CSIS Discussion Paper No. 115

A Study on Urban Mobility and Dynamic Population Estimation
by Using Aggregate Mobile Phone Sources
携帯電話情報を用いた都市 おける人々の移動特性分析と動的
人口推定手法 関する研究

ホラノン

ティーラユット

I

ABSTRACT

Sensing is becoming increasingly mobile and people-centric in a network
enabled society. Urban activities are recognized from the footprint of the usagesof ubiquitous urban infrastructure. This new paradigm is a combination of urban
infrastructures, information and communication technologies and digital networks.
The rapid technology development in the area of digital network andtelecommunications has a significant impact on our societies, lifestyles and theway we interact with the city. The technological advances and the benefits
resulting from the use of these technologies are capable of improving current
urban management, and introduce new schemes for urban mobility studies. Thisdissertation explores a novel practice concerning how mobile devices can be
employed as a new urban mobility sensor, location-aware and human in the loop
system that enables city wide information collection and analysis.

Despite the growing concern for public health, urban securities and natural
disasters, these studies have initially been developed to improve operational
efficiency or to plan public space management. Understanding the population
dynamics at high spatial and temporal resolutions, especially when incorporated
into established population distribution models, will hopefully allow for detailed
investigations of populations impacted by natural and manmade disasters and,
subsequently, could lead to more appropriate emergency planning and responses,
as well as better informed policy decisions.

Another promising application in utilizing digital footprints from the mobile
devices is public marketing analysis. The distribution of a population at different
points of time in each city space could be an ideal source for marketing
executives to decide on a place to activate premium urban advertising
opportunities or campaigns that are geographically based with more targetedand efficient marketing. These finsights would allow them to offer more specificad-funded and LBS services that stimulate uptake and increase their profitability.

AbstractII
In this research we explore and analyze the daily population distribution
patterns in the city, using a unique data source from aggregate calling activity
over time in a mobile phone network. This study will therefore address the following
research questions: Would modification of calling activities be confirmed tocorrespond to actual population in the area? How do such patterns vary in
different parts of the city? And how can human mobility be reconstructed fromcalling records of mobile phones? The questions are meant to explore whether the
population distribution follows a trend of mobile phone call detail records (CDRs)
and to investigate using such data for potential develop dynamic population
estimation models.

To test the provided research questions, the study had been conducted inseveral major cities of the world starting from a test base scenario in central
Bangkok in 2008. We developed a system call “Mobile Sensing”, a web based 2D
and 3D GIS, to use as a fundamental tool for mobile phone data visualization andanalysis. The initial results depicted and captured time series of interpolated mobile
phone call traffic in a grid density surface. This observation leads to the speculation
about how one part of the central city is upscale, crowded and how long the area
remains busy until people move to another part of the city. The analysis of themobile phone activities during one day and the mean transformation within amonth has been examined to extract each cityscape’s communal pattern. In
addition, results of the study not only provided a tool for area or zoning analysis but
also could be used to specify hidden problems of the particular space over a
period of time.

In 2009, millions of mobile phone users in Massachusetts were examined. It was
the first time that a huge amount of mobile phone traces were used to analyze themobility of the city. The numerical algorithms were developed to extract the revisit
points of individuals, for instance, home and work locations. We preliminarily
analyzed this data by characterizing mobility in a profile-based space(activity-aware map) that describes most probable activity associated with a
specific area of space. This, in turn, allowed us to capture the individual daily
activity pattern and analyze the correlations among different people’s work area’s
profile. We also investigated how good a correlation exists between the presenceof people and the mobile phone activities by inventing “SIM Mobility”, a mobile
phone traffic simulation system. The product outcome has been validated with

AbstractIII
simulated trajectories and MassGIS census data. The results yielded evidence
confirming that the mobile phone activities are significantly correlated to theexistence of people.

The last part of this dissertation was implemented in the Tokyo metropolitan
area. The Tokyo’s mobile phone traffic or call detail records (CDRs) weregenerated from the SIM Mobility simulation system. The calling patterns of theGreater Tokyo region were built from analyzing mobile phone usage surveys, andthe simulated trajectories were retrieved from Tokyo Person Trip data. Besides this,we developed new dynamic population estimation methodologies involving two
different approaches and investigated the accuracy of results. The first
methodology was implemented using a group based approach, and the
population weighted factor has been modified by time-dependent OD metrics
extracted from the observed mobile phone calling traffic. The second
methodology was implemented using an individual based approach utilizing data
assimilation process. The estimation results shown greatly accurate prediction andcapable of being enhanced the existing algorithm previously developed by The
Tokyo Metropolitan Region Transportation Planning Commission. By using acomplete scale of this novel data type, the proposed methods would enablereal-time reporting of city-wide population estimations and potentially paint a
complex and dynamic portrait of the urban dynamic in which users are based.

Using mobile phones as sensing devices to accumulate aggregate“crowdsourced” data for urban analysis is still in the early phases of development.
The contributions of this research would pave the way for future extensions tolarger and more complex analysis.

Abstract

IV

CONTENTSABSTRACT .......................................................................................................................I
CONTENTS .................................................................................................................... IV
LIST OF FIGURES ........................................................................................................... VI
LIST OF TABLES ............................................................................................................... X
Chapter 1
INTRODUCTIONS ..............................................................................................................1
1.1 From existing and beyond: Toward the ubiquitous city ............................................... 1
1.2 Communications networks as urban fabric: ................................................................. 2
1.3 Research structure and main finding ............................................................................. 4
1.4 Research questions and objectives ............................................................................... 5

Chapter 2
PRINCIPLE OF MOBILE COMUNICATION AND REVIEW OF PREVIOUS WORKS ......................7
2.1 Evolution of mobile communication: past and future ................................................. 7
2.2 Definitions and terminology ............................................................................................ 8
2.3 Literature reviews ............................................................................................................ 122.3.1 The chronological structure and scale of the population dynamic ............. 14
2.3.2 Person Trip Survey data and its potential use in this research ........................ 15
2.4 Limitations and difficulties in approach ....................................................................... 16

Chapter 3
DEVELOPMENT OF REAL-TIME MOBILE SENSING PLATFORM ..............................................18
3.1 Research initiative .......................................................................................................... 18
3.1.1 Study area ............................................................................................................ 20
3.1.2 Mobile phone log data ....................................................................................... 21
3.2 System architecture ....................................................................................................... 22
3.2.1 Mobile sensing platform ...................................................................................... 22
3.2.2 Data Processing and Services API ...................................................................... 23

Contents

V
3.2.3 Data visualization ................................................................................................. 23
3.2.4 Data manipulation .............................................................................................. 25
3.2.5 Interpolation methods and population density ............................................... 26
3.3Results and Discussions.................................................................................................... 29
3.3.1 Evolution of urban activities ................................................................................ 29
3.3.2 Abnormally detection ......................................................................................... 30
3.3.2 Activities patterns and Land Use Classification ................................................ 31
3.4 Future perspective ......................................................................................................... 34

Chapter 4
TELEMATIC MAP: IDENTIFYING HUMAN DAILY ACTIVITY PATTERN......................................35
4.1 Introduction ..................................................................................................................... 35
4.1.1 related work ......................................................................................................... 36
4.2 Methodology .................................................................................................................. 37
4.2.1 Data Preparation ................................................................................................. 37
4.2.2 Spatial Proﬁling ..................................................................................................... 40
4.3Daily Activity Patterns...................................................................................................... 43
4.4Work Area’s Proﬁle and Similarity in Daily Activity Patterns ....................................... 44
4.5 Conclusions ..................................................................................................................... 49

Chapter 5
CALLING PATTERNS AND CALL SIMULATION OF CROWD MOVEMENT ................................. 50
5.1 Introduction ..................................................................................................................... 50
5.2 Call patterns analysis ..................................................................................................... 52
5.2.1Mobile phone traffic and population distribution ........................................... 52
5.2.2 Call patterns extraction methodd ..................................................................... 54
5.3 The SIM Mobility platform ............................................................................................... 62
5.4 Results and discussions ................................................................................................... 66

Chapter 6
PREDICTION OF THE CURRENT: Integration of Mobile Phone Calling Records and Person
Trip Data ................................................................................................................................... 72
6.1 Introduction ..................................................................................................................... 72
6.2 Toyko Person trip: Tokyo today as seen by movement of people ........................... 73
6.2.1 Preprocessing and data structure ..................................................................... 75
6.3 The mobile phone usage survey................................................................................... 76Contents

VI
6.4 Methodology .................................................................................................................. 86
6.4.1 Data preprocessing and system configuration ................................................ 86
6.4.2 Simulation of call traffic ....................................................................................... 88
6.5 Time-dependent dynamic OD weight modification method .................................. 93
6.5.1Population reconstruction ................................................................................... 97
6.5.2 O-D weight modification function ..................................................................... 99
6.5.3 Experimental results and conclusions .............................................................. 101
6.6 Data assimilation method ........................................................................................... 107
6.6.1 Assimilation Process ........................................................................................... 107
6.6.2 Results and discussions ...................................................................................... 111

Chapter 7
CONCLUSIONS AND FUTURE PROSPECT ........................................................................114
7.1 Conclusions ................................................................................................................... 114
7.1.1 Summary of contributions and main finding .................................................. 114
7.2 Future prospect and collaboration ............................................................................ 116

REFERENCES ...............................................................................................................119

Contents

VII

LIST OF FIGURES

Figure 1.1 Urban and rural population of the world, 1950 - 2030 ........................................ 2
Figure 1.2 World cellular network coverage (2008) ................................................................3
Figure 1.3 A conceptual idea of information sensing ............................................................ 5
Figure 2.1 Monthly forecasts of Mobile data traffic from 2008 to 2013 ................................ 8
Figure 2.2Laptops and mobile broadband handsets drive traffic growth from 2008 to2013 ........................................................................................................................... 8
Figure 2.3 Commutation basis of GSM system ........................................................................ 9
Figure 2.4 Main component of GSM system ......................................................................... 11
Figure 2.5 Cell Tower Triangulation ......................................................................................... 17
Figure 3.1 Study area in central Bangkok .............................................................................. 20
Figure 3.2 Daily Erlang distribution in one month .................................................................. 21
Figure 3.3 System architecture of mobile sensing platform ................................................ 22
Figure 3.4A prototype system with temporal control and time based analysis ............... 24
Figure 3.5 A prototype system with 3D flow pattern animate on Google Earth Plug-in .. 25
Figure 3.6 Mobile antenna, horizontal plane range ............................................................ 27
Figure3.7 Increase interpolated resolution by using voronoi tessellation and theangle of antenna ................................................................................................... 28
Figure 3.8 Day and month statistics from cumulative mobile usages data ...................... 29
Figure 3.9 The flow pattern from early morning to late evening in central Bangkok ....... 30
Figure 3.10 Comparison of density contour in Pathumwan area onFriday and

Sundayat 1.00 pm .............................................................................................................. 31
Figure 3.11 The 24 hour graph images illustrates the urban signature in specificland use .................................................................................................................. 32
Figure 3.12 Individual mobile phone signature in one day ................................................. 33
Figure 4.1 Area of study, cropped by yellow line ................................................................. 39
Figure 4.2 POI search results on the map with 500x500 m2 visual grids .............................. 42
Figure 4.3 Crisp activity distribution map. .............................................................................. 43
Figure 4.4 The eight 3-hour temporal windows are used to frame .................................... 44
Figure 4.5 Shows higher degree in similarity within the group ............................................. 47
Contents

VIII
Figure 4.6 Dissimilarity in daily activity patterns is measured by average
Hamming distance ................................................................................................ 48
Figure 5.1 Night time cell phone users density, calculated at the census tract level ...... 53
Figure 5.2 Population densities, calculated at the census tract level ............................... 53
Figure 5.3 Distributions of outgoing call (a) and incoming call (b) in one month ............ 54
Figure 5.4 Illustrate number of calls and callers .................................................................... 55
Figure 5.5 The data mining results after clustering using K-Means method ...................... 57
Figure 5.6 Characteristic of 5 calling patterns in Massachusetts ........................................ 58
Figure 5.7 Calling patterns with 5 clusters .............................................................................. 60
Figure 5.8Subsequence profile patterns of the main cluster .............................................. 60
Figure 5.9 The simulated trajectories deriving methods ...................................................... 62
Figure 5.10 SIM Mobility flow diagrams .................................................................................. 64
Figure 5.11Capture of simulated call activities in Metro-Boston (Suffolk County) ............ 65
Figure 5.12 Illustrate aggregate call activities of part of greater Boston .......................... 66
Figure 5.13 Correlation of actual population and call activities in one week.................. 67
Figure 5.14 Area interpolations ofMassGIS land use data ................................................... 69
Figure 6.1 The coverage area of Person Trip Survey in greater Tokyo region ................... 74
Figure 6.1(b)Person Trip Survey describes overall trips of a person in one day ................. 75
Figure 6.2 Accumulated call activity by area in 24 hours normalized bynumber of observations.......................................................................................... 78
Figure 6.3 Accumulated call(a), email(b) and internet(c) activity by age groups ......... 79
Figure 6.4Accumulated call(a), email(b) and internet(c) activity
categorized by gender .......................................................................................... 80
Figure6.5 Accumulated call, email and internet activity categorized by activity types 81
Figure 6.6 Overall accumulated mobile phone activities during one business day ........ 82
Figure 6.7 Accumulated call activities of each career groups in 24 hours ....................... 83
Figure 6.8 Accumulated email activities of each career groups in 24 hours ................... 83
Figure6.9 Area of study cropped by Tokyo 23 wards .......................................................... 86
Figure 6.10 Grid dimension of the simulation system ........................................................... 87
Figure6.11 Uniform mobile base station................................................................................. 87
Figure6.12 Structure and procedural steps of simulation system ....................................... 88
Figure 6.13 A SIM Mobility prototype system illustrates Tokyo activity ................................ 90
Figure 6.14 Illustrate call activities in simulated grid config at 250 x 250 meters .............. 90
Figure 6.15 (a) Interpolation of mobile phone activities at morning rush hours (6-8 am.)91
Figure6.15 (b) Interpolation of mobile phone activities at morning office hours(10-12am.) ......................................................................................................................... 91

Contents

IX
Figure 6.15 (c) Interpolation of mobile phone activities at afternoon office hours(1-3 pm.) ................................................................................................................. 91
Figure 6.15 (d) Interpolation of mobile phone activities at evening commuting time
(6-8 pm.) .................................................................................................................91
Figure6.16 Visualization of mobile phone activities at morning commuting time overlay
on map with train network.................................................................................. 92
Figure 6.17Visualization of mobile phone activities at 10 am. on normal business day
overlay on map with train network ................................................................... 92
Figure 6.18 Home and work location of samples in Tokyo 23 wards .................................. 96
Figure 6.19 A day time O-D mapping graph of Tokyo 23 Words ........................................ 97
Figure 6.20 Plot of estimated population and real population numbers ....................... 101
Figure 6.21 Plot of RMSE of the O-D modification methods compare with previous Person
Trip Survey method .......................................................................................... 103
Figure 6.22 Plot of R2 of the O-D modification methods compare with previous Person Trip
Survey method ................................................................................................... 103
Figure 6.23Configuration of the method, the selected samples share the same homelocation ............................................................................................................... 108
Figure 6.24 Giving sample traces of each occupation types in difference colors ........ 109
Figure 6.25Comparethe RMSE results of previous PTand assimilation method on different
iterated scenario ............................................................................................... 111
Figure 6.26Compare the RMSE results of previous PT and assimilation methodondifferent observed population scenario ......................................................... 113

Contents

X

LIST OF TABLES

Table 3.1

Sample Data from the Base Station Controller (BSC) ....................................... 26

Table3.2

Data modification with voronoi-based segmentation method ....................... 28

Table4.1

List of the counties and their area covered by this study ................................. 38

Table 4.2

Considered activities and keywords used for POIs search .............................. 40

Table 4.3

Signature of each group based on work cell’s profile ..................................... 45

Table 4.4

Average within-group distance .......................................................................... 46

Table 4.5

Average between-group distance .................................................................... 46

Table 5.1Ak-means clustering was applied to the subsetsamples with k= 5using the data
mining software weka........................................................................................... 61
Table 5.2 Land use code definitions ...................................................................................... 69
Table 6.1

The preprocess trajectories in tabular format .................................................... 76

Table 6.2

Average Call activity of each occupation type .............................................. 84

Table 6.3

Average Mail activity of each occupation type .............................................. 85

Table 6.4

Real population and estimated population at one square kilometer ........... 94

Table 6.5

O-D matrices explain link flow from Chiyoda-ku ............................................... 98

Table 6.6

O-D matrices explain link flow to Chiyoda-ku ................................................... 99

Table6.7Average Call activity of each occupation type ..................................................105
Table 6.8

The results of individual weight modification through the data assimilation
process ..................................................................................................................111

Table 6.9

Compare the results of estimated population .................................................112

Contents

1

CHAPTER 1
Introductions

1.1 From existing and beyond: Toward the ubiquitous city
Today’s cities develop at a rate that outpaces architects’ and planners’ efforts
to shape them. Cities, home to more than half the world’s population, can be seenascomplexnetworks

ofcomponents:

citizens,

businesses,

transport,

communications, utilities and other systems. Urban spaces become increasinglyinterconnected and intelligent in nature. Cities have the chance to accelerate theirjourney towards sustainable prosperity by making use of new “smart” solutions andmanagement practices.
Focusing on urban mobility, the percentage of the population living in urban
areas is expected to rise from 13 % in 1900 to approximately 60 % by 2030, meaningthat a large and increasing proportion of the population will limit their daily travel toshort / medium distances of less than 100 kilometers, often within entirely the urban
environment. (Figure 1.1) Particularly relevant to the urban mobility scenarios, thedemand for people transportation will further increase over the next decades,
requiring new solutions to optimize human fluidity and energy efficiency in specificsituations.

Chapter1: Introductions

2

Figure 1.1 Urban and rural population of the world, 1950 - 2030
Source: UN Population Division.

The study of movements of people within a city has had a long history. The
population flows in and out of the city are highly dynamic especially in several
worlds’ mega cities. For instance, Manhattan island in New York City, the island ishome to about 1.6 million (2008 - http://en.wikipedia.org/) inhabitants, with roughly
double that number during any work day. These highly dynamic situations in busy
city centers still pose considerable challenges for state-of-the-art approaches.

The accuracy of estimating the total number of population for an urban area
during the day also remains highly questionable and problematic. Many urban
planners, sociologists, and even economists have tried various means of estimating
populations by using standard multipliers and complex formulas for making
inferences from census records. The challenges ahead are significant andcomplicated and thus will be focused of this research.

1.2 Communications networks as urban fabric
Telecommunication network enables cities to gather more high-quality humanmobility data in a timely fashion than ever before. Thinking in term of “scales of
networks”, mobile communication provides us an ideal solution to create a hugeurban fabric in which urban populations simply become part of a network. These
networks can be telematic, physical, or even social when people are all engaged.
Chapter1: Introductions

3
Implicitly this research underscores the opportunistic of these territories as much as
they

constructafootingfornewintervention

practices.

In

principle,

telecommunication infrastructure may be seen as providing a service to people.
This means people and infrastructure interact, making new interfaces and ways ofrepresenting the existence of any entity that can be seen by the network. The
footprints from this interaction clearly become a new source to unambiguously
identify people in the real world.

Another reality is that over 4.3 billion people own mobile phones today (March
2010, http://www.gsacom.com/), and the increasing reach of mobile networks
creates an unprecedented opportunity for researchers to derive valuable high-level
human behavior information in realms of urban planning, mobility and social
interactions.

Figure 1.2 World cellular network coverage (2008)
Source: The Global mobile Suppliers Association (GSA).

