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Kim, Ki-ByoungTransportation Service Innovation through Big Data- Best Practices in Seoul Metropolitan Gov. - Apr. 28. 2016.Chief Data Officer / DirectorData & Statistics DivisionSeoul Metropolitan Governmente-mail: pskbkim@gmail.com

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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

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ContentsIntroductione-Government of SMG Foundation of Seoul eGov.3. Data to communication Open data initiative4. Communication to collaboration Bukchon IoT Living lab5. Collaboration to innovation Data based public services6. What Seoul has learned 7. Plan & direction

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IoT UI , IoT .

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More than 90% of Seoul citizens are Smart Phone users

Ranked 1st one-Governance survey By Rutgers Univ, NJ, USAFor 6 times since 2003Concentration 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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Area: Seoul 605km2, Mumbai 603 km2(4,355km2), 7 self governments, 15 small councilsPopulation 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.

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25 districts605 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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8:00 8:30 - 3 best practices of Seoul metropolitan government. - additional on going projects4

Background - Open data initiativeOpen information1Open documentsOpen dataIntegration & 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 its small1Open documentsOpen dataIntegration & visualizationListen to the citizen2m Voting, mobile complaint app120 call center Big data analysis

Open information

64,226,068 callssince 2007

as of the end of 2014as of the end of 2013as of the end of 201325.5%?73.5%?

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Find insight of voices even its small, then response!1Open documentsOpen dataIntegration & visualization23Listen again by data & DoLate night bus routesTaxi analysisReduction of car accidents

Listen to the citizenOpen information

m Voting, mobile complaint app120 call center Big data analysis

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Communication to Collaboration

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8:00 8:30 - 3 best practices of Seoul metropolitan government. - additional on going projects8

Demand driven communication based on Citizens needsData collaboration with citizen

Connect to openAPI(real time data)

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2014 , - - neuro associates dls

app, Bus app, taxi app

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

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Voting through smart phones and PCsSelected 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) Citizens 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-votingm-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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Many ICT analysis experts mentions 2015 will become a year of IoTWhat about government side?- Theres 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

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Protect Minorities Smart Parking

LTE

WiFi Zigbee

WiFi Zigbee

Safety, welfare, transportation, environmentsSmart Trashbox

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Smart Street LampSmart meterSmart Parcel Box

SMS,

WiFi []A-123 . 1913

25%50%100%50%

IoT 6LoWPAN

6LoWPAN

(,,)

LTE

u-Service Net

Safety, welfare, transportation, environments

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Collaboration to Innovation

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8:00 8:30 - 3 best practices of Seoul metropolitan government. - additional on going projects15

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

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1720131201519201410201410Number of big data based administration

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Night bus route optimization13.06~13.07Location analysis of Life double cropping center13.10~14.02Location analysis ofSenior welfare center13.10~14.02City PR booth analysis13.10~14.02Flow analysis of foreign tourists13.10~14.02Location analysis for ATM of civil service14.11~15.02Taxi analysis14.07~15.02Traffic accident analysis for minorities14.07~15.03Disabled taxi analysis14.07~15.07Street business zone analysis14.10~15.10Location analysis for public WIFI15.04~15.07

Analysis of regional festivals15.10~16.05Mobility analysis of disabled15.10~16.05Tuberculosis analysis15.10~16.05Village bus route optimization15.10~16.05Effectiveness of traffic signs15.10~16.05Analysis of traffic accident zones15.10~16.05Parking analysis15.10~16.05Analysis of Shinchon water fest. 15.10~16.05

Big data based administration1111111112222234333

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Transportation/safety = 9 , welfare = 5, Small business = 4, and, Health/environment = 1 , Total 19 projects has been doing or finished at this moment.

