1,721,109 research outputs found
Time-focused clustering of trajectories of moving objects
No full textSpatio-temporal, geo-referenced datasets are growing rapidly, and will be more in
the near future, due to both technological and social/commercial reasons. From the data mining
viewpoint, spatio-temporal trajectory data introduce new dimensions and, correspondingly,
novel issues in performing the analysis tasks. In this paper, we consider the clustering
problem applied to the trajectory data domain. In particular, we propose an adaptation of a
density-based clustering algorithm to trajectory data based on a simple notion of distance
between trajectories. Then, a set of experiments on synthesized data is performed in order
to test the algorithm and to compare it with other standard clustering approaches. Finally, a
new approach to the trajectory clustering problem, called temporal focussing, is sketched,
having the aim of exploiting the intrinsic semantics of the temporal dimension to improve
the quality of trajectory clustering
Individual and Collective Stop-Based Adaptive Trajectory Segmentation
Full text linkIdentifying 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 perspec tive, 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
On the pursuit of Graph Embedding Strategies for Individual Mobility Networks
Full text linkAn 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
City Indicators for Mobility Data Mining
Full text linkClassifying cities and other geographical units is a classical task in
urban geography, typically carried out through manual analysis
of specific characteristics of the area. The primary objective of
this paper is to contribute to this process through the definition
of a wide set of city indicators that capture different aspects
of the city, mainly based on human mobility and automatically
computed from a set of data sources, including mobility traces
and road networks. The secondary objective is to prove that such
set of characteristics is indeed rich enough to support a simple
task of geographical transfer learning, namely identifying which
groups of geographical areas can share with each other a basic
traffic prediction model. The experiments show that similarity in
terms of our city indicators also means better transferability of
predictive models, opening the way to the development of more
sophisticated solutions that leverage city indicators
City Indicators for Geographical Transfer Learning: An Application to Crash Prediction
Full text linkThe 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 tackle the problem of predicting the crash risk of a car driver in the long term. This is a very challenging task, requiring a deep knowledge of both the driver and their surroundings, yet it has several useful applications to public safety (e.g. by coaching high-risk drivers) and the insurance market (e.g. by adapting pricing to risk). We model each user with a data-driven approach based on a network representation of users’ mobility. In addition, we represent the areas in which users moves through the definition of a wide set of city indicators that capture different aspects of the city. These indicators are based on human mobility and are automatically computed from a set of different data sources, including mobility traces and road networks. Through these city indicators we develop a geographical transfer learning approach for the crash risk task such that we can build effective predictive models for another area where labeled data is not available. Empirical results over real datasets show the superiority of our solution
From Fossil Fuel to Electricity: Studying the Impact of EVs on the Daily Mobility Life of Users
No full textElectric Vehicles (EVs) currently provide a major opportunity to decarbonize urban areas and improve their quality of life, however, the mass transition towards electric mobility requires understanding and solving the potential issues that they might cause to users. In this work, we propose a process that, through a mix of mobility data analytics, efficient trip planning, and simulation heuristics, is able to analyze the current fuel-based mobility of a user and quantitatively describe the impact of switching to EVs on their mobility lifestyle. We apply our process to a large dataset of real trips, analyzing both the impact of EVs on the collectivity and on the individuals, providing a case study with insights at the level of single users
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