ASEAN8Japan)Seminar)on)Utilization)of)
Mobile)Phone)Big8Data(MBD),)Jan.)16,)2019

Utilization)of)MBD)analysisfor)transport)policy)in)Asia
Yoshihide SEKIMOTO,)Associate)Prof.,Institute)of)Industrial)Science,)University)of)Tokyo1• 2002About)me– PhD,)University)of)Tokyo• 200282007– National)Institute)for)Land,)Infrastructure)and))Management)(NILIM),)MLIT• 20078– Univ.)of)Tokyo

http://sekilab.iis.u1tokyo.ac.jp

2

People)flow)project)(PFLOW)
http://pflow.csis.u1tokyo.ac.jp8(20081 )Key8factor8is8rough8understanding8in8whole8target8area8!

32.)PFLOW)from)PT)(2008*)

4At)the)first)stage…

PFLOW)from)PT(2008*)

5Person)trip)survey)??
Place&staying&at&3:00&in&the&morning
1st place&to&go&&&&&&&&
2nd place&to&go&
Home&&&&&&&Office&or&School&
Other&places&(Rough&address)
Other&places&(Place&name)Kind&of&place

Trip

Departure&time
Arrival&time
Trip&object
Purpose

SubGtripTransportation& mode
Travel&time
Transfer&point&

Main%part%of%PT%survey%sheet%(from%the%Tokyo%Metropolitan%Region%Transportation%Planning
Commission%web%site%"http://www.tokyo@pt.jp/data/file/tebiki.pdf")
6

Spatio8temporal)interpolation)from)OD)data

Station(11:45)
Home(11:42)

Sub%trip%2(Railway)• XXXX

11:42%(Lat1,Lon1)
11:43%(Lat2,Lon2)
11:44%(Lat3,Lon3)
11:45%(Lat4,Lon4)
11:46%(Lat5,Lon5)
11:47%(Lat6,Lon6)
11:48%(Lat7,Lon7)
11:49%(Lat8,Lon8)
11:50%(Lat9,Lon9)
11:51%(Lat10,Lon10)
11:52%(Lat11,Lon11)
11:53%(Lat12,Lon12)
11:54%(Lat13,Lon13)

Sub%trip%1(Walking)

Office(11:54)

Station(11:49)Sub%trip%3(Walking)a)#Geocoded#OD#of(a) each#sub#trip

Railway%geometry(Green% line)

Railway% rough%route(Blue%line)
Walking%rough%route(Red%line)Road%
topology(Gray% line)Road%
geometry(Gray% line)c)#Interpolation#at#each#1#minute>intervals(c)Railway%
topology(Green% line)b)#Route#choice#along#road/railway#topology(b)*)Y.8Sekimoto8et8al.8PFLOW:8Reconstruction8of8people8flow8
by8recycling8large1scale8fragmentary8social8survey8data,8IEEE#Pervasive#Computing,8Vol.10(4)8pp.27135,82011.
73D)visualization

3D%visualization%with%1@km2%mesh

8Archiving)of)PFLOW(14)metropolitan)areas)and)totally)3.5)million)

Council8for8
Minister8of8Land,8regional8transport8
Infrastructure8
or and8Transport
plannning
Apply

Approve

Approve

Apply

CSIS,8UT
Application8for8
joint8research(*)

Approve

User(*) limited8in8public8use8for8industry,
government8and8academic
9Many)joint)researches)through)“People)Flow)Data)Set”&Transportation'#)Research)on)improving)the)efficiency)of)urban)transport)systems)using)portable)personal)
mobility.(iTransport Lab,)Ltd.)#)A)simulation)of)tourist)flow)patterns)in)the)Sendai)metropolitan)area)using)the)People)Flow)
Analysis)Platform.)Masayoshi)Tanishita (Chuo)University)#)Utilization)of)statistical)data)in)urban)transport)planning.)(Ritsumeikan Asia)Pacific)University)
Department)of)Asian)Pacific)Studies)&Spatio@temporal%analysis'#)Detection)of)patterns)in)travel)routes)using)position)information)and)travel)times)(Kobe)University)
Graduate)School)of)Engineering)#)Development)of)a)spatio8temporal)data)model)for)analysis)of)spatio8temporal)behavior)using)GIS.)(Tokyo)Metropolitan)University)&Risk%analysis'#)A)model)for)the)transmission)of)novel)infectious)diseases.
(University)of)Tokyo)Institute)of)Industrial)Science)#)An)investigation)of)of disaster)risk)using)GIS.)(Aichi)Institute)of)Technology)Department)of)Environmental)Engineering)&Personal%information%and%security'#)On)the)anonymization of)personal)information)and)its)two8dimensional)use)(Information)Grand)
Voyage)Project).)(Mitsubishi)Research)Institute,)Inc.)&Environment'#)Development)of)a)scenario)for)fine)spatial)output)and)changes)in)land)use)via)unified)system)
analysis.)(National)Institute)for)Environmental)Studies)&Marketing'#)A)study)of)consumer)respiration)models)using)person8trip)data.)(Fine)Analysis,)LLC)
10

