TOST: A Topological Semantic Model for GPS Trajectories Inside Road Networks

Wu, Tao; Qin, Jianxin; Wan, Yat-wah

doi:10.3390/ijgi8090410

Cited by 13 publications

(7 citation statements)

References 31 publications

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“…These trajectory semantic information construction studies did not discuss spatiotemporal topological semantics in detail. However, spatiotemporal topological semantics are critical elements in a semantic trajectory model (Hove & Keller, 2010; Shen et al, 2020; Wu et al, 2019; Xiang et al, 2017), particularly for movements in a limited space, such as driving inside a road network. Spatiotemporal topological semantics are spatiotemporal relations that are invariant under topological transformations (Abraham & Roddick, 1999), such as rotation and scaling.…”

Section: Related Workmentioning

confidence: 99%

A topological model of trajectories with road network space

Zhu

Xiang

et al. 2022

Transactions in GIS

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Spatiotemporal topological semantic information has attracted a lot of attention recently when exploring high‐level human movement features in geographic contexts. Although topological semantics between a trajectory and individual spatial objects have been studied extensively, few studies have focused on movements in the specific context of spatial infrastructures, such as road networks. This study proposes a topological trajectory model that integrates the topological semantics of trajectory data related to road networks and regions in a unified framework. The proposed model considers key points during a trajectory and establishes a topological intersection model to describe key points’ semantics. With the topological semantics of the trajectory–road network obtained, the road network’s semantic relation is derived directly from the topological relation between road networks and regions. This model can intuitively express the semantic relations of the three elements, trajectory, road network, and region, which helps spatial queries and analyses of network constrained movements. A case study of real traffic data shows that more detailed semantic information can be obtained based on this model.

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Section: Related Workmentioning

confidence: 99%

A topological model of trajectories with road network space

Zhu

Xiang

et al. 2022

Transactions in GIS

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“…This will help to direct certain applications of location-based services and make them more convenient to use, which is the primary concern of new research. More specifically, trajectory data often includes human movement connected to the geographical context, which is becoming increasingly important in the representation and interpretation of real information embedded in movements and further processing [11]. Some studies have therefore switched to concentrate on rich contexts from other data sources to provide a semantic view of the trajectories.…”

Section: Introductionmentioning

confidence: 99%

Extracting Stops from Spatio-Temporal Trajectories within Dynamic Contextual Features

Shen

Qin

et al. 2021

Sustainability

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Identifying stops from GPS trajectories is one of the main concerns in the study of moving objects and has a major effect on a wide variety of location-based services and applications. Although the spatial and non-spatial characteristics of trajectories have been widely investigated for the identification of stops, few studies have concentrated on the impacts of the contextual features, which are also connected to the road network and nearby Points of Interest (POIs). In order to obtain more precise stop information from moving objects, this paper proposes and implements a novel approach that represents a spatio-temproal dynamics relationship between stopping behaviors and geospatial elements to detect stops. The relationship between the candidate stops based on the standard time–distance threshold approach and the surrounding environmental elements are integrated in a complex way (the mobility context cube) to extract stop features and precisely derive stops using the classifier classification. The methodology presented is designed to reduce the error rate of detection of stops in the work of trajectory data mining. It turns out that 26 features can contribute to recognizing stop behaviors from trajectory data. Additionally, experiments on a real-world trajectory dataset further demonstrate the effectiveness of the proposed approach in improving the accuracy of identifying stops from trajectories.

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“…These raw data of trajectories are designated as Raw Trajectories of Moving Objects (RTMO) and do not contain semantic annotations and nor metadata. The growing availability of RTMO imposes the need of analysis, semantical enrichment, and sharing with communities that are interested in their application [4][5][6]. These semantically enriched trajectories are named STMO and correspond to a sequence of spatial-temporal points with associated semantic information (e.g., information on the place visited, type of transport of the moving object, the aim of the movement etc.).…”

Section: Introductionmentioning

confidence: 99%

Towards the Semantic Enrichment of Trajectories Using Spatial Data Infrastructures

Vidal-Filho

Times

Filho

et al. 2021

IJGI

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The term Semantic Trajectories of Moving Objects (STMO) corresponds to a sequence of spatial-temporal points with associated semantic information (for example, annotations about locations visited by the user or types of transportation used). However, the growth of Big Data generated by users, such as data produced by social networks or collected by an electronic equipment with embedded sensors, causes the STMO to require services and standards for enabling data documentation and ensuring the quality of STMOs. Spatial Data Infrastructures (SDI), on the other hand, provide a shared interoperable and integrated environment for data documentation. The main challenge is how to lead traditional SDIs to evolve to an STMO document due to the lack of specific metadata standards and services for semantic annotation. This paper presents a new concept of SDI for STMO, named SDI4Trajectory, which supports the documentation of different types of STMO—holistic trajectories, for example. The SDI4Trajectory allows us to propose semi-automatic and manual semantic enrichment processes, which are efficient in supporting semantic annotations and STMO documentation as well. These processes are hardly found in traditional SDIs and have been developed through Web and semantic micro-services. To validate the SDI4Trajectory, we used a dataset collected by voluntary users through the MyTracks application for the following purposes: (i) comparing the semi-automatic and manual semantic enrichment processes in the SDI4Trajectory; (ii) investigating the viability of the documentation processes carried out by the SDI4Trajectory, which was able to document all the collected trajectories.

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TOST: A Topological Semantic Model for GPS Trajectories Inside Road Networks

Cited by 13 publications

References 31 publications

A topological model of trajectories with road network space

A topological model of trajectories with road network space

Extracting Stops from Spatio-Temporal Trajectories within Dynamic Contextual Features

Towards the Semantic Enrichment of Trajectories Using Spatial Data Infrastructures

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