Chapter1: Introductions

4
1.3 Research structure and main findings
This dissertation is organized into seven chapters. The First chapter describes
here introduce the backgrounds, problems and new potentials of this research. The
remaining sections of this chapter explain the objectives of this research,
motivations and research questions.
Chapter 2 briefly describes each of the components of a telecommunication
system, definition and terminology and points out the difficulties in examining themobile phone data for human flow analysis. Also reviews of current and previous
works.
Chapter 3 demonstrates the development of research tools, a prototype ofmobile sensing platform and web API. The system provides an easy visualizing ofmobile phone activity from base station log data. The principle functions of spatial
analysis such as Inverse Distance Weighted (IDW), Kriging model, contour
interpolation and basic graph analysis had been implemented. A new voronoi
based sector interpolation has been introduced in this chapter.
Chapter 4 expresses a use case of analyzing mobile phone traces to identify
human daily activity patterns. The article discusses various methodologies andmodels to extract mobility patterns and spatial profiles from the real mobile traces.
Chapter 5 investigates call-usage patterns mining from real dataset of millions
of mobile phone users. A study was conducted to find a minimum behavioral clusterof mobile user groups in inner-city environment. Beside, algorithms for mobile traces
reconstruction had been invented to find a common trajectory of the individuals.
Last, the correlation of population distribution and mobile phone traffic had been
confirmed.
Chapter 6 builds on the concepts introduced in Chapter 5 and focuses on
algorithms and models to reconstruct population distribution over time. Tokyo
mobile phone usage survey has been conducted to understand calling behavior ofuser groups in Tokyo. Subsequently, a platform calls “SIM Mobility” was developed
for assembling mobile phone call. We applied SIM Mobility to generate the virtual
call traffic by utilizing pre-processed Person Trip data and behavioral models.
Time-dependent OD matrix method and assimilation process method have been
invented to reconstruct human mobility from calling records of mobile phones. The
methods contribute significantly to improve the accuracy of population estimation
models.

Chapter1: Introductions

5
Chapter 7 discusses new findings, future prospect and the possible extension of
the proposed algorithm to more complex analysis and concludes the paper.

1.4 Research questions and objectives
Motivations

From the introduction, it is the first time in history that more people live in cities
than in the countryside. The accelerated rate of growth of the city today makes
urban life steadily more complex than ever. This sudden concentration of
population is often raising the problem of traffic congestion, public health andurban securities. Therefore, we need a novel method in order to capture oflarge-scale quantitative data related to human behavior and their mobility. Figure
1.3 illustrates the conceptual idea and potential use in general.

However, to date, the majority of research in understanding human flow andurban mobility has been done with very limited sample sizes. The estimatedpopulation density is mainly meant to census data which is static and refers to nighttime population. Nevertheless, the vast penetration of mobile phone users andadvance development through the innovative use of mobile network are changing
our daily lives and also the way to research on the city motion. We now have taken
great steps forward to truly become the Ubiquitous city.

Figure 1.3 A conceptual idea of information sensing

Chapter1: Introductions

6
Our hypothesis is that in the urban area where mobile phone penetration rate ishigh, the mobile device could be represented to the existence of the people andthe population density could be determined by mobile phone usage density.

Research questions

In particular, this research examines the extent to which the use of mobile
phones traffic data helps to understand human mobility patterns and leads to thedevelopment of population dynamic model. The following research questions weredeveloped to examine this aspect.

How can actual population at a specific time-frame be estimated from callingrecords of mobile phones?



How can dynamic people mobility be reconstructed from calling records ofmobile phones?

Research objectives

From above research questions, our objective is to develop a methodologicalframework based on the analysis of mobile phone call detail records (CDRs) data.
The research objectives pursued in order to answer the research questions are:

To implement a standard platform to access, manipulate and visualize mobile
phone Call Detail Records (CDRs) and to use this platform to help understandthe distribution of calling traffic in the area.



To confirm the correlation between mobile phone usage and population
density.



To develop a methodology for estimating population density and dynamic
mobility of people.

Chapter1: Introductions

7

CHAPTER 2
Principle of Mobile Communication andReview of Previous Works

2.1 Evolution of mobile communication: past and future
The first generation of mobile communication was introduced in the late 1970s
and this 1G cellular system still transmits only analogue voice information. The
development of 2G cellular systems was driven by the need to improve transmission
quality, system capacity and coverage. Speech transmission still dominates the
airways, but the demand for shortgrowing

rapidly.

2G

cellularmessagesystemsandincludedata

Globaltransmissions
SystemforareMobileCommunication (GSM), Digital AMPS (D-AMPS), code division multiple access(CDMA) and Personal Digital Communication (PDC). GSM is the most successful
family of cellular standards and therefore is the main system in our first study which
we will discuss more in the next session. GSM became the dominant technology
and is also the basis for one of the two leading 3G technologies UMTS and W-CDMA.
The first pre-commercial 3G network was launched by NTT DoCoMo in Japanbranded FOMA, in May 2001 on a pre-release of W-CDMA technology. The 3G
systems were capable of data transmission at speeds up to 384K bps but can now
support downloads in the megabit per second range. Figure 3 illustrates monthly
forecasts of Mobile data traffic from 2008 to 2013 and figure 4 shows the laptops
and mobile broadband handsets drive traffic growth. The trends help confirm thatmobile phone is already part of everyday life with penetration rates rising to 70 per
cent and more in many countries. According to a survey amongst more than 1000
Internet leaders, activists and analysts the mobile device will be the primaryconnection tool to the Internet for most people in the world in 2020 (pew Internet &American life project, December 2008: www. pewinternet.org). In particular, mobile
broadband is going to change the way the world lives and to dramatically change
the way we interact with the city. This research introduces how mobile phone
networks can provide the ubiquitous mobility services in a world where everyone

Chapter 2: Principle of Mobile Communication and Review of Presence Works

8
are connected.

Figure 2.1 Monthly forecasts of Mobile data traffic from 2008 to 2013

Figure 2.2 Laptops and mobile broadband handsets drive traffic growth from2008 to 2013

2.2 Definitions and terminology
There are several words and terminologies use in this research paper that arestrongly connected with information and communication technology. Some of themost important basic terminologies of GSM system are described in this section.

Chapter 2: Principle of Mobile Communication and Review of Presence Works

9
Global System for Mobile Communication (GSM)

A GSM system is basically designed as a combination of three major subsystems,
the Network Subsystem (NSS), the Radio Subsystem (BSS) and the Operation support
subsystem (B&CCS & IN). In general, we collect the data from Base Station
Controller (BSC) which is a part of Radio Subsystem. When users place a call, a cell
phone or mobile station (MS) will establish a communication with the closest BaseTransceiver Station (BTS). A coverage area of a single base station indicates thecapacity of mobile usage in the area. The initial research, for example in chapter 3,
we accumulate the call traffic (Erlang) and other information in each base station
with an hour interval. In a very high traffic area, the coverage could be reduced toa hundred-meter radius. However in the suburb, the coverage could be increased,varying from several hundreds to a kilometer. In the case of our study area, in
Bangkok, the coverage varies from two to five hundred meters depending on thecapacity of the peak usage time.

Figure 2.3 Commutation basis of GSM system

Erlang data

An Erlang is defined as a unit of telecommunications traffic measurement.
Erlang represents the continuous use of one voice path and it is used to describe
the total traffic volume of one hour going through a particular cell antenna. The
erlang is a dimensionless unit that is used in telephony as a statistical measure ofChapter 2: Principle of Mobile Communication and Review of Presence Works

10
offered load. Traffic of one erlang refers to a single resource being in continuous use,or two channels being at fifty percent use each, and so on.

Call Detail Records (CDRs)

For every mobile telephone call there should be a corresponding Call Detail
Records (CDRs). It can contain information that the mobile network operator uses
for subscriber identification, call charging, services obtained, call routing, Erlang
and etc. In this research, we received CDRs data from two mobile operator, namely
Advanced Info Service (AIS – Thailand) and Airsage Inc. (Untied State).

Mobile Station (MS)The mobile station is made up of the subscriber identity module (SIM) and the
Mobile Equipment. The SIM card and phone (ME) both have internationally unique
identification. The number for the SIM card is called the IMSI (International Mobile
Subscriber Identity) and for the phone it is called the IMEI (International Mobile
Equipment Identity).

Base Station Transceivers (BTS)

BTSs are always grouped into geographical areas called Location Areas (LA).
When a call is established to an MS, the Home Location Register (HLR) will know inwhich LA the subscriber is. Only the BTSs in that LA will page all the phones in order tofind the targeted phone. This saves a lot of signaling resources. When a phonemoves from one LA to another, it will request a position update and the HLR will
update its database with the new location of the phone.

Base Station Controllers (BSC)

A single BSC (Base Station Controller) controls a number of BTSs and MSs. It
handles the radio resource (RR) management of the BTSs such as frequency
allocation, timeslot allocation, and handles handovers between cells under its
control.

Chapter 2: Principle of Mobile Communication and Review of Presence Works

11
Mobile services Switching Centre (MSC)The Mobile services Switching Centre coordinates the call setup andtermination of MSs in its area and performs the switching between MSs in the local
mobile network (PLMN).

Visitor Location Register (VLR)

The Visitor Location Register (VLR) is a temporary database used by an MSC.
The VLR will update a subscriber’s location when the subscriber moves from one
location area (LA) to another in its area. When the subscriber mover from one LA toanother under the control of a new VLR, then the old VLR will delete the informationof the MS and the new VLR will draw the information from the HLR and also update
the HLR with the new location.

Home Location Register (HLR)

The HLR is the central database where a subscriber’s profile is held against the
IMSI of the SIM card. It contains permanent information such as allowed services,
status of supplementary services (such as call diverts) as well as other dynamic
information such as the subscriber location.

Figure 2.4 Main component of GSM system

Chapter 2: Principle of Mobile Communication and Review of Presence Works

12
2.3 Literature reviews
In recent years, the large deployment of mobile and wireless technologies has
provided new means to understand the dynamics of a city. Scientists are
discovering that a world buzzing with cell phone calls and text messages has a sideincredible benefit: reams of data about who calls whom and about where they areat what time. Researchers are beginning to use that information to answer questions
about how people behave, where they travel, and the social networks thatconnect them.

It is therefore not surprising that considerable research has gone in this new andinteresting directions, in particular, developing techniques to bring out and explore
large scale human mobility.( Azevedo et al., 2009; Candia et al.,2008; Eagle et al.,2007; Gonzalez et al., 2008 )In reality mining project (Eagle and Pentland, 2007), the project is using data
gathered by cell phones to learn more about human behavior and social
interactions. The dataset was collected over nine months by monitoring the cell
phone usage of 84 participants. The personal reality mining infers humanrelationships and behavior by applying data-mining algorithms to informationcollected by cell-phone sensors that can measure location, physical activity, andmore. With the aid of some algorithms, they posit, that information could help us
identify things to do or new people to meet. It could also make devices easier touse for instance, by automatically determining security settings. More significant,cell phone data could shed light on workplace dynamics and on the well-being ofcommunities. It could even help project the course of disease outbreaks andprovide clues about individuals’ health.

While the promise of reality mining is great, the idea of collecting so muchpersonal information naturally raises many questions about privacy. Some people
are nervous about trailing digital bread crumbs behind them. It’s crucial thatbehavior-logging technology not be forced on anyone. But legal statutes arelagging behind data collection abilities which makes it all the more important tobegin discussing how the technology will be used.

In Real-time Rome project, the collaboration between Telecom Italia and MIT’s

Chapter 2: Principle of Mobile Communication and Review of Presence Works

13
SENSEable City Laboratory in 2007, they explored how researchers might be able touse mobile phone data for an entire metropolitan region to analyze urban
dynamics. The Erlang data which is a measure of network bandwidth usage
typically collected at the antenna level. The data was collected over four months inlate 2006 and covering a region of 47 km2. The findings suggested that signature
analysis can provide an important new way of looking at the city as a holistic,
dynamic system. In particular, the mobile phone network lets us develop a real-time
representation of those dynamics at the city and city-region scale. This approach
can complement traditional collection techniques, which are often outdated by
the time they’re available to policy makers and the general public (Ratti et al.,2007)

In earlier research on human mobility patterns (Gonzalez et al., 2008), Gonzalez
and her team observed the trajectory of 100,000 anonymized mobile phone users
whose position is tracked for a six-month period. The results had shown a highdegree of temporal and spatial regularity, each individual being characterized bya time-independent characteristic travel distance and a significant probability toreturn to a few highly frequented locations. This is describing the recurrence andtemporal periodicity inherent to human mobility.

In 2009, Kyunghan Lee studied a mobility model for human walks by proposing
a new mobility model called SLAW (Self-similar Least Action Walk) to improve theperformance of networking applications. As wireless devices are mostly attached tohumans,understandinghumanmobility

patterns

leads

to

an

accurate

performance prediction of protocols used for these networks (Lee et al., 2009). In
contrast of this perspective, understanding the activities occur in the network canlead to a potential predictable presence of human or their mobility patterns in theurban system.

A recent study in Northeastern University (Barabasi et al., 2010), Barabasi usedcell phone billing data for 50,000 people in a European country to show thatpeople's travel patterns are extremely predictable. He found that most people
travel very little on a daily basis, for instance, 5 to 10 kilometers or so. There were a
few individuals, who on a daily basis travel hundreds of kilometers. This finding alsosuggests that, for the vast majority of the people, there is an average of 93 percent
predictability across the user base.

Chapter 2: Principle of Mobile Communication and Review of Presence Works

14
There have been other considerable studies done to understand humanmobility and improve the spatial resolution of static population counts through theuse of census data (Dobson et al. 2000, Bhaduri et al. 2007). They introduced aproject call “LandScan”, a population distribution model created by Oak Ridge
National Laboratory, seeks to overcome the limitations of aggregated and static
population counts by estimating high spatial resolution population locations for bothday and night. The project suggested a multi-dimensional dasymetric modelingapproach, which has allowed the creation of a very high-resolution population
distribution data both over space and time. At a spatial resolution of 3 arc seconds(~90 m), the initial LandScan USA database contains both a nighttime residential aswell as a baseline daytime population distribution that incorporates movement ofworkers and students.

Therefore, any estimate of populations impacted or exposed must consider that
census data represent nighttime residential locations and are not accurate fordaytime population estimation. By estimating daytime location and disaggregating
census blocks for accurate population location, high-resolution population data
have already been proven to be vital tools in research and planning forenvironmental, public health, and disaster concerns (Bhaduri et al. 2007).

2.3.1

The chronological structure and scale of the population dynamic

When we talk about the population dynamic, there are several terminologies toexpress how to count the population in space. In case of census data or population
count within the national level, the population density may describe as household
population collected by annual or even longer since if we see the at the national orcity scale, the city population may not change rapidly. Population dynamics deals
with the way populations are affected by birth and death rates, and by immigrationand emigration, and studies topics such as aging populations or population decline.
Within this context, population data are constrained both in space and time and do
not capture the population dynamics as functions of space and time. In fact, non
uniform distribution of human population is quite obvious from simple visualobservation of any landscape. From a temporal perspective, the resolution of census
information is typically at anywhere between 1 and 10 year cycles and represented
as a nighttime residential population. Usage of traditional census counts in a daytime

Chapter 2: Principle of Mobile Communication and Review of Presence Works

15
event is irrational. Because of this uncertainty, there is significant potential to
misclassify people with respect to their location, These limitations, to a large degree,
can be overcome by developing population data with a finer resolution in both
space and time at sub-census levels. Geodemographic data at such scales will
represent a more realistic non-uniform distribution of population.

Population distribution during the day

Population distribution during the day can be defined as distribution of
population in an area during the daytime hours. The question on how to define andintegrate daytime movements and collective travel habits into a single measure to
produce a better representation of where people are located during an average
day is crucial important.

Dynamic day time population or we describe it “ambient population”, as it
opposes to residential population, it takes into account the movements of individuals
through a given area. For instance, no individuals live on an interstate, but they do
travel on it from time to time. A resident population would show no one living on theinterstate, whereas the ambient population would indicate the presence ofindividuals based on factors specific to the interstate.

Accurate estimation and representation of dynamic population distribution
poses significant challenges and in fact few models have been developed toestimate day time population. The previous known effort to develop an analogous
model has been at the Los Alamos National Laboratory (McPherson and Brown 2004;
McPherson et al. 2006) that developed nighttime residential and daytime distribution
data at a 250 m resolution. Another is LandScan project developed by Oak Ridge
National Laboratory. (Bhaduri 2002, 2007) However none of the previous studies
provide a methodology in estimating high temporal dynamic population distribution.

2.3.2

Person Trip Survey data and its potential use in this research

Person Trip Survey (PTS), a large scale household interview survey, is conducted
to grasp passenger movement or to build travel demand forecasting models. The
data has been used by academic researchers for various aspects including urban
transportation planning, job accessibility and human flow. (Minamoto et al, 2202;
Chapter 2: Principle of Mobile Communication and Review of Presence Works

16
Kawabata, 2003; Sato et al, 2008)

Since Person Trip data provide a details

movement of people including mode of transportation, trip purpose and trip time,and because of the difficulty in validating real human mobility and counting theexisting number of population in large dimension, thus with this aspect, PTS data
would be an ideal source of information for post validation of this research

2.4 Limitations and difficulties in approach
Privacy

Privacy concerns arise when there is a possibility of discovering personal
information such as the personal habits, locations, behaviors and lifestyles ofindividuals inside cities, and to use this information for secondary purposes. Mobile
phones are ubiquitous in corporate life, supplying voice, email and browser accessto data whenever and wherever information is needed. Although mobile phones
are a valued and powerful tool for communication, there are aspects of mobile
phones which are problematic, that have arisen due to the power and ubiquity ofmobile phones. The ability to not only transmit voice but also to implicitly collect andrecord geographical locations has created new challenges for researchers in termsof new conflicts between technology Innovation and rights to privacy. This newform of location availability is considered to be very positive, but at the same timecreates uneasiness among users. New conflicts and challenges are not only
relevant to mobile phones but to other new forms of new communication
technology.

Accuracy

One major factor that could impact continued advancement of local analysis
on these mobile devices is accuracy of their geolocation estimated position (EP).
There are two methods for pinpointing the location of mobile phone users. First iswith Global Positioning System (GPS) capability, the mobile phone use signals fromsatellites to pinpoint location very accurately. But this is not in the normal case since
the data sent from the mobile devices normally not include this kind of information.

Chapter 2: Principle of Mobile Communication and Review of Presence Works

17

Figure 2.5 Cell Tower Triangulation
Source: http://www.intomobile.com/

The second and less-accurate method but mostly available is often called “Cell
Tower Triangulation”, referring to how the cell towers which receive a phone’s signal
may be used to calculate its geophysical location. In most case the accuracy fromthis method vary from 50-300 meters in dense urban environments.

Availability of dataIn almost every country, a new form of researches and studies on mining mobile
phones data has limited to the availability of the data from the mobile phone
operator. Although the datasets have somehow become available in recent years
and have opened the possibility for researchers to study on large-scale urban andsocial analysis however the support from mobile industry and data availability arestill very limited in our experiment.

Data discontinuity

At some point we can consider the estimated mobile phone positions as GPS
positions since the logging information include all geographical location and timestamp. Nevertheless, the crucial difference is that there is no continuity of eachconsecutive point from the Call Detail Record (CDR) since the CDRs are generated
only when people use the mobile phones. This dissertation introduces various views
to resolve this problem and bring in how CDR could become an effective
monitoring probe for real-life application.

