Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems最新文献_第6页

Location-based social networking for obtaining personalised driving advice 基于位置的社交网络，用于获取个性化驾驶建议

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820789

Abdullah AlDwyish, E. Tanin, S. Karunasekera

{"title":"Location-based social networking for obtaining personalised driving advice","authors":"Abdullah AlDwyish, E. Tanin, S. Karunasekera","doi":"10.1145/2820783.2820789","DOIUrl":"https://doi.org/10.1145/2820783.2820789","url":null,"abstract":"Major navigation companies have resorted to crowdsourcing for obtaining traffic data to improve their services. In fact, a new generation of navigation systems are emerging for the sole purpose of connecting drivers on the road (Waze.com) so that they benefit from each other. The focus of these efforts is on real-time traffic estimation. While obtaining real-time data is desirable and has great benefits, it is still limited, given the potential of the detailed individual data from drivers. For example, commuters in a city have valuable knowledge about their city as a result of their daily commute over many years. Using drivers as mere traffic sensors and aggregating their data desposes off any human knowledge that exists in their choices. In this work, we demonstrate a social navigation system that can help drivers avoid road incidents using expert advice in real-time without explicitly consulting drivers but through an automated system built on top of a location-based social network. In our system, we do not aggregate data from individual users and we instead outsource the task of finding a route that best avoids incidents by looking up drivers' old choices.","PeriodicalId":241302,"journal":{"name":"Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117317369","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

A framework for multi-criteria optimal location selection 多准则最优选址框架

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820784

Ji Zhang, Wei-Shinn Ku, Xunfei Jiang, X. Qin, Min-Te Sun, Hua Lu

引用次数: 2

Design and representation of complex objects in database systems 数据库系统中复杂对象的设计和表示

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820842

Lin Qi, Huiyuan Zhang, Markus Schneider

引用次数: 1

Task matching and scheduling for multiple workers in spatial crowdsourcing 空间众包中多工作者的任务匹配与调度

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820831

Dingxiong Deng, C. Shahabi, Linhong Zhu

{"title":"Task matching and scheduling for multiple workers in spatial crowdsourcing","authors":"Dingxiong Deng, C. Shahabi, Linhong Zhu","doi":"10.1145/2820783.2820831","DOIUrl":"https://doi.org/10.1145/2820783.2820831","url":null,"abstract":"A new platform, termed spatial crowdsourcing, is emerging which enables a requester to commission workers to physically travel to some specified locations to perform a set of spatial tasks (i.e., tasks related to a geographical location and time). The current approach is to formulate spatial crowdsourcing as a matching problem between tasks and workers; hence the primary objective of the existing solutions is to maximize the number of matched tasks. Our goal is to solve the spatial crowdsourcing problem in the presence of multiple workers where we optimize for both travel cost and the number of completed tasks, while taking the tasks' expiration times into consideration. The challenge is that the solution should be a mixture of task-matching and task-scheduling, which are fundamentally different. In this paper, we show that a baseline approach that performs a task-matching first, and subsequently schedules the tasks assigned per worker in a following phase, does not perform well. Hence, we add a third phase in which we iterate back to the matching phase to improve the assignment per the output of the scheduling phase, and thus further improves the quality of matching and scheduling. Even though this 3-phase approach generates high quality results, it is very slow and does not scale. Hence, to scale our algorithm to large number of workers and tasks, we propose a Bisection-based framework which recursively divides all the workers and tasks into different partitions such that assignment and scheduling can be performed locally in a much smaller and promising space. Our experiments show that this approach is three orders of magnitude faster than the 3-phase approach while it only sacrifices 4% of the results' quality.","PeriodicalId":241302,"journal":{"name":"Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115274592","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: 90

Location selection for ambulance stations: a data-driven approach 救护站的位置选择:数据驱动的方法

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820876

Yuhong Li, Yu Zheng, Shenggong Ji, Wenjun Wang, Leong Hou U, Zhiguo Gong

引用次数: 51

Flag-verify-fix: adaptive spatial crowdsourcing leveraging location-based social networks Flag-verify-fix:利用基于位置的社交网络的自适应空间众包

