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Towards Mobility-based Clustering
學生:黃群凱
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Outline
 INTRODUCTION
 PRELIMINARIES
 SPOT CROWDEDNESS FUNDAMENTAL
 SPOT CROWDEDNESS IN PRACTICE
 HOT SPOTS AND HOT REGIONS
 FIELD STUDY EVALUATION
 RELATED WORK
 CONCLUSION
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Introduction
 Identifying hot spots of moving vehicles in an urban area is essential
to many smart city applications.
 The ultimate goal of this research is to have a better
understanding of the city traffic via a quantitative research on
hot spots.
 To define and quantify the vehicle crowdedness of an area.
 To picture the crowdedness distribution of the city and identify
the hot spots.
 To investigate the evolution of hot spots.
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Introduction
 The first major challenge is incomplete information. Existing
algorithms (for static or mobile) are all density-based
approaches that use inter-node distances as a critical measure.
 The sample object set is a specific type of vehicles.
 It has very limited to represent general vehicles.
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Introduction
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PRELIMINARIES
 Raw dataset characteristics
 The dataset is originated from the City Traffic Bureau.
 The instant speed
 The geographic location
 The status of occupied or unoccupied of the taxi.
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PRELIMINARIES
 Road network grid
 The road topology and type will impact the vehicle, not only
the speed, but also the drive pattern, hence we study the
following problems based on road network grid.
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PRELIMINARIES
 Observations and design principles.
 The first one is that vehicles prefer high mobility in a sparse
region. To the opposite, for security concerns vehicles will drive
slowly when the nearby area is crowded. Motivated by it, we
employ vehicles as sensors using their instant speed to sense the
vehicle crowdedness of vicinity.
 The second one is that the reported locations can be erroneous,
while the reported speeds are usually quite accurate because
they are directly obtained from the speedometers installed on
taxis. In addition, for safety concerns sudden changes of speeds
are rare.
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SPOT CROWDEDNESS FUNDAMENTAL
 Assumptions and notations
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SPOT CROWDEDNESS FUNDAMENTAL
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SPOT CROWDEDNESS IN PRACTICE
 cumulated distribution function(CDF):分布函數
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HOT SPOTS AND HOT REGIONS
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HOT SPOTS AND HOT REGIONS
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FIELD STUDY EVALUATION
 Mean Absolute Difference (MAD):絕對平均差
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FIELD STUDY EVALUATION
 F-score is the weighted harmonic mean of precision and
recall
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Rwlated Work
 Object clustering is a well studied problem
 A great deal of research efforts being devoted in. One of the
most promising approaches for spatial static clustering can be
found in the research work of DBSCAN. Recently, clustering
moving objects is becoming a hot research issue.
 Of related work focuses on the analysis of mobile traffic
object data. They are mainly interested in the detecting areas
of high traffic load.
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CONCLUSION
 In this paper, we proposed mobility-based clustering, a novel
approach to identify hot spots and hot regions in a highly
mobile environment with extremely limited and biased
samples.
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