Chapter 2: Principle of Mobile Communication and Review of Presence Works

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CHAPTER 3
Development of Real-Time Mobile Sensing Platform

3.1 Research initiative

In our everyday activities we leave behind footprints from our interaction withthe urban environment and its digital infrastructures. This chapter aims to explore a
new technique for urban monitoring by mapping of mobile phone usage and its
location to represent the urban activities and their evolution through space andtime. The massive movement of the people and rise of city congestion could be
visualized in near real time on a web-based interface. We implemented mobile
sensing platform which interpolate the aggregate mobile sources from theantenna-masts position to predict the population in grid-density surfaces. We thenanalyze urban patterns at a point of time to illustrate how people experience theircity. Furthermore, the urban land-use could be classified from the uniquecharacteristic use of mobile phone in each urban space. We analyzed the activities
during one day and examined the mean transformation within a month to extract
each cityscape communal pattern. This initial research could derive valuablehigh-level human behavior information in realms of urban planning, mobility andsocial interactions.
As we discussed earlier, considerable research has gone into this new andinteresting directions. In particular, developing techniques to bring out very large
scale urban sensing by leveraging the increasing capabilities found in cell phones(Nicholas D. Lane et al, 2008).

Data collected from the cell phones give thefoundation for exciting people-centric applications. And the analysis of cell phone
use can provide an important new way of looking at the city as a holistic, dynamic
system (Reades J. et al, 2007). People-movement maps could lead to

Chapter 3: Development of Real-Time Mobile Sensing Platform

19
improvements in transport planning, event management and modelling the spreadof biological and mobile viruses (Marta C. González et al, 2008, Ratti C. 2007).

Today, maps are not only intelligent, they are ubiquitous and the ubiquity ofmaps as a form of visual representation produces a new understanding of space.
The usage of mobile devices can be treated as a medium for data collection. We
developed a system called mobile sensing, an interactive web-based platform formobile phone and web services data integration. The system analyzes contents
and visualizes information in a map interface, which users can access via theinternet. This article presents findings in the course of designing and examining how
invisible activities can be realized. Obviously this would change the way we see
how people interact with their built environment.

For the large scale monitoring of urban space, clusters of Erlang data frommobile base stations are excellent at providing indirect interpretation of spatial
patterns of urban life and its temporal dynamic. Erlang data that represent a
distribution of call duration in the Global System for Mobile Communication (GSM)
network could be performed as aggregate data sources to estimate thepopulation density of a city. This aspect is very useful in the view of public monitoring.
It could potentially become a new way to extract or identify invisible problem spots
from the complex urbanized areas. Furthermore, mobile sensing is potentiallyapplicable for public marketing analysis. The distribution of a population at different
points of time in each city space could be an ideal source to help people decide aplace for urban advertising or for opening a shop.

In addition, an exploration ofmobile sensing data would give the urban planner a better understanding on
flowing patterns of people at specific times of a day.

If we look at broader

contexts, then population transfers from city to city in during special events or
holidays could also be determined.

The applications of mobile sensing could be extended to various domains ofurban and infrastructure management. Since more than 4 billion people carrymobile phones today, and we are beginning to notice mobile phones’ ability to
become the ultimate data collection machines.

Chapter 3: Development of Real-Time Mobile Sensing Platform

20

3.1.1

Study area
The research takes place at Bangkok Metropolitan, Thailand. Bangkok city has

a well developed mobile network and has a high degree of mobile usage. We
received the support of the Erlang data and base station locations from Advanced
Info Service PLC (AIS), a leading mobile operator in Thailand.

Figure 3.1

Study area in central Bangkok. This is a good example for thisexperiment since the area compose of mix land use including business area andhigh-rise building zone, Shopping mall, horse racing field and the university.

Theoverlaid voronoi give an idea how base stations and their coverage distribute overthe dense city area. The yellow points virtually illustrate the centroid of service areaof each antenna range.

Chapter 3: Development of Real-Time Mobile Sensing Platform

21

3.1.2

Mobile phone log data

In this study, mobile phone log or Call Detail records (CDRs) data had beencollected during the period from February to April 2008 that cover a part of thecentral Bangkok. We collected the data from 50 base transceiver stations and 150
antennas with an hour interval. The data compose of several attributed information,
including cell antenna id, base station location, time stamp, handover information,
call attempts and erlang data. Figure 3.2 show a plot of Erlang distribution over one
month period,

Figure 3.2 Daily Erlang distribution in one month

Chapter 3: Development of Real-Time Mobile Sensing Platform

22

3.2 System architecture
3.2.1

Mobile sensing platform

The system is intended to visualize the aggregate mobile data sources andperform analysis of temporal human-flow in the city. We therefore developed
fundamental tools of spatial exploration and visualization which permitted the data
to be obtained, integrated and displayed quickly, easily and flexibly.

Since theinternet has now made distance virtually disappear and enables us to enjoy amuch greater richness of information, we questioned the traditional method ofurban monitoring by enhancing the way to instantaneously integrate and obtainlarge amounts of data via the network.

The key characteristic of the system is that it provides an easy visualizing of
spatial data exploration and analysis through the web interfaces. With cell-based
probe analysis, it is possible to handle multi-temporal, dynamic information,enhancing the ability to detect and represent changes associated with events andflows.

Figure 3.3 System architecture of mobile sensing platform

Chapter 3: Development of Real-Time Mobile Sensing Platform

23
The mobile sensing platform concept could be described in two different
components: a core application along with spatial analysis services that have been
implemented in our local environment and the public domain services that acted
as data sources for the system (Figure 3.2).

3.2.2

Data Processing and Services API

To maximize compatibility and interoperability, open standard such as XML andweb services are utilized for data exchange and sharing. Data analysis is assisted by
the open source statistical package R (http://www.r-project.org), integrated into
the PostgreSQL database via the PL/R procedural language (Conway, 2003). The
current system handles two different services in order to analyze flow of the city, one
to obtain statistics service and the other to obtain interpolation service.

Obtain statistics: the statistic service manages two kinds of requests. It can
retrieve daily statistics and monthly statistics of the interested area from a request
made by a user services interface. This function executes a query request to spatial
database servers and returns the processed data back to perform a graphrendering at the browser front end.

Obtain interpolation: the interpolation service provides a method of
constructing new data points within the range of a discrete set of known data
points. A user service parses a request with relevant parameters including date, timeand interpolation method to the spatial analysis server, which query the spatial
database server for retrieving mobile points and related information to generate aninterpolated image. Finally, the system asynchronously sends the result image backto the originating user service.

3.2.3

Data visualization

The web browser we use as a universal front end, an Ajax mashup, is a hybrid
web application which presents a rich UI to update and integrate contents
asynchronously from multiple sources. This makes combining data easier, not only
spatial data from the host server but also third-party sources from the services
available on the internet.

The calls can also be made directly to the third-party

Chapter 3: Development of Real-Time Mobile Sensing Platform24sources from the browser or back to the originating server, which acts as a proxy forthe third-party content.

We have developed a 2D and 3D visualization prototype system byimplementing map APIs from various open source Javascript libraries and Google
Earth Plug-in (Figure 3.3). The prototype system could evaluate the grid population
density at a point of time and animate them to illustrate the flow patterns of people
in the city throughout the day. The dynamic activities in each urban spot could beanalyzed as a time signature in a daily and monthly based graph.

Various statistics

options are implemented to investigate, compare and highlight the hidden intensityof each urban space.

Figure 3.4

A prototype system with temporal control and time based analysis

We applied Google Earth Plug-in that is a subset of the Google Earth 3Dgraphics rendering engine with KML support for 3D visualization and analysis. At thetime we implemented this system, the plug-in has not yet supported two KML’s timeelements: TimeStamp and TimeSpan which are derived from TimePrimitive. We haveto create a separate KML file and display it on a frame by frame basis (Figure 3.4).

Chapter 3: Development of Real-Time Mobile Sensing Platform

25

Figure 3.5

Illustrate “Pulse of a City” using Mobile Sensing platform, a browser
based prototype 3D system with animated flow patterns

3.2.4

Data manipulation

This section will explain the process by which we first simulated a connection
with a mobile operator in our local environment instead of communicating directly
to the mobile operator system. The mobile log data during the period from February
to April 2008 that cover a part of the central Bangkok area were transformed andinputted into the database.

The data established in the database mainly include

cell-id, the base station geographic position, update time and Erlang data (Table 1).
The Erlang data which is calculated from the call duration are performed as asample distribution in order to estimate the population density of the whole area.

The study area is covered by 2 Base Station Controllers (BSC) which arecomposed of 50 base stations and approximately 150 attached cell antennas. The
data derived from BSC generally update in hourly intervals.

Basically, more than

100,000 of records are fed into the database in one month. We analyze this sample
data to see how people experience their city.

Chapter 3: Development of Real-Time Mobile Sensing Platform

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CellidLatitudeLongitude

Start time

ErlangAPGA1

13.75697

100.5594

2008/03/01 9:00

33.98

APGA2

13.75697

100.5594

2008/03/01 9:00

18.93

APGA3

13.75697

100.5594

2008/03/01 9:00

33.17

ARWG1

13.75138

100.5402

2008/03/01 9:00

20.75

ARWG2

13.75138

100.5402

2008/03/01 9:00

17.93

ARWG3

13.75138

100.5402

2008/03/01 9:00

33.07

Table 3.1

3.2.5

Sample Data from the Base Station Controller (BSC)

Interpolation methods and population density

In order to present population data in a continuous space, we needinterpolation techniques to generate a surface from discrete points. There are many
interpolation techniques each with their own weaknesses and strengths. For
point-based interpolation, the typical examples are conditions based ongeostatistical concepts (Kriging), locality (nearest neighbours and finite element
methods, IDW, TIN), smoothness and tension (spline), or ad hoc functional forms(polynomials, multi-quadrics).

These have been discussed by several researchers

including Demers (2000), Mitas et al (1997, 1999) and Pariente (1994). In this paper,
we introduced an inverse distance weighted (IDW) and ordinary kriging method topredict population density in our prototype system. The IDW method uses a
distance-decay weighting function to determine weights of known centroids forpredicting values at positions where observations are not available. The kriging
method computes an empirical semivariogram based on observed data toestimate spatial autocorrelation of the interested variable. Based on the spatial
autocorrelation evaluated, estimates at unobserved positions can be predictedwith minimal kriging error.

As the number and distribution of sample points can greatly influence theaccuracy of spatial interpolation, we attempt to increase interpolated resolution by
calculating the weight in each cell separately instead of using the mean value. We
first generate voronoi tessellation over the discrete set of base station points. Since

Chapter 3: Development of Real-Time Mobile Sensing Platform27
the base station is a three-sector cells type, that is, each cell is transmitting in one
direction instead of broadcasting all around. (Figure 3.4) We divided each voronoi
cell by 120 degrees refer to the antenna installation direction.

At last, we

re-calculated the new antenna points from the centroid of the segment voronoi(Figure 3.5).

Figure 3.6

Mobile antenna, horizontal plane range

Source: www.matequipement.com

Chapter 3: Development of Real-Time Mobile Sensing Platform

28

Figure 3.7

Increase interpolated resolution by using voronoi tessellation and theangle of antenna. Left image present original coverage of base station and right image
present a sub sampling coverage by this method

CellidLatitudeAPGA1

13.75806

100.56073

2008/03/01 9:00

33.98

APGA2

13.75387

100.56028

2008/03/01 9:00

18.93

APGA3

13.75683

100.55761

2008/03/01 9:00

33.17

Table 3.2

Longitude

Start time

ErlangData modification with voronoi-based segmentation method

Chapter 3: Development of Real-Time Mobile Sensing Platform

29
3.3 Results and Discussions
We started by presenting our first results on querying the cumulative usage ofthe mobiles over an hour interval. Histogram and time series statistics could be
retrieved from specific locations on a map and displayed in a time-plot basedgraph. The first two types of graphs, day and month, were produced by
implementing a getStatistic webAPI in conjunction with Timeplot, a DHTML-based
AJAX widget.

The exploration leads to a better understanding of each area’s

activities during one day as well as the different characteristics between weekdays
and weekends.

In Figure 5(a), we picked up an office area in central Bangkok,

and the time plot shows the trends of increasing activities from early morning up to
the peak at noon then decreasing gradually after 5.00 pm.

Figure 5(b) and (c)illustrate the overall activity on a monthly basis. We could capture a weekly rhythmof this area which clearly defines high activities on the weekdays and appears todecrease on Saturday and Sunday. Figure 5(c) presents a pulse in April: we can see
a week of flat low activities since it was a long holiday in Thailand.

(a)

(b)

(c)
Figure 3.8

Day and month statistics from cumulative mobile usages data

3.3.1 Evolution of urban activities

Another approach which can be employed to explore the data is to generate
a surface flow pattern by interpolating the aggregate call traffic. Exploratory
analysis of temporal data can give a clear view on how people flow into and out of
the city throughout the day. Figure 6 shows the flow patterns in one local area from6.00 am. until 8.00 pm. This observation leads to the speculation on how one part ofthe central city is upscale, crowded and how long the area keeps busy until people

Chapter 3: Development of Real-Time Mobile Sensing Platform30
move to another part of the city. It is extremely useful for the urban planner to figureout how types of land use, street networks and other urban landscapes could affectthe flow and density of the city. Furthermore, results of the study not only provide a
new tool for area or zoning analysis but also could be used to specify hidden
problems of the particular space over a period of time.

Figure 3.9

3.3.2

The flow pattern from early morning to late evening in central Bangkok

Abnormally detection

In order to clearly highlight extreme values in distribution surfaces, we generate
the volume of call density with an overlay contour diagram using a specific color
palate.

In Figure 3.9, to illustrate how mobile density could reflect the real world daily
activities, we capture Pathumwan area. This is one of the most active spots in
Bangkok that has a mixed land use, for example high rise office buildings and alarge-scale shopping complex. If we compare the same period of time at 1.00 pm.
on Friday and Sunday, the result demonstrates that on Friday the activities aredense at the office area and in contrast, a peak density moves toward the
shopping area on Sunday.

Chapter 3: Development of Real-Time Mobile Sensing Platform

31

Figure 3.10

Comparison of density contour in Pathumwan area on
Friday and Sunday at 1.00 pm.

Obviously enough, this kind of hot spot extraction could be useful to capturesome hidden aspects in the urban space. We are planning to collect a longer spanof archive data and make a base urban signature in order to implement a real time
signature recognition and hot spot extraction.

3.3.2 Activities patterns and Land Use Classification

In general, land use could be roughly determined from the intensity of usage.
Low-intensity uses are those that preserve some of the natural features of their sites,
involve little construction, and require few public services and facilities, parks andfarms, for example. High-intensity uses require major alteration of their sites, a lot ofbuilding and a maximum of supporting services and facilities, shopping centers andhigh-rise apartments, for instance.

The footprints from mobile phone activities could also be presented as a new
approach to extract urban land use information from the usage intensity andpatterns of use in each area at different timeframes during a day. It is easy to
imagine that the activities of people in residential areas and business areas would
be different since people regularly go to work or to school in the morning and returnhome in the evening.

Chapter 3: Development of Real-Time Mobile Sensing Platform32
We selected four points from different land use on the map which represent theresidential area, the business area, the shopping center and the university
respectively. In figure 8 we calculated a mean value with an hour interval in one
month. The results clearly describe the unique characteristics of each urban spacein term of human activities. The residential area considers increasingly more
activities from the evening until late night (Figure 8-1). On the other hand, thebusiness area keeps overcrowding during the day and gradually release after theoffice hour (Figure 8-2). The Shopping center tended to have a significant
increasing of activities after 11 am. after the shops open (Figure 8-3). And the
University has less activity in any time of the day because of the data source has
been collected during March-April which is the semester break period in Thailand(Figure 8-4).

(8-1)

(8-2)

(8-3)

(8-4)

Figure 3.11

The 24 hour graph images illustrates the urban signaturein specific land use

Since the study area covers only the center part of Bangkok, it lacks someinteresting findings from other kinds of land use such as the night club area, low andhighincomehousingarea,

national

event

placesand

someimportanttransportation nodes such as park and ride. A study on the broader area would
leads to a better understanding of each urban land use by evaluating the usage
intensity and temporal signature of mobile activities.

Chapter 3: Development of Real-Time Mobile Sensing Platform

33

Figure 3.12 Individual mobile phone signature in one day

Chapter 3: Development of Real-Time Mobile Sensing Platform

34
3.4

Future perspective
In this paper, we have presented the concept design, implemented andillustrated the evolution of city by using the mobile sensing platform. We areplanning to extend the study area to other cities as well as include additional
sensing data from the public domain services.

The initial results of this paper only

presented the analysis of urban activities from the mobile phone usages. In the
future, we plan to analyze the flow of the city with real time traffic data and urban
environmental information from mobile sensor networks. We would like to make theplatform as flexible as possible in order to integrate with the public web services. Thismeans the system would not only be used for the data visualization but also data
analysis and processing tasks.

Using mobile phones as sensing devices and aggregating “crowdsourced”
data for urban analysis is still in the early phases of concept development. Mobile
sensing can potential paint a complex and dynamic portrait of the urban
environment in which users are based.

Acknowledgements
We would like to thank Advanced Info Services PLC for a support of mobile
data and Spatial Dimension Solutions (SDS) for a support of base map and Ikonos
satellite images.

Chapter 3: Development of Real-Time Mobile Sensing Platform

35

Chapter 4
TELEMATIC BEHAVIOR:
Identifying Human Daily Activity Pattern
Using Mobile Phone Data

4.1 Introduction
Dynamics of human mobility is essential for urban planning and transportation
management. Besides geographic space, in this chapter, we characterize mobility
in a profile-based space (activity-aware map) that describes most probable activity
associated with a specific area of space. This, in turn, allows us to capture theindividual daily activity pattern and analyze the correlations among different
people’s work area’s profile. Based on a large mobile phone data of nearly one
million records of the users in the central Metro-Boston area, we find a strong
correlation in daily activity patterns within the group of people who share acommon work area’s profile. In addition, within the group itself, the similarity inactivity patterns decreases as their work places become apartThis chapter aims to explore to what extent people working in different part ofcities and towns has similarity in daily activity patterns by analyzing their individual
mobility pattern from the mobile phones usages. For better understanding of theeffects of human movement, characterizing human mobility patterns is crucial. For
example, without such characterization, the impact of inhabit dynamics in the city
cannot be understood. As spatiotemporal and geo-referenced datasets are
growing rapidly because of the daily collection of transaction data throughdatabasesystems,

networktraffic

controllers,sensor

networks,

andtelecommunication data from mobile phones and other location-aware devices,
the large availability of these forms of data allows researchers to bettercharacterize human mobility. The additional information of activities associatedwith human mobility further provides a unique opportunity to better understand thecontext of human movement, and hence better urban planning and management.

