Traffic Flow Analysis by the Use of Wi-Fi Packets ReceiverJunji NISHIDA1・Tomoyuki ADACHI2・Kazuhiko MAKIMURA3
1 Japan Research Institute for Social Systems (550-0002 1-10-27 Edobori Nishi-ku Osaka City, Osaka, Japan)

E-mail:nishida@jriss.jp
2 Overseas Business Dept., West Nippon Expressway Company Ltd. (530-0003 1-6-20 Dojima, Kita-ku, Osaka City,

Osaka, Japan) E-mail:t.adachi.af@w-nexco.co.jp
3 Institute of Behavioral Sciences (162-0845 2-9 Ichigayahonmura-cho, Shinjuku-ku, Tokyo, Japan)

E-mail:kmakimura@ibs.or.jp

There has been sharp increase of mobile communication terminals such as smartphones and tablet computers with
Wi-Fi function in use. When a smartphone performs packet communication, it transmits a unique ID number of thedevice at regular intervals even in standby mode, so it is possible to track the movement of mobile bodies by receiving packets at multiple locations on roadsides and analyzing them.
This paper shows the results of experiments analyzing traffic and person flows by the use of Wi-Fi packet receiver
which was uniquely developed by the authors. The authors measure traffic flows on an expressway and ordinary roadand calculate travel time both in Japan and abroad. Person flows at a service area on expressway and a fast-food restaurant in an urban area are also measured in order to analyze residence time and usage situation of the facilities.
The possibility of further applications to the breakdown of traffic-flow and/or low-cost Origin-Destination surveys
by additional allocation of sensors in wider areas will be also discussed in the paper. The traffic-flow analysis using
Wi-Fi packet data from smartphones should be more beneficial especially in developing countries with insufficienttraffic-flow observation systems.

Key Words : system analysis, travel behavior survey, road planning, public transport planning, traffic information, ITS

1. Research Objectives and backgroundmeasure traffic flow by acquiring unique wireless packet information,
we must use a method that pays the utmost care to protection of

Due to the rapid dissemination of smartphones in recent years,

personal privacy, or else even if all of the technical problems weremany mobile bodies have become equipped with communications

resolved, it would be difficult to gain the understanding of the public.

terminals that have Wi-Fi functionality. Since many smartphones

We developed the hardware and software for using Wi-Fi packet

transmit a type of Wi-Fi packet that includes a unique ID number for

sensors to analyze traffic flow, applied them in multiple fields, andthe device, by receiving and analyzing these packets, it is possible totook many measurements. In addition to capable of measuring traffic

ascertain the traffic flow of a broad range of mobile bodies.

flow of vehicles on expressways and ordinary roads, we were able to

By receiving Wi-Fi packets on roadside and comparing data at

confirm that the Wi-Fi packet sensors could be effective as sensors tomultiple locations, it is possible to measure the movement speed overascertain the flow of people in congested areas such as service areas

distances (travel speed) of automobiles and people at extremely low

and metropolitan areas. Moreover, making the acquired data

cost. Furthermore, if we measure the start and end time of Wi-Fi

anonymous and conforming to personal information protection laws

packets that are continuously observed at the same location, we canis indispensable. We consider measures to delete the data from users

measure residence time of people and automobiles.

who do not wish to be acquired (in other words, an “opt-out” method),

These traffic flow sensors that can easily observe traffic flows fromand propose a method of operating the sensors in a way that does not

the side of the road could be utilized in many regions. In particular,cause any legal or socio-ethical problems.

they hold much promise for practical use in developing countries,

2. Wi-Fi Packet Receiver

where the facilities for traffic flow measurement infrastructure areinsufficient.
In the past few years, a variety of approaches and research for

In recent years, mobile information devices with Wi-Fi

implementing analysis methods of traffic flow through the acquisition

communication functionality have rapidly disseminated. The terminalof unique wireless packet information have been carried out in manywith the highest rate of dissemination is the smartphone, but otherplaces. However, many of these use Bluetooth, and not muchdevices, from notebook PCs to mobile gaming devices and recently

research on Wi-Fi packets has accumulated. Moreover, in order toeven digital cameras, are also outfitted with Wi-Fi communication1

functionality.

