Semantic Trajectory Research Articles

Understanding Occupant Behaviors in Dynamic Environments using OBiDE framework

Occupants’ movements and presence are fundamental and the pre-requisites for any type of occupant behaviors’ understanding which tells whether a building location is occupied, the number of occupants or an occupant with a specific profile in a certain location. Numerous studies have been conducted over the past few decades to model occupant behaviors stochastically for an improved understanding of their activities for different facility management applications. Despite many research efforts to model dynamic behaviors of building occupants, the understanding of their behaviors by incorporating the contextual information linked to the evolution of the building environment is still not adequately explored. The contextual information linked to locations in dynamic environments changes often over time in terms of position, size, properties and relationships with the environment. This changing building environment affects the occupants’ movements and presence inside the facility which ultimately degrades the process of inferring their accurate activities based on the location context. Henceforth, the evolving building information is required to be mapped with occupant movements for an improved understanding of their changing behaviors. To fill this research gap, a framework named ‘Occupant Behaviors in Dynamic Environments’ (OBiDE) is designed for providing a ‘blueprint map’ to integrate existing DNAS (Drivers, Needs, Systems, Actions) ontology (i.e. a scheme to model occupant behaviors) with our semantic trajectory enrichment model to better understand the occupant behaviors by tracking the dynamicity of building locations. The proposed framework extends the usability of DNAS by providing a centralized knowledge base that holds the movements of occupants with relevant historicized contextual information of the building environment to study occupant behaviors for different facility management applications.

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

To organize trajectory data is a challenging issue for both studies on spatial databases and spatial data mining in the last decade, especially where there is semantic information involved. The high-level semantic features of trajectory data exploit human movement interrelated with geographic context, which is becoming increasingly important in representing and analyzing actual information contained in movements and further processing. This paper argues for a novel semantic trajectory model named TOST. It considers both semantic and geographic information of trajectory data happens along network infrastructure simultaneously. In TOST, a flexible intersection-based semantic representation is designed to express movement typically constrained by urban road networks by combining sets of local semantic details along the time axis. A relational schema based on this model was instantiated against real datasets, which illustrated the effectivity of our proposed model.

Analytical queries on semantic trajectories using graph databases

AbstractThis article studies the analysis of moving object data collected by location‐aware devices, such as GPS, using graph databases. Such raw trajectories can be transformed into so‐called semantic trajectories, which are sequences of stops that occur at “places of interest.” Trajectory data analysis can be enriched if spatial and non‐spatial contextual data associated with the moving objects are taken into account, and aggregation of trajectory data can reveal hidden patterns within such data. When trajectory data are stored in relational databases, there is an “impedance mismatch” between the representation and storage models. Graphs in which the nodes and edges are annotated with properties are gaining increasing interest to model a variety of networks. Therefore, this article proposes the use of graph databases (Neo4j in this case) to represent and store trajectory data, which can thus be analyzed at different aggregation levels using graph query languages (Cypher, for Neo4j). Through a real‐world public data case study, the article shows that trajectory queries are expressed more naturally on the graph‐based representation than over the relational alternative, and perform better in many typical cases.

A System of Mining Semantic Trajectory Patterns from GPS Data of Real Users

Positioning devices allow users’ movement to be recorded. The GPS (Global Positioning System) trajectory data typically consists of spatiotemporal points, which make up the major part of the big data concerning urban life. Existing knowledge extraction methods about the trajectory share a general limitation—they only investigate data from a spatiotemporal aspect, but fail to take the semantic information of trajectories into consideration. Therefore, extracting the semantic information of trajectories with the context of big data is challenging pattern recognition task that has practical application prospects. In this paper, a system is proposed to extract the semantic trajectory patterns of positioning device users. Firstly, a spatiotemporal threshold and clustering based pre-processing model is proposed to process the raw data. Then, we design a probabilistic generative model to annotate the semantic information of each trajectory after the pre-processing procedure. Finally, we apply the PrefixSpan algorithm to mine the semantic trajectory patterns. We verify our system on a large dataset of users’ real trajectories over a period of 5 years in Beijing, China. The results of the experiment indicate that our system produces meaningful patterns.

