Distributed Mining of Popular Paths in Road Networks

International audienceWe consider the problem of finding large scale mobility patterns. A common challenge in mobility tracking systems is that large quantity of data is spread out spatially and temporally across many tracking sensors. We thus devise a spatial sampling and information exchange protocol that provides probabilistic guarantees on detecting prominent patterns. For this purpose, we define a general notion of significant popular paths that can capture many different types of motion. We design a summary sketch for the data at each tracking node, which can be updated efficiently, and then aggregated across devices to reconstruct the prominent paths in the global data. The algorithm is scalable, even with large number of mobile targets. It uses a hierarchic query system that automatically prioritizes important trajectories – those that are long and popular. We show further that this scheme can in fact give good results by sampling relatively few sensors and targets, and works for streaming spatial data. We prove differential privacy guarantees for the randomized algorithm. Extensive experiments on real GPS data show that the method is efficient and accurate, and is useful in predicting motion of travelers even with small samples

Characterizing and Removing Oscillations in Mobile Phone Location Data

IEEE WoWMoM 2019, 20th IEEE International symposium on a World of Wireless, Mobile and Multimedia Networks, Washington, ETATS-UNIS, 10-/06/2019 - 12/06/2019International audienceHuman mobility analysis is a multidisciplinary research subject that has attracted a growing interest over the last decade. A substantial amount of such recent studies is driven by the availability of original sources of real-world information about individual movement patterns. An important task in the analysis of mobility data is reliably distinguishing between the stop locations and movement phases that compose the trajectories of the monitored subjects. The problem is especially challenging when mobility is inferred from mobile phone location data: here, oscillations in the association of mobile devices to base stations lead to apparent user mobility even in absence of actual movement. In this paper, we leverage a unique dataset of spatiotemporal individual trajectories that allows capturing both the user and network operator perspectives in mobile phone location data, and investigate the oscillation phenomenon. We present probabilistic and machine learning approaches for detecting oscillations in mobile phone location data, and a filtering technique for removing those. Our analyses and comparison with state-of-the-art approaches demonstrate the superiority of our solution, both in terms of removed oscillations and of error with respect to ground-truth trajectories

On the Sampling Frequency of Human Mobility

International audienceIn this paper, we aim at answering the question " at what frequency should one sample individual human movements so that they can be reconstructed from the collected samples with minimum loss of information? ". Our quest for a response unveils (i) seemingly universal spectral properties of human mobility, and (ii) a linear scaling law of the localization error with respect to the sampling interval. We conduct analyses using fine-grained GPS trajectories of 119 users worldwide. Our findings have potential applications in ubiquitous computing and mobile service design, in terms of energy efficiency, location-based service operations, active probing of subscribers' positions in mobile networks and trajectory data compression

L' étude de la fréquence d' échantillonnage des mouvements des humains

International audienceDes études récentes ont profité des techniques de suivi basées sur les technologies de positionnement d' étudier afin la mobilité humaine. Ces recherches ont révélé, entre autres, une grande régularité spatio-temporelle des mouvements individuels. Sur la base de ces résultats, nous visons à répondrè a la question "a quelle fréquence doit-on échantillonner les mouvements humains individuels afin qu'ils puissent être reconstruits à partir des échantillons recueillis avec un minimum de perte d'information?". Notre quête d'une réponse a conduit à la découverte de (i) propriétés spectrales apparemment universelles de la mobilité humaine, et (ii) une loi de misè a l' échelle linéaire de l'erreur de localisation par rapport à l'intervalle d' échantillonnage. Nos résultats sont basés sur l'analyse des trajectoires GPS de 119 utilisateurs dans le monde entier. Les applications de nos résultats sont liées un certain nombre de domaines pertinents pour l'informatique omniprésente, tels que l'informatique mobilé econome en énergie, les opérations de service basées sur l'emplacement, le sondage actif des positions des abonnés dans les réseaux mobiles et la compression des données de trajectoire

