Pub Date : 2023-07-27DOI: 10.1080/17489725.2023.2239190
Nina Wiedemann, Henry Martin, Esra Suel, Ye Hong, Yanan Xin
{"title":"Influence of tracking duration on the privacy of individual mobility graphs","authors":"Nina Wiedemann, Henry Martin, Esra Suel, Ye Hong, Yanan Xin","doi":"10.1080/17489725.2023.2239190","DOIUrl":"https://doi.org/10.1080/17489725.2023.2239190","url":null,"abstract":"","PeriodicalId":44932,"journal":{"name":"Journal of Location Based Services","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48547663","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}
Pub Date : 2023-07-25DOI: 10.1080/17489725.2023.2238658
I. Kveladze, Marina Georgati, C. Kessler, H. Hansen
{"title":"Analytics of historical human migration patterns: use cases of Amsterdam and Copenhagen","authors":"I. Kveladze, Marina Georgati, C. Kessler, H. Hansen","doi":"10.1080/17489725.2023.2238658","DOIUrl":"https://doi.org/10.1080/17489725.2023.2238658","url":null,"abstract":"","PeriodicalId":44932,"journal":{"name":"Journal of Location Based Services","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44447672","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}
Pub Date : 2023-07-24DOI: 10.1080/17489725.2023.2238661
Rui Li
{"title":"Augmented reality landmarks on windshield and their effects on the acquisition of spatial knowledge in autonomous vehicles","authors":"Rui Li","doi":"10.1080/17489725.2023.2238661","DOIUrl":"https://doi.org/10.1080/17489725.2023.2238661","url":null,"abstract":"","PeriodicalId":44932,"journal":{"name":"Journal of Location Based Services","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44243401","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}
Pub Date : 2023-07-20DOI: 10.1080/17489725.2023.2238663
Kenzo Milleville, S. Van Ackere, Jana Verdoodt, S. Verstockt, P. de Maeyer, N. van de Weghe
{"title":"Exploring the potential of social media to study environmental topics and natural disasters","authors":"Kenzo Milleville, S. Van Ackere, Jana Verdoodt, S. Verstockt, P. de Maeyer, N. van de Weghe","doi":"10.1080/17489725.2023.2238663","DOIUrl":"https://doi.org/10.1080/17489725.2023.2238663","url":null,"abstract":"","PeriodicalId":44932,"journal":{"name":"Journal of Location Based Services","volume":"1 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41555767","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}
Pub Date : 2023-06-28DOI: 10.1080/17489725.2023.2229285
Fateme Teimouri, Kai-Florian Richter, H. Hochmair
ABSTRACT Navigation services are essential for daily navigation, providing turn-by-turn instructions to help wayfinders reach their destinations. These services often differ from the heuristics wayfinders use, resulting in a poor user experience. Researchers have attempted to address this issue by developing algorithms that find less complex routes, by integrating prominent locations along the route to make wayfinding easier and to improve a wayfinder’s knowledge about the environment. These approaches, however, have taken a bottom-up approach, involving a limited number of participants navigating in real or virtual environments which may limit generalisability of results. In this study, we took a top-down approach by analysing a large dataset of GPS-based trips in the real world. Using the Geolife dataset, we analysed individual heuristics for route selection in terms of complexity and prominent locations, and found that wayfinders prefer less complex routes, such as routes that require fewer turns or involve simpler intersections. Additionally, we found that wayfinders choose routes with fewer prominent locations, such as routes that bypass well-known landmarks or busy commercial areas. These findings suggest that simplicity and ease of use are prioritized when selecting a route, while overly complex routes or areas with many points of interest are avoided.
