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2023 International Conference on Networking and Network Applications, NaNA 2023

作者： Gao, LuLu Li, XiaoNan Wei, ZhiCheng Key Lab of Network and Information Security Hebei Province Shijiazhuang China College of Computer and Cyber Security Hebei Province Shijiazhuang China Hebei Normal University Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Security Hebei Province Shijiazhuang China

ISBN: (纸本)9798350327380

With the widespread use of mobile apps, empty taxis can easily receive new orders without having to roam around. Therefore, it is necessary to plan taxi stations where empty taxis can park. In the location selection problem, the spatial and temporal distribution of passenger demand needs to be considered to ensure that the selected location can meet the actual needs of passengers. In this paper, a Spatio-Temporal Cube Demand Coverage Model (STC-DCM) is proposed for taxi station location selection. Firstly, a spatiotemporal demand cube is constructed to obtain fine-grained user demands based on time and space, and genetic algorithm is used to maximize the demand coverage rate. Then, the final location selection results are obtained using the fusion module and adjustment optimization module, where the fusion module integrates multiple location selection solutions using ensemble learning method. Experimental results show that our proposed model can better cover dynamic passenger demand compared to traditional models. © 2023 IEEE.

关键词： Genetic algorithms

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STGAT: A Spatio-Temporal Graph Attention Network for Travel Demand Prediction

STGAT: A Spatio-Temporal Graph Attention Network for Travel ...

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2023 International Conference on Networking and Network Applications, NaNA 2023

作者： Zhang, Tao Yi Wang, YuBo Wei, ZhiCheng Key Lab of Network and Information Security China College of Computer and Cyberspace Security China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Security Hebei Normal University Hebei Province Shijiazhuang China

ISBN: (纸本)9798350327380

Forecasting travel demand is a challenging task due to the complex spatial dependencies and dynamic temporal correlation of the traffic data. Furthermore, a limited representation of the given spatial graph structure may restrict the model's ability to effectively learn spatial-temporal dependencies. To extract latent semantic features from traffic data, this paper proposes a method to construct a traffic graph using the Markov cluster algorithm. The traffic semantic correlation matrix is constructed based on a traffic graph to obtain the deep semantic information. Specifically, this study proposes a Spatio-Temporal Graph Attention Network(STGAT) for travel demand prediction. STGAT uses a graph attention layer based on the Node2Vec graph embedding algorithm, two convolutional layers based on the Markov cluster algorithm, and a long short-term memory network to capture spatial-temporal dependencies. Experimental results on the NYC Taxi dataset and the Chengdu online car dataset demonstrate that STGAT achieved state-of-the-art performance compared to other baseline models. © 2023 IEEE.

关键词： Forecasting

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A Deep Reinforcement Learning Approach for Online Taxi Dispatching

A Deep Reinforcement Learning Approach for Online Taxi Dispa...

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2023 International Conference on Networking and Network Applications, NaNA 2023

作者： Wang, YuBo Zhang, TaoYi Wei, ZhiCheng Key Lab of Network and Information Security China College of Computer and Cyber Security China Hebei Prov. Eng. Res. Center for Supply Chain Big Data Analytics Security Hebei Normal University Hebei Province Shijiazhuang China

ISBN: (纸本)9798350327380

With the development of smart city transportation systems, developing reasonable dispatching strategies for idle ride-hailing vehicles has become an urgent research problem. In this paper, we address the short-sightedness issue of existing dispatching strategies and design a ride-hailing dispatching model based on deep reinforcement learning in the Markov decision process(MDP) framework to obtain long-term dispatching strategies with foresight. Firstly, we define the basic framework of agent and optimize the dispatching strategy of ride-hailing services by defining a metric that combines instant revenue with the demand situation of the next state in the action space as a reward function. Then, we construct an order-matching system simulator based on historical order data sets to facilitate interaction between the intelligent agent and the environment. Finally, based on the Bellman optimality equation, we approximate the value function using the Eval Net and Target Net, and train the model using Temporal Difference Learning. Simulation experiments demonstrate that this approach can increase driver income, provide more services to passengers, and effectively improve the operating efficiency of the transportation network. © 2023 IEEE.

