8,447 research outputs found
Context Trees: Augmenting Geospatial Trajectories with Context
Exposing latent knowledge in geospatial trajectories has the potential to
provide a better understanding of the movements of individuals and groups.
Motivated by such a desire, this work presents the context tree, a new
hierarchical data structure that summarises the context behind user actions in
a single model. We propose a method for context tree construction that augments
geospatial trajectories with land usage data to identify such contexts. Through
evaluation of the construction method and analysis of the properties of
generated context trees, we demonstrate the foundation for understanding and
modelling behaviour afforded. Summarising user contexts into a single data
structure gives easy access to information that would otherwise remain latent,
providing the basis for better understanding and predicting the actions and
behaviours of individuals and groups. Finally, we also present a method for
pruning context trees, for use in applications where it is desirable to reduce
the size of the tree while retaining useful information
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
Detecting Stops from GPS Trajectories: A Comparison of Different GPS Indicators for Raster Sampling Methods
With the increasing prevalence of GPS tracking capabilities on smartphones, GPS
trajectories have proven to be useful for an extensive range of research topics. Stop
detection, which estimates activity locations, is fundamental for organizing GPS
trajectories into semantically meaningful journeys. With previous methods
overwhelmingly dependent on thresholds, contextual information or a pre-understanding
of the GPS records, this paper addresses the challenge by contributing a ‘top-down’ raster
sampling method which samples pre-calculated GPS indicators and clusters the raster cells
with significantly different values as stops. We report a comparison of a set of precalculated
GPS indicators with two baseline methods. By referencing a ground truth travel
dairy, the raster sampling method demonstrates good and reliable capabilities on producing
high accuracy, low redundancy and close proximity to the ground truth in three distinct
travel use cases. This further indicates a good generic stop detection method
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