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Adaptive Spatio-Temporal Graph Learning for Bus Station Profiling 用于公交车站轮廓分析的自适应时空图学习
IF 1.9 Q4 REMOTE SENSING Pub Date : 2023-12-07 DOI: 10.1145/3636459
Mingliang Hou, Feng Xia, Xin Chen, V. Saikrishna, Honglong Chen
Understanding and managing public transportation systems require capturing complex spatio-temporal correlations within datasets. Existing studies often use predefined graphs in graph learning frameworks, neglecting shifted spatial and long-term temporal correlations, which are crucial in practical applications. To address these problems, we propose a novel bus station profiling framework to automatically infer the spatio-temporal correlations and capture the shifted spatial and long-term temporal correlations in the public transportation dataset. The proposed framework adopts and advances the graph learning structure through the following innovative ideas: (1) Designing an adaptive graph learning mechanism to capture the interactions between spatio-temporal correlations rather than relying on pre-defined graphs; (2) Modeling shifted correlation in shifted spatial graphs to learn fine-grained spatio-temporal features; (3) Employing self-attention mechanism to learn the long-term temporal correlations preserved in public transportation data. We conduct extensive experiments on three real-world datasets and exploit the learned profiles of stations for the station passenger flow prediction task. Experimental results demonstrate that the proposed framework outperforms all baselines under different settings and can produce meaningful bus station profiles.
理解和管理公共交通系统需要在数据集中捕捉复杂的时空相关性。现有的研究通常在图学习框架中使用预定义的图,而忽略了在实际应用中至关重要的位移空间和长期时间相关性。为了解决这些问题,我们提出了一个新的公交站点分析框架,以自动推断时空相关性,并捕获公共交通数据集中的空间和长期时间相关性。该框架采用并推进了图学习结构,创新思路如下:(1)设计了一种自适应图学习机制,以捕捉时空相关性之间的相互作用,而不是依赖于预定义的图;(2)对移位空间图的移位相关性进行建模,学习细粒度的时空特征;(3)利用自注意机制学习公共交通数据中保存的长期时间相关性。我们在三个真实世界的数据集上进行了广泛的实验,并利用学习到的车站概况进行车站客流预测任务。实验结果表明,该框架在不同设置下优于所有基线,可以生成有意义的公交车站轮廓。
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引用次数: 0
Forecasting Urban Sensory Values through Learning Attention-adjusted Graph Spatio-Temporal Networks 通过学习注意力调整图时空网络预测城市感官值
IF 1.9 Q4 REMOTE SENSING Pub Date : 2023-12-04 DOI: 10.1145/3635140
Yi-Ju Lu, Cheng-Te Li
Forecasting spatio-temporal correlated time series of sensor values is crucial in urban applications, such as air pollution alert, biking resource management, and intelligent transportation systems. While recent advances exploit graph neural networks (GNN) to better learn spatial and temporal dependencies between sensors, they cannot model time-evolving spatio-temporal correlation (STC) between sensors, and require pre-defined graphs, which are neither always available nor totally reliable, and target at only a specific type of sensor data at one time. Moreover, since the form of time-series fluctuation is varied across sensors, a model needs to learn fluctuation modulation. To tackle these issues, in this work, we propose a novel GNN-based model, Attention-adjusted Graph Spatio-Temporal Network (AGSTN). In AGSTN, multi-graph convolution with sequential learning is developed to learn time-evolving STC. Fluctuation modulation is realized by a proposed attention adjustment mechanism. Experiments on three sensor data, air quality, bike demand, and traffic flow, exhibit that AGSTN outperforms the state-of-the-art methods.