{"title":"Analysis of route choice based on path characteristics using Geolife GPS trajectories","authors":"Fateme Teimouri, Kai-Florian Richter, H. Hochmair","doi":"10.1080/17489725.2023.2229285","DOIUrl":"https://doi.org/10.1080/17489725.2023.2229285","url":null,"abstract":"ABSTRACT Navigation services are essential for daily navigation, providing turn-by-turn instructions to help wayfinders reach their destinations. These services often differ from the heuristics wayfinders use, resulting in a poor user experience. Researchers have attempted to address this issue by developing algorithms that find less complex routes, by integrating prominent locations along the route to make wayfinding easier and to improve a wayfinder’s knowledge about the environment. These approaches, however, have taken a bottom-up approach, involving a limited number of participants navigating in real or virtual environments which may limit generalisability of results. In this study, we took a top-down approach by analysing a large dataset of GPS-based trips in the real world. Using the Geolife dataset, we analysed individual heuristics for route selection in terms of complexity and prominent locations, and found that wayfinders prefer less complex routes, such as routes that require fewer turns or involve simpler intersections. Additionally, we found that wayfinders choose routes with fewer prominent locations, such as routes that bypass well-known landmarks or busy commercial areas. These findings suggest that simplicity and ease of use are prioritized when selecting a route, while overly complex routes or areas with many points of interest are avoided.","PeriodicalId":44932,"journal":{"name":"Journal of Location Based Services","volume":"17 1","pages":"271 - 297"},"PeriodicalIF":2.3,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46157268","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}
Pub Date : 2023-05-27DOI: 10.1080/17489725.2023.2216670
Bruno de Moura Morceli, A. Poz
ABSTRACT This paper proposes a method for road centerline extraction from dense Global Navigation Satellite System (GNSS) trajectories, collected by using low-cost GNSS-devices, i.e. smartphones. The proposed method basically consists in generating a frequency image by tracking the GNSS trajectories and then by applying the Steger line detector to the generated image to extract the road centerlines. The main motivation of using the Steger algorithm is its capability to detect lines with sub-pixel accuracy. To evaluate the obtained results, reference road centerlines are manually extracted from a georeferenced orthomosaic. The experiments performed demonstrate the high potential of applying the Steger line detector to frequency images, generated by using dense GPS (Global Positioning System) trajectories. The completeness and correctness values for the accomplished experiments were 98% and 99%, respectively. Additionally, the RMSE (Root Mean Square Error) ranged from 0.63 m to 2.39 m, or approximately 1/16 to 1/4 of the expected accuracy (about 10 m) of a point determined by the Single-Point Positioning (SPP) method, which is the GNSS positioning method usually employed by smartphones. KEY POLICY HIGHLIGHTS In this paper we propose to extract roads by using dense GNSS trajectories based on frequency images. GNSS trajectories were collected from low-cost devices (smartphones). Unlike optical images, trajectory frequency images show only roads, thereby preventing problems such as extracting non-road objects. The experiments showed the high potential of using the Steger line detector for road extraction. Profiles drawn cross-sectionally to the roads actually exhibit behaviour similar to the normal distribution. The results obtained were between 16 times and four times better than the expected accuracy of the GNSS positioning method via the SPP positioning method.
