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Transportation service innovation through big data

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The document outlines the Seoul Metropolitan Government's innovative transportation services through the use of big data, focusing on their e-governance framework, open data initiatives, and collaboration with citizens. It details various projects aimed at improving public services, such as optimizing late-night bus routes and enhancing taxi availability based on data analysis. Key takeaways emphasize the importance of problem definition, citizen participation in decision-making, and the need for public-private data collaboration.

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0 / 33 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
1 / 33 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
2 / 33 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
3 / 33 25 districts 605 km2 490 ICT systems - USD 25 billion dollars - 46,500 city officials (including fire fighters and district officials) Seoul Metropolitan Government
4 / 33 Data to Communication
5 / 33 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
6 / 33 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%?
7 / 33 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
8 / 33 Communication to Collaboration
9 / 33 Demand driven communication based on Citizen’s needs Data collaboration with citizen ConnecttoopenAPI(realtimedata)
10 / 33 Participation for prepare new policy via mVoting m-Voting for listening the citizen toward new policies
11 / 33 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
12 / 33 Living lab for Internet of Things @ Bukchon Living lab = Innovation eco-system where users can participate proactively
13 / 33 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
14 / 33 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
15 / 33 Collaboration to Innovation
16 / 33 https://www.youtube.com/watch?v=NV-QeQ-6yOw
17 2013 1 2015 192014년 102014 10 Number of big data based administration
18 / 33 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
19 / 33 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
20 / 33 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?
21 / 33 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
22 / 33 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
23 / 33 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
24 / 33 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
25 / 33 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 26 / 42
26 / 33 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
27 / 33 Traffic accident analysis - approaches Problem definition Modeling Analysis (EDA) Hot spot analysis Assumption & Proof Preparation of policy
28 / 33 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
Learning & Direction
30 / 33 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
31 / 33 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
32 / 33 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
33 / 33 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
34 / 33 Thank you
Appendix
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
Modeling SimplifiedOriginal 605km2 1,252 Hexagons a set of lines lines with thickness
Analysis Analysis Optimization based on population
Analysis Finding solution through data Utilization floating population of weekdays and weekend N26 N37 Establish interval based on population
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% ↑)
Establish 9 owl bus routes
Establish 9 owl bus routes Initial routes of Seoul owl bus in 2013
Establish 9 owl bus routes
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)
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
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.)
• 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
What did big data find? 운행 대수  기준 : 4시~3시 운행 대수
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 23 / 42
Suggestion - Seoul taxi map 14 15 19 홍대 입구 종각 역 동대 문역 건대 입구 가로 수길 강남 역 로데 오거
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
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
Speed Accident by lane Traffic facilities Floating populati on Weather Traffic volume Event Driving behavior Safety zone Composite modeling approaches
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
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
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]
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
Hot spot analysis of children/senior citizen’s traffic accidents [어린이 보행자 교통사고 온도지도] [노인 보행자 교통사고 온도지도] [일반 보행자 교통사고 온도지도] VS
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
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]
[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
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
• 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
64 / 33 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
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

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Transportation service innovation through big data

  • 1. 0 / 33 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
  • 2. 1 / 33 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
  • 3. 2 / 33 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
  • 4. 3 / 33 25 districts 605 km2 490 ICT systems - USD 25 billion dollars - 46,500 city officials (including fire fighters and district officials) Seoul Metropolitan Government
  • 5. 4 / 33 Data to Communication
  • 6. 5 / 33 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
  • 7. 6 / 33 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%?
  • 8. 7 / 33 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
  • 9. 8 / 33 Communication to Collaboration
  • 10. 9 / 33 Demand driven communication based on Citizen’s needs Data collaboration with citizen ConnecttoopenAPI(realtimedata)
  • 11. 10 / 33 Participation for prepare new policy via mVoting m-Voting for listening the citizen toward new policies
  • 12. 11 / 33 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
  • 13. 12 / 33 Living lab for Internet of Things @ Bukchon Living lab = Innovation eco-system where users can participate proactively
  • 14. 13 / 33 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
  • 15. 14 / 33 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
  • 16. 15 / 33 Collaboration to Innovation
  • 19. 18 / 33 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
  • 20. 19 / 33 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
  • 21. 20 / 33 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?
  • 22. 21 / 33 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
  • 23. 22 / 33 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
  • 24. 23 / 33 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
  • 25. 24 / 33 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
  • 26. 25 / 33 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 26 / 42
  • 27. 26 / 33 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
  • 28. 27 / 33 Traffic accident analysis - approaches Problem definition Modeling Analysis (EDA) Hot spot analysis Assumption & Proof Preparation of policy
  • 29. 28 / 33 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
  • 31. 30 / 33 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
  • 32. 31 / 33 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
  • 34. 33 / 33 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
  • 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
  • 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 23 / 42
  • 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
  • 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
  • 65. 64 / 33 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

