UrbComp '13最新文献_第2页

From data to knowledge: city-wide traffic flows analysis and prediction using bing maps 从数据到知识:使用必应地图分析和预测全市交通流量

UrbComp '13 Pub Date : 2013-08-11 DOI: 10.1145/2505821.2505831

A. Ribeiro, Fatima de L. P. Duarte-Figueiredo, R. Assunção, Juliana F. S. Salles, A. Loureiro

引用次数: 61

Inferring human activities from GPS tracks 根据GPS轨迹推断人类活动

UrbComp '13 Pub Date : 2013-08-11 DOI: 10.1145/2505821.2505830

Barbara Furletti, Paolo Cintia, C. Renso, L. Spinsanti

{"title":"Inferring human activities from GPS tracks","authors":"Barbara Furletti, Paolo Cintia, C. Renso, L. Spinsanti","doi":"10.1145/2505821.2505830","DOIUrl":"https://doi.org/10.1145/2505821.2505830","url":null,"abstract":"The collection of huge amount of tracking data made possible by the widespread use of GPS devices, enabled the analysis of such data for several applications domains, ranging from traffic management to advertisement and social studies. However, the raw positioning data, as it is detected by GPS devices, lacks of semantic information since this data does not natively provide any additional contextual information like the places that people visited or the activities performed. Traditionally, this information is collected by hand filled questionnaire where a limited number of users are asked to annotate their tracks with the activities they have done. With the purpose of getting large amount of semantically rich trajectories, we propose an algorithm for automatically annotating raw trajectories with the activities performed by the users. To do this, we analyse the stops points trying to infer the Point Of Interest (POI) the user has visited. Based on the category of the POI and a probability measure based on the gravity law, we infer the activity performed. We experimented and evaluated the method in a real case study of car trajectories, manually annotated by users with their activities. Experimental results are encouraging and will drive our future works.","PeriodicalId":157169,"journal":{"name":"UrbComp '13","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129150607","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: 119

A comparison of Foursquare and Instagram to the study of city dynamics and urban social behavior Foursquare和Instagram对城市动态和城市社会行为研究的比较

UrbComp '13 Pub Date : 2013-08-11 DOI: 10.1145/2505821.2505836

Thiago H. Silva, Pedro O. S. Vaz de Melo, J. Almeida, Juliana F. S. Salles, A. Loureiro

{"title":"A comparison of Foursquare and Instagram to the study of city dynamics and urban social behavior","authors":"Thiago H. Silva, Pedro O. S. Vaz de Melo, J. Almeida, Juliana F. S. Salles, A. Loureiro","doi":"10.1145/2505821.2505836","DOIUrl":"https://doi.org/10.1145/2505821.2505836","url":null,"abstract":"Social media systems allow a user connected to the Internet to provide useful data about the context in which they are at any given moment, such as Instagram and Foursquare, which are called participatory sensing systems. Location sharing services are examples of participatory sensing systems. The sensed data is a check-in of a particular place that indicates, for instance, a restaurant in a specific location, and also a signal from a user expressing his/her preference. From a participatory sensing system we can derive a participatory sensor network. In this work we compare two different participatory sensor networks, one derived from Instagram, and another one derived from Foursquare. In Instagram, the sensed data is a picture of a specific place. On the other hand, in Foursquare the sensed data is the actual location associated with a specific category of place (e.g., restaurant). Using those social networks we can extract information in many ways. In this work we are interested in comparing two datasets of Foursquare and two datasets of Instagram. We analyze those datasets to investigate whether we can observe the same users' movement pattern, the popularity of regions in cities, the activities of users who use those social networks, and how users share their content along the time. In answering those questions, we want to better understand location-related information, which is an important aspect of the urban phenomena.","PeriodicalId":157169,"journal":{"name":"UrbComp '13","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129818755","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: 88

Understanding urban human activity and mobility patterns using large-scale location-based data from online social media 利用来自在线社交媒体的大规模基于位置的数据了解城市人类活动和流动模式

UrbComp '13 Pub Date : 2013-08-11 DOI: 10.1145/2505821.2505823

Samiul Hasan, Xianyuan Zhan, S. Ukkusuri

引用次数: 318

Fast and exact network trajectory similarity computation: a case-study on bicycle corridor planning 快速精确的网络轨迹相似度计算——以自行车道规划为例

UrbComp '13 Pub Date : 2013-08-11 DOI: 10.1145/2505821.2505835

Michael R. Evans, Dev Oliver, S. Shekhar, F. Harvey

引用次数: 24

Finding frequent sub-trajectories with time constraints 寻找具有时间约束的频繁子轨迹

UrbComp '13 Pub Date : 2013-08-11 DOI: 10.1145/2505821.2505824

Xin Huang, Jun Luo, Xin Wang

引用次数: 3

A review of urban computing for mobile phone traces: current methods, challenges and opportunities 回顾城市计算的移动电话痕迹:目前的方法，挑战和机遇

UrbComp '13 Pub Date : 2013-08-01 DOI: 10.1145/2505821.2505828

Shan Jiang, G. Fiore, Yingxiang Yang, J. Ferreira, Emilio Frazzoli, Marta C. González

{"title":"A review of urban computing for mobile phone traces: current methods, challenges and opportunities","authors":"Shan Jiang, G. Fiore, Yingxiang Yang, J. Ferreira, Emilio Frazzoli, Marta C. González","doi":"10.1145/2505821.2505828","DOIUrl":"https://doi.org/10.1145/2505821.2505828","url":null,"abstract":"In this work, we present three classes of methods to extract information from triangulated mobile phone signals, and describe applications with different goals in spatiotemporal analysis and urban modeling. Our first challenge is to relate extracted information from phone records (i.e., a set of time-stamped coordinates estimated from signal strengths) with destinations by each of the million anonymous users. By demonstrating a method that converts phone signals into small grid cell destinations, we present a framework that bridges triangulated mobile phone data with previously established findings obtained from data at more coarse-grained resolutions (such as at the cell tower or census tract levels). In particular, this method allows us to relate daily mobility networks, called motifs here, with trip chains extracted from travel diary surveys. Compared with existing travel demand models mainly relying on expensive and less-frequent travel survey data, this method represents an advantage for applying ubiquitous mobile phone data to urban and transportation modeling applications. Second, we present a method that takes advantage of the high spatial resolution of the triangulated phone data to infer trip purposes by examining semantic-enriched land uses surrounding destinations in individual's motifs. In the final section, we discuss a portable computational architecture that allows us to manage and analyze mobile phone data in geospatial databases, and to map mobile phone trips onto spatial networks such that further analysis about flows and network performances can be done. The combination of these three methods demonstrate the state-of-the-art algorithms that can be adapted to triangulated mobile phone data for the context of urban computing and modeling applications.","PeriodicalId":157169,"journal":{"name":"UrbComp '13","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123054682","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: 266