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A novel algorithm to identifying vehicle travel path in elevated road area based on GPS trajectory data

Frontiers of Earth Science 2012年第4期6卷 354-363页

作者： Xianrui XU Xiaojie LI Yujie HU Zhongren PENG School of Transportation Engineering Tongji University Shanghai 201804 China ZTE Corporation Nanji-ng 210012 China Department of Geography and Anthropology Louisiana State University Baton Rouge LA 70803 USA Department of Urban and Regional Planning University of Florida Gainesville FL 32611 USA

In recent years, the increasing development of traffic information collection technology based on floating car data has been recognized, which gives rise to the establishment of real-time traffic information dissemina- tion system in many cities. However, the recent massive construction of urban elevated roads hinders the processing of corresponding GPS data and further extraction of traffic information （e.g., identifying the real travel path）, as a result of the frequent transfer of vehicles between ground and elevated road travel. Consequently, an algorithm for identifying the travel road type （i.e., elevated or ground road） of vehicles is designed based on the vehicle traveling features, geometric and topological characteristics of the elevated road network, and a trajectory model proposed in the present study. To be specific, the proposed algorithm can detect the places where a vehicle enters, leaves or crosses under elevated roads. An experiment of 10 sample taxis in Shanghai, China was conducted, and the comparison of our results and results that are obtained from visual interpretation validates the proposed algo- rithm.

关键词： GPS trajectory trajectory segmentation road road vehicle status identification network modeling elevated

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Optimizing segmented trajectory data storage with HBase for improved spatio-temporal query efficiency

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International Journal of Digital Earth 2023年第1期16卷 1124-1143页

作者： Yi Bao Zhou Huang Xuri Gong Yuyang Zhang Ganmin Yin Han Wang Institute of Remote Sensing and Geographical Information Systems Peking UniversityBeiingPeople's Republic of China Beijing Key Lab of Spatial Information Integration&Its Applications Peking UniversityBejjingPeople's Republic of China

The surging accumulation of trajectory data has yielded invaluable insights into urban systems,but it has also presented challenges for data storage and management *** response,specialized storage systems based on non-relational databases have been developed to support large data quantities in distributed ***,these systems often utilize storage by point or storage by trajectory methods,both of which have *** this study,we evaluate the effectiveness of segmented trajectory data storage with HBase optimizations for spatio-temporal *** develop a prototype system that includes trajectory segmentation,serialization,and spatio-temporal indexing and apply it to taxi trajectory data in *** indicate that the segmented system provides enhanced query speed and reduced memory usage compared to the Geomesa system.

关键词： trajectory storage HBase trajectory segmentation spatio-temporal query

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DR-XGBoost: An XGBoost model for field-road segmentation based on dual feature extraction and recursive feature elimination

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International Journal of Agricultural and Biological Engineering 2023年第3期16卷 169-179页

作者： Yuzhen Xiao Guozhao Mo Xiya Xiong Jiawen Pan Bingbing Hu Caicong Wu Weixin Zhai College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China Key Laboratory of Agricultural Machinery Monitoring and Big Data Application Ministry of Agriculture and Rural AffairsBeijing 100083China Kunlun Beidou Intelligence Technologies Co. Ltd.Beijing 102200China

Field-road segmentation is one of the key tasks in the processing of the trajectory of agricultural *** improve the accuracy of the field-road segmentation,this study proposed an XGBoost model based on dual feature extraction and recursive feature elimination called ***-XGBoost takes only a small amount of agricultural machine trajectory features as ***,the model adopted the dual feature extraction method we designed to rapidly expand the number of features and then adequately extract local trajectory features by the time window and feature extraction ***,the model applies the recursive feature elimination algorithm to eliminate redundant features from the perspective of the model segmentation effect and thus reduce the computational consumption of model ***,it trains XGBoost to complete the trajectory *** evaluate the effectiveness of DR-XGBoost,we conducted a series of experiments on a real trajectory dataset of agricultural *** model achieves a 98.2%Macro-F1 score on the dataset,which is 10.9%higher than the previous *** proposal of DR-XGBoost fills the knowledge gap of trajectory feature extraction for agricultural machinery and provides a reasonable and effective feature selection scheme for the field-road segmentation problem.

关键词： trajectory segmentation feature extraction recursive feature elimination time window XGBoost

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An Improvement of the trajectory Segmented DDPC:Utilizing LQ Decomposition

An Improvement of the Trajectory Segmented DDPC:Utilizing LQ...

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第43届中国控制会议

作者： Jingqian Yan Malika Sader Yucheng Li Zhongxin Liu College of Artificial Intelligence Nankai University Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Nankai University Department of Automation Tsinghua University

Behavioral systems theory has inspired the emergence of the data-driven predictive control methods(DDPC).These DDPC methods are influenced by non-causal terms,which are primarily responsible for the poor performance of systems in noisy *** the existing trajectory segmentation DDPC methods enforce causality,they increase the dimension of decision variables,resulting in computational *** address this problem,an improvement of trajectory segmentation DDPC utilizing LQ decomposition is proposed in this ***,the equivalence between the proposed DDPC and the existing DDPC is demonstrated in a noise-free *** proposed method decreases computational complexity and improves noise robustness by reducing decision variable dimensions and matching initial conditions *** in noisy conditions,the proposed DDPC method effectively solves problems without needing extra ***,the efficacy of the proposed method is shown through a numerical case study by varying parameters like noise level.

关键词： Data-driven predictive control trajectory segmentation LQ decomposition

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