The Purpose of Motion: Learning Activities from Individual Mobility Networks Salvatore Rinzivillo*, Lorenzo Gabrielli*, Mirco Nanni*, Luca Pappalardo*tt, Dino Pedreschit and Fosca Giannotti* *Institute of Information Science and Technology (ISTI), National Research Council (CNR), Pisa, Italy tDepartment of Computer Science, University of Pisa, Italy +Budapest University of Technology and Economics (BME), Budapest, Hungary Email: [email protected] Ahstract-The large availability of mobility data allows us successful only if the predictive accuracy of the classifier is to investigate complex phenomena about human movement. extremely high. Unfortunately, none of the methods for activity However this adundance of data comes with few information recognition proposed so far reach adequate performance for about the purpose of movement. In this work we address the issue semantic amplification. Some existing methods, such as Condi­ of activity recognition by introducing Activity-Based Cascading tional Random Fields (CRF) [5], obtain very accurate learners (ABC) classification. Such approach departs completely from that, given a past history of activity-labelled movements of probabilistic approaches for two main reasons. First, it exploits a individuals, predict the activity associated to future unlabeled set of structural features extracted from the Individual Mobility Network (IMN), a model able to capture the salient aspects of trips of the same individuals. As we discuss in this paper, individual mobility. Second, it uses a cascading classification as such learners exhibit poor performance when used to predict a way to tackle the highly skewed frequency of activity classes. the activity associated to the movements of other individuals, We show that our approach outperforms existing state-of-the­ whose data were not used in the learning process; which is the art probabilistic methods. Since it reaches high precision, ABC situation we face in semantic amplification of unlabeled Big classification represents a very reliable semantic amplifier for Big Data. Data. In this paper, we describe a model for activity recognition targeted explicitly at the semantic amplification of big mobility I. INTRODUCTION data, called Activity-Based Cascading (ABC) classification. Human mobility is driven by people's daily activities, such ABC departs completely from probabilistic approaches for as going to work or school, shopping, transporting kids, and so two main reasons: (i) it exploits a set of structural features on. The digital mobility traces collected through a variety of extracted from the Individual Mobility Network (IMN), a technologies, from navigation devices to smart phones, allow model able to capture the salient aspects of individual mobility; us to understand people's movements in great detail [1], [2]. (ii) it uses a cascading classification as a way to tackle the skewed frequency of activity classes (home and work are However, they generally fail to capture the purpose of such movements, i.e. the kind of activity behind each travel. This generally very frequent compared to shopping and leisure). deficiency is a hard obstacle to the deployment of Big Data in We use a dataset of approximately 7,000 activity-annotated many domains such as urban planning, traffic management, in­ trips obtained from GPS receivers on board of private cars, telligent transportation systems, socio-demographic simulation and show how ABC classification reaches high precision (up and nowcasting, and emergency management [3]. For all such to 0.98) and outperfonns both state-of-the-art probabilistic methods (CRF) and Decision Tree classifiers. In summary, the applications, information on why people move is crucial. The availability of society-wide data about mobility and activity of novel contributions of the paper are the following: people would be a driver for a better comprehension of our • Summarization of individual mobility by introducing a complex society, and for smarter knowledge services for the novel graph-based representation: Individual Mobility individual and the collective sphere. Networks (Section III); It is not a surprise, hence, that several researchers tackled • Selection of predictive features extracted from the the problem of activity recognition, i.e. how to infer the kind IMNs to improve decision tree classification (Section of activity associated to a travel on the only basis of the IV-A); observed mobility patterns [4]. Show me how you move, I'll tell what you do. The rationale behind such research follows a • Enhancing Cascade Classification with label propaga­ two steps method. First, use a small training mobility dataset tion through successive steps (Section IV-B). annotated with activity information, obtained for instance by • Comparison with state-of-the-art methods to assess surveying some volunteers, to learn a classifier that maps validity of the approach (Section V); mobility-related features into the different kinds of activities. Second, apply the classifier to unlabeled Big Data, to obtain II. RELATED WORKS large-scale mobility data annotated with activity