关键词： Reinforcement learning

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Taxi Station Location Model Based on Spatio-Temporal Demand Cube

Taxi Station Location Model Based on Spatio-Temporal Demand ...

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International Conference on Networking and Network Applications (NaNA)

作者： LuLu Gao XiaoNan Li ZhiCheng Wei Key Lab of Network and Information Security Shijiazhuang Hebei Province China College of Computer and Cyber Security Shijiazhuang Hebei Province China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Security Hebei Normal University Shijiazhuang Hebei Province China

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STGAT: A Spatio-Temporal Graph Attention Network for Travel Demand Prediction

STGAT: A Spatio-Temporal Graph Attention Network for Travel ...

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International Conference on Networking and Network Applications (NaNA)

作者： Tao Yi Zhang YuBo Wang ZhiCheng Wei Key Lab of Network and Information Security College of Computer and Cyberspace Security Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Security Hebei Normal University Shijiazhuang Hebei Province China

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A Deep Reinforcement Learning Approach for Online Taxi Dispatching

A Deep Reinforcement Learning Approach for Online Taxi Dispa...

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International Conference on Networking and Network Applications (NaNA)

作者： YuBo Wang TaoYi Zhang ZhiCheng Wei Key Lab of Network and Information Security College of Computer and Cyber Security Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Security Hebei Normal University Shijiazhuang Hebei Province China

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Mandari: Multi-Modal Temporal Knowledge Graph-aware Sub-graph Embedding for Next-POI Recommendation

Mandari: Multi-Modal Temporal Knowledge Graph-aware Sub-grap...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiaoqian Liu Xiuyun Li Yuan Cao Fan Zhang Xiongnan Jin Jinpeng Chen School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing China The Technology Innovation Center of Cultural Tourism Big Data of Hebei Province Chengde China Hebei Normal University for Nationalities Chengde China Knowledge Discovery and Data Mining Research Center Zhejiang Lab Hangzhou China

Next-POI recommendation aims to explore from user check-in sequence to predict the next possible location to be visited. Existing methods are often difficult to model the implicit association of multi-modal data with user choices. Moreover, traditional methods struggle to fully explore the variation of user preferences at variable time intervals. To tackle these limitations, we propose a Multi-Modal Temporal Knowledge Graph-aware Sub-graph Embedding approach (Mandari). We first construct a novel Multi-Modal Temporal Knowledge Graph. Based on the proposed knowledge graph, we integrate multi-modal information and leverage the graph attention network to calculate sub-graph prediction probability. Next, we implement a temporal knowledge mining method to model the segmentation and periodicity of user check-in and obtain temporal prediction probability. Finally, we fuse temporal prediction probability with the previous sub-graph prediction probability to obtain the final result. Extensive experiments demonstrate that our approach outperforms existing state-of-the-art methods.

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Identification of macrophage-associated diagnostic biomarkers and molecular subtypes in gestational diabetes mellitus based on machine learning

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Artificial cells, nanomedicine, and biotechnology 2025年第1期53卷 20-33页

作者： Kai Wei Liyun Yuan Yongsheng Ge Han Yu Guoping Zhao Guoqing Zhang Guohua Liu Children's Hospital Affiliated to Shandong University Jinan China. Jinan Children's Hospital Jinan China. Bio-Med Big Data Center Shanghai Institute of Nutrition and Health Chinese Academy of Science Shanghai China. Medical AI Lab The First Hospital of Hebei Medical University Shijiazhuang China. Key Laboratory of Systems Health Science of Zhejiang Province School of Life Science Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou China.

Gestational diabetes mellitus (GDM) is a common metabolic disorder during pregnancy, involving multiple immune and inflammatory factors. Macrophages play a crucial role in its development. This study integrated scRNA-seq and RNA-seq data to explore macrophage-related diagnostic genes and GDM subtypes. For scRNA-seq data, cell clusters were annotated using the SingleR package and validated with marker gene expression profiles, while hdWGCNA analysis identified three gene modules related to macrophages. A diagnostic model for GDM derived from endothelial cell transcriptomes was constructed by employing a variety of machine learning ensemble algorithms, achieving an AUC of 0.887. The model identified five differentially expressed genes (ZEB2, MALAT1, HEBP1, AHSA1, and TTC3) as potential diagnostic biomarkers. The CB-DSNMF algorithm was proposed to identify two distinct GDM subtypes from RNA-seq data, revealing significant differences in biological behaviours. This algorithm outperformed other baselines in multiple clustering metrics. Mendelian randomisation analysis identified ZEB2 as a gene causally related to GDM risk. A transcription factor (TF)-gene regulatory network was constructed for these genes using the ENCODE database. The study highlights the importance of macrophages in GDM, provides a high-precision diagnostic model, and offers new insights into personalised treatment strategies, contributing to a better understanding of GDM pathophysiology.

