Vehicular traffic is one of the major sources of air pollution in urban settings, making it essential to clearly understand how much and where vehicle emissions impact residents. Recent approaches manage to yield pollution maps at the microscopic level by processing GPS trajectories of vehicles. That is achieved by applying mathematical models to estimate instantaneous emissions from GPS data, extending estimates to areas without data through missing data imputation, and further considering air dispersion factors. In this work, we leverage such inferred knowledge to implement an emission-aware pedestrian routing strategy and to study its impact on the reduction of exposure to vehicular pollutants and walking time. The study is realized through simulations of large masses of pedestrians over a medium-sized city in Italy, analyzing the interplay between the two factors - exposure versus walking time - in terms of time efficiency of paths and changes over existing habits both at a global and at an individual level. Experiments suggest that exposure-aware routing can yield a significant margin of improvement in health over most paths with minor effects on mobility, making it feasible and effective.

The growing availability of time series data has increased the usage of classifiers for this data type. Unfortunately, state-of-the-art time series classifiers are black-box models and, therefore, not usable in critical domains such as healthcare or finance, where explainability can be a crucial requirement. This paper presents a framework to explain the predictions of any black-box classifier for univariate and multivariate time series. The provided explanation is composed of three parts. First, a saliency map highlighting the most important parts of the time series for the classification. Second, an instance-based explanation exemplifies the black-box’s decision by providing a set of prototypical and counterfactual time series. Third, a factual and counterfactual rule-based explanation, revealing the reasons for the classification through logical conditions based on subsequences that must, or must not, be contained in the time series. Experiments and benchmarks show that the proposed method provides faithful, meaningful, stable, and interpretable explanations.

An Individual Mobility Network (IMN) is a graph representation of the mobility history of an individual that highlights the relevant locations visited (nodes of the graph) and the movements across them (edges), also providing a rich set of annotations of both nodes and edges. Extracting representative features from an IMN has proven to be a valuable task for enabling various learning applications. However, it is also a demanding operation that does not guarantee the inclusion of all important aspects from the human perspective. A vast recent literature on graph embedding goes in a similar direction, yet typically aims at general-purpose methods that might not suit specific contexts. In this paper, we discuss the existing approaches to graph embedding and the specificities of IMNs, trying to find the best matching solutions. We experiment with representative algorithms and study the results in relation to IMN characteristics. Tests are performed on a large dataset of real vehicle trajectories.

Traffic assignment (TA) is crucial in optimizing transportation systems and consists in efficiently assigning routes to a collection of trips. Existing TA algorithms often do not adequately consider realtime traffic conditions, resulting in inefficient route assignments. This paper introduces Metis, a coordinated, one-shot TA algorithm that combines alternative routing with edge penalization and informed route scoring. We conduct experiments in several cities to evaluate the performance of Metis against state-of-the-art one-shot methods. Compared to the best baseline, Metis significantly reduces CO2 emissions by 18% in Milan, 28% in Florence, and 46% in Rome, improving trip distribution considerably while still having low computational time. Our study proposes Metis as a promising solution for optimizing TA and urban transportation systems.

Columnar data formats, such as Apache Parquet, are increasingly popular nowadays for scalable data storage and querying data lakes, due to compressed storage and efficient data access via data skipping. However, when applied to spatial or spatio-temporal data, advanced solutions are required to go beyond pruning over single attributes and towards multidimensional pruning. Even though there exist solutions for geospatial data, such as GeoParquet and SpatialParquet, they fall short when applied to trajectory data (sequences of spatio-temporal positions). In this paper, we propose TrajParquet, a format for columnar storage of trajectory data, which is highly efficient and scalable. Also, we present a query processing algorithm that supports spatio-temporal range queries over TrajParquet. We evaluate TrajParquet using real-world data sets and in comparison with extensions of GeoParquet and SpatialParquet, suitable for handling spatio-temporal data.

GPS devices generate spatio-temporal trajectories for different types of moving objects. Scientists can exploit them to analyze migration patterns, manage city traffic, monitor the spread of diseases, etc. Many current state-of-the-art models that use this data type require a not negligible running time to be trained. To overcome this issue, we propose the Trajectory Interval Forest (TIF) classifier, an efficient model with high throughput. TIF works by calculating various mobility-related statistics over a set of randomly selected intervals. These statistics are used to create a tabular representation of the data, which can be used as input for any classical classifier. Our results show that TIF is comparable to or better than state-of-art in terms of accuracy and is orders of magnitude faster.

