580 research outputs found
Discovering Urban Functional Zones By Latent Fusion of Users GPS Data and Points of Interests
With rapid development of socio-economics, the task of discovering functional
zones becomes critical to better understand the interactions between social
activities and spatial locations. In this paper, we propose a framework to
discover the functional zones by analyzing urban structures and social
behaviors. The proposed approach models the inner influences between spatial
locations and human activities by fusing the semantic meanings of both Point of
Interests (POIs) and human activities to learn the latent representation of the
regions. A spatial based unsupervised clustering method, Conditional Random
Filed (CRF), is then applied to aggregate regions using both their spatial
information and discriminative representations. Also, we estimate the
functionality of the regions and annotate them by the differences between the
normalized POI distributions which properly rank various functionalities. This
framework is able to properly address the biased categories in sparse POI data,
when exploring the unbiased and true functional zones. To validate our
framework, a case study is evaluated by using very large real-world users GPS
and POIs data from city of Raleigh. The results demonstrate that the proposed
framework can better identify functional zones than the benchmarks, and,
therefore, enhance understanding of urban structures with a finer granularity
under practical conditions
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
Inferring land use from mobile phone activity
Understanding the spatiotemporal distribution of people within a city is
crucial to many planning applications. Obtaining data to create required
knowledge, currently involves costly survey methods. At the same time
ubiquitous mobile sensors from personal GPS devices to mobile phones are
collecting massive amounts of data on urban systems. The locations,
communications, and activities of millions of people are recorded and stored by
new information technologies. This work utilizes novel dynamic data, generated
by mobile phone users, to measure spatiotemporal changes in population. In the
process, we identify the relationship between land use and dynamic population
over the course of a typical week. A machine learning classification algorithm
is used to identify clusters of locations with similar zoned uses and mobile
phone activity patterns. It is shown that the mobile phone data is capable of
delivering useful information on actual land use that supplements zoning
regulations.Comment: To be presented at ACM UrbComp201
Trajectory data mining: A review of methods and applications
The increasing use of location-aware devices has led to an increasing availability of trajectory data. As a result, researchers devoted their efforts to developing analysis methods including different data mining methods for trajectories. However, the research in this direction has so far produced mostly isolated studies and we still lack an integrated view of problems in applications of trajectory mining that were solved, the methods used to solve them, and applications using the obtained solutions. In this paper, we first discuss generic methods of trajectory mining and the relationships between them. Then, we discuss and classify application problems that were solved using trajectory data and relate them to the generic mining methods that were used and real world applications based on them. We classify trajectory-mining application problems under major problem groups based on how they are related. This classification of problems can guide researchers in identifying new application problems. The relationships between the methods together with the association between the application problems and mining methods can help researchers in identifying gaps between methods and inspire them to develop new methods. This paper can also guide analysts in choosing a suitable method for a specific problem. The main contribution of this paper is to provide an integrated view relating applications of mining trajectory data and the methods used
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