1% detection of Wi-Fi, leading to the conclusion that Bluetooth was

Most of these devices transmit management packets, called “Probefavorable. However, in fields other than roads, there is research5) by

Request Packets,” that search for Wi-Fi routers to connect to even in

Ryusuke Nakano and others on approximating the degree ofstandby mode. There is some discrepancy between the frequency at

crowding inside train cars by attempting to estimate the number ofwhich different devices transmit this signal, but most generally

passengers inside the cars using probe request receivers installed

transmit them at intervals of around 30 to 90 seconds. Since thisinside the cars.

packet includes a device-specific address assigned to each device (a(3) Comparison of Bluetooth and Wi-Fi System

MAC address), by comparing the MAC addresses of packets
acquired by sensors in multiple locations, we can analyze many types

As stated above, based on the existing research, there are manyof traffic flows.

examples concluding that measurements by Bluetooth are more

It is not possible to identify an individual solely based on thefavorable than those by Wi-Fi.

unique device information included in the packet. If, however, one

The reason for this is that with Bluetooth measurements, first thewere to obtain the MAC address of an individual and maliciously

sensor transmits an API connection request (a request for coupling),

connect it to the person’s personal information, it would be possible towaits for the other terminal to respond, and then acquires the MAC

track that individual’s activity. So we convert the acquired MAC

address included in the reply from the other device, so acquisition ofaddress inside the sensor through a one-way hash function so that thethe other device’s MAC address is simple. Furthermore, we can infer

analysis can be processed anonymously.

that since the dissemination of Bluetooth preceded Wi-Fi for

We call this sensor, which receives Probe Requests withtransmission for devices inside automobiles, such as mobile telephoneanonymous MAC addresses, the Anonymous MAC address Probeheadsets and devices to connect to the sound system, the result of thesensor, or AMP.

measurement was that the rate of detection of Wi-Fi was higher thanthat of Wi-Fi.

3. Literature Review

However, in analyzing traffic flow on expressways, whenmeasuring with Bluetooth, it takes a certain amount of time (8 – 13

(1) Traffic Flow Analysis using Bluetooth packets

seconds) between transmitting the connection request and reply from

It is possible to measure travel speed of automobiles by receiving

the device. So during that time the automobile may leave the sensor’s

packets transmitted from Bluetooth devices in automobiles andeffective range and it has been pointed out 5)in detection of high-speed

comparing them with MAC addresses as identifiers. Much traffic
flow analysis research using this method has been presented sincevehicles, the detection rate drops. In the aforementioned research by
Kitazawa et al.3）, the number of MAC addresses detected at both

2010, particularly abroad. Trung Vo1) used this method to measure

points on the Hanshin Expressway in Japan was quite small, at only 2

travel speed of automobiles on trunk roads.

Compared to– 17 in a 2 hour interval. A cause of this was, in addition to themeasurement methods using license plate identification, magnetic

response time for coupling, the problem of the wave strength

sensors or RFID tags, the measurements were effective with respectapproved for Bluetooth. Namely, in other countries the wave strength

to accuracy, cost superiority, and anonymity. He has conducted

approved for Bluetooth is 100mW, but in the Japanese Radio Law

various other researches2) on analysis of traffic flow with this method.

only half that strength, 50mW, is approved.

2)

In Japan, Kitazawa and Shiomi have attempted researchtoOn the other hand, the recent rapid dissemination of smartphones

measure travel speed on the Hanshin Expressway, and theyhas drastically increased the number of automobiles with Wi-Fi

succeeded at measuring travel speed by comparing the MAC

devices in a short time. In an article in ITS World presented ataddresses of packets transmitted via Bluetooth measured at two points

approximately the same time as our research introducing theon the Hanshin Expressway.

measurement results7) of Blip Systems A/S from the Netherlands, it
was reported that although previously the detection rate for Bluetooth(2) Traffic Flow Analysis using Wi-Fi packets

was clearly higher, recently the detection rate for Wi-Fi has exceeded

Compared to analysis of traffic flow using Bluetooth, there is little

that of Bluetooth.

research on analysis of traffic flow using Wi-Fi packets.