Chapter4: TELEMATIC BEHAVIOR: Identifying Human Daily Activity Pattern Using Mobile Phone Data

36This chapter, we develop the activity-aware map, which provides information
about the most probable activity associated with a specific area in the map. With
the activity-aware map and an analysis of a large mobile phone data of nearly one
million records of location traces, we are able to construct the individual daily
activity patterns. This allows us to carry out a correlation analysis of work area’s
profile and similarity in daily activity patterns.
4.1.1 Related work
A rapidly increasing number of mobile phone users has motivated researchers
from various fields to study its social (Turner et al., 2003; Nickerson et al., 2008; Liu etal., 2010) and economic (Kauﬀman, R.J., 2005; Giray et al., 2009; Li & McQueen,2008) impact. With the extensive records of mobile phone data such as calling
pattern and location of the mobile phone user, analyses have been performed on
numerous aspects including behavioral routine (Eagle et al., 2006), social proximity(Clauset & Eagle 2007), call prediction, social closeness (Phithakkitnukoon et al.,2007), and human mobility. (Azevedo et al., 2009; Lee et al., 2009; Candia et al.,2008; Gonzalez et al.,2008 )Understanding dynamics of social networks is beneficial to urban planning,
public transport design, traffic engineering, disease outbreaks control, andemergency response management. To study dynamics in human mobility, GPSreceiver has been handy for researchers in collecting large real-life traces. Azevedo
et al. study pedestrian mobility behavior using GPS traces captured at Quinta da
Boa Vista’s Park in Rio de Janeiro (Brazil). Movement elements are analyzed from
120-pedestrian collected data. They find that the velocity and acceleration
elements follow a normal distribution while the direction angle change and thepause time measure fit better to lognormal distribution. Based on 226 daily GPS
traces of 101 subjects, Lee et al. develop a mobility model that captures the effectof human mobility patterns characterized by some fundamental statistical functions.
With analytical and empirical evidence, they show that human movement can be
expressed using gaps among fractal waypoints. (Rhee et al.,2008) People are more
attracted to more popular places.
With a large set of mobile phone data, Candia et al. study spatiotemporalhuman dynamics as well as social interactions. They investigate the patterns inanomalous events, which can be useful in real-time detection of emergency

Chapter4: TELEMATIC BEHAVIOR: Identifying Human Daily Activity Pattern Using Mobile Phone Data

37
situation. At the individual level, they find that the interevent time of consecutive
calls can be described by heavy-tailed distribution, which is consistent with theprevious reports on other human related activities. Gonzalez et al. examine
six-month trajectory of 100,000 mobile phone users and find a high regularity degree
in human trajectories contrasting with estimation by Levy flight and random walkmodels. People tend to return a few frequent locations and follow simple repeatedpatterns despite the diversity of their travel history. The most recent study in humanmobility based on a large mobile phone data by Song et al., whose result is
consistent with Gonzalez et al.’s that human mobility is highly predictable. Based ondata from 50,000 mobile phone users, they find that predictability in human mobility
is independent of distance that each individual regularly travel and show that thepredictability is stabled at 93% for all regular traveled distances of more than 10km.
In contrast with other work in human mobility, this work is focusing on humanmobility concerning the spatial profile (i.e. type of space or surrounding area suchas dinning, shopping, and entertainment) rather than geographical location.

4.2

Methodology
A number of literature have described geographical human mobility patternconcerning movement of people between multiple locations. Here we areinterested in characterizing the mobility not by geographic location but its
associated spatial profile. This spatial profile-based mobility pattern, in turn,
becomes a human activity pattern. In addition, our interest expands to investigationof relationship between this activity pattern and demographic of people. Therefore,
in this section, we will describe our methodology used in characterizing space,capturing daily activity pattern, as well as preprocessing our dataset.

4.2.1 Data Preparation
In this part, we use anonymous mobile phone data collected during the period
from July 30th, 2009 to September 12th, 2009 by Airsage Inc. of about one million
users in the state of Massachusetts, which account for approximately 20% of
population, equally spread over space. This includes 130 million anonymous
location estimations in (latitude, longitude)-coordinates, which are recorded when
the users are engaged in communication via the cellular network. Specifically, theChapter4: TELEMATIC BEHAVIOR: Identifying Human Daily Activity Pattern Using Mobile Phone Data

38
locations are estimated at the beginning and the end of each voice call placed or
received, when a short message is sent or received, and while internet is connected.
Note that these location estimations have an average uncertainty of 320 metersand median of 220 meters as reported by Airsage Inc. based on internal andindependent tests. For our analysis, we consider the mobile phone data within anarea of 33x42 km2, which includes 52 cities (Boston, Cambridge, and others) in the
county of Essex, Middlesex, Suffolk, and Norfolk as shown in Fig. 1. The list of thecounties and their corresponding area covered (in km2) by this study are shown in
Table 4.1.

CountyTable 4.1

Area Covered (km2)Essex

110.30

Middlesex452.52

Suffolk

154.39

Norfolk

26.12

List of the counties and their area covered by this study

Within this area in the map, we need to extract mobility traces of each userfrom the mobile phone data. As the estimation of the user’s location is aggregatedonly when network connection is established, mobility thus can be derived as atemporal sequence of locations. To segment these traces into trajectories so thatdaily mobility pattern of each individual can be indentifies, we describe here somebasic algorithms to extract trajectory and stop.
Let Xk denote a set of sequential traces of user k such that Xk = {xk (1), xk (2), xk(3), ...} where xk (i) is a position i of user k. A trajectory can then be obtained bysegmenting Xk with the spatial threshold △S. If a distance between adjacentpositions is greater than the threshold ( distance (xk (i), xk (i + 1)) > △S), then theearly position xk (i) becomes the end position of the last trajectory while the later
position xk (i+1) becomes the starting position of the next trajectory. Once theChapter4: TELEMATIC BEHAVIOR: Identifying Human Daily Activity Pattern Using Mobile Phone Data

39
trajectories are detected, a stop can be identified as an event during which theuser stays in a specific location for a sufficiently long period of time. As each position
i contains location and timestamp, i.e. xk (i) = (lat(i), long(i), t(i)), extraction of a stop
depends on time and space. A stop is thus regarded as a sequence of positions{ x(j), x(j + 1), x(j + 3), ..., x(j + m) } where the distance between any adjacentpositions is less than a spatial threshold Sth i.e., distance(x(j), x(j + 1)) < Sth , and timespent within the location is greater than a time threshold Tth i.e., t(m)Figure 4.1

t(j) > Tth .

Area of study, cropped by yellow line

After stops have been identified, work location of each user is then estimatedas a most frequent stop during the day hours. The information about work location
allows us to derive the mobility choices of the users, and detect activity patterns
throughout the day.

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40
4.2.2

Spatial Profiling
To model the space, we construct a virtual grid reference by dividing the map

into square cells of size 500 meters by 500 meters. Since our interest is in the activities
associated with the space, we thus characterize space based on the type ofactivities expected to be performed within given space. For example, if restaurants
were clustered within a particular area, then this area would be associated witheating activity. In this study, we consider four different human activities in whichpeople typically spend time engaging on daily basis. These activities areconcerning eating, shopping, entertainment, and recreational. Profiling the map
according to these activities requires information about the types of places withineach cell. To acquire the information regarding these activities, we search for Points
of Interest (POIs) for each cell location. We use pYsearch (Python APIs for Y! searchservices) version 3.1 for POI search service, and Reverse Geocoding with Geopy (AGeocoding Toolbox for Python) for translating (latitude, longitude) coordinate intoa physical address. For each activity category of each cell, we make three searchattempts using different keywords. The keywords used for each activity category
are listed in Table 4.2. With the limit of 5,000 queries per day restricted by Yahoo, an
extensive amount of search time is required inevitably.

Table 4.2

Activity

Keyword used

EatingRestaurant, Bakery, Coffee shop

Shopping

Mall, Store, Market

Entertainment

Theater, Bowling, Night club

Recreational

Park, Gym, Fitness

Considered activities and keywords used for POIs searchOnce POI searches are completed, the number of POIs associated with eachactivity category is recorded for each cell. The raw activity distribution map is thencomposed of 500x500 m2 cells where each cell contains distribution of each activity.
Each cell Ci contains normalized portion of each activity:

Chapter4: TELEMATIC BEHAVIOR: Identifying Human Daily Activity Pattern Using Mobile Phone Data

41

Ci = [αi (1), αi (2), αi (3), αi (4)](1)

where i = 1, 2, 3, ..., N, N is the total number of cells, and normalized portion ofeach activityα i (a) in cell i is computed as

α i (a) =nαi ( a )

∑ nα
N
i =1

(2)
i (a)

where α i (a) denotes the number of POIs associated with activity a within thecell i and a = 1, 2, 3, 4 corresponds to eating, shopping, entertainment, andrecreational activity, respectively.
Based on our POI search, Figure 4.2 shows a map with the visual grids and POIs
found by 12 different keywords (described in Table 4.2) in different colors. To further
classify these cells into a more crisp distribution map, we apply k-means algorithmwith k=4. The resulting crisp activity distribution map is depicted in Fig. 3 where eachcell is classified to one of the four activities according to Bayes theorem:

P(a | nαi ( a ) ) =P(nαi ( a ) | a) P(a)(3)

nαi ( a )The interest here is to find the most probable activity category a for each of the
k clusters. Therefore, for each cluster, we find a that maximizes a posteriori (MAP
method). So we use Bayes theorem above to compute the posterior

Chapter4: TELEMATIC BEHAVIOR: Identifying Human Daily Activity Pattern Using Mobile Phone Data

42

Figure 4.2

POI search results on the map with 500x500 m2 visual grids.

probability of each activity category as follows:

aMAP ≡ arg max P(a | nαi ( a ) )a= arg maxaP(nαi ( a ) | a) P(a)nαi ( a )= arg max P(nαi ( a ) | a) P(a).
a(4)

Chapter4: TELEMATIC BEHAVIOR: Identifying Human Daily Activity Pattern Using Mobile Phone Data

43
4.3

Daily Activity Patterns
Generally, people perform different activities throughout the day. A lot of these
activities are repeated on daily basis, e.g. eating around 12pm (noon), jogging in
the evening, and hence producing recognizable patterns. With our mobile phonedata, each user is more likely to engage in an activity during “stop” rather than on
the move. Therefore, for each stop, activity is identified according to the crisp
activity distribution map.
To infer a daily activity pattern for each user, we divide 24-hour time scale into
eight 3-hour segments starting at 5AM as shown in Figure 4.4. So, daily activity
pattern is simply a sequence of activities performed by the user during each stop
throughout the day. For each user, daily activity patterns are collected over thecourse of the data collection period. Note that, in this study, we consider only
weekdays (Monday, Tuesday, Wednesday, Thursday, Friday) as our speculation isthat weekday pattern is different from weekend pattern due to typical work

Figure 4.3

Crisp activity distribution map.

Chapter4: TELEMATIC BEHAVIOR: Identifying Human Daily Activity Pattern Using Mobile Phone Data

44
schedule and hence different daily activity sequences – this will be addressed
and further discussed in our future work.

Figure 4.4

The eight 3-hour temporal windows are used to frame
the daily activity pattern.

To derive the representative daily activity pattern of each user, we simply assign
each segment with the most frequent activity during that time interval over theperiod of data collection. Precisely, ifλad(t) represents the count of activity a on

d-th day during time segment t (where t = 1, 2, 3.., 8), thenMz (t ) = arg max ∑ λad (t )
a

(5)

d =1

where z(t) is the assigned activity for time segment t and M is the total number of
days.

4.4

Work Area’s Profile and Similarity in Daily Activity Patterns
The activity map and individual daily activity patterns developed in theprevious section allows us to conduct a number of studies that can be useful for
better understanding of human behavior in the city. In this present research, we areparticularly interested in relationship between people’s daily activity patterns andthe characteristic of their work area. Do people who work in the same area’s
category (e.g. eating, shopping, etc.) also have similar daily activity patterns? With
the same type of work area, how does distance impact the similarity in their daily
activity patterns (e.g. do people who work in an urban shopping area have similar

Chapter4: TELEMATIC BEHAVIOR: Identifying Human Daily Activity Pattern Using Mobile Phone Data

45
activity pattern with people who work in a distant shopping area)? In this current
study, we are attempting to answer these two questions.
As a first step, we classify the users into four groups based on their work cell’s
profiles. Each group then consists of a number of different individual daily activity
patterns who have a common work cell’s profile. To represent each group’s activity
pattern, we need to find a group signature for further correlation analyses. The
representative daily activity pattern or signature of each group can be obtained ina similar fashion with the individual patterns described in the previous section (using
Eq. (5)). The derived signatures are shown in Table 4.3.

Table 4.3

Group

Group’s daily activity pattern

Eating

W–W–W–W–Sho.–Rec.–Rec.–Sho.

Shopping

W–W–W–W–Rec.–Rec.–W–W

Entertainment

Sho.–W–W–W–W–Rec.–Sho.–Sho.

Recreational

W–W–W–W–W–Sho.–Sho.–Sho.

Signature of each group based on work cell’s profile. Note: Eat. = Eating,

Sho. = Shopping, Ent. = Entertainment, Rec. = Recreational, W = Work cell.

It can be noticed that there is no Eating element appears in any of other group
signatures beside its own group (showing in form of a working activity, W). Our
speculation is that it could be caused by first, people normally eat at home(breakfasts) and at work or somewhere nearby workplace (lunches), and second,people are not frequently involved in a phone communication while at eating area.
Note also that the patterns are derived from weekday’s activities so ifweekends-only activities are considered, Eating elements could emerge in thegroup patterns.
To answer the ﬁrst question, we need to measure similarity in daily activity
patterns among individuals within the same group as well as among other groups.
To measure distance (dissimilarity) between two daily activity patterns, we use
Chapter4: TELEMATIC BEHAVIOR: Identifying Human Daily Activity Pattern Using Mobile Phone Data

46
Hamming distance, which is normally used to measure distance between two
strings of equal length. The distance is essentially the number of positions at which
the corresponding symbols are different, which is quite suitable for our case as aseries of activities can be considered as symbols. The result of the average
Hamming distance within the group is shown in Table 4.4.

Work cell’s profile

Average distance

Eating4.78

Shopping

2.58

Entertainment

4.67

Recreational

3.61

Table 4.4

Average within-group distance.

Using group signatures obtained earlier, we then measure dissimilarity between
each group signature and other group’s individual patterns. The result of thisbetween-group distance is shown in Table 4.5 in forms of average Hamming
distance.

Eating

Shopping

Entertainment

Recreational

Eating

-

6.53

6.60

6.96

Shopping

4.90

-

4.92

5.05

Entertainment

6.43

6.88

-

7.00

Recreational

5.04

4.81

5.13

-

Table 4.5

Average between-group distance.

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47
As the result of our first investigation, Figure 4.5 illustrates a bar plot intended tomake a comparison between within-group and between-group distances whereorangebars

represent

within-group

distance

while

blue

bars

represent

between-group distance. Clearly, it shows that within-group distances are less thanbetween group distances. This implies that people who have a common work cell’s
profile tend to exhibit more similar daily activity patterns than people who havedifferent work cell’s profile.

Figure 4.5 When users are grouped together based on their work cell’s profiles,
within group and between-group distances are illustrated with red and blue bars
respectively. This shows higher degree in similarity within the group than between
groups.

For the second investigation about the impact of physical distance on thesimilarity in activity patterns, we decide to proceed by placing a growing spatial
window (a circle of an arbitrary radius) onto the map then measure similarity
between the users’ activity pattern whose work cell located at the center of thewindow and other users whose work cells are within the vicinity of the spatial
window. The similarity is being measured while the radius of the window grows froma small to larger value. The process is repeated for each activity category. This way,
we can see the change (if any) in similarity for each work profile as we move awayfrom the center area. Precisely, we choose to grow the spatial window from thecenter of Boston area with the radius varying from 0.5km to 30km. The result for each

Chapter4: TELEMATIC BEHAVIOR: Identifying Human Daily Activity Pattern Using Mobile Phone Data

48
work category is shown in Figure 4.6. We can observe that, overall, the similarity inactivity patterns decreases as radius increases, which implies that physical distancehas an impact on similarity in daily activity patterns. People whose work area’s
profile are although the same, their activity patterns tend to deviate more as theywork areas become further apart. In summary, we have observed a strong
correlation in daily activity patterns within the group of people who share acommon work area’s profile. Addition, within the group itself, the similarity in activity
patterns decreases as the distance between them increases.

Figure 4.6 Dissimilarity in daily activity patterns is measured by average Hamming
distance as the radius varies from 0.5km to 30km for each work cell’s profile. The
center of the growing radius is near the center of city of Boston. Dissimilarity is
between the users whose work cells are within the 0.5km radius and other users
covered by growing radius.

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49
4.5

Conclusions
In this chapter, we have developed an activity-aware map that contains mostprobable activity associated with a specific area in the map based on POIs
information. With activity-aware map, we are able to extract individual daily activity
patterns from analyzing a large mobile phone data of nearly one million records.
Results from our correlation analysis show a strong correlation in daily activity
patterns within the group of people who share a common work area’s profile. In
addition, within the group itself, the similarity in activity patterns decreases as the
distance between them increases. This study is the first report of many more tocome in using activity-aware map to study inhabitant behavior. So as our future
direction, we will continue to investigate on daily activity pattern and its dynamics
for better understanding of human dynamics, which in turn benefits urban planningand management.
Limitations of the Study
There are a number of limitations of this study. First and foremost, the lack ofcontinuity of mobility traces due to the fact that the location is estimated frommobile phone data only when connection with a cellular network is made throughvoice, text, or data communication, which constricts us to a smaller number of usersthat can be analyzed. Secondly, our POI search is constrained by Yahoo’s search
limit and capability. Lastly, home and work locations are estimated intuitively
according to the data provided. Although ground-truth validation is desired, itwould be very difficult to perform due to the privacy issue.

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50

Chapter 5
Calling Pattern and Call Simulation of Crowd Movement

5.1 Introduction
Sensing is going mobile and people-centric in a network enabled society.
Urban motilities are recognized from the footprint of the usages of mobile phone.
This research introduces how mobile devices can be employed as a new urban
sensor and how mobile activities can be represented as dynamic data sources for
population estimation. The paper aims to evaluate the presence of People from the
Cell Phone Trace Analysis. We demonstrate this assumption by using crowd
simulation approach. We first analyze the calling patterns and the mobility patternsof the individual to create a user model. The simulated trajectories are generated
from a group of the most active users and therefore are selected in proportion to
the number of population in census tract. The random call patterns are assigned toindividual agents to formulate the mobile activities in the simulation space. The
simulated results express the call volume and the existing of caller location at the
point in time. This paper illustrates how the digital footprint from mobile devices canhelp understand the population distribution of the area in today's excessive
dynamic society.

Populations of the cities are intermittently dynamic changing as people
commute from place to place, technical speaking across two separate
geographic boundaries. The question in estimating population density in hightemporal resolutions has reached unprecedented levels, and is therefore the focusof the present study. In this chapter, the usage of mobile devices was treated as a
medium for data collection. The fundamental tools have been developed tovisualize the mobile phone activities and urban mobility in spatio-temporal space.
We calculate the estimated ambient population base on crowd simulation
approaches by utilizing a novel data source of mobile phone activities and other

Chapter 5: Calling Pattern and Call Simulation of Crowd Movement

51
ancillary information. The results formulate aggregate call volume and estimatedpopulation density. This paper illustrates how digital footprint from mobile devices
can potentially be used as an indicator to estimate the population distribution.

The main purpose of this chapter is to examine the population count at a hightemporal resolution. The key problem is how to acquire and estimate the number of
population in the whole city space. In this paper, the usage of mobile devices wastreated as a medium for data collection. The call distribution in mobile
communication network was applied as aggregate data sources to estimate thepopulation density. Previous studies had suggested that erlang measures atparticular cells seem to be a decent indicator of actual presence of people.
(Horanont and Shibasaki, 2008; Reades et al., 2007) However, erlang data itself
could not yield the existing of people who are not using the mobile phone.

It is therefore not surprising that considerable research has gone in this new andinteresting direction, in particular, developing techniques to bring out and explore
large scale human mobility. Marta C. Gonzalez et al. observed the trajectory of
100,000 anonymized mobile phone users whose position is tracked for a six-month
period. The results had shown a high degree of temporal and spatial regularity,each individual being characterized by a time-independent characteristic travel
distance and a significant probability to return to a few highly frequented locations.
This is describing the recurrence and temporal periodicity inherent to humanmobility.

We often find that data sensed and incorporated in real-time offers additional
value to their analysis and simulation models. Live geospatial data provides animmediacy and relevance to applications in domains such as situational awareness,population dynamics, transportation, and evacuation planning.

The previous

paper (Lieu 2003) described that dynamic approach offers an effective strategy to
migrate static application systems to more real-time data-intensive applications
which are often the domain of GIS models and users.