预测传感器值的时空相关时间序列在城市应用中至关重要,如空气污染预警、自行车资源管理和智能交通系统。虽然最近的研究进展利用图神经网络(GNN)来更好地学习传感器之间的时空依赖关系,但它们不能对传感器之间的时间演化时空相关性(STC)进行建模,并且需要预先定义的图,这些图既不总是可用的,也不是完全可靠的,并且一次只针对特定类型的传感器数据。此外,由于时间序列波动的形式在传感器之间是不同的,因此模型需要学习波动调制。为了解决这些问题,在这项工作中,我们提出了一种新的基于gnn的模型,即注意力调整图时空网络(AGSTN)。在AGSTN中,采用时序学习的多图卷积来学习随时间变化的STC。通过提出的注意力调节机制实现波动调制。对空气质量、自行车需求和交通流量这三个传感器数据的实验表明,AGSTN优于最先进的方法。
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引用次数: 0
Deformation Gated Recurrent Network for Lane-Level Abnormal Driving Behavior Recognition 用于车道级异常驾驶行为识别的变形门控递归网络
IF 1.9 Q4 REMOTE SENSING Pub Date : 2023-12-02 DOI: 10.1145/3635141
Guojiang Shen, Juntao Wang, Xiangjie Kong, Zhanhao Ji, Bing Zhu, Tie Qiu
As a significant part of traffic accident prevention, abnormal driving behavior recognition has been receiving extensive attention. However, the granularity of existing abnormal driving behavior recognition is mostly at road-level, and these methods’ high complexity leads to high overhead on training and recognition. In this article, we propose a deformation gated recurrent network for lane-level abnormal driving behavior recognition. Firstly, we use conditional random field model to calculate the lane change necessity of the vehicle, which helps us to distinguish whether the lane-changing behavior is reasonable. Secondly, we propose deformation gated recurrent network (DF-GRN) and trajectory entropy to capture the implicit relationship between trajectories and shorten recognition time. Finally, we get classified results including aggressive, distracted and normal driving behavior from the network. Distracted and aggressive behavior will be marked as anomaly. The effectiveness and real-time nature of the network are verified by experiments on Hangzhou and Chengdu location datasets.
异常驾驶行为识别作为交通事故预防的重要组成部分,一直受到人们的广泛关注。然而,现有异常驾驶行为识别的粒度多在道路层面,这些方法的高复杂度导致训练和识别开销较大。本文提出了一种用于车道级异常驾驶行为识别的变形门控递归网络。首先,利用条件随机场模型计算车辆变道必要性,从而判断车辆变道行为是否合理;其次,我们提出变形门控递归网络(DF-GRN)和轨迹熵来捕捉轨迹之间的隐式关系,缩短识别时间。最后,我们从网络中得到分类结果,包括攻击性、分心和正常驾驶行为。分心和攻击性行为会被标记为异常。通过在杭州和成都定位数据集上的实验,验证了该网络的有效性和实时性。
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引用次数: 0
HTF: Homogeneous Tree Framework for Differentially-Private Release of Large Geospatial Datasets with Self-Tuning Structure Height. 基于结构高度自调优的大型地理空间数据集差分私有发布的同构树框架
IF 1.9 Q4 REMOTE SENSING Pub Date : 2023-12-01 Epub Date: 2023-11-20 DOI: 10.1145/3569087
Sina Shaham, Gabriel Ghinita, Ritesh Ahuja, John Krumm, Cyrus Shahabi

Mobile apps that use location data are pervasive, spanning domains such as transportation, urban planning and healthcare. Important use cases for location data rely on statistical queries, e.g., identifying hotspots where users work and travel. Such queries can be answered efficiently by building histograms. However, precise histograms can expose sensitive details about individual users. Differential privacy (DP) is a mature and widely-adopted protection model, but most approaches for DP-compliant histograms work in a data-independent fashion, leading to poor accuracy. The few proposed data-dependent techniques attempt to adjust histogram partitions based on dataset characteristics, but they do not perform well due to the addition of noise required to achieve DP. In addition, they use ad-hoc criteria to decide the depth of the partitioning. We identify density homogeneity as a main factor driving the accuracy of DP-compliant histograms, and we build a data structure that splits the space such that data density is homogeneous within each resulting partition. We propose a self-tuning approach to decide the depth of the partitioning structure that optimizes the use of privacy budget. Furthermore, we provide an optimization that scales the proposed split approach to large datasets while maintaining accuracy. We show through extensive experiments on large-scale real-world data that the proposed approach achieves superior accuracy compared to existing approaches.

