UBICOMP '14 ADJUNCT, SEPTEMBER 13 - 17, 2014, SEATTLE, WA, USA

A Pedestrian Flow Analysis Systemusing Wi-Fi Packet Sensors to a
Real Environment
Yuki Fukuzaki
Ritsumeikan University
1-1-1 Noji-Higashi
Kusatsu, Shiga 5258577
JAPAN
yuuki@ubi.cs.ritsumei.ac.jp

Nobuhiko Nishio
Ritsumeikan University
1-1-1 Noji-Higashi
Kusatsu, Shiga 5258577
JAPANnishio@cs.ritsumei.ac.jp

Masahiro Mochizuki
Ritsumeikan University
1-1-1 Noji-Higashi
Kusatsu, Shiga 5258577
JAPAN
moma@ubi.cs.ritsumei.ac.jp
Kazuya Murao
Ritsumeikan University
1-1-1 Noji-Higashi
Kusatsu, Shiga 5258577
JAPANmurao@cs.ritsumei.ac.jp

Abstract
The authors have been developing the system, whichanalyzes pedestrian flow using Wi-Fi packet sensors. The
sensors collect Wi-Fi packet called probe request packet,
which is transmitted from a smartphone to search Wi-Fi
access points. In addition, the cloud storage server isrunning to manage observed packets centrally and tocompute pedestrian flow in real time. Additionally, user
movement history is vitally important and we have to pay
close attention to handling that kind of data. Therefore,
the system runs with an anonymization method and acryptographic function. Some kinds of demonstration
experiments were held in real environment. As a result, it
was confirmed that we can analyze the rough tendency ofpedestrian flow using the present system and simple
analysis methods.

Author Keywords

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Publication rights licensed to ACM. ACM 978-1-4503-3047-3/14/09…$15.00.
http://dx.doi.org/10.1145/2638728.2641312

721

Pedestrian Flow Analysis, Ubiquitous Computing, Sensing
Network, Human Activity Recognition

ACM Classification Keywords
K.6.4 [MANAGEMENT OF COMPUTING AND
INFORMATION SYSTEMS]: System ManagementQuality assuranceUBICOMP '14 ADJUNCT, SEPTEMBER 13 - 17, 2014, SEATTLE, WA, USA

Introduction
Since The Great East Japan Earthquake has occurred,techniques of analyzing human flow characteristics in realtime have been gathering attention. Especially, it is said
that analyzing human flow in urban areas is eﬀective.
Nowadays, most people carry their own smartphone.
Therefore, we focus on collecting and utilizing probe
request packet because the packet is transmitted
periodically and has a MAC address of the smartphone.
First, we developed Anonymous MAC Address Probe
Sensor (AMP sensor), which enables to collect MAC
addresses from transmitted packets. The sensor also has
upload function, and collected MAC addresses are sent toanalytical server. Installing multiple sensors, we aim toanalyze human flow in real time and develop a system
which uses analyzed data eﬀectively for making disasterprevention plans and taking measures to deal with naturalcalamities.
Most smartphones, which have Wi-Fi functions, transmit
periodically probe request frame to associate with Wi-Fi
access point and the frame transmission interval is from30 sec to 120 sec (depends on device). That frame
includes MAC address. Therefore, we use that as a key,
because MAC address enables us to identify each device.
Thus, the system can analyze the flow and distribution of
persons carrying information devices. It goes without
saying that movement history is a kind of personal
information. Accordingly, proposed system generates
secure hash value (AMAC address) from MAC addressof an information device to assure anonymity for Wi-Fi
device users.
As for the usage of AMP sensors, the number of Wi-Fi
devices around an AMP sensor helps to estimating therelative number of person there. In addition, using AMAC

722

addresses uploaded by AMP sensors installed in multiple
places, pedestrians’ point-to-point transit pattern can becomputed. Similarly, migration velocity is also calculated.
The processing of AMAC addresses enables a system tocomprehend not only walking locus of pedestrians but also
the trajectory data of person who travels in a car, a train
and so on.
Needless to say that it is too important to analyze apedestrian flow and a pedestrian distribution in time ofdisaster. However, the flow and distribution of pedestrian
are also quite serviceable for urban planning and agovernment scheme at ordinary times. Moreover, thatkinds of data become valuable information for a private
enterprise such as the distribution industry and theadvertising industry.
In the meantime, there is an additional approach which
contrary to assure anonymity. Trajectory tracking isinvaluable for watching over a child and a physicallyhandicapped person. They are encouraged to resister
personal attribute with AMAC address in advance. If so
that, the system can track their real-time trajectory dataas necessary. In case of an emergency, proposed system isable to function as a watching system.