{"title":"Road extraction from low-cost GNSS-device dense trajectories","authors":"Bruno de Moura Morceli, A. Poz","doi":"10.1080/17489725.2023.2216670","DOIUrl":"https://doi.org/10.1080/17489725.2023.2216670","url":null,"abstract":"ABSTRACT This paper proposes a method for road centerline extraction from dense Global Navigation Satellite System (GNSS) trajectories, collected by using low-cost GNSS-devices, i.e. smartphones. The proposed method basically consists in generating a frequency image by tracking the GNSS trajectories and then by applying the Steger line detector to the generated image to extract the road centerlines. The main motivation of using the Steger algorithm is its capability to detect lines with sub-pixel accuracy. To evaluate the obtained results, reference road centerlines are manually extracted from a georeferenced orthomosaic. The experiments performed demonstrate the high potential of applying the Steger line detector to frequency images, generated by using dense GPS (Global Positioning System) trajectories. The completeness and correctness values for the accomplished experiments were 98% and 99%, respectively. Additionally, the RMSE (Root Mean Square Error) ranged from 0.63 m to 2.39 m, or approximately 1/16 to 1/4 of the expected accuracy (about 10 m) of a point determined by the Single-Point Positioning (SPP) method, which is the GNSS positioning method usually employed by smartphones. KEY POLICY HIGHLIGHTS In this paper we propose to extract roads by using dense GNSS trajectories based on frequency images. GNSS trajectories were collected from low-cost devices (smartphones). Unlike optical images, trajectory frequency images show only roads, thereby preventing problems such as extracting non-road objects. The experiments showed the high potential of using the Steger line detector for road extraction. Profiles drawn cross-sectionally to the roads actually exhibit behaviour similar to the normal distribution. The results obtained were between 16 times and four times better than the expected accuracy of the GNSS positioning method via the SPP positioning method.","PeriodicalId":44932,"journal":{"name":"Journal of Location Based Services","volume":"17 1","pages":"251 - 270"},"PeriodicalIF":2.3,"publicationDate":"2023-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47047694","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}
Pub Date : 2023-01-22DOI: 10.1080/17489725.2023.2168078
A. Chehreghan, E. Saadatzadeh, R. Abbaspour
ABSTRACT One of the main elements of location-based services (LBS) is the awareness and knowledge of the user’s location information inside the smart buildings. In this study, a smartphone sensor-based indoor positioning system (IPS) is proposed to track a person’s location in Texting and Pocket carrying modes in a smart building. The gravity, gyroscope, and magnetometer sensors data were combined using a gradient descent algorithm (GDA) to estimate the heading angle. This system was implemented in three straight, complex, and rectangular paths. The mean (M) and standard deviation (SD) of the absolute heading error of each step were obtained as (1.68°, 1.97°) in the Texting mode and (4.39°, 5.22°) in the Pocket mode, respectively. Acceleration and angle-based models were employed to estimate the step length in the Texting and Pocket modes, respectively. The mean relative error (MRE) of the distance in the Texting and Pocket modes were obtained as %4.8 and %4.37, respectively. Experimental results indicated the MRE of the final position along the three paths in the two carrying modes of Texting and Pocket by magnetic and proposed methods reduced from %3.75 to %2.66 and %7.02 to %4.24, respectively.
{"title":"Infrastructure-free indoor navigation based on smartphone sensors in smart buildings","authors":"A. Chehreghan, E. Saadatzadeh, R. Abbaspour","doi":"10.1080/17489725.2023.2168078","DOIUrl":"https://doi.org/10.1080/17489725.2023.2168078","url":null,"abstract":"ABSTRACT One of the main elements of location-based services (LBS) is the awareness and knowledge of the user’s location information inside the smart buildings. In this study, a smartphone sensor-based indoor positioning system (IPS) is proposed to track a person’s location in Texting and Pocket carrying modes in a smart building. The gravity, gyroscope, and magnetometer sensors data were combined using a gradient descent algorithm (GDA) to estimate the heading angle. This system was implemented in three straight, complex, and rectangular paths. The mean (M) and standard deviation (SD) of the absolute heading error of each step were obtained as (1.68°, 1.97°) in the Texting mode and (4.39°, 5.22°) in the Pocket mode, respectively. Acceleration and angle-based models were employed to estimate the step length in the Texting and Pocket modes, respectively. The mean relative error (MRE) of the distance in the Texting and Pocket modes were obtained as %4.8 and %4.37, respectively. Experimental results indicated the MRE of the final position along the three paths in the two carrying modes of Texting and Pocket by magnetic and proposed methods reduced from %3.75 to %2.66 and %7.02 to %4.24, respectively.","PeriodicalId":44932,"journal":{"name":"Journal of Location Based Services","volume":"17 1","pages":"145 - 184"},"PeriodicalIF":2.3,"publicationDate":"2023-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47007571","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}
