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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
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