Related Works
There are various approaches to analyze pedestrian tracks.
For example, built-in devices on mobile phones, such as
GPS receivers and accelerometers are used for recognising
persons’s tracks[1][2]. These kinds of approaches do notneed infrastructures to collect necessary data for analyzing
pedestrian trajectory, but specified applications need to beinstalled on persons’s mobile phone. Systems developers
need to consider how to spread that applications such as
giving incentives for persons. Therefore, it can be said

WORKSHOP: HASCA

that they are unpractical ways to collect a great numberof persons’ trajectory data.

AMPセンサAMP sensor	

the number	of persons	Figure 1: Pedestrian Flow
Analysis as Particles

time	

calculate the number of persons	and their residence time 	
for each sensor.	

In other words, if systems developers prepare an
infrastructure to track pedestrians, it is unnecessary to let
persons install specific applications on their mobile
phones. With regard to analyzing crowd of persons, it iseﬀective to prepare sensing infrastructure. Musa et al. [3]reported pedestrian tracking method using Wi-Fi monitor
to collect probe request packets from smartphone. Viterbi
algorithm is applied for estimating pedestrian trajectory. It
is not required any extra configurations for smartphone.
In order to improve the precision of estimating the flow
and distribution of pedestrians, they take some measures.
(1) emulating as popular free Wi-Fi hotspots. (2)emulating as Wi-Fi access points which have destination
SSID of Directed Probe Request sent by smartphone. (3)transmitting RTS packets periodically to detected
smartphone in order to receive CTS packet. Thus, they
increase the number of receive packet from smartphones
for improving the precision of analyzing pedestrian flow.
Packet capturing concept such that is also realized using
Bluetooth[4].

variation of 	pedestrian flow	

Moreover, there are already commercialized devices for
capturing probe request packet. For example, Meshlium
If(variation == 0)	
Xtreme[5] is a multi-protocol router made by Libelium.
the path is 	
impassable.	
That provides a function to detects devices which have	
Wi-Fi interface or Bluetooth interface. There is also
Mobility Services Engine (MSE)[6] which is provided bytime	
calculate variation of 	
Cisco Systems, Inc.. MSE collects probe request packets
pedestrian flow for each path	and RSSI data from Wi-Fi access points made by Cisco
Systems, Inc. through a Wireless LAN Controller (WLC).
Figure 2: Pedestrian Flow
Then gathered data is used for estimating location
Analysis as Fluid
information of mobile devices.
There are other kinds of pedestrian flow analysis methods

723

using stereo cameras[7][8] and laser range scanners[9],[10].
With regard to using stereo camera, it is, however, quite
diﬃcult to recognize person trip data of each pedestrianby image processing because visibility of cameras arelimited. For recognising individual person, it is necessary
to install cameras all over the testing area or contrive atechnique to identify each person on each camera. On theother hand, laser range scanner is expensive and it is also
diﬃcult to identify each person. Therefore, tracking
pedestrian in large area such as a shopping mall, it costs a
lot to prepare an environment for sensing person trip data.

System Concept and Architecture
Considering the introduced related works, we chose theway to prepare a infrastructure to collect Wi-Fi packet
called probe request packet from Wi-Fi devices such as
smartphones (It is under consideration to collect bluetooth
packet in parallel), because it enables us to collect a great
number of persons’ data. In addition, the reason why
utilizing Wi-Fi packet is that we can use MAC addresses
to identify the each person, so it is easier to recognize
individual person than image processing approach.
Additionally, Our proposed system has two aspects which
are pedestrian flow analysis as particle (Figure 1) andpedestrian flow analysis as fluid (Figure 2). As for theformer aspect, we extract a person trip data focusing oneach pedestrian’s trajectory. To comprehend pedestrian
flow, an Origin-Destination table is generated based on
person trip data. Person trip data are also used to watch
over specific persons such as children and physicallyhandicapped persons. Regarding the latter aspect, thesystem generates a complete graph by connecting witheach sensor. The complete graph is used to analyze datasuch as variation of pedestrian flow, the number ofresident persons and their resident time.

