A new approach to COVID-19 data mining: A deep spatial–temporal prediction model based on tree structure for traffic revitalization index

Lv, Zhiqiang; Wang, Xiaotong; Cheng, Zesheng; Li, Jianbo; Li, Haoran; Xu, Zhihao

doi:10.1016/j.datak.2023.102193

Cited by 17 publications

(3 citation statements)

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“…The construction of intelligent transportation systems needs to fully consider urban development planning and demographic changes to achieve long-term sustainable development. The traffic condition of the target area is affected by the surrounding area or the further area, which makes the urban traffic area have a certain spatial dependence [18]. And, the movement of humans in cities is an important reference standard for the division of urban functional regions [19].…”

Section: Resultsmentioning

confidence: 99%

A survey of sustainable development of intelligent transportation system based on urban travel demand

Yan,

2024

Sustain. Social. Dev.

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<p>This paper provides a comprehensive exploration of urban travel demand forecasting and its implications for intelligent transportation systems, emphasizing the crucial role of intelligent transportation systems in promoting sustainable urban development. With the increasing challenges posed by traffic congestion, environmental pollution, and diverse travel needs, accurate prediction of urban travel demand becomes essential for optimizing transportation systems, fostering sustainable travel methods, and creating opportunities for business development. However, achieving this goal involves overcoming challenges such as data collection and processing, privacy protection, and information security. To address these challenges, the paper proposes a set of strategic measures, including advancing intelligent transportation technology, integrating intelligent transportation systems with urban planning, enforcing policy guidance and market supervision, promoting sustainable travel methods, and adopting intelligent transportation technology and green energy solutions. Additionally, the study highlights the role of intelligent transportation systems in mitigating traffic congestion and environmental impact through intelligent road condition monitoring, prediction, and traffic optimization. Looking ahead, the paper foresees an increasingly pivotal role for intelligent transportation systems in the future, leveraging advancements in deep learning and information technology to more accurately collect and analyze urban travel-related data for better predictive modeling. By combining data analysis, public transportation promotion, shared travel modes, intelligent transportation technology, and green energy adoption, cities can build more efficient, environmentally friendly transportation systems, enhancing residents’ travel experiences while reducing congestion and pollution to promote sustainable urban development. Furthermore, the study anticipates that intelligent transportation systems will be intricately integrated with urban public services and management, facilitating efficient and coordinated urban functions. Ultimately, the paper envisions intelligent transportation systems playing a vital role in supporting urban traffic management and enhancing the overall well-being of urban construction and residents’ lives. In conclusion, this research not only enhances our understanding of urban travel demand forecasting and the evolving landscape of intelligent transportation systems but also provides valuable insights for future research and practical applications in related fields. The study encourages greater attention and investment from scholars and practitioners in the research and practice of intelligent transportation systems to collectively advance the progress of urban transportation and sustainable development.<strong></strong></p>

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Section: Resultsmentioning

confidence: 99%

A survey of sustainable development of intelligent transportation system based on urban travel demand

Yan,

2024

Sustain. Social. Dev.

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“…Through experimentation with varied prediction ranges and training data volumes, their algorithm demonstrated superior performance compared to alternative methods. Lv et al [ 25 ] introduced an advanced spatio-temporal prediction model for the traffic revitalization index utilizing a tree structure. This model comprises key components, namely the spatial convolution module, temporal convolution module, and matrix data fusion module, resulting in improved prediction outcomes.…”

Section: Related Workmentioning

confidence: 99%

Expressway traffic flow prediction based on MF-TAN and STSA

Zhang,

Ren,

Zhang

et al. 2024

PLoS ONE

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Highly accurate traffic flow prediction is essential for effectively managing traffic congestion, providing real-time travel advice, and reducing travel costs. However, traditional traffic flow prediction models often fail to fully consider the correlation and periodicity among traffic state data and rely on static network topology graphs. To solve this problem, this paper proposes a expressway traffic flow prediction model based on multi-feature spatial-temporal adaptive periodic fused graph convolutional network (MFSTAPFGCN). First, we make fine preprocessing of the raw data to construct a complete and accurate dataset. Second, by deeply investigating the correlation properties among section speed, traffic flow, and section saturation rate, we incorporate these features into a multi-feature temporal attention mechanism in order to dynamically model the correlation of traffic flow in different time periods. Next, we adopt a spatial-temporal adaptive fusion graph convolutional network to capture the daily cycle similarity and potential spatial-temporal dependence of traffic flow data. Finally, the superiority of the proposed MFSTAPFGCN model over the traditional baseline model is verified through comparative experiments on real Electronic Toll Collection (ETC) gantry transaction data, and the effectiveness of each module is demonstrated through ablation experiments.

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“…In recent years, deep convolutional neural networks (DCNNs) [19][20][21][22][23] have demonstrated stateof-the-art and sometimes even human-level performance in solving many computer vision problems, such as image classification, 24,25 object detection, 26,27 image segmentation, 28,29 etc. For line detection, [30][31][32][33] DCNN-based methods have also been proposed for tasks such as edge detection, contour detection, boundary segmentation, etc. These deep architectures construct high-level functions from low-level primitives by hierarchical convolutional sensory input.…”

Section: Introductionmentioning

confidence: 99%

FSDGNet: frequency and spatial dual guide network for crack detection

Liu,

Yuan,

et al. 2024

J. Electron. Imag.

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Cracks are one of the major threats to the safe operation of civil infrastructures, so timely and accurate detection of cracks is crucial for accident prevention. However, in practical applications, the poor continuity and low contrast of many cracks (e.g., pavement cracks) pose a great challenge to image-based crack detection. In previous approaches, the detection results are often unsatisfactory due to the limitation of the dataset and the singularity of the detection method. To solve this problem, we propose a frequency and spatial dual guide network for crack detection. First, global frequency domain information is processed by the fast Fourier transform to extract low-level features, and then the convolutional neural network uses global frequency domain information to learn local features of the image to extract highlevel features. Finally, the feature maps of the two are weighted and fused element by element to obtain the final detection result. Extensive experiments show that our method is superior to any other existing method.

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A new approach to COVID-19 data mining: A deep spatial–temporal prediction model based on tree structure for traffic revitalization index

Cited by 17 publications

References 50 publications

A survey of sustainable development of intelligent transportation system based on urban travel demand

A survey of sustainable development of intelligent transportation system based on urban travel demand

Expressway traffic flow prediction based on MF-TAN and STSA

FSDGNet: frequency and spatial dual guide network for crack detection

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