Given a sequence S of temporally ordered observations, non necessarily of spatial nature, the segmentation task partitions S in a set of disjoint sub-sequences si, .., sn - the segments - such that ∪i∈[1, n] si = S. Typically, segments represents sub-sequences that are somehow homogeneous with respect to some criteria. Depending on the context and the nature of observations, segments can be given an approximated representation, for example segments can be assigned a descriptive label or one of the data points is chosen as representative of the whole sub-sequence. The final result is a summarized representation of the sequence. This simple and intuitive mechanism has been extensively studied in literature, for example, for the summarization of time series. Interestingly, the notion of segment is also at the basis of the most recent trajectory data models. For example, segments are the informative units in the semantic trajectories, where they are called episodes. Episodes are spatial sub-trajectories that can be semantically annotated using application-dependent descriptions, e.g. place names [1]. Similarly the recent symbolic trajectory data model [2] describes the individual movement as a sequence of temporally annotated labeled states s1, ..sn, where each state si is associated with a time interval. Beyond data modeling, segmentation can be employed for the indexing of trajectories in moving object databases while another major role is to support data analysis, especially for the extraction of individual mobility patterns. The concept of trajectory segment is thus emerging as shared and perhaps unifying concept across data modeling, indexing and analysis.
给定一个时间有序的观测序列S，不一定具有空间性质，分割任务将S划分为一组不相交的子序列si，…， sn—段—使得∪i∈[1,n] si = s。通常，段表示在某些条件下是齐次的子序列。根据上下文和观测的性质，可以给片段一个近似的表示，例如，可以给片段分配一个描述性标签，或者选择一个数据点作为整个子序列的代表。最后的结果是序列的总结表示。这种简单直观的机制在文献中得到了广泛的研究，例如，用于时间序列的总结。有趣的是，分段的概念也是最近的轨迹数据模型的基础。例如，片段是语义轨迹中的信息单位，它们被称为情节。情节是空间子轨迹，可以使用依赖于应用程序的描述进行语义注释，例如地名[1]。类似地，最近的符号轨迹数据模型[2]将个体运动描述为一系列时间标注的标记状态s1，…Sn，其中每个状态si都与一个时间间隔相关联。除了数据建模之外，分割还可以用于对移动对象数据库中的轨迹进行索引，而另一个主要作用是支持数据分析，特别是对个人移动模式的提取。因此，轨迹段的概念正在成为跨数据建模、索引和分析的共享的、也许是统一的概念。
{"title":"Spatial trajectories segmentation: trends and challenges","authors":"M. Damiani","doi":"10.1145/3004725.3007201","DOIUrl":"https://doi.org/10.1145/3004725.3007201","url":null,"abstract":"Given a sequence S of temporally ordered observations, non necessarily of spatial nature, the segmentation task partitions S in a set of disjoint sub-sequences si, .., sn - the segments - such that ∪i∈[1, n] si = S. Typically, segments represents sub-sequences that are somehow homogeneous with respect to some criteria. Depending on the context and the nature of observations, segments can be given an approximated representation, for example segments can be assigned a descriptive label or one of the data points is chosen as representative of the whole sub-sequence. The final result is a summarized representation of the sequence. This simple and intuitive mechanism has been extensively studied in literature, for example, for the summarization of time series. Interestingly, the notion of segment is also at the basis of the most recent trajectory data models. For example, segments are the informative units in the semantic trajectories, where they are called episodes. Episodes are spatial sub-trajectories that can be semantically annotated using application-dependent descriptions, e.g. place names [1]. Similarly the recent symbolic trajectory data model [2] describes the individual movement as a sequence of temporally annotated labeled states s1, ..sn, where each state si is associated with a time interval. Beyond data modeling, segmentation can be employed for the indexing of trajectories in moving object databases while another major role is to support data analysis, especially for the extraction of individual mobility patterns. The concept of trajectory segment is thus emerging as shared and perhaps unifying concept across data modeling, indexing and analysis.","PeriodicalId":154980,"journal":{"name":"Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems","volume":"134 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114090894","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}
Trajectory analysis is a central problem in the era of big data due to numerous interconnected mobile devices generating unprecedented amounts of spatio-temporal trajectories. Unfortunately, datasets of spatial trajectories are quite difficult to analyse because of the computational complexity of the various existing distance measures. A significant amount of work in comparing two trajectories stems from calculating temporal alignments of the involved spatial points. With this paper, we propose an alignment-free method of representing spatial trajectories using low-dimensional feature vectors by summarizing the combinatorics of shape-derived string sequences. Therefore, we propose to translate trajectories into strings describing the evolving shape of each trajectory, and then provide a sparse matrix representation of these strings using frequencies of adjacencies of characters (n-grams). The final feature vectors are constructed by approximating this matrix with low-dimensional column space using singular value decomposition. New trajectories can be projected into this geometry for comparison. We show that this construction leads to low-dimensional feature vectors with surprising expressive power. We illustrate the usefulness of this approach in various datasets.