UBICOMP '14 ADJUNCT, SEPTEMBER 13 - 17, 2014, SEATTLE, WA, USA

It is also necessary to develop a device observing probe
平成２５年度 成果報告書（研究分担者所属機関）

request packets at a low price. There are some relevant
ットセンサーで平日昼間に 1 分間に 350 件程度のプローブリクエストの取得（1 分間の通行
devices and techniques to collect probe request packets.
者数約 500 人）できることを確認したことで達成した。
They are, however, not developed for the purpose of the
Wi-Fi adapter
SD cardRaspberry PI（Model B）をベースにプロトタイプ（図 1 参照）を開発し、今後のシステム構築
public benefit. The feature of proposed system is not theに資する基本性能評価実験を実施した。Wi-Fi パケットセンサーのベースとなる Raspberry PI は市
development of AMP sensor as one of a hardware
販価格で 5,000 円程度であり、無線通信用 USB アダプタを含めて一式 8,000 円程度で構築できるため、1 万円を切る価格という目標を達成した。このタイプのセンサノードを便宜上、P 型と呼ぶ事にplatforms. The main purpose is the spread of a functionする。Raspberry PI の概略仕様は以下の通り。
to collect and anonymize MAC addresses. In addition, we
aim to build the AMP sensor network eﬃciently in a short
CPU: ARM1176JF-S 700MHz clock (GPU: Broadcom VideCore IV)
Raspberry Pi
Memory (SDRAM): 512MB battery	
period of time using existing ICT infrastructures.
I/O: USB x 2, HDMI1.4, SD Card, Ethernet, GPIO, UART, I2C, SPI
Wi-Fi: Logitec LAN-W150NU2AW

Figure 3: A prototype of AMP
sensorP)
OS: Raspbian (Linux
Debian(TypeDistribution
based)

Hardware

CloudStack上のcloud storage server	
クラウドストレージサーバ
on CloudStack	

電源: 700mA, 5V

HTTP
POST

Table 1: Specifications for AMP Sensors

OpenTSDB2.0

Itemform factors
size

HBase

db
brokerHDFS

exec
fluentd

forwardAMP Sensors
Considering the installation in multiple places, It is
desirable that the AMP sensor is developed as asmall-sized device and developed at a low price. In
addition, it is also desirable that the only thing to bootand run the AMP sensor is just supplying electricity. In
case of mass production, the objective is developing the
AMP sensor under one hundred dollars. An AMP sensor
has an anonymization method and a cryptographic
function to prevent the leak of personal information.

fluentdCPUmain memory

Type P
Raspberry PI Type B
100 (wide) × 64 (depth) ×
30 (height) mm
ARM1176JZF-S 700 MHz
512 MB{ timestamp, ampid, amac, rssi }SSL SSL接続
connection 	secure_forward

図 2. Intel NUC プラットフォームベースの Wi-Fi パケットセンサープロトタイプ

Figure 4: A prototype of AMP
sensor (Type N)
アプリケーションレベルでの高度な処理を想定し、やや処理能力に余裕のある Intel NUC プラット
Raspberry PI ベースのセンサーでも十分な性能を確保出来る事を確認したが、分散データ処理やフォームベースのプロトタイプも開発して端末内処理のバリエーションを試行した。このタイプのセンサーを便宜上 N 型と呼ぶ事にする。このタイプの概略仕様は以下の通り。

ampcapstorage
networkLog

tailfluentd

AMP sensor	

power supply

AMPセンサ

8 GB
wired network:
10/100Mbps ethernet (RJ45)
wireless network: Logitec
LAN-W150NU2AW
5 V, 3.5 W AC adapter

Type NIntel NUC (DC3217IYE)
116.6 (wide) × 112 (depth)
× 39 (height) mm
Intel Core i3 3217-U
SO-DIMM 8 GB DDR3(1600 MHz)128 GB SSD (mSATA)
wired network: Integrated
Intel PRO 10/100/1000
wireless network: Intel Centrino Advanced-N 6235
19 V, 65 W AC adapter

CPU: Intel Core i3 3217-U

Figure 5: System Architecture

Chipset: Intel QS77 Express Chipset
Memory: SO-DIMM 8GB DDR3 (1600MHz)
Disk: 128GB SSD (mSATA)
Wi-Fi: Intel Centrino Advanced-N6235 (via USB)
I/O: USB2.0 x 3, Integrated Intel PRO 10/100/1000 Network-2-

The architecture of the proposed system is shown in
Figure 5. To handle a large amount of data, the cloudstorage server should provide scalability and high-speed
data processing capability. That is why that a server isbuilt on a cloud server and with the processing engine of
distributed database. The data captured by AMP sensors
are encrypted and communication path are also secured.

