Kim, Ki-Byoung, Chief Data Officer / Director Data & Statistics Division Seoul Metropolitan Government

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Kim, Ki-Byoung
Transportation Service Innovation through Big Data
- Best Practices in Seoul Metropolitan Gov. -
Apr. 28. 2016.
Chief Data Officer / Director
Data & Statistics Division
Seoul Metropolitan Government
e-mail: pskbkim@gmail.com

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Contents
1. Introduction
2. e-Government of SMG – Foundation of Seoul eGov.
3. Data to communication – Open data initiative
4. Communication to collaboration – Bukchon IoT Living lab
5. Collaboration to innovation – Data based public services
6. What Seoul has learned
7. Plan & direction

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More than 90% of Seoul citizens are
Smart Phone users
Ranked 1st on
e-Governance survey
By Rutgers Univ, NJ, USA
For 6 times since 2003
Concentration of ICT Resources
Population: 10,370,000 (2015)
20% of Korean population
Total Area: 605.26km2 GRDP : USD 319 billion(2015)
25% of Korean GDP
Seoul - Outlook

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25 districts
605 km2
490 ICT systems
- USD 25 billion dollars
- 46,500 city officials
(including fire fighters
and district officials)
Seoul Metropolitan Government

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Data to Communication

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Background - Open data initiative
Open information1
- Open documents
- Open data
- Integration & visualization
-Real time bus operational information
-Real time metro information
-Taxi vacancy information by big data
-Fine-dust, Tax revenue, expenditure, .. more than 4,000 data set

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Listen to voices of the citizen – even it’s small…
1
- Open documents
- Open data
- Integration & visualization
Listen to the citizen2
-m Voting, mobile complaint app
-“120” call center
-Big data analysis
Open information
64,226,068 calls
since 2007
as of the end of 2014 as of the end of 2013 as of the end of 2013
25.5%? 73.5%?

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Find insight of voices even it’s small, then response!
1
- Open documents
- Open data
- Integration & visualization
2
3 Listen again by data & Do
-Late night bus routes
-Taxi analysis
-Reduction of car accidents
Listen to the citizen
Open information
-m Voting, mobile complaint app
-“120” call center
-Big data analysis

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Communication to Collaboration

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Demand driven communication based on Citizen’s needs
Data collaboration with citizen
ConnecttoopenAPI(realtimedata)

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Participation for prepare new policy via mVoting
m-Voting for listening the citizen toward new policies

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Voting through smart phones and PCs
Selected projects are planned according to
the m-Voting results
- Participation ratio
- Citizens(m-Voting): 45%
- Citizen participatory budget committee: 45%
- Survey: 10%
Citizen Participatory Budget Project (July 16th~25th, ‘15)
Citizen’s participation on the selection of city projects
Seoul citizens can decide where the $50
million city budget will be spent in 2016
 Everybody can participate in m-voting
m-Voting for listening the citizen toward new policies

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Living lab for Internet of Things @ Bukchon
Living lab = Innovation eco-system where users can participate proactively

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Protect Minorities
Smart Parking
위급상황
전송
이상징후
감지 온도
동작
음성
이상징후
전송
LTE라우
터
비상벨
데이터 분석
위급상황 판단
관제서버
담당복지사
전송
담당복지사복지대상자
주차인식센서
싱크노드
무선메쉬
WiFi
Zigbee
주차인식센서
싱크노드
무선메쉬
WiFi
Zigbee
▶Safety, welfare, transportation, environments
Smart Trashbox

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Smart Street Lamp Smart meter
Smart Parcel Box
SMS, 앱
전송
수령자
핸드폰번호
입력
택배기사 택배함 중앙시스템
WiFi
비밀번호설정
수령자
[문자내용]
A-123번
보관함에
택배가 보관.
비밀번호 1913
인체감지
환경정보수집
25% 50% 100% 50%
북촌 IoT유무선
네트워크
6LoWPAN
조명색 조절
6LoWPAN
가로등 중앙관제
(고장,감시,제어)
계량기
전송기
계량기
계량기
전송기
소출력
무선통신
계량기
전송기
집중기 LTE라우터
u-Service Net
중앙시스템
고지서송부 주민
사용량 분석
▶ Safety, welfare, transportation, environments

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Collaboration to Innovation

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https://www.youtube.com/watch?v=NV-QeQ-6yOw