{"title":"A low-dimensional feature vector representation for alignment-free spatial trajectory analysis","authors":"M. Werner, Marie Kiermeier","doi":"10.1145/3004725.3004733","DOIUrl":"https://doi.org/10.1145/3004725.3004733","url":null,"abstract":"Trajectory analysis is a central problem in the era of big data due to numerous interconnected mobile devices generating unprecedented amounts of spatio-temporal trajectories. Unfortunately, datasets of spatial trajectories are quite difficult to analyse because of the computational complexity of the various existing distance measures. A significant amount of work in comparing two trajectories stems from calculating temporal alignments of the involved spatial points. With this paper, we propose an alignment-free method of representing spatial trajectories using low-dimensional feature vectors by summarizing the combinatorics of shape-derived string sequences. Therefore, we propose to translate trajectories into strings describing the evolving shape of each trajectory, and then provide a sparse matrix representation of these strings using frequencies of adjacencies of characters (n-grams). The final feature vectors are constructed by approximating this matrix with low-dimensional column space using singular value decomposition. New trajectories can be projected into this geometry for comparison. We show that this construction leads to low-dimensional feature vectors with surprising expressive power. We illustrate the usefulness of this approach in various datasets.","PeriodicalId":154980,"journal":{"name":"Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124715859","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}
This paper describes a new Network-constrained Moving objects indexing structure, which extends the state-of-the-art for this kind of data. The indexing structure we propose is called Temporally Enhanced Network-Constrained R-tree (TENC R-tree), which solves the shortcomings in other Network-Constrained access methods like the FNR-tree [7], MON-tree [1] and UTR-tree. These existing indexing methods are designed to store and retrieve the moving objects based on spatial features, followed by their temporal ones. They are generally not efficient when a query has only temporal constraints, or when a specific moving object id is also part of the query conditions. In such cases, existing methods have to scan the entire database to retrieve the result. Furthermore, the aforementioned methods are not efficient in processing Strict-path query, which is a query type that retrieves trajectories that follow all the edges in the queried path [10]. Our proposed TENC R-tree index allows good performance for almost all types of queries on moving objects in a constrained network, whether the constraints are spatial, temporal, or based on object id. Also, the TENC R-tree out-performs other access methods on the case of Path queries. Our experiments show the performance has been improved by 10 to 100 times for such queries.