724

Regarding the hardware of the AMP sensor, Wi-Fi adapter
which runs as monitor mode and PC are needed. At thismoment, two diﬀerent prototypes are developed; one is an
AMP sensor (Type P) based on Raspberry PI Model B
shown in Figure 3 and the other is AMP sensor (Type N)
based on Intel NUC Platform shown in Figure 4.
Specifications for each AMP Sensor are described in Table
1.

WORKSHOP: HASCARSSI[dBm]	

SoftwareOperating system (OS) of an AMP sensor (Type P) is
Linux-based OS named Raspbian3.6.1+. OS of an AMP
sensor (Type N) is Ubuntu12.04LTS. At this moment,
when AMP sensor observed a probe request packet, theMAC address is hashed by SHA-1(160bit) and the sensor
archives hashed value, RSSI, observed time andobservation point as a log. The archived logs are
periodically uploaded to cloud storage server using afluentd. An OpenSSL library is used for hashing MAC
address and a libcap is used for capturing packets. AMP
sensor software is written in C and C++.
distance between device and AMP sensor[m] 	

RSSI[dBm]	

Figure 6: Inside a Building

distance between device and AMP sensor[m] 	

Figure 7: Outside a Building

Basic Characteristic
We made an experiment to measure the basic
characteristics of AMP sensor. We verified RSSI
attenuation by distance between a smartphone and an
AMP sensor. In this experiment, we prepared Android OS
smartphone named Galaxy Nexus (indoor environment :Figure 6, outdoor environment : Figure 7). An RSSI value
is clearly high within around 15 m. If the distance is over
15 m, it is diﬃcult to say that there is a proportional
connection between an RSSI value and a distance. RSSI
also depends on the environment and the way of holding asmartphone. Experimental results regarding packet
transmission interval is shown in Table 2. As for eachdevice, we confirmed that each device transmits probe
request packets periodically. However, the transmissioninterval depends on the device. In addition, there are somedevices which do not transmit probe request packets.

Demonstration Experiment

Experiment in ActiveLabThis experiment was made at ActiveLab in GRANDFRONT OSAKA. The Experimental period is fromJanuary/29/2012 16:00 to January/31/2012 18:00. In
addition, we made reference points and an experimenter
moved between designated points at a specified time
while holding a smartphone which has an already‐known
AMAC address (from January/29/2012 18:00). The
person who holds that device made a one-minute stay ateach reference point. We show an experimental result
regarding reference points. A floor map, installation
locations of AMP sensors (J1 to J6) and reference points(R1 to R20) are shown in Figure 8. A list of reference
point names and the start time of staying is shown in
Table 3. During the observation in regard to underlinedreference points, smartphone was put just above
underlined reference points.
R2
R1

ACTIVE Lab.

R7

R8

3F

J6

J5
R5

J2

R4
R6

R3

R10

R12 R19
R11

R9

R16
J4

ACTIVE Lab.

2F

ACTIVE Studio

J1
R15

J3

R14
R17

R13 R20

R18

Figure 8: ActiveLab Floor Map

We made experiments in a real environment. The purpose
is to verify whether a simple pedestrian flow analysis
method is applicable or not in a real environment.

725

UBICOMP '14 ADJUNCT, SEPTEMBER 13 - 17, 2014, SEATTLE, WA, USA

Table 2: The Transmission Intervals of Probe RequestOS
AndroidiOS

device name
Galaxy Nexus
Nexus 4
Xperia Tablet S
Eee Pad TF201
iPhone4S
iPad 1G

maximum interval(s)
247.7
14.8
—
—
481.2
483.1

remarks
repeat a regular set (30s×4times，60s×4times，120s×4times，240s×4times)
approximately 15s interval
can’t observe any probe request packets
can’t observe any probe request packets
approximately 480s interval
approximately 480s interval