使用位置数据的移动应用程序无处不在,涵盖交通、城市规划和医疗保健等领域。位置数据的重要用例依赖于统计查询,例如,识别用户工作和旅行的热点。这样的查询可以通过构建直方图来有效地回答。然而,精确的直方图可以暴露个人用户的敏感细节。差分隐私(DP)是一种成熟且广泛采用的保护模型,但大多数符合DP的直方图方法都是以数据独立的方式工作的,导致准确性较差。少数提出的依赖于数据的技术试图根据数据集特征调整直方图分区,但由于增加了实现DP所需的噪声,它们的性能不佳。此外,他们使用特别的标准来决定分区的深度。我们将密度均匀性确定为驱动符合DP的直方图准确性的主要因素,并构建了一个数据结构来分割空间,使数据密度在每个生成的分区内均匀。我们提出了一种自调整方法来决定分区结构的深度,从而优化隐私预算的使用。此外,我们提供了一种优化,在保持准确性的同时,将所提出的分割方法扩展到大型数据集。我们通过对大规模真实世界数据的大量实验表明,与现有方法相比,所提出的方法实现了更高的精度。
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引用次数: 0
STICAP: Spatio-Temporal Interactive Attention for Citywide Crowd Activity Prediction STICAP:用于全城人群活动预测的时空互动注意力
IF 1.9 Q4 REMOTE SENSING Pub Date : 2023-12-01 DOI: 10.1145/3603375
Huiqun Huang, Suining He, Xi Yang, Mahan Tabatabaie
Accurate citywide crowd activity prediction (CAP) can enable proactive crowd mobility management and timely responses to urban events, which has become increasingly important for a myriad of smart city planning and management purposes. However, complex correlations across the crowd activities, spatial and temporal urban environment features and their interactive dependencies, and relevant external factors (e.g., weather conditions) make it highly challenging to predict crowd activities accurately in terms of different venue categories (for instance, venues related to dining, services, and residence) and varying degrees (e.g., daytime and nighttime). To address the above concerns, we propose STICAP, a citywide spatio-temporal interactive crowd activity prediction approach. In particular, STICAP takes in the location-based social network check-in data (e.g., from Foursquare/Gowalla) as the model inputs, and forecasts the crowd activity within each time step for each venue category. Furthermore, we have integrated multiple levels of temporal discretization to interactively capture the relations with historical data. Then three parallel Residual Spatial Attention Networks (RSAN) in the Spatial Attention Component exploit the hourly, daily, and weekly spatial features of crowd activities, which are further fused and processed by the Temporal Attention Component for interactive CAP. Along with other external factors such as weather conditions and holidays, STICAP adaptively and accurately forecasts the final crowd activities per venue category, enabling potential activity recommendation and other smart city applications. Extensive experimental studies based on three different real-word crowd activity datasets have demonstrated that our proposed STICAP outperforms the baseline and state-of-the-art algorithms in CAP accuracy, with an average error reduction of 35.02%
准确的全市人群活动预测(CAP)可以实现前瞻性的人群流动管理和及时响应城市事件,这对于无数智慧城市规划和管理目的变得越来越重要。然而,人群活动之间的复杂相关性、城市环境的时空特征及其相互依赖关系,以及相关的外部因素(如天气条件),使得在不同的场地类别(如与餐饮、服务和居住相关的场地)和不同程度(如白天和夜间)上准确预测人群活动具有很高的挑战性。为了解决上述问题,我们提出了一种城市范围的时空互动人群活动预测方法——STICAP。特别是,STICAP将基于位置的社交网络签到数据(例如,来自Foursquare/Gowalla)作为模型输入,并预测每个场地类别在每个时间步内的人群活动。此外,我们还集成了多层次的时间离散化,以交互式地捕捉与历史数据的关系。然后,空间注意分量中的三个平行剩余空间注意网络(RSAN)利用人群活动的小时、日、周空间特征,再通过时间注意分量进行融合和处理,形成交互式CAP。STICAP与天气条件、节假日等外部因素一起,自适应准确预测每个场地类别的最终人群活动。支持潜在的活动推荐和其他智慧城市应用。基于三种不同的真实世界人群活动数据集的广泛实验研究表明,我们提出的STICAP在CAP精度方面优于基线和最先进的算法,平均误差降低35.02%
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引用次数: 0
Editorial: Special Issue on the Best Papers from the 2021 ACM SIGSPATIAL Conference 编辑:2021 年 ACM SIGSPATIAL 会议最佳论文特刊
IF 1.9 Q4 REMOTE SENSING Pub Date : 2023-11-30 DOI: 10.1145/3632619
W. Aref
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引用次数: 0
Adaptive Joint Spatio-Temporal Graph Learning Network for Traffic Data Forecasting 用于交通数据预测的自适应时空联合图学习网络
IF 1.9 Q4 REMOTE SENSING Pub Date : 2023-11-28 DOI: 10.1145/3634913
Tianyi Wang, Shu-Ching Chen
Traffic data forecasting has become an integral part of the intelligent traffic system. Great efforts are spent developing tools and techniques to estimate traffic flow patterns. Many existing approaches lack the ability to model the complex and dynamic spatio-temporal relations in the traffic data, which are crucial in capturing the traffic dynamic. In this work, we propose a novel adaptive joint spatio-temporal graph learning network (AJSTGL) for traffic data forecasting. The proposed model utilizes static and adaptive graph learning modules to capture the static and dynamic spatial traffic patterns and optimize the graph learning process. A sequence-to-sequence fusion model is proposed to learn the temporal correlation and combine the output of multiple parallelized encoders. We also develop a spatio-temporal graph transformer module to complement the sequence-to-sequence fusion module by dynamically capturing the time-evolving node relations in long-term intervals. Experiments on three large-scale traffic flow datasets demonstrate that our model could outperform other state-of-the-art baseline methods.
