Region-Level Traffic Prediction Based on Temporal Multi-Spatial Dependence Graph Convolutional Network from GPS Data

Yang, Haiqiang; Zhang, Xinming; Cui, Jianxun

doi:10.3390/rs14020303

Cited by 64 publications

(25 citation statements)

References 37 publications

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“…In recent years, neural network has been extensively utilized for traffic prediction, which offers superior modeling performance compared to traditional methods ( Guo et al, 2021 ). Most studies utilized graph convolution network to capture spatial feature ( Li et al, 2022 ; Wang et al, 2022 ; Zhu et al, 2022 ; Yao et al, 2023 ; Yang et al, 2022 ). GraphSAGE has also been used to model spatial dependency for inductive learning ( Liu et al, 2023 ; Liu, Ong & Chen, 2022 ).…”

Section: Literary Reviewmentioning

confidence: 99%

Dynamic multiple-graph spatial-temporal synchronous aggregation framework for traffic prediction in intelligent transportation systems

Yu,

Bao,

Shi

2024

PeerJ Computer Science

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Accurate traffic prediction contributes significantly to the success of intelligent transportation systems (ITS), which enables ITS to rationally deploy road resources and enhance the utilization efficiency of road networks. Improvements in prediction performance are evident by utilizing synchronized rather than stepwise components to model spatial-temporal correlations. Some existing studies have designed graph structures containing spatial and temporal attributes to achieve spatial-temporal synchronous learning. However, two challenges remain due to the intricate dynamics: (a) Accounting for the impact of external factors in spatial-temporal synchronous modeling. (b) Multiple perspectives in constructing spatial-temporal synchronous graphs. To address the mentioned limitations, a novel model named dynamic multiple-graph spatial-temporal synchronous aggregation framework (DMSTSAF) for traffic prediction is proposed. Specifically, DMSTSAF utilizes a feature augmentation module (FAM) to adaptively incorporate traffic data with external factors and generate fused features as inputs to subsequent modules. Moreover, DMSTSAF introduces diverse spatial and temporal graphs according to different spatial-temporal relationships. Based on this, two types of spatial-temporal synchronous graphs and the corresponding synchronous aggregation modules are designed to simultaneously extract hidden features from various aspects. Extensive experiments constructed on four real-world datasets indicate that our model improves by 3.68–8.54% compared to the state-of-the-art baseline.

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Section: Literary Reviewmentioning

confidence: 99%

Dynamic multiple-graph spatial-temporal synchronous aggregation framework for traffic prediction in intelligent transportation systems

Yu,

Bao,

Shi

2024

PeerJ Computer Science

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“…In this model, the GHOA was used for tuning the parameters of the RF model. Hybrid deep learning techniques are effective approaches for solving a wide range of problems such as traffic prediction 21 , 22 , trajectory prediction 23 , and so on. Also, optimization techniques have been used in many researches to improve the efficiency of learning models 24 .…”

Section: Literature Reviewmentioning

confidence: 99%

A hybrid machine learning-based model for predicting flight delay through aviation big data

Dai

2024

Sci Rep

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The prediction of flight delays is one of the important and challenging issues in the field of scheduling and planning flights by airports and airlines. Therefore, in recent years, we have witnessed various methods to solve this problem using machine learning techniques. In this article, a new method is proposed to address these issues. In the proposed method, a group of potential indicators related to flight delay is introduced, and a combination of ANOVA and the Forward Sequential Feature Selection (FSFS) algorithm is used to determine the most influential indicators on flight delays. To overcome the challenges related to large flight data volumes, a clustering strategy based on the DBSCAN algorithm is employed. In this approach, samples are clustered into similar groups, and a separate learning model is used to predict flight delays for each group. This strategy allows the problem to be decomposed into smaller sub-problems, leading to improved prediction system performance in terms of accuracy (by 2.49%) and processing speed (by 39.17%). The learning model used in each cluster is a novel structure based on a random forest, where each tree component is optimized and weighted using the Coyote Optimization Algorithm (COA). Optimizing the structure of each tree component and assigning weighted values to them results in a minimum 5.3% increase in accuracy compared to the conventional random forest model. The performance of the proposed method in predicting flight delays is tested and compared with previous research. The findings demonstrate that the proposed approach achieves an average accuracy of 97.2% which indicates a 4.7% improvement compared to previous efforts.

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“…This work mainly focuses on incorporating feature-selecting techniques with the DNN for vehicular traffic noise modelling. Yang et al [17] devised a novel DL technique called TmS-GCN for forecasting region-level traffic data made up of gated recurrent unit (GRU) and GCN. The GCN part will capture spatial dependence between regions, whereas the GRU part will capture the dynamic traffic change in the regions.…”

Section: Literature Reviewmentioning

confidence: 99%

Parameter Tuned Deep Learning Based Traffic Critical Prediction Model on Remote Sensing Imaging

Ahmed¹,

Al-Zebari²,

Zebari³

et al. 2023

Computers, Materials &Amp; Continua

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Remote sensing (RS) presents laser scanning measurements, aerial photos, and high-resolution satellite images, which are utilized for extracting a range of traffic-related and road-related features. RS has a weakness, such as traffic fluctuations on small time scales that could distort the accuracy of predicted road and traffic features. This article introduces an Optimal Deep Learning for Traffic Critical Prediction Model on High-Resolution Remote Sensing Images (ODLTCP-HRRSI) to resolve these issues. The presented ODLTCP-HRRSI technique majorly aims to forecast the critical traffic in smart cities. To attain this, the presented ODLTCP-HRRSI model performs two major processes. At the initial stage, the ODLTCP-HRRSI technique employs a convolutional neural network with an auto-encoder (CNN-AE) model for productive and accurate traffic flow. Next, the hyperparameter adjustment of the CNN-AE model is performed via the Bayesian adaptive direct search optimization (BADSO) algorithm. The experimental outcomes demonstrate the enhanced performance of the ODLTCP-HRRSI technique over recent approaches with maximum accuracy of 98.23%.

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Region-Level Traffic Prediction Based on Temporal Multi-Spatial Dependence Graph Convolutional Network from GPS Data

Cited by 64 publications

References 37 publications

Dynamic multiple-graph spatial-temporal synchronous aggregation framework for traffic prediction in intelligent transportation systems

Dynamic multiple-graph spatial-temporal synchronous aggregation framework for traffic prediction in intelligent transportation systems

A hybrid machine learning-based model for predicting flight delay through aviation big data

Parameter Tuned Deep Learning Based Traffic Critical Prediction Model on Remote Sensing Imaging

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