Estimating destination flows

8/3/2019 Estimating destination flows

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36 PERVASIVEcomputing Published by the IEEE CS n 1536-1268/11/$26.00 2011 IEEE

L a r g e - S c a L e O p p O r t u n i S t i c S e n S i n g

esimig Oigi-

Dsiio Flowsusig Mobil phoLoio D

Origin-destination (OD) matri-

ces represent one o the most

important sources o inor-

mation used in the strategic

planning and management o

transportation networks. A precise calculationo OD matrices is an essential component in

enabling administrative authorities to optimize

the use o their transportation networks, not

only to benet users on their

daily journeys, but also to

plan investments required to

adapt these inrastructures

to envisaged uture needs. Tra-

ditionally, urban planning and

transportation engineering

use household questionnaires

or census and road surveys todevelop methodologies or OD matrix estima-

tion. This approach has two main drawbacks:

calculating an OD matrix, rom the initial

data gathering to the exploitation o the rst

results, can take years and produce only a

snapshot o the travel demand; and

the collected data has shortcomings in terms

o both spatial and temporal scale.

Sensor-based OD estimation methods use

street sensors such as loop detectors and video

cameras together with trac-assignment mod-

els. Analogous methods have been developed

using probe vehicles, in which vehicle traces

serve as data sources.1,2 Those methods are,

however, limited by the act that models are

oten underdetermined because the number oparameters to be estimated is typically larger

than the number o monitored network links.3

On the other hand, the wide deployment o

pervasive computing devices (mobile phone,

smart cards, GPS devices, digital cameras, and

so on) provides unprecedented digital ootprints,

telling where people are and when theyre there.

Earlier projects have used dierent methodolo-

gies or detecting the presence and movement o

crowds through their digital ootprints (Flickr

photo, mobile phone logs, smart card records,

and taxi/bus GPS traces).4 6 This ne-grainedanalysis could dramatically increase our un-

derstanding o the use o space and daily com-

muting fows or urban mobility planning and

management. Thus, its no surprise that the idea

o using mobile phones to monitor trac con-

ditions isnt new, as we discuss in the Related

Work in Analyzing Trac Flow sidebar.

Although the results rom these other studies

show great potential or using cellular probe tra-

jectory inormation to estimate travel demand,

all methods must overcome several shortcomings

beore they can be put into practice. Indeed, as

Using an algorithm to analyze opportunistically collected mobile

phone location data, the authors estimate weekday and weekend travel

patterns of a large metropolitan area with high accuracy.

Francesco Calabrese

and Giusy Di Lorenzo

IBM Dublin Research Laboratory

Liang Liu and Carlo Ratti

Massachusetts Instituteof Technology


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OCTOBERDECEMBER 2011 PERVASIVEcomputing 37

Yi Zhang and his colleagues note,7 eld

tests are needed or the ollowing reasons:

real coverage areas o cell phone tow-

ers are quite dierent rom simulated

ones, and vary rom urban to rural

areas;

validation o methods to determine

a trips origin and destination shouldbe perormed using real individual

mobility data;

real mobility and calling patterns

should be included in the analysis

because they crucially infuence the

methods perormance;

existing OD matrices should be used

as ground truth to veriy the correct-

ness o the estimated results.

Our methodology uses opportunis-

tically collected mobile phone location

data to estimate dynamic OD matrices.

We address concerns using a real mo-

bility and calling dataset rom 1 million

mobile phone users. We use the Boston

metropolitan area as a case study and

validate our methodology using census

survey data or both county and census-

tract levels.8 To our knowledge, both

the methodology developed and thedata precision and amount are unique.

Mobil pho DsThe considered dataset consists o

anonymous location measurements

generated each time a device connects

to the cellular network, including:

when a call is placed or received (both

at the beginning and end o a call);

when a short message is sent or

received;

when the user connects to the Inter-

net (or example, to browse the Web,

or through email programs that peri-

odically check the mail server).

