Zilong Zhao

Zilong Zhao

Wuhan University

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

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Posts

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Research

awards

education

project

publications

Toward urban traffic scenarios and more: A spatio-temporal analysis empowered low-rank tensor completion method for data imputation.

Published in 2023,37(9), 1900

Funded by the National Key R&D Program of China (No. 2017YFB0503604)
• Adopt a manifold embedding approach to depict the local geometric structure of spatio-temporal domains, and propose a novel Spatio-Temporal constrained Low-Rank Tensor Completion (ST-LRTC) method.
• The proposed method achieves stable and accurate imputation results even in extreme scenarios with large missing rates or non-random missing patterns.
• This study won the Grand Prize of the 12th National College Students’ Science and Technology Thesis Competition on Surveying and Mapping; Excellent Bachelor’s Thesis of Wuhan University (2021).

Recommended citation: Zilong Zhao, Luliang Tang, Mengyuan Fang, Xue Yang, Chaokui Li, Qingquan Li (2023). Toward urban traffic scenarios and more: a spatio-temporal analysis empowered low-rank tensor completion method for data imputation. International Journal of Geographical Information Science, 1-34. https://www.tandfonline.com/doi/abs/10.1080/13658816.2023.2234434?journalCode=tgis20

Research on application of differential grey neural network-AR model based on wavelet decomposition in the settlement prediction of metro tunnel.

Published in Bulletin of Surveying and Mapping, 2020(S1):99-103., 1900

Funded by Wuhan University (No. S2019214021)
• A wavelet decomposition-based differential gray neural network-AR model is proposed to address the impact of non-stationary sequences on the prediction accuracy of gray neural networks.
• This study won the First Prize (Top 2) in the 15th Science and Technology Paper Competition of School of Geodesy and Geomatics, Wuhan University.

Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1). http://zilzhao.github.io/files/Grey_neural_network.pdf

The impact of community shuttle services on traffic and traffic-related air pollution.

Published in 2022,19(22), 1900

Funded by the Fundamental Research Funds for the Central Universities
• Propose a complete framework to quantitatively assess the positive impacts of community shuttle services.
• Develop a novel method to adaptively generate shuttle stops with maximum service capacity based on crowd movement data, and design shuttle routes with minimum mileage by genetic algorithm.
• Conduct a fine-grained quantitative assessment of the extent to which community shuttle services alleviate traffic congestion and reduce traffic-related air pollution.

Recommended citation: Zilong Zhao, Mengyuan Fang, Luliang Tang, Xue Yang, Zihan Kan, and Qingquan Li. (2022). The impact of community shuttle services on traffic and traffic-related air pollution. International Journal of Environmental Research and Public Health, 19(22), 15128. http://zilzhao.github.io/files/Community_Shuttle_Services.pdf

Identifying Critical Urban Intersections from a Fine-grained Spatio-Temporal Perspective.

Published in 2023,33, 1900

Funded by the National Key R&D Program of China (No. 2017YFB0503604)
• Adopt a manifold embedding approach to depict the local geometric structure of spatio-temporal domains, and propose a novel Spatio-Temporal constrained Low-Rank Tensor Completion (ST-LRTC) method.
• The proposed method achieves stable and accurate imputation results even in extreme scenarios with large missing rates or non-random missing patterns.
• This study won the Grand Prize of the 12th National College Students’ Science and Technology Thesis Competition on Surveying and Mapping; Excellent Bachelor’s Thesis of Wuhan University (2021).

Recommended citation: Zilong Zhao, Luliang Tang, Mengyuan Fang, Xue Yang, Chaokui Li, Qingquan Li (2023). Toward urban traffic scenarios and more: a spatio-temporal analysis empowered low-rank tensor completion method for data imputation. International Journal of Geographical Information Science, 1-34. http://zilzhao.github.io/files/Traffic_data_imputation.pdf

talks

To what extent can community shuttle services enhance transport efficiency and improve the surrounding environment?

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Community shuttle services have the potential to alleviate traffic congestion and reduce traffic pollution caused by massive short-distance taxi-hailing trips. In this study, we developed a novel method to adaptively generate shuttle stops with maximum service capacity based on residents’ origin–destination (OD) data, and designed shuttle routes with minimum mileage by genetic algorithm. For traffic congestion alleviation, we identified trips that can be shifted to shuttle services and their potential changes in traffic flow. The decrease in traffic flow can alleviate traffic congestion and indirectly reduce unnecessary pollutant emissions. We utilized the COPERT III model and the spatial kernel density estimation method to finely analyze the reduction in traffic emissions by eco-friendly transportation modes to support detailed policymaking regarding transportation environmental issues.

Spatio-temporal low-rank sparse tensor model and its application in urban anomaly analysis

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In this study, we propose a comprehensive framework for traffic anomaly diagnosis based on tensor theory and GIScience methods, which is divided into three parts: road traffic status acquisition, traffic anomaly detection, and urban anomaly analysis. In terms of traffic anomaly detection, a novel Spatio-Temporal constrained Low-Rank Sparse Tensor (ST-LRST) method is proposed to decompose urban traffic data into normal and anomalous components. For anomaly analysis, we analyze the characteristics of the anomalies detected by ST-LRST, such as duration, impact scope, and impact intensity. Then, combined with crowdsourced geographic information, we conduct a comprehensive analysis of the spatial and temporal anomalous variations in the road network and explain the inducing factors of urban anomalies.

ST-LRTC: A Spatio-Temporal analysis empowered Low-Rank Tensor Completion method for missing traffic data imputation

Published:

In this study, we propose a novel Spatio-Temporal constrained Low-Rank Tensor Completion (ST-LRTC) method. The method utilizes a low-rank tensor to extract global features of traffic data, and a manifold embedding approach to depict the local geometric structure of spatiotemporal domains. Specifically, under the low-rank assumption, the method introduces temporal constraints based on the continuity and periodicity of traffic flow and a spatial constraint matrix reflecting the traffic flow transmission mechanism. We embed low-dimensional spatiotemporal constraint matrices into the low-rank tensor completion solving process to fully utilize the global features and local spatiotemporal characteristics of the traffic tensor.