It is not a matter of whether to use detection by Bluetooth andLuber Andreas et al. compared measurements of travel speed on

detection by Wi-Fi, but by using a hybrid sensor that responds to both4)

roads using Bluetooth and Wi-Fi and conducted research totypes of signals, it is possible to analyze traffic flow with a higher

reevaluate the superiority of each. However, upon setting up Wi-Fi

detection rate. Blip Systems A/S of the Netherlands alreadyand Bluetooth wireless devices on roadside gantry and comparing thedeveloped7) a sensor that measures both types simultaneously.

detection of each, they found 6.5% detection of Bluetooth and only2

4. Organization of the System

The unit was intended to be installed indoors. Thus, in order to set
up outdoors, it needs to be produced and take into consideration of

To develop the AMP sensor, we aimed to make it easily installed

temperature changes and weather resistance such as using industrial

in a variety of environments and developed the following two items.

circuit board.

A. Development of sensor software that could be operated on
general -purpose PCs

Wi-Fi Antenna

B. Development of low-cost, small sensor hardware

SD card （OS/System，
for recording log）

(1) Development of Sensor Software Operated with
General PC
We have completed development of sensor software that operates
on three different operating systems (OS), Macintosh, Windows, andLinux.
The structure of the AMP sensor software is described in Fig. 1.
After capturing the Wi-Fi packet, it is immediately hashed with SHA1 and after storing the acquired log on the internal memory, it isBattery （UPS function）

uploaded to cloud storage server each designated period of time.
The time for each sensor is synchronized with NTP, so since the

Raspberry PI

Fig. 2 Structure of the AMP Sensor Hardware

timestamps of multiple AMP sensors are synchronized, it is possible(3) Cloud Storage Server

to accurately measure speed with the data from AMP sensors
between multiple points.

The West Nippon Expressway Company Ltd. (NEXCO-West)has cloud storage servers that can be accessed via the Internet for the
purpose of collecting, analyzing, and servicing a large amount ofprobe data. It is simple to scale up this cloud storage server byincreasing the amount of data, and it has a distributed databaseprocessing engine that can process large quantities of data.
The collected data transmitted from the AMP sensor records the
SHA-1 hashed MAC address, time received, and signal strength in asingle line and stores the acquired log on the server each designatedperiod of time. The amount of data transmitted from one AMP sensor
is not large, but in the future, in order to smoothly process data if
many AMP sensors are set up and continuously operated, wedeveloped the environment capable to use NEXCO West’s high-Fig. 1 Structure of the AMP Sensor System

powered cloud storage server.
(2) Development of Affordable and Small Sensor Hardware

The data uploaded to the server is immediately aggregated by the

We used hardware, Raspberry PI Model B. The CPU is an ARM

analysis system, and from the difference in time between the

1176JZF-S 700MHz. The storage is SDRAM 512MB. The cabled

observations of the same ID measured at two designated locations,connection is a 10/100 BaseT Ethernet socket in the main unit, which

the distribution of travel speed for each designated period between

uses USB wireless LAN adapter connected via USB 2.0 for wirelesseach section is calculated.

signals. It consumes 3.5W of electricity, and the OS is the Linux(4) Wi-Fi Packets Accessible Distancebased Raspbian 3.6.1+.
When the unit is connected to the Internet, such as via Ethernet

Using the system explained in (1) - (3), we conducted various

cable, it automatically uploads the recorded data to a cloud storage

functionality tests of the AMP sensor operation and data processing.

server. However, when it is set up autonomously, it records its log on

The Wi-Fi probe packets had the quite significant travel distance ofan SD card and can operate continuously for about one month.

200 – 350m. However we measured the signal strength of each

Furthermore, in order to equip it with UPS function (Uninterruptible

packet, and it was possible to judge the distance using this signal

Power Supply), when operating on a mobile battery, it can function

strength (-35db ~ -95db). We have just begun to consider thefor approximately 3 hours even without electricity.

possibility of structure that identifies location based on signal strength3

by installing multiple AMP sensors at designated distances, and it will

The measurement was conducted to monitor packets using

be the subject of future research.

