Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems最新文献_第3页

Urban Travel Time Prediction using a Small Number of GPS Floating Cars 基于少量GPS浮动车的城市出行时间预测

Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2017-11-07 DOI: 10.1145/3139958.3139971

Yongni Li, D. Gunopulos, Cewu Lu, L. Guibas

{"title":"Urban Travel Time Prediction using a Small Number of GPS Floating Cars","authors":"Yongni Li, D. Gunopulos, Cewu Lu, L. Guibas","doi":"10.1145/3139958.3139971","DOIUrl":"https://doi.org/10.1145/3139958.3139971","url":null,"abstract":"Predicting the travel time of a path is an important task in route planning and navigation applications. As more GPS floating car data has been collected to monitor urban traffic, GPS trajectories of floating cars have been frequently used to predict path travel time. However, most trajectory-based methods rely on deploying GPS devices and collect real-time data on a large taxi fleet, which can be expensive and unreliable in smaller cities. This work deals with the problem of predicting path travel time when only a small number of GPS floating cars are available. We developed an algorithm that learns local congestion patterns of a compact set of frequently shared paths from historical data. Given a travel time prediction query, we identify the current congestion patterns around the query path from recent trajectories, then infer its travel time in the near future. Experimental results using 10-15 taxis tracked for 11 months in urban areas of Shenzhen, China show that our prediction has on average 5.4 minutes of error on trips of duration 10-75 minutes. This result improves the baseline approach of using purely historical trajectories by 2-30% on regions with various degree of path regularity. It also outperforms a state-of-the-art travel time prediction method that uses both historical trajectories and real-time trajectories.","PeriodicalId":270649,"journal":{"name":"Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129454453","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: 21

Game-Theoretic Solutions for Constrained Geo-Social Event Organization 约束地理社会事件组织的博弈论解

Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2017-11-07 DOI: 10.1145/3139958.3139974

Lefteris Ntaflos, George Trimponias, D. Papadias

{"title":"Game-Theoretic Solutions for Constrained Geo-Social Event Organization","authors":"Lefteris Ntaflos, George Trimponias, D. Papadias","doi":"10.1145/3139958.3139974","DOIUrl":"https://doi.org/10.1145/3139958.3139974","url":null,"abstract":"In Geo-Social Event Organization (GSEO), each user of a geo-social network is assigned to an event, so that the distance and social costs are minimized. Specifically, the distance cost is the total distance between every user and his assigned event. The social cost is measured in terms of the pairs of friends in different events. Intuitively, users should be assigned to events in their vicinity, which are also recommended to their friends. Moreover, the events may have constraints on the number of users that they can accommodate. GSEO is an NP-Hard problem. In this paper, we utilize a game-theoretic framework, where each user constitutes a player that wishes to minimize his own social and distance cost. We demonstrate that the Nash Equilibrium concept is inadequate due to the capacity constraints, and propose the notion of pairwise stability, which yields better solutions. In addition, we develop a number of optimization techniques to achieve efficiency. Our experimental evaluation on real datasets demonstrates that the proposed methods always outperform the state-of-the-art in terms of solution quality, while they are up to one order of magnitude faster.","PeriodicalId":270649,"journal":{"name":"Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124172225","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

Extracting Hotspots without A-priori by Enabling Signal Processing over Geospatial Data 基于地理空间数据信号处理的无先验热点提取方法

Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2017-11-07 DOI: 10.1145/3139958.3140002

