27,293 research outputs found
A personal route prediction system based on trajectory data mining
This paper presents a system where the personal route of a user is predicted using a probabilistic model built from the historical trajectory data. Route patterns are extracted from personal trajectory data using a novel mining algorithm, Continuous Route Pattern Mining (CRPM), which can tolerate different kinds of disturbance in trajectory data. Furthermore, a client–server architecture is employed which has the dual purpose of guaranteeing the privacy of personal data and greatly reducing the computational load on mobile devices. An evaluation using a corpus of trajectory data from 17 people demonstrates that CRPM can extract longer route patterns than current methods. Moreover, the average correct rate of one step prediction of our system is greater than 71%, and the average Levenshtein distance of continuous route prediction of our system is about 30% shorter than that of the Markov model based method
Analysing Human Mobility Patterns of Hiking Activities through Complex Network Theory
The exploitation of high volume of geolocalized data from social sport
tracking applications of outdoor activities can be useful for natural resource
planning and to understand the human mobility patterns during leisure
activities. This geolocalized data represents the selection of hike activities
according to subjective and objective factors such as personal goals, personal
abilities, trail conditions or weather conditions. In our approach, human
mobility patterns are analysed from trajectories which are generated by hikers.
We propose the generation of the trail network identifying special points in
the overlap of trajectories. Trail crossings and trailheads define our network
and shape topological features. We analyse the trail network of Balearic
Islands, as a case of study, using complex weighted network theory. The
analysis is divided into the four seasons of the year to observe the impact of
weather conditions on the network topology. The number of visited places does
not decrease despite the large difference in the number of samples of the two
seasons with larger and lower activity. It is in summer season where it is
produced the most significant variation in the frequency and localization of
activities from inland regions to coastal areas. Finally, we compare our model
with other related studies where the network possesses a different purpose. One
finding of our approach is the detection of regions with relevant importance
where landscape interventions can be applied in function of the communities.Comment: 20 pages, 9 figures, accepte
Modeling Taxi Drivers' Behaviour for the Next Destination Prediction
In this paper, we study how to model taxi drivers' behaviour and geographical
information for an interesting and challenging task: the next destination
prediction in a taxi journey. Predicting the next location is a well studied
problem in human mobility, which finds several applications in real-world
scenarios, from optimizing the efficiency of electronic dispatching systems to
predicting and reducing the traffic jam. This task is normally modeled as a
multiclass classification problem, where the goal is to select, among a set of
already known locations, the next taxi destination. We present a Recurrent
Neural Network (RNN) approach that models the taxi drivers' behaviour and
encodes the semantics of visited locations by using geographical information
from Location-Based Social Networks (LBSNs). In particular, RNNs are trained to
predict the exact coordinates of the next destination, overcoming the problem
of producing, in output, a limited set of locations, seen during the training
phase. The proposed approach was tested on the ECML/PKDD Discovery Challenge
2015 dataset - based on the city of Porto -, obtaining better results with
respect to the competition winner, whilst using less information, and on
Manhattan and San Francisco datasets.Comment: preprint version of a paper submitted to IEEE Transactions on
Intelligent Transportation System
PATH: Person Authentication using Trace Histories
In this paper, a solution to the problem of Active Authentication using trace
histories is addressed. Specifically, the task is to perform user verification
on mobile devices using historical location traces of the user as a function of
time. Considering the movement of a human as a Markovian motion, a modified
Hidden Markov Model (HMM)-based solution is proposed. The proposed method,
namely the Marginally Smoothed HMM (MSHMM), utilizes the marginal probabilities
of location and timing information of the observations to smooth-out the
emission probabilities while training. Hence, it can efficiently handle
unforeseen observations during the test phase. The verification performance of
this method is compared to a sequence matching (SM) method , a Markov
Chain-based method (MC) and an HMM with basic Laplace Smoothing (HMM-lap).
Experimental results using the location information of the UMD Active
Authentication Dataset-02 (UMDAA02) and the GeoLife dataset are presented. The
proposed MSHMM method outperforms the compared methods in terms of equal error
rate (EER). Additionally, the effects of different parameters on the proposed
method are discussed.Comment: 8 pages, 9 figures. Best Paper award at IEEE UEMCON 201
A survey on Human Mobility and its applications
Human Mobility has attracted attentions from different fields of studies such
as epidemic modeling, traffic engineering, traffic prediction and urban
planning. In this survey we review major characteristics of human mobility
studies including from trajectory-based studies to studies using graph and
network theory. In trajectory-based studies statistical measures such as jump
length distribution and radius of gyration are analyzed in order to investigate
how people move in their daily life, and if it is possible to model this
individual movements and make prediction based on them. Using graph in mobility
studies, helps to investigate the dynamic behavior of the system, such as
diffusion and flow in the network and makes it easier to estimate how much one
part of the network influences another by using metrics like centrality
measures. We aim to study population flow in transportation networks using
mobility data to derive models and patterns, and to develop new applications in
predicting phenomena such as congestion. Human Mobility studies with the new
generation of mobility data provided by cellular phone networks, arise new
challenges such as data storing, data representation, data analysis and
computation complexity. A comparative review of different data types used in
current tools and applications of Human Mobility studies leads us to new
approaches for dealing with mentioned challenges
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