Pedestrian network generation based on crowdsourced tracking data     

Xue Yang  Chang Ren  Yang Chen  Zhong Xie  Qingquan Li 《International journal of geographical information science》2020,34(5):1051-1074

ABSTRACT

Pedestrian networks play an important role in various applications, such as pedestrian navigation services and mobility modeling. This paper presents a novel method to extract pedestrian networks from crowdsourced tracking data based on a two-layer framework. This framework includes a walking pattern classification layer and a pedestrian network generation layer. In the first layer, we propose a multi-scale fractal dimension (MFD) algorithm in order to recognize the two different types of walking patterns: walking with a clear destination (WCD) or walking without a clear destination (WOCD). In the second layer, we generate the pedestrian network by combining the pedestrian regions and pedestrian paths. The pedestrian regions are extracted based on a modified connected component analysis (CCA) algorithm from the WOCD traces. We generate the pedestrian paths using a kernel density estimation (KDE)-based point clustering algorithm from the WCD traces. The pedestrian network generation results using two actual crowdsourced datasets show that the proposed method has good performance in both geometrical correctness and topological correctness.  相似文献   

A hybrid integrated deep learning model for the prediction of citywide spatio-temporal flow volumes     

Yibin Ren  Huanfa Chen  Tao Cheng  Yang Zhang  Ge Chen 《International journal of geographical information science》2020,34(4):802-823

ABSTRACT

The spatio-temporal residual network (ST-ResNet) leverages the power of deep learning (DL) for predicting the volume of citywide spatio-temporal flows. However, this model, neglects the dynamic dependency of the input flows in the temporal dimension, which affects what spatio-temporal features may be captured in the result. This study introduces a long short-term memory (LSTM) neural network into the ST-ResNet to form a hybrid integrated-DL model to predict the volumes of citywide spatio-temporal flows (called HIDLST). The new model can dynamically learn the temporal dependency among flows via the feedback connection in the LSTM to improve accurate captures of spatio-temporal features in the flows. We test the HIDLST model by predicting the volumes of citywide taxi flows in Beijing, China. We tune the hyperparameters of the HIDLST model to optimize the prediction accuracy. A comparative study shows that the proposed model consistently outperforms ST-ResNet and several other typical DL-based models on prediction accuracy. Furthermore, we discuss the distribution of prediction errors and the contributions of the different spatio-temporal patterns.  相似文献   

Detecting regional dominant movement patterns in trajectory data with a convolutional neural network     

Can Yang  Győző Gidófalvi 《International journal of geographical information science》2020,34(5):996-1021

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MARC: a robust method for multiple-aspect trajectory classification via space,time, and semantic embeddings     

Lucas May Petry  Camila Leite Da Silva  Andrea Esuli  Chiara Renso  Vania Bogorny 《International journal of geographical information science》2020,34(7):1428-1450

ABSTRACT

The increasing popularity of Location-Based Social Networks (LBSNs) and the semantic enrichment of mobility data in several contexts in the last years has led to the generation of large volumes of trajectory data. In contrast to GPS-based trajectories, LBSN and context-aware trajectories are more complex data, having several semantic textual dimensions besides space and time, which may reveal interesting mobility patterns. For instance, people may visit different places or perform different activities depending on the weather conditions. These new semantically rich data, known as multiple-aspect trajectories, pose new challenges in trajectory classification, which is the problem that we address in this paper. Existing methods for trajectory classification cannot deal with the complexity of heterogeneous data dimensions or the sequential aspect that characterizes movement. In this paper we propose MARC, an approach based on attribute embedding and Recurrent Neural Networks (RNNs) for classifying multiple-aspect trajectories, that tackles all trajectory properties: space, time, semantics, and sequence. We highlight that MARC exhibits good performance especially when trajectories are described by several textual/categorical attributes. Experiments performed over four publicly available datasets considering the Trajectory-User Linking (TUL) problem show that MARC outperformed all competitors, with respect to accuracy, precision, recall, and F1-score.  相似文献   

流塑淤泥地层深基坑钢板桩围堰位移突变原因分析及处理          下载免费PDF全文

杨勇  詹元林 《探矿工程》2020,47(3):69-74

广东省珠海市洪鹤大桥主墩承台位于珠江西江流域的流塑状淤泥地层，采用钢板桩围堰进行基坑支护，基坑开挖过程中，钢板桩围堰发生较大的变形。经详细分析，发现导致事故的主要原因有地下水位持续升高导致土体力学性能显著下降、边跨侧钢板桩长度不足、基坑边缘集中荷载过大、施工控制不严、内支撑体系施工精度不足等。为了确保深基坑支护的安全，在全面分析总结了钢板桩围堰变形原因的基础上，结合实际情况，采取了增设穿透淤泥质土层的钢管桩围堰、加强内支撑体系等加固处理措施，并在实施过程中进行持续监测，最终安全地完成了基坑工程的施工。  相似文献   

