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学者姓名：丁治明
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Abstract ：
With the widespread use of the Global Positioning System (GPS) in the fields such as traffic monitoring, sports navigation, and track recording, the trajectory data recording users' spatial and temporal information has grown dramatically. The huge volume of trajectory data causes high cost and poses a great challenge to data storage, network transmission, query and analysis. Therefore, the compression of trajectory data becomes a crucial issue. This paper proposes an online trajectory compression algorithm based on vector extraction (VOLTCom), which aims to achieve efficient data compression while retaining more effective information, and is mainly applied to trajectory recording and analysis in the traffic field. VOLTCom first generates vectors for trajectory data according to customized vector features, and then performs real-time vector extraction to achieve online trajectory compression. The vector extraction of the trajectory data ensures the stability of the compression time per unit and achieves efficient compression. Experiments on real datasets show that VOLTCom can retain the information of object velocity variation by vector density and outperforms traditional algorithms in terms of error, compression rate, and execution time. The algorithm is $O(1)$ in compression time complexity and has better compression performance.
Keyword ：
vector vector synchronous Euclidean distance synchronous Euclidean distance Online trajectory compression Online trajectory compression trajectory compression ratio trajectory compression ratio
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| GB/T 7714 | Cai, Zhi , Dong, Qian , Shi, Meihui et al. VOLTCom: A Novel Online Trajectory Compression Method Based on Vector Processing [J]. | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2023 , 24 (12) : 14982-14993 . |
| MLA | Cai, Zhi et al. "VOLTCom: A Novel Online Trajectory Compression Method Based on Vector Processing" . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 24 . 12 (2023) : 14982-14993 . |
| APA | Cai, Zhi , Dong, Qian , Shi, Meihui , Su, Xing , Guo, Limin , Ding, Zhiming . VOLTCom: A Novel Online Trajectory Compression Method Based on Vector Processing . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2023 , 24 (12) , 14982-14993 . |
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Abstract ：
With the continuous enrichment of traffic Internet-of-Things data acquisition methods, more and more spatiotemporal data on road networks is collected in real time by various sensors and multimedia devices. The data-driven deep learning approach can make full use of real-time data from a road network to predict future traffic status. By mining the spatiotemporal relationships between road units, the ability to predict network evolutionary behaviors is improved, which provides a new method of traffic management. There are strong semantic relations between road intersections or road sections in terms of traffic evolution. Modeling the network only from a shallow spatial topological perspective ignores the important intrinsic association of the dynamic network. In this paper, we propose a semantic associative neural network (SANN) for traffic evolution analysis by modeling the propagation effects and similarity patterns between road units. Considering the inadequacy of the fixed adjacent matrix, graph convolution is used to encode the semantic features of a road network and embed them in a bidirectional recurrent neural network for sequence prediction. Finally, the experiments are conducted based on speed data sets to prove the effectiveness of the proposed method. The model achieved a well-predicted accuracy of 95.33% and 84.08% on Pems-Bay and Los Angeles data sets.
Keyword ：
Traffic prediction Traffic prediction Traffic evolution Traffic evolution Semantic features Semantic features Dynamic similarity Dynamic similarity Spatiotemporal neural network Spatiotemporal neural network
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| GB/T 7714 | Chang, Mengmeng , Ding, Zhiming , Guo, Limin et al. Traffic Propagation in Road Network from a Data-Driven Analysis Perspective [J]. | JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS , 2023 , 149 (2) . |
| MLA | Chang, Mengmeng et al. "Traffic Propagation in Road Network from a Data-Driven Analysis Perspective" . | JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS 149 . 2 (2023) . |
| APA | Chang, Mengmeng , Ding, Zhiming , Guo, Limin , Zhao, Zilin . Traffic Propagation in Road Network from a Data-Driven Analysis Perspective . | JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS , 2023 , 149 (2) . |
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Abstract ：
Missing traffic data collected by IoT sensors is a common issue. Having complete traffic data can help people with their studies and work in real world. A spatio-temporal enhanced k nearest neighbor (ST-KNN) method is proposed in this paper to interpolate missing traffic data according to its corresponding spatio-temporal dependence. The proposed method is improved in three aspects: initially, localized data are involved in the computation, the distance metric formula is re-designed secondly, and the data regression model is improved. We conducted our experimental evaluations on publicly available real dataset, and the results are compared to those from existing state-of-the-art models. The results of our experiments show that the method proposed in this paper can effectively improve traffic data interpolation accuracy.