18. 17
2013
1
2015
192014년
102014
10
Number of big data based administration

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Night bus route optimization
‘13.06~
‘13.07
Location analysis of
Life double cropping center
‘13.10~
’14.02
Location analysis of
Senior welfare center
‘13.10~
’14.02
City PR booth analysis
‘13.10~
’14.02
Flow analysis of foreign tourists
‘13.10~
’14.02
Location analysis for ATM of civil service
‘14.11~
’15.02
Taxi analysis
‘14.07~
‘15.02
Traffic accident analysis for minorities
‘14.07~
‘15.03
Disabled taxi analysis
‘14.07~
‘15.07
Street business zone analysis
‘14.10~
‘15.10
Location analysis for public WIFI
‘15.04~
‘15.07
Analysis of regional festivals
‘15.10~
‘16.05
Mobility analysis of disabled
‘15.10~
‘16.05
Tuberculosis analysis
‘15.10~
‘16.05
Village bus route optimization
‘15.10~
‘16.05
Effectiveness of traffic signs
‘15.10~
‘16.05
Analysis of traffic accident zones
‘15.10~
‘16.05
Parking analysis
‘15.10~
‘16.05
‘ Analysis of Shinchon water fest.
‘15.10~
‘16.05
Big data based administration
1
1
1
1
1
1
1
1
1
2
2
2
2
2
3
4
3
3
3

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Area Topic Period Objectives Results
Transporta
tion
Night bus route
optimization
`13.1H Route optimization
Night bus coverage > 42% with 30
buses (daytime buses >7,000)
Transporta
tion
Traffic accident
analysis for minorities
`14.2H
Prepare policies to reduce
traffic accidents of children and
senior citizen
Number of accidents will become
50% in 3 years
Transporta
tion
Taxi analysis `14.2H
Provide more chance to catch
a taxi during mid night
Provide 5% more chance to catch a
taxi in midnight
Welfare
Location analysis of
Life double cropping
center
`14.1H
Select the best location of life
double-cropping center
Every facility will provide best
coverage for the citizen
Welfare
Location analysis of
Senior welfare center
`14.1H
Select the best location of
senior leisure centers
Every facility will provide best
coverage for the citizen
Welfare Disabled taxi analysis `14.2H
Reduce waiting time of
disabled taxi
Reduce waiting time by 10%
Admin City PR booth analysis `14.1H
Select best location of public
PR
More PR’s with same booths
Admin
Location analysis for
ATM of civil service
`14.2H Select prioritized ATM locations
Provide more ATM services with
planned number of devices
Tourism
Flow analysis of
foreign tourists
`14.1H
Promote foreign tourism with
better service
Increase foreign tourist by 10%
Results from big data based administration

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Response of the City
No public transportation
in 01:00 AM ~ 05:00 AM
Subway Bus Taxi
“Buses don’t run by the
time I get off work. I
don’t have a car.
I hope there will be
buses available at late
night..!!”
@gu****
Late-night bus story
Let’s set-up Late night bus routes
Facing Problems
1.Limited resources
– bus, drivers, budget
2. Where is traffic demand?
Why Late-night bus?

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Approach – night bus route optimization
Big data problem definition Modeling Analysis
Night bus routes Routes optimization Finalized routes
Original problem(big problem)  Big data problem(small, manageable problem)
Set-up mid-night buses with limited resources Floating population and moving directions of 1,252 cells

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Results
•Citizen’s evaluation
A new way to go home at mid-night
8.9% reduction of refusal rate to
passengers of mid-night taxi
11.8% increase of women’s
mid-night activities
•Administration perspective
Proof of administration decision
to settle civil complaints
Max. 10% of PAX increased without
increasing new routes or buses
42% coverage of citizens
PassengersWanderer due to refusal of taxi Women returning home late
Daily PAX
Student BusinessmanProxy driver
8.9%▽ of refusal passengers 11.8% △ of midnight women’s activity
Source: News jelly