水源钻机在超深土层绳索取心钻进中的应用          下载免费PDF全文

杨联锋  段云星 《探矿工程》2020,47(11):44-50

山西省清徐县区域地质调查项目设计800、2000、3000 m科学钻探孔，以调查填补新生界底板埋深控制空白区，各孔钻入基岩30 m完钻。要求全孔取心，岩心采取率≮85%，岩心直径≮60 mm，采用塑料保护管采取原状岩样。针对超深软土层、各组地层特性及厚度未知、钻遇基岩完钻深度未知、大直径高保真全孔取心、项目价格远低于目前市场成本等难题，经过“水源钻机+大提钻取心+长裸眼孔段”实施800 m孔、“岩心钻机+绳索取心+套管固井”实施640 m参数对比孔，创新性使用“水源钻机+绳索取心+长裸眼孔段”工艺完成了2000 m孔的施工。该工艺岩心采取率达到93%，孔径和孔斜符合地质要求，为3000 m孔顺利施工打下了坚实的基础，为同类型项目提供了经验和借鉴。  相似文献   

基于表层岩土渗透对三峡井网井水位降雨影响的精准分析          下载免费PDF全文

周洋  吴艳霞  罗棋  李查玮 《地震工程学报》2020,42(2):460-467,528

为研究三峡井网表层岩土渗透对井水位降雨的影响,采取井区表层岩土垂向渗透性测试方法试验,测得表层岩土垂向渗透性,并建立数学模型,用于降雨渗入补给分析。在此模型基础上,通过三峡井网8口井水位、气象三要素的对比观测资料对井水位日动态、月动态、年动态的影响进行精准分析与验证。结果表明:这种影响的特征是相当复杂的,同一个降雨过程在不同井上产生的影响特征不同,这一方面可能与各井的水文地质条件不同有关,另一方面可能还与各井点的降雨过程的差异也有关。  相似文献   

Evaluating techniques for mapping island vegetation from unmanned aerial vehicle (UAV) images: Pixel classification,visual interpretation and machine learning approaches     

《International Journal of Applied Earth Observation and Geoinformation》2020

We evaluate three approaches to mapping vegetation using images collected by an unmanned aerial vehicle (UAV) to monitor rehabilitation activities in the Five Islands Nature Reserve, Wollongong (Australia). Between April 2017 and July 2018, four aerial surveys of Big Island were undertaken to map changes to island vegetation following helicopter herbicide sprays to eradicate weeds, including the creeper Coastal Morning Glory (Ipomoea cairica) and Kikuyu Grass (Cenchrus clandestinus). The spraying was followed by a large scale planting campaign to introduce native plants, such as tussocks of Spiny-headed Mat-rush (Lomandra longifolia). Three approaches to mapping vegetation were evaluated, including: (i) a pixel-based image classification algorithm applied to the composite spectral wavebands of the images collected, (ii) manual digitisation of vegetation directly from images based on visual interpretation, and (iii) the application of a machine learning algorithm, LeNet, based on a deep learning convolutional neural network (CNN) for detecting planted Lomandra tussocks. The uncertainty of each approach was assessed via comparison against an independently collected field dataset. Each of the vegetation mapping approaches had a comparable accuracy; for a selected weed management and planting area, the overall accuracies were 82 %, 91 % and 85 % respectively for the pixel based image classification, the visual interpretation / digitisation and the CNN machine learning algorithm. At the scale of the whole island, statistically significant differences in the performance of the three approaches to mapping Lomandra plants were detected via ANOVA. The manual digitisation took a longer time to perform than others. The three approaches resulted in markedly different vegetation maps characterised by different digital data formats, which offered fundamentally different types of information on vegetation character. We draw attention to the need to consider how different digital map products will be used for vegetation management (e.g. monitoring the health individual species or a broader profile of the community). Where individual plants are to be monitored over time, a feature-based approach that represents plants as vector points is appropriate. The CNN approach emerged as a promising technique in this regard as it leveraged spatial information from the UAV images within the architecture of the learning framework by enforcing a local connectivity pattern between neurons of adjacent layers to incorporate the spatial relationships between features that comprised the shape of the Lomandra tussocks detected.  相似文献   

路网约束条件下热力规则设计与试验     

杜晓  周玉秀  周琦  郑义  张俊辉  林尚纬  陈利军 《测绘通报》2020,(7):18-21

热力图是一种能直观准确展示空间观测值的有效工具，在多个领域具有广泛应用。本文在分析设定不同道路层权重、道路技术等级分类的基础上，以反距离权重、直方图均衡化、密度补偿、参数多次迭代等方法，研究构建了以热力方式展示道路网分布及发达程度的相关规则体系。热力规则通过道路赋权、路线曲面化、密度协调、图面综合等多套规则的有机结合，实现了道路网总体发达水平信息的提取与展示，并以全球地理信息资源建设项目路网成果中的亚洲和非洲部分国家数据为例，进行了信息提取与热力分布试验。  相似文献   

北斗一张网业务管理与服务系统设计与实现     

唐晓霏  车一鸣  孙小超  魏以宽  赵鹏 《测绘通报》2020,(1):142-145

针对北斗一张网体系建设与运营项目的实际需求与现状，整合已有基础设施资源，深入研究挖掘了潜在的信息资源，搭建了一个标准化管理与服务应用统一平台——业务管理与服务系统，提高了系统的移动管理能力、系统管理水平及系统服务效率，改变了传统CORS的管理与运维模式，同时也为海量用户提供了更高效便捷的个性化服务，真正从应用层面上实现了规范化、体系化与便捷化。在这一系统的支撑下，北斗一张网服务体系确立了全新的管理机制，也为北斗未来更广泛地推广应用打下坚实基础。  相似文献