Navigation apps use routing algorithms to suggest the best path to reach a user's desired destination. Although undoubtedly useful, navigation apps' impact on the urban environment (e.g., CO2 emissions and pollution) is still largely unclear. In this work, we design a simulation framework to assess the impact of routing algorithms on carbon dioxide emissions within an urban environment. Using APIs from TomTom and OpenStreetMap, we find that settings in which either all vehicles or none of them follow a navigation app's suggestion lead to the worst impact in terms of CO2 emissions. In contrast, when just a portion (around half) of vehicles follow these suggestions, and some degree of randomness is added to the remaining vehicles' paths, we observe a reduction in the overall CO2 emissions over the road network. Our work is a first step towards designing next-generation routing principles that may increase urban well-being while satisfying individual needs.

This paper demonstrates a simulation framework that collects data about connected vehicles' locations and surroundings in a realistic traffic scenario. Our focus lies on the capability to detect parking spots and their occupancy status. We use this data to train machine learning models that predict parking occupancy levels of specific areas in the city center of San Francisco. By comparing their performance to a given ground truth, our results show that it is possible to use simulated connected vehicle data as a base for prototyping meaningful AI-based applications.

Abstract Identifying the portions of trajectory data where movement ends and a significant stop starts is a basic, yet fundamental task that can affect the quality of any mobility analytics process. Most of the many existing solutions adopted by researchers and practitioners are simply based on fixed spatial and temporal thresholds stating when the moving object remained still for a significant amount of time, yet such thresholds remain as static parameters for the user to guess. In this work we study the trajectory segmentation from a multi-granularity perspective, looking for a better understanding of the problem and for an automatic, user-adaptive and essentially parameter-free solution that flexibly adjusts the segmentation criteria to the specific user under study and to the geographical areas they traverse. Experiments over real data, and comparison against simple and state-of-the-art competitors show that the flexibility of the proposed methods has a positive impact on results.

DataMod 2020 aims to bring together practitioners and researchers from academia, industry and research institutions interested in the combined application of computational modelling methods with data-driven techniques from the areas of knowledge management, data mining and machine learning. Modelling methodologies of interest include automata, agents, Petri nets, process algebras and rewriting systems. Application domains include social systems, ecology, biology, medicine, smart cities, governance, security, education, software engineering, and any other field that deals with complex systems and large amounts of data. Papers can present research results in any of the themes of interest for the symposium as well as application experiences, tools and promising preliminary ideas. Papers dealing with synergistic approaches that integrate modelling and knowledge management/discovery or that exploit knowledge management/discovery to develop/syntesise system models are especially welcome.

The massive and increasing availability of mobility data enables the study and the prediction of human mobility behavior and activities at various levels. In this paper, we address the problem of building a data-driven model for predicting car drivers' risk of experiencing a crash in the long-term future, for instance, in the next four weeks. Since the raw mobility data, although potentially large, typically lacks any explicit semantics or clear structure to help understanding and predicting such rare and difficult-to-grasp events, our work proposes to build concise representations of individual mobility, that highlight mobility habits, driving behaviors and other factors deemed relevant for assessing the propensity to be involved in car accidents. The suggested approach is mainly based on a network representation of users' mobility, called Individual Mobility Networks, jointly with the analysis of descriptive features of the user's driving behavior related to driving style (e.g., accelerations) and characteristics of the mobility in the neighborhood visited by the user. The paper presents a large experimentation over a real dataset, showing comparative performances against baselines and competitors, and a study of some typical risk factors in the areas under analysis through the adoption of state-of-art model explanation techniques. Preliminary results show the effectiveness and usability of the proposed predictive approach.

