Fabio Pinelli

Trajectory pattern mining

The increasing pervasiveness of location-acquisition technologies (GPS, GSM networks, etc.) is leading to the collection of large spatio-temporal datasets and to the opportunity of discovering usable knowledge about movement behaviour, which fosters novel applications and services. In this paper, we move towards this direction and develop an extension of the sequential pattern mining paradigm that analyzes the trajectories of moving objects. We introduce trajectory patterns as concise descriptions of frequent behaviours, in terms of both space (i.e., the regions of space visited during movements) and time (i.e., the duration of movements). In this setting, we provide a general formal statement of the novel mining problem and then study several different instantiations of different complexity. The various approaches are then empirically evaluated over real data and synthetic benchmarks, comparing their strengths and weaknesses.

WhereNext: a location predictor on trajectory pattern mining

The pervasiveness of mobile devices and location based services is leading to an increasing volume of mobility data.This side eect provides the opportunity for innovative methods that analyse the behaviors of movements. In this paper we propose WhereNext, which is a method aimed at predicting with a certain level of accuracy the next location of a moving object. The prediction uses previously extracted movement patterns named Trajectory Patterns, which are a concise representation of behaviors of moving objects as sequences of regions frequently visited with a typical travel time. A decision tree, named T-pattern Tree, is built and evaluated with a formal training and test process. The tree is learned from the Trajectory Patterns that hold a certain area and it may be used as a predictor of the next location of a new trajectory finding the best matching path in the tree. Three dierent best matching methods to classify a new moving object are proposed and their impact on the quality of prediction is studied extensively. Using Trajectory Patterns as predictive rules has the following implications: (I) the learning depends on the movement of all available objects in a certain area instead of on the individual history of an object; (II) the prediction tree intrinsically contains the spatio-temporal properties that have emerged from the data and this allows us to define matching methods that striclty depend on the properties of such movements. In addition, we propose a set of other measures, that evaluate a priori the predictive power of a set of Trajectory Patterns. This measures were tuned on a real life case study. Finally, an exhaustive set of experiments and results on the real dataset are presented.

Unveiling the complexity of human mobility by querying and mining massive trajectory data

The technologies of mobile communications pervade our society and wireless networks sense the movement of people, generating large volumes of mobility data, such as mobile phone call records and Global Positioning System (GPS) tracks. In this work, we illustrate the striking analytical power of massive collections of trajectory data in unveiling the complexity of human mobility. We present the results of a large-scale experiment, based on the detailed trajectories of tens of thousands private cars with on-board GPS receivers, tracked during weeks of ordinary mobile activity. We illustrate the knowledge discovery process that, based on these data, addresses some fundamental questions of mobility analysts: what are the frequent patterns of people’s travels? How big attractors and extraordinary events influence mobility? How to predict areas of dense traffic in the near future? How to characterize traffic jams and congestions? We also describe M-Atlas, the querying and mining language and system that makes this analytical process possible, providing the mechanisms to master the complexity of transforming raw GPS tracks into mobility knowledge. M-Atlas is centered onto the concept of a trajectory, and the mobility knowledge discovery process can be specified by M-Atlas queries that realize data transformations, data-driven estimation of the parameters of the mining methods, the quality assessment of the obtained results, the quantitative and visual exploration of the discovered behavioral patterns and models, the composition of mined patterns, models and data with further analyses and mining, and the incremental mining strategies to address scalability.

Mining mobility user profiles for car pooling

In this paper we introduce a methodology for extracting mobility profiles of individuals from raw digital traces (in particular, GPS traces), and study criteria to match individuals based on profiles. We instantiate the profile matching problem to a specific application context, namely proactive car pooling services, and therefore develop a matching criterion that satisfies various basic constraints obtained from the background knowledge of the application domain. In order to evaluate the impact and robustness of the methods introduced, two experiments are reported, which were performed on a massive dataset containing GPS traces of private cars: (i) the impact of the car pooling application based on profile matching is measured, in terms of percentage shareable traffic; (ii) the approach is adapted to coarser-grained mobility data sources that are nowadays commonly available from telecom operators. In addition the ensuing loss in precision and coverage of profile matches is measured.

ABACUS: frequent pAttern mining-BAsed Community discovery in mUltidimensional networkS

Community discovery in complex networks is the problem of detecting, for each node of the network, its membership to one of more groups of nodes, the communities, that are densely connected, or highly interactive, or, more in general, similar, according to a similarity function. So far, the problem has been widely studied in monodimensional networks, i.e. networks where only one connection between two entities may exist. However, real networks are often multidimensional, i.e., multiple connections between any two nodes may exist, either reflecting different kinds of relationships, or representing different values of the same type of tie. In this context, the problem of community discovery has to be redefined, taking into account multidimensional structure of the graph. We define a new concept of community that groups together nodes sharing memberships to the same monodimensional communities in the different single dimensions. As we show, such communities are meaningful and able to group nodes even if they might not be connected in any of the monodimensional networks. We devise frequent pAttern mining-BAsed Community discoverer in mUltidimensional networkS (ABACUS), an algorithm that is able to extract multidimensional communities based on the extraction of frequent closed itemsets from monodimensional community memberships. Experiments on two different real multidimensional networks confirm the meaningfulness of the introduced concepts, and open the way for a new class of algorithms for community discovery that do not rely on the dense connections among nodes.

Mining sequences with temporal annotations

In this paper we propose an extension of the sequence mining paradigm to (temporally-)annotated sequential patterns, where each transition in a sequential pattern is annotated with a typical transition time derived from the source data. Then, we present a basic solution for the novel mining problem based on the combination of sequential pattern mining and clustering, and assess this solution on two realistic datasets, illustrating how potentially useful patterns of the new form are extracted.

AllAboard: a system for exploring urban mobility and optimizing public transport using cellphone data

This paper describes a system to leverage data on cell phones to understand mobility patterns and presents a large-scale network design model for public transit while considering existing service offerings. The work is motivated by the rapid urbanization in growth market cities across the world, where adequate resources to develop detailed travel demand models may be absent. Urban growth is coupled with high cell phone penetration which transit operators can leverage to better match observed demand for travel with service offerings. Based on call detail records from a telecommunications operator in Abidjan, Cote d’Ivoire, this paper describes analytics and optimization techniques that result in system-wide journey time improvements of 10%. The main contribution of this paper is to demonstrate how big data analytics and optimization tools can transform data from opportunistic sensing in the real-world to improve urban mobility outcomes.

Inferring Unusual Crowd Events from Mobile Phone Call Detail Records

The pervasiveness and availability of mobile phone data offer the opportunity of discovering usable knowledge about crowd behavior in urban environments. Cities can leverage such knowledge to provide better services e.g., public transport planning, optimized resource allocation and safer environment. Call Detail Record CDR data represents a practical data source to detect and monitor unusual events considering the high level of mobile phone penetration, compared with GPS equipped and open devices. In this paper, we propose a methodology that is able to detect unusual events from CDR data, which typically has low accuracy in terms of space and time resolution. Moreover, we introduce a concept of unusual event that involves a large amount of people who expose an unusual mobility behavior. Our careful consideration of the issues that come from coarse-grained CDR data ultimately leads to a completely general framework that can detect unusual crowd events from CDR data effectively and efficiently. Through extensive experiments on real-world CDR data for a large city in Africa, we demonstrate that our method can detect unusual events with 16% higher recall and over 10

Anonymity preserving sequential pattern mining

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