Table 3: A List of Reference Points

Table 4: A Result of Samplingat Reference Points

name
R5
R6
R7
R15
R16
R20

answer
J5
J2
J6
J1
J4
J3

result
J5
J5
J6
J1
J4
J3

name
R1
R2
R3
R4
R5
R6
R7
R8
R9
R10

observing start time
18:18
18:20
18:22
18:24
18:26
18:28
18:30
18:33
18:35
18:39

name
R11
R12
R13
R14
R15
R16
R17
R18
R19
R20

observing start time
18:41
18:43
18:45
18:47
18:49
18:51
18:53
18:55
19:05
19:07

answer is J2, however the estimated AMP sensor ID is J5.
Although there were a little discrepancies, we could obtain
expected results. Therefor RSSI can be used to estimate
users location.
Visits to Reference Points
J6

R1 R2 R3 R4 R5 R6 R7

R8 R9

R11 R13 R15R17R10
R12 R14 R16 R18

R19 R20

J5

J4

J3

Experiment Result Regarding Reference Point
Figure 9 shows time-series plot of AMP sensor ID whichobserved target AMAC address. Collected data areresampled at one minute intervals and we consider thatsmartphone exits near the AMP sensor whose RSSI is max
value within a resampling interval. Vertical lines meanobserving start timings on each reference point. In
addition, a solid line among them means the casesmartphone was put just above a reference point. With
regard to each reference point showed as solid line, thecomparison of ground truth AMP sensor ID and estimated
AMP sensor ID which is calculated from RSSI value isshown in Table 4. Regarding reference point R6, the

726

J2

J1

Figure 9: Visits to Reference Points

Experiment in Osaka Electro-Communication University
This experiment is made during the graduation work
exhibition called Nawaten in OsakaWORKSHOP: HASCA

FOUR	  AMP	  	
  SENSORS(C,D,E,F)	
   bldg6

bldg7

TWO	
  AMP	  	
  SENSORS(G,H)	
  bldg10TWO	
  AMP	  	
  SENSORS(I,J)	
  

bldg1

TWO	
  AMP	  	
  SENSORS(A,B)	
  
bus	
  
stop

Figure 10: Osaka
Electro-Communication
University Campus Map

Table 5: Classification of
Observed Devices

type

14:27:00	
14:33:00	

building1	
building10	
...	

14:55:00	
15:05:00	

building10	
building7	
...	

727

observed time	AMP Sensor 	
14:21:00	
building1 	
...	

mean valueof time(min)
55.4
40.0
36.4
39.2

Observed	
  data	  of	
  portable	
  device	
  X	

...	

building
numberbuilding1
building6
building7
building10

Generation of Pedestrian’s Trajectory Data
As for analyzing a pedestrian flow, it is the first step togenerate pedestrian’s trajectory data. Figure 11 shows theprocess of generating trajectory data regarding a portabledevice.

...	

Table 6: Mean Value of Resident
TimeClassification of Wi-Fi Device
Not only portable devices such as smartphones but also
fixed devices such as Wi-fi base stations transmit probe
request packets. Therefore, we need to classified the typeof Wi-Fi devices. We divided them into three types(portable devices, fixed devices and undetermineddevices). The devices which were observed in at least twobuildings were regarded as portable devices. The devices
which were observed all through the night were regarded
as fixed devices. Concretely, we devided 9 hours fromFebruary/8/2012 21:00 to February/9/2012 6:00 into 9
periods, and devices observed in every period were treatedas fixed devices. The devices satisfying both portabledevice condition and fixed device condition were regarded
as fixed devices. After that, remaining devices weretreated as undetermined devices which were diﬃcult todecide as portable devices or fixed devices. The
classification result is shown in Table 5. The unique
number of observed devices was 1277. The number of
portable devices was detected 320 and that of fixed device
was 385. 572 devices were regard as undetermineddevices.

...	

portable
fixedundeterminedΣquantityof devices
320
385
572
1277

Electro-Communication University. The experimental
period was from February/8/2012 11:00 to
February/9/2012 17:00. Figure 10 shows campus map ofOsaka Electro-Communication University and installation
locations of AMP sensors. There is a bus stop near thebuilding1, and most participants used buses because ofsite condition. In addition, participants of this exhibition
had to sign in at the reception desk in building1. That is
why who comes into university and who goes away fromuniversity was expected to walk by building1. Graduation
works were displayed in each building. The graduation
work exhibition was held from 11:00 to 17:00 in both days.
There were also special events, one was social gathering
held from 15:00 to 17:00 on the first day in building6, theother was commendation ceremony from 15:00 to 16:15
on the second day in building7. Almost all participants
attended above events. During this experiment, almost all
the AMP sensors could not observe correct RSSI value.
That is why we did not use RSSI values in this analysis.
For example, if the probe request transmitted by a device
is observed Sensor A or B, we consider that the device isobserved in building1. Resampling interval is 2 minutes,
and the smartphone is regarded that it is in the building
which observed the smartphone’s AMAC address withhighest frequency within the resampling interval.