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Subsidy $150M / year
Why Taxi Matchmaking? Response of the City
Taxi big data analysis
•According to 120 Seoul Dasan call center
• - 25.5% of the citizens’ complaints are on
transportation!
- Among them, 73.5% are related to taxis!
Provide more supplies of taxis,
without additional no. of taxis
- Taxi DTG (Digital Tacho-graph)
-X,Y coordinate, height, date, heading, speed, status per 10 secs
- Data are collected in every 150 seconds
Private
Taxis
49,424
Corporate
Taxis
22,801
Status of taxi registered in Seoul
It seems to be short supply of taxi
during 11PM to 1AM
while Taxis in Seoul are oversupplied
Facing Problems

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Approach – taxi analysis
Big data problem definition Modeling Analysis
Vacancy vs. Demand Preparation of policy Reinforcing eco-system
Original problem(big problem)  Big data problem(small, manageable problem)
More taxi supply without increasing no. of taxis Decrease vacancy rate of taxis
No vacant
taxis during
23-01hr
Vacant rate is
HIGH!
Instead of providing more taxis,
what about
reduction of vacancy rate?
Vacant rate is HIGH!Vacant rate is HIGH!
Refined node/link
Refined link length : 150m
-2 min. walking distance
-Can count taxi with speed of 60km/h
High vacancy
High demand
1
3
2

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Occupied
Transfer
21.2L Saving
1.5L
Empty
Transfer
13.9L
Reduction of
1.5 liter/day
1ML 1ML 1ML 1ML 1ML 1ML 1ML
1ML 1ML 1ML 1ML 1ML 1ML 1ML 1ML 1ML 1ML
1ML 1ML 1ML 1ML 1ML 1ML 1ML 1ML 1ML 1ML
27,000,000 liter / year
(7M gallon / year)
* 44,932 ton / year
CO2
* Applied IPCC formula for tCO2 conversion
Expected results
• Provide more chance to catch a cab by reducing empty rate of taxi
• We will reduce empty rate of taxi by 10% from now……
- Average empty rate of taxi in Seoul = 42% (Korea Transport Institute , 2012)
5% more chance
to catch a taxi
$40M annual
cost savings
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Traffic accident analysis for children & seniors
Why traffic accidents? Response of the city
Death
Accident
Bodily Injury
Car Injury
Prededing
Clause
1
Assailant
2
Road
3
Vehicle
4
Others
5
Victim
Facilities
structure
Lane
Change Sharp
Curve
Conjuction
Management
Problem
No
Facility
Facilities
Road
Structure
Events
Bad
Weather
Night
Perception
Ability
Carelessness
Safety
Utility
Drinking
Inattention Intentional
Overspeed
- Heinrich’s law (1:29:300)
Facing problems
1.Reduce accident rate of children safety
zone and senior citizen zone
2.Where are traffic accidents of children
and senior citizens?
Assumption
1.0 1.4 1.6
2.4
4.8
코펜하겐 베를린 도쿄 런던 서울
<’2009, person/100k person>
2-4 times more traffic accident than
global cities
Walk to death accident among fatal accidents=57%
Senior accident among walk accident=38% (#1)
Protect children & senior citizen from the
traffic by reducing no. of accidents by half

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Traffic accident analysis - approaches
Problem definition Modeling Analysis (EDA)
Hot spot analysis Assumption & Proof Preparation of policy

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More cases…
1.Blind citizen’s facility operation
2.Location selection of braille blocks
3.Effectiveness analysis of traffic signs
4.Festival analysis in Seoul
5.Tuberculosis analysis
6.Route recommendation for midnight village buses
7.Traffic accident analysis
8.Parking analysis
9.Shuttle services for minorities
10.Relocation of Taxi stops

30. Learning & Direction

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1.Problem definition is important!
- Try to find problems by analysis? Good question makes good analysis
2. Objective of analysis?
- Quest for pearl in a grain of sand? Analysis without objectives may lead wrong direction
3. Administration problems into data problems
- Much better to analysis data based transformed problem
4. How to model data?
- Analysis raw data? Better to analysis with simplified but problem oriented model/data
5. Focused only on big data analysis?
- Good insights are sometimes coming from traditional data analysis.as well as big data
6. Apply analyzed results
- Provide analyzed results to proper departments. They will demonstrate results, not by you
What we’ve learned
*Bus route problem
-> floating population & direction problem
*Floating population
-> population of hexagons with 500 m radius
-> 605km2 of Seoul -> 1,252 cells

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1. Preparation of data
2. Sharing data/
analysis results
Big Data Campus
Integrated
Analysis
How to start big data innovation in major cities?
Transportation
Administration
-Cell phone data
-Sensors, IoT
-Community mapping 3. Private/Public
collaboration
Floating
population