The avalanche of mobility data like GPS and GSM daily produced by each user through mobile devices enables personalized mobility-services improving everyday life. The base for these mobility-services lies in the predictability of human behavior. In this paper we propose an approach for reproducing the user's personal mobility agenda that is able to predict the user's positions for the whole day. We reproduce the agenda by exploiting a data-driven personal mobility model able to capture and summarize different aspects of the systematic mobility behavior of a user. We show how the proposed approach outperforms typical methodologies adopted in the literature on four different real GPS datasets. Moreover, we analyze some features of the mobility models and we discuss how they can be employed as agents of a simulator for what-if mobility analysis.

Mining a large number of datasets recording human activities for making sense of individual data is the key enabler of a new wave of personalized knowledge-based services. In this paper we focus on the problem of clustering individual transactional data for a large mass of users. Transactional data is a very pervasive kind of information that is collected by several services, often involving huge pools of users. We propose txmeans, a parameter-free clustering algorithm able to efficiently partitioning transactional data in a completely automatic way. Txmeans is designed for the case where clustering must be applied on a massive number of different datasets, for instance when a large set of users need to be analyzed individually and each of them has generated a long history of transactions. A deep experimentation on both real and synthetic datasets shows the practical effectiveness of txmeans for the mass clustering of different personal datasets, and suggests that txmeans outperforms existing methods in terms of quality and efficiency. Finally, we present a personal cart assistant application based on txmeans

Customer segmentation is one of the most traditional and valued tasks in customer relationship management (CRM). In this article, we explore the problem in the context of the car insurance industry, where the mobility behavior of customers plays a key role: Different mobility needs, driving habits, and skills imply also different requirements (level of coverage provided by the insurance) and risks (of accidents). In the present work, we describe a methodology to extract several indicators describing the driving profile of customers, and we provide a clustering-oriented instantiation of the segmentation problem based on such indicators. Then, we consider the availability of a continuous flow of fresh mobility data sent by the circulating vehicles, aiming at keeping our segments constantly up to date. We tackle a major scalability issue that emerges in this context when the number of customers is large—namely, the communication bottleneck—by proposing and implementing a sophisticated distributed monitoring solution that reduces communications between vehicles and company servers to the essential. We validate the framework on a large database of real mobility data coming from GPS devices on private cars. Finally, we analyze the privacy risks that the proposed approach might involve for the users, providing and evaluating a countermeasure based on data perturbation.

One of the key tasks in mobility data analysis is the study of the individual mobility of users with reference to their personal locations, i.e. the places or areas where they stop to perform any kind of activities. Correctly discovering such personal locations is therefore a very important problem, which is yet not very well addressed in literature. In this work we propose a robust, efficient, statistically well-founded and parameter-free personal location detection process. The algorithm, called TOSCA (TwO-Steps parameter free Clustering Algorithm), combines two clustering strategies and applies statistical tests to drive the selection of the needed parameters. The proposed solution is tested against a large set of competitors and several datasets, including synthetic and real ones. The empirical results show its ability to automatically adapt to different contexts yielding good accuracy and a good efficiency.

The large availability of mobility data allows us to investigate complex phenomena about human movement. However this adundance of data comes with few information about the purpose of movement. In this work we address the issue of activity recognition by introducing Activity-Based Cascading (ABC) classification. Such approach departs completely from probabilistic approaches for two main reasons. First, it exploits a set of structural features extracted from the Individual Mobility Network (IMN), a model able to capture the salient aspects of individual mobility. Second, it uses a cascading classification as a way to tackle the highly skewed frequency of activity classes. We show that our approach outperforms existing state-of-the-art probabilistic methods. Since it reaches high precision, ABC classification represents a very reliable semantic amplifier for Big Data.

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.

Tracking technologies are able to provide high-resolution movement data that can advance research in different fields, such as tourism management. In this specific field, developing methods to extract moving flock patterns from such data are particularly relevant to enable us to improve our knowledge of the nature of recreational use interactions, which is crucial for a good management of attractions and for designing sustainable development policies. However, ‘flocking’ has been usually associated with the form of collective movement of a large group of birds, fish, insects and certain mammals as well. Very few research efforts have been devoted in finding flock patterns associated with pedestrian movement. In this work, we propose a moving flock pattern definition and a corresponding extraction algorithm based on the notion of collective coherence. We use the term collective coherence to refer to the spatial closeness over some time duration with a minimum number of members. Furthermore, we evaluate the proposed algorithm by applying it to two different pedestrian movement datasets, which have been gathered from visitors of two recreational parks. The results show that the algorithm is capable of extracting moving flock patterns, disqualifying the patterns with flock members that remain stationary in a common place during the considered time interval.