交通数据预测已成为智能交通系统不可或缺的一部分。人们花费了大量精力来开发估算交通流模式的工具和技术。许多现有方法缺乏对交通数据中复杂动态的时空关系建模的能力,而时空关系对于捕捉交通动态至关重要。在这项工作中,我们提出了一种用于交通数据预测的新型自适应时空联合图学习网络(AJSTGL)。该模型利用静态和自适应图学习模块来捕捉静态和动态空间交通模式,并优化图学习过程。我们提出了一个序列到序列融合模型来学习时间相关性,并结合多个并行编码器的输出。我们还开发了一个时空图转换器模块,通过动态捕捉长期时间间隔内不断变化的节点关系来补充序列到序列融合模块。在三个大规模交通流数据集上的实验证明,我们的模型优于其他最先进的基线方法。
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引用次数: 0
Exploiting Network Structure in Multi-criteria Distributed and Competitive Stationary-resource Searching 在多标准分布式竞争性固定资源搜索中利用网络结构
IF 1.9 Q4 REMOTE SENSING Pub Date : 2023-11-20 DOI: 10.1145/3569937
Fandel Lin, Hsun-Ping Hsieh
Transportation between satellite cities or inside the city center has always been a crucial factor in contributing to a better quality of life. This article focuses on multi-criteria distributed and competitive route planning for stationary resources in regions where neither real-time nor historical availability of the targeted resource is accessible. We propose an inference-than-planning approach, with an availability inference for stationary resources in areas with no sensor coverage and a distributed routing where no information is shared among agents. We leverage the inferred availability and network structure in the searching space to suggest a two-stage algorithm with three relaxing policies: adjacent cruising, on-orbital annealing, and orbital transitioning. We take two publicly accessible parking-slot datasets from San Francisco and Melbourne for evaluation. Overall results show that the proposed availability inference model can retain decent performance. Furthermore, our proposed routing algorithm maintains the quality of solutions by achieving the Pareto-optimal between searching experience and resource utilization among baseline and state-of-the-art methods under various circumstances.
卫星城市之间或城市中心内部的交通一直是提高生活质量的关键因素。本文的重点是在目标资源的实时可用性和历史可用性都无法获取的地区,对固定资源进行多标准分布式竞争路线规划。我们提出了一种 "推断而非规划 "的方法,即在没有传感器覆盖的地区对固定资源进行可用性推断,并在代理之间不共享信息的情况下进行分布式路由规划。我们利用推断出的可用性和搜索空间中的网络结构,提出了一种两阶段算法,其中包含三种放松策略:邻近巡航、轨道退火和轨道转换。我们利用旧金山和墨尔本的两个可公开访问的停车位数据集进行了评估。总体结果表明,所提出的可用性推理模型可以保持不错的性能。此外,在各种情况下,我们提出的路由算法在搜索体验和资源利用率之间达到了基准方法和最先进方法的帕累托最优,从而保持了解决方案的质量。
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引用次数: 0
VxH: A systematic determination of efficient hierarchical voxel structures VxH:系统地确定有效的分层体素结构
Q4 REMOTE SENSING Pub Date : 2023-11-09 DOI: 10.1145/3632404
Mouad Rifai, Lennart Johnsson
3D maps with many millions to billions of points are now used in an increasing number of applications, with processing rates in the hundreds of thousands to millions of points per second. In mobile applications, power and energy consumption for managing such data and extracting useful information thereof are critical concerns. We have developed structures and methodologies with the purpose of minimizing memory usage and associated energy consumption for indexing and serialization of voxelized point-clouds. The primary source of points in our case is airborne laser scanning, but our methodology is not restricted to only such setting. Our emulated results show a memory usage reduction factor of roughly up to 200 × that of Octree/Octomap, and a file size reduction factor of up to 1.65 × compared the predominating compression scheme for airborne Lidar data, LASzip. In addition, our structures enable significantly more efficient processing since they are included in a hierarchical structure that captures geometric aspects.
