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W I WV

Handclap Traffic Tidal

Sheep Flock

AnimalMobility

AnimalBehavior

Fish School

All contain patterns

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W I WV

PhysicalSpace Pattern

Space

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Z OWIT

Input:

• / I IT MU WZIT I MZV• GP KP IZMI IZM V PM IUM I IT I MZV 2 +B G• GPI MU WZIT I MZV M V Z IV ZINN K2 2• :W ZINN K WKK Z M MMV I IT I MZV P L NNMZMV MU WZITI MZV 2 28 4 B 8 88 6B G 4 7 D

Urbanbigdata

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Our Works

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I ZWIL

TaxiGPSinBeijing BeijingMap BeijingPOI

Input:

Tools:

RegularizedNon-negativeTensorDecomposition

Output:

Spatio-temporal

patterns

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AZW TMU LMN V WV

•0Z O V M VI WV 3UM MV WZ• ! ! P MTMUMV / PM ZINN K WT UM NZWU PWZ O V bWVM W P LM VI WV bWVM V P UM

ODnetwork

Tensor

AODTtensorTimeevolving

ODnetwork

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AZW TMU LMN V WV

•6MKWU W M PM MV WZ I PZMM ZWRMK WVUI Z M IVL I KWZM MV WZ

ODTtensor

Coretensor

O projectionMatrix

D projectionMatrix

T projectionMatrix

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C I IT I MZV ZWRMK WV

Urbanzones Urbancommunities

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DMU WZIT I MZV ZWRMK WV

UrbanrhythmUrbantraffic

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min

BMO TIZ bML ?WV VMOI M D KSMZ 6MKWU W WV

• 4I K D KSMZ LMKWU W WV

• 0 86 8 9G 6 B

•5PITTMVOM• FIT M V PM MV WZ MZa IZ M WVTa - VWV bMZWMTMUMV• EZ IV ZINN K I MZV PI M KTW M ZMTI WV P Z IVKWV M ! KP I A

ODTtensor OMatrix TMatrix

Coretensor DMatrix

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• WLMT Z IV KWV M IT VNWZUI WV• 3 U WV/ IZMI P U TIZ A VNWZUI WVPW TL PI M PM U TIZ Z IV Z K ZM

• 5WV Z K IZMI IZMI U TIZ a UI Z

•0 86 8 9G 6 B

min

BMO TIZ bML ?WV VMOI M D KSMZ 6MKWU W WV

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BMO TIZ bML ?WV VMOI M D KSMZ 6MKWU W WV

• ISM I MZV UWZM M TI VI TM•?WV VMOI M KWV ZI V

• = ZMO TIZ bI WV• 5WZM MV WZ/ VTa SMM ZWVO V MZIK WV

• AZWRMK WV UI Z / UISM MIKP KT MZ UWZM UMIV VON T IVLMVPIVKM V MVM

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BMO TIZ bML ?WV VMOI M D KSMZ 6MKWU W WV

• 8 VIT W RMK WV N VK WV

•: UIV UW T a IVL Z IV KWV M IZM RW V TaW U bML

Modelurban

contextual

information (POI)

Model traffic

information

Makeresults

sparse

Makeresults

non-negative

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U bI WV

•4TWKS 5WWZL VI M 6M KMV 456

•3T MZVI VO AZW UIT 9ZIL MV 3A9

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Experiments

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6I I

•4M R VO 9AC LI I• 9AC ZIRMK WZa WN %%%% 4M R VO I M ! %%- IVL %

•4M R VO ZINN K IVITa bWVM UI• +%% bWVM V PM P Z VO ZWIL V 4M R VO

•AW WV WN V MZM VO A V 4M R VO• (-%!%%% W V

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4M R VO PW KI M

ODTtensor

Coretensor

O projectionMatrix

D projectionMatrix

T projectionMatrix

Spatialpatterns

Temporalpatterns

Relations

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DMU WZIT I MZV

Morningpeak

Mid-day

Aftereveningpeak

Eveningpeak

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DMU WZIT I MZV

2008 2012

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4M R VO PW KI M

ODTtensor

Coretensor

O projectionMatrix

D projectionMatrix

T projectionMatrix

Spatialpatterns

Temporalpatterns

Relations

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C I IT I MZV

2008 2012

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C I IT I MZV

2008 2012

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C I IT I MZV

2008 2012

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C I IT I MZV

2008 2012

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4M R VO PW KI M

ODTtensor

Coretensor

O projectionMatrix

D projectionMatrix

T projectionMatrix

Spatialpatterns

Temporalpatterns

Relations

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5WZM MV WZ

Morning Midday

Evening Night

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WZV VO

Origin Destination

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LLIa

Origin Destination

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7 MV VO

Origin Destination

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? OP

Origin Destination

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8 VK WV WN Z IV bWVM

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8 VK WV WN Z IV bWVM

2008 2012

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WhereisthecenterofBeijing?

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s

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Obama

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s

R• r m• g m• r m

h R• h i h i

No-congestion

Congestion

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s

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Input Matrix Convolution Pooling Error

feedback Output

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there exist a number of local patterns (spatio-temporal trends) in the spatio-temporal input matrix

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Convolution layer: using several filters to convolute the input matrix

Pooling layer: using average pooling to down sampling the convolution neuro matrix

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ARIMA: Auto Regression Integrated Moving Average; SVR: Support Vector Regression; SAE: Stacked Auto Encoders; 1D-CNN: 1D Convolutional Neural Network; CNN: Convolutional Neural Network

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