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Transcript of Siyuan Liu *#, Yunhuai Liu *, Lionel M. Ni *# +, Jianping Fan #, Minglu Li + * Hong Kong University...
![Page 1: Siyuan Liu *#, Yunhuai Liu *, Lionel M. Ni *# +, Jianping Fan #, Minglu Li + * Hong Kong University of Science and Technology # Shenzhen Institutes of.](https://reader035.fdocuments.net/reader035/viewer/2022062314/56649f1f5503460f94c37333/html5/thumbnails/1.jpg)
Siyuan Liu*#, Yunhuai Liu*, Lionel M. Ni*# +, Jianping Fan#, Minglu Li+
*Hong Kong University of Science and Technology#Shenzhen Institutes of Advanced Technology, Chinese Academy of
Sciences+ Shanghai Jiao Tong University
July 27th, 2010@SIGKDD 2010
Towards Mobility-based Clustering
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OutlineIntroductionRelated workMobility based clusteringField study evaluationConclusion
![Page 3: Siyuan Liu *#, Yunhuai Liu *, Lionel M. Ni *# +, Jianping Fan #, Minglu Li + * Hong Kong University of Science and Technology # Shenzhen Institutes of.](https://reader035.fdocuments.net/reader035/viewer/2022062314/56649f1f5503460f94c37333/html5/thumbnails/3.jpg)
OutlineIntroductionRelated workMobility based clusteringField study evaluationConclusion
![Page 4: Siyuan Liu *#, Yunhuai Liu *, Lionel M. Ni *# +, Jianping Fan #, Minglu Li + * Hong Kong University of Science and Technology # Shenzhen Institutes of.](https://reader035.fdocuments.net/reader035/viewer/2022062314/56649f1f5503460f94c37333/html5/thumbnails/4.jpg)
Smart City [1]
China's urbanizationMassive issues and problems
City monitoring and managementPervasive information and knowledge
Digital technologyData collection, storage and miningReal life data sets
Vehicle GPS data sets (one year, two cities)Mobile phone networks data sets (one year, two
cities)
Hot spot detection in the city
[1] Smart City Research Group. http://www.cse.ust.hk/scrg
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Motivation
Vehicle instant locations of sample taxis at 13:00PM on 12th Dec, 2006
Traffic congesti
on
Event detectio
n
Commercial
promotion
Crowded spots and areas in the
city
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Data setIdeal case: we should have all information
of all vehicles in the cityReality: only a sample set of all vehicles
Taxi GPS data (ID, location, speed, time, direction, status)
0.3% of the two million vehicles in Shanghai
Could we utilize such a very limited sample set to detect
hot spot in the city?
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Challenges
Extremely limited sample
set
Dense?
Sparse?
Sparse!
Dense!Notable location
error
Could density based clustering handle it?
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MethodologyObservation
The low speed may indicate that the area is crowded
MethodMobility-based clusteringStudy the speed (mobility) instead of the
density
Moving objects as sensors
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OutlineIntroductionRelated workMobility based clusteringField study evaluationConclusion
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Related Work
•Partitioning methods•Hierarchical methods•Density based methods•Grid based methods•Model based methodsetc.
•Raw data based methods•Feature based methods•Model based methodsetc.
What if the mobility is high?What if the density is poor?What if the location is lossy?
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Density based clustering
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OutlineIntroductionRelated workMobility based clusteringField study evaluationConclusion
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Mobility-based Clustering
12Roadmap
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Object Mobility ModelSpeed estimation
Road network gridInterpolation
Direction distinguishing
Speed spectrum of road direction
Speed spectrum of reverse direction
NanPu Bridge13
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Spot Crowdedness ModelLinear crowdedness function
Statistical crowdedness function
( )( )( ) max
max min
( ) ( )( ) ( ) (1 )
( ) ( )l
ttt
l lL Lv l v l
l lv l v l
( )( ) ( )( ) ( ) (1 ) (1 ( ( )))ltt tl lS Sl l v v l
14
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Crowdedness Model ValidationValidation
1. Taxis traces 2. Buses tracesO r i g i n a l d a t a s e t Input
set Ф
Test set Фc
Randomly split to two parts
Crowdedness computation
Mobility estimation
ValidationMobility
error
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Learning in Practice
Characterizing spots
α, г
Sensor object profiling
Hot spots and hot regions
Temporal hot spots
Evolutionary hot regions
Spot crowdednes
s
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Learning in Mobility Based Clustering
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Learning in PracticeCharacterizing spots
17
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Learning in PracticeSensor object profiling
18
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Learning in PracticeSensor object profiling
19
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Learning in PracticeHot spots and hot regions
( ) ( ) ( ) { | 0, ' , ( ', ) ( ') ( )}t t ts sHot spot Ls l l A dist l l l l
( ) ( ) ( ) { | , ( ) }t tth s thHot region Rs l l A l
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Hot spot, even sparse sample points
NOT hot spot, even dense sample points
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Learning in PracticeTemporal hot spots
Event detection Temporal consistence
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Learning in PracticeEvolutionary hot regions
Area difference ratioCrowdedness difference ratio
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OutlineIntroductionRelated workMobility based clusteringField study evaluationConclusion
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Field Study Evaluation
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Field Study Evaluation
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OutlineIntroductionRelated workMobility based clusteringField study evaluationConclusion
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Conclusion and Future WorkContributions
Mobility-based clustering modelKey factors on spot crowdednessHot spots and hot regions
Future workMore accurate speed informationMore accurate location information
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Thanks for your attention!