Modeling semantic trajectories including multiple viewpoints and explanatory factors: application to life trajectories

International audienceIn this paper, we propose a semantic trajectory model handling complex trajectories. We consider that a trajectory is composed of multiple aspects which are all different viewpoints from which it can be observed. This model also allows for the representation of explanatory factors that influenced the episodes that compose a trajectory. We here deal with a particular type of trajectory: the life trajectories of individuals or citizens. The residential aspect in life trajectories is particularly relevant for decision makers and urban planning experts who try to understand the residential choices of inhabitants in order to adjust their decisions. Those choices depend on factors related to the home (type, size...), and to the surroundings (accessibility, amenities ...) but they can only be fully apprehended by taking into account the life circumstances of the individuals, along their family, or professional or even spare-time activities viewpoints

SeTraStream: Semantic-Aware Trajectory Construction over Streaming Movement Data

Location data generated from GPS equipped moving objects are typically collected as streams of spatio-temporal (x,y,t) points that when put together form corresponding {\em trajectories}. Most existing studies focus on building ad-hoc querying, analysis, as well as data mining techniques on formed trajectories. As a prior step, trajectory construction is evidently necessary for mobility data processing and understanding -- including tasks like trajectory data cleaning, compression, and segmentation to identify semantic trajectory episodes like stops (e.g. while sitting and standing) and moves (while jogging, walking, driving etc). However, such methods in the current literature, are typically based on offline procedures, which is not sufficient for real life trajectory applications that rely on timely delivery of computed trajectories to serve real time query answers. Filling this gap, our paper proposes a platform, namely SeTraStream, for real-time semantic trajectory construction. Our online framework is capable of providing real-life trajectory data {\em cleaning}, {\em compression}, {\em segmentation} over streaming movement data

Multi-Predictor Fusion: Combining Learning-based and Rule-based Trajectory Predictors

Trajectory prediction modules are key enablers for safe and efficient planning of autonomous vehicles (AVs), particularly in highly interactive traffic scenarios. Recently, learning-based trajectory predictors have experienced considerable success in providing state-of-the-art performance due to their ability to learn multimodal behaviors of other agents from data. In this paper, we present an algorithm called multi-predictor fusion (MPF) that augments the performance of learning-based predictors by imbuing them with motion planners that are tasked with satisfying logic-based rules. MPF probabilistically combines learning- and rule-based predictors by mixing trajectories from both standalone predictors in accordance with a belief distribution that reflects the online performance of each predictor. In our results, we show that MPF outperforms the two standalone predictors on various metrics and delivers the most consistent performance

Semantic Trajectories:Computing and Understanding Mobility Data

Thanks to the rapid development of mobile sensing technologies (like GPS, GSM, RFID, accelerometer, gyroscope, sound and other sensors in smartphones), the large-scale capture of evolving positioning data (called mobility data or trajectories) generated by moving objects with embedded sensors has become easily feasible, both technically and economically. We have already entered a world full of trajectories. The state-of-the-art on trajectory, either from the moving object database area or in the statistical analysis viewpoint, has built a bunch of sophisticated techniques for trajectory data ad-hoc storage, indexing, querying and mining etc. However, most of these existing methods mainly focus on a spatio-temporal viewpoint of mobility data, which means they analyze only the geometric movement of trajectories (e.g., the raw ‹x, y, t› sequential data) without enough consideration on the high-level semantics that can better understand the underlying meaningful movement behaviors. Addressing this challenging issue for better understanding movement behaviors from the raw mobility data, this doctoral work aims at providing a high-level modeling and computing methodology for semantically abstracting the rapidly increasing mobility data. Therefore, we bring top-down semantic modeling and bottom-up data computing together and establish a new concept called "semantic trajectories" for mobility data representation and understanding. As the main novelty contribution, this thesis provides a rich, holistic, heterogeneous and application-independent methodology for computing semantic trajectories to better understand mobility data at different levels. In details, this methodology is composed of five main parts with dedicated contributions. Semantic Trajectory Modeling. By investigating trajectory modeling requirements to better understand mobility data, this thesis first designs a hybrid spatio-semantic trajectory model that represents mobility with rich data abstraction at different levels, i.e., from the low-level spatio-temporal trajectory to the intermediate-level structured trajectory, and finally to the high-level semantic trajectory. In addition, a semantic based ontological framework has also been designed and applied for querying and reasoning on trajectories. Offline Trajectory Computing. To utilize the hybrid model, the thesis complementarily designs a holistic trajectory computing platform with dedicated algorithms for reconstructing trajectories at different levels. The platform can preprocess collected mobility data (i.e., raw movement tracks like GPS feeds) in terms of data cleaning/compression etc., identify individual trajectories, and segment them into structurally meaningful trajectory episodes. Therefore, this trajectory computing platform can construct spatio-temporal trajectories and structured trajectories from the raw mobility data. Such computing platform is initially designed as an offline solution which is supposed to analyze past trajectories via a batch procedure. Trajectory Semantic Annotation. To achieve the final semantic level for better understanding mobility data, this thesis additionally designs a semantic annotation platform that can enrich trajectories with third party sources that are composed of geographic background information and application domain knowledge, to further infer more meaningful semantic trajectories. Such annotation platform is application-independent that can annotate various trajectories (e.g., mobility data of people, vehicle and animals) with heterogeneous data sources of semantic knowledge (e.g., third party sources in any kind of geometric shapes like point, line and region) that can help trajectory enrichment. Online Trajectory Computing. In addition to the offline trajectory computing for analyzing past trajectories, this thesis also contributes to dealing with ongoing trajectories in terms of real-time trajectory computing from movement data streams. The online trajectory computing platform is capable of providing real-life trajectory data cleaning, compression, and segmentation over streaming movement data. In addition, the online platform explores the functionality of online tagging to achieve fully semantic-aware trajectories and further evaluate trajectory computing in a real-time setting. Mining Trajectories from Multi-Sensors. Previously, the focus is on computing semantic trajectories using single-sensory data (i.e., GPS feeds), where most datasets are from moving objects with wearable GPS-embedded sensors (e.g., mobility data of animal, vehicle and people tracking). In addition, we explore the problem of mining people trajectories using multi-sensory feeds from smartphones (GPS, gyroscope, accelerometer etc). The research results reveal that the combination of two sensors (GPS+accelerometer) can significantly infer a complete life-cycle semantic trajectories of people's daily behaviors, both outdoor movement via GPS and indoor activities via accelerometer

Privacy Preservation of Semantic Trajectory Databases using Query Auditing Techniques

ABSTRACT Existing approaches that publish anonymized spatiotemporal traces of mobile humans deal with the preservation of privacy operating under the assumption that most of the information in the original dataset can be disclosed without causing any privacy violation. However, an alternative strategy considers that data stays in-house to the hosting organization and privacy-preserving mobility data management systems are in charge of privacy-aware sharing of the mobility data. Furthermore, human trajectories are nowadays enriched with semantic information by using background geographic information and/or by user-provided data via location-based social media. This new type of representation of personal movements as sequences of places visited by a person during his/her movement poses even greater privacy violation threats. To facilitate privacy-aware sharing of mobility data, we design a semantic-aware MOD engine were all potential privacy breaches that may occur when answering a query, are prevented through an auditing mechanism. Moreover, in order to improve user friendliness and system functionality of the aforementioned engine, we propose Zoom-Out algorithm as a distinct component, whose objective is to modify the initial query that cannot be answered at first due to privacy violation, to the 'nearest' query that can be possibly answered with 'safety'