Pandemic)simulation)based)on)PFLOW

Day 3
Day 27
Day 46

Aihara & Suzuki lab in IIS, Univ. of Tokyo
11

JICA1PT8data

CityPopulatio Survey% Sample% Number% Ratios%of%various%modes%of%
main%transportationnyear
sizeof%trips(2@(million)
wheeler/car/taxi/bus/rail)

Manila%9.45

1996

231,889 471,035

2%/10%/25%/58%/4%

Kuala%Lumpur
Damascus
Managua
Bucharest
Phnom%Penh
Chengdu
Belem

1.39
3.08
1.20
2.151.15)
3.09
1.78

1997
1998
1998
1998
2000
2000
2000

80,56038,49024,85467,50918,664
31,18824,04329%/44%/2%/23%/2%
4%/25%/15%/56%/0%
2%/25%/4%/69%/0%
0%/19%/0%/27%/54%
89%/11%*1/8/8
81%/10%/4%/4%/0%
15%/13%/2%/70%/0%

Jakarta

2.10

2000

Tripoli

0.33

Cairo
Ho%Chi%Minh%
CityHanoi
NairobiLima

2001

218,460
81,69854,138143,31140,36970,19959,5291,083,28
423,237
0
3,608
7,615

35%/29%/17%/19%/0%

14.4

2001

136,070 268,360

2%/13%/46%/29%/10%

3.18

2002

27,412

71,89096%/2.4%*1/1.8%/8

7.16
4.04

2004
2004

63,716
20,980188,949
46,82889%/3.6%*1/6.7%/82%/29%*1/68%/1%

8.04

2004

115,728 270,384

2%/0%/42%/56%/0%

17.2%*2/13.7%/69.1%/8 12

13For)the)data)set)preparation…
In#case#of#Tokyo#metropolitan#area

Processing%items

Calculation%timeData)cleaning)(partly)manual)

Several)days

Geo8coding)from)zone)to)lat/lon

Several days

Spatio8temporal)interpolation

84)daysCreation)of)query)tables)for)DB14 days

143.)PFLOW)from)mobile)phone)GPS(2011*)

15Tsunami)Evacuation)activity)on)the)disaster)day16

Evacuation)activity)after)the)earthquake

From815th March8to830th April

17Understanding)of)recovery)status)
comparing)tourist)flow

Legend18“Density)map”)from)Auto8GPS)data

Density(map(from(Auto1GPS(dataZENRIN(DataCom(CO.,LTD.(http://lab.its1mo.com/densitymap/

194.)PFLOW)from)mobile)phone)CDR(2012*)

20

Call)detail)record)(CDR))from)mobile)phone)base)station21

Comparison#of#GPS#(pink)#and#CDR#(blue)

21Mechanism)of)mobile)phone)base)station
Mobile%Station

Base%Station%
Subsystem

Network%Switching%
Subsystem

Public%Switched%
Telephone%Network
Visitor%Location%
Register

Base%Transceiver%
StationMobile%Services%
Switching%Center
Gateway%MSC

Home%Location%
Register

Base%Station%
Controller

Equipment%ID%
Register

22Mobile)base)station)data
Anonymized CDR)(Call)detail)record)data))per)person                                                   "                             "
 

 
 
 
 
          

 
 
 
 
 



Aggregated)as)erlang value)per)each)cell)station)of)base)station       "        


