM Model

Zhang

Lü

et al. 2023

Teh. vjesn.

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“…They evaluated the model's performance by comparing the prediction results with individual ANN, LSTM, and Bagging to show the enhanced accuracy of the model. Zhang et al [17] proposed an interpretable model using a graph community neural network and a time-series fuzzy decision tree for train delay prediction. Their model captures the influence of spatiotemporal characteristics, train community structure, and multi-factor in high-speed train station delays.…”

Section: Related Workmentioning

confidence: 99%

A Global Modeling Pruning Ensemble Stacking With Deep Learning and Neural Network Meta-Learner for Passenger Train Delay Prediction

Boateng,

Yang

2023

IEEE Access

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Train Operators can improve railway passengers' service quality and traffic management by accurately predicting travel arrangements and delays. Precise prediction of train delays is vital for creating feasible scheduled timetables. The import of pruning stacked ensemble deep neural networks into train delay prediction helps improve model prediction accuracy and computational time. In this study, we propose a novel pruning stacked ensemble learning model that uses pruned multilayer perceptron (MLP) neural networks as a meta-learner and heterogeneous neural networks as sub-models to improve the prediction accuracy for passenger train arrival delays and evaluate the model performance using Amtrak, the most extensive US passenger railroad service data, to determine the enhanced model accuracy. We used stacked generalization regression neural networks for artificial neural networks and deep learning neural networks to create the stacked ensemble models. We optimized the models using OPTUNA and produced a pruning stacked ensemble model using the best neural network sub-models and an MLP metalearner. Our methodology comprises data preprocessing, feature engineering, modeling, case studies, and evaluation phases. Our experiments demonstrate that our proposed pruning stacked ensemble multilayer perceptron Neural Network (PST-NN) model has various degrees of improvement in model evaluation indicators compared to the existing model by 35.20%, 53.40% in terms of accuracy and prediction error and outperforms the benchmark models by 85.22% in terms prediction error in Passenger train delay prediction respectively, which provides a new approach to building models efficiently in Passenger train arrival delay prediction.

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“…A BP (Backpropagation) neural network, a fundamental component of artificial neural networks, stands as a powerful tool in the realm of machine learning and pattern recognition [11]. Comprising interconnected nodes organized into layers, it utilizes a supervised learning technique to adjust weights and biases iteratively, thereby minimizing the difference between predicted and actual outputs [12]. This process, known as backpropagation, involves propagating errors backward from the output layer to the input layer, enabling the network to learn and adapt its parameters over time.…”

Section: Introductionmentioning

confidence: 99%

Simulation of a Mathematical Model of Rail Transportation Scheduling Based on Bp Neural Network

Xiaoguang Hu

2024

jes

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Rail transportation, a cornerstone of modern logistics and passenger transit systems, plays a pivotal role in facilitating the efficient movement of goods and people across vast distances. Operating on a network of interconnected tracks, rail systems offer a reliable and environmentally sustainable mode of transportation, particularly for long-distance travel and freight shipments. The paper presents a comprehensive investigation into the application of advanced computational techniques in the realm of rail transportation management. Specifically, Mamdani fuzzy logic and Backpropagation (BP) Neural Networks are employed to address critical challenges in scheduling and classification within rail networks. The utilization of Mamdani fuzzy logic facilitates nuanced decision-making in scheduling processes, considering uncertainties and complexities inherent in rail operations. Through linguistic rules and fuzzy sets, the scheduling system can effectively adapt to various operational constraints and disruptions, leading to more resilient and efficient scheduling solutions. Additionally, the integration of BP Neural Network enhances classification accuracy and prediction capabilities, enabling precise forecasting of train movements, passenger flows, and other key variables.

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An Interpretable Station Delay Prediction Model Based on Graph Community Neural Network and Time-Series Fuzzy Decision Tree

Cited by 16 publications

References 31 publications

Internet Financial Credit Risk Assessment with Sliding Window and Attention Mechanism LSTM Model

Internet Financial Credit Risk Assessment with Sliding Window and Attention Mechanism LSTM Model

A Global Modeling Pruning Ensemble Stacking With Deep Learning and Neural Network Meta-Learner for Passenger Train Delay Prediction

Simulation of a Mathematical Model of Rail Transportation Scheduling Based on Bp Neural Network

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