THESIS
2014
viii, 91 pages : illustrations ; 30 cm
Abstract
The value of large amount of trajectory data has received wide attention in many applications
including human behavior analysis, urban transportation planning, and various location-based
services (LBS). Nowadays, both scientific and industrial communities are encouraged
to collect as much trajectory data as possible, which brings many opportunities and problems,
including: 1) the raw data collected by the GPS device requires reorganization and
preprocessing to enable further analysis; 2) it is expensive and challenging to store and
process such big trajectory data efficiently; and 3) by leveraging effective spatial-temporal
queries from the trajectory data, it enables us to discover various knowledge that are difficult
to identify intuitively. In this thesis, we propose a comple...[
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The value of large amount of trajectory data has received wide attention in many applications
including human behavior analysis, urban transportation planning, and various location-based
services (LBS). Nowadays, both scientific and industrial communities are encouraged
to collect as much trajectory data as possible, which brings many opportunities and problems,
including: 1) the raw data collected by the GPS device requires reorganization and
preprocessing to enable further analysis; 2) it is expensive and challenging to store and
process such big trajectory data efficiently; and 3) by leveraging effective spatial-temporal
queries from the trajectory data, it enables us to discover various knowledge that are difficult
to identify intuitively. In this thesis, we propose a complete system from the preprocessing
of raw trajectory data, to the storage and query processing of the trajectories, and finally
the discovery of the hidden knowledge from such data. For each component of the system,
we study and solve a specific problem regarding to the component, as: 1) inferring the road
type in crowdsourced map services; 2) exploring the use of diverse replicas for big location
tracking data; and 3) recommending trajectories for effective and efficient hunting of taxi
passengers.
For the road type inference problem, we propose a combined model based on stacked
generalization to infer the types of road segments, and conduct eight experiments based on
different classifiers to show that our method is much better than the baseline methods. For
the use of diverse replicas for big trajectory data, we propose BLOT, a system abstraction
which tries to find the optimal set of diverse replicas that suitable for the costs of a set of trajectory queries. Based on our greedy strategy, the experiments show that our solution is
effective and efficient. For the taxi hunting route recommendation problem, we introduce
the HUNTS system based on the estimations of the benefits of road segments, and make
reasonable hunting trajectory recommendations for taxi drivers to make more money.
Due to the low-density, sparsity, and uncertainty of trajectories, handling such data may
face many challenges. In this thesis, we will discuss the important challenges and research
issues of each aspect, and compare the differences between our methods and the state-of-the-art.
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