15:21:00	

building7	

lp	
 15:21:00	
  first point	

bldg7

transition point	
bldg6
transition point	last point	
extrac,ng	
  three	  types	
  of	  point	
  

bldg1

tp	
 15:05:00	
  tp	
 14:33:00	
  fp	
 14:21:00	
  genera,ng	
  trajectory	
  data	  	
  Figure 11: Generation of Trajectory Data

Three types of points (first point, transition point andlast point) are extracted. The point, where the portabledevice X was observed at first, is regarded as the firstpoint. The point, which changes observed point, istreated as a transition point. The other, where theportable device X was observed the last, is regarded as thelast point. After extracting each point, pedestrian’strajectory data is generated by connecting those points inchronological order.

120	
  portable	
  undetermined	
  

60	
  
40	
  

February 8	

February 9	

120	
  
100	
  

portable	
  undetermined	
  

80	
  

40	
  

16:45	
  15:45	
  

14:45	
  13:45	
  12:45	
  11:45	
  

9:45	
  

8:45	
  

10:45	  19:00	  18:00	
  17:00	
  16:00	
  15:00	
  14:00	
  13:00	
  12:00	
  

20	
  
0	
  

February 9	

OriginFebruary 8	

120	
  portable	
  undetermined	
  

80	
  

commenda!on*ceremony*
exhibi!on

exhibi!on
16"

Destination
bldg6 bldg7
131
11
0
416
402
0
266
9
799
436

bldg10
27
272
15
0
314

Σ
169
778
428
315
1690

12"
10"
8"
6"
4"
2"
0"

February*8

come"into"university"

February*9
go"out"of"university"

!me



40	
  

0	
  

8:45	
  
9:45	
  
10:45	
  11:45	
  12:45	
  13:45	
  
14:45	
  15:45	
  16:45	  20	
  February 9	

February 8	(c) building7
120	
  
100	
  

portable	
  undetermined	
  

80	
  

Calculation of Resident TimeAt least one transition point is generated, resident time
can be roughly calculated. The diﬀerence time between
the transition point and the previous transition point is
considered as resident time. Mean value of resident time
regarding this experiment is shown in Table 6.

Figure 13: The Number of Persons Come into University/Goout of University

Counting the Persons Comes into/Goes out of University
Considering the locations of bus stop and a reception
desk, almost all participants may be considered to walk byor drop at building1. Therefore, we detect a time when
the person comes into university or goes out of university
using trajectory data. If the first point is generated inbuilding1, that time is regarded as a time when the person

Management Of Undetermined Devices
The way to treat undetermined devices should beconsidered. The comparison regarding the numbers of
portable devices and undetermined devices at 30-minuteintervals are shown in Figure 12. According to the results,
the waveforms regarding portable devices and that ofundetermined devices look quite similar. An undetermined

According to the results, most persons who come into
university is tend to appear until 13:00 o’clock, and most
persons who goes out of university is tend to leave around17:00 o’clock.

60	
  
40	
  

February 8	

16:45	
  15:45	
  

14:45	
  13:45	
  12:45	
  11:45	
  

9:45	
  

10:45	
  

8:45	
  

19:00	  18:00	
  17:00	
  16:00	
  15:00	
  14:00	
  13:00	
  0	
  12:00	  20	
  11:00	
  the number of residence persons	

bldg1
bldg6
bldg7
bldg10
Σ

bldg1
0
90
11
40
141

60	
  

11:00	
  12:00	
  13:00	
  14:00	
  15:00	
  16:00	
  17:00	  18:00	  19:00	
  the number of residence persons	(b) building6

100	
  

social*gathering*

18"

14"

Table 7: Origin-Destination Table60	
  11:00	
  the number of residence persons	(a) building1

Origin-Destination Table
Using the transition points, the system can know where
the pedestrian comes from and where he/she goes. For
generating a Origin-Destination table, the data withregard to the relation between origin and destination were
aggregated. Table 7 is Origin-Destination table generatedusing extracted transition points.