information - the activity classifier acts as a semantic amplifier for Big The task of activity recogmtIOn consists in assigning a Data. It goes without saying that this second step can be label to a movement according to its relevant characteristics. The vast literature on the subject may be organized according to the type of movement observed. Many works focus on the movement of individuals to recognize gestures, indoor activities, physical activity levels, surveillance, outlier and intrusion detection [6], [7]. We are mainly interested in those works considering the movement as the physical change in position of the individual, thus leaving a geographical place to reach another one. We can identify two large groups of inference methods: supervised and unsupervised. Among the supervised approaches, some methods try to infer the mode of transportation [8], [9], the activity performed in a specific location [10], [11] and a combination of the two [4]. The methods that deals with the transportation mode try to infer if the individual is moving by foot, by car, by bike or by public transportation. This annotation exploits several features of the movements, speed, acceleration and, when available, other context data like accelerometer measurements. Fig. 1. The IMN extracted from the mobility of an individual. Edges represent the existence of a route between the locations. The function w ( e) indicates The learning approaches are based on discriminative meth­ the number of trips performed on the edge e, while T (X ) the total time spent ods, like decision trees [9] and conditional random fields in a location x. (CRF) [4], [12]; or on generative methods, like Hidden Markov Models (HMM). In our context we are not interested in the transportation mode, since our focus is the prediction of the Nodes represent locations and edges represent movements be­ activity at destination. When considering the activity from tween locations. We attach to both nodes and edges statistical movement we can distinguish two main approaches: sequence information by means of structural annotations: edges provide learning approaches consider the activity of an individual as information about the frequency of movements through the w a sequence in a fixed temporal period (usually one day) and function; nodes provide an estimation of the time spent in each try to predict the labels for the whole sequence [4]; episode location through the T function. To clarify the concept of IMN, learning approaches try to label each single movement episode let us consider the network in Figure l. It describes the IMN independently from the others [9]. extracted from the mobility of an individual who visited 19 distinct locations. Location a has been visited a total of 18 Unsupervised methods are mainly based on clustering time units (days in the example), since T( a) = 18. The edge techniques [13], [14], [15] or dimensionality reduction [13], = = = e ( a, b) has weight w(e ) w( a, b) 20, indicating that the [16]. In [l3] the authors analyze an activity-based travel individual moved twenty times from location a to location b. survey conducted in the Chicago metropolitan area with the aim of exploring the daily activity structure of people. They The IMN of an individual is an abstraction of her mobility describe how the considered population can be clustered into behavior. A location is an abstract entity without any reference eight (weekdays) or seven (weekends) representative groups to the geographic space. It can be interpreted as a subjective according to the activities performed by the individuals. point of interest, a place around which the mobility of that individual gravitates. This allows the modeling of locations that are meaningful only for that individual, like her home or III. INDIVIDUAL MOBILITY NETWORKS work place, etc. Accordingly, given the IMNs of two distinct An Individual Mobility Network (lMN) describes the indi­ individuals we are not able to determine whether they have vidual mobility of an individual through a graph representation visited the same location. This limitation, on the other hand, of her locations and movements, grasping the relevant proper­ allow to hide the actual places visited by the individual, ties of individual mobility and removing unnecessary details. providing a protection layer of sensitive information. The computation of a IMN starts fromthe ordered sequence Definition 3. I: An Individual Mobility Network (IMN) of an individual's trajectories. It works in a "streaming" of an individual u is a directed graph Gu = (V, E), where V is the set of nodes and E is the set of edges. On nodes and fashion: every time the individual performs a new trip, her edges the following functions are defined: IMN is updated according to the new trajectory. The origin and destination points of the new trajectory are mapped to locations • w: E --+ N returns the weight of an edge (i.e. the in the IMN. The locations are obtained by aggregating all the number of travels performed by u on that edge); origin and destination points of the past trajectories within a spatial threshold J.