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AreaTopicPeriodObjectives ResultsTransportationNight bus route optimization`13.1HRoute optimization Night bus coverage > 42% with 30 buses (daytime buses >7,000)TransportationTraffic accident analysis for minorities`14.2HPrepare policies to reduce traffic accidents of children and senior citizenNumber of accidents will become 50% in 3 yearsTransportationTaxi analysis`14.2HProvide more chance to catch a taxi during mid nightProvide 5% more chance to catch a taxi in midnightWelfareLocation analysis of Life double cropping center`14.1HSelect the best location of life double-cropping centerEvery facility will provide best coverage for the citizenWelfareLocation analysis ofSenior welfare center`14.1HSelect the best location of senior leisure centersEvery facility will provide best coverage for the citizenWelfareDisabled taxi analysis`14.2HReduce waiting time of disabled taxiReduce waiting time by 10%AdminCity PR booth analysis`14.1HSelect best location of public PRMore PRs with same boothsAdminLocation analysis for ATM of civil service`14.2HSelect prioritized ATM locationsProvide more ATM services with planned number of devicesTourismFlow analysis of foreign tourists`14.1HPromote foreign tourism with better serviceIncrease foreign tourist by 10%

Results from big data based administration

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Response of the CityNo public transportationin 01:00 AM ~ 05:00 AM

SubwayBusTaxi

Buses dont run by the time I get off work. I dont have a car. I hope there will be buses available at late night..!! @gu**** Late-night bus story Lets 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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13:00 15:00

Theres 9 Late-night bus route in Seoul, we call it Owl bus. Owl bus started running since 2013.

Id 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, its stated from the very small communication with citizen. Its small but, Seoul didnt miss the value of this small voice.

Now the night bus task had been set-upBut, still there were lot more problems in front of us.-Facing problem

April CIO Mr Kim approach . Communication participation .

. ** .

.. .

, .( ) ? .

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Approach night bus route optimization

Big data problem definitionModelingAnalysisNight bus routesRoutes optimizationFinalized routesOriginal 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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15:00 20:00Transportation problem -> Big data problemBig data problem -> manageable modelAnd, analyze

Prepare - Define task & problemTransform original problems(Big problem) into big data problem(Smaller problem)Establish manageable model, but a huge big and complex modelAnalyze goal-oriented approach. remember objective is not a big data21

ResultsCitizens evaluationA new way to go home at mid-night8.9% reduction of refusal rate to passengers of mid-night taxi11.8% increase of womens mid-night activities

Administration perspectiveProof of administration decision to settle civil complaintsMax. 10% of PAX increased withoutincreasing new routes or buses42% coverage of citizens

PassengersWanderer due to refusal of taxiWomen returning home lateDailyPAXStudentBusinessmanProxy driver8.9% of refusal passengers11.8% of midnight womens activitySource: News jelly

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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. Im going to show you one of the citizens evaluation.22

Subsidy $150M / yearWhy Taxi Matchmaking?Response of the CityTaxi 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 Taxis49,424Corporate Taxis 22,801Status of taxi registered in Seoul It seems to be short supply of taxiduring 11PM to 1AM while Taxis in Seoul are oversuppliedFacing Problems

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7:00 8:00

Id 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, its 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.

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Approach taxi analysisBig data problem definitionModelingAnalysisVacancy vs. DemandPreparation of policyReinforcing eco-systemOriginal 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 aboutreduction of vacancy rate?

Vacant rate is HIGH!Vacant rate is HIGH!Refined node/link

Refined link length : 150m2 min. walking distanceCan count taxi with speed of 60km/h

High vacancyHigh demand132

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15:00 20:00Transportation problem -> Big data problemBig data problem -> manageable modelAnd, analyze

Prepare - Define task & problemTransform original problems(Big problem) into big data problem(Smaller problem)Establish manageable model, but a huge big and complex modelAnalyze goal-oriented approach. remember objective is not a big data24

Occupied Transfer21.2LSaving1.5LEmpty Transfer13.9LReduction of1.5 liter/day

1ML

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1ML27,000,000 liter / year(7M gallon / year)

* 44,932 ton / yearCO2* Applied IPCC formula for tCO2 conversionExpected resultsProvide more chance to catch a cab by reducing empty rate of taxiWe 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

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8:30 9:00

Ok, now, Id 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 citizens perspective, Citizen can have 5% more chance to catch a cab with same number of taxi in Seoul.