Pub Date : 2022-12-22DOI: 10.1080/17489725.2022.2157898
Elias Issawy, B. Levy, S. Dalyot
ABSTRACT An important feature of mobile devices relates to positioning, mainly relying on the global navigation satellite system sensor. In optimal conditions, this sensor provides horizontal positioning sufficient for most location-based services. The elevation, on the other hand, still lacks sufficient accuracy and reliability – mostly due to mobile device inadequacies that stem from technological limitations and environmental and physical conditions, which impact the observations quality. We suggest augmenting the elevation measurements of this sensor with measurements from supplementary embedded mobile device sensors, such as barometers and accelerometers, and with data from external mapping and environmental databases, namely topography and weather. We developed an artificial neural network deep-learning model that identifies parameter values for producing the highest predictive accuracy of the elevation value while relying on a comprehensive set of measurements. Our findings indicate very promising results, whereby we enhanced the elevation accuracy of testing data by 428%, while significantly reducing the elevation variance. These results show that using supplementary measurements and data improves elevation values while significantly reducing errors commonly associated with mobile device global navigation satellite system sensors. The proposed method has the capacity to improve outdoor kinematic positioning for location-based services, with a focus on urban and concealed areas.
{"title":"Elevation accuracy improvement in mobile devices by implementing artificial neural networks","authors":"Elias Issawy, B. Levy, S. Dalyot","doi":"10.1080/17489725.2022.2157898","DOIUrl":"https://doi.org/10.1080/17489725.2022.2157898","url":null,"abstract":"ABSTRACT An important feature of mobile devices relates to positioning, mainly relying on the global navigation satellite system sensor. In optimal conditions, this sensor provides horizontal positioning sufficient for most location-based services. The elevation, on the other hand, still lacks sufficient accuracy and reliability – mostly due to mobile device inadequacies that stem from technological limitations and environmental and physical conditions, which impact the observations quality. We suggest augmenting the elevation measurements of this sensor with measurements from supplementary embedded mobile device sensors, such as barometers and accelerometers, and with data from external mapping and environmental databases, namely topography and weather. We developed an artificial neural network deep-learning model that identifies parameter values for producing the highest predictive accuracy of the elevation value while relying on a comprehensive set of measurements. Our findings indicate very promising results, whereby we enhanced the elevation accuracy of testing data by 428%, while significantly reducing the elevation variance. These results show that using supplementary measurements and data improves elevation values while significantly reducing errors commonly associated with mobile device global navigation satellite system sensors. The proposed method has the capacity to improve outdoor kinematic positioning for location-based services, with a focus on urban and concealed areas.","PeriodicalId":44932,"journal":{"name":"Journal of Location Based Services","volume":"17 1","pages":"122 - 144"},"PeriodicalIF":2.3,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44580853","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}
Pub Date : 2022-11-30DOI: 10.1080/17489725.2022.2151658
Nadjet Azzaoui, A. Korichi, B. Brik, Hanane Amirat
ABSTRACT Internet of vehicles (IoV) is rapidly growing as key enablers of new applications related to Intelligent Transportation System (ITS), including: autonomous driving, teleoperation, cooperative manoeuvre and perception, etc. The design of such new applications relies mainly on the performance of data dissemination techniques, which enable vehicles to exchange data with their surroundings. In fact, these new applications are coming with new requirements such as ultra-low latency, high bandwidth and communication reliability. This limits also the use of data dissemination techniques designed for traditional vehicular network, especially with the increasing number of connected vehicles on roads. In this paper, we review a taxonomy of data dissemination techniques for IoV based on four new classes: networking-based class, intelligent-based class, traditional-based class, and hybrid class. Furthermore, the paper not only reviews some recent contributions addressing data dissemination in IoV, but also emphasis their enabling technologies, services, architectures, their used simulation tools, and open challenges.