Editor's Notes

  • #2: 0:00 – 0;30 Hi everyone, First of all, thank you for inviting me here today. Introduction – - SMG integrates all data related roles such as big data, traditional data and statistical data together into one organization - Set-up a new org. Data and Statistics Division - Data based scientific administration - Administration based on big data analysis - Public data disclosure project for opening, sharing, and communication - Statistical strategies and researches
  • #3: IoT는 디바이스 관점에서는 UI를 대체하는 수단으로 부터, 방대한 데이터를 생성하는 센서까지 다양한 분야에 사용되고 있으며 빅데이터는 IoT의 활용기반이 된다.
  • #4: Area: Seoul 605km2, Mumbai 603 km2(4,355km2), 7 self governments, 15 small councils Population 10M vs. 13M Why is data so important? Every city cannot destroy current infra and re-build them on it. If current city infrastructure should be kept and need to be optimized, data becomes so important.
  • #6: 8:00 – 8:30 - 3 best practices of Seoul metropolitan government. - additional on going projects
  • #10: 8:00 – 8:30 - 3 best practices of Seoul metropolitan government. - additional on going projects
  • #11: 2014년 하반기, 서울시 데이터를 활용한 다양한 시도 추진 - 서울시 데이터를 시각디자인 – 랜덤웤스의 민세희 대표 - neuro associates에서 서울시 제정정보를 시민들이 직관적으로 볼 수 있게 디자dls 지하철 app, Bus app, taxi app 등
  • #14: Many ICT analysis experts mentions 2015 will become a year of IoT What about government side? - There’s no experiences nor cases of new technology. - So, we cannot prepare plan until a good case will be unveiled by others - From the economy perspective, leading position will be not mine but yours! Seoul MG made a strategic decision to establish IoT zone in Bukchon near the Seoul city hall
  • #17: 8:00 – 8:30 - 3 best practices of Seoul metropolitan government. - additional on going projects
  • #19: 올빼미버스에서 현재까지 19건의 빅데이터 프로젝트를 진행함
  • #20: Transportation/safety = 9 , welfare = 5, Small business = 4, and, Health/environment = 1 , Total 19 projects has been doing or finished at this moment.
  • #22: 13:00 – 15:00 There’s 9 Late-night bus route in Seoul, we call it Owl bus. Owl bus started running since 2013. I’d like to explain it from the perspective of communication and participation At the beginning, owl bus discussion started from one small twitter message from a university student. Yes, it’s stated from the very small communication with citizen. It’s small but, Seoul didn’t miss the value of this small voice. Now the night bus task had been set-up But, still there were lot more problems in front of us. -Facing problem 설명 지난 April 서울시 CIO인 Mr Kim은 올빼미 버스의 approach와 성과에 대해 소개한 바 있습니다. 오늘 저는 이를 Communication과 participation 관점에서 얘기를 해보고자 합니다. 올빼미 버스는 대표적으로 시민의 소리와 참여를 반영한 서울의 정책 사례입니다. 대학생 김**씨는 트위터를 통해 심야대중교통의 불편함을 서울시장에게 호소하였습니다. 서울시는 즉시 심야버스를 검토하였는데요.. 여러가지 해결해야 할 문제점들이 나열되었습니다. 심야라는 특성 상, 서울시 전역을 커버할 수 있는 많은 버스노선과 지하철을 운행할 수 없었습니다. (서울은 세계에서 가장 편리한 대중교통시스템을 운영하는 도시입니다) 두번째는 사람들이 퇴근한 심야시간대에 어디에 승객들이 많이 모여있느냐는 것이죠 세번째는 이러한 승객들이 어디로 갈 것인가?라는 문제입니다.
