Inferring Travel Purpose from Crowd-Augmented

Human Mobility Data

!Zack Zhu, Ulf Blanke, and Gerhard Tröster

Wearable Computing Lab @ ETH Zurich !

The First International Conference on IoT in Urban Space, Rome, Italy !

October 27, 2014

Urban Travel

*Gibson, J. J. The theory of affordances. Hilldale, USA (1977)

Affordance*“The affordances of the environment are what it offers the animal, what it

provides or furnishes, either for good or ill.”

*Gibson, J. J. The theory of affordances. Hilldale, USA (1977)

Affordance*(Urban)“The affordances of the environment are what it offers the animal, what it

provides or furnishes, either for good or ill.”

*Gibson, J. J. The theory of affordances. Hilldale, USA (1977)

Affordance*(Urban)“The affordances of the environment are what it offers the animal, what it

provides or furnishes, either for good or ill.”

*Gibson, J. J. The theory of affordances. Hilldale, USA (1977)

Affordance*(Urban)“The affordances of the environment are what it offers the animal, what it

provides or furnishes, either for good or ill.”

*Gibson, J. J. The theory of affordances. Hilldale, USA (1977)

Affordance*(Urban)“The affordances of the environment are what it offers the animal, what it

provides or furnishes, either for good or ill.”

Why do people travel?

Why do people travel?

WHO?

WHEN?

WHERE? (what’s-there)

Why do people travel?

WHO?

WHEN?

What is at the origin and what is at the destination?

When did we depart and arrive? How long did we stay?

What is my age? What do I do?

WHERE? (what’s-there)

Why do people travel?

WHO?

WHEN?

What is at the origin and what is at the destination?

When did we depart and arrive? How long did we stay?

What is my age? What do I do?

Given who, when, where, can we infer why?

WHERE? (what’s-there)

Puget Sound Research Council Travel Survey*

•10,372 individuals •87,000 trips •48-hour survey period •Geo-coordinates + travel

purpose class

* Puget Sound Regional Council. 2006 Household Activity Survey: General User’s Guide. http://www.psrc.org/assets/4358/2006HHSurveyUserGuide.pdf, 2006.

Puget Sound Research Council Travel Survey*

•10,372 individuals •87,000 trips •48-hour survey period •Geo-coordinates + travel

purpose class

* Puget Sound Regional Council. 2006 Household Activity Survey: General User’s Guide. http://www.psrc.org/assets/4358/2006HHSurveyUserGuide.pdf, 2006.

CLASSES INSTANCESHOME 37,122WORK 11,776

PERSONAL BUSINESS 7,877

ACCOMPANYING OTHERS 7,819

SHOPPING 7,524RECREATION 4,766EATING OUT 4,041ATTENDING

SCHOOL 3,468

SOCIAL 3,202

Work School Social RecreationHome

Students Workers

Work School Social RecreationHome

Students Workers

Modelling Procedure• Feature-level fusion of who,

when, where

• Interpersonal 10-fold cross-validation

• L1-regularized Linear SVM

• Suitable for high-dimensional features spaces

• Feature coefficient magnitude useful for model interpretation

Who When Where Label

Why do people travel?

WHO?

WHEN?

WHERE? What is at the origin and what is at the destination?

When did we depart and arrive? How long did we stay?

What is my age? What do I do?

Given who, when, where, can we infer why?

Feature ConstructionWHO?WHEN?

Feature ConstructionWHO?WHEN?

4:34 pm

5:23 pm

Feature ConstructionWHO?WHEN?

4:34 pm

5:23 pm

[…, 0, 1,1, 0, …]

Feature ConstructionWHO?WHEN?

4:34 pm

5:23 pm

6:00 pm

[…, 0, 1,1, 0, …]

Feature ConstructionWHO?WHEN?

4:34 pm

5:23 pm

6:00 pm

[…, 0, 1,1, 0, …]

47 min

0.0326 days

Feature ConstructionWHO?WHEN?

4:34 pm

5:23 pm

6:00 pm

[…, 0, 1,1, 0, …]

47 min

0.0326 days

AGE GROUP

Under 5

5-10

16-17

18-24

25-34

35-44

45-54

55-64

65-74

75-84

85+

OCCUPATION

FT Worker

PT Worker

Retired Non-Worker

Other Non-Worker

Adult Student

Grade School 16+

Child Age 5-15

Child Age 0-4

Feature ConstructionWHERE? Coordinates (X,Y) -> Coordinates (X,Y)

Feature ConstructionWHERE? Coordinates (X,Y) -> Coordinates (X,Y)

The , also known as the Flavian Amphitheatre is an elliptical amphitheatre

in the centre of the city of Rome, Italy.

Originally known as the Flavian Amphitheatre, was commisioned in AD 72 by Emperor Vespasian.