In this article, we reer to these events

as network connections. The events

represent a superset o those in the call

detail records used elsewhere.9,10

We analyzed 829 million mobile

location data or 1 million devices

collected by AirSage (www.airsage.

com). This data included not only the

ID o the cell tower the mobile phone

was connected to, but also an estima-

tion o its position within the cell,

which is generated through triangu-

lation by AirSages Wireless Signal

Extraction technology. Each loca-

tion measurement miM is charac-

terized by a position pmi expressed

S

everal related studies on analyzing trac fow have been

published in recent years.Raaele Bolla and Franco Davoli presented a model or

estimating trac using an algorithm that calculates trac para-

meters on the basis o mobile phone location data.1 Researchers

in Rome developed a case study or real-time urban monitor-

ing using aggregated mobile phone data to monitor trac and

movement o vehicles and pedestrians.2 Randal Cayord and

Tigran Johnson analyzed the main parameters to be consid-

ered, namely precision, metering requency, and the number

o localizations necessary to achieve accurate trac descrip-

tions.3 Several companies worldwide, including ITIS Holdings

(Britain), Delcan (Canada), CellInt (Israel), and AirSage and

IntelliOne (USA), have begun developing commercial applica-tions o mobile phone-based trac monitoring.

With the speciic goal o measuring OD lows, dierent

mobile phone signaling datasets have been considered

and simulated to evaluate the easibility o estimating trips.

Initial work used billing data, consisting o cell phone

tower inormation every time a phone received or made a

call.4 Other research has used mobile phone positions every

two hours to iner trips,5 location updates to iner mobile

phone movement,6 and cell phone tower handover inorma-

tion.7 A recent eort estimated the daily OD demand using

simulated cellular probe trajectory inormation (extracted

rom location updates, handover, and transition o timing

advance values) and tested the methodology via the VisSim

simulation.8

REfEREnCES

1. R. Bolla and F. Davoli, Road Trac Estimation rom Location Track-

ing Data in the Mobile Cellular Network, Proc. IEEE Wireless Comm.

and Networking Conf., vol. 3, IEEE Press, 2000, pp. 11071112.

2. F. Calabrese et al., Real-Time Urban Monitoring Using Cell Phones:

A Case Study in Rome, IEEE Trans. Intelligent Transportation Systems,

vol. 12, no. 1, 2011, pp. 141151.

3. R. Cayord and T. Johnson, Operational Parameters Aecting Use o

Anonymous Cellphone Tracking or Generating Trac Inormation,

Proc. Transportation Research Board Ann. Meeting, 2003.

4. J. White and I. Wells, Extracting Origin Destination Inormationrom Mobile Phone Data, International Conerence on Road Trans-

portation and Control, IEE, 2002, pp. 3034.

5. C. Pan et al., Cellular-Based Data-Extracting Method or Trip Distri-

bution,J. Transportation Research Board, vol. 1945, 2006, pp. 3339.

6. N. Caceres, J. Wideberg, and F. Benitez, Deriving Origin Destination

Data rom a Mobile Phone Network, Intelligent Transport Systems,

vol. 1, no. 1, 2007, pp. 15 26.

7. K. Sohn and D. Kim, Dynamic Origin-Dest ination Flow Estimation

Using Cellular Communication System, IEEE Trans. Vehicular Technol-

ogy, vol. 57, no. 5, 2008, pp. 2703 2713.

8. Y. Zhang et al., Daily O-D Matrix Estimation Using Cellular Probe

Data, Proc. Transportation Research Board Ann. Meeting, 2010.

rld Wok i alyzig tffi Flow


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38 PERVASIVEcomputing www.computer.org/pervasive

Large-ScaLe OppOrtuniStic SenSing

in latitude and longitude and atimestamp t

mi.

To iner trips rom these measure-

ments, we rst characterized the indi-

vidual calling activity and veried that

its requent enough to allow monitor-

ing the users movement over time with

a ne enough resolution. For each user,

we measured the interevent timethat

is, the time interval between two con-

secutive network connections (similar

to what Marta Gonzlez and her col-

leagues measured10). The average inter-event time measured or the entire pop-

ulation was 260 minutes, much lower

than Gonzlez and her colleagues mea-

surement (500 minutes) because were

also considering mobile Internet con-

nections. Because the distribution o

interevent times or a user spans several

temporal scales, we urther character-

ized each calling activity distribution

by its rst and third quantile and the

median. Figure 1 shows the distribu-

tion o the rst and third quantile and

the median or all available users intothe dataset. The arithmetic average

o the medians is 84 minutes (the geo-

metric average o the medians is 10.3 min-

utes) with results small enough to de-

tect changes o location where the user

stops or as little as 1.5 hours.

