A high-speed railway network dataset from train operation records and weather data

Zhang, Dalin; Peng, Yunjuan; Xu, Yi; Du, Chenyue; Zhang, Yumei; Wang, Nan; Chong, Yunhao; Wang, Hongwei; Wu, Daohua; Liu, Jintao; Zhang, Hailong; Lu, Lixin; Liu, Jiqiang

doi:10.1038/s41597-022-01349-8

Cited by 11 publications

(4 citation statements)

References 29 publications

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“…The driving force behind the influence of rainfall on common subgrade defects lies in its capacity to increase the moisture content of the pavement soil. We have found that defects such as subgrade settlement, frost damage, mud pumping, and uplift deformation are intricately linked to the presence of water, which is consistent with the research results of many researchers 20 , 27 , 44 – 46 . In the case of frost damage, soil moisture undergoes crystallization into ice, filling soil voids during temperature drops, resulting in relative displacement of the subgrade particles.…”

Section: Discussionsupporting

confidence: 91%

Multi-defect risk assessment in high-speed rail subgrade infrastructure in China

Wang,

Zhang,

Wang

et al. 2024

Sci Rep

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This study addresses the escalating risk of high-speed railway (HSR) infrastructure in China, amplified by climate warming, increased rainfall, frequent extreme weather, and geohazard events. Leveraging a georeferenced dataset of recent HSR defects obtained through an extensive literature review, we employ machine learning techniques for a quantitative multi-defect risk assessment. Climatic, geomorphological, geohydrological, and anthropogenic variables influencing HSR subgrade safety are identified and ranked. Climatic factors significantly impact frost damage and mud pumping, while geomorphological variables exhibit greater influence on settlement and uplift deformation defects. Notably, frost damage is prevalent in the northeast and northwest, mud pumping along the southeast coast, and settlement and uplift deformation in the northwest and central areas. The generated comprehensive risk map underscores high-risk zones, particularly the Menyuan Hui Autonomous and Minle County sections of the Lanzhou-Urumqi HSR, emphasizing the need for focused attention and preventive actions to mitigate potential losses and ensure operational continuity.

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Section: Discussionsupporting

confidence: 91%

Multi-defect risk assessment in high-speed rail subgrade infrastructure in China

Wang,

Zhang,

Wang

et al. 2024

Sci Rep

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“…We collected, collated relevant data, and published it on nature scientific data [5] for researchers in related fields. The distribution of stations and lines in the dataset is shown in Fig.…”

Section: A High-speed Train Delay Datasetmentioning

confidence: 99%

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

Zhang

Peng

et al. 2023

IEEE Trans. Fuzzy Syst.

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High-speed train delay prediction has always been one of the important research issues in the railway dispatching. Accurate and interpretable delay prediction can enable staff to implement preventive measures and scheduling decisions in advance, and guide relevant departments to cooperate in completing complex transportation tasks, so as to improve rail transit operations, service quality, and the efficiency of train operation. This article proposes a new interpretable model based on graph community neural network and time-series fuzzy decision tree. This model can well capture the influence of spatiotemporal characteristics, train community structure, and multifactor in high-speed train station delay prediction. Besides, the time series fuzzy decision tree based on multiobjective optimization and reduced error pruning can mine potential decision rules to improve the model's interpretability, transparency, and high reliability. Finally, we prove that the prediction effect of the proposed model is superior than the other seven state-of-the-art models and our model is interpretable.

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“…we evaluate the performance of DCGCN on three real-world datasets: PeMSD4 [32], PeMSD8 [32] and CHSD [33]. The detailed information can be illustrated as follows:…”

Section: A Datasetsmentioning

confidence: 99%

“…(3) CHSD (China High-speed railway network dataset) [33]. This HSR dataset contains the train operation data in China from 8/Oct/2019 to 27/Jan/2020, with 727 stations and 3399 high-speed trains with a time interval of 5 It contains seven traffic measurements, i.e.…”

Section: A Datasetsmentioning

confidence: 99%

DCGCN: Dynamic community graph convolutional network for traffic forecasting

Xu¹,

Zhang²,

Peng³

et al. 2022

Preprint

Self Cite

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<p>Traffic forecasting is one of the core issues in transportation systems. Graph convolution based spatiotemporal model can process the complex and highly nonlinear traffic data, but rely heavily on the graph construction, some creative works make innovations in dynamic graph modeling and expansion of the graph. However, these works ignored the dynamic community structure of traffic graph and the inter-community traffic flow interaction, and not fully exploited the spatiotemporal characteristics. In this paper, we propose a dynamic community graph convolution model (DCGCN), which can more precisely characterize the dynamic traffic network and capture the dynamic spatiotemporal dependencies by capturing the community properties and community interactions of traffic flow. First, we propose a noval method to capture inter-community traffic flow interactions. Second, we take advantage of the spatiotemporal properties of traffic data and introduce a community-based dynamic traffic graph learning mechanism to construct traffic networks adaptively. Finally, we fuse inter-community traffic flow interactions and dynamic traffic graph to update the traffic graph, introduce a multi-attention spatiotemporal convolution for traffic forecasting. We conduct experiments on three large-scale real datasets containing multiple traffic scenarios, prove the superiority of our model compared with SOTA models such as ASTGCN, MATGCN, and DCRNN. Meanwhile, the visualization in these datasets shows the method can effectively identify the stations that have a large impact in the forecasting process and dynamic community changes, and the extracted inter-station dependencies and traffic flow region interactions are interpretable. The source code is available athttps://github.com/1998XuYi/DCGCN.</p>

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A high-speed railway network dataset from train operation records and weather data

Cited by 11 publications

References 29 publications

Multi-defect risk assessment in high-speed rail subgrade infrastructure in China

Multi-defect risk assessment in high-speed rail subgrade infrastructure in China

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

DCGCN: Dynamic community graph convolutional network for traffic forecasting

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