Proceedings of the 28th International Conference on Advances in Geographic Information Systems最新文献_第9页

A Web-Based System for Efficient Contact Tracing Query in a Large Spatio-Temporal Database 基于web的大型时空数据库接触者追踪查询系统

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-11-03 DOI: 10.1145/3397536.3422350

Shadman Saqib Eusuf, Kazi Ashik Islam, Mohammed Eunus Ali, S. M. Abdullah, Abdus Salam Azad

{"title":"A Web-Based System for Efficient Contact Tracing Query in a Large Spatio-Temporal Database","authors":"Shadman Saqib Eusuf, Kazi Ashik Islam, Mohammed Eunus Ali, S. M. Abdullah, Abdus Salam Azad","doi":"10.1145/3397536.3422350","DOIUrl":"https://doi.org/10.1145/3397536.3422350","url":null,"abstract":"In this demonstration, we present a web based system for the novel contact tracing query (CTQ) that finds users who have come into direct contact with the query user or indirect contact via the already contacted users from a large spatio-temporal database. The CTQ is of paramount importance in the era of new COVID-19 pandemic world for identifying people who came into close spatial and temporal proximity with persons carrying an infectious disease. We demonstrate a multi-level index named QzR-tree, that considers the space coverage and the co-visiting patterns of the trajectories to group users who are likely to meet. More specifically, we use a quadtree to partition user movement traces along with a linear ordering and use the space-time mapping to group users with an R-tree. We develop a web-based demo system to show the effectiveness of the QzR-tree for the CTQ. The web-based system essentially uses a PostgreSQL database to store user trajectories, and indexes these trajectories using the QzR-tree, and finally uses a web interface to take user query and display the results in a map.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128118937","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: 3

Cycling-Net: A Deep Learning Approach to Predicting Cyclist Behaviors from Geo-Referenced Egocentric Video Data Cycling-Net:一种基于地理参考的以自我为中心的视频数据预测骑自行车者行为的深度学习方法

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-11-03 DOI: 10.1145/3397536.3422258

Yichen Ding, Xun Zhou, Han Bao, Yanhua Li, C. Hamann, Steven Spears, Zhuoning Yuan

{"title":"Cycling-Net: A Deep Learning Approach to Predicting Cyclist Behaviors from Geo-Referenced Egocentric Video Data","authors":"Yichen Ding, Xun Zhou, Han Bao, Yanhua Li, C. Hamann, Steven Spears, Zhuoning Yuan","doi":"10.1145/3397536.3422258","DOIUrl":"https://doi.org/10.1145/3397536.3422258","url":null,"abstract":"Cycling, as a green transportation mode, provides an environmentally friendly transportation choice for short-distance traveling. However, cyclists are also getting involved in fatal accidents more frequently in recent years. Thus, understanding and modeling their road behaviors is crucial in helping improving road safety laws and infrastructures. Traditionally, people understand road user behavior using either purely spatial trajectory data, or videos from fixed surveillance camera through tracking or predicting their paths. However, these data only cover limited areas and do not provide information from the cyclist's field of view. In this paper, we take advantage of geo-referenced egocentric video data collected from the handlebar cameras of cyclists to learn how to predict their behaviors. This approach is technically more challenging, because both the observer and objects in the scene might be moving, and there are strong temporal dependencies in both the behaviors of cyclists and the video scenes. We propose Cycling-Net, a novel deep learning model that tracks different types of objects in consecutive scenes and learns the relationship between the movement of these objects and the behavior of the cyclist. Experiment results on a naturalistic trip dataset show the Cycling-Net is effective in behavior prediction and outperforms a baseline model.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128605582","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: 4

Gain Without Pain: Enabling Fingerprinting-based Indoor Localization using Tracking Scanners 无痛获得:使用跟踪扫描仪实现基于指纹的室内定位

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-11-03 DOI: 10.1145/3397536.3422207

