Short-Term Passenger Flow Prediction Using a Bus Network Graph Convolutional Long Short-Term Memory Neural Network Model

Baghbani, Asiye; Bouguila, Nizar; Patterson, Zachary

doi:10.1177/03611981221112673

Cited by 16 publications

(3 citation statements)

References 48 publications

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“…LSTM is a special type of recurrent neural network (RNN), commonly used for dynamically capturing time dependencies in data [13]. During training, the original RNN is prone to gradient explosion or vanishing as the training time increases and the number of network layers increases, resulting in the inability to process longer sequence data and obtain information from long-distance data.…”

Section: Lstmmentioning

confidence: 99%

An Integrated Convolutional Neural Network-Bidirectional Long Short-Term Memory-Attention Mechanism Model for Enhanced Highway Traffic Flow Prediction

Kan,

Li,

Wang

2024

JUDM

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The burgeoning expansion of the Internet of Things (IoT) technology has propelled Intelligent Traffic Systems (ITS) to the forefront of IoT applications, with accurate highway traffic flow prediction models playing a pivotal role in their development. Such models are essential for mitigating highway traffic congestion, reducing accident rates, and informing city planning and traffic management strategies. Given the inherent periodicity, nonlinearity, and variability of highway traffic data, an innovative model leveraging a Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Attention Mechanism (AM) is proposed. In this model, feature extraction is accomplished via the CNN, which subsequently feeds into the BiLSTM for processing temporal dependencies. The integration of an AM enhances the model by weighting and fusing the BiLSTM outputs, thereby refining the prediction accuracy. Through a series of experiments and the application of diverse evaluation metrics, it is demonstrated that the proposed CNN-BiLSTM-AM model surpasses existing models in prediction accuracy and explainability. This advancement positions the model as a significant contribution to the field, offering a robust and insightful tool for highway traffic flow prediction.

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Section: Lstmmentioning

confidence: 99%

An Integrated Convolutional Neural Network-Bidirectional Long Short-Term Memory-Attention Mechanism Model for Enhanced Highway Traffic Flow Prediction

Kan,

Li,

Wang

2024

JUDM

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“…There are several types of connectivity, which are either caused by roads such as motorways and highways, or by public transportation, such as subways. A connectivity matrix, also called "physical matrix" [85], is a kind of adjacency matrix that has been used in several studies to demonstrate such associations between nodes [86], [112], [113].…”

Section: A Graph Construction In Graph Neural Networkmentioning

confidence: 99%

Graph Neural Networks for Intelligent Transportation Systems: A Survey

Rahmani

Baghbani

Bouguila

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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Graph neural networks (GNNs) have been extensively used in a wide variety of domains in recent years. Owing to their power in analyzing graph-structured data, they have become broadly popular in intelligent transportation systems (ITS) applications as well. Despite their widespread applications in different transportation domains, there is no comprehensive review of recent advancements and future research directions that covers all transportation areas. Accordingly, in this survey, for the first time, we provide an overview of GNN studies in the general domain of ITS. Unlike previous surveys, which have been limited to traffic forecasting problems, we explore how GNN frameworks have evolved for different ITS applications, including traffic forecasting, demand prediction, autonomous vehicles, intersection management, parking management, urban planning, and transportation safety. Also, we micro-categorize the studies based on their transportation application to identify domainspecific research directions, opportunities, and challenges, which have been missing in previous surveys. Moreover, we identify unique and undiscussed research opportunities and directions, which is the result of reviewing a wide range of transportation applications. The neglected role of edge and graph learning in ITS applications, developing multi-modal models, and exploiting the power of unsupervised and reinforcement learning methods for developing more powerful GNNs are some examples of such new discussions in this survey. Finally, we have identified popular baseline models and datasets in each transportation domain, which facilitate the development and evaluation of future GNNbased frameworks.

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“…In addition, to verify the capability of the SAE-GCN-BiLSTM-based network model, the ARIMA (Su, C., 2020), LSTM , and ConvLSTM (Baghbani, et al, 2023) Hidden layer structure (SAE) [14,12,10,8,10,12,14] Optimizer (GCN-BiLSTM) Adam Optimizer Batch size (GCN-BiLSTM) 32 re 7 da 1…”

Section: Model Prediction Error Analysismentioning

confidence: 99%

A Passenger Flow Prediction Method Using SAE-GCN-BiLSTM for Urban Rail Transit

Liu

2023

International Journal of Swarm Intelligence Research

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To address the problems of existing passenger flow prediction methods such as low accuracy, inadequate learning of spatial features of station topology, and inability to apply to large networks, a SAE-GCN-BiLSTM-based passenger flow forecasting method for urban rail transit is proposed. First, the external features are extracted layer by layer using stacked autoencoder (SAE). Then, graph convolutional network (GCN) is used to capture the spatial features of station topology, and bi-directional long and short-term memory network (BiLSTM) is used to extract the bi-directional temporal features, realizing the extraction of the spatio-temporal features. Finally, external features and spatio-temporal features are fused for accurate prediction of urban rail transit passenger flow. The experimental results show that the proposed method is higher than several other advanced models in the evaluation indexes under different granularities, indicating that the model effectively develops the accuracy and robustness of urban rail transit passenger flow prediction.

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Short-Term Passenger Flow Prediction Using a Bus Network Graph Convolutional Long Short-Term Memory Neural Network Model

Cited by 16 publications

References 48 publications

An Integrated Convolutional Neural Network-Bidirectional Long Short-Term Memory-Attention Mechanism Model for Enhanced Highway Traffic Flow Prediction

An Integrated Convolutional Neural Network-Bidirectional Long Short-Term Memory-Attention Mechanism Model for Enhanced Highway Traffic Flow Prediction

Graph Neural Networks for Intelligent Transportation Systems: A Survey

A Passenger Flow Prediction Method Using SAE-GCN-BiLSTM for Urban Rail Transit

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