MacBook Air, Linux notebook PC, etc. set next to a parked car onroadside (Fig. 4). For this reason, there were problems of bias in the

5. Traffic Flow Analysis on Roads

packet acquisition according to driving direction. Since we did not set
up a special antenna, the reception sensitivity was low as well.

We measured travel speed on roads at following locations using
either the AMP sensor we already developed or AMP sensor
prototype under development.
A) Japan: Packet Acquisition test on National Route 163
Date/Time: Multiple days in April, 2013
B) Japan: Travel speed measurement on the Sanyo Expressway
Date/Time: July 5, 2013

11:50 - 12:50

C) Japan: Residence time measurement at Miki Service Area on

Fig. 4 Scenes of the Experiment on the Sanyo Expressway

the Sanyo Expressway
Date/Time: July 5, 2013

13:35 - 14:25

However, even though we employed this simple measurement

D) Japan: Travel speed measurement in Osaka
Date/Time: January 12, 2014

method, as seen in Table 1, in 60 minutes of measurement we were

10:00 - 12:00

able to acquire 134 samples on the Miki-Higashi side, and 139 on theE) Indonesia: Travel speed measurement in Makassar

Kakogawa side. We had 22 valid samples that were detected at both

Date/Time: January 22, 2014

sites.

In this article, we will discuss the measurement results of the firsttest conducted, “B) Japan: Travel speed measurement on the Sanyo
Expressway,” as well as “E) Indonesia: Travel speed measurement in
Makassar”.
(1) Measuring Travel Speed of Section on Sanyo

Table 2 shows the vehicle travel speed calculated using theExpressway

acquired data. They almost match with the speed measured by the

At the beginning stage of development of the AMP sensor, using

traffic counters installed in nearby locations shown in Table 1.

the sensor software developed for the internal Wi-Fi antenna in

Samples 20 – 22 had extremely slow speed of 19.1 – 45.6 km/h, but

general-purpose PCs (Macintosh, MacBook Air, Linux PC), we

since there is a service area between these two points, it can beinstalled the devices over an 11.4km interval on the Sanyoassumed that these vehicles stopped there. Suppose the average speed

expressway between Miki-Higashi and Kakogawa and conducted

is 80 km/h, the amount of time spent at the Miki Service Area would

test measurements to see if it would be possible to receive Wi-Fi

be between 6 and 27 minutes.

packets from high-speed vehicles and to measure travel distances inThrough this experiment, we were able to see that Wi-Fi packet

that section by comparing MAC addresses.

sensing is well suited to the measurement of the travel speed of

Before this experiment, in the preliminary experiment conductedvehicles on expressways and ordinary roads.

along the side of an ordinary road, we were able to confirm receipt ofa certain number of unique IDs, but we conducted this experiment toTable 1 Acquired Packets on the Sanyo Expressway

confirm whether it would be possible to measure the travel speed of(60 minutes of measurement)

high-speed vehicles. The section measured in this experiment isshown in Fig. 3.

Location

Number ofvehicleAveragespeed intravelinglanes

Averagespeed inpassinglanes

Acquired
packets

Acquisition
rate

Mikihigashi

1,092

87.0km/h

103.9km/h

13412.20%

Kakogaw a

1,247

87.6km/h

105.4km/h

13911.10%

22

1.76%

Commonly acquired
packetsNote) Number of vehicle and travel speed by lane: traffic near the observation point

１１．４ｋｍValue measured with counter (provided by NEXCO-West)

Fig. 3 Travel Speed Measurement Experiment on Sanyo Expressway
4a1 b1
Airport①

b2
a2

Table 2 Travel Speed Calculated from the Acquired Data②

b3

SampleTimedifference(seconds)

Traveling
speed (km/h)

1342.481119.8

2356.781115

3

369.128111.2

4374.231109.7

5395.383103.8

6407.784100.6

7408.896100.4

8441.04593.1

9

443.94592.4

10444.56292.3

11447.32291.7

12453.15190.6

13458.65889.5

14460.46589.1

15484.48484.7

16488.56384

17494.11283.1

18516.35379.5

19525.361

78.1

Time stay ed atMiki serv ice
area assuming
driv ing at
av erage speed
80km/h (min.)20