Vaibhav Kulkarni, A. Moro, B. Chapuis, B. Garbinato

{"title":"Extracting Hotspots without A-priori by Enabling Signal Processing over Geospatial Data","authors":"Vaibhav Kulkarni, A. Moro, B. Chapuis, B. Garbinato","doi":"10.1145/3139958.3140002","DOIUrl":"https://doi.org/10.1145/3139958.3140002","url":null,"abstract":"The proliferation of mobile devices equipped with internet connectivity and global positioning functionality (GPS) has resulted in the generation of large volumes of spatiotemporal data. This has led to the rapid evolution of location-based services. The anticipatory nature of these services, demand exploitation of a broader range of user information for service personalization. Determining the users' places of interest, i.e. hotspots is critical to understand their behaviors and preferences. Existing techniques to detect hotspots rely on a set of a-priori determined parameters that are either dataset dependent or derived without any empirical basis. This leads to biased results and inaccuracies in estimating the total number of hotspots belonging to a user, their shape and the average dwelling time. In this paper, we propose a parameter-less technique for extracting hotspots from spatiotemporal trajectories without any a-priori assumptions. We eliminate parameter dependence by treating trajectories as spatiotemporal signals and rely on signal processing algorithms to derive hotspots. We experimentally show that, our technique does not necessitate any spatiotemporal or behavior dependent bounds, which makes it suitable to extract hotspots from a larger variety of datasets and across users having disparate mobility behaviors. Our evaluation results on a real world dataset, show accuracy rates exceeding 80% and outperforms traditional clustering techniques used for hotspot detection.","PeriodicalId":270649,"journal":{"name":"Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126999905","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: 9

Generating Concise and Robust Driving Directions 生成简洁和稳健的驾驶方向

Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2017-11-07 DOI: 10.1145/3139958.3140010

S. Funke, C. Haag, Sabine Storandt

引用次数: 1

Fighting Statistical Re-Identification in Human Trajectory Publication 反对人类轨迹出版物中的统计再识别

Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2017-11-07 DOI: 10.1145/3139958.3140045

J. Ding, Chien-Chun Ni, Jie Gao

引用次数: 5

From ITDL to Place2Vec: Reasoning About Place Type Similarity and Relatedness by Learning Embeddings From Augmented Spatial Contexts 从ITDL到Place2Vec:基于增强空间上下文学习嵌入的地点类型相似性和相关性推理

Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2017-11-07 DOI: 10.1145/3139958.3140054

Bo Yan, K. Janowicz, Gengchen Mai, Song Gao

{"title":"From ITDL to Place2Vec: Reasoning About Place Type Similarity and Relatedness by Learning Embeddings From Augmented Spatial Contexts","authors":"Bo Yan, K. Janowicz, Gengchen Mai, Song Gao","doi":"10.1145/3139958.3140054","DOIUrl":"https://doi.org/10.1145/3139958.3140054","url":null,"abstract":"Understanding, representing, and reasoning about Points Of Interest (POI) types such as Auto Repair, Body Shop, Gas Stations, or Planetarium, is a key aspect of geographic information retrieval, recommender systems, geographic knowledge graphs, as well as studying urban spaces in general, e.g., for extracting functional or vague cognitive regions from user-generated content. One prerequisite to these tasks is the ability to capture the similarity and relatedness between POI types. Intuitively, a spatial search that returns body shops or even gas stations in the absence of auto repair places is still likely to satisfy some user needs while returning planetariums will not. Place hierarchies are frequently used for query expansion, but most of the existing hierarchies are relatively shallow and structured from a single perspective, thereby putting POI types that may be closely related regarding some characteristics far apart from another. This leads to the question of how to learn POI type representations from data. Models such as Word2Vec that produces word embeddings from linguistic contexts are a novel and promising approach as they come with an intuitive notion of similarity. However, the structure of geographic space, e.g., the interactions between POI types, differs substantially from linguistics. In this work, we present a novel method to augment the spatial contexts of POI types using a distance-binned, information-theoretic approach to generate embeddings. We demonstrate that our work outperforms Word2Vec and other models using three different evaluation tasks and strongly correlates with human assessments of POI type similarity. We published the resulting embeddings for 570 place types as well as a collection of human similarity assessments online for others to use.","PeriodicalId":270649,"journal":{"name":"Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115856647","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: 134

Fast exact parallel 3D mesh intersection algorithm using only orientation predicates 仅使用方向谓词的快速精确并行3D网格相交算法

Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2017-11-07 DOI: 10.1145/3139958.3140001

S. V. G. Magalhães, W. Randolph Franklin, M. Andrade

引用次数: 7

Inferring Venue Visits from GPS Trajectories 从GPS轨迹推断地点访问

Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2017-11-07 DOI: 10.1145/3139958.3140034

Qihang Gu, Dimitris Sacharidis, M. Mathioudakis, G. Wang

引用次数: 10

City-wide Traffic Volume Inference with Loop Detector Data and Taxi Trajectories 基于环路检测器数据和出租车轨迹的城市交通量推断

Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2017-11-07 DOI: 10.1145/3139958.3139984

Chuishi Meng, Xiuwen Yi, Lu Su, Jing Gao, Yu Zheng

{"title":"City-wide Traffic Volume Inference with Loop Detector Data and Taxi Trajectories","authors":"Chuishi Meng, Xiuwen Yi, Lu Su, Jing Gao, Yu Zheng","doi":"10.1145/3139958.3139984","DOIUrl":"https://doi.org/10.1145/3139958.3139984","url":null,"abstract":"The traffic volume on road segments is a vital property of the transportation efficiency. City-wide traffic volume information can benefit people with their everyday life, and help the government on better city planning. However, there are no existing methods that can monitor the traffic volume of every road, because they are either too expensive or inaccurate. Fortunately, nowadays we can collect a large amount of urban data which provides us the opportunity to tackle this problem. In this paper, we propose a novel framework to infer the city-wide traffic volume information with data collected by loop detectors and taxi trajectories. Although these two data sets are incomplete, sparse and from quite different domains, the proposed spatio-temporal semi-supervised learning model can take the full advantages of both data and accurately infer the volume of each road. In order to provide a better interpretation on the inference results, we also derive the confidence of the inference based on spatio-temporal properties of traffic volume. Real-world data was collected from 155 loop detectors and 6,918 taxis over a period of 17 days in Guiyang China. The experiments performed on this large urban data set demonstrate the advantages of the proposed framework on correctly inferring the traffic volume in a city-wide scale.","PeriodicalId":270649,"journal":{"name":"Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121686664","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: 83

Smart-phone based Spatio-temporal Sensing for Annotated Transit Map Generation 基于智能手机的时空感知标注交通地图生成

Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2017-11-07 DOI: 10.1145/3139958.3140005

Rohit Verma, Surjya Ghosh, Niloy Ganguly, Bivas Mitra, Sandip Chakraborty

{"title":"Smart-phone based Spatio-temporal Sensing for Annotated Transit Map Generation","authors":"Rohit Verma, Surjya Ghosh, Niloy Ganguly, Bivas Mitra, Sandip Chakraborty","doi":"10.1145/3139958.3140005","DOIUrl":"https://doi.org/10.1145/3139958.3140005","url":null,"abstract":"City transit maps are one of the important resources for public navigation in today's digital world. However, the availability of transit maps for many developing countries is very limited, primarily due to the various socio-economic factors that drive the private operated and partially regulated transport services. Public transports at these cities are marred with many factors such as uncoordinated waiting time at bus stoppages, crowding in the bus, sporadic road conditions etc., which also need to be annotated so that commuters can take informed decision. Interestingly, many of these factors are spatio-temporal in nature. In this paper, we develop CityMap, a system to automatically extract transit routes along with their eccentricities from spatio-temporal crowdsensed data collected via commuters' smart-phones. We apply a learning based methodology coupled with a feature selection mechanism to filter out the necessary information from raw smart-phone sensor data with minimal user engagement and drain of battery power. A thorough evaluation of CityMap, conducted for more than two years over 11 different routes in 3 different cities in India, show that the system effectively annotates bus routes along with other route and road features with more than 90% of accuracy.","PeriodicalId":270649,"journal":{"name":"Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"36 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123277163","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: 11