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Integrated
Analysis
Transportation
16
Facilities
10
Population
12
Shop/Enterprise
6
Spending
6
Environments
4
Complaint/Social
2
Real estate
4
Locations
2
Income
1
Big data of Seoul - category

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What are we going to do with data?
Transport/Safety1
Welfare2
3 Small Business
4 Environment
-Reduction of car accidents
-Optimization of local bus routes
-Automatic allocation of disabled taxi
-Optimization of civil service kiosks
-Mobility service for the disabled
-Big data for small business develop.
-Analysis for Seoul city festival
-Tuberculosis analysis
and more…
2015 ~ 2017

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Thank you

36. Appendix

37. Problem definition & transformation to data problems
1.Where are the passengers
in the mid-night?
2. Where do they want to go?
Data problemsAdmin. Problems
3 billion mobile call data

38. Modeling
SimplifiedOriginal
605km2 1,252 Hexagons
a set of lines lines with thickness

39. Analysis
Analysis Optimization based on population

40. Analysis
Finding solution through data
Utilization floating population of weekdays and weekend
N26
N37
Establish interval based on population

41. Finding answers from data - optimization
<Route optimization in detail>
Route 4 Route 5
Route 7 Route 8
- Route4:Gil-dong~Myung-ilsta.
- Route5:ButtyHill ~Yaksusta.
- Route7:Expressbusterminal
- Route8:Nambuterm,Konkukuniv.
Optimization of routes
95,335 ⇒ 105,261 (10% ↑) 161,483 ⇒ 175,233 (8.5% ↑)
90,785 ⇒ 95,146 (5% ↑) 205,719 ⇒ 2201582 (7% ↑)

42. Establish 9 owl bus routes

43. Establish 9 owl bus routes
Initial routes of Seoul owl bus in 2013

44. Establish 9 owl bus routes

45. Problem definition & transformation to data problems
1.Provide more taxis without
increasing no. of taxis
2. Increase utilization of taxis
Data problemsAdmin. Problems
Vacant rate =
𝑽𝒂𝒄𝒂𝒏𝒕 𝒓𝒖𝒏 (𝒕𝒊𝒎𝒆 𝒐𝒓 𝒅𝒊𝒔𝒕𝒂𝒏𝒄𝒆)
𝑻𝒐𝒕𝒂𝒍 𝒓𝒖𝒏(𝒕𝒊𝒎𝒆 𝒐𝒓 𝒅𝒊𝒔𝒕𝒂𝒏𝒄𝒆)
Hired rate =
𝑯𝒊𝒓𝒆𝒅 𝒓𝒖𝒏(𝒕𝒊𝒎𝒆 𝒐𝒓 𝒅𝒊𝒔𝒕𝒂𝒏𝒄𝒆)
𝑻𝒐𝒕𝒂𝒍 𝒓𝒖𝒏(𝒕𝒊𝒎𝒆 𝒐𝒓 𝒅𝒊𝒔𝒕𝒂𝒏𝒄𝒆)

46. 1
2
3
4
Get on Get off
Vacant
Hired
Status of taxi Get on Get off Vacant run Hired run
Hired run 1
Vacant run 1
Hired run  get off 
Vacant run
1 1 1
Vacant run  get off 
Hired run
1 1 1
1
2
3
4
Modeling – Status of taxi

47. Modeling – Seoul metropolitan area
Original
Standard node/link
Refined
Refined node/link
Average link length: 330m,
Longest link length: 30km
Refined link length : 150m
-2 min. walking distance
-Can count taxi with speed of 60km/h
-(move 150m in every 10 sec.)