8:15"
8:45"
9:15"
9:45"
10:15"
10:45"
11:15"
11:45"
12:15"
12:45"
13:15"
13:45"
14:15"
14:45"
15:15"
15:45"
16:15"
16:45"

0	
  

8:45	
  
9:45	
  
10:45	
  11:45	
  12:45	
  13:45	
  
14:45	
  15:45	
  16:45	  20	
  11:00"
11:30"
12:00"
12:30"
13:00"
13:30"
14:00"
14:30"
15:00"
15:30"
16:00"
16:30"
17:00"
17:30"
18:00"
18:30"
19:00"
19:30"

80	
  comes into university. On the other hand, If the last pointis generated in building1, that time is regarded as a time
when the person goes out of university. The each numberof both persons at 30-minute intervals is shown in Figure
13. A blue line is the number of persons who come into
university, and a red line is the number of persons who goout of university.

the*number*of*person

100	
  

11:00	
  12:00	
  13:00	
  14:00	
  15:00	
  16:00	
  17:00	  18:00	  19:00	
  the number of residence persons	UBICOMP '14 ADJUNCT, SEPTEMBER 13 - 17, 2014, SEATTLE, WA, USA

February 9	(d) building10

Figure 12: The Number ofPortable/Undetermined728

WORKSHOP: HASCA

may be considered as a portable device. At least in thisanalysis, these undetermined devices were treated as
portables devices.
The Number of Resident Person in Each Building
The number of resident persons at 30-minute intervals
with regard to each building is shown in Figure 14. The
time of the exhibition and that of the special event arealso shown. In addition, the number of resident persons
are estimated using information of portable devices andundetermined devices.
social*gathering*180"

commenda!on*ceremony*
exhibi!on

exhibi!on
160"

the*number*of*residence*persons*

140"
120"
100"
80"
60"
40"

February*8

building1"

building6"

8:15"
8:45"
9:15"
9:45"
10:15"
10:45"
11:15"
11:45"
12:15"
12:45"
13:15"
13:45"
14:15"
14:45"
15:15"
15:45"
16:15"
16:45"

0"

11:00"
11:30"
12:00"
12:30"
13:00"
13:30"
14:00"
14:30"
15:00"
15:30"
16:00"
16:30"
17:00"
17:30"
18:00"
18:30"
19:00"
19:30"

20"

February*9

building7"!mebuilding10"

Figure 14: The Number of Resident Persons Based On
Portable and Grey Devices

On the first day, a social gathering was held from 15:00 to
17:00 in building6. Therefore, the number of the resident
person in building6 peaked during the event period. On
the other hand, a commendation ceremony was held inbuilding7 from 15:00 to 16:15 on the second day. almost
all the participants took part in the event. The number ofresident person in building7, however, decreased.
Furthermore, the number of resident persons in each729

building decreased sharply at the event duration. We
guess due to the diﬃculty of observing probe request
packets from smartphone in the event hall because of thesensor location. Therefore, only the considerable decline
in each building were confirmed from the result. In
addition, the number of the resident persons was tend toincrease until 13:00 o’clock, and that number was tend to
decrease after ending time of the exhibition.

Conclusion
Most smartphones, which have Wi-Fi functions, usuallysends probe request frame to associate with a Wi-Fi
access point and the frame transmitting interval is from30 sec to 120 sec (depends on device). A probe request
frame includes MAC address, therefore, we collect each
MAC address as a hashed value to analyze the pedestrian
flow. We described the result of some demonstration
experiments held in a real environment. We confirmedthat we can analyze the rough tendency of the pedestrianflow using the present system and simple analysis
methods. As for future issues, it is necessary to generate
more accurate person trip data because it becomes
fundamental information in our analysis. Moreover, we areplanning to implement a system which can analyze apedestrian flow in real time on a server, as soon as sensor
data are uploaded.

Acknowledgements
We received generous support from Strategic Information
and Communications R&D Promotion Programme(No.132307011) organised by Ministry of Internal Aﬀairs
and Communications.

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UBICOMP '14 ADJUNCT, SEPTEMBER 13 - 17, 2014, SEATTLE, WA, USA

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