Train:

Test:

Colosseum or Coliseum

Feature ConstructionWHERE? Coordinates (X,Y) -> Coordinates (X,Y)

The , also known as the Flavian Amphitheatre is an elliptical amphitheatre

in the centre of the city of Rome, Italy.

Originally known as the Flavian Amphitheatre, was commisioned in AD 72 by Emperor Vespasian.

Train:

Test:

Colosseum or Coliseum

Feature ConstructionWHERE? Coordinates (X,Y) -> Coordinates (X,Y)

The , also known as the Flavian Amphitheatre is an elliptical amphitheatre

in the centre of the city of Rome, Italy.Train:

Test:

Colosseum or Coliseum

Colosseum or Coliseum

WHERE?

Feature Constructionembedding (X,Y) -> embedding (X,Y)

WHERE?

Feature Construction

Foursquare as platform for semantic augmentation

embedding (X,Y) -> embedding (X,Y)

WHERE?

583 low-level venue semantics in 10 high-level categories

Feature Constructionembedding (X,Y) -> embedding (X,Y)

WHERE?

Rich, crowd-generated textual features

Feature Constructionembedding (X,Y) -> embedding (X,Y)

Feature Construction

Feature Construction

Cafe Business

[ 4 … … … … 7 …]

Feature Construction

Cafe Business

[ 4 … … … … 7 …]

best coffee in town!

bustling street great for people watching

try the salted caramel, bliss.

[ 1.23 … 0.24 … 0.32 …]

Tf-Idf weighted n-grams

What is “nearby”?

What is “nearby”?

Which aspect is important for inferring why?

42.38%'

50.09%'

66.23%'

75.28%'72.54%'

69.27%'

55.56%'

0.00%'

10.00%'

20.00%'

30.00%'

40.00%'

50.00%'

60.00%'

70.00%'

80.00%'

90.00%'

100.00%'

Demographical' Temporal' Spa<al' Fused' Without'Demographical'

Without'Temporal'

Without'Spa<al'

Average'Tes*ng'Accuracy'

Accuracy'Comparison'for'Combina*ons'of'Feature'Sets'

Which aspect is important for inferring why?

42.38%'

50.09%'

66.23%'

75.28%'72.54%'

69.27%'

55.56%'

0.00%'

10.00%'

20.00%'

30.00%'

40.00%'

50.00%'

60.00%'

70.00%'

80.00%'

90.00%'

100.00%'

Demographical' Temporal' Spa<al' Fused' Without'Demographical'

Without'Temporal'

Without'Spa<al'

Average'Tes*ng'Accuracy'

Accuracy'Comparison'for'Combina*ons'of'Feature'Sets'

Which aspect is important for inferring why?

42.38%'

50.09%'

66.23%'

75.28%'72.54%'

69.27%'

55.56%'

0.00%'

10.00%'

20.00%'

30.00%'

40.00%'

50.00%'

60.00%'

70.00%'

80.00%'

90.00%'

100.00%'

Demographical' Temporal' Spa<al' Fused' Without'Demographical'

Without'Temporal'

Without'Spa<al'

Average'Tes*ng'Accuracy'

Accuracy'Comparison'for'Combina*ons'of'Feature'Sets'

-19.72%

Which aspect is important for inferring why?

42.38%'

50.09%'

66.23%'

75.28%'72.54%'

69.27%'

55.56%'

0.00%'

10.00%'

20.00%'

30.00%'

40.00%'

50.00%'

60.00%'

70.00%'

80.00%'

90.00%'

100.00%'

Demographical' Temporal' Spa<al' Fused' Without'Demographical'

Without'Temporal'

Without'Spa<al'

Average'Tes*ng'Accuracy'

Accuracy'Comparison'for'Combina*ons'of'Feature'Sets'

Crowd-augmented “where”

0"

0.1"

0.2"

0.3"

0.4"

0.5"

0.6"

0.7"

0.8"

0.9"

1"

Accompanying"Others"

A=ending"School"

EaBng"Out" Home" Personal"Business"

RecreaBon" Shopping" Social" Work"

F1#Score)

F1#Score)Comparison)for)Using)Various)Spa6al)Features)

Venue"Category"L"High"Level"

Venue"Category"L"Low"Level"

Venue"Tips"

All"SpaBal"InformaBon"

Crowd-augmented “where”

0"

0.1"

0.2"

0.3"

0.4"

0.5"

0.6"

0.7"

0.8"

0.9"

1"

Accompanying"Others"

A=ending"School"

EaBng"Out" Home" Personal"Business"

RecreaBon" Shopping" Social" Work"

F1#Score)

F1#Score)Comparison)for)Using)Various)Spa6al)Features)

Venue"Category"L"High"Level"

Venue"Category"L"Low"Level"

Venue"Tips"

All"SpaBal"InformaBon"

Crowd-augmented “where”