{"title":"Temporally enhanced network-constrained (TENC) R-tree","authors":"M. Fouladgar, R. Elmasri","doi":"10.1145/3004725.3004736","DOIUrl":"https://doi.org/10.1145/3004725.3004736","url":null,"abstract":"This paper describes a new Network-constrained Moving objects indexing structure, which extends the state-of-the-art for this kind of data. The indexing structure we propose is called Temporally Enhanced Network-Constrained R-tree (TENC R-tree), which solves the shortcomings in other Network-Constrained access methods like the FNR-tree [7], MON-tree [1] and UTR-tree. These existing indexing methods are designed to store and retrieve the moving objects based on spatial features, followed by their temporal ones. They are generally not efficient when a query has only temporal constraints, or when a specific moving object id is also part of the query conditions. In such cases, existing methods have to scan the entire database to retrieve the result. Furthermore, the aforementioned methods are not efficient in processing Strict-path query, which is a query type that retrieves trajectories that follow all the edges in the queried path [10]. Our proposed TENC R-tree index allows good performance for almost all types of queries on moving objects in a constrained network, whether the constraints are spatial, temporal, or based on object id. Also, the TENC R-tree out-performs other access methods on the case of Path queries. Our experiments show the performance has been improved by 10 to 100 times for such queries.","PeriodicalId":154980,"journal":{"name":"Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132338919","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}
Advancement in mobile and GPS technologies have enabled users to record and publish their route activities or trajectories through location based social networking sites. Existing research mainly focus on finding popular routes and recommending suitable routes for the users based on the historical movements of users between different Point of Interests (POIs). However, users often spend most of their time around different POIs (e.g., Colosseo) and less time traveling between POIs. Thus, existing methods fail to capture the detailed movement of users around a POI, which we call Region of Interest (ROI). A major challenge of identifying patterns of routes inside an ROI comes from the inaccurate and incomplete data of user trajectories. In this paper we propose a novel technique to find the most popular path within an ROI from historical trajectory data by rephrasing trajectories into smaller part and eliminating noisy points from trajectories. We then devise an algorithm to produce the most popular path inside each ROI. We perform experiments on a real dataset extracted from Flickr to show the effectiveness of our approach.
{"title":"Recommending most popular travel path within a region of interest from historical trajectory data","authors":"Samia Shafique, Mohammed Eunus Ali","doi":"10.1145/3004725.3004728","DOIUrl":"https://doi.org/10.1145/3004725.3004728","url":null,"abstract":"Advancement in mobile and GPS technologies have enabled users to record and publish their route activities or trajectories through location based social networking sites. Existing research mainly focus on finding popular routes and recommending suitable routes for the users based on the historical movements of users between different Point of Interests (POIs). However, users often spend most of their time around different POIs (e.g., Colosseo) and less time traveling between POIs. Thus, existing methods fail to capture the detailed movement of users around a POI, which we call Region of Interest (ROI). A major challenge of identifying patterns of routes inside an ROI comes from the inaccurate and incomplete data of user trajectories. In this paper we propose a novel technique to find the most popular path within an ROI from historical trajectory data by rephrasing trajectories into smaller part and eliminating noisy points from trajectories. We then devise an algorithm to produce the most popular path inside each ROI. We perform experiments on a real dataset extracted from Flickr to show the effectiveness of our approach.","PeriodicalId":154980,"journal":{"name":"Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130794562","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}
S. Yun, Sanghyun Yoon, Sungha Ju, Won Seob Oh, J. Ma, J. Heo
Currently there are demands for maximization of taxi services and also for saving fuel usage within massive cities. Spatial big data extracted from taxi service records and GPS can be used to suggest optimal routing options to achieve these goals. The taxi cab ride data contains 7,000 unique taxies being serviced in Seoul, South Korea. In this study one week worth of data with the size of 3.13GB were used. Also road network data provided by Ministry of Land, Infrastructure and Transport (MOLIT), which contains 19,229 nodes and 22,192 links, and census map provided by Statistics Korea were used as base-map. Lastly floating population data of Seoul city area, gathered with mobile phones, has been used as an index of demand for taxi service. By using taxi cab ride data, which contains trajectory with time and 2D coordinates, and information about whether passenger is on the taxi or not, hot spots were analyzed for 1) taxies without passengers whom are available to pick-up passengers, 2) places where people are experiencing difficulty hailing a taxi due to high demand for taxi. Combination of these two types of hot spots can provide new insight for both public and commercial sectors to maximize the efficiency of taxi service and to reduce idle fuel usage. Afterwards the floating population data is used to provide indices for taxi usage in Seoul area, providing further insights. Utilizing the time stamp records on the taxi GPS data, hourly based hot spots for both 'demand' and 'supply' for taxi cab ride can be derived, and this outcome can be practically used to guide taxi drivers to high demanding places and avoid high supplying places.