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Traffic accident analysis for children & seniorsWhy traffic accidents?Response of the city

DeathAccidentBodily Injury Car Injury Prededing Clause

1 Assailant2Road3 Vehicle4 Others5 Victim

Facilities

structure

LaneChange

SharpCurve

Conjuction

ManagementProblem

NoFacility

Facilities

RoadStructure

Events

BadWeather

Night

PerceptionAbility

Carelessness

SafetyUtility

Drinking

Inattention

Intentional

Overspeed- Heinrichs law (1:29:300)Facing problemsReduce accident rate of children safety zone and senior citizen zoneWhere are traffic accidents of children and senior citizens?Assumption

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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36:00 37:00 26

Traffic accident analysis - approachesProblem definitionModelingAnalysis (EDA)

Hot spot analysisAssumption & ProofPreparation of policy

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More cases

Blind citizens facility operationLocation selection of braille blocksEffectiveness analysis of traffic signsFestival analysis in SeoulTuberculosis analysisRoute recommendation for midnight village busesTraffic accident analysisParking analysisShuttle services for minoritiesRelocation of Taxi stops

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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 youWhat weve 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 CampusIntegratedAnalysisHow to start big data innovation in major cities?TransportationAdministration

Cell phone dataSensors, IoTCommunity mapping3. Private/PubliccollaborationFloating population

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One of the most useful data is floating population in real timeor every hour or every day.

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IntegratedAnalysisTransportation 16

Facilities10

Population12

Shop/Enterprise6

Spending6

Environments 4

Complaint/Social 2

Real estate4

Locations2

Income1Big data of Seoul - category

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What are we going to do with data?Transport/Safety1Welfare23Small Business4Environment

Reduction of car accidentsOptimization of local bus routesAutomatic allocation of disabled taxiOptimization of civil service kiosksMobility service for the disabledBig data for small business develop.Analysis for Seoul city festivalTuberculosis analysis and more

2015 ~ 2017

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13:30- 15:00With 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.

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Thank you

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Appendix

Problem definition & transformation to data problems1.Where are the passengers in the mid-night?

2. Where do they want to go?Data problems

Admin. Problems

3 billion mobile call data

Modeling

SimplifiedOriginal

605km21,252 Hexagonsa set of lineslines with thickness

Analysis

AnalysisOptimization based on population

AnalysisFinding solution through dataUtilization floating population of weekdays and weekend

N26N37Establish interval based on population

Finding answers from data - optimization

Route 4

Route 5

Route 7

Route 8

Route 4 : Gil-dong ~ Myung-il sta. Route 5 : Butty Hill ~ Yaksu sta. Route 7: Express bus terminal Route 8: Nambu term, Konkuk univ.Optimization of routes95,335 105,261 (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 problems1.Provide more taxis without increasing no. of taxis

2. Increase utilization of taxis

Data problems

Admin. Problems

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1234Get onGet offVacantHiredStatus of taxiGet onGet offVacant runHired runHired run1Vacant run1Hired run get off Vacant run111Vacant run get off Hired run111

1234

Modeling Status of taxi

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Modeling Seoul metropolitan area OriginalStandard node/linkRefinedRefined node/link

Average link length: 330m, Longest link length: 30kmRefined link length : 150m2 min. walking distanceCan count taxi with speed of 60km/h(move 150m in every 10 sec.)

Why 150m? , 150m (150m 2 , 60km(10 150m) )46

: 2014 10~11, 23 ~ 01 : ( 34,306)

1284567310911121314151617181920112111891 21654 31501 4()1473 51316 61256 71136 81112 91109 10()1041 11979 12975 13952 14929 15876 16855 17844 18835 19832 20()815 21750

AnalysisTop demand spots

Top demand spot

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What did big data find?