Erlang is8the8total8amount8of8
resources used8over8a8period8of8time

23“Mobile)Spatial)Statistics”)from)base)station)data

Mobile(Spatial(Statistics

Operational(dataDe1identification
Privilege
Aggregation
Aggregated(population

Mobile(Spatial(StatisticsFrom(NTTDocomo(web(site:(
http://www.nttdocomo.co.jp/corporate/disclosure/mobile_spatial_statistics/24Commercial mobility)data(in)Japan)• Personal)information)is)not)linked)under)the)personal)information)revised)law)(2015)• Grid8based– ZENRIN)DataCom (Hourly),)NTT)Docomo (Hourly),)Agoop• Link8based– Navitime,)Pioneer• Dot8based– Agoop• OD8based25Joint)research)with)Bangladesh
Period2012/6/19*7/18# of user

6.85 million# of mobile
phone base 1,362 (1125 in DMA)
stationTarget data!" #"!
!!""
( $% )

Anonymized individual timeand station ID when talkoccur

5.8 / 1day (The # of target# of averageis 4 million people whoselog
home can be extracted)Sparse)!!

26Trips)accumulating)sparse)CDRs)data

Target (30%days%in%total)# of%trip%pattern

All users who have CDR
Users whose home can be extracted
Users limited in related Voronoi area
Magnified based on real populations
Vehicle users

695,637
677,670
647,469
647,469
619,219#%of%trip
77,798,677
70,014,818
55,383,314
172,843,289
126,921,796#%of%user
6,854,189
3,995,353
2,877,197
7,597,2568#%of%user%whose%trip%
can%be%extracted
4,790,888
3,311,932
2,430,487
8827OD)table)of)trips

28

Past)hourly)link)traffic)volume)based)on)PT)
survey)data)(questionnaire)data)
Hourly)trip)volume)based)on)PT)data  
2,500,000

2,000,000
1,500,000
1,000,000
500,0000

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
time29

Simple)route)assignment)based)on)“ITA)
method”(Chen etc.,)1991)• Divide)traffic)volume)into)40%,)30%,)20%)and)
10%)blocks)and)calculate)trip)time)(ta))and)
update)trip)time!" = !$ 1 + ' ()* + ,,,,,,' = 0.15, 1=4!$ : 4567,869:,6;,<5::,<=>?
,,,,,,,,,!" : 4567,869:,@AB:C,>;,D>;E:B86>;,(GHI),,,,,,,,,()*: D>;E:B86>;:,=6;K,85A<<6D,L>=M9:
NOPQ,RPSPRTUV

30Target%of%our%traffic%and%people%flow%groupDevelop)dynamic)social)observation)and)assessment)system)with)mobile)sensor)in)order)to)understand)actual)condition flow)of)traffic)and)crowd

Calculate)link)traffic)volume)from)cell8phone)base)station

Cell8phone)communication)information)in)Dhaka by)
GrameenPhone (Base)station’s)CDR:)Call)Detail)Record)

31Assigned)link)based traffic)volumefrom)OD)table
t.OD   24 
180000160000SP13SP17
SP18 SP09

140000SP07

SP16SP08

120000SP12100000

SP01

SP10
SP11SP15SP02

80000

SS04 SP14
SP04
SP03

60000

SS05
SP05
SS08
SS16
SS15
SS03
SS17
SS14
SS10
SS13SS12
SS18
SS01
SS07
SS20 SS02
SS21
SP06

SS09
SS22 SS19

40000
20000
0

SS06

0

SS2320000

40000

60000

80000

SS11100000

1200002009   24 

14000032Joint)research)with)Myanmar(JICA/JST)SATREPS)Project)

Science)and)Technology)Research)
Partnership)for)Sustainable)Development

33“ANZEN”)Project)in)SATREPS)(201582020)“Development%of%a%Comprehensive%Disaster%Resilience%and%Collaboration%Platform%in%Myanmar”

34

Phase#III(Implementation)

Phase#II(Integration)

Phase#I(Individual8research)

Implementation#framework

35

Transport%and%People%Mobility%Group

Assoc.#Prof.#Sekimoto
IIS,#UT

Dr.#KoKo Lwin

Mr.#Fukushima#

Proj.#Assist.#Prof.#IIS,#UT

Researcher,#IIS,#UT

Prof.#Kato

Prof.#Shibasaki

Civil#Engineering,#UT(Advisor)

Lecturer.#Daw Kyaing
Transportation,#YTU

Assoc.#Professor.#
Ms.#
Htay Win
AyeHninHninNaing
Transportation,#YTU

Transportation,#YTU

CSIS,#UT(Advisor)