{"title":"A survey on data dissemination in internet of vehicles networks","authors":"Nadjet Azzaoui, A. Korichi, B. Brik, Hanane Amirat","doi":"10.1080/17489725.2022.2151658","DOIUrl":"https://doi.org/10.1080/17489725.2022.2151658","url":null,"abstract":"ABSTRACT Internet of vehicles (IoV) is rapidly growing as key enablers of new applications related to Intelligent Transportation System (ITS), including: autonomous driving, teleoperation, cooperative manoeuvre and perception, etc. The design of such new applications relies mainly on the performance of data dissemination techniques, which enable vehicles to exchange data with their surroundings. In fact, these new applications are coming with new requirements such as ultra-low latency, high bandwidth and communication reliability. This limits also the use of data dissemination techniques designed for traditional vehicular network, especially with the increasing number of connected vehicles on roads. In this paper, we review a taxonomy of data dissemination techniques for IoV based on four new classes: networking-based class, intelligent-based class, traditional-based class, and hybrid class. Furthermore, the paper not only reviews some recent contributions addressing data dissemination in IoV, but also emphasis their enabling technologies, services, architectures, their used simulation tools, and open challenges.","PeriodicalId":44932,"journal":{"name":"Journal of Location Based Services","volume":"17 1","pages":"207 - 250"},"PeriodicalIF":2.3,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44908211","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}
Pub Date : 2022-09-19DOI: 10.1080/17489725.2022.2148008
Alexandra Kapp, Saskia Nuñez von Voigt, Helena Mihaljevic, Florian Tschorsch
ABSTRACT The importance of human mobility analyses is growing in both research and practice, especially as applications for urban planning and mobility rely on them. Aggregate statistics and visualizations play an essential role as building blocks of data explorations and summary reports, the latter being increasingly released to third parties such as municipal administrations or in the context of citizen participation. However, such explorations already pose a threat to privacy as they reveal potentially sensitive location information, and thus should not be shared without further privacy measures. There is a substantial gap between state-of-the-art research on privacy methods and their utilization in practice. We thus conceptualize a mobility report with differential privacy guarantees and implement it as open-source software to enable a privacy-preserving exploration of key aspects of mobility data in an easily accessible way. Moreover, we evaluate the benefits of limiting user contributions using three data sets relevant to research and practice. Our results show that even a strong limit on user contribution alters the original geospatial distribution only within a comparatively small range, while significantly reducing the error introduced by adding noise to achieve privacy guarantees.
{"title":"Towards mobility reports with user-level privacy","authors":"Alexandra Kapp, Saskia Nuñez von Voigt, Helena Mihaljevic, Florian Tschorsch","doi":"10.1080/17489725.2022.2148008","DOIUrl":"https://doi.org/10.1080/17489725.2022.2148008","url":null,"abstract":"ABSTRACT The importance of human mobility analyses is growing in both research and practice, especially as applications for urban planning and mobility rely on them. Aggregate statistics and visualizations play an essential role as building blocks of data explorations and summary reports, the latter being increasingly released to third parties such as municipal administrations or in the context of citizen participation. However, such explorations already pose a threat to privacy as they reveal potentially sensitive location information, and thus should not be shared without further privacy measures. There is a substantial gap between state-of-the-art research on privacy methods and their utilization in practice. We thus conceptualize a mobility report with differential privacy guarantees and implement it as open-source software to enable a privacy-preserving exploration of key aspects of mobility data in an easily accessible way. Moreover, we evaluate the benefits of limiting user contributions using three data sets relevant to research and practice. Our results show that even a strong limit on user contribution alters the original geospatial distribution only within a comparatively small range, while significantly reducing the error introduced by adding noise to achieve privacy guarantees.","PeriodicalId":44932,"journal":{"name":"Journal of Location Based Services","volume":"17 1","pages":"95 - 121"},"PeriodicalIF":2.3,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47913474","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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