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

OutlineIntroductionRelated workMobility based clusteringField study evaluationConclusion

OutlineIntroductionRelated workMobility based clusteringField study evaluationConclusion

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

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

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?

Challenges

Extremely limited sample

set

Dense?

Sparse?

Sparse!

Dense!Notable location

error

Could density based clustering handle it?

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

OutlineIntroductionRelated workMobility based clusteringField study evaluationConclusion

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?

10

Density based clustering

OutlineIntroductionRelated workMobility based clusteringField study evaluationConclusion

Mobility-based Clustering

12Roadmap

Object Mobility ModelSpeed estimation

Road network gridInterpolation

Direction distinguishing

Speed spectrum of road direction

Speed spectrum of reverse direction

NanPu Bridge13

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

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

15

Learning in Practice

Characterizing spots

α, г

Sensor object profiling

Hot spots and hot regions

Temporal hot spots

Evolutionary hot regions

Spot crowdednes

s

16

Learning in Mobility Based Clustering

Learning in PracticeCharacterizing spots

17

Learning in PracticeSensor object profiling

18

Learning in PracticeSensor object profiling

19

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

20

Hot spot, even sparse sample points

NOT hot spot, even dense sample points

Learning in PracticeTemporal hot spots

Event detection Temporal consistence

Learning in PracticeEvolutionary hot regions

Area difference ratioCrowdedness difference ratio

22

OutlineIntroductionRelated workMobility based clusteringField study evaluationConclusion

Field Study Evaluation

24

Field Study Evaluation

25

OutlineIntroductionRelated workMobility based clusteringField study evaluationConclusion

Conclusion and Future WorkContributions

Mobility-based clustering modelKey factors on spot crowdednessHot spots and hot regions

Future workMore accurate speed informationMore accurate location information

Thanks for your attention!