48. • 기준 : 2014년 10월~11월, 금요일 23시 ~ 토요일 01시
• 대상 : 개인 및 법인택시(운행대수 약 34,306대)
1
2
8
4
5
6
7
3
109
11
1213
14
15
16
17
18
19
20
1㎞
1㎞
21
연번 지역 횟수
1 홍대입구 1891
2 강남역 1654
3 가로수길 1501
4 선릉역(동) 1473
5 신림역 1316
6 건대입구 1256
7 로데오거리 1136
8 압구정역 1112
9 신논현역 1109
10 선릉역(서) 1041
11 사당역 979
12 동대문역 975
13 종각역 952
14 영등포역 929
15 이태원역 876
16
서울대입구
역
855
17 천호역 844
18 양재역 835
19 몽촌토성역 832
20 홍대입구(북) 815
21 강동역 750
Analysis
Top demand spots

49. What did big data find?
운행
대수
 기준 : 4시~3시
운행
대수

50. Location Date Begin End period Vacant taxi Estimation Real vacancy rate
1. Hotel Seogyo 2014-12-11 23:00 23:30 42 89 47%
2. Seonghwa bd. 2014-12-11 23:50 00:20 28 37 76%
3. Hotel Yaja 2014-12-11 00:30 01:00 47 55 85%
승객대비 공차
많은 위치
승차 집중 위치
1
3
2
Implications
Example: Hongik Univ: Seogyo Hotel – High demand vs. Donggyoro – low demand
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51. Suggestion - Seoul taxi map
14
15
19
홍대
입구
종각
역
동대
문역
건대
입구
가로
수길
강남
역
로데
오거

52. Accelerate eco-system of big data
Information of
Boarding and
Departing Time,
Location, and
Traveling Course
Weather
Information
Information on the
Floating Population
Node-Link
SMG,Taxis utilize shared big data
25 / 42

53. Problem definition & transformation to data problems
1.Reduce traffic accidents
of senior citizen & children
2. Reduce traffic accidents
of safety zones
Data problemsAdmin. Problems
1. Accident frequencies along with
cell model
2. Driving behavior on node/link model
3. Forecast index of accidents from
- Floating population
- Location of metro station
- Location of Crosswalk
- Location of junction
- Location of Bus stop in the middle

54. Speed
Accident
by lane
Traffic
facilities
Floating
populati
on
Weather
Traffic
volume
Event
Driving
behavior
Safety
zone
Composite modeling approaches

55. Exploratory Data Analysis (탐색적 자료 분석)
Time series analysis
Characteristics External
causes
Geo-spatial analysis
• 사고유형
• 가해자/피해자
• 연령/성별
• 차종 등
기상
유동
인구
DTG
교통
속도
도로
특성
시설물
EDA
(Exploratory Data Analysis, 탐색적 자료 분석)
• Finding characteristics of traffic accidents from external causes
• Finding hot spot of traffic accidents
• Forecast future traffic accidents along with time/location through time series
analysis and geo-spatial analysis
다차원
상관분석

56. Children’s traffic accidents
[어린이/중고생 보행자 교통사고 - 시간대별]
[성인]
[어린이]
[중/고생]
• 어린이 교통사고는 등/하교시간에
집중적으로 발생되며 등교보다는
하교시간에 더 많이 발생됨
[어린이 보행자 교통사고 - 연령별]
• 초등학교 1학년인 만 7세에 사고가 급증하며,
7~9세의 비중이 34.4%로 이 연령층에
대해서는 별도의 대책이 필요함

57. Senior citizen’s traffic accidents
• 노인 보행자 사고는 오전 4시에 급증한 후
출근시간을 지나 오전 10시경에 다시 급증하는
형태가 나타나고, 저녁 7시에 급감
구성비
Senior vs. non Senior pedestrian – Injuries
• 비노인 중상이상 비율은 42.3%인
반면, 노인 보행자는 65.4%의
중상이상 피해가 발생
• 일단 보행자 사고가 발생할 경우
노인이 더 심각한 피해를 입는다고
할 수 있음
구성비
[노인/비노인 보행자 교통사고 – 시간대별]

58. Forecast model of traffic accidents by location/time
Floating
population
Metro
Station
Cross
Walk
Junctions
Bus stops
Frequencies of
pedestrian accidents
Based on initial analysis, traffic accidents
in Seong-buk district(one of 25 districts in Seoul)
are highly correlated with floating population,
distance from metro station entrance, cross-
walk, junctions, and distance from bus stops.
Forecast model of
pedestrian traffic accidents

59. Hot spot analysis of children/senior citizen’s traffic
accidents
[어린이 보행자 교통사고 온도지도]
[노인 보행자 교통사고 온도지도]
[일반 보행자 교통사고 온도지도]
VS