Effective spatio-temporal semantic trajectory generation for similar pattern group identification

The daily trajectories of individual movements convey a concise overview of their behaviors, with different social roles having different trajectory patterns. Therefore, we can identify users or groups based on the similar of their trajectory patterns. However, most existing trajectory analysis focuses only on the spatial and temporal analyses of the raw trajectory data and misses essential semantic information concerning behaviors. In this paper, we propose a new trajectory semantics calculation method to identify groups with similar behaviors. We first propose a fast and efficient two-phase method for identifying stay regions within daily trajectories and enriching the stay regions with semantic labels based on points of interest to generate semantic trajectories. Furthermore, we design a semantic similarity measure model using geographic and semantic similarity factors to measure the similarity between semantic trajectories. We also propose a pruning strategy using time entropy to decrease the number of complex calculations and comparisons to improve performance. The results of our extensive experiments on the real trajectory dataset of the Geolife project show that our proposed method is both effective and efficient.

SMSM: a similarity measure for trajectory stops and moves

ABSTRACTFor many years trajectory similarity research has focused on raw trajectories, considering only space and time information. With the trajectory semantic enrichment, emerged the need for similarity measures that support space, time, and semantics. Although some trajectory similarity measures deal with all these dimensions, they consider only stops, ignoring the moves. We claim that, for some applications, the movement between stops is as important as the stops, and they must be considered in the similarity analysis. In this article, we propose SMSM, a novel similarity measure for semantic trajectories that considers both stops and moves. We evaluate SMSM with three trajectory datasets: (i) a synthetic trajectory dataset generated with the Hermoupolis semantic trajectory generator, (ii) a real trajectory dataset from the CRAWDAD project, and (iii) the Geolife dataset. The results show that SMSM overcomes state-of-the-art measures developed either for raw or semantic trajectories.

Privacy preserving semantic trajectory data publishing for mobile location-based services

The development of wireless technologies and the popularity of mobile devices is responsible for generating large amounts of trajectory data for moving objects. Trajectory datasets have spatiotemporal features and are a rich information source. The mining of trajectory data can reveal interesting patterns of human activities and behaviors. However, trajectory data can also be exploited to disclose users’ privacy information, e.g., the places they live and work, which could be abused by a malicious user. Therefore, it is very important to protect the users’ privacy before publishing any trajectory data. While most previous research on this subject has only considered the privacy protection of stay points, this paper distinguishes itself by modeling and processing semantic trajectories, which not only contain spatiotemporal data but also involve POI information and the users’ motion modes such as walking, running, driving, etc. Accordingly, in this research, semantic trajectory anonymizing based on the k-anonymity model is proposed that can form sensitive areas that contain k − 1 POI points that are similar to the sensitive points. Then, trajectory ambiguity is executed based on the motion modes, road network topologies and road weights in the sensitive area. Finally, a similarity comparison is performed to obtain the recordable and releasable anonymity trajectory sets. Experimental results show that this method performs efficiently and provides high privacy levels.

Semantic trajectory insights for worker safety in dynamic environments

Existing studies reveal that unsafe worker movement behaviors are one of the major reasons of construction site fatalities resulting in serious collisions with site objects and machinery. For understanding worker movements in dynamic construction environments which involve moving and changing objects, a system named ‘WoTAS’ (Worker Trajectory Analysis System) is proposed. First, a real-time Bluetooth Low Energy (BLE) beacons-based data collection and trajectory pre-processing subsystem is built for extracting multifaceted trajectory characteristics and stay regions of the workers that will help in recognizing the important regions in the building for categorizing the worker movements. Second, to enable the desired semantic insights for better understanding the underlying meaningful worker movements using the contextual data repositories related to the building environment, an ontology-based ‘STriDE’ (Semantic Trajectories for Dynamic Environments) model is applied which tracks the evolution of moving and changing building objects and outputs semantic trajectories. For extracting insights from the semantic trajectories, the Hidden Markov Model (HMM) is used which is one of the probabilistic approaches present in the literature for describing the object behavior in time. Using the HMMs, a set of trajectories belonging to a stay region is analyzed by categorizing the worker movements into four different states. In the end, the output of the Viterbi algorithm is visualized using a BIM model for identifying the most probable high-risk locations involving sharp worker movements and rotations. The developed ‘WoTAS’ system will help safety managers in monitoring and controlling building activities remotely in dynamic environments by understanding the worker movements for improved safety management in day-to-day building operations. Eventually, understanding the worker movements will contribute towards reducing the chances of near-miss incidents on sites which have the potential to cause serious accidents.