Hamada Rizk, H. Yamaguchi, M. Youssef, T. Higashino

{"title":"Gain Without Pain: Enabling Fingerprinting-based Indoor Localization using Tracking Scanners","authors":"Hamada Rizk, H. Yamaguchi, M. Youssef, T. Higashino","doi":"10.1145/3397536.3422207","DOIUrl":"https://doi.org/10.1145/3397536.3422207","url":null,"abstract":"Robust and accurate indoor localization has been the goal of several research efforts over the past decade. Towards achieving this goal, WiFi fingerprinting-based indoor localization systems have been proposed. However, fingerprinting involves significant effort; especially when done at high density; and needs to be repeated with any change in the deployment area. While a number of recent systems have been introduced to reduce the calibration effort, these still trade overhead with accuracy. In this paper, we present LiPhi: an accurate system for enabling fingerprinting-based indoor localization systems without the associated data collection overhead. This is achieved by leveraging the sensing capability of transportable laser range scanners (LRSs) to automatically label WiFi signal scans, which can subsequently be used to build (and maintain) localization models. As part of its design, LiPhi has modules to associate WiFi scans with the unlabeled traces obtained from as few as one LRS as well as provisions to train a robust deep learning model. Evaluation of LiPhi using Android phones in two realistic testbeds shows that it can match the performance of manual fingerprinting techniques under the same deployment conditions without the overhead associated with the traditional fingerprinting process. In addition, LiPhi improves upon the median localization accuracy obtained from crowdsourcing-based and fingerprinting-based systems by 181% and 297% respectively, when tested with data collected a few months later.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125972708","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: 24

Noise Prediction for Geocoding Queries using Word Geospatial Embedding and Bidirectional LSTM 基于词地理空间嵌入和双向LSTM的地理编码查询噪声预测

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-11-03 DOI: 10.1145/3397536.3422201

Tin Vu, Solluna Liu, Renzhong Wang, Kumarswamy Valegerepura

引用次数: 2

Estimation of Road Transverse Slope Using Crowd-Sourced Data from Smartphones 基于智能手机众包数据的道路横向坡度估算

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-11-03 DOI: 10.1145/3397536.3422239

Abhishek Gupta, Abhinav Khare, Haiming Jin, A. Sadek, Lu Su, C. Qiao

{"title":"Estimation of Road Transverse Slope Using Crowd-Sourced Data from Smartphones","authors":"Abhishek Gupta, Abhinav Khare, Haiming Jin, A. Sadek, Lu Su, C. Qiao","doi":"10.1145/3397536.3422239","DOIUrl":"https://doi.org/10.1145/3397536.3422239","url":null,"abstract":"Integration of information on road transverse geometric features such as cross slope and superelevation in digital maps can widen the scope of its applications, which is primarily navigation, by enabling driving safety and efficiency applications such as Advanced Driver Assistance Systems (ADAS). The huge scale and dynamic nature of road networks make sensing such road geometric features a challenging task. Traditional methods oftentimes suffer from high cost, limited scalability and update frequency, as well as poor sensing accuracy. To overcome these problems, we propose a cost-effective and scalable road transverse slope estimation framework using sensor data from smartphones. Based on error characteristics of smartphone sensors, we intelligently combine data from accelerometer, gyroscope and GPS to estimate road transverse slope profile of a road segment. To improve accuracy and robustness of the system, the estimations of road transverse slope from multiple sources/vehicles are crowd-sourced to compensate for the effects of varying quality of sensor data from different sources. Extensive experimental evaluation on a test route of 9km demonstrates the superior performance of our proposed method, achieving 350% improvement on road transverse slope estimation accuracy over existing methods, with 90% of errors below 0.5°.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114997564","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

QarSUMO: A Parallel, Congestion-optimized Traffic Simulator QarSUMO:一个并行的，拥堵优化的交通模拟器

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-10-07 DOI: 10.1145/3397536.3422274

Hao Chen, Ke Yang, S. Rizzo, Giovanna Vantini, Phillip Taylor, Xiaosong Ma, S. Chawla