Keyword ：
IoT sensors IoT sensors Spatio-temporal dependence Spatio-temporal dependence ST-KNN ST-KNN Traffic data interpolation Traffic data interpolation
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| GB/T 7714 | Cai, Zhi , Shu, Yuyu , Su, Xing et al. A traffic data interpolation method for IoT sensors based on spatio-temporal dependence [J]. | INTERNET OF THINGS , 2023 , 21 . |
| MLA | Cai, Zhi et al. "A traffic data interpolation method for IoT sensors based on spatio-temporal dependence" . | INTERNET OF THINGS 21 (2023) . |
| APA | Cai, Zhi , Shu, Yuyu , Su, Xing , Guo, Limin , Ding, Zhiming . A traffic data interpolation method for IoT sensors based on spatio-temporal dependence . | INTERNET OF THINGS , 2023 , 21 . |
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Abstract ：
The skyline query is one of the most important supporting technologies for the location-based query services in the road network. Usually, when a user queries the skyline points in the road network, the query area is a user-centered circle or rectangle area, without considering the impact of the current movement speed and direction of the user on the formation of the query area. In this context, a speed and direction aware skyline query method is proposed, which can provide the skyline query area for the users by considering their moving speed and direction. Since the efficiency to directly obtain points of interest from speed and direction aware query area is not high, a Voronoi based speed and direction query area generation algorithm is proposed to approximate the query area, so as to improve the obtaining efficiency of points of interest in the area. The experiments on road networks and points of interest data of Beijing show the performance of the proposed method in terms of query efficiency and quality.
Keyword ：
road network road network Skyline query Skyline query speed and direction speed and direction
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| GB/T 7714 | Cai, Zhi , Cui, Xuerui , Su, Xing et al. Speed and Direction Aware Skyline Query for Moving Objects [J]. | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2022 , 23 (4) : 3000-3011 . |
| MLA | Cai, Zhi et al. "Speed and Direction Aware Skyline Query for Moving Objects" . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 23 . 4 (2022) : 3000-3011 . |
| APA | Cai, Zhi , Cui, Xuerui , Su, Xing , Guo, Limin , Ding, Zhiming . Speed and Direction Aware Skyline Query for Moving Objects . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2022 , 23 (4) , 3000-3011 . |
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Abstract ：
With the development of mobile networks and the rapid prevalence of location-based social networks (LBSN), a massive volume of spatiotemporal data has been generated, which is valuable for points of interest (POI) recommendation. However, current studies have not unleashed the full power of such spatiotemporal data, which either explore only a single dimension of the data or consider multiple factors in an asynchronous fashion. In this article, we propose a novel spatiotemporal network-based recommender framework (STNBR) to effectively recommend POIs for users. Specifically, we first establish a comprehensive conceptual model of spatiotemporal data, involving various essential factors for POIs recommendation. On top of the conceptual model, we design a series of meaningful meta-paths that simultaneously consider the time and location factors to precisely capture the semantics of user behaviours. By profiling users based on their embedded meta-paths, our approach can yield meaningful POIs recommendations. We have evaluated our proposal using a realistic dataset obtained from Foursquare and Gowalla, the results of which show that our STNBR model outperforms existing approaches.