Mapping knowledge domains of non-biomedical modalities: A large-scale co-word analysis of literature 1987–2017

This paper presents a systematic mapping of the disparate literature on non-biomedical therapeutic modalities using co-word analysis. Non-biomedical modalities are defined in this paper as therapeutic modalities that exist in separation, but not isolation from, biomedicine. Bibliometric visualisation based on co-word analysis, a method sensitive to the configuration of socio-cognitive networks of knowledge, is employed to create a semantic topography of thirty years’ literature from across different disciplines. The proliferation of terminologies to describe non-biomedical modalities from different disciplines raises important issues about the structure of scholarly knowledge about this area, particularly with regards to domains of meaning and conceptual spaces that lay dormant within this discourse. Drawing from a bibliographic dataset of 17,163 peer-reviewed publications written in English between 1987 and 2017 (retrieved on September 31, 2018), this paper presents a rigorous map with which to navigate the highly complex and interdisciplinary literature on non-biomedical knowledge and practices. Arguing that knowledge production about non-biomedical modalities in scholarly literature resembles that of problematic networks of interest, this paper substantiates the separation from biomedicine that contradistinguishes non-biomedical modalities. It does so by analysing the semantic trajectories of the most widely used terminologies in this domain, namely traditional medicine, alternative medicine, herbal medicine, and unclassified drug. Although all equally problematic, these contested terminologies are unlikely to replace one another in any form of paradigmatic shift in the foreseeable future. Their persisting conceptual usefulness is anchored in their own respective clusters of meaning, and researchers wishing to engage in the production of knowledge in this domain should be mindful of the pitfalls associated with their terminology use. Non-biomedical modalities as a term might be better equipped to capture the diversity as well as the historical continuities and discontinuities of therapeutic traditions and practices at the margin of mainstream scientific medicine.

Semantic-aware aircraft trajectory prediction using flight plans

Aircraft trajectory prediction (TP) is a challenging and inherently data-driven time-series modeling problem. Adding annotation or enrichment parameters further increases the search space complexity, especially when ‘blind’ optimization algorithms are employed. In this paper, flight plans, localized weather and aircraft properties are introduced as trajectory annotations that enable modeling in a space higher than the typical 4-D spatio-temporal. A multi-stage hybrid approach is employed for a new variation of the core TP task, the so-called Future Semantic Trajectory Prediction, including clustering the enriched trajectory data using a semantic-aware similarity function as distance metric. Subsequently, a separate predictive model is trained for each cluster, using a nonuniform graph-based grid that is formed by the waypoints of each flight plan. In practice, flight plans constitute a constrained-based training of each predictive model, one for each waypoint, independently. The proposed method is formulated and experimentally validated with real aviation dataset (flight plans and IFS radar tracks) and localized weather data for a 1-month time frame of flights in the Spanish airspace. Various types of predictive models are tested, including hidden Markov model (HMM), linear regressors, regression trees and feed-forward neural networks. The results show very narrow confidence intervals for the per-waypoint TP errors in HMM, while the more efficient linear and nonlinear regressors exhibit 3-D spatial accuracy much lower than the current state of the art, up to a factor of five compared to ‘blind’ TP for complete flights, in the order of 2–3 km compared to the actual flight routes.

Semantic trajectory compression via multi-resolution synchronization-based clustering

With the pervasive use of location-aware devices and rapid development of location sensing technology, trajectory data has been generated in diverse fields. How to store and manage these large amounts of data is a non-trivial task and thus trajectory compression has gained increasing attention in recent years. As traditional compression algorithms often treat trajectories as sequences of lines in geometric space, the global statistics and the semantics embedded in trajectories are not well considered. In this paper, inspired by the powerful concept of synchronization, we introduce a new semantic trajectory compression approach to yield multi-resolution trajectory abstractions with semantic enrichment. The basic idea is to introduce a multi-resolution synchronization-based clustering model to produce semantic regions of interest (ROIs) in a hierarchical way. Specifically, by imposing constraints on points with semantic information in the interaction model, all neighboring points with similar semantics will group together automatically. Afterwards, each trajectory is compressed as a sequence of semantic ROIs and is further represented as a hierarchical ROI network. Moreover, we further extend our model on the data stream setting. The extensive experiments on synthetic data and four real-world data sets demonstrate the effectiveness and efficiency of our proposed model.