Mid8term)Activity)Report,)27)October)2017,)Yangon)Technological)University,)Yangon,)Myanmar
ANZEN)SATREPS)|)The)Development)of)a)Comprehensive)Disaster)Resilience)System)and)Collaboration)Platform)in)Myanmar

36

Property%of%CDR%data• CDR)is)call)detail)record)of)mobile)phone
PeriodThe)number of)CELL_ID

Dec. 187,)2015
14,284The)number)of)subscriber’s)ID

4,435,321

The average)number)of)daily)records

16,161,366

Mid8term)Activity)Report,)27)October)2017,)Yangon)Technological)University,)Yangon,)Myanmar
ANZEN)SATREPS)|)The)Development)of)a)Comprehensive)Disaster)Resilience)System)and)Collaboration)Platform)in)Myanmar

37

PFLOW%from%reconstructed%trip%data

Mid8term)Activity)Report,)27)October)2017,)Yangon)Technological)University,)Yangon,)Myanmar
ANZEN)SATREPS)|)The)Development)of)a)Comprehensive)Disaster)Resilience)System)and)Collaboration)Platform)in)Myanmar38Traffic%simulation%for%the%future%Yangon! Input! OD)matrix)with)each)transportation)mode:)Past)person)trip)survey)and)CDR)data! Road)network:)JICA)survey! Output! Main)is)hourly)link8based)traffic)status)(traffic)volume,)speed)and)congestion=vol/capacity)! Others)are)some)animations.

! First)target)! See)traffic)congestion)changing)traffic)demand)between)201682035)
with)current)infrastructure)(road,)railway…)! Adding)item)1,)see)the)congestion)increasing)road)capacity)(e.g.)the)number)of)lane)

Mid8term)Activity)Report,)27)October)2017,)Yangon)Technological)University,)Yangon,)Myanmar
ANZEN)SATREPS)|)The)Development)of)a)Comprehensive)Disaster)Resilience)System)and)Collaboration)Platform)in)Myanmar

39

JICA%traffic%demand%prediction

Mid8term)Activity)Report,)27)October)2017,)Yangon)Technological)University,)Yangon,)Myanmar
ANZEN)SATREPS)|)The)Development)of)a)Comprehensive)Disaster)Resilience)System)and)Collaboration)Platform)in)Myanmar

40

Vehicle%OD%visualization! Vehicle)OD)volume)at)9)a.m.

Mid8term)Activity)Report,)27)October)2017,)Yangon)Technological)University,)Yangon,)Myanmar
ANZEN)SATREPS)|)The)Development)of)a)Comprehensive)Disaster)Resilience)System)and)Collaboration)Platform)in)Myanmar

41

Hourly%traffic%congestion
03:00

Volume/Capacity)Ratio

07:00

Volume/Capacity)Ratio

11:00

Volume/Capacity)Ratio

15:00

Volume/Capacity)Ratio

19:00

Volume/Capacity)Ratio

23:00

Volume/Capacity)Ratio

Mid8term)Activity)Report,)27)October)2017,)Yangon)Technological)University,)Yangon,)Myanmar
ANZEN)SATREPS)|)The)Development)of)a)Comprehensive)Disaster)Resilience)System)and)Collaboration)Platform)in)Myanmar

42

Result%1(Congestion%change%depending%on%the%future%demand)! Can)confirm)increase)in)trip)time)by)increase)of)traffic)volume)in)
2035)(1.7)x)in)some)times)

Mid8term)Activity)Report,)27)October)2017,)Yangon)Technological)University,)Yangon,)Myanmar
ANZEN)SATREPS)|)The)Development)of)a)Comprehensive)Disaster)Resilience)System)and)Collaboration)Platform)in)Myanmar

43

Result%2(Congestion%depending%on%the%demand%&%road%capacity)! Can)confirm)the)save)of)trip)time)by)increase)the)number)of)one)lane)on)major)roadRed)road:)assumed)
increasing)link)(+1)lane)

Mid8term)Activity)Report,)27)October)2017,)Yangon)Technological)University,)Yangon,)Myanmar
ANZEN)SATREPS)|)The)Development)of)a)Comprehensive)Disaster)Resilience)System)and)Collaboration)Platform)in)Myanmar

44MBD)trial)analysis)in)Hanoi,)Vietnam

45

Don’t)hesitate)to)contact)me)!!
http://sekilab.iis.u8tokyo.ac.jp
sekimoto@iis.u8tokyo.ac.jp

46