{"title":"Taxi cab service optimization using spatio-temporal implementation to hot-spot analysis with taxi trajectories: a case study in Seoul, Korea","authors":"S. Yun, Sanghyun Yoon, Sungha Ju, Won Seob Oh, J. Ma, J. Heo","doi":"10.1145/3004725.3004732","DOIUrl":"https://doi.org/10.1145/3004725.3004732","url":null,"abstract":"Currently there are demands for maximization of taxi services and also for saving fuel usage within massive cities. Spatial big data extracted from taxi service records and GPS can be used to suggest optimal routing options to achieve these goals. The taxi cab ride data contains 7,000 unique taxies being serviced in Seoul, South Korea. In this study one week worth of data with the size of 3.13GB were used. Also road network data provided by Ministry of Land, Infrastructure and Transport (MOLIT), which contains 19,229 nodes and 22,192 links, and census map provided by Statistics Korea were used as base-map. Lastly floating population data of Seoul city area, gathered with mobile phones, has been used as an index of demand for taxi service. By using taxi cab ride data, which contains trajectory with time and 2D coordinates, and information about whether passenger is on the taxi or not, hot spots were analyzed for 1) taxies without passengers whom are available to pick-up passengers, 2) places where people are experiencing difficulty hailing a taxi due to high demand for taxi. Combination of these two types of hot spots can provide new insight for both public and commercial sectors to maximize the efficiency of taxi service and to reduce idle fuel usage. Afterwards the floating population data is used to provide indices for taxi usage in Seoul area, providing further insights. Utilizing the time stamp records on the taxi GPS data, hourly based hot spots for both 'demand' and 'supply' for taxi cab ride can be derived, and this outcome can be practically used to guide taxi drivers to high demanding places and avoid high supplying places.","PeriodicalId":154980,"journal":{"name":"Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131835035","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}
This paper presents a system architecture of a web GIS that is used to develop a web mapping app for real-time macroeconomic impact decision support tool. It incorporates web GIS on the cloud with an autonomous software system for real-time situational awareness (outage statue and economic loss) from power & electric utilities. Our web GIS is a system of systems, and we deployed ESRI's ArcGIS platform, Amazon Web Services (AWS), enterprise spatial database, C#, RESTful API, and JSON format. The system implementation results in a web GIS that contains a GIS server with a set of REST APIs of GIS web services (map service, geodata service, etc) on the cloud that can be used by web mapping apps, mobile GIS apps, or desktop programs to share, display, analyze, and update a geodatabase, which is embedded in cloud. To evaluate our approach, we developed a web map application and an operations dashboard that used the created GIS web services and APIs. Our web GIS is applicable for the "Internet of Things" domain, public safety, cloud communication, crisis response, web map application, location-based services, and real-time GIS.
{"title":"System architecture of cloud-based web GIS for real-time macroeconomic loss estimation","authors":"R. Nourjou, Joel Thomas","doi":"10.1145/3004725.3004731","DOIUrl":"https://doi.org/10.1145/3004725.3004731","url":null,"abstract":"This paper presents a system architecture of a web GIS that is used to develop a web mapping app for real-time macroeconomic impact decision support tool. It incorporates web GIS on the cloud with an autonomous software system for real-time situational awareness (outage statue and economic loss) from power & electric utilities. Our web GIS is a system of systems, and we deployed ESRI's ArcGIS platform, Amazon Web Services (AWS), enterprise spatial database, C#, RESTful API, and JSON format. The system implementation results in a web GIS that contains a GIS server with a set of REST APIs of GIS web services (map service, geodata service, etc) on the cloud that can be used by web mapping apps, mobile GIS apps, or desktop programs to share, display, analyze, and update a geodatabase, which is embedded in cloud. To evaluate our approach, we developed a web map application and an operations dashboard that used the created GIS web services and APIs. Our web GIS is applicable for the \"Internet of Things\" domain, public safety, cloud communication, crisis response, web map application, location-based services, and real-time GIS.","PeriodicalId":154980,"journal":{"name":"Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121388286","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}
This paper focuses on the study of detecting real-time pedestrian flow by using Bluetooth on smartphones to show evacuation route. When a natural disaster strikes, knowing real-time accurate pedestrian density and flow in wide range is very important to lead people to the safe places. Evacuation route can be calculated by agent-based simulation and the real-time pedestrian flow. One promising way to detect real-time pedestrian density and flow is mobile sensing. With devices the evacuee have, the pedestrian density of near area can be detected. In this paper, we introduce the method of detecting pedestrian density and propose how to apply this method to a disaster for solving crowded situation.