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52
Consequently we address the following questions: Would modification of
calling activities be confirmed to correspond to actual population in the area? And
would land use affect to the consistency between number of call and actual
population? We have scheduled the research into two phases. First is to extract
calling patterns from a million of mobile phone users collected from July to October
2009 in Massachusetts. Second, we developed a platform to simulate a virtual
urban system and call activity to test the population prediction assumption and find
how much confidence in estimating the population density by using only
anonymous mobile phone traffic based on simulation approach.

5.2 Call patterns analysis
5.2.1

Mobile phone traffic and population distribution

In the fourth quarter 2009, there is over 4.12 billion people subscribed to mobile
phone communication networks that mean nearly two-third of the world population
own the mobile phone today. And the increasing reach of mobile networks creates
an unprecedented opportunity to describe the existing of the entire population
especially in the big city where the subscriber percentage is high.

In general, we collect the data from Base Station Controller (BSC) which is apart of Radio Subsystem in mobile communication. When people use their mobile
phone, their estimated position and time will be logged into BSC database. Our
primary assumption remark that there is a possibility to estimate population numbers
from the mobile traffic and also we could possible predicts flow and movement ofpeople in the entire city from everyday routine use of the mobile phone. Hence, it isnecessary to confirm a correlation between mobile phone CDRs and population
density.

The initial exploration has been done by mapping the night time mobile phoneuser density (figure 5.1) comparing with the census data in tract level. (figure 5.2)
The visualization result generally confirms a good match of two dataset.

Chapter 5: Calling Pattern and Call Simulation of Crowd Movement

53

Figure 5.1 Night time cell phone users density, calculated at the census tract level(generally one square mile)

Figure 5.2 Population densities, calculated at the census tract level (generally one
square mile)

Chapter 5: Calling Pattern and Call Simulation of Crowd Movement

54
5.2.2 Call patterns extraction methods

The call traffic pattern is an ideal source in designing a calling behavior of eachagent in the simulated space. First, by observing the features of the database andthen extracting the attributes needed to be mined. Basically Airsage data composeof 3 specific types of cell phone traffic that is while placing a call, receiving a calland using the data connection package. Figure 5.3 illustrate overall distribution ofcall and incoming call of one million observations in Massachusetts.

(a)

(b)

Figure 5.3 Distributions of outgoing call (a) and incoming call (b) in one month.
While y axis is population count in log scale and x axis is frequency of call in log
scale.

Each single activity comes with the estimated location of the mobile device,method of estimation, approximated distance error and time stamp. Data from thespecific group are then summed in an hour interval and examined to assemble acall traffic pattern. The results illustrate how people in Massachusetts behave inusing their mobile phone in normal weekdays. We set up the overall process offinding and interpreting patterns from the data by the following steps;

Selection

We developed criteria by first studying a simple conceptual overview of mobile
phone activities. We selected a trial by subset a thousand of samples from the

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55
entire users collected in October 2009. The result set were examined separately
base on activity group, namely call, receive and data connection activity. The
selected procedure had been conducted by plotting the number of mobile
activities against the number of people who generate the activity (Figure 5.4). Since
we want to make sure that the subset is fully represented to the entire dataset,therefore we have to select them in fair distribution as possible. A straightforward
way to cope with this problem is by using binning techniques in which the sample isreduced in resolution to a sufficient degree to ensure that a given peak remains in
its bin.

Figure 5.4

Illustrate number of calls and callers

Preprocessing

We aggregate the call activities by an hour and calculate the mean usage ofeach type in one month. As we focus on the normal working day simulation, thedata on weekends and national holidays will be discarded. We then derived thedata by formulating the calculation as discussed below.

For each user k, the mean usage for each hour slot t where t = 1,2,3,…,24 canbe computed as

Chapter 5: Calling Pattern and Call Simulation of Crowd Movement

56

xk (t ) =1 Ni d∑ xk (t )Ni i =1(1)

Where Ni is the total number of days in experiment and hence d denotes day(d = 1,2,3,...,Ni ). Such that mean usage of user k can be expressed as a series ofxk (t ) for t = 1,2,3,...,24 as

X k = {xk (1), xk (2), xk (3),..., xk (24)}(2)

The data which contains a missing value or zero activity in each category wasfiltered out before process to the next step.

Data mining

Data mining methods are algorithms designed to analyze data or to extractfrom data patterns into specific categories. In this study we use K-means
unsupervised clustering approach. The clustering methods repeatedly run to
discover the satisfy patterns in appropriate range of sum of squared errors. Figure 5.5
shows five categories of call clustering produced by k-means in heat map andfigure 5.6 illustrate individual graph of each cluster.

Interpretation

This section we discuss on the clustering results, three sets of graph indicate theactivity patterns of call, receive and data connection are placed at the end of thissection. First, we interpret the call activity; figure 5.7 illustrate the clustering of call
activity by K-means. We repeat the algorithm to classify it into 4, 5 and 6 clusters andthe results demonstrate that the 5 clusters seems to be a decent one. Surprisingly,
nearly 60% of people in Massachusetts have very low average call in the weekdays.
We further check with the raw data and found out that 15% of population only uses
a mobile phone for receiving call and text. Apart from that, 25% of the population
has as average call only once a day. The similar results also take place in the caseof receiving call and data connection.

Since the 60% of the population is large,

Chapter 5: Calling Pattern and Call Simulation of Crowd Movement

57
we continue investigate this cluster to find out whether there is any difference
characteristic in this low activity group. Figure 5.8 reveal the secondary clusteringresults. The finding yield that more than 50% of this group has very low average callat all time and 12% of population has a possibility to make a call during the day, 11%of the population has peak call in late afternoon and 7% of this group has more callfrom late afternoon to evening.

Figure 5.5 The data mining results after clustering using K-Means method

We are focusing on the calling patterns as it directly describe the user behavior
rather than receiving call and data connection in order to generate the user modelfor the simulation system.

Chapter 5: Calling Pattern and Call Simulation of Crowd Movement

58

Figure 5.6 (a): 59% of thesamples have very less callat all time.

Figure 5.6 (b): 24% of thesamples have average one
call during the day

Figure 5.6 (c): 12% of thesamples have peak call in
the afternoon

Chapter 5: Calling Pattern and Call Simulation of Crowd Movement

59

Figure 5.6 (d): 3% of thesamples have peak call inthe evening

Figure 5.6 (e): 2% of thesamples create very highcall activity during the day

Figure 5.6 Characteristic of 5 calling patterns in Massachusetts, most of people
tends to have less call at all time.

Chapter 5: Calling Pattern and Call Simulation of Crowd Movement

60

Figure 5.7

Figure 5.8

Calling patterns with 5 clusters

Subsequence profile patterns of the main cluster

Chapter 5: Calling Pattern and Call Simulation of Crowd Movement

61
Time

c1

c2

c3

c4

c5

1

0.041

0.444

2.46311.84080.22552

0.00750.05550.4581

0.4862

0.056430.00540.02490.29620.28690.0315

40.00280.02820.20040.44620.015

50.00210.03620.11690.33690.013160.0101

0.11390.16230.43770.049870.03380.41330.1912

1.11770.165880.0664

1.14290.42772.37150.4496

90.1022

1.61950.3962

3.56850.6561100.13522.064

0.61463.90620.73

110.14082.206

0.85814.01920.8158120.16212.47461.28695.31080.8597130.16742.65871.52465.67150.924414

0.15362.596

1.28315.74230.966315

0.1892

3.0933

2.14356.42691.132316

0.214

2.91472.0246

6.4115

1.1621170.23742.92522.2212

7.53771.2629180.22372.7832

2.23737.7492

1.3907190.17762.57442.34

6.6362

1.0685200.14562.01152.28656.21540.876321

0.095

1.61242.14625.59850.7227220.06571.13882.61274.25540.508123

0.05

0.64192.60962.86620.301124

0.006

0.02680.13

0.18230.0174

Table 5.1

A k-means clustering was applied to the subset samples with k= 5
using the data mining software weka

K-Means
Scheme: weka.clusterers.SimpleKMeans -N 5 -A "weka.core.EuclideanDistance -R first-last" -I 500-S 10Relation:subset_nordist_1000_avg
Instances: 810
Attributes: 24
Number of iterations: 14
Within cluster sum of squared errors: 65.20075258546107
Missing values globally replaced with mean/mode
Clustered Instances0
480 ( 59%)1
94 ( 12%)2
26 ( 3%)3
13 ( 2%)4196 ( 24%)

Chapter 5: Calling Pattern and Call Simulation of Crowd Movement

62
5.3

The SIM Mobility

In order to test the assumption on measuring the call traffic as a determinantfactor to predict actual population numbers, we have developed “SIM Mobility” a2-dimensional spatial simulation system to simulate movement and virtual call
activity of the entire city. The Sim Mobility take the particles or swarm simulation
approach which each particle is considered as an individual entity and theinteractions between it and the other particles are individually modeled according
to their physical movement and their calling patterns. We start testing ourassumption by limit the simulation space with in Suffolk County. The simulated
trajectories were derived from the generalizing mobile phone location point data ofnearly one million records in central Metro-Boston area. (figure 5.9)

Figure 5.9 The simulated trajectories deriving methods. Dots referred to themobile phone activities of the sample user where the green lines connectedconsecutive points. The red square defined as stop area of this user.

Please note that there is a limitation on lack of continuity of mobility traces dueto the fact that the location is estimated from mobile phone data only whenconnection with a cellular network is made throughcommunication,
be analyzed.

which

constricts

ustoeithervoice, text,

or

data

a smaller number of users that can

Besides the user models which perform the calling functions aredefined as;
Chapter 5: Calling Pattern and Call Simulation of Crowd Movement

63
Let {x1, x2, x3,..., xN} be a set of unclassified agents, {y1, y2, y3,..., yN} denote a
set of classified agents, and fi ( ) represent the population distribution function ofclass i where i = 1,2,3,4,5 and N is the total number of agents. The unclassified agentsare designated to each class according to the population distribution, that isyk = f i ( xk ),(1)

Where xk and yk are unclassified and classified agent k, respectively.

For each population-based classified agent, the call volume per day is to be
assigned corresponding to call distribution function derived from the actual callvolume, i.e.

g i = qi (t ) ±σ (t ),Where gi(t) is the call volume per day during t time slot, qi(t) is the call
distribution function,

denotes the standard deviation of call volume of time slot t,

and t = 1,2,3,...,24 represents hourly time slot. The sign ± alternates according toBernoulli distribution of p = 0.5, such that half of agents are assigned withthan the expected value (mean) and the other half is assigned togreatergreater thanthe expected value.

The mobile phone usage activities are generated by replicating calling functions
from random given call patterns. Figure 5.10 show the flow diagram of SIM Mobility
system, demonstrate how virtual call traffics were simulated base on the simulated
trajectories and user models.

Chapter 5: Calling Pattern and Call Simulation of Crowd Movement

64

Figure 5.10 SIM Mobility flow diagrams

Chapter 5: Calling Pattern and Call Simulation of Crowd Movement

65

Figure 5.11 Capture of simulated call activities in Metro-Boston (Suffolk County)
at 4:00, 8:00, 12:00, 16:00, 20:00 and 24:00 on normal weekday

Chapter 5: Calling Pattern and Call Simulation of Crowd Movement

66
5.4 Results and discussions
The simulation results provide detail measurements of presence population andnumber of calls at 1 hour temporal resolution and 500 meters spatial resolution.
Figure 5.12 visualize the simulated results of center Boston at 10 am.

We question how much confident the call activity can be correlated with theexisting number of people in each coverage at all time.

We then simply repeating

run the simulation replicate the normal working day in one month and calculated
the mean call volume of each grid cell. In addition, to evaluate the degree ofconsistency, the correlation coefficient was computed to estimate how good thecall volume fit to the presence population. Figure 5.13 show calculated R2 in anhour time slot of a complete weekday from Monday to Friday. The result explainsthat call activity from mobile phone show a positive correlation during the day.
More frequent the mobile phone information occurrence a higher correlation we
can get.

Figure 5.12 Illustrate aggregate call activities of part of greater Boston in 500 by 500
meter grid cell at 10:00 am

A graph show a high correlation during the day with two distinctive peak in themorning and in the evening while the correlation drop significantly at night due tothe less mobile phone usage. As previous extensive research had confirmed that

Chapter 5: Calling Pattern and Call Simulation of Crowd Movement

67
mobile phone call volume has unique characteristic in each specific land use, we
further investigate whether different type of land use affect to the consistency
between number of call and actual population in the area. MassGIS 2005 land use
data was utilized to test our assumption; the land use data was converted into 500
meters grid in order to compare with the simulation results. (figure 5.14)

Figure 5.13 Correlation of actual population and call activities in one week
period from Monday to Friday

Land Use Code1

Land Use Description
Cropland

Detailed Definition
Generally tilled land used to grow row crops. Boundaries follow
the shape of the fields and include associated buildings (e.g.,barns). This category also includes turf farms that grow sod.

2

PastureFields and associated facilities (barns and other outbuildings)
used for animal grazing and for the growing of grasses for hay.

3

ForestAreas where tree canopy covers at least 50% of the land. Bothconiferous and deciduous forests belong to this class.

4

Non-Forested
Wetland

DEP Wetlands (1:12,000) WETCODEs 4, 7, 8, 12, 23, 18, 20, and 21.

5

MiningIncludes sand and gravel pits, mines and quarries. The
boundaries extend to the edges of the site’s activities, including
on-site machinery, parking lots, roads and buildings.

6

Open Land

Vacant land, idle agriculture, rock outcrops, and barren areas.
Vacant land is not maintained for any evident purpose and it
does not support large plant growth.

7

Participation
Recreation

8

Spectator
Recreation

Facilities used by the public for active recreation. Includes ballfields, tennis courts, basketball courts, athletic tracks, ski areas,
playgrounds, and bike paths plus associated parking
lots. Primary and secondary school recreational facilities are inthis category, but university stadiums and arenas are considered
Spectator Recreation. Recreation facilities not open to the public
such as those belonging to private residences are mostly labeledwith the associated residential land use class not participation
recreation. However, some private facilities may also be
mapped.
University and professional stadiums designed for spectators aswell as zoos, amusement parks, drive-in theaters, fairgrounds,
race tracks and associated facilities and parking lots.

Chapter 5: Calling Pattern and Call Simulation of Crowd Movement

68
9

Water-Based
Recreation

10

Multi-Family
Residential

11

14High Density
Residential
Medium Density
Residential
Low Density
Residential
Saltwater Wetland

15

Commercial

16

Industrial

17

Transitional

18

Transportation

19

Waste Disposal

20

Water

23

Cranberry bog

Both active and recently inactive cranberry bogs and the sandy
areas adjacent to the bogs that are used in the growing process.
Impervious features associated with cranberry bogs such as
parking lots and machinery are included. Modified from DEP
Wetlands (1:12,000) WETCODE 5.

24Powerline/Utility

25

Saltwater Sandy
Beach

Powerline and other maintained public utility corridors andassociated facilities, including power plants and their parking
areas.
DEP Wetlands (1:12,000) WETCODEs 1, 2, 3, 6, 10, 13, 17 and 19

26

Golf Course

12
13

Swimming pools, water parks, developed freshwater andsaltwater sandy beach areas and associated parking lots. Also
included are scenic areas overlooking lakes or other water
bodies, which may or may not include access to the water (suchas a boat launch). Water-based recreation facilities related touniversities are in this class. Private pools owned by individual
residences are usually included in the Residential category.
Marinas are separated into code 29.
Duplexes (usually with two front doors, two entrance pathways,
and sometimes two driveways), apartment buildings,
condominium complexes, including buildings and maintained
lawns.
Note: This category was difficult to assess via photo interpretation,
particularly in highly urban areas.
Housing on smaller than 1/4 acre lots. See notes below for details
on Residential interpretation.
Housing on 1/4 - 1/2 acre lots. See notes below for details on
Residential interpretation.
Housing on 1/2 - 1 acre lots. See notes below for details on
Residential interpretation.
DEP Wetlands (1:12,000) WETCODEs 11 and 27.
Malls, shopping centers and larger strip commercial areas, plus
neighborhood stores and medical offices (not hospitals). Lawn
and garden centers that do not produce or grow the productare also considered commercial.
Light and heavy industry, including buildings, equipment andparking areas.
Open areas in the process of being developed from one land
use to another (if the future land use is at all uncertain). Formerly
identified as "Urban Open".
Airports (including landing strips, hangars, parking areas andrelated facilities), railroads and rail stations, and divided highways(related facilities would include rest areas, highway maintenanceareas, storage areas, and on/off ramps). Also includes docks,
warehouses, and related land-based storage facilities, andterminal freight and storage facilities. Roads and bridges less than
200 feet in width that are the center of two differing land use
classes will have the land use classes meet at the center line of
the road (i.e., these roads/bridges themselves will not be
separated into this class).
Landfills, dumps, and water and sewage treatment facilities suchas pump houses, and associated parking lots. Capped landfills
that have been converted to other uses are coded with theirpresent land use.
DEP Wetlands (1:12,000) WETCODEs 9 and 22.

Includes the greenways, sand traps, water bodies within thecourse, associated buildings and parking lots. Large forest
patches within the course greater than 1 acre are classified as
Forest (class 3). Does not include driving ranges or miniature golfcourses.

Chapter 5: Calling Pattern and Call Simulation of Crowd Movement

69
29

Marina31

Urban
Public/Institutional

34

Cemetery

35

Orchard36

Nursery37

Forested Wetland

38

Very Low Density
Residential
Junkyard

39
40

Brushland/Successio
nalInclude parking lots and associated facilities but not docks (inclass 18)
Lands comprising schools, churches, colleges, hospitals,
museums, prisons, town halls or court houses, police and firestations, including parking lots, dormitories, and university housing.
Also may include public open green spaces like town commons.
Includes the gravestones, monuments, parking lots, road
networks and associated buildings.
Fruit farms and associated facilities.
Greenhouses and associated buildings as well as any surrounding
maintained lawn. Christmas tree (small conifer) farms are alsoclassified as Nurseries.
DEP Wetlands (1:12,000) WETCODEs 14, 15, 16, 24, 25 and 26.
Housing on > 1 acre lots and very remote, rural housing. See notes
below for details on Residential interpretation.
Includes the storage of car, metal, machinery and other debris as
well as associated buildings as a business.
Predominantly (> 25%) shrub cover, and some immature trees notlarge or dense enough to be classified as forest. It also includes
areas that are more permanently shrubby, such as heath areas,
wild blueberries or mountain laurel.

Table 5.2 Land use code definitions

Figure 5.14 Area interpolations of MassGIS land use data, left show the original land use
polygon and right show the process data in 500 meters grid

We selected 6 major land use types out of 33 (Table 5.2) in our study area
includingMulti-Family

Residential,

High

Density

Residential,

Urban

Public/Institutional, Transportation, Commercial and Industrial. Individual land
use type show its distinctive characteristic where five of selected samples
suggest highly and positively correlate among call activity and presence
population during the day except for the industrial area. (Figure 5.15)
Transportation type which covers the major road and public transport giving a
Chapter 5: Calling Pattern and Call Simulation of Crowd Movement

70highest correlation with distinguish peak in morning and evening commuting time.
Commercial and Urban Public/Institutional are having fairly good translation of
calling traffic to population density in the daytime but giving the lowest correlation
at night. Besides, we found average correlation in High density residential area
during the day; however the nighttime correlations are significantly better than theother land use types. Furthermore, the Industrial areas in Suffolk are having slightly
difference interpretation where night time correlations are higher than in thedaytime.