Mobile-phone-derived location data

has lower resolution than GPS data.

Internal and independent testing sug-

gest an average uncertainty radius o

320 meters, and a median o 220 me-

ters. Moreover, at some peak usage pe-riods, additional location errors can be

introduced when users are automati-

cally transerred by the network rom

the closest cellular tower to one thats

urther away but less heavily loaded.

Oigi-Dsiioesimio MhodThe procedure or estimating dynamic

OD matrices consists o two steps: trip

determination and origin-destination

estimation.

To alleviate the eects o localization

errors and event-driven location measure-

ments on individual trip determination,

we apply a low-pass lter with a 10-minute

resampling rate to the raw data. This ol-lows an approach tested with data rom

Rome, Italy.11 In addition, because ewer

localization errors might still generate

ctitious trips, we adapt a preprocess-

ing step used to analyze GPS traces. This

step uses clustering to identiy minor

oscillations around a common location.

The approach used to handle loca-

tion errors and identiy meaningul

locations in a users travel history can

be understood as ollows:

We begin with a measurement series

Ms={mq,mq+1,,mz}Mz-q-1,q>z,

derived rom a series o network con-

nections over a certain time interval

T t tm mz q= > 0.

We dene an area with radius S

(in this case, 1 km to account or the

localization errors estimated by Air-

Sage), such that

max ,

, .

distance ( ) < Sp p

i j z

m mi j

qAll consecutive points pjMs or

which this condition holds can be

used together such that the centroid

becomes a virtual location

p z q ps mi q

i z

i=

=

=

( ) 1 (the centroido the points),

that is a trips origin or destination.

Once the virtual locations are

detected, we can evaluate the stops

(virtual locations) and trips as pathsbetween users positions at conse-

cutive virtual locations. Each trip

trip(u, o, d, t) is characterized by

user ID u, origin location o, desti-

nation location d, and starting time t.

Once trips are extracted, we use the

ollowing procedure to derive OD fows:

The geographical area under analy-

sis is divided into regions: regioni,

i= 1,, n.

Figure 1. Caracterizatio o idividua caig activity or te etire popuatio,

i terms o time betee to etor coectio evets. Graps so te

distributios o te media (soid ie), frst quatie (das-dotted ie), ad tird

quatie (dased ie) o idividua iterevet time.

102 101 100 101 102 103 104 1050

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.10

Interevent time (minutes)

Distribution

First quantile Median Third quantile


4/9


Origin and destination regions, to-

gether with starting time, are ex-

tracted or each trip o each user

trip(u, o, d, t).

Trips with the same origin and desti-

nation regions are grouped together

at dierent temporal windows tw,or example, weekly, daily, and

hourly:

m i j tw

trip u o d to region d regioni j

( ), ,

( , , , )

, ,

=

t tw

.

The result is a 3D matrix M3 whose

element m(i,j, tw) represents the num-

ber o trips rom origin region i to desti-

nation regionj starting within the time

window tw.

cs Sdy d comisoBased on the area covered by the mo-

bile phone locations dataset, we ana-

lyzed movement among areas in eight

counties in eastern Massachusetts

(Middlesex, Suolk, Essex, Worcester,

Norolk, Bristol, Plymouth, and Barns-

table) with an approximate population

o 5.5 million people. To simpliy the

analysis, we randomly selected 25 per-

cent o the available users and extracted

traces or them.

ti chizio

As a rst analysis, we studied the trip-

length distribution (see Figure 2a), show-

ing that trips range rom 1 to 300 km.

We determined the trip length x by cal-

culating the Euclidean distances be-

tween the trips origins and destinations.The distribution is well approximated

by P(x) = (x + 14.6) -0.78exp(-x/60)

with R2= 0.98, which conrms previ-

ous ndings.10 The slightly dierent

coecients ound in this case could be

because o the dierent build environ-

ments in Europe and the US.12 To check

the plausibility o our segmentation o

the trajectory in trips, we compute the

same statistics computed on the number

o individual trips per day. Figure 2b

shows the distribution over the entirepopulation, separating weekday and

weekend trips. We obtain an average

o 5 trips per day during the week,

and 4.5 on weekends. This number is

reasonable when compared to the US

National Household Travel Survey (see

http://nhts.ornl.gov), which evaluated

this number to be between 4.18 during

the week and 3.86 on weekends. (Rea-

sons or these dierences include seve-

ral years o dierence between when

the two datasets were collected, and the

act that NHTS is based on a sample

over the entire US population, so not

ocused on the behavior o people in the

Boston metropolitan area.)