{"title":"Pedestrian flow detection using Bluetooth for evacuation route finding","authors":"Miku Hoshino, Masaki Ito, K. Sezaki","doi":"10.1145/3004725.3004726","DOIUrl":"https://doi.org/10.1145/3004725.3004726","url":null,"abstract":"This paper focuses on the study of detecting real-time pedestrian flow by using Bluetooth on smartphones to show evacuation route. When a natural disaster strikes, knowing real-time accurate pedestrian density and flow in wide range is very important to lead people to the safe places. Evacuation route can be calculated by agent-based simulation and the real-time pedestrian flow. One promising way to detect real-time pedestrian density and flow is mobile sensing. With devices the evacuee have, the pedestrian density of near area can be detected. In this paper, we introduce the method of detecting pedestrian density and propose how to apply this method to a disaster for solving crowded situation.","PeriodicalId":154980,"journal":{"name":"Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126897344","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}
Citizens in various locations can report local infrastructure issues to the government by posting reports on certain websites, such as Chiba Report and FixMyStreet. Recently, these systems have begun operating worldwide. In these systems, a large volume of information is collected on infrastructure problems that are identified by citizens (e.g., broken paving slabs, fly tipping, graffiti, potholes). This information is expected to be utilized for infrastructure maintenance. However, local problems (especially road defection) identified by citizens are sometimes not deemed an urgent matter for road managers. This is because it is difficult for an average person to determine road damage status. Furthermore, non-critical reports may be a burden for local government because each report requires visual confirmation. We therefore propose a smartphone application based on a deep neural network that can determine road damage status using only photographs of the road. This application is based on a deep neural network model trained by citizen reports and road manager inspection results, which are gathered daily on a government server. The application updates the model parameters each time it launches and thereby becomes increasingly more intelligent and effective. The proposed system enables average citizens to easily determine road damage status using only a smartphone application. In addition, because not only expert road managers but also local government officials without expert knowledge can inspect the road, the proposed system can be useful for local governments that lack expert road managers.
{"title":"Lightweight road manager: smartphone-based automatic determination of road damage status by deep neural network","authors":"Hiroya Maeda, Y. Sekimoto, Toshikazu Seto","doi":"10.1145/3004725.3004729","DOIUrl":"https://doi.org/10.1145/3004725.3004729","url":null,"abstract":"Citizens in various locations can report local infrastructure issues to the government by posting reports on certain websites, such as Chiba Report and FixMyStreet. Recently, these systems have begun operating worldwide. In these systems, a large volume of information is collected on infrastructure problems that are identified by citizens (e.g., broken paving slabs, fly tipping, graffiti, potholes). This information is expected to be utilized for infrastructure maintenance. However, local problems (especially road defection) identified by citizens are sometimes not deemed an urgent matter for road managers. This is because it is difficult for an average person to determine road damage status. Furthermore, non-critical reports may be a burden for local government because each report requires visual confirmation. We therefore propose a smartphone application based on a deep neural network that can determine road damage status using only photographs of the road. This application is based on a deep neural network model trained by citizen reports and road manager inspection results, which are gathered daily on a government server. The application updates the model parameters each time it launches and thereby becomes increasingly more intelligent and effective. The proposed system enables average citizens to easily determine road damage status using only a smartphone application. In addition, because not only expert road managers but also local government officials without expert knowledge can inspect the road, the proposed system can be useful for local governments that lack expert road managers.","PeriodicalId":154980,"journal":{"name":"Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121644191","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}
Many indoor positioning methods and systems exhibit high inaccuracies and structural positioning biases, when deployed and evaluated in real-world environments. This holds especially for signal-strength-based positioning, the prevalent means for position tracking in environments, that are not suitable for GNSS positioning, such as large building complexes. In such environments though strong positioning inaccuracies and biases result from the many building elements with different attenuation properties. We propose and evaluate deviation maps as a means for capturing, and thereby reducing, positioning errors and biases as prevalent in different parts of deployment's building complex.