In conclusion, this chapter has investigated the opportunity to use mobile
phone call detail records (CDRs) to evaluate how mobile phone activity correlate
to the actual presence population. The simulation results showed that higher mobile
phone activities yield a better correlation between calling records and presence
population. Cross-comparison analysis was undertaken against specific land use todescribe how similar and different correlations are formed in each land use types.
These findings indicate that land use and land cover play a role in shaping the
dynamics population estimation within the mobile context. Among the new ideathat emerged in this period, this study describes an important scenario in which
mobile devices with user-driven contents are fully changing the way of urban
mobility research and how to estimate the population dynamics.

Chapter 5: Calling Pattern and Call Simulation of Crowd Movement

71

Figure 5.15 Correlation of actual population and call activities by land use type

Chapter 5: Calling Pattern and Call Simulation of Crowd Movement

72

Chapter 6
PREDICTION OF THE CURRENT:
Integration of Mobile Phone calling records
and Person Trip Data

6.1 Introduction
The existing crowd flow simulation models fall into two major categories: those
based on space syntax theories, and those based on the vehicular micro-simulation
models incorporating origin-destination matrix. (Sharma, 2007) The space-syntax
based models have a unique strength in their ability to analyze the spatial geometry
quickly and generating valuable information about the configuration of space.
However it suffers from the exclusion of dynamic effects of flow that are driven by
the needs of people to go from A to B. The origin-destination based models arefundamentally micro-simulation models that provide for these dynamic effects.
These models provide the valuable information on interaction of agents anddensities as a function of time. However these models tend to be complex, both in
pre-processing and execution, and reliable information on space effectiveness isonly available at the end of a number of simulation runs.

The present work we are focusing on the techniques to effectively predict theprobability of human flow from analyzing coarse-grained mobile phone traces. Thisis different from ordinary GPS or roadside sensor for origin-destination flow estimationin traffic engineering. Unlike GPS and other sensor tracking data, mobile phone log
or Call Detail Records (CDRs) data provide uninformed report on locations and time.
However, the most beneficial in utilizing CDRs data is a large-scale of analysis canbe performed without install additional devices. The individual locations could bedetermined when the phones are actively activated, namely when the people
place or receive calls, sending text messages or using the internet.

Chapter 6: PREDICTION OF THE CURRENT: Integration of Mobile Phone Derived Time‐Dependent OD Matrix Model and Person Trip Data

73
In this chapter, we describe the design methodology used to produce statisticalmobility model from CDRs data, incorporated with Person Trip Survey (PTS) data toperform an estimation of day time population distribution in the study area. We first
analyze calling patterns and mobility patterns of the individual from the mobile
phone usage survey to create user behavior models. More detail discussions areplaced in the third section of this chapter. The simulated trajectories are generated
from PTS data through pre-process of place geocoding and trip mode associatedroute matching. A brief background of Person Trip and data preparation methods
will be discussed in the next section.

Section 4 will provide a methodology framework on how to virtually create
mobile phone traffic or Call Detail Records (CDRs) by using simulation.

The last partof this chapter describes the mythologies in using these mobile phone call records
to estimate dynamic population.

6.2 Tokyo Person Trip Survey: Tokyo as seen by movement of people
The Person Trip Survey (PTS) data has been used in several researches ontransportation and planning. It provides valuable information on demographic (age,sex and occupations), travel mode and trip purpose. The main use of PTS data in thisresearch is to generate substantial daily trajectories for the simulation system and forthe validation of estimated population results.

The Tokyo Person Trip Survey was conducted in the Tokyo Metropolitan region
including Tokyo, Kanagawa, Saitama and Chiba Prefectures and the southern
region of Ibaraki Prefecture. This is the arena for the lives and activities of the 34
million people. It is also the world’s biggest metropolitan area, playing a pivotal rolein the politics, economy and culture of Japan. The movement of people in theTokyo Metropolitan Region extends widely beyond the metropolitan region, andthe provision of suitable transportation to support the diverse activities of thesepeople must be studied as a wide-ranging issue that looks at the whole of themetropolitan region. The Tokyo Metropolitan Region Transportation Planning
Commission was established for this purpose. It has been engaged in mutual
cooperation and coordination between the prefectures within the Tokyo
Metropolitan Region and cities and relevant organizations specified by government
ordinance.

The

Commission’s

activities

began

with

the

first

large-scale

Chapter 6: PREDICTION OF THE CURRENT: Integration of Mobile Phone Derived Time‐Dependent OD Matrix Model and Person Trip Data

74
transportation fact-finding survey (Person Trip Survey - PTS) in 1968, followed by asecond survey in 1978, a third in 1988 and a fourth in 1998, in order to study
metropolitan transportation policy from a wide-ranging, comprehensive viewpoint.
The results of the surveys have also been widely used as basic data for solving
transportation issues in various localities. The 5th Person Trip Survey was conductedin October 2008, and an outline of the results has now been summarized.

Figure 6.1 The coverage area of Person Trip Survey in greater Tokyo region
Survey forms were collected from about 880,000 people who cooperated in thissurvey. Households are randomly selected and people who are 5 years or more are
surveyed. This represents a sample size of approximately 3%.

In principle, we areconsidering PTS data as the moving sampling of a person in simulated environment.
This has greatly benefited since the original survey purpose are described as follow;•"Person trip" refers to the movement of a person•

The Person Trip Survey aims to capture all movements on one day by
investigating "what kind of person" moved "when", "for what purpose", "fromwhere", "to where", and "by what modes of transportation".

•

The objective of the survey is to obtain an overall grasp and analysis of the

Chapter 6: PREDICTION OF THE CURRENT: Integration of Mobile Phone Derived Time‐Dependent OD Matrix Model and Person Trip Data

75
realities of transportation in the metropolitan region on the basis of these survey
data, and to explore suitable urban transportation systems for the metropolitan
region.

In addition, PTS is basically a person trip diary where the respondents enter thefollowing information for all the trips made in the day.

1. Trip start time
2. Trip origin
3. Modes of travel (e.g. train, bus, private car)
4. Trip purpose (e.g. work, shopping, education)
5. Trip destination
6. Trip end time.

Figure 6.1(b) Person Trip Survey describes overall trips of a person in one day

PTS data also provides detail information of commuting mode including
automobile, bus, two-wheeled vehicle, walking and train.

6.2.1 Preprocessing and data structure

Person Trip data were pre-processed before being GIS-mapped. Field address
data was first geocode to geographic position. Commuting modes and trip time
were used to determine stop points and further for route matching algorithms.
Furthermore, in order to work with the SIM Mobility, all traces were interpolated into
points with 1 minute interval. These interpolated points represent the existence ofpeople in certain time and hence help confirm the number of populations in thevalidation process. After that the interpolated points are ported into PostgreSQL, asophisticated open-source spatial database, which help manage this large

Chapter 6: PREDICTION OF THE CURRENT: Integration of Mobile Phone Derived Time‐Dependent OD Matrix Model and Person Trip Data

76
spatio-temporal dataset. Table 6.1 show the preprocess trajectories in tabularformat.

pid

pdate

longitude

latitude

sex

age

work

magfac

datum

1020

10:25:00

139.9096

35.72799

1

10

4

37

6

1020

10:26:00

139.9093

35.72864

1

10

4

37

6

1020

10:27:00

139.9081

35.72910

1

10

4

37

6

1020

10:28:00

139.9080

35.72990

1

10

4

37

6

1020

10:29:00

139.9083

35.73092

1

10

4

37

6

1020

10:30:00

139.9087

35.73112

1

10

4

37

6

1020

10:30:00

139.9087

35.73112

1

10

4

37

97

Table 6.1 the preprocess trajectories in tabular format.

6.3 The mobile phone usage survey
In order to simulate individual call activities, the user behavior models have tobe developed. Our interest in conducting this survey is to study the behavior of thephone user and therefore create calling patterns of each user groups by using data
mining techniques to identify characteristics or patterns found in our survey samples.
This section demonstrates the results of this survey to evaluate how people generate
their call and data traffics in a normal working day. The results are fairly consistent to
the major calling patterns we had extracted from the real CDRs data in Boston inchapter five. We conducted online mobile phone usage surveys which reported
quantitative results of calls, messages and internet uses of 1,063 people in the same
region that Person Trip surveys were accomplished. The survey samples wereselected consistent with the PTS data based on sample’s ages and occupations. A
summary of the results of the surveys is explained in this section. The original online
questionnaire can be obtained from the appendix I and the details analysis can beobtained from the appendix II. The following explain a summary of the general
results of the surveys in bulleted lists.

•

100% of the samples have mobile phone.

•

8% of the samples have more than 1 phone.

•

6 % of the samples use smart phone. (ie. iPhone、BlackBerry)

Chapter 6: PREDICTION OF THE CURRENT: Integration of Mobile Phone Derived Time‐Dependent OD Matrix Model and Person Trip Data77•

66% of the samples use fixed rate service package.

•

45 % of the samples spent 2,000-5,000 yen a month for mobile phone bill and 38%
spent 5,000-10,000 yen, 7%spent more than 10,000 and 10% spent less than 2,000.

•

Nearly 60% of the samples never turn the mobile phone off, 20% turn it off in
specific event such as in the theater, concert hall, etc. And 10% turn it off during
sleep, 7% during work and 5% turn the mobile phone off when they commuting
on the train.

•

62% of the samples have mobile phone with them most of the time and 30% stayclose with mobile phone 24 hours.

•

Surprisingly the most frequent use of the mobile phone is email/text service at
37%, following with internet service at 18%. It was reported that only 13% havemost frequent use their mobile for calling purpose. Also, they use their mobile tobrowse internet and email more often than making a call.

•

35% of the samples making a call only few times a week, 26% making call onceor twice a day and 25% of the samples are calling with their mobile phone few
times a month. Only 12% of the samples reported that they are making call 3-6
times a day and 4% make more frequent call than 6 times a day.

•

63% of the samples have average talk about 1-5 minutes, 19% had reported of
5-15 minutes and 10% have average talk less than 1 min.

•At the work place or school, it was reported that 21% of the samples use theirmobile phone at the office only few times a month and 19% never use theirmobile phone at work. 15% use their mobile phone at work few times a week,
13% use once or twice a day and 6% use 3-6 times a day.

•During commuting to work or school, 31% report that they never talk on thephone and 19% talk over commuting few times a month. 13% talk few times aweek and 9% talk 1-2 time(s) a day. Please note that 25% of the samples neednot to travel on a daily basis.

•

At home, 35% of the samples make a call with their mobile phone only few times
a month, 31% few times a week, 21% once or twice a day and 6% placing call
3-6 times a day. Also 5% they never talking on the phone with their mobile phone
at home.

•

It was reported that people more frequent talk on the phone at home, when
walking on the street, at the shop or department store and at the office.

•

Email and internet services have the same usage trend, people seem to use
more frequent at home, during commuting with public transportation and when
they are at the station.

Chapter 6: PREDICTION OF THE CURRENT: Integration of Mobile Phone Derived Time‐Dependent OD Matrix Model and Person Trip Data78•

60% of the samples tend to use mobile phone when they are stuck on the roador public transportation.

•

34% of the samples tend to use mobile phone during participating the events,
nevertheless 55% tend to not use at this point.

•

45% of the samples tend to use mobile phone in case of accident or disasteroccurred and 28% tend to not use.

In conclusion, above summaries help confirm the potential use of analyzing
mobile phone CDR as an indicator to describe the population in dense urban
environment. The last part of this questionnaire particularly focuses on quantitativeuse of mobile phone services in a normal working day. We built user behavior
models from statistic count of call and email record. Figure 6.2 - 6.6 show the overall
results of this survey categorized by area, age, sex, occupation and activity types(call, email and internet) of 1,063 samples in 24 hours.

Since different age groups behave differently and male and female also
behave differently. In chapter 4, we demonstrated the use case of using mobile
phone CDR as location-aware devices to analyze the activities of each work area’s
profile. The results explain the correlation of work area’s profile and similarity in daily
activity patterns.

Figure 6.2 Accumulated call activity by area in 24 hours normalized by number ofobservations

Chapter 6: PREDICTION OF THE CURRENT: Integration of Mobile Phone Derived Time‐Dependent OD Matrix Model and Person Trip Data

79

(a)

(b)

(c)

Figure 6.3 Accumulated call(a), email(b) and internet(c) activity by age groups

Chapter 6: PREDICTION OF THE CURRENT: Integration of Mobile Phone Derived Time‐Dependent OD Matrix Model and Person Trip Data

80

(a)

(b)

(c)

Figure 6.4 Accumulated call(a), email(b) and internet(c) activity categorized by gender

Chapter 6: PREDICTION OF THE CURRENT: Integration of Mobile Phone Derived Time‐Dependent OD Matrix Model and Person Trip Data

81

Figure 6.5 Accumulated call, email and internet activity categorized by activity types

Chapter 6: PREDICTION OF THE CURRENT: Integration of Mobile Phone Derived Time‐Dependent OD Matrix Model and Person Trip Data

82

Figure 6.6 Overall accumulated mobile phone activities during one business day

It is clear that the overall mobile phone activities are significant low. From our
examination, one issue is that some samples had not replied this question and we
judged those to be outliers. Note that, in the experiment we discard simulated
internet activity since less than 50% of the samples replied in this part. Figure 6.3 and6.4 show the accumulated call and email activities of each career groups in 24
hours.

We further calculate an average call and email activities by occupationgroups. We consider that occupation groups offer better results in classifying user
behavior patterns more than age and sex. The average call and email patterns ofeach slot t (hour) in any occupation types. Note that the can be computed as

X (t ) =1 n∑ X i (t )n i =1

Where t = 1,2,3,…,24
and n is the total number of samples exclude the outliers

Table 6.2 present calling patterns of each specific occupation groups in 24
hours.

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83

Unemployed
Housewife and househusband
Student
Other
Management
Specialist
Office worker
Security service workers
Transportation and communication
Service industry
Sales
Technological and production worker
MiningAgriculture/forestry/fishery
0

500

1000

1500

2000

2500

Figure 6.7 Accumulated call activities of each career groups in 24 hours

Unemployed
Housewife and househusband
Student
Other
Management
Specialist
Office worker
Security service workers
Transportation and communication
Service industry
Sales
Technological and production worker
MiningAgriculture/forestry/fishery
0

1000

2000

3000

4000

5000

6000

Figure 6.8 Accumulated email activities of each career groups in 24 hours

Chapter 6: PREDICTION OF THE CURRENT: Integration of Mobile Phone Derived Time‐Dependent OD Matrix Model and Person Trip Data

84

T

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

1

1

3

3

9

12

10

3

3

5

5

3

6

5

1

5

1

1

2

0

2

0

1

3

7

8

6

4

5

5

4

4

3

2

4

4

2

2

1

0

3

1

1

0

0

7

1

3

4

3

6

4

3

3

4

1

0

0

0

0

4

1

1

3

3

4

2

3

4

1

1

3

2

3

4

3

3

3

1

0

5

0

1

1

4

3

4

5

2

1

2

1

2

4

3

3

1

3

2

0

6

1

1

1

7

9

4

6

2

1

4

4

3

3

2

3

3

1

1

0

7

0

2

1

4

5

1

3

3

3

4

2

3

3

3

3

1

2

2

0

8

1

3

2

3

0

0

10

1

2

2

1

1

1

2

2

6

3

5

0

9

1

1

1

3

4

3

2

2

2

3

1

3

3

3

2

2

2

0

0

10

2

0

5

3

0

0

0

3

2

4

2

3

2

1

0

0

1

2

2

Table 6.2 Average Call activity of each occupation typeType 1: Agriculture

2: Mining

3: Skill worker

4: Sales

5: Service industry

6: Transportation

7:Protective service

8: Business

9: specialist

10: Manager

11: Other

12: Student

13: Housewife

14: unemployed

Type

h1

h2

h3

h4 h5

h6

h7

h8

h9 h10

h11

h12

h13

h14

h15

h16

h17

h18

h19

h20

h21

h22

h23

h24

Chapter 6: PREDICTION OF THE CURRENT: Integration of Mobile Phone Derived Time‐Dependent OD Matrix Model and Person Trip Data

85
0

0

0

0

0

0

9

9

0

0

0

9

0

0

0

0

9

9

9

0

0

9

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

12

0

0

6

0

6

0

0

0

3

15

12

0

6

3

3

0

0

0

0

0

0

0

0

5

10

16

7

5

5

12

3

3

7

2

5

13

12

4

5

6

4

2

0

1

0

0

0

0

2

5

9

8

9

9

10

6

5

9

2

5

11

7

7

8

8

5

1

1

0

0

0

0

0

3

4

8

3

1

1

21

4

4

1

4

1

8

10

8

5

4

5

0

0

0

0

0

0

0

0

0

0

0

0

0

0

55

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

2

8

15

5

3

4

13

4

4

5

3

6

12

10

10

8

9

6

1

0

0

0

0

0

0

2

7

12

8

6

6

18

3

3

4

3

6

7

7

9

8

8

6

1

0

0

0

0

0

0

2

13

10

5

7

6

10

4

2

7

3

7

7

11

12

6

6

4

0

0

0

0

0

0

0

2

10

11

5

9

6

11

4

4

6

10

13

11

9

9

8

6

10

0

0

0

0

0

0

0

2

13

18

11

10

19

15

8

5

8

8

15

11

13

17

21

13

11

1

0

0

0

0

0

0

2

5

7

12

9

4

7

7

6

5

6

4

8

12

6

7

5

4

0

0

0

0

0

0

0

1

6

8

7

11

7

4

8

7

4

4

12

7

6

4

7

6

6

0

0

Table 6.3 Average Mail activity of each occupation typeType 1: Agriculture

2: Mining

3: Skill worker

4: Sales

5: Service industry

6: Transportation

7:Protective service

8: Business

9: specialist

10: Manager

11: Other

12: Student

13: Housewife

14: unemployed

Note: Pattern probabilities above are maximized by a distribution over 20 days

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86
6.4 Methodology
6.4.1 Data preprocessing and system configuration
The preprocess PTS data contain 1.26 x 109 rows in total. Since the data is large,
we subset and use the most active area in central Tokyo to test our hypothesis andproposed algorithms. The main Tokyo 23 wards are considered as a simulated
environment. (Figure 6.9) We random select 30,000 samples out of 880,000 as
observations and use the total PTS data as a ground truth value. The assumption ofthis experiment is that if the subset samples of 30,000 people could predict the
dynamic day time population of 880,000 people then it is feasible to use the same
method to predict the real world population from the original PTS data. We finallyevaluate how close the sample estimate matches the true value in the whole PTS
population.

Figure 6.9 Area of study cropped by Tokyo 23 wards.
We have developed the Grid-based mobile phone simulation called “SIM
Mobility”. The system utilize the preprocess PTS data as simulated trajectories of theindividual agents and apply the calling patterns derived from section 6.3 togenerate mobile phone activities as user behavior models. Figure 6.10 illustrate thegrid based simulation scenario with initial configuration of 250 meters grid as spatial
resolution and use 15 minutes interval to accumulate the mobile phone activities as
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87
temporal resolution.

Figure 6.10 Grid dimension of the simulation system
Apart from simulated grid dimension, we configure using 1 kilometer uniform
grid to represent mobile phone base station in the simulated environment. In Urban
and sub urban the mobile based station can vary from few hundred meters to few
kilometers, figure 6.11 giving an example, illustrates the uniform mobile base stationand its coverage.

Figure 6.11 Uniform mobile base station, in denser area the coverage may reduce
to few hundred meters however the larger grid can be applied for suburb area

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88First, we use pre-process PTS trajectories and behavior model to simulate mobile phone
activities, followed by post-processing methods as illustrated in Figure 6.12.