To evaluate whether we have sam-

pling biases in our data, we computed

the home location distribution esti-mated rom the mobile phone data, and

compared it with data rom the US 2000

Census. To detect a home location, we

rst group together geographic regions

that are close in space, creating a grid in

space where the side o every cell is 500

meters long. For each cell, we evaluate

the number o nights the user connects

to the network in the nighttime interval

while in that cell, and select as a home

location the cell with the greatest value.

(We used 6 p.m. to 8 a.m. as the night-time interval based on statistics rom

the American Time Use Survey; www.

bls.gov/tus/charts/work.htm.)

To validate the home location distri-

bution, we compared it with population

data rom the US 2000 Census, at the

census-tract level.13 Within the eight

selected counties are 1,171 distinct

census tracts, with populations rang-

ing rom 70 to 12,000 people (on aver-

age 4,705), and an area ranging rom

0.08 to 203 km2 (on average 10.8 km2).

0 0.5 1.0 1.5 2.0 2.5

104

103

102

101

100

Trip length (log10 km)

Distribution

1 0.5 0 0.5 1.0 1.5 2.0

106

105

104

103

102

101

100

Number of trips (log10)

Distribution

(a) (b)

Weekday

Weekend

Figure 2. Statistics o te trips detected rom te mobie poe ocatio data: (a) trip-egt distributio (curve iterpoated

it P(x) = (x+ 14.6)-0.78exp(-x/60) it R2 = 0.98), ad (b) umber o idividua trips per day distributio.


5/9



The census tract population estimated

using cell phone users home locations

scales linearly with the census popula-

tion, as Figure 3 shows, corresponding

to an average 4.3 percent o the popula-

tion being monitored.

OD Flows chizio

To validate the accuracy o the OD

matrices produced using the mobile

phone traces, we used the most re-

cent tracttract worker fows dataset

rom the Census Transportation Plan-

ning Package.8 CTPP is a tabulation

o responses rom households com-

pleting the census long orm. It is the

only census product that summarizes

data by place o work and tabulates

the fow o workers between home and

work.

The tracttract worker fows data

shows the number o workers in each

tract o work by tract o residence.

Workers are dened as people 16 years

old and older who were employed andat work, ull or part time, during the

census reerence week (generally the

last week o March). The data contains

the number o workers in the fow who

were allocated to tract, place, and

county o work.

Given the two levels o granular-

ity (tract and county) available in the

CTPP dataset, we computed our OD

estimates at two levels o aggregation.

Because commuting fow generally ac-

counts or two trips (home to workand work to home), we considered un-

directed fows between two locations to

compare our OD estimations. For each

granularity, we computed the average

daily number o trips:

m i j

K

daysm i j tw m j i tw

All

All

tw day

( , )

#( ( , , ) ( , , ))= +

=

,,

,..., ,...,i n j i= = 1 1 1

where KAll is a scaling actor we use tocompare our estimation with the census

estimations.

Moreover, because according to the

denition, the census dataset includes

only commuting trips, we evaluated the

average daily number o trips made only

on weekday mornings (6 to 10 a.m.)

rom the estimated home to estimated

work location (estimated as the most

requent stop area on weekday morn-

ings between 8 and 10 a.m.):

m i j

K

weekdaysm i j tw m j i tw

WM

WM

tw wm

( , )

#( ( , , ) ( , , ))= +

=

= =

,

,..., ,...,i n j i1 1 1

where KWM is a scaling actor.

