KIBIM 2016. 11. 18

신동윤dongyoun79@gmail.com

Big Data Urban Simulation: Sensing Cities

http://en.wikipedia.org/wiki/Direct_democracy#/media/File:Landsgemeinde_Glarus_2006.jpg

A Landsgemeinde of the Canton of Glarus, on 7 May 2006, Switzerland

Direct democracy: people participate in the decision making process

James Murray, the Oxford English Dictionary editor call public participation in compiling the dictionary. 2,500,000 quotation was obtained until 1884.

Wikipedia (Listeni/ˌwɪkɨˈpiːdiə/ or Listeni/ˌwɪkiˈpiːdiə/ wik-i-pee-dee-ə) is the world’s largest encyclopaedia that supports a free-access, free content by Internet.

48%

3%3%4%4%4%5%5%5%

6%14%

EnglishSwedishGermanDutchFranchWaray-WarayCebuanoRussianItalianSpanishOther

34,780,589 articles in different language editions (March 2015)

Collected 2,500,000 quotations for 15 years Collected 35,000,000 articles for 15 years

Crowdsourcing with new media

0

5,000,000

10,000,000

15,000,000

20,000,000

25,000,000

30,000,000

35,000,000

Number of articles

ScriptoriumWikipedia

Image source: https://smartcitizen.me/

Lightweight data collection: crowdsourcing + sensors

Motivation

Crowdsourcing

Mobile network

Lightweight form of urban trip data collection to support urban planning

People-centric sensing

Walking Walking Walking Walking

Transportation mode classification

date, time / x-axis acceleration value / y-axis acceleration value / z-axis acceleration value / latitude / Longitude

Raw data

Acce

lera

tion

(m/s

^2)

Time (s)

BUSCarWalking WalkingElevator

In a officeWalking Walking Walking

Time series visualization

Classified as

Ground truth

Walk Train/Tram

Car Bus

Walk 100% 0% 0% 0%

Train/Tram

0% 88.46% 11.54% 0%

Car 0% 3.85% 80.77% 15.38%

Bus 0% 3.85% 19.23% 76.92%

Acce

lera

tion

(m/s

^2)

Time (s)

BUSCarWalking WalkingElevator

In a officeWalking Walking Walking

No VehicleAverage Acceleration: 0.158 Average Acceleration: 0.926

Overall Accuracy: 82.05%

Transportation mode classification

Hierarchical data analysis (1Hz of sensing frequency)

Extract each homogenous

activities

Distance: 3.9 kmSpeed: 39 km/h

Distance: 3.1 kmSpeed: 23.8 km/h

Distance: 0.1 kmSpeed: 0.7 km/h

Vehicle Vehicle

CAR BUS

No VehicleAverage Acceleration: 0.158 Average Acceleration: 0.926

Vehicle type detection

Analysis

Acce

lera

tion

(m/s

^2)

BUSCarWalking WalkingElevator

PostOfficeWalking Walking WalkingSensing

Walking Walking Walking Walking

Detect walking & separate

Linear data analysis (25-60Hz of sensing frequency)

Hemminki S., Nurmi P. et al. Helsinki institute (2013)

Co2go

Comparison with other classification methods

Mazoni.V., Maniloff.D. et al. SENSEable City Lab, MIT (2012)

Mobile application: Citying

Case study: Singapore

Total number of users: 78

Total number of trips: 1344

Screenshots of the mobile application

Case study: students at ETH Hönggerberg

Case study: transpiration mode and route analysis

Bus Tram

Train

Car

Walking (>5min) Bicycle

Zürich HB

ETH Hönggerberg8.9 %

10.9 %

2.1 %

9.8 %

7.8 % 2.1 %

29.2 %

2.1 %

12.0 %

4.7 %4.7 %

5.7 %

ETH Hönggerberg

Bhf. Oerlikon

Bucheggplatz

AltstettenHardbrücke

Zurich HB

Meierhofplatz

semestervacationsum

Num

ber o

f trip

s

Hours

Case study: transportation mode use analysis

9%15%

47%

4%

21%5%

TrainTramCarBusWalkingBicycle

13%

30%29%

5%

19%3%

Total number of travels (2012)

10%

20%

41%

4%

20%4%

Number of travels by time

Semester Vacation

Case study: Start/end/transfer place analysis

Total travel

0 125 250

ETH Hönggerberg Bhf. Oerlikon

Bucheggplatz

Altstetten

HardbrükeZurich HB

Meierhofplatz

Daytime travel

Nighttime travel

Daytime (travel to Daytime (travel to non

0 9 18

Nighttime (travel to

0 11 33

Nighttime (travel to non 0 83 166

0 42 84

0 83 166 0 42 84(19:00 to 24:00)

(7:00 to 19:00)

Image source: http://www.bljesak.info/

Private Costs

Social costs analysis

Generally speaking though, social costs can be distinguished from private costs so as to indicate only external costs (Walters 1961).

In this thesis, the social costs does not include private costs but only external costs as “pure” social costs.

Verhoef (1996)

0

7.5

15

22.5

30

car

train bu

stra

mbic

ycle

CongestionNoiseAccidentsAirpollutionClimate changeNature and landscapeSoil & water pollution

Rp./pkm

Social costs analysis: social costs by transportation mode

Maibach, Schreyer, et al. (2008), Handbook on estimation of external costs in the transport sector, CE Delft Solutions for environment, economy and technology (www.ce.nl)

Social costs analysis: tool for social costs estimation

Social costs (CHF) =

Travel distance (km) xSocial costs of selected travel mode (per Kilometer) (CHF: car, train, bus, tram, bicycle)

- Different modes of travel exert high leverage effects on social costs

Short-range / walking dominant

x6x50

Trip patterns analysis: short-range trip vs large-range trip

Large-range / car dominant

Trip patterns analysis: mid-range trip patterns

- 75% of the travels have travel distance between 110 km – 170 km per week, and mostly in the mixed-use travel mode.

- The mixed-use trip patterns has rather similar social costs around CHF 4.20 – CHF 6.20 per week.

Mid-range / tram dominant Mid-range / bus dominant

Social costs analysis Urban sensing

BigData informed design

Dongyoun Shin (2016), Urban sensing by crowdsourcing: Analyzing of urban trip behavior in Zurich, International Journal of Urban and Resonal Research, accepted (SSCI).

CH

F (p

er w

eek)

Case B

Social costs analysis: student housing

1

2

3

4

5

Simulation platformBig data informed urban planning

Student housing simulation

Travel to non ETH Hönggerberg

0 42 84

Social costs analysis: student housing

ETH Hönggerberg

Zurich HB

Bhf. Oerlikon

Bucheggplatz

Altstetten

Hardbrücke

Meierhofplatz

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Machine Learning based Urban Big Data Analysis for the future cities

NRF (2016-2019 / 300,000,000 won)

THANK YOU!FOR YOUR ATTENTION

KIBIM 2016. 11. 18