Figure 6.12 Structure and procedural steps of simulation system

6.4.2 Simulation of call traffic
We use discrete event simulation in order to analyze the distribution of mobile
phone activities. Sim Mobility assigns a unique numerical ID when each agent is created.
The call behavior patterns are assigned to each individual agent based on theoccupation attribute data. To generate the call activity, a uniform random probability isused to determine whether a call will be occurred regarding the present underlying
distribution of each calling patterns. A uniform random number from 0 to 20, UR[0, 20], isfirst obtained. The call occurs if the result number is lying within the distribution function
displayed in table 6.2 and 6.3. A generated call timestamp would be the solution of t forR(T, t) = UR[0, 60]. Besides, the calling probability is computed based on spatial locationand event; specifically no call will be made when the transportation mode is motorbike
or bicycle. The same approach is repeated for every agent for 24 hours. In thisexperiment, we continued simulate the weekday activities for one month, which is 20
times running task had been carried out in our preliminary test.

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89
Let {x1, x2, x3,..., xN} be a set of PT agents, {y1, y2, y3,..., yN} denote a set of classified
agents(base on occupation), and fi(.) represent the population distribution function ofoccupation class i where i = 1,2,3,…,15 and N is the total number of agents.

yk = fi ( xk ),Where xk and yk are PT and classified agent k, respectively.

gi (t ) = qi (t ) ± σ (t ),where gi(t) is the call volume per day during t time slot, qi(t) is the call distributionfunction,denotes the standard deviation of call volume of time slot t, and t =5,6,7,...,24
Visualization of the simulated call information can play a big role in our
understanding of urban mobility and human flow. Preliminary simulation results areparsed to interpolated module developed in chapter 3 to the grid interpolation images.
Figure 6.13, 6.14, 6.15 display a capture of mobile phone usages in Tokyo’s 23 words in
one day.

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90

Figure 6.13 A SIM Mobility prototype system illustrates Tokyo activity at 05:00 am.(upper
left ), 12:00 am.(upper right), 6:00 pm.(lower left) and 12 pm.(lower right)

Figure 6.14 Illustrate call activities in simulated grid config at 250 x 250 meters

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91

Figure 6.15 (a) Interpolation of mobile phone activities at morning rush hours (6-8 am.)Figure 6.15 (b) Interpolation of mobile phone activities at morning office hours(10-12 am.)Figure 6.15 (c) Interpolation of mobile phone activities at afternoon office hours(1-3 pm.)

Figure 6.15 (d) Interpolation of mobile phone activities at evening commuting time
(6-8 pm.)

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92

Figure 6.16 Visualization of mobile phone activities at morning commuting time overlay
on map with train network

Figure 6.17 Visualization of mobile phone activities at 10 am. on normal business day
overlay on map with train network

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93
6.5 Time-dependent dynamic OD weight modification
The theoretical framework of the time-dependence based Origin destination(O-D) matrix analysis is discussed in this section. In general, O-D matrices are notobservable unless the vast majority if people can be tracked on a continuous basis,
which become reality with the increasing penetration of mobile phones. The
fundamental approach is to utilize mobile phone traffic as real time flow indicator tomodify magnification weight factor in order to reconstruct the population density inthe area. In the previous section, we discussed how to simulate mobile phone
activities from the pre-process PTS trajectories and call behavior model, thefollowed section will explain the post-processing methods, how to generate the O-D
matrix from mobile phone CDRs and how to utilize generated O-D matrices for
estimating population density.

The original Person Trip Survey (PTS) data come with a magnification factor
which is a ratio used to reconstruct real number of population in the area. (Table
6.1) However, range of estimated error for the total population becomes large
when person move cross the zone in the day time. Table 6.4 present the estimatedpopulation with PT magnification factor and the true value. Note that, the numbersof population are calculated from counting the person who exists in specific gridcell and in specific time frame. In the following case, we use Japan mesh level 3 (1 x
1 kilometer) grid cell with time interval of 15 minutes.

code

True Value

PT magnify

53393514

1188

2320

53393515

865

920

53393516

1437

1956

53393517

927

1665

53393518

2340

4109

53393519

832

1263

5339352085

24153393521

582

622

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94

Table 6.4

53393522

1080

1606

53393523

1809

3669

53393524

1749

3888

Real population and estimated population at one square kilometer
grid and 15 minutes time frame.

Where code is the 1x1 kilometer grid id from Japan mesh level 3, True value isthe estimated population count from the entire 880,000 people of original PT
data, PT magnify is the estimated population count from the subset 3 % of PT
data and multiply by its magnification factor.

Since the mobile phone activities are related to the actions of people andthereby to the overall dynamics of cities, how they function and evolve over time,
meaning that it represented stochastic mobility patterns of the entire population in
the city. (In contrast to PTS data that only give mobility patterns of the samples). In
this case, our assumption is that by combining the mobility patterns from the mobile
phone, we could improve the accuracy of estimated population at the certaintime.

In order to understand the flow of the city, the first question is to reconstruct theindividual trace from the CDRs data. However, the frequency of simulated daily call
activities is less we then simply demonstrate this methodology by applying it toestimate day time population. The optimal strategy for generating flow in thiscontext is to evaluate home and work locations in order to create trip O-D matrix inthe study area. A likely location of home place is estimated from a simple modelwith two criteria measurements. First is the frequency of call and another is theregularity of revisit to the observed places. In this experiment, we define theresolution of estimated home-work locations by using 1 x 1 kilometer grid and use
time frame of 5 - 8 am. in the morning and 10 – 12 pm. at night to calculate homelocations. The time between 10 am to 4 am is considered to calculate work
locations.

The computation is defined as;

Chapter 6: PREDICTION OF THE CURRENT: Integration of Mobile Phone Derived Time‐Dependent OD Matrix Model and Person Trip Data

95nHome location =max ∑ H ij (t ) * n −1

(1)

i =1

Where:
Hij = Number of call at base station locationt = Expected time at home (5,6,7,22,23)
n = Number of simulation days (20 days)nWork location =max ∑ Wij (t ) * n −1

(2)

i =1

Where:
Wij = Number of call at base station locationt = Expected time at work (10,11,12,13,14,15)
n = Number of Simulation days (20 days)

Figure 6.18 show home and work location from above methodology link withstraight line connecting home-work nodes. In our framework, to evaluate dynamic
population estimation, the Origin-Destination (O-D) matrices provide a crucial input
as a city-wide mobility indicator. Since a dynamic O-D matrix describes theaggregate demand for trip interchange between specific zones of the network
over a series of time intervals. We further aggregate the process home–work nodes
from grid-level to larger geographic zone, for instance, Tokyo wards area in ourcase.

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96

Figure 6.18 Home and work location of samples in Tokyo 23 wards
Consider a network composed of G nodes, H and W be the amount of origins
and destinations from and to which samples are allocated and P be the amount of
O-D pairs traversed by flows. Eh denote the number of samples departing from origin
h to destination w during estimation interval T ∈τand contributing to the flows
traversing links during count interval τ∈t , where T is the study period that typicallyrefers to the business hours period. Then, the dynamic O-D matrix estimation at aninterval T can be expressed as:

The resulting O-D matrices are used as input to a Dynamic Time-dependent O-D
matrix population estimator for mapping the estimated zone mobility into a set ofpath and link flows. The first set of origins and destinations defined based on theestimated home-work locations is illustrated by figure 6.18

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97

Figure 6.19 A day time O-D mapping graph of Tokyo 23 Words
The above O-D mapping graph is considered as time-dependent relationshipof travel demand and link volumes between zones, as defined by the fractions ofeach extracted home-work pairs, which constitute the assignment matrix.

6.5.1 Population reconstruction

The fundamental concept of population reconstruction was explained bymagnification weight factor in the Person Trip Survey. We therefore are proposing
an approach to calibrate this method by using O-D Matrix Adjustment. The O-D
matrix adjustment gives logic on the use of simplified rules to represent the
movement and interaction between zones. Then modification of magnification
weight is accomplished by weighted mean function. A sample O-D matrix thatprovided in Table 6.5 and 6.6 describe link flow from and to Chiyoda-ku, one ofward area in Tokyo.

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98h_jcodew_jcodeoutflow

13101

13113

2

13101

13109

2

13101

13108

2

13101

13101

20

13101

13103

4

13101

13106

1

13101

13104

1

13101

13102

2

Table 6.5 O-D matrices explain link flow from Chiyoda-ku

Whereh_jcode = home administrative area
W_jcode = work place administrative areah_jcodew_jcodeinflow

13104

13101

2

13105

13101

5

13106

13101

3

13107

13101

2

13108

13101

2

13109

13101

6

13110

13101

2

13111

13101

3

13112

13101

8

13113

13101

1

13114

13101

3

13115

13101

6

13116

13101

2

13117

13101

4

13118

13101

2

13119

13101

6

Chapter 6: PREDICTION OF THE CURRENT: Integration of Mobile Phone Derived Time‐Dependent OD Matrix Model and Person Trip Data

99
13120

13101

5

13121

13101

5

13122

13101

3

13123

13101

6

Table 6.6 O-D matrices explain link flow to Chiyoda-ku

Whereh_jcode = home administrative area
W_jcode = work place administrative area

6.5.2

O-D weighted modification function

The new approach using O-D weighted modification function is based on
estimated weight of interchange population group in the area. This caused toreduce the estimated error by changing the weights of each individual in thespecific zone through the existing mobility. To reduce complexity, we demonstrate
this algorithm by using a single area; the input–output pairs are listed in table 6.5
and 6.6

From table 6.5, the specific zone (jcode -13101) is composed of 14 outflow links
and 20 nodes still remain in this area. We first calculate weighted mean of eachgroup in this zone. Note that, group refers to the occupation groups and weightrefers to the magnification weight specify in Person Trip Survey data.

The weighted

mean can be computed by:X=∑ w ⋅x∑ wn
x =i iinx =i iWhere
wi represent the bounds of the partial sample. In this case, bound refer tosmaller zones used in computing home and work location, partial sample mean
outflow numbers of specific occupation group in the same zone.

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100
The process was repeated for every zone in the study area (23 wards). The
results give values of gradient weighted mean of each occupation group by zone.

We next calculate the inflow weighted mean of destination zone, refer to table
6.6, we use the same formulation as for the case described in previous step, that is;∑ w ⋅xX=∑ wn
x =i iinx =i i

Where:
wi represent the bounds of the partial sample. In this case, bound refer tosmaller zones used in computing home and work location, partial sample mean
inflow numbers of specific occupation group in the same zone.

For more informative, the formula can be rewrite as;

w a + w2 a2 + w3a3 + .... + wn aa
a= 1 1
w1 + w2 + w3 + ... + wn
Where
w=

inflow numbers from each of origin zones

a = weighted mean of an occupation group of origin zones
a = weighted mean of an occupation group of destination zone

To reconstruct population of each zone we use the result gradient weighted
mean from the previous step. In general, we can proceed multiple gradientweighted mean by applying specific time-dependent O-D matrices. For instance,
the same process could derive the gradient weighted mean at time t+1 if the O-D
matrices at time t are identified. In this example, we apply the results weighted
mean to estimate day time population since we merely process day time average
OD matrix (work place).

The population reconstruction can be computed as;

Chapter 6: PREDICTION OF THE CURRENT: Integration of Mobile Phone Derived Time‐Dependent OD Matrix Model and Person Trip Data

101
k

n

P = ∑∑ X j * xi
j =1 i =1

Where
P = estimated populationk = number of groups in the area (occupation)n = number of members in group

X i = gradient weighted mean of group j

6.5.3 Experimental results and conclusions

The comparison between the estimated population distribution and the real
distribution allows the assessment of the quality of this novel approach. The resulting
population distribution matches relatively well with the real distribution. This section
aims to present the findings of the experimental validation, and to address some
limitations and future improvement of this method.

Figure 6.20 show a quick demonstration of regression graph visually depicting
the relationship between the estimated population from the proposed method andreal population numbers in 250 meters grid of 23 wards area. The results correlation
coefficient showed that the estimated population had highly significant andpositive correlation with the real population numbers.

Figure 6.20

Plot of estimated population(y) and real population numbers (x).

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102
In order to examine the accuracy of the model, the Root Mean Square Error(RMSE) had been used. Table 6.7 shows the R2 and RMSE values at two
representatives, one is from the estimated population using PTS magnification factor
and the second is the estimated population using time-dependent O-D
modification methods.

The estimated results of the O-D modification methods may be compared tothose obtained using a previous PTS magnification factor. The correlation
coefficient and root mean square error (RMSE) are computed. Obviously enough,the proposed method show significant improve of prediction for all area in Tokyo 23
wards.

The correlation coefficient returned slight increases, while decreasing the

RMSE mean of all zone.

As O-D matrices explain the overall mobility trends, our assumption is that more
we can defined the O-D link between zones more the accuracy will be increased.
We further investigate the results by increasing the population size in simulated
space to 105 samples or about 12% of the total PTS population.

Again the results show a clear trend emerges in this test sample size, it was
giving a better estimated of R2 and RMSE. Figure 6.21, 6.22 illustrate graph in 15
minutes time slot from 5.00 to 24:00.

Chapter 6: PREDICTION OF THE CURRENT: Integration of Mobile Phone Derived Time‐Dependent OD Matrix Model and Person Trip Data

103

Figure 6.21

Plot of RMSE of the O-D modification methods(orange) comparewith previous Person Trip Survey method (blue)

Figure 6.22

Plot of R2 of the O-D modification methods(orange) compare withprevious Person Trip Survey method (blue)

Chapter 6: PREDICTION OF THE CURRENT: Integration of Mobile Phone Derived Time‐Dependent OD Matrix Model and Person Trip Data

104
PREF

WARD

J CODECITYNAME

EST R2EST STD ERR

EST R2(*)

EST STD ERR(*)

AREA

TOKYO

CHIYODA-KU

13101

千代田区

0.888424.50290.894923.75904

136

TOKYO

CHUO-KU

13102

中央区

0.909626.17430.915825.24061

113

TOKYO

MINATO-KU

13103

港区

0.882312.92391

0.886612.70453

242

TOKYO

SINJUKU-KU

13104

新宿区

0.967710.644817

0.972

9.902468

213

TOKYO

BUNKYO-KU

13105

文京区

0.799413.44848

0.8223

12.6389

131

TOKYO

TAITO-KU

13106

台東区

0.714516.74609

0.722916.43207

115

TOKYO

SUMIDA-KU

13107

墨田区

0.63529.626270.64629.50941

160

TOKYO

KOTO-KU

13108

江東区

0.61484.72715

0.63624.60034

426

TOKYO

SHINAGAWA-KU

13109

品川区

0.75899.620910.76069.59281

221

TOKYO

MEGURO-KU

13110

目黒区

0.632

9.12461

0.67228.81624

158

TOKYO

OTA-KU13111

大田区

0.72852.86724

0.73622.82886

739

TOKYO

SETAGAYA-KU

13112

世田谷区

0.64393.028170.658

2.97619

781

TOKYO

SHIBUYA-KU

13113

渋谷区

0.8616

14.63037

0.868

14.2843

183

TOKYO

NAKANO-KU

13114

中野区

0.7176

7.13603

0.72657.01375

176

TOKYO

SUGINAMI-KU

13115

杉並区

0.74643.34208

0.76233.23188

448

TOKYO

TOSHIMA-KU

13116

豊島区

0.925314.51910.928314.20364

147

TOKYO

KITA-KU

13117

北区

0.74775.99440.76855.73527

248

TOKYO

ARAKAWA-KU

13118

荒川区

0.527613.78381

0.5357

13.55524

113

TOKYO

ITABASHI-KU

13119

板橋区

0.7492

3.797830.74853.80382

418

TOKYO

NERIMA-KU

13120

練馬区

0.56952.826450.58212.7933652

TOKYO

ADACHI-KU

13121

足立区

0.60712.35355

0.62042.32141706

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105
TOKYO

KATSUSHIKA-KU

13122

葛飾区

0.60413.02952

0.611

3.00239

444

TOKYO

EDOGAWA-KU

13123

江戸川区

0.63182.39153

0.63412.38388648

Table 6.7 Average Call activity of each occupation type

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106
The results based on the correlation coefficients, Root Mean Square Error, andscatter plots tell us that the proposed model has a potential for further development
and using the mobile phone log data for estimating dynamic population in thedense city area is a promising application. However, this model is sensitive to thegiven total number of estimated O-D matrix which refers to the overall mobile
phone activities in the area thus needs to be identified as a requirement. Somelimitations are explained as follow;

Limitations
会 The simulation (experiment) has several limitations such as the user behavior
models were created from a survey of 103 samples and it lacks of frequent
generated activity users. The previous study suggested average traffic of ½ houras an appropriated rate to reconstruct the mobile phone trajectory.
解 From the experiment, we could not derived O-D matrix based on interval (t = 1hour), A better results from this algorithm can be expected if revisit points could
be captured in every time frame. This could be achievable by use either data
mining or machine learning method with larger extent or longer period of mobile
CDR data.

From the experiment, we give assumptions that the accuracy of estimatedpopulation will be increased if the specific conditions can be performed:

会

The accuracy will be improved if the population is large (the best scenario
is using the entire population)

解The accuracy will be improved if average area of calculated zone
decrease, for instance, from district level to sub district level or the mostmicro scale at base station level but the complexity of calculation will be
multiplied.

回

The accuracy will be improved if more specific O-D matrix can be
determined. For instance, the time-dependent O-D matrix can be
performed at suggested specific time frame:

06:00 - 09:00 as morning rush hours
09:00 - 12:00 as morning office hours

Chapter 6: PREDICTION OF THE CURRENT: Integration of Mobile Phone Derived Time‐Dependent OD Matrix Model and Person Trip Data

107
12:00 - 15:00 as afternoon (lunch, etc)
15:00 - 18:00 as late afternoon
18:00 - 21:00 as evening commuting time

From the initial experiment, we did test with limited numbers of population, anduse ward area as an appropriated zone size of estimated O-D matrix. The results
gave an estimated day time population of the area.

6.6 Data assimilation method
Data assimilation is a novel, versatile methodology for estimating variables. The
estimation of a quantity of interest via data assimilation involves the combination ofobservational data with the underlying dynamical principles governing the system
under observation. The melding of data and dynamics is a powerful methodology,
which makes possible efficient, accurate and realistic estimations which might nototherwise be feasible. In case of dynamic population estimation, one importantcomplexity is associated with the vast range of phenomena and the multitude ofinteractive scales of space and time.

6.6.1

Assimilation process

In this implementation, first, we give a state variable definition which holds avalue that represents the real population in the area. We utilize magnification
weight factor associated with PTS data as basic definitions in order to reconstruct
the population. An approximate dynamics which govern the evolution of the scaleof state variables are computed by applying the simulated mobile phone traffic.

In general, a data assimilation method consists of three components: a set ofobservations, a dynamical model, and a data assimilation scheme or melding
scheme. A set of observations is basically referred to the calling probability of eachbase station grid-cell. The algorithm giving the dynamic model is detailed in thefollowing steps. First we make an assumption with regard to the population
parameter that each calling records can be associated with the user types, forinstance occupation, sex, and age. By following this assumption, the PT data canbe taken it into account. Next, we pick up all PT trajectories that share the same
home place. By this we mean that the calling records can be compared within the

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108
same user types which represented in the same base station grid-cell. The callingprobability is computed by using a previously developed mobile phone simulation
platform called “SIM Mobility”. At each time step, we compare the computed
calling records with the observed calling records. If the computed calling records
are greater than the observed calling records, then we decrease the weights of all
trajectories that passing through the subsequent grid-cell. In contrast, if thecomputed calling records less than the observed calling records, then we increase
the weight of all trajectories. Figure 4 illustrate this approach in a simple scenario.

Figure 6.23 Configuration of the method, the selected samples share the same
home location

The quantitative basis of melding is the relative uncertainties of the observations
and the dynamics computed call probability. Thus, the aspect of assimilation
schemes does not degrade the reliable information of the observational data but
rather enhances that information content.