: 4~3

LocationDateBeginEnd periodVacant taxiEstimationReal vacancy rate1. Hotel Seogyo2014-12-1123:0023:30 42 89 47%2. Seonghwa bd.2014-12-1123:5000:20 28 37 76%3. Hotel Yaja2014-12-1100:3001:00 47 55 85%

132ImplicationsExample: Hongik Univ: Seogyo Hotel High demand vs. Donggyoro low demand23 / 42

, .49

Suggestion - Seoul taxi map

141519

Accelerate eco-system of big data

Link ID

Information of Boarding and Departing Time, Location, and Traveling Course

Weather Information Information on the Floating Population

Node-Link

SMG,Taxis utilize shared big data25 / 42

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Problem definition & transformation to data problems1.Reduce traffic accidents of senior citizen & children

2. Reduce traffic accidents of safety zones

Data problems

Admin. Problems1. 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

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SpeedAccidentby laneTraffic facilitiesFloatingpopulationWeather

Traffic volumeEventDriving behaviorSafety zoneComposite modeling approaches

37:00 38:00

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Exploratory Data Analysis ( )Time series analysisCharacteristicsExternal causesGeo-spatial analysis// DTGEDA(Exploratory Data Analysis, )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

Childrens traffic accidents[/ - ]

[][][/] /

[ - ]

1 7 , 7~9 34.4%

Senior citizens traffic accidents 4 10 , 7

Senior vs. non Senior pedestrian Injuries 42.3% , 65.4%

[/ ]

Forecast model of traffic accidents by location/timeFloating populationMetro StationCrossWalkJunctionsBus stopsFrequencies 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 citizens traffic accidents

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[ ]

[ ]VS

Findings on childrens traffic accidents

[A ]Assumption

A1. Traffic accidents are outside of safety zones

A2. Over speed prevention facilities reduce traffic accidents

Results

300m , ,

Findings on senior citizens traffic accidents

6[ ][ ][ ][ ]Assumption

A1 Traffic accidents are outside of safety zone for senior citizens

A2. Traffic accidents are occurred near the senior citizen facilities

Results

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[][][][]

[] New facilities to prevent cross the roadsNew facilities to prevent cross the road in traditional market, parkVoice guided facilities at hot spots Relocation of senior citizen safety zones (escalation to central gov.)

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Training, promotion center (Mar. 2015~) - Deliver safety guidelines at the senior citizen facilities

Promotions for senior citizenPreparation of policies senior citizen

Preparation of policies - children

Campaign & training for lower grade childrenCustomized contents, video clips, class tools () (2014.10~2015.6)All year campaign Focused campaign in Mar Apr.

[()]

Facilities to prevent over speedTraffic accident hot spot, new spots outside of safety zonesRelocation of traffic signs or safety facilities along with hot spots Expand best practices of selected schoolsPromote schools with best practicesShare best practices with other schools

25 10 / 60 1 1 562 273 - 7 244 1952 196 177 168 159 1510 14

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Seoul has been ranked as #1 for 6 times by Global e-Governance Survey by Rutgers Univ. (NJ, USA)

20032005200720092011-12

2013-14e-Governance survey of Rutgers University includes - Digital government(delivery of public services) - Digital democracy(citizen participation in governance)

What we achieved and major considerationsCitizen & Society Engagement

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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 drivenResults- ranked as #1 e-government city among global top 100 cities for 6 times since 2003

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Big Data Collaboration Campus

Big Data Analysis Platform`Big Data CloudB.D. Collaboration Lab. of Seoul Metropolitan Government

CitizenResearchInstituteStart-upVirtualization

Partners

Trans. 16

Facilities 10

Pop. 12

Company 6

Spending 6

Env. 4

Complaint 2

R. est. 4

Loc. 2

Income 1Big Data BaseClosed collaboration space

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