  • #23: 15:00 – 20:00 Transportation problem -> Big data problem Big data problem -> manageable model And, analyze Prepare - Define task & problem Transform original problems(Big problem) into big data problem(Smaller problem) Establish manageable model, but a huge big and complex model Analyze goal-oriented approach. remember objective is not a big data…
  • #24: 20:00 – 22:00 - The results was amazing. With same, limited budget and resources, we can create bus route with 10% more passengers. And, only 9 routes of night bus, SMG can cover more than 42% of citizens. At the beginning of my talk today, I mentioned Seoul opened all the data and information to the citizen. As a results, citizen can evaluate the results of Owl bus. I’m going to show you one of the citizen’s evaluation.
  • #25: 7:00 – 8:00 I’d like to share another example of communication and participation in Seoul. Recently, 621 thousand complaint call has been analyzed. After that, we find top most complaint was transportation, it’s 25%, And, among them, 73% are related with Taxi. Immediately, my team started analyzing taxi Digital Tachgraph data. There are 130 billion case data in a year.
  • #26: 15:00 – 20:00 Transportation problem -> Big data problem Big data problem -> manageable model And, analyze Prepare - Define task & problem Transform original problems(Big problem) into big data problem(Smaller problem) Establish manageable model, but a huge big and complex model Analyze goal-oriented approach. remember objective is not a big data…
  • #27: 8:30 – 9:00 Ok, now, I’d like to show you the results of taxi match making. As the problem is defined as reduction of empty transfer rate, if empty transfer rate become decreased by 10%, we can have 27 million liter gas saving in a year, and reduce 45 k CO2 emission in a year. 27 million liter = 7 million gallon… $50M cost reduction.. From the citizen’s perspective, Citizen can have 5% more chance to catch a cab with same number of taxi in Seoul.
  • #28: 36:00 – 37:00
  • #31: 11:00- 11:30
  • #33: One of the most useful data is floating population in real time or every hour or every day.
  • #35: 13:30- 15:00 With connected data and data administration, Seoul city have finished 8 big data projects since 2013. For 2015, SMG plan to do 7 projects such as … They are Safety, Welfare, Small business development and environment.
  • #48: Why 150m? 최대한 짧은 간격일수록 좋으나, 너무 짧으면 지나가는 택시가 해당 링크에 카운트안될 가능성이 높아지기 때문에 그 적정선을 150m로 선정 (150m는 도보로 이동하는데 2분 미만이 걸리고, 시속 60km(10초당 약 150m) 달리는 택시도 카운트 가능함)
  • #49: Top demand spot은 당연히 찾는 것임
  • #51: 심야시간대의 홍대입구역에서 합정역까지의 양화로에는 빈택시는 많으나 택시 승차가 집중되어 택시잡기가 어렵지만, 서쪽 이면도로인 동교로에서는 승차횟수가 적어 비교적 빈택시 발견하기가 용이한 것으로 나타났다.
  • #53: 데이터 수집-분석-가공하여 열린데이터 광장에 데이터셋 개방 개방된 데이터를 활용하여, 승객- 택시잡기 좋은 곳 운전자 – 승객태우기 좋은 곳 서비스 개발
  • #55: 37:00 – 38:00
  • #66: 1:30 – 2:00 Rutgers E-governance survey - Digital government(delivery of public services) - Digital democracy(citizen participation in governance) E-Government of Seoul - Passion & Concentrated ICT resources - Well established ICT strategies & plans - Strong power of executions - And, citizen – centered approaches, citizen – oriented result driven Results - ranked as #1 e-government city among global top 100 cities for 6 times since 2003