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109

Figure 6.24 Giving sample traces of each occupation types in difference colors
where all daily traces share the same home location (red square).

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110
The initial implementation was conducted by observing from 75000 samples
that live within Tokyo’s 23 wards area which user types covered the person who
work in sale, service industrial, transportation, service workers, business, technical,
manager, student, housewife and unemployed. A dynamic model used to improve
the state parameter was computed as:

Wk = f[ck, ok]
Where
W = weight magnification improvementc = Computed call probability at the associated base stationo = Observed call probability at the associated base stationk = time from 5 to 24 in an hour intervalf(.) denotes the linear model

The updates can be performed several times, either as several iterations at asingle time in order to enhance the smoothness of corrections, or as several
corrections distributed in time.

Regarding post validation, we use root mean square error (RMSE) to test theaccuracy of this approach by evaluating two different scenarios which is supposed
to take part and offer the key to distinguish the effect parameters. The two
configurations were configured like so;

Scenario 1:

Number of iterations

1.

20 days observation period

2.

40 days observation period3.

60 days observation period

Scenario 2:

Number of population

1.

Full population (100%)2.

50% of population

3.

25% of population

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111
6.6.2

Results and discussions

The RMSE of the estimates decreases significantly with this method at all
time. Assimilation process reduces the error more at high mobility stage during
7-8 am. in the morning and around 6 pm. in the evening. As for the first scenario,
the accuracy improvement after increasing the simulated iteration can be seen.
However, there was no major effect to the improvement of this estimation byincreasing the simulation date. (Figure 6.25)

Figure 6.25

Compare the RMSE results of the previous PT method and theassimilation method on different iterated scenario

a

a
.
.
.
.
.
.
.
.
.

Chapter 6: PREDICTION OF THE CURRENT: Integration of Mobile Phone Derived Time‐Dependent OD Matrix Model and Person Trip Data

112

.
.
.

Table 6.8

The results of the individual weight modification through thedata assimilation process

T

_ a

PT_ a

a
Da a_aTable 6.9

Compare the results of estimated population

We performed the additional test by changing the configuration of thenumber of population. The simulation results in the 2nd scenario described agreater degree of improvement when the observed population increase andusing the entire observation yield the best estimation at all time. (Figure 6.26)

Chapter 6: PREDICTION OF THE CURRENT: Integration of Mobile Phone Derived Time‐Dependent OD Matrix Model and Person Trip Data

113

Figure 6.26

Compare the RMSE results of the previous PT method and theassimilation method on different observed population scenario

In this chapter, we have developed new methodologies for population
estimation by integrating mobile phone calling records and Person Trip data. The
results suggest a considerable improvement of estimated accuracy over the priormethod giving by Person Trip Survey.

Our future direction is to provide broader

scenarios to cover a range of issues that currently limit the feasibility of using thisvaluable mobile phone data for this kind of research.

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114

Chapter 7
Conclusions and Future Prospect

7.1 Conclusions
This study explores the potential of using mobile phones in the new context andbroader advances towards the understanding of today’s excessive mobility. The
research has contributed to the underlying cause initiative of evaluating mobile
phone footprint as a new component of urban system. This dissertation presents a
combination of system development, statistical analysis, design of algorithms, data
mining, survey study, simulation framework and population estimation model. The
rest of this chapter we are going to summarize the major contributions of theresearch, discuss the future prospect and collaboration.

7.1.1 Summary of contributions and main finding

This research designs to express a novel approach of using digital footprints oruser-generated mobile phone data to draw the answers of the questions of thisstudy.

To help answer the research question, Mobile Sensing system, a standard
platform for visualizing city-wide human activities and human flow in the complex
build environment has been suggested in chapter 3. Ultimately, the world has acommon platform only if the key platform elements are open and standard. The
mobile sensing system was implemented on web-based interface in order tomaximize compatibility and interoperability. We developed standard webAPIs,
Internet-based services; to presents the way to visualize time series of aggregatedmobile phone sources in pseudo-color contour maps and grid density surfaces. Theinterface synchronized with interactive graph to give analyses and to explain theactivities of mobile phone use in the specific area.

The system dedicates a great

Chapter 7: Conclusions and Future Prospect115
deal of effort to the initiated study of using mobile phone for exploring a wide rangeof urban dynamics.

The great concern on utilizing this mobile phone data lies on the first researchquestion that is “How can actual population at a specific time-frame be estimatedfrom calling records of mobile phones”.

We have investigated the real mobile

phone Call Detail Records (CDRs) data of central Bangkok and Boston area
(Chapter 3 and 5). The initial analyses were evaluated using interpolated time seriesof datasets through the visual analysis. The diagnostic task was done by observing
particular land use at specific time frame. The results confirmed the correspondent
between urban activity and mobile phone usage.

A more informative case was conducted by analyzing CDRs data from millions
of mobile phone users in Massachusetts. Data mining and clustering techniques
were applied to examine the calling behavior patterns. The call characteristic in
Massachusetts can finally be classified into 5 clusters, which the results minimized therange of sum of squared errors and we discovered that 59% of the population in
Massachusetts has very low average call in the weekdays. 15% of population onlyuses a mobile phone for receiving call and text. Apart from that, 25% of the
population has as average call only once a day. The similar finding was found in the
result of questionnaire survey in Tokyo. (Chapter 6) Furthermore, the number ofmobile phone users has been confirmed with the Tract level planning of census 2000
database. The results showed a good correlation between number of mobile
phone users and population in census data.

Additional finding of this remarkable dataset is to analyze human mobility. We
find a strong correlation in daily activity patterns within the group of people whoshare a common work area’s profile (Chapter 4). It was report that, within the groupitself, the similarity in activity patterns decreases as the distance between themincreases.

The finding in chapter 6 has been answered to the problem of how dynamic
people mobility can be reconstructed from calling records of mobile phones. The
design methodologies used statistical mobility collected from mobile phone Call
Detail Records (CDRs) data, incorporated with Person Trip Survey (PTS) to calibrate
the estimation of population density. We demonstrate how this approach can be

Chapter 7: Conclusions and Future Prospect116
applied to real world application by developing a simulation system call “SIM
Mobility” which generates virtual mobile phone activities. The first design
methodology utilized a stochastic global mobility of the O-D trip as flow distribution
trends and therefore modifies the magnification factor in the original PTS data. The
outcome demonstrated its ability to provide results of satisfactory accuracy with
fast implementing and, hence, potential to support the deployment of dynamic
urban population estimation procedure. The second design methodology usedassimilation process to fine-tune the weight factor where the benefits of assimilation
are much longer lasting and essentially giving a better predictor of longer term
estimation. These studies provide a preliminary prediction result and would certainly
benefit for further analysis.

Among the new idea that emerged in this period, this paper describes animportant scenario in which mobile devices with user-driven contents are fullychanging the way of urban mobility research as well as the way to estimate thepopulation dynamics. A city could be understood as a seamless system and fully
integrated within the co-creation process of new services, products andsocietal-urban infrastructures.

7.2 Future prospect and collaboration
Building telecommunications networks was mostly a private sector activity fromplanning, construction and operation. This was in sharp contrast to urban planning,water, power and transportation infrastructure, which were exclusively a public
sector manage by government or local government. More specifically, in terms of
future policies, government and mobile phone operator need to begin develop an
intellectual framework for looking at telecommunication system as real massive
mobility and interactive city’s sensor.

Generally, mobile phone providers tend not to retain mobile phone CDRs data,in part because there's no business reason to store the data, and in part because
the storage costs would be prohibitive. However, a coming increasingly aware ofthis data, it will eventually encourage the mobile phone providers to open andcollaborate with researcher since they could see the benefits from their early
involvement in this new area.

Chapter 7: Conclusions and Future Prospect

117
However, one of the most discussed topics in doing this research is how to avoid
privacy violations problems. The problems of privacy concerns are not new; people
have been talking about privacy since the being use of surveillance cameras or
GPS phones. Nevertheless, we need to consider the future use of this great andpromising data sources from the fast developed mobile technology and this may
require the government to obtain a warrant that mobile phone information will not
be used unless it is trusted for privacy leak free.

Presence awareness on human mobility also appears likely to be a very
promising in adapting this research idea to the real society. Crowd management
and public emergency spot forecasting could be one of good examples. A longterm capture of mobile phone data would create a city-wide native signature
where the anomaly events can be detected or forecasted in real time. In next few
years, we believe that user-generated mobile phone sources will become one of
the most valuable contents to improve our understanding of urban dynamic,
human mobility, real time population density, large-scale social networks and social
interactions.

Another aspect in utilizing this research idea is by using different available
dynamic information such as internet traffic, email or social media data sources.
The results would give extra values with global scale image since we can break thelimitation of distance or country boundary that normally happens in case of mobile
phone.

Finally, the importance of this research ultimately lies in how it can bepractically applied and utilized massive amount of mobile phone CDRs data tounderstand the city in motion. This raises important questions about how to collect,
store the data and aggregate them then analysis and transforming the findings into
solutions. Methodologies such as data visualization, data mining and statisticalanalysis could be used, possibly applied within a specific thematic domain. Thisdissertation has answered some crucial start up questions of this area, namely,
studying and uncovering the hidden aspect of human mobility and estimateddynamic population distribution from the user-generated mobile phone sources. I,
the author, therefore encourage all potential readers to continue explore thespecific contributions of this novel approach since using mobile phones as sensing
devices and aggregating “crowdsourced” data for urban analysis is still in the early

Chapter 7: Conclusions and Future Prospect

118
phases of theoretical development. Mobile sensing can potentially paint a complex
and dynamic portrait of the urban environment in which users are based.

Chapter 7: Conclusions and Future Prospect

119

REFERENCES

A. Akkerman, The Urban Household Pattern of Daytime Population Change, The Annals
of Regional Science, Volume 29 No.1, pp1-16, 1995

A. Okabe, B. Boots, K. Sugihara, S.N. Chiu, Spatial Tessellations: Concepts andApplications of Voronoi Diagrams, 2nd Edition, John Wiley, 2000

A.

Pashtan, 2005 Mobile Web Services (Aware Networks, Illinois)

A. T. Campbell, S. B. Eisenman, N. D. Lane, E. Miluzzo, and R. A. Peterson,“People-centric urban sensing,” in ACM/IEEE WICON 2006. Boston, MA, USA.

A. Tatem, Y. Qiu, D. Smith, O.

Sabot, A. Ali and B. MoonenThe, use of mobile phonedata for the estimation of the travel patterns and imported Plasmodium falciparum
rates among Zanzibar residents, 8:287, Malaria Journal 2009.

B. Bhaduri, LandScan USA: A High Resolution Population Distribution Model , theInterface 2002 Conference, 2002.

B. Friedrich, Y. Wang, Improvement of OD Estimation Based on Disaggregated Flow
Information, In Proceeding: the 16th Mini - EURO Conference and 10th Meeting of
EWGT, 2005.

C. Kaiser and M. Kanevski, Population Distribution Modelling for Calibration ofMulti-Agent Traffic Simulation, In proceeding 13 th AGILE International Conference
on Geographic Information Science 2010, Portugal

C. Ratti, “Go with the flow” The Economist Technology Quarterly 10 March, page 12-13,
2007.

References

120
C. Ratti, R. M. Pulselli, S. Williams, and D. Frenchman, Mobile landscapes: Using location
data from cell-phones for urban analysis, Environment & Planning, 33(5), pp. 727–748,
2006.

C. Song, Z. Qu, N. Blumm, A.L. Barabási, Limits of Predictability in Human Mobility,
Science. 2010 Feb 19;327(5968):1018-21.

D. Brockmann, L. Hufnagel, and T. Geisel. The scaling laws of human travel. Nature,
439:462–465, January 2006.

D. Brocklehurst, People Flow Modelling Benefits and Applications within Industry,
Loughborough University, 2005

D. Mountain and J. Raper, Modelling human spatio-temporal behaviour: A challenge forlocation-based services, In Proceedings of the 6th International Conference on
GeoComputation, Australia, 24-26 Sep, 2001

D. Pariente, “Geographic interpolation and extrapolation by means of neural networks”
Proceedings of the Fifth European Conference and Exhibition on Geographic
Information Systems, volume 1, 684 – 693, 1994

E. Chung, and M. Kuwahara, Mapping personal trip OD from probe data. International
Journal of Intelligent Transportation Systems Research, 5(1). pp. 1-6, 2007.

F. Aurenhammer and R. Klein, “ Voronoi diagrams” In: Handbook of computational
geometry Eds J.-R. Sack and J. Urrutia (North-Holland, Amsterdam) pp 201 – 290,
2000.

I. Rhee, M. Shin, S. Hong, K. Lee, and S. Chong. On the levy walk nature of humanmobility. In Proceedings of INFOCOM 2008, Phoenix, AZ, April 2008.

I.

Yasuyuki, K. Shin’ichi, T. Niwat, S. Ryohei, S. Kaoru and T. Yoshito., An Implicit andUser-Modifiable Urban Sensing Environment, International Workshop on Urban,
Community, and Social Applications of Networked Sensing Systems November 4,
2008, Raleigh, NC, USA.

References

121
J. Conway, 2003, “PostgreSQL-embedded Statistical Analysis with PL/R” O’Reilly Open
Source Convention, 7 – 11 July.

J. Hutchins, A. Ihler, and P. Smyth, 2008, Probabilistic Analysis of a Large-Scale Urban
Traffic Sensor Data Set. Second International Workshop on Knowledge Discovery
from Sensor Data (ACM SIGKDD Conference, KDD-08)

J. Reades, F. Calabrese, A. Sevtsuk, and C. Ratti, 2007, “Cellular Census: Explorations in
Urban Data Collection” IEEE Pervasive Computing 6(3) 30-38

J. Schlaich, T. Otterstätter , and M. Friedrich, Generating Trajectories from Mobile Phone
Data, TRB 89th Annual Meeting Compendium of Papers, Transportation Research
Board of the National Academies, 2010

J. Yoon, B. D. Noble, M. Liu M. Kim, Building Realistic Mobility Models fromCoarse-Grained Traces, in proceedings of the 4th international conference on
Mobile systems, applications and services, pp 177 - 190, 2006

K. Farrahi and D. Gatica-Perez, Discovering Human Routines from Cell Phone Data with
Topic Models, IEEE International Symposium on Wearable Computers (ISWC), 2008

K. Lee ,S. Hong, S. Joon Kim, I. Rhee, S. Chong, SLAW: A Mobility Model for Human Walks, ,In Proceeding, INFOCOM, Rio de Janeiro, Brazil, 2009,

K. Lee, S. Hong, S. Joon Kim, I. Rhee and S. Chong, Demystifying Levy Walk Patterns in
Human Walks

K. Sakamura and N. Koshizuka, Ubiquitous Computing Technologies for Ubiquitous
Learning, Proceedings of the First Annual International Conference on Mobile andUbiquitous Systems: Networking and Services (MobiQuitous’04), IEEE 2004.

K. Sato, Y. Sekimoto, R. Shibasaki, Study for Reconstruction of People Flow in the City with
Person Trip Data, In Proceedings of AsiaGIS, 2008

L. Bertolini, M. Dijst, 2003, “Mobility Environments and Network Cities” Journal of Urban
Design 8(1) 27–43.

References

122
L. Mitas, H. Mitasova, 1999, “Spatial Interpolation”, In: Geographical Information Systems:
Principles,Techniques,Management

andApplications

Eds

P.Longley,

M.F.

Goodchild, D.J. Maguire, D.W.Rhind, (Wiley, New York) pp 481 – 492.

M. C. Gonzalez, C. A. Hidalgo, and A.-L. Barabasi. Understanding individual humanmobility patterns. Nature, 453:779–782, June 2008.

M. DeMers, 2000 Fundamentals of Geographic information system (Wiley and sons, NewYork)

M. Erwig, R. H. Guting, M. Schneider and M. Vazirgiannis, Spatio-Temporal Data Types:
An

ApproachtoModeling

andQuerying

MovingObjects

inDatabases

GeoInformatica, 3:269–296, 1999.

M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, L. H. Witten; The WEKA Data
Mining Software: An Update; SIGKDD Explorations, Vol. 11, Issue 1, 2009.

M. Kim, D. Kotz, and S. Kim. Extracting a mobility model from real user traces. In Proc. ofIEEE INFOCOM 2006.

M. Kawabata, A GIS-Based Analysis of Jobs, Workers and Job Access in Tokyo, CSIS
Discussion Paper No. 57, July, 2003

M. Piorkowski, Sampling Urban Mobility Through Online Repositories of GPS Tracks, ACM
Hot Planet, June 2009

M. Piorkowski, N. SarafijanovocDjukic, M. Grossglauser A Parsimonious

Model of Mobile Partitioned Networks with Clustering, COMSNETS, January 2009.

M. Seshadri, S. Machiraju, A. Sridharan, J. Bolot, C. Faloutsos, and J. Leskove. 2008
“Mobile call graphs: beyond power-law and lognormal distributions” Proceeding of
the 14th ACM SIGKDD international conference on Knowledge discovery and data
mining, pp 596 – 604, AMC

M. Srivastava et al., “Network system challenges in selective sharing and verification for
personal, social, and urban-scale sensing applications,” in HotNets ’06, Irvine,

References

123
California, USA, November 29 2006, pp. 37–42.

N. D. Lane, S. B. Eisenman, M. Musolesi, E. Miluzzo, A. T. Campbell, 2008, “Urban Sensing
Systems: Opportunistic or Participatory” Proceedings of the 9th workshop on Mobile
computing systems and applications 11-16

N. Eagle, A. Pentland , D. Lazer, Inferring social network structure using mobile phonedata. PNAS (2007)

N. J. Boucher, 2001 The cellular radio handbook : a reference for cellular system
operation. 4th ed (Wiley and sons, New York)

N. Ohmori, Application of Information on Human Activity-Travel Behavior in Urban Spaceand Time in the Information Age, Spatial Data Infrastructure for Urban Regeneration,
pp 127-145, 2008

P. Sistla, O. Wolfson, S. Chamberlain and S. Dao, Modeling and Querying Moving
Objects, IEEE International Conference on Data Engineering, Apr 1997, Birmingham,
U.K.

R. Neuwirth, Shadow cities: a billion squatters, a new urban world, New York: Routledge,
2006.

S. Ng, C. Cheung and S. Leung, Magniﬁed Gradient Function With Deterministic Weight
Modiﬁcation in Adaptive Learning, IEEE Transaction on Neural Networks, Vol.15, No. 6,
2004.

T. Abdelzaher, Y. Anokwa, P. Boda, J. Burke, D. Estrin, L. Guibas, A. Kansal, S. Madden,
and J. Reich, “Mobiscopes for human spaces,” IEEE Pervasive Computing - Mobile
and Ubiquitous Systems, vol. 6, no. 2, April - June 2007.

T. Hengl, 2007, “A Practical Guide to Geostatistical Mapping of Environmental Variables”
JRC Scientific and Technical Reports

T. Tsekeris, L.Dimitriou, and A.Stathopoulos, Simultaneous Origin-Destination Matrix
Estimation in Dynamic Traffic Networks with Evolutionary Computing, Lecture Notes

References

124
in Computer Science, Volume 4448, pp 668-677, 2007.

Y. Asakura and E. Hato, Tracking survey for individual travel behaviour using mobile
communication instruments, Transportation Research Part C 12 (2004) 273–291

W. Hsu, K. Merchant, H. Shu, C. Hsu and A. Helmy, Weighted waypoint mobility modeland its impact on ad hoc networks, ACM SIGMOBILE MC2R 9, pp. 59-63, January
2005

Working Group, 2005, “Urban Mobility Initiative” European Conference of Transport
Research Institutes, Report ECTRI number 2005-06.

References