Journal of Location Based Services最新文献

{"title":"Classification-based symbolic indoor positioning over the Miskolc IIS Data-set","authors":"J. Tamás, Zsolt Tóth","doi":"10.1080/17489725.2018.1455992","DOIUrl":"https://doi.org/10.1080/17489725.2018.1455992","url":null,"abstract":"Abstract Determination of indoor position is vital for the creation of smart environments. Symbolic indoor positioning algorithms determine the location as a well-defined part of the building, such as a room, a corridor or a floor. Performance analysis of classification-based symbolic indoor positioning methods are presented in this paper. Symbolic positioning can be considered as a classification task, where position denotes the category and the attributes are the measured values. Evaluation and comparison of the selected classification methods are performed over a hybrid data-set which was recorded by the ILONA (Indoor Localisation and Navigation) System. These experiments were performed in RapidMiner and the Weka framework. Accuracy is the base of comparison and the following classification methods were used: k–NN, Naive Bayes, Decision Tree, Rule Induction and Artificial Neural Network. Comparison is used to recommend a classification-based symbolic indoor positioning method to be implemented in the ILONA System. Experimental results show that the k–NN, Naive Bayes with 1 kernel and ANN classifiers achieved better than 90% accuracy. As a result of our experiments, k–NN, Naive Bayes with 1 kernel- and ANN-based classification methods are recommended.","PeriodicalId":44932,"journal":{"name":"Journal of Location Based Services","volume":"12 1","pages":"18 - 2"},"PeriodicalIF":2.3,"publicationDate":"2018-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/17489725.2018.1455992","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60103657","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}

{"title":"A multidimensional model for selecting personalised landmarks","authors":"Eva Nuhn, S. Timpf","doi":"10.1080/17489725.2017.1401129","DOIUrl":"https://doi.org/10.1080/17489725.2017.1401129","url":null,"abstract":"Abstract Route instructions for pedestrians usually include many references to landmarks. The specific landmark used depends on the perceived familiarity of the traveller with the environment. While a human informant is able to perceive this personal dimension and react accordingly, existing landmark selection algorithms neglect the individual spatial knowledge of a wayfinder. In this paper, we share our ideas for incorporating a personal dimension into the definition of landmark salience. This complements current models, which identify landmark salience based on attributes of landmarks or integrate route-dependent landmarks based on environmental factors. We propose a conceptual framework for a multidimensional model for personalised landmarks that integrates three dimensions: a dimension describing the landmark, an environmental dimension and a personal dimension, which has been lacking in existing landmark salience models. We identify and discuss attributes and attribute values for each of the dimensions. Furthermore, salience measures for the attributes of the individual dimensions are developed. The novelty of this research consists of integrating all known attributes and potential values within a single model of salience, while focusing on the personal dimension.","PeriodicalId":44932,"journal":{"name":"Journal of Location Based Services","volume":"11 1","pages":"153 - 180"},"PeriodicalIF":2.3,"publicationDate":"2017-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/17489725.2017.1401129","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48553981","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}

{"title":"Seamless navigation using GNSS and Wi-Fi/IN with intelligent checkpoints","authors":"Hannes Hofer, G. Retscher","doi":"10.1080/17489725.2017.1415385","DOIUrl":"https://doi.org/10.1080/17489725.2017.1415385","url":null,"abstract":"Abstract The aim of this work is to combine smartphone components which can be used for navigation in order to improve the accuracy of positioning. Intelligently chosen checkpoints along the user’s trajectory are selected and recognised by the wireless module using a location fingerprinting strategy. These waypoints (referred to as iCPs) are placed following a logical sequence with their ascending orders so that they must be passed and can be distinguished well. Moreover, the inertial sensors and the digital compass are used to detect changes in positioning continuously. While walking, the performed dynamic recognition of the iCPs reduces sensor drift as their recognition serves as absolute position update. Conducted experiments in a multi-storey office building indicated that positioning accuracy of 1.9 m on average is achievable if Wi-Fi and sensors data are combined. In contrast, with standalone GPS deviations of 19.8 m on average were obtained. Furthermore, an improvement of inertial navigation from around 6.0 m for the destination point to 1.5 m is achieved on average by the integrated positioning approach. This achievement goes along with a reduction of workload by three quarter using this novel approach as the number of reference points for training is reduced significantly..","PeriodicalId":44932,"journal":{"name":"Journal of Location Based Services","volume":"11 1","pages":"204 - 221"},"PeriodicalIF":2.3,"publicationDate":"2017-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/17489725.2017.1415385","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49340023","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}