Keyword ：
spatiotemporal data spatiotemporal data meta-path meta-path network embedding network embedding POI recommendation POI recommendation
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| GB/T 7714 | Song, Rui , Li, Tong , Dong, Xin et al. An effective points of interest recommendation approach based on embedded meta-path of spatiotemporal data [J]. | EXPERT SYSTEMS , 2022 , 40 (2) . |
| MLA | Song, Rui et al. "An effective points of interest recommendation approach based on embedded meta-path of spatiotemporal data" . | EXPERT SYSTEMS 40 . 2 (2022) . |
| APA | Song, Rui , Li, Tong , Dong, Xin , Ding, Zhiming . An effective points of interest recommendation approach based on embedded meta-path of spatiotemporal data . | EXPERT SYSTEMS , 2022 , 40 (2) . |
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Abstract ：
In order to improve the effect of path planning in emergencies, the missing position imputation and velocity restoration in vehicle trajectory provide data support for emergency path planning and analysis. At present, there are many methods to fill in the missing trajectory information, but they basically restore the missing trajectory after analyzing a large number of datasets. However, the trajectory reduction method with few training sets needs to be further explored. For this purpose, a novel trajectory data cube model (TDC) is designed to store time, position, and velocity information hierarchically in the trajectory data. Based on this model, three trajectory Hierarchical Trace-Back algorithms HTB-p, HTB-v, and HTB-KF are proposed in this paper. Finally, experiments verify that conduct in a different number of sample sets, it has a satisfactory performance on information restoration of individual points of the trajectory and information restoration of trajectory segments.
Keyword ：
transportation network transportation network traffic condition restoration traffic condition restoration trajectory data completion trajectory data completion spatio-temporal data mining spatio-temporal data mining
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| GB/T 7714 | Yang, Bowen , Liu, Zunhao , Cai, Zhi et al. A Novel Traffic Flow Reduction Method Based on Incomplete Vehicle History Spatio-Temporal Trajectory Data [J]. | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION , 2022 , 11 (3) . |
| MLA | Yang, Bowen et al. "A Novel Traffic Flow Reduction Method Based on Incomplete Vehicle History Spatio-Temporal Trajectory Data" . | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 11 . 3 (2022) . |
| APA | Yang, Bowen , Liu, Zunhao , Cai, Zhi , Li, Dongze , Su, Xing , Guo, Limin et al. A Novel Traffic Flow Reduction Method Based on Incomplete Vehicle History Spatio-Temporal Trajectory Data . | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION , 2022 , 11 (3) . |
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Abstract ：
In recent years, with the development of various types of public transportation, they are also more and more closely connected. Among them, subway transportation has become the first choice of major cities. However, the planning of subway stations is very difficult and there are many factors to consider. Besides, few methods for selecting optimal station locations take other public transport in to consideration. In order to study the relationship between different types of public transportation, the authors collected and analyzed the travel data of subway passengers and the passenger trajectory data of taxis. In this paper, a method based on LeaderRank and Gaussian Mixed Model (GMM) is proposed to conduct subway station locations selection. In this method, the author builds a subway-passenger traffic zone weighted network and a station location prediction model. First, we evaluate the nodes in the network, then use the GPS track data of taxis to predict the location of new stations in future subway construction, and analyze and discuss the land use characteristics in the prediction area. Taking the design of the Beijing subway line as an example, the suitability of this method is illustrated.
Keyword ：
station ranking station ranking subway site forecast subway site forecast urban transportation urban transportation spatio-temporal data spatio-temporal data
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| GB/T 7714 | Cai, Zhi , Wang, Jiawei , Li, Tong et al. A Novel Trajectory Based Prediction Method for Urban Subway Design [J]. | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION , 2022 , 11 (2) . |
| MLA | Cai, Zhi et al. "A Novel Trajectory Based Prediction Method for Urban Subway Design" . | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 11 . 2 (2022) . |
| APA | Cai, Zhi , Wang, Jiawei , Li, Tong , Yang, Bowen , Su, Xing , Guo, Limin et al. A Novel Trajectory Based Prediction Method for Urban Subway Design . | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION , 2022 , 11 (2) . |
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Abstract ：
Students have produced a large number of data in the teaching life of colleges and universities. At present, the development trend of university data is to gradually form a high-dimensional data storage system composed of student status information, educational administration information, behavior information, etc. It is of great significance to make use of the existing data of students in Colleges and universities to carry out deep-seated and personalized data mining for college education decision-making, implementation of education and teaching programs, and evaluation of education and teaching. Student portrait is the extension of user portrait in the application of education data mining. According to the data of students’ behavior in school, a labeled student model is abstracted. To address above problems, a hybrid neural network model is designed and implemented to mine the data of college students and build their portraits, so as to help students’ academic development and improve the quality of college teaching. In this paper, experiments are carried out on real datasets (the basic data of a college’s students in Beijing and the behavior data in the second half of 2018–2019 academic year). The results show that the hybrid neural network model is effective. © 2021, Springer Nature Switzerland AG.