Finally, we also compared our pre-

dictions with the well-known and

widely used gravity model:14

m i j

K

P P

d i n j i

Gravity

G

i j

i j

( , )

, ,..., ,...,,

=

= = 2 1 1 1,,

Figure 3. US 2000 cesus tract popuatio desity ad ce poe users estimated

ome ocatios desity. (a) Cesus tract popuatio desity derived rom ce poe

users estimated ome ocatios. (b) To compare tese to desities, e mutipied

ce poe-based popuatio desity by 100/4.3 to accout or te percetage o te

popuatio beig moitored. Error bars represet te stadard error.

Cell phone user density (person/sq mi)

0.000000000122.6

122.7287.3

287.4521.8

521.9847.9

848.01222

12231653

16542158

21592888

28894353

43547267

(a)

5 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.07

6

5

4

3

2

1

0

Estimatedtractpopulationdensity(log10)

Census tract population density (log10)(b)


6/9


where KG is a scaling actor, and di,j is

the Euclidean distance (in kilometers)

between the centroids o the regions.

Figure 4a shows the county-level re-

sults. The plots correspond to models

that minimize the least square errors,

using KAll= 16.9 or the predictionmade with the average number o trips

in a day mAll, KWM= 71.4 or the pre-

diction made with the average number

o trips on weekday mornings mWM ,

and KG= 58.4 or the gravity model

mGravity.

Correlations show encouraging re-

sults, with R2= 0.59 or the gravity

model, R2= 0.73 or the prediction

made with all trips, and the best result

R2= 0.76 or predictions made with

weekday morning trips only. The re-sulting high correlation shows that the

estimated OD matrices resemble OD

matrices generated using completely

dierent inormation.

Using the best model mWM, we com-

pared our results with the tract-level

census data. At this level, noise is more

evident (see Figure 4b), but we can still

see on average a good linear relation-

ship between census estimation and

our estimationR2= 0.36 in this case,

which is high compared to the R2= 0.10

o the gravity model. We also evaluated

more sophisticated gravity-like models

by optimizing the dexponent and sub-

stituting the populations with the total

estimated number o trips outgoing or

incoming or an area, but still obtained

R2< 0.3.The relatively low value oR2 com-

pared to the county-level analysis is

partially because the relationship seems

less linear when the census estimates

ewer than 10 trips rom tract to tract.

This might be because census fows are

estimated rom a subsample, which

might result in small numbers or par-

ticular pairs o census tracts. Moreover,

census estimates werent available or

the same year as the mobile phone data,

and trip origins and destinations mighthave slightly changed (at this high level

o spatial detail) between the two moni-

tored periods.

We note that the scaling actor KWM

used or the last model mWM corre-

sponds to a share o monitored trips

thats about 1.4 percent compared

to the census estimations. This ac-

tor can be explained by the percent-

age o mobile phones selected (about

4.3 percent) and by the calling activity,

which isnt very high in the morning.

Other elements, such as the act that were

monitoring not only commuting fows,

might explain the remaining dierence.

Estimating KWM lets us extrapolate the

ODs computed using the mobile phone

data to the whole population.

nw poilWe see a great deal o potential or this

approach. We should note, though,

that OD fows data estimated through

census surveys have the ollowing

limitations:8

The decennial census monitors

usual days to avoid local or re-

gional anomalies, such as transit

strikes or severe weather, on a single

sampling day. This tends to hide theless common uses, such as individu-

als telecommuting once every two

weeks or carpooling once a week be-

cause o ever-changing lie and work

patterns.

According to the denition, the cen-

sus dataset doesnt include nonwork

trips, and modelers must develop

relationships between work and non-

work trips.

The census data is based on a

xed-point snapshot approach, so

Figure 4. Compariso betee mobie poe ad cesus origi-destiatio (OD) estimates. Error bars so oe stadard

deviatio rom te average: (a) couty eve soig a trips, eeday morig trips, ad te gravity mode; ad (b) tract eve

soig eeday morig trips.

0 1 2 3 4 5 60

1

2

3

4

5

6

Census number of trips (log10)

Estimatednumberoftrips(lo

g10)

All trips

Weekday mornings trips

Gravity model

(a) (b)

0.5 0 0.5 1.0 1.5 2.0 2.5 3.0 3.50

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Census number of trips (log10)

Estimatednumberoftrips(lo

g10)


7/9



transportation planners can only

interpret data over geographic space,

rather than over time.