{"title":"Deviation maps: enhancing robustness and predictability of indoor positioning systems","authors":"H. Blunck, Sylvie Temme, J. Vahrenhold","doi":"10.1145/3004725.3004727","DOIUrl":"https://doi.org/10.1145/3004725.3004727","url":null,"abstract":"Many indoor positioning methods and systems exhibit high inaccuracies and structural positioning biases, when deployed and evaluated in real-world environments. This holds especially for signal-strength-based positioning, the prevalent means for position tracking in environments, that are not suitable for GNSS positioning, such as large building complexes. In such environments though strong positioning inaccuracies and biases result from the many building elements with different attenuation properties. We propose and evaluate deviation maps as a means for capturing, and thereby reducing, positioning errors and biases as prevalent in different parts of deployment's building complex.","PeriodicalId":154980,"journal":{"name":"Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131333552","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}
B. Chapuis, A. Moro, Vaibhav Kulkarni, B. Garbinato
We put forth a system, to predict distant-future positions of multiple moving entities and index the forecasted trajectories, in order to answer predictive queries involving long time horizons. Today, the proliferation of mobile devices with GPS functionality and internet connectivity has led to a rapid development of location-based services, accounting for user mobility prediction as a key paradigm. Mobility prediction is already playing a major role in traffic management, urban planning and location-based advertising, which demand accurate and long time horizon forecasting of user movements. Existing prediction methodologies either use motion patterns or techniques based on frequently visited places for predicting the next move. However, when it comes to distant-future, human mobility is too complex to be represented by such statistical functions. Therefore, the existing techniques are not well suited to answer distant-future queries with a satisfactory level of accuracy. To tackle this problem, we introduce a novel spatial object, 'Representative Trajectory', which embodies the movements of users amongst their zones of interest. We propose means to empirically evaluate the quality of this object and dynamically adapt its extraction method based on user mobility behaviour. We rely on an inverted index to store the predicted trajectories that scales well with the number of moving entities. Our evaluation results show that the technique achieves more than 70% accurate predictions with the best extraction technique. This shows that longer query time horizons do not necessarily demand complex spatial indexing schemes, which have to be rebalanced as they grow and which is a constantly experienced problem while answering predictive queries.
{"title":"Capturing complex behaviour for predicting distant future trajectories","authors":"B. Chapuis, A. Moro, Vaibhav Kulkarni, B. Garbinato","doi":"10.1145/3004725.3004730","DOIUrl":"https://doi.org/10.1145/3004725.3004730","url":null,"abstract":"We put forth a system, to predict distant-future positions of multiple moving entities and index the forecasted trajectories, in order to answer predictive queries involving long time horizons. Today, the proliferation of mobile devices with GPS functionality and internet connectivity has led to a rapid development of location-based services, accounting for user mobility prediction as a key paradigm. Mobility prediction is already playing a major role in traffic management, urban planning and location-based advertising, which demand accurate and long time horizon forecasting of user movements. Existing prediction methodologies either use motion patterns or techniques based on frequently visited places for predicting the next move. However, when it comes to distant-future, human mobility is too complex to be represented by such statistical functions. Therefore, the existing techniques are not well suited to answer distant-future queries with a satisfactory level of accuracy. To tackle this problem, we introduce a novel spatial object, 'Representative Trajectory', which embodies the movements of users amongst their zones of interest. We propose means to empirically evaluate the quality of this object and dynamically adapt its extraction method based on user mobility behaviour. We rely on an inverted index to store the predicted trajectories that scales well with the number of moving entities. Our evaluation results show that the technique achieves more than 70% accurate predictions with the best extraction technique. This shows that longer query time horizons do not necessarily demand complex spatial indexing schemes, which have to be rebalanced as they grow and which is a constantly experienced problem while answering predictive queries.","PeriodicalId":154980,"journal":{"name":"Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127385423","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}
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