Semantic Modelling of Ship Behavior in Harbor Based on Ontology and Dynamic Bayesian Network

Recognizing ship behavior is important for maritime situation awareness and intelligent transportation management. Some scholars extracted ship behaviors from massive trajectory data by statistical analysis. However, the meaning of the behaviors, i.e., semantic meanings of behaviors and their relationships, are not explicit. Ship behaviors are affected by navigational area and traffic rules, so their meanings can be obtained only in specific maritime situations. The work establishes the semantic model of ship behavior (SMSB) to represent and reason the meaning of the behaviors. Firstly, a semantic network is built based on maritime traffic rules and good seamanship. The corresponding detection methods are then proposed to identify basic ship behaviors in various maritime scenes, including dock, anchorage, traffic lane, and general scenes. After that, dynamic Bayesian network (DBN) is used to reason potential ship behaviors. Finally, trajectory annotation and semantic query of the model are validated in the different scenes of harbor. The basic behaviors and potential behaviors in all typical scenes of any harbor can be obtained accurately and expressed conveniently using the proposed model. The model facilitates the ships behavior research, contributing to the semantic trajectory analysis.

Semantic enrichment of spatio-temporal trajectories for worker safety on construction sites

Thousands of fatalities are reported from the construction industry every year and a high percentage of them are due to the unsafe worker movements which resulted in falling from heights, transportation accidents, exposure to harmful environments, and striking against or being struck by the moving equipment. To reduce such fatalities, a system is proposed to monitor worker movements on a construction site by collecting their raw spatio-temporal trajectory data and enriching it with the relevant semantic information. To acquire the trajectories, the use of an indoor positioning system (IPS) is considered. Bluetooth beacons are used for collecting spatio-temporal information of the building users. By means of an Android-based mobile application, neighboring beacons’ signals are selected, and a geo-localization technique is performed to get the unique pairs of users’ location coordinates. After pre-processing this collected data, three semantic enrichment techniques are used to construct semantically enriched trajectories which are as follows: (1) enrichment with the semantic points which maps site location identification to the trajectory points; (2) enrichment with the semantic lines which relies on the speed-based segmentation approach to infer user modes of transportation; (3) enrichment with the semantic region for mapping a complete trajectory on an actual building or a construction site zone. The proposed system will help in extracting multifaceted trajectory characteristics and generates semantic trajectories to enable the desired semantic insights for better understanding of the underlying meaningful worker movements using the contextual data related to the building environment. Generated semantic trajectories will help health and safety (H&S) managers in making improved decisions for monitoring and controlling site activities by visualizing site-zones’ density to avoid congestion, proximity analysis to prevent workers collisions, identifying unauthorized access to hazardous areas, and monitoring movements of workers and machinery to reduce transportation accidents.

SEABIG: A Deep Learning-Based Method for Location Prediction in Pedestrian Semantic Trajectories

Pedestrian destination prediction of a user is known as an important and challenging task for LBSs (location-based services) like traffic planning and travelling recommendation. The typical method generally applies statistical model to predict the future location based on the raw trajectory. However, while predicting, existing approaches fall short in accommodating long-range dependency and ignore the semantic information existing in the raw trajectory. In this paper, we proposed a method named semantics-enriched attentional BiGRU (SEABIG) to solve the two problems. Firstly, we designed a probabilistic model based on the GMM (Gaussian mixture model) to extract stopover points from the raw trajectories and annotate the semantic information on the stopover points. Then we proposed an attentional BiGRU-based trajectory prediction model, which can jointly learn the embeddings of the semantic trajectory. It not only takes the advantage of the BiGRU (Bidirectional Gated Recurrent Unit) for sequence modeling, but also gives more attention to meaningful positions that have strong correlations w.r.t. destination by applying attention mechanism. Finally, we annotate the most likely semantic on the predicted position with the probabilistic model. Extensive experiments on Beijing real datasets demonstrate that our proposed method has higher prediction accuracy.

Semantic Trajectory Based Behavior Generation for Groups Identification

Semantic Trajectory Based Behavior Generation for Groups Identification

‘Next to godliness?’ exploring cleanliness in peace and war

ABSTRACTIn the history of English, the early moral centring of cleanliness is conventionally depicted as having been eroded. This paper aims instead to explore its continued moral dynamism, using language as prime resource. Examining the complex semantic trajectories of cleanliness from Middle English onwards, it documents its shifting status in a number of disparate registers, including chastity, domestic virtue, and health, alongside the forms of moral expression these reveal. The conventionalised alliance of cleanliness with godliness forms part of this process of critical re-evaluation. The salience of cleanliness as moral device comes to the fore in a detailed study of language practice in the First World War. Across a range of discourse types, cleanliness is shown to retain potent moral and ideological force, not least in constructing both nationhood and enemy in a time of war.