{"title":"Location- and time-dependent meeting point recommendations for shared interurban rides","authors":"Paul Czioska, Aleksandar Trifunović, Sophie Dennisen, Monika Sester","doi":"10.1080/17489725.2017.1421779","DOIUrl":"https://doi.org/10.1080/17489725.2017.1421779","url":null,"abstract":"Abstract Drivers offering spare seats in their vehicles on long-distance (interurban) trips often have to pick up or drop off passengers in cities en route. In that case, it is necessary to agree on a meeting point. Often, this is done by proposing well-known locations like train stations, which frequently induces unnecessary detours through the inner-city districts. In contrast, meeting points in the vicinity of motorways and arterial roads with good public transport connection can reduce driving time and mileage. This work proposes a location-based approach to enable a fast and automatic recommendation of suitable pick up (and drop off) points for drivers and passengers using a GIS workflow and comprehensive precomputation of travel times.","PeriodicalId":44932,"journal":{"name":"Journal of Location Based Services","volume":"11 1","pages":"181 - 203"},"PeriodicalIF":2.3,"publicationDate":"2017-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/17489725.2017.1421779","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46895108","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}

{"title":"Detecting traffic congestion propagation in urban environments – a case study with Floating Taxi Data (FTD) in Shanghai","authors":"A. Keler, J. Krisp, L. Ding","doi":"10.1080/17489725.2017.1420256","DOIUrl":"https://doi.org/10.1080/17489725.2017.1420256","url":null,"abstract":"Abstract Traffic congestion in urban environments has severe influences on the daily life of people. Due to typical recurrent mobility patterns of commuters and transport fleets, we can detect traffic congestion events on selected hours of the day, so called rush hours. Besides the mentioned recurrent traffic congestion, there are non-recurrent events that may be caused by accidents or newly established building sites. We want to inspect this appearance using a massive Floating Taxi Data (FTD) set of Shanghai from 2007. We introduce a simple method for detecting and extracting congestion events on selected rush hours and for distinguishing between their recurrence and non-recurrence. By preselecting of similar velocity and driving direction values of the nearby situated FTD points, we provide the first part for the Shared Nearest Neighbour (SNN) clustering method, which follows with a density-based clustering. After the definition of our traffic congestion clusters, we try to connect ongoing events by querying individual taxi identifications. The detected events are then represented by polylines that connect density core points of the clusters. By comparing the shapes of congestion propagation polylines of different days, we try to classify recurrent congestion events that follow similar patterns. In the end, we reason on the reasonability of our method and mention further steps of its extension.","PeriodicalId":44932,"journal":{"name":"Journal of Location Based Services","volume":"11 1","pages":"133 - 151"},"PeriodicalIF":2.3,"publicationDate":"2017-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/17489725.2017.1420256","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43261274","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}

{"title":"Methodological aspects of using geocoded data from mobile devices in transportation research","authors":"R. Ahas, J. Krisp, T. Toivonen","doi":"10.1080/17489725.2017.1427020","DOIUrl":"https://doi.org/10.1080/17489725.2017.1427020","url":null,"abstract":"There is an increasing need for information on human mobility patterns in various fields of research from health research to urban planning and civil engineering, and particularly in transport research. Traditionally, research on spatial mobility and travel behaviour has been collected using questionnaires, observations, censuses and so on. Recently, geocoded spatial data from mobile phones and GPS devices have been increasingly utilised to analyse and understand the spatial mobility patterns and travel behaviour of citizens. In this special issue in Journal of Location-Based Services researchers make use of, for example, Call Detail Records (CDR) of mobile operators or Floating Taxi Data (FTD) to identify and map mobility trajectories for mobility studies and transportation research. The data from mobile phones and GPS devices are substantially different from the traditional data sources, which poses new methodological challenges for researchers. Most importantly, the data tends to be secondary, meaning that it has not been collected purposefully for research but is created as a by-product of other activities (Chen et al. 2016; Schwanen 2016). For example, the CDR data are originally generated to keep account of calls and prepare telephone bills, while FTD is collected to organise the logistics of taxi companies. Traditionally transport-related questionnaires and other methods in transport studies primarily involve purposeful questions being asked from a limited number of people about precise activities, trajectories, means of transport, motivations for travelling, etc. These questionnaire-based studies may be planned with a representative sample and adequate questions, which makes them an excellent research material. Compared to these, large secondary data-sets from technical devices generally have a lot of respondents and geolocated data points, but they are often less precise and are/can be only indirectly related to the research objectives (Calabrese et al. 2013; Järv, Ahas, and Witlox 2014). Despite the potential quality issues, the new types of digital data with a large sample size are appealing to researchers because such data are easier to obtain and often cheaper than separate questionnaires. They are also less intrusive and do not require time and effort from volunteers to collect data. Additionally, computational methods needed to process these data are developing at a quick pace and there are a large number of enthusiastic researchers and IT companies that develop business models based on the new data. The improvements in digital data flow makes data collection cycles faster and smoother. In relation to these developments, the Eurostat feasibility study ‘BIG data’ that was composed in 2014 (Eurostat 2014), and several other solid studies, highlight the need for a systematic development of methods regarding new data and validation procedures (Bernardin et al. 2017; Toole et al. 2015). This special issue of the Journal of Location-Base","PeriodicalId":44932,"journal":{"name":"Journal of Location Based Services","volume":"11 1","pages":"75 - 77"},"PeriodicalIF":2.3,"publicationDate":"2017-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/17489725.2017.1427020","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48754166","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}