The growing availability of mobile devices produces an enor- mous quantity of personal tracks which calls for advanced analysis methods capable of extracting knowledge out of massive trajectories datasets. In this paper we present an experiment on a real world scenario that demonstrates the strong analytical power of massive, raw trajectory data made available as a by-product of telecom services, in unveiling the complexity of urban mobility. The experiment has been made possible by the GeoPKDD system, an integrated plat- form for complex analysis of mobility data. The system com- bines spatio-temporal querying capabilities with data min- ing and semantic technologies, thus providing a full support for the Mobility Knowledge Discovery process.

In the past few years there has been an increasing interest in the analysis of process logs. Several proposed techniques, such as workflow mining, are aimed at automatically deriving the underlying workflow models. However, current approaches only pay little attention on an important piece of information contained in process logs: the timestamps, which are used to define a sequential ordering of the performed tasks. In this work we try to overcome these limitations by explicitly including time in the extracted knowledge, thus making the temporal information a first-class citizen of the analysis process. This makes it possible to discern between apparently identical process executions that are performed with different transition times between consecutive tasks.

Preserving individual privacy when publishing data is a problem that is receiving increasing attention. According to the fc-anonymity principle, each release of data must be such that each individual is indistinguishable from at least k - 1 other individuals. In this paper we study the problem of anonymity preserving data publishing in moving objects databases. We propose a novel concept of k-anonymity based on co-localization that exploits the inherent uncertainty of the moving object's whereabouts. Due to sampling and positioning systems (e.g., GPS) imprecision, the trajectory of a moving object is no longer a polyline in a three-dimensional space, instead it is a cylindrical volume, where its radius delta represents the possible location imprecision: we know that the trajectory of the moving object is within this cylinder, but we do not know exactly where. If another object moves within the same cylinder they are indistinguishable from each other. This leads to the definition of (k,delta) -anonymity for moving objects databases. We first characterize the (k, delta)-anonymity problem and discuss techniques to solve it. Then we focus on the most promising technique by the point of view of information preservation, namely space translation. We develop a suitable measure of the information distortion introduced by space translation, and we prove that the problem of achieving (k,delta) -anonymity by space translation with minimum distortion is NP-hard. Faced with the hardness of our problem we propose a greedy algorithm based on clustering and enhanced with ad hoc pre-processing and outlier removal techniques. The resulting method, named NWA (Never Walk .Alone), is empirically evaluated in terms of data quality and efficiency. Data quality is assessed both by means of objective measures of information distortion, and by comparing the results of the same spatio-temporal range queries executed on the original database and on the (k, delta)-anonymized one. Experimental results show that for a wide range of values of delta and k, the relative error introduced is kept low, confirming that NWA produces high quality (k, delta)-anonymized data.

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.

Sequential patterns mining received much attention in recent years, thanks to its various potential application domains. A large part of them represent data as collections of time-stamped itemsets, e.g., customers' purchases, logged web accesses, etc. Most approaches to sequence mining focus on sequentiality of data, using time-stamps only to order items and, in some cases, to constrain the temporal gap between items. In this paper, we propose an efficient algorithm for computing (temporally-)annotated sequential patterns, i.e., sequential patterns where each transition is annotated with a typical transition time derived from the source data. The algorithm adopts a prefix-projection approach to mine candidate sequences, and it is tightly integrated with an annotation mining process that associates sequences with temporal annotations. The pruning capabilities of the two steps sum together, yielding significant improvements in performances, as demonstrated by a set of experiments performed on synthetic datasets.

We consider an extension of Datalog with mechanisms for temporal, nonmonotonic, and nondeterministic reasoning, which we refer to as Datalog++. We show, by means of examples, its flexibility in expressing queries concerning aggregates and data cube. Also, we show how iterated fixpoint and stable model semantics can be combined to the purpose of clarifying the semantics of Datalog++ programs and supporting their efficient execution. Finally, we provide a more concrete implementation strategy on which basis the design of optimization techniques tailored for Datalog++ is addressed.