Compared with traditional census

data, however, our methodology to de-

tect OD matrices rom mobile phonetraces has several advantages:

It can capture the weekday and

weekend patterns as well as seasonal

variations.

It can capture work and nonwork

trips, which is essential or trip chain-

ing and activity-based modeling.

Thus, these matrices could then com-

plement traditionally generated OD

matrices, providing a ine-grainedspatiotemporal pattern o mobility.

tmol alysis

Whereas the census gives only static

inormation about OD fows, the OD

matrices derived rom mobile phone

data let us appreciate the dierences

in travel demand over time. Figure 5a

shows the total daily travel demand or

three weeks in October 2009. A weekly

pattern clearly appears in the travel de-

mand, with the minimum on weekends

(especially Sundays) and the maximum

on Fridays. Moreover, Figure 5a shows

a change in travel demand on the sec-

ond Monday (day 9 in the gure), corre-

sponding to Columbus Day. For a better

look at this pattern, we plot the hourly

travel demand or Columbus Day com-pared to other Mondays (see Figure 5b).

We clearly see a higher travel demand

in the rst two hours o the day, ol-

lowed by lower demand rom hour 4 to

hour 9, and rom hour 12 to hour 20,

because o the holiday.

Siomol alysis

Our methodology can capture ine-

grained OD matrices in both spatial

and temporal scale, essential data or

understanding transport demand andtransport modeling, especially during

special events. For example, Figure 6

compares the incoming fows toward

Fenway Park, Bostons baseball sta-

dium. We compare two days: Sunday,

11 October, when the Boston Red Sox

played the Los Angeles Angels in a

postseason game, and a Sunday with

no special events. As the gure shows,

we can capture the increasing incom-

ing low caused by the event, both

in terms o new trip origins, and in

fow volume. Further studies with the

same dataset have also shown regular

spatial patterns o attendee origins based

on event type, inormation that would

be valuable or event management.5

As this study shows, perva-sive datasets such as mo-

bile phone traces provide

rich inormation to support

transportation planning and operation.

Meanwhile, some related limitations

should also be addressed when apply-

ing these datasets in mobility analysis.

The localization error, or example,

limits the minimum size o the regions

that can be considered.

Other elements that can aect the

statistical results include:

the market share o the mobile phone

operator rom which the dataset is

obtained,

the potential nonrandomness o the

mobile phone users (or example,

teenagers),

calling plans, which can limit the

number o samples acquired at each

hour or day, and

the number o devices that each per-

son carries.

Figure 5. Tempora variatio i te umber o trips detected rom te mobie poe ocatio data: (a) umber o trips per day,

over a tree-ee iterva; ad (b) umber o trips per our, over tree dieret Modays.

1.5

1.6

1.7

1.8

1.9

2.0

2.110

6

Numberoftrips

S

un

M

on

T

ue

W

ed

T

hu

Fri

Sat

S

un

M

on

T

ue

W

ed

T

hu

Fri

Sat

S

un

M

on

T

ue

W

ed

T

hu

Fri

Sat

ColumbusDay

(a) (b)

0 5 10 15 20

0

5

10

15104

Hour

Numberoftrips

Columbus Day

Other Mondays


8/9


Moreover, because the considered data-

set is event driven (location measure-

ments available only when the device

makes network connections), users

connection patterns can aect the

possibility o capturing more or ewer

trips. This last limitation could be

solved by continuous location readings

rom GPS devices, which would require

the users consent. A hybrid approach

could integrate both event-driven and

continuous location measurements,

as the current method can be easily

generalized to dierent datasets with

dierent spatiotemporal resolutions.

Nonetheless, the analysis perormed

on the interevent time, the spatial dis-

tribution o mobile phone users, and

comparisons with census estimations

conrm that the mobile phone data

represent a reasonable proxy or human

mobility.

Future work will involve reproducing

the analysis or other cities to understand

which parameters infuence the scaling

actors to be used to extrapolate the

ODs computed using the mobile phone

data to the whole population. The re-

search output will give transportation

planners an automatic and systematic

way to understand the dynamics o

daily mobility in a real complex metro-

politan area.

francesco Calabrese is an advisory research

sta member at the IBM Dublin Research Labo-

ratory and a research aliate at the SENSEable

City Laboratory o the Massachusetts Institute

o Technology. His research interests include

ubiquitous computing, intelligent transportation

systems, urban network analysis, and distributed

control systems design. Calabrese has a PhD in

computer and systems engineering rom the Uni-

versity o Naples Federico II, Italy. Hes a member

o IEEE and the IEEE Control Systems Society.Contact him at [email protected].