900.668

45.6

6.3

21

1,011.09

40.6

8.2

Fig. 6 Scenes of Measurement at the Side of the Expressway

22

2,153.62

19.1

27.2

(a3 Location)a3

Miki SA(minutes)

City
Center

③

b6

b5

b4

Fig. 5 Sections where Travel Speed was Measured in Makassar(2) Measuring Travel Speed in Kota Makassar in Indonesia

There were frontage roads on the expressways measured, andAutomotive traffic is rapidly increasing in developing countries

there were both four-wheeled automobiles and two-wheeledsuch as Indonesia, but since there is insufficient infrastructure, such as

motorcycles driving on the frontage road in the a1-a2 section. In

traffic counters, to measure traffic flow, the traffic situation on theparticular, there were many section s of the frontage road in the a2-a3

roads cannot be ascertained. For this reason, provision of information

section that were narrow and were only for two-wheeled vehicles.

on route selection and expressway use is insufficient. ThroughTwo-wheeled vehicles (motorcycles) were not permitted on the trunk

introduction of ITS, we measured travel speed on Makassar’sroad of the expressway.

expressways and ordinary roads for the purpose of building a system

Since, as noted in 4(4), the traveling distance of the Wi-Fi packets

to provide information on road and traffic status.

was 200 – 350m, there was the problem that the AMP sensor set on

Fig. 5 shows the section s where traffic speed was measured on site.

the trunk road would measure the travel speed of vehicles driving on

Section 1. is an expressway and section s 2. And 3. are ordinary roads

the frontage road as well.

that run alongside expressways. In all three section s, we installedmeasuring points at three sites, on both ends and in between them andNext we will explain the situation of section 2. Section 2 in Fig. 5

took measurements.

(measurement points b1, b2, and b3) is a trunk road from the airport
to the city center. It is a trunk road with mostly one lane traveling ineach direction, and there are commercial facilities alongside the road.
Section 3 (measurement points b4, b5, and b6) is a trunk road with 2
or 3 lanes of traffic in either direction, but it is near the city center andthere are many traffic jams mainly during peak hours in the morningand evening.
This means that on ordinary roads, measurements of travel speed
5

would not be only of automobiles, but of bicycles and pedestrians as

for the a2-a3 section are shown in

well.

0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100

20
18
16
14
12
10
8
6
4
2
0

上り

下り

Fig. 8.
Fig. 7 State of Traffic on Ordinary Road (b5-b6 Section)

20
18
16
14
12
10
8
6
4
2
0

Table 3 shows the results of measurements taken by the AMP
sensor in sections 1 – 3.
Table 3 Measurement Results in Makassar
Number ofobservedunique ID at 2
points per
minute

7.1%
10.0%
5.3%

74
94

0.82
1.04

2.6

3.5%

56

0.93

Fig. 8 Travel Speed Measurement Results on Expressway

4.1

3.8%

83

1.38

217

3.6

3.7%

(a2-a3 Section)

12,738

471

7.9

3.7%

171

2.85

The lighter bar shows the inbound route (from the airport into theb512,120

761

12.7

6.3%

217

3.62

city center), and the black bar shows the outbound route (from the cityb6

13,794

459

7.7

3.3%

Point

Number ofvehicle duringobser-vation

Observednumber of
unique ID

Observati
on time(min.)

Number of
unique ID per
minuteAcquisi-tion
ratio (%)

Highway
1.

a1
a2
a3

2,025
3,177
3,258

143
318
173

90

1.6
3.5
1.9

b1

4,512

158

b2

6,456

246

b3

5,898

b4

OrdinaryEast
3.

60

60

0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100

Observednumber of
unique ID at 2
pointsSectionOrdinaryEast
2.