60. Findings on children’s traffic accidents
과속방지턱
어린이 보행자 교통사고
[A초등학교 인근 어린이 보행자 사고 및 과속방지시설 분포]
Assumption
A1. Traffic accidents are outside of safety zones
A2. Over speed prevention facilities reduce
traffic accidents
Results
• 초등학교 정문으로부터 반경 300m이내는 모두
어린이 보호구역으로 지정되어 있고
과속방지시설이 대부분 많이 설치되어 있으나,
• 어린이 보행자 사고가 많은 초등학교 인근을
보면 과속방지시설이 전혀 설치되어 있지 않은
곳들을 발견할 수 있으며,
• 이들 지역에 대한 집중적인 과속방지시설
설치가 필요함

61. Findings on senior citizen’s traffic accidents
불광시장
경동시장
신림6동시장
신신림시장
청량리도매시장
월곡시장
[불광역 인근 – 노인 보행자 사고] [신신림시장 인근 – 노인 보행자 사고]
[청량리역 인근 – 노인 보행자 사고] [월곡역 인근 – 노인 보행자 사고]
Assumption
A1 Traffic accidents are
outside of safety zone for
senior citizens
A2. Traffic accidents are
occurred near the senior
citizen facilities
Results
• 현행 노인보호구역은
노인정, 노인복지관,
요양원 중심으로
천편일률적으로 지정
• 전통시장이나 공원 등
노인 사고 다발지역
중심으로 노인보호구역
지정이 필요함
제일시장
[일반] [일반]
[일반] [일반]

62. [무단횡단금지시설]
 New facilities to prevent cross the roads
 New facilities to prevent cross the road in traditional market, park
 Voice guided facilities at hot spots
 Relocation of senior citizen safety zones (escalation to central gov.)
[보행신호 음성안내 보조장치]
 Training, promotion center (Mar. 2015~)
- Deliver safety guidelines at the senior citizen facilities
 Promotions for senior citizen
Preparation of policies – senior citizen

63. Preparation of policies - children
 Campaign & training for lower grade children
 Customized contents, video clips, class tools (수업교구)
(2014.10~2015.6)
 All year campaign →Focused campaign in Mar –Apr.
[영상컨텐츠(안)]
과속방지턱 신규설치 Facilities to prevent over speed
 Traffic accident hot spot, new spots outside of safety zones
 Relocation of traffic signs or safety facilities along with hot spots
 Expand best practices of selected schools
 Promote schools with best practices
 Share best practices with other schools

64. • 성북구는 25개 자치구 중 10대 이하 어린이/청소년과 60대
이상 노년층에서 높은 보행자 사고 발생률을 보임
순위 성북구 사고 다발 지점 연간 사상자 수
1 성신여대 입구역 인근 56
2 성북구 보건소 인근 27
3 길음역 인근 - 7번출구 앞 24
4 성북성심의원 인근 19
5 길음2동 주민센터 인근 19
6 진각종 인근 오거리 17
7 종암동 주민센터 인근 16
8 길음역 인근 – 버스중앙차로 15
9 서울 장위초등학교 인근 15
10 장위로 인근 14
[성북구 사고다발지점 위치]
성북구는 다른 구에 비해 어린이/청소년/노인의
보행자 사고 비중이 높음
빅데이터 기반의 교통사고 예측모형 개발
• 유동인구, 날씨 등의 빅데이터와
횡단보도 등 안전시설물과 도로,
지하철 입구와 같은 교통시설물
등의 독립변수를 바탕으로
• 보행자 교통사고 예측모델 개발
• 이를 통해 잠재 교통사고 위험
지역 파악

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Seoul has been ranked as #1 for 6 times by Global
e-Governance Survey by Rutgers Univ. (NJ, USA)
2003
2005
2007
2009
2011-12
2013-14
e-Governance survey of Rutgers University includes
- Digital government(delivery of public services)
- Digital democracy(citizen participation in governance)
What we achieved and major considerations…
Citizen & Society Engagement

66. 65 / 33
Big Data Collaboration Campus
Big Data Analysis Platform`Big Data Cloud
B.D. Collaboration Lab. of Seoul Metropolitan Government
Citizen Research
Institute
Start-up
복제
Virtualization
Partners
융합
분석
Trans.
16
Facilities 10
Pop. 12
Company 6
Spending
6
Env. 4
Complaint 2
R. est. 4
Loc. 2
Income 1
Big Data Base Closed collaboration space