44,832 research outputs found
Moving Object Trajectories Meta-Model And Spatio-Temporal Queries
In this paper, a general moving object trajectories framework is put forward
to allow independent applications processing trajectories data benefit from a
high level of interoperability, information sharing as well as an efficient
answer for a wide range of complex trajectory queries. Our proposed meta-model
is based on ontology and event approach, incorporates existing presentations of
trajectory and integrates new patterns like space-time path to describe
activities in geographical space-time. We introduce recursive Region of
Interest concepts and deal mobile objects trajectories with diverse
spatio-temporal sampling protocols and different sensors available that
traditional data model alone are incapable for this purpose.Comment: International Journal of Database Management Systems (IJDMS) Vol.4,
No.2, April 201
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
Virtual environment trajectory analysis:a basis for navigational assistance and scene adaptivity
This paper describes the analysis and clustering of motion trajectories obtained while users navigate within a virtual environment (VE). It presents a neural network simulation that produces a set of five clusters which help to differentiate users on the basis of efficient and inefficient navigational strategies. The accuracy of classification carried out with a self-organising map algorithm was tested and improved to in excess of 85% by using learning vector quantisation. This paper considers how such user classifications could be utilised in the delivery of intelligent navigational support and the dynamic reconfiguration of scenes within such VEs. We explore how such intelligent assistance and system adaptivity could be delivered within a Multi-Agent Systems (MAS) context
Invariant template matching in systems with spatiotemporal coding: a vote for instability
We consider the design of a pattern recognition that matches templates to
images, both of which are spatially sampled and encoded as temporal sequences.
The image is subject to a combination of various perturbations. These include
ones that can be modeled as parameterized uncertainties such as image blur,
luminance, translation, and rotation as well as unmodeled ones. Biological and
neural systems require that these perturbations be processed through a minimal
number of channels by simple adaptation mechanisms. We found that the most
suitable mathematical framework to meet this requirement is that of weakly
attracting sets. This framework provides us with a normative and unifying
solution to the pattern recognition problem. We analyze the consequences of its
explicit implementation in neural systems. Several properties inherent to the
systems designed in accordance with our normative mathematical argument
coincide with known empirical facts. This is illustrated in mental rotation,
visual search and blur/intensity adaptation. We demonstrate how our results can
be applied to a range of practical problems in template matching and pattern
recognition.Comment: 52 pages, 12 figure
The topography of the environment alters the optimal search strategy for active particles
In environments with scarce resources, adopting the right search strategy can
make the difference between succeeding and failing, even between life and
death. At different scales, this applies to molecular encounters in the cell
cytoplasm, to animals looking for food or mates in natural landscapes, to
rescuers during search-and-rescue operations in disaster zones, as well as to
genetic computer algorithms exploring parameter spaces. When looking for sparse
targets in a homogeneous environment, a combination of ballistic and diffusive
steps is considered optimal; in particular, more ballistic L\'evy flights with
exponent {\alpha} <= 1 are generally believed to optimize the search process.
However, most search spaces present complex topographies, with boundaries,
barriers and obstacles. What is the best search strategy in these more
realistic scenarios? Here we show that the topography of the environment
significantly alters the optimal search strategy towards less ballistic and
more Brownian strategies. We consider an active particle performing a blind
search in a two-dimensional space with steps drawn from a L\'evy distribution
with exponent varying from {\alpha} = 1 to {\alpha} = 2 (Brownian). We
demonstrate that the optimal search strategy depends on the topography of the
environment, with {\alpha} assuming intermediate values in the whole range
under consideration. We interpret these findings in terms of a simple
theoretical model, and discuss their robustness to the addition of Brownian
diffusion to the searcher's motion. Our results are relevant for search
problems at different length scales, from animal and human foraging to
microswimmers' taxis, to biochemical rates of reaction
- …