拥有数百万到数十亿个点的3D地图现在被越来越多的应用程序所使用,其处理速度在每秒数十万到数百万个点。在移动应用程序中,管理此类数据并从中提取有用信息的功率和能源消耗是关键问题。我们已经开发了结构和方法,目的是最大限度地减少内存使用和相关的能量消耗,用于体素化点云的索引和序列化。在我们的情况下,点的主要来源是机载激光扫描,但我们的方法并不仅限于这样的设置。我们的仿真结果表明,与主要的机载激光雷达数据压缩方案LASzip相比,该压缩方案的内存使用减少系数大约是Octree/Octomap压缩方案的200倍,文件大小减少系数高达1.65倍。此外,我们的结构可以显著提高处理效率,因为它们包含在捕获几何方面的层次结构中。
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引用次数: 1
Spatio-Temporal Dual Graph Neural Networks for Travel Time Estimation 时空对偶图神经网络的旅行时间估计
Q4 REMOTE SENSING Pub Date : 2023-10-28 DOI: 10.1145/3627819
Guangyin Jin, Huan Yan, Fuxian Li, Jincai Huang, Yong Li
Travel time estimation is one of the core tasks for the development of intelligent transportation systems. Most previous works model the road segments or intersections separately by learning their spatio-temporal characteristics to estimate travel time. However, due to the continuous alternations of the road segments and intersections in a path, the dynamic features are supposed to be coupled and interactive. Therefore, modeling one of them limits further improvement in accuracy of estimating travel time. To address the above problems, a novel graph-based deep learning framework for travel time estimation is proposed in this paper, namely Spatio-Temporal Dual Graph Neural Networks (STDGNN). Specifically, we first establish the node-wise and edge-wise graphs to respectively characterize the adjacency relations of intersections and that of road segments. In order to extract the joint spatio-temporal correlations of the intersections and road segments, we adopt the spatio-temporal dual graph learning approach that incorporates multiple spatial-temporal dual graph learning modules with multi-scale network architectures for capturing multi-level spatial-temporal information from the dual graph. Finally, we employ the multi-task learning approach to estimate the travel time of a given whole route, each road segment and intersection simultaneously. We conduct extensive experiments to evaluate our proposed model on three real-world trajectory datasets, and the experimental results show that STDGNN significantly outperforms several state-of-art baselines.
行车时间估计是智能交通系统发展的核心问题之一。以往的研究大多是通过学习路段或交叉路口的时空特征,分别对其进行建模来估计行车时间。然而,由于道路上的路段和交叉口的不断变化,动态特征必须是耦合和交互的。因此,对其中一个模型的建模限制了进一步提高估计旅行时间的精度。为了解决上述问题,本文提出了一种基于图的深度学习框架,即时空对偶图神经网络(STDGNN)。具体来说,我们首先建立节点和边缘图,分别表征交叉口和路段的邻接关系。为了提取交叉口和路段的联合时空相关性,我们采用了时空对偶图学习方法,该方法将多个时空对偶图学习模块与多尺度网络架构相结合,从对偶图中获取多层次时空信息。最后,我们采用多任务学习方法同时估计给定整条路线、每个路段和交叉口的行驶时间。我们在三个真实世界的轨迹数据集上进行了大量的实验来评估我们提出的模型,实验结果表明STDGNN显著优于几种最先进的基线。
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引用次数: 7
期刊
ACM Transactions on Spatial Algorithms and Systems
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