Investigations sur la fréquence d’échantillonnage de la mobilité

Recent studies have leveraged tracking techniques based on positioning technologiesto discover new knowledge about human mobility. These investigations have revealed, amongothers, a high spatiotemporal regularity of individual movement patterns. Building on these findings,we aim at answering the question “at what frequency should one sample individual humanmovements so that they can be reconstructed from the collected samples with minimum loss of information?”.Our quest for a response leads to the discovery of (i) seemingly universal spectralproperties of human mobility, and (ii) a linear scaling law of the localization error with respectto the sampling interval. Our findings are based on the analysis of fine-grained GPS trajectoriesof 119 users worldwide. The applications of our findings are related to a number of fields relevantto ubiquitous computing, such as energy-efficient mobile computing, location-based service operations,active probing of subscribers’ positions in mobile networks and trajectory data compression.Des études récentes ont mis à profit des techniques de suivi basées sur des technologiesde positionnement pour étuder la mobilité humaine. Ces recherches ont révélé, entreautres, une grande régularité spatio-temporelle des mouvements individuels. Sur la base de cesrésultats, nous visons à répondre à la question «à quelle fréquence doit-on échantillonner lesmouvements humains individuels afin qu’ils puissent être reconstruits à partir des échantillonsrecueillis avec un minimum de perte d’information? Notre recherche d’une réponse à cette questionnous a conduit à la découverte de (i) propriétés spectrales apparemment universelles de lamobilité humaine, et (ii) une loi de mise à l’échelle linéaire de l’erreur de localisation par rapportà l’intervalle d’échantillonnage. Nos résultats sont basés sur l’analyse des trajectoires GPS de119 utilisateurs dans le monde entier. Les applications de nos résultats sont liées à un certainnombre de domaines pertinents pour l’informatique omniprésente, tels que l’informatique mobileéconome en énergie, les opérations de service basées sur l’emplacement, le sondage actif despositions des abonnés dans les réseaux mobiles et la compression des données de trajectoire

Multi-resolution sketches and locality sensitive hashing for fast trajectory processing

International audienceSearching for similar GPS trajectories is a fundamental problem that faces challenges of large data volume and intrinsic complexity of trajectory comparison. In this paper, we present a suite of sketches for trajectory data that drastically reduce the computation costs associated with near neighbor search, distance estimation, clustering and classification, and subtrajectory detection. Apart from summarizing the dataset, our sketches have two uses. First, we obtain simple provable locality sensitive hash families for both the Hausdorff and Fréchet distance measures, useful in near neighbour queries. Second, we build a data structure called MRTS (Multi Resolution Trajectory Sketch), which contains sketches of varying degrees of detail. The MRTS is a user-friendly, compact representation of the dataset that allows to efficiently answer various other types of queries. Moreover, MRTS can be used in a dynamic setting with fast insertions of trajectories into the database. Experiments on real data show effective locality sensitive hashing substantially improves near neighbor search time. Distances defined on the skteches show good correlation with Fréchet and Hausdorff distances

A Novel Sensor-Free Location Sampling Mechanism

In recent years, mobile device tracking technologies based on various positioning systems have made location data collection an ubiquitous practice. Applications running on smartphones record location samples at different frequencies for varied purposes.The frequency at which location samples are recorded is usually pre-defined and fixed but can differ across applications; this naturally results in big location datasets of various resolutions. What is more, continuous recording of locations results usually in redundant information, as humans tend to spend significant amount of their time either static or in routine trips, and drains the battery of the recording device. In this paper, we aim at answering the question "at what frequency should one sample individual human movements so that they can be reconstructed from the collected samples with minimum loss of information?". Our analyses on fine-grained GPS trajectories from users around the world unveil (i) seemingly universal spectral properties of human mobility, and (ii) a linear scaling law of the localization error with respect to the sampling interval. Building on these results, we challenge the idea of a fixed sampling frequency and present a lightweight, energy efficient, mobility aware adaptive location sampling mechanism. Our mechanism can serve as a standalone application for adaptive location sampling, or as complimentary tool alongside auxiliary sensors (such as accelerometer and gyroscope). In this work, we implemented our mechanism as an application for mobile devices and tested it on mobile users worldwide. The results from our preliminary experiments show that our method adjusts the sampling frequency to the mobility habits of the tracked users, it reliably tracks a mobile user incurring acceptable approximation errors and significantly reduces the energy consumption of the mobile device