{"title":"GIS-based mobile coverage estimation to reduce uncertainty of cellular trajectories","authors":"M. Forghani, F. Karimipour","doi":"10.1080/17489725.2017.1422562","DOIUrl":"https://doi.org/10.1080/17489725.2017.1422562","url":null,"abstract":"Abstract Mobile positioning data are normally generated as scattered points in CDRs (Call Detail Records), which include the IDs of connected cell towers for call activities. To interpret mobile associations with towers, one needs to model the area where the mobile phone user is likely to be when connected. Voronoi cells, which define the area closest to each tower, are usually employed to this end. However, the accuracy of Voronoi tessellation is influenced by various environmental factors due to propagation of signals in different manners, such as diffraction, scattering and reflection. This non-specificity of cell shapes causes a major source of uncertainty in, say, trajectory reconstruction. This paper evaluates the effect of imprecision of cell shape on accuracy of CDR-based trajectory reconstruction. It further aims to reconstruct more accurate trajectories from the coarse CDR data through considering the wave propagation models in estimating the location of mobile phone users. Since many of the factors in radio wave propagation are geographical, GIS techniques will be incorporated into this procedure.","PeriodicalId":44932,"journal":{"name":"Journal of Location Based Services","volume":"11 1","pages":"117 - 98"},"PeriodicalIF":2.3,"publicationDate":"2017-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/17489725.2017.1422562","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42622374","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}

{"title":"Correlation between digital and physical world, case study in Thessaloniki","authors":"Josep Maria Salanova Grau, I. Toumpalidis, Emmanouil Chaniotakis, N. Karanikolas, Georgia Aifandopoulou","doi":"10.1080/17489725.2017.1420257","DOIUrl":"https://doi.org/10.1080/17489725.2017.1420257","url":null,"abstract":"Abstract This study investigates the use of geo-referenced social media data for enhancing the transportation planning capabilities. A methodological framework is defined for the identification of the citizens’ mobility patterns using social media data, which consists of a number of check-in events that are made through the Facebook service and 1200 taxis’ floating car data for the city of Thessaloniki, during the period of February of 2016. Focus is paid on the derivation of taxi demand forecasting models using the number of check-in events through spatial statistic and analytical tools. The results illustrate the actual correlation of demand for taxis to the check-in events, while it is noted that models should take into account the temporal and spatial offset of the demand production to the check-in events. The results show that the correlation is better during the evening and night hours, since during the morning and afternoon most tips are homework based and people does not check-in at their place of work or at home.","PeriodicalId":44932,"journal":{"name":"Journal of Location Based Services","volume":"11 1","pages":"118 - 132"},"PeriodicalIF":2.3,"publicationDate":"2017-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/17489725.2017.1420257","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44893837","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}

{"title":"Extracting regular mobility patterns from sparse CDR data without a priori assumptions","authors":"Oliver Burkhard, R. Ahas, Erki Saluveer, R. Weibel","doi":"10.1080/17489725.2017.1333638","DOIUrl":"https://doi.org/10.1080/17489725.2017.1333638","url":null,"abstract":"Abstract In this work we present two methods that can extract habitual movement patterns and reconstruct the underlying movement of users from their call detail records (CDR) in a way that works for users with only moderate numbers of CDRs and that does not make any prior assumptions on the behaviour of the users. The methods allow for a more comprehensive user base in large-scale studies due to the fact that users that might otherwise have to be discarded can also be analysed. The first one is computationally not overly intense and is based on association mining. The second one, which we named DAMOCLES, is based on extracting idiosyncratic daily patterns from clustered daily activities. The methods are evaluated on real data of 140 users over an average of 200 days against benchmarks using assumptions commonly found in the literature such as a work week from Monday to Friday on GPS ground truth. Both methods clearly outperform the benchmarks and for many users retrieve similar regularities. Additionally a simulation study is performed that allows to evaluate the methods in a more controlled environment.","PeriodicalId":44932,"journal":{"name":"Journal of Location Based Services","volume":"11 1","pages":"78 - 97"},"PeriodicalIF":2.3,"publicationDate":"2017-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/17489725.2017.1333638","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43263980","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}

{"title":"Framework for location-aware search engine","authors":"A. Tabarcea, Najlah Gali, P. Fränti","doi":"10.1080/17489725.2017.1407001","DOIUrl":"https://doi.org/10.1080/17489725.2017.1407001","url":null,"abstract":"Abstract Nowadays, a large part of the multimedia data on the Internet is generated with devices that automatically annotate them with location information However, free-form content on websites does not implicitly contain any geographical information. This is the biggest challenge for building a location-aware search engine. In this paper, we study how to extract location-aware information from the web. The key challenges are to detect location from a web page and to extract relevant information related to that location. We detect locations by identifying postal addresses using freely available gazetteers. Additional information for summarising the search results are titles and representative images, which we mine from the content using simple rule-based approaches utilising the structure of web pages. This information can be used to personalise search results for mobile users so that the results are relevant to their location.","PeriodicalId":44932,"journal":{"name":"Journal of Location Based Services","volume":"11 1","pages":"50 - 74"},"PeriodicalIF":2.3,"publicationDate":"2017-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/17489725.2017.1407001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48447543","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}