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820870

U. Hassan, E. Curry

{"title":"Flag-verify-fix: adaptive spatial crowdsourcing leveraging location-based social networks","authors":"U. Hassan, E. Curry","doi":"10.1145/2820783.2820870","DOIUrl":"https://doi.org/10.1145/2820783.2820870","url":null,"abstract":"This paper introduces the flag-verify-fix pattern that employs spatial crowdsourcing for city maintenance. The patterns motivates the need for appropriate assignment of dynamically arriving spatial tasks to a pool for workers on the ground. The assignment is aimed at maximizing the coverage of tasks spread over spatial locations; however, the coverage depends of willingness of workers to perform tasks assigned to them. We introduce the maximum coverage assignment problem that formulates two design issues of dynamic assignment. The quantity issue determines the number of worker required for a task and selection issue determines the set of workers. We propose an adaptive algorithm that uses location diversity based on a location-based social network to address the quantity issue and employs Thompson sampling for selecting the workers by learning their willingness. We evaluate the performance of the proposed algorithm in terms of coverage and number of assignments using real world datasets. The results show that our proposed algorithm achieves 30%--50% more coverage than the baseline algorithms, while requiring less workers per task.","PeriodicalId":241302,"journal":{"name":"Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114542650","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: 8

Cheap approximate localization using FM radio 廉价的近似定位使用调频收音机

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820868

Piyush Kumar, Tathagata Mukherjee, E. Pasiliao, Liqin Xu

引用次数: 6

coMobile: real-time human mobility modeling at urban scale using multi-view learning coMobile:使用多视图学习的城市尺度实时人类移动建模

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820821

Desheng Zhang, Juanjuan Zhao, Fan Zhang, T. He

{"title":"coMobile: real-time human mobility modeling at urban scale using multi-view learning","authors":"Desheng Zhang, Juanjuan Zhao, Fan Zhang, T. He","doi":"10.1145/2820783.2820821","DOIUrl":"https://doi.org/10.1145/2820783.2820821","url":null,"abstract":"Real-time human mobility modeling is essential to various urban applications. To model such human mobility, numerous data-driven techniques have been proposed. However, existing techniques are mostly driven by data from a single view, e.g., a transportation view or a cellphone view, which leads to over-fitting of these single-view models. To address this issue, we propose a human mobility modeling technique based on a generic multi-view learning framework called coMobile. In coMobile, we first improve the performance of single-view models based on tensor decomposition with correlated contexts, and then we integrate these improved single-view models together for multi-view learning to iteratively obtain mutually-reinforced knowledge for real-time human mobility at urban scale. We implement coMobile based on an extremely large dataset in the Chinese city Shenzhen, including data about taxi, bus and subway passengers along with cellphone users, capturing more than 27 thousand vehicles and 10 million urban residents. The evaluation results show that our approach outperforms a single-view model by 51% on average.","PeriodicalId":241302,"journal":{"name":"Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116827012","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: 41

LiDAR-based pedestrian-flow analysis for crowdedness equalization 基于激光雷达的拥挤均衡行人流量分析

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820805

A. Asahara, Nobuo Sato, Masatsugu Nomiya, Satomi Tsuji

引用次数: 4

Toponym disambiguation in online social network profiles 在线社交网络档案中的地名歧义消除

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820812

Mohammad Ghufran, Gianluca Quercini, N. Bennacer

{"title":"Toponym disambiguation in online social network profiles","authors":"Mohammad Ghufran, Gianluca Quercini, N. Bennacer","doi":"10.1145/2820783.2820812","DOIUrl":"https://doi.org/10.1145/2820783.2820812","url":null,"abstract":"In online social networks individuals are given the option to reveal on their online profiles some personal information about themselves including, among others, their home location that, if specified, is typically referred to with a toponym. A toponym disclosed by an individual on her profile, or self-reported toponym (e.g., \"London\"), is often ambiguous and may be interpreted as the name of different geographic locations (\"London, U.K.\", \"London, Ontario, Canada\", \"London, Arkansas, USA\"). The determination of the right interpretation of a self-reported toponym is challenging because the profile of an individual usually lacks the contextual information that can be used to disambiguate the toponym. In this paper, we propose LocusRank, an algorithm for the disambiguation (or resolution) of the self-reported toponyms in online social network profiles. More precisely, LocusRank chooses the right interpretation of a self-reported toponym of an individual by using the (possibly ambiguous) self-reported toponyms of her friends. Besides, LocusRank can also be used to infer with good accuracy the home location of profiles that do not even have a self-reported toponym. We evaluated LocusRank with good results on a dataset of profiles obtained from two real social networks, namely Flickr and LiveJournal.","PeriodicalId":241302,"journal":{"name":"Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121566415","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