Distance：2,580m
Date：January 23, 2014
Time：12:00～13:30
Weather：Rainy

上り
下り OutboundInboundcenter to the airport). Overall, the reason that the number of*Unique ID is the number of unique ID included in Wi-Fi packet measured at eachobservation location. It matches the number of observed Wi-Fi devices.
*Number of vehicles on ordinary roads includes the number of two-wheelers (motorcycle).
* “a2” observation location is near the toll road exit, and vehicles tend to slow down.
* “b5” observation location is near a commercial complex.

observations of inbound vehicles was greater is thought to be because
the observing vehicle was parked along the inbound side of the roadto take measurements. We interpret the reason that the distribution isdivided in two to be that the lower speed group was vehicles travelingon the frontage road, and the higher speed group was vehicles

Based on these results, we can see that the capture rate is low

traveling on the expressway. As there was tropic squall about 10

compared to the measurements in Japan in July, 2013 on the Sanyominutes after beginning the measurements on the expressway, heavy

Expressway. The number of IDs that was measured at two locations,vehicles that slip easily, such as container trucks lowered their speed.

and the number of observations that could be used to measure travel

The reason that the speed distribution of vehicles diving on the

speed was average of 0.8 to 1.0 per minute on the expressway, andexpressway was wide, from 35 – 75 km/h, is due to the effects of the

0.9 to 3.6 per minute on ordinary roads. In a time period with similarsquall.

levels of traffic, if setting the time units for travel speed measurement

Next, the results of the b5-b6 section from among the travel speed

at 15 minutes, it is possible to acquire above average sample numbers

measurements of the ordinary roads are shown in Fig. 9.

in 15 minutes on both inbound and outbound routes on theexpressway. Locally, the rate of dissemination of smartphones isincreasing, so we expect that the accuracy of this measuring methodof travel speed will improve in the future. Furthermore, as at location
a2, near the tollbooth on expressways where drivers slow down, thecapture rate improves. If we install sensors at these locations giving
priority, the capture rate would increase further.
Next, we calculated the traveling time between two locations from thepackets that could be measured at two points. At each measured section
we looked for the distribution of travel speed. The measurement results
6through the store, and left. The results in Fig. 10 show that many of45

Distance：3,050m
Date：January 23, 2014
Time：17:20～18:20
Weather：Cloudy, sprinkle

40
35
30

the users during the time at the service area facility were there for only
a short period of time.
50分
50

20

45分
45

1

15

40
40分

1

10

35
35分

5

30
30分

0

25
25分

0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100

25

Date：July 5, 2013
Time：13:35～14:25
Weather：Sunny

3

3
2
3

20
20分

6

15
15分

Inbound上り
下りOutbound

10

10
10分

12

5
5分

Fig. 9 Travel Speed Measurement Results on Ordinary Road

19

minutes00(b5-b6 Section)

5

10

15

20

Fig. 10 Distribution of Residence Time at Miki Service Area

In Makassar, there are still few traffic lights on ordinary roads, andduring rush hours, cars attempting to turn right stop the flow of(2) Distribution of residence time at fast food restaurant in front of
the train station

oncoming traffic, and intense traffic results. The time when theThis example of analysis is not related to roads, but with themeasurements were taken overlapped with the evening rush hour,cooperation of a fast food restaurant in front of the train station, weand especially on the outbound lane where people were returning

took continuous measurements over the course of 17 days. We are

home, a decrease in travel speed due to traffic was measured.

unable to publicize the name of the restaurant and the date and timethus, the speed is high without traffic jams, 40 – 50 km/h. However,measurements were taken, but are able to use the measurement data

6. Residence Time Analysis

taken exclusively for academic purposes.
the number of customers at the point-of-service (POS) registers

In the traffic flow analysis by the AMP sensor, in addition toand the number of AMP sensor measurements for five of those 17

analyzing travel speed between two points, by aggregating the timedays including a weekend. Based on these results, we can see that therecords when the same ID was measured continuously, it is possible

number of customers counted at the POS registers in the fast foodto find distribution of residence time. On the expressway, it isrestaurant in front of the station matches accurately the number ofeffective in analyzing usage tendencies and residence time of service

customers staying in the restaurant measured by the AMP sensor.

areas and parking areas.

The correlation coefficient between these two numbers was 0.945.

We will next introduce an example of this residence time analysis.