Giusy Di Lorenzo is a research scientist at the

IBM Dublin Research Laboratory and a research

aliate at the SENSEable City Laboratory o the

Massachusetts Institute o Technology. Her re-

search explores the analysis o urban dynamics

and human mobility using data gathered rom

sensor networks. In particular, shes interested

in developing context-aware applications to

improve urban living experiences o citizens.

Di Lorenzo has a PhD in computer and systems

engineering rom the University o Naples

Federico II. Contact her at [email protected].

Liang Liu is the deputy director o the Creative In-

dustries Park in Shanghai and Changzhou. He was a

postdoctoral associate at the SENSEable City Labo-

ratory o the Massachusetts Institute o Technol-

ogy. Liu has a PhD in urban planning and manage-

ment rom Tongji University, China. Contact him at

[email protected].

Carlo Ratti is an architect, engineer, and associate

proessor o practice at the Massachusetts Institute

o Technology, and director o MITs SENSEable City

Laboratory. Ratti has an MSc in civil structural engi-

neering rom both the Polytechnic University o Turin

and the School o International Management (ENPC)

in Paris, and a PhD in architecture rom the Univer-

sity o Cambridge. He is a member o the Order o

Engineers in Turin and the Architects Registration

Board (UK). Contact him at [email protected].

the AUThORS

(b)(a)

Figure 6. Icomig trips i te Feay Par area. Eac ie coects a detected trip origi area to te Feay Par: (a) orma

Suday, ad (b) day o a Red Sox game. Fo voume is represeted by te ies ticess.


9/9



ACknOwlEDGMEnTS

We thank Moshe E. Ben-Akiva or his eedback

and AirSage or providing the mobile phone loca-

tion dataset.

REFEREnCES

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2. S. Baek et al., Method or EstimatingPopulation OD Matrix Based on ProbeVehicles, Korean Soc. Civil Engineers(KSCE) J. Civil Eng., vol. 14, no. 2, 2010,pp. 231235.

3. M.L. Hazelton, Some Comments onOrigin-Destination Matrix Estimation,Transportation Research Part A: Policy andPractice, vol. 37, no. 10, 2003, pp. 811822.

4. F. Girardin et al., Digital Footprinting:Uncovering Tourists with User-Generated

Content, IEEE Pervasive Computing,vol. 7, no. 4, 2008, pp. 36 43.

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6. D. Quercia et al., Mobile Phones and Out-door Advertising: Measurable Advertis-ing, IEEE Pervasive Computing, vol. 10,no. 2, 2011, pp. 2836.

7. Y. Zhang et al., Daily O-D MatrixEstimation Using Cellular Probe Data,Transportation Research Board Ann.Meeting, 2010.

8. CTPP Census Transportation Plan-ning Package, US Department o

Transportation, Census Transporta-tion Planning Products, 2010; www.hwa.dot.gov/planning/census_issues/ctpp/.

9. J. White and I. Wells, Extracting OriginDestination Inormation rom MobilePhone Data, International Conerence

on Road Transportation and Control,IEE, 2002, pp. 3034.

10. M. Gonzalez, C. Hidalgo, and A.-L. Bara-basi, Understanding Individual HumanMobility Patterns, Nature, vol. 453,no. 7196, 2008, pp. 779782.

11. F. Calabrese et al., Real-Time UrbanMonitoring Using Cell Phones: A CaseStudy in Rome, IEEE Trans. IntelligentTransportation Systems, vol. 12, no. 1,2011, pp. 141151.

12. L. Liu et al., The Law o InhabitantTravel Distance Distribution, Proc.European Conf. Complex Systems, 2009.

13. MassGIS, Census 2000 Tracts Data-layer Description, 2010; www.mass.gov/mgis/cen2000_tracts.htm.

14. J. Anderson, A Theoretical Foundationor the Gravity Equation, Am. EconomicRev., vol. 69, no. 1, 1979, pp. 106116.

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