{"title":"QarSUMO: A Parallel, Congestion-optimized Traffic Simulator","authors":"Hao Chen, Ke Yang, S. Rizzo, Giovanna Vantini, Phillip Taylor, Xiaosong Ma, S. Chawla","doi":"10.1145/3397536.3422274","DOIUrl":"https://doi.org/10.1145/3397536.3422274","url":null,"abstract":"Traffic simulators are important tools for tasks such as urban planning and transportation management. Microscopic simulators allow per-vehicle movement simulation, but require longer simulation time. The simulation overhead is exacerbated when there is traffic congestion and most vehicles move slowly. This in particular hurts the productivity of emerging urban computing studies based on reinforcement learning, where traffic simulations are heavily and repeatedly used for designing policies to optimize traffic related tasks. In this paper, we develop QarSUMO, a parallel, congestion-optimized version of the popular SUMO open-source traffic simulator. QarSUMO performs high-level parallelization on top of SUMO, to utilize powerful multi-core servers and enables future extension to multi-node parallel simulation if necessary. The proposed design, while partly sacrificing speedup, makes QarSUMO compatible with future SUMO improvements. We further contribute such an improvement by modifying the SUMO simulation engine for congestion scenarios where the update computation of consecutive and slow-moving vehicles can be simplified. We evaluate QarSUMO with both real-world and synthetic road network and traffic data, and examine its execution time as well as simulation accuracy relative to the original, sequential SUMO.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"176 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124341362","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: 6

1D and 2D Flow Routing on a Terrain 地形上的1D和2D流路由

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-09-17 DOI: 10.1145/3397536.3422269

Aaron Lowe, Svend C. Svendsen, P. Agarwal, L. Arge

{"title":"1D and 2D Flow Routing on a Terrain","authors":"Aaron Lowe, Svend C. Svendsen, P. Agarwal, L. Arge","doi":"10.1145/3397536.3422269","DOIUrl":"https://doi.org/10.1145/3397536.3422269","url":null,"abstract":"An important problem in terrain analysis is modeling how water flows across a terrain creating floods by forming channels and filling depressions. In this paper we study a number of flow-query related problems: given a terrain Σ represented as a triangulated xy-monotone surface with n vertices, and a rain distribution R which may vary over time, determine how much water is flowing over a given edge as a function of time. We develop internal-memory as well as I/O-efficient algorithms for flow queries. This paper contains four main results: (i) An internal-memory algorithm for answering terrain-flow queries: preprocess Σ into a linear-size data structure so that given a rain distribution R the flow-rate functions of all edges of Σ can be reported quickly. (ii) I/O-efficient algorithms for answering terrain-flow queries. (iii) An internal memory algorithm for answering edge-flow queries: preprocess Σ into a linear-size data structure so that given a rain distribution R, the flow-rate function of an edge under the single-flow direction (SFD) model can be computed quickly. (iv) We present an efficient algorithm that given a path in Σ computes the two-dimensional channel along which water flows.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126838603","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: 1

A Deep Learning Approach to Geographical Candidate Selection through Toponym Matching 一种基于地名匹配的地理候选物深度学习方法

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-09-17 DOI: 10.1145/3397536.3422236

Mariona Coll Ardanuy, Kasra Hosseini, Katherine McDonough, A. Krause, Daniel Alexander van Strien, F. Nanni

引用次数: 8

Learning Behavioral Representations of Human Mobility 学习人类移动性的行为表征

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-09-10 DOI: 10.1145/3397536.3422255

M. Damiani, A. Acquaviva, F. Hachem, M. Rossini

引用次数: 5

HAP-SAP: Semantic Annotation in LBSNs using Latent Spatio-Temporal Hawkes Process 基于潜在时空Hawkes过程的LBSNs语义标注

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-09-05 DOI: 10.1145/3397536.3422233

Manisha Dubey, P. K. Srijith, M. Desarkar

引用次数: 1