0"

0.1"

0.2"

0.3"

0.4"

0.5"

0.6"

0.7"

0.8"

0.9"

1"

Accompanying"Others"

A=ending"School"

EaBng"Out" Home" Personal"Business"

RecreaBon" Shopping" Social" Work"

F1#Score)

F1#Score)Comparison)for)Using)Various)Spa6al)Features)

Venue"Category"L"High"Level"

Venue"Category"L"Low"Level"

Venue"Tips"

All"SpaBal"InformaBon"

Eating Out

Socializing

Attend School

Conclusion & Future Work• Augmentation of physical-world coordinates with

virtual-world contextualization cues • Travel inference with 75.28% accuracy (9 classes) • Feature importance: “where” > “when” > “who” • Model-driven indication of purpose context

• Feature engineering of “nearby” to consider travel time and venue importance weighting

• Validate model with real-time inference field study

Conclusion & Future Work• Augmentation of physical-world coordinates with

virtual-world contextualization cues • Travel inference with 75.28% accuracy (9 classes) • Feature importance: “where” > “when” > “who” • Model-driven indication of purpose context

• Feature engineering of “nearby” to consider travel time and venue importance weighting

• Validate model with real-time inference field study

Zack Zhu, Wearable Computing Lab, ETH Zurich zack.zhu@ife.ee.ethz.ch

What is “nearby”?

We investigate two notions of “nearby” for feature construction:

What is “nearby”?

We investigate two notions of “nearby” for feature construction:

Physical Radius

What is “nearby”?

We investigate two notions of “nearby” for feature construction:

Physical Radius

Rank Distance

What is “nearby”?

What is “nearby”?

What is “nearby”?

What is “nearby”?

What is “nearby”?

What is “nearby”?

Rank distance is adaptive against varying data density

What is “nearby”?

68.51%'

75.28%' 74.95%'73.83%' 73.11%' 72.79%'

58.93%'

65.92%'

71.89%'

72.74%' 72.52%'71.53%' 70.67%'

0' 100' 200' 300' 400' 500' 600' 700' 800' 900' 1000'

50.00%'

55.00%'

60.00%'

65.00%'

70.00%'

75.00%'

80.00%'

85.00%'

90.00%'

95.00%'

100.00%'

0' 20' 40' 60' 80' 100'

Physical)Distance)Radius)in)Meters)

Tes4ng)Accuracy)

Rank)Distance)Radius)in)Nearest)Neighbours)

Rank'Distance'

Physical'Distance'

Rank distance is adaptive against varying data density

Crowd-augmented “where”

Feature Rank

Feat

ure

Gro

ups

Demographical Features

Temporal Features

Venue Tips

Venue Category−Low Level

Venue Category−High Level

0 2000 4000 6000

●

●

●

●

●

o o o

o ooooo ooooooooooo

o

Accompanying Others

●

●

●

●

●o

Attending School

0 2000 4000 6000

●

●

●

●

●

o o o

oooooooooo

Eating Out

Demographical Features

Temporal Features

Venue Tips

Venue Category−Low Level

Venue Category−High Level

●

●

●

●

●

o

o

oo

Home

●

●

●

●

●

o o o o o o

ooo oooooooo

o

Personal Business

●

●

●

●

●

oo

o oo oo

oo ooooo oooooooooo

Recreation

Demographical Features

Temporal Features

Venue Tips

Venue Category−Low Level

Venue Category−High Level

●

●

●

●

●

o o o

ooooooooo

o

Shopping

0 2000 4000 6000

●

●

●

●

●

o o

oo o o oooooo

o o

Social

●

●

●

●

●

o

ooo oooooo

ooo

Work

Crowd-augmented “where”

Feature Rank

Feat

ure

Gro

ups

Demographical Features

Temporal Features

Venue Tips

Venue Category−Low Level

Venue Category−High Level

0 2000 4000 6000

●

●

●

●

●

o o o

o ooooo ooooooooooo

o

Accompanying Others

●

●

●

●

●o

Attending School

0 2000 4000 6000

●

●

●

●

●

o o o

oooooooooo

Eating Out

Demographical Features

Temporal Features

Venue Tips

Venue Category−Low Level

Venue Category−High Level

●

●

●

●

●

o

o

oo

Home

●

●

●

●

●

o o o o o o

ooo oooooooo

o

Personal Business

●

●

●

●

●

oo

o oo oo

oo ooooo oooooooooo

Recreation

Demographical Features

Temporal Features

Venue Tips

Venue Category−Low Level

Venue Category−High Level

●

●

●

●

●

o o o

ooooooooo

o

Shopping

0 2000 4000 6000

●

●

●

●

●

o o

oo o o oooooo

o o

Social

●

●

●

●

●

o

ooo oooooo

ooo

Work

Venue tips consistently rank with the highest (median) feature importance