Semantic-Enhanced Multi-Dimensional Markov Chains on Semantic Trajectories for Predicting Future Locations.

In this work, we investigate the performance of Markov Chains with respect to modelling semantic trajectories and predicting future locations. In the first part, we examine whether and to what degree the semantic level of semantic trajectories affects the predictive performance of a spatial Markov model. It can be shown that the choice of the semantic level when describing trajectories has a significant impact on the accuracy of the models. High-level descriptions lead to better results than low-level ones. The second part introduces a multi-dimensional Markov Chain construct that considers, besides locations, additional context information, such as time, day and the users’ activity. While the respective approach is able to outperform our baseline, we could also identify some limitations. These are mainly attributed to its sensitivity towards small-sized training datasets. We attempt to overcome this issue, among others, by adding a semantic similarity analysis component to our model that takes the varying role of locations due each time to the respective purpose of visiting the particular location explicitly into consideration. To capture the aforementioned dynamics, we define an entity, which we refer to as Purpose-of-Visit-Dependent Frame (PoVDF). In the third part of this work, we describe in detail the PoVDF-based approach and we evaluate it against the multi-dimensional Markov Chain model as well as with a semantic trajectory mining and prefix tree based model. Our evaluation shows that the PoVDF-based approach outperforms its competition and lays a solid foundation for further investigation.

Multi-level temporal autoregressive modelling of daily activity satisfaction using GPS-integrated activity diary data

ABSTRACTIn this research, we match web-based activity diary data with daily mobility information recorded by GPS trackers for a sample of 709 residents in a 7-day survey in Beijing in 2012 to investigate activity satisfaction. Given the complications arising from the irregular time intervals of GPS-integrated diary data and the associated complex dependency structure, a direct application of standard (spatial) panel data econometric approaches is inappropriate. This study develops a multi-level temporal autoregressive modelling approach to analyse such data, which conceptualises time as continuous and examines sequential correlations via a time or space-time weights matrix. Moreover, we manage to simultaneously model individual heterogeneity through the inclusion of individual random effects, which can be treated flexibly either as independent or dependent. Bayesian Markov chain Monte Carlo (MCMC) algorithms are developed for model implementation. Positive sequential correlations and individual heterogeneity effects are both found to be statistically significant. Geographical contextual characteristics of sites where activities take place are significantly associated with daily activity satisfaction, controlling for a range of situational characteristics and individual socio-demographic attributes. Apart from the conceivable urban planning and development implications of our study, we demonstrate a novel statistical methodology for analysing semantic GPS trajectory data in general.

Efficient Similarity Search Based on Semantic Trajectories in Road Networks

In recent years, a few researches focus on the similarity measure of semantic trajectories in road networks, since semantic trajectories in road networks have smaller volumes, higher qualities and can better reflect user behaviors. However, these works do not further discuss how to efficiently search similar trajectories. Thus, to implement an efficient similarity search, we design an index called SIET based on the structures of road networks. Then, we propose a novel algorithm called SSN-BF to search similar trajectories efficiently by using best-first strategy. At last, we take the experimental evaluations on real dataset and prove the efficiency of our algorithm.

Mining Semantic Trajectory Patterns from Geo-Tagged Data

User-generated social media data tagged with geographic information present messages of dynamic spatio-temporal trajectories. These increasing mobility data provide potential opportunities to enhance the understanding of human mobility behaviors. Several trajectory data mining approaches have been proposed to benefit from these rich datasets, but fail to incorporate aspatial semantics in mining. This study investigates mining frequent moving sequences of geographic entities with transit time from geo-tagged data. Different from previous analysis of geographic feature only trajectories, this work focuses on extracting patterns with rich context semantics. We extend raw geographic trajectories generated from geo-tagged data with rich context semantic annotations, use regions-of-interest as stops to represent interesting places, enrich them with multiple aspatial semantic annotations, and propose a semantic trajectory pattern mining algorithm that returns basic and multidimensional semantic trajectory patterns. Experimental results demonstrate that semantic trajectory patterns from our method present semantically meaningful patterns and display richer semantic knowledge.