关键词： Gestational diabetes mellitus immune microenvironment machine learning macrophages non-negative matrix factorisation

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Atmospheric fine particles in a typical coastal port of Yangtze River Delta

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Journal of Environmental Sciences 2020年第12期32卷 62-70页

作者： Qi Yuan Xiaomi Teng Shaoxuan Tu Binxin Feng Zhiyu Wu Hang Xiao Qiuliang Cai Yinxiao Zhang Qiuhan Lin Zhaoce Liu Mengmeng He Xiaokun Ding Weijun Li Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province Department of Atmospheric SciencesSchool of Earth SciencesZhejiang UniversityHangzhou 310027China Center for Excellence in Regional Atmospheric Environment&Key Lab of Urban Environment and Health Institute of Urban EnvironmentChinese Academy of SciencesXiamen 361021China Key Laboratory of Urban Environmental Processes and Pollution Control of Zhejiang Province Ningbo Urban Environment Observation and Research StationChinese Academy of SciencesNingbo 315830China School of Earth Science and Engineering Hebei University of EngineeringHandan 056038China Department of Chemistry Zhejiang UniversityHangzhou 310027China

In recent decades, coastal ports have experienced rapid development and become an important economic and ecological hub in China. Atmospheric particle is a research hotspot in atmospheric environmental sciences in inland regions. However, few studies on the atmospheric particle were conducted in coastal port areas in China, which indeed suffers atmospheric particle pollution. Lack of the physicochemical characteristics of fine particles serves as an obstacle toward the accurate control for air pollution in the coastal port area in China. Here, a field observation was conducted in an important coastal port city in Yangtze River Delta from March 6 to March 19, 2019. The average PM2.5 concentration was 63.7 ±27.8 μg/m^3 and NO3^-, SO4^2-, NH4^+, and organic matter accounted for ?60% of PM 2.5. Fe was the most abundant trace metal element and V as the ship emission indicator was detected. Transmission electron microscopy images showed that SK-rich, soot, Fe, SK-soot and SK-Fe were the major individual particles in the coastal port. V and soluble Fe were detected in sulfate coating of SK-Fe particles. We found that anthropogenic emissions, marine sea salt, and secondary atmosphere process were the major sources of fine particles. Backward trajectory analysis indicated that the dominant air masses were marine air mass, inland air mass from northern Zhejiang and inland-marine mixed air mass from Shandong and Shanghai during the sampling period. The findings can help us better understand the physicochemical properties of atmospheric fine particles in the coastal port of Eastern China.

关键词： Coastal port Water-soluble ion Organic matter Trace metal Individual particle

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Real-time detection of vehicle speed based on video image 12

Real-time detection of vehicle speed based on video image

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12th International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2020

作者： Cheng, Genyuan Guo, Yubin Cheng, Xiaochun Wang, Dongliang Zhao, Jiandong Tangshan Municipal Transportation Bureau Hebei Province Tangshan063000 China School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China Beijing National Day School Beijing100039 China Tangshan NolMiddle School Hebei Province Tangshan063000 China Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9781728170817

With the continuous development and improvement of Intelligent Transportation System, more and more attention has been paid to the real-time and accurate speed information detection. Video vehicle speed detection based on machine vision has attracted the attention of the researchers due to its practical convenience and other advantages. In this paper, we propose a target tracking method based on video image feature matching vehicle to simplify the algorithm complexity and obtain high accuracy. The background subtraction method based on KNN algorithm is used to identify vehicle targets and obtain good initial vehicle characteristics. Experimental results show that the method has high real-time and the relative error of vehicle speed detection can be controlled at about 5%. © 2020 IEEE.

关键词： Target tracking

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