ACM Transactions on Spatial Algorithms and Systems最新文献

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

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2023-12-07 DOI: 10.1145/3636459

Mingliang Hou, Feng Xia, Xin Chen, V. Saikrishna, Honglong Chen

{"title":"Adaptive Spatio-Temporal Graph Learning for Bus Station Profiling","authors":"Mingliang Hou, Feng Xia, Xin Chen, V. Saikrishna, Honglong Chen","doi":"10.1145/3636459","DOIUrl":"https://doi.org/10.1145/3636459","url":null,"abstract":"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.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":"50 3","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138591142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2023-12-04 DOI: 10.1145/3635140

Yi-Ju Lu, Cheng-Te Li

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

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2023-12-02 DOI: 10.1145/3635141

Guojiang Shen, Juntao Wang, Xiangjie Kong, Zhanhao Ji, Bing Zhu, Tie Qiu

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

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2023-12-01 Epub Date: 2023-11-20 DOI: 10.1145/3569087

Sina Shaham, Gabriel Ghinita, Ritesh Ahuja, John Krumm, Cyrus Shahabi

{"title":"HTF: Homogeneous Tree Framework for Differentially-Private Release of Large Geospatial Datasets with Self-Tuning Structure Height.","authors":"Sina Shaham, Gabriel Ghinita, Ritesh Ahuja, John Krumm, Cyrus Shahabi","doi":"10.1145/3569087","DOIUrl":"10.1145/3569087","url":null,"abstract":"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.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":"1 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10881200/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44219469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2023-12-01 DOI: 10.1145/3603375

Huiqun Huang, Suining He, Xi Yang, Mahan Tabatabaie

{"title":"STICAP: Spatio-Temporal Interactive Attention for Citywide Crowd Activity Prediction","authors":"Huiqun Huang, Suining He, Xi Yang, Mahan Tabatabaie","doi":"10.1145/3603375","DOIUrl":"https://doi.org/10.1145/3603375","url":null,"abstract":"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%","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":" 11","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138621022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2023-11-30 DOI: 10.1145/3632619

W. Aref

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

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2023-11-28 DOI: 10.1145/3634913

Tianyi Wang, Shu-Ching Chen

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

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2023-11-20 DOI: 10.1145/3569937

Fandel Lin, Hsun-Ping Hsieh

{"title":"Exploiting Network Structure in Multi-criteria Distributed and Competitive Stationary-resource Searching","authors":"Fandel Lin, Hsun-Ping Hsieh","doi":"10.1145/3569937","DOIUrl":"https://doi.org/10.1145/3569937","url":null,"abstract":"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.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":"127 1","pages":"1 - 33"},"PeriodicalIF":1.9,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139258484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2023-11-09 DOI: 10.1145/3632404

Mouad Rifai, Lennart Johnsson

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

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2023-10-28 DOI: 10.1145/3627819

Guangyin Jin, Huan Yan, Fuxian Li, Jincai Huang, Yong Li

{"title":"Spatio-Temporal Dual Graph Neural Networks for Travel Time Estimation","authors":"Guangyin Jin, Huan Yan, Fuxian Li, Jincai Huang, Yong Li","doi":"10.1145/3627819","DOIUrl":"https://doi.org/10.1145/3627819","url":null,"abstract":"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.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":"21 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136158626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7