Keyword ：
Neural networks Neural networks Decision making Decision making Digital storage Digital storage Education computing Education computing Students Students Clustering algorithms Clustering algorithms Data mining Data mining
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| GB/T 7714 | Ding, Zhiming , Li, Xuyang . College Students’ Portrait Technology Based on Hybrid Neural Network [C] . 2021 : 165-183 . |
| MLA | Ding, Zhiming et al. "College Students’ Portrait Technology Based on Hybrid Neural Network" . (2021) : 165-183 . |
| APA | Ding, Zhiming , Li, Xuyang . College Students’ Portrait Technology Based on Hybrid Neural Network . (2021) : 165-183 . |
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Abstract ：
How to detect and predict the critical situation in large-scale activities is a very important research issue. The existing researches of emergency prediction are mainly focus on the micro events in some specific fields. Applying existing results directly to predict the critical situation in large-scale activity is a big challenge. In this paper, we show a novel method to predict emergency based on historical data analysis. We integrate relevant research results into a unified spatiotemporal model. Firstly, constructing the historical spatiotemporal context time series based on historical activity data. Then, dividing the time series into time period and time window. Finally, exploiting the time series’ spatiotemporal patterns to predict the emergency of current activity. Experimental results show that the proposed method can achieve better prediction of large-scale activity emergencies in a specific venue. © 2021, Springer Nature Switzerland AG.
Keyword ：
Forecasting Forecasting Time series Time series
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| GB/T 7714 | Zhao, Zilin , Ding, Zhiming , Cao, Yang . A Method of Emergency Prediction Based on Spatiotemporal Context Time Series [C] . 2021 : 14-28 . |
| MLA | Zhao, Zilin et al. "A Method of Emergency Prediction Based on Spatiotemporal Context Time Series" . (2021) : 14-28 . |
| APA | Zhao, Zilin , Ding, Zhiming , Cao, Yang . A Method of Emergency Prediction Based on Spatiotemporal Context Time Series . (2021) : 14-28 . |
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Abstract ：
随着互联网和移动应用平台的快速发展,围绕移动应用所产生的海量用户数据已经成为精确分析用户需求偏好的重要数据源.尽管已有不少学者从这些数据中分析和挖掘用户需求,但现有的方法通常只研究了数据的少数维度的特征,未能有效地挖掘多维移动应用信息以及他们之间的关联.提出一种基于元路径嵌入的移动应用需求偏好分析方法,能够为用户进行个性化移动应用推荐.具体地,首先分析移动应用的文本信息中的语义主题,挖掘用户需求偏好的分析维度.其次,将移动应用信息的语义特征构建了一个融合移动应用多维信息的概念模型,涵盖了能够表征用户需求偏好的多维度数据.基于概念模型的语义,设计了一组有意义的元路径集合,以精确地捕捉用户需求偏好...
Keyword ：
用户需求偏好 用户需求偏好 概念模型 概念模型 嵌入学习 嵌入学习 移动应用 移动应用 元路径 元路径
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| GB/T 7714 | 宋蕊 , 李童 , 董鑫 et al. 基于元路径嵌入的移动应用需求偏好分析方法 [J]. | 计算机研究与发展 , 2021 , 58 (04) : 749-762 . |
| MLA | 宋蕊 et al. "基于元路径嵌入的移动应用需求偏好分析方法" . | 计算机研究与发展 58 . 04 (2021) : 749-762 . |
| APA | 宋蕊 , 李童 , 董鑫 , 丁治明 . 基于元路径嵌入的移动应用需求偏好分析方法 . | 计算机研究与发展 , 2021 , 58 (04) , 749-762 . |
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