There were some discrepancies, depending on the hour, between
the number of customers at the POS registers and the numbers(1) Distribution of residence time at the Miki Service Area

measured by the AMP sensor; the numbers for customers at the POS

On the same date and time that we measured travel time on theregisters is higher during lunchtime on weekdays, and the numbers

Sanyo Expressway between Miki-Higashi and Kakogawa as

observed with the AMP sensor were higher in the evenings. As this

explained in 5(1), we measured the residence time distribution at therestaurant locates in suburb, there were many groups of middle-aged

food court in the Miki Service Area. The results of thesehousewives during the day, and there were many students andmeasurements are shown in Fig. 10.

businessmen in the evenings. Therefore, we infer that the difference in

The food court at the Miki Service Area is located next to retailownership rate of smartphones among customer types and the hours

stores but is distant from restaurant and toilet. In these results, we see

affected the results.

that during the 50 minutes that measurements were taken, we
measured about 60 samples. Here, we defined delays as thosesamples measured continuously for over one minute. Samples that
did not stay inside the building and just passed through in front of thebuildings were excluded.
Since we started at 13:35, after lunchtime, but we observed many
people eating at the service area. However, we also observed many
who purchased drinks from vending machines, browsed quickly
7

conduct analysis of traffic flow by sensing Wi-Fi packets from

Coefficient of correlation: 0.9448

smartphones, etc. that have been disseminated rapidly in recent years.
We also showed applications in measuring travel speed and residence
time analysis. The AMP sensor we developed shows high potential to
be used for analysis of traffic flow in vehicles on roads including
expressways, and people flow. Furthermore, we proposed measures
to protect personal information and personal data, which are both
legal and ethical concerns once the system is implemented in practice.
Wi-Fi packet sensors are a measurement method that has become
Numberof customersatPOS

practical rapidly in the past one to two years. We expect that it can be

ProbeRequest (Staymorethan300seconds)

used widely in society.

Fig. 11 Number of POS Registers and Numbers Observed by AMP

In the future, we hope to continue with not only analyzing travel

Sensor at Fast Food Restaurant in Front of the Station

speed and residence time at a few locations as in this article, but also

7. Sensing and Personal Information Protection

by installing numerous sensors widely, we hope to continue withsystems to examine wide-area traffic flow and applications forIn the AMP sensor system, we took the utmost care to protect

Origin-Destination surveys at low cost and so on. Moreover, we hopepersonal information; the measured MAC addresses were madeto further research in various fields, such as disaster planning andanonymous using hashing function, and the coding and hacking

operations management of urban facilities, etc.

countermeasures on the server were set at high level.
The information acquired with this system is not categorized as

Acknowledgement:

Section 4. of this research was carried out

personal information stipulated under the Japanese Personalunder the commission of the Strategic Information and Communica-

Information Protection Law, but it has characteristic of personal data

tions R&D Promotion Programme (SCOPE) (Receipt numberthat can be used to recognize individual’s behavior. Currently in

132307011). Furthermore, the results of section 5. (2) are the results

Japan, there will be amendment of the Personal Informationof the research project funded by the New Energy and Industrial

Protection Law, and a bill will be submitted in 2015. In advance ofTechnology Development Organization (NEDO). We wish to ex-

the amendment, in December 2013, the “Policy Outline of thepress our appreciation for them.

8)

Institutional Revision for Utilization of Personal Data” related topersonal data protection was publicized. Following this policy outline,

Reference

we developed principle on the system taking the following measures:

1)

1.

Define the purpose of use for data collected2.

Define the content of data collected and how it is to behandled3.

2)

Define prevention measures for those who do not wish to beobserved (such as by turning off the Wi-Fi functionality on
their smartphone)

4.

3)

Define the contact information for deleting data if anyone’s
personal data was obtained against their wishes, andpreparation method for handling (opt-out measures)

5.

Set a period of data storage for analysis, and clearly state to
not disclosing data to any third parties

4)

If measurements were to be taken in small area, the above 1. to 5.
could be posted around the sensor, etc. If it is wide area, they could be5)posted on website or issued a press release. It would be necessary to
publicize the information with appropriate methods.

6)

8. Conclusion
In this article, we described the sensor software and hardware to8

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