Analysis of Wi-Fi-based and Perceptual Congestion

Masaki Igarashi, Atsushi Shimada, Kaito Oka and Rin-ichiro Taniguchi

Kyushu University, 744, Motooka, Nishi-ku, Fukuoka, Japan

{igarashi, atsushi, kaito}@limu.ait.kyushu-u.ac.jp, rin@kyudai.jp

Keywords:

Congestion Estimate, Perceptual Congestion, Probe Request.

Abstract:

Conventional works for congestion estimates focus on estimating quantitative congestion (e.g., actual number

of people, mobile devices, and crowd density). Meanwhile, we focus on perceptual congestion rather than

quantitative congestion toward providing perceptual congestion information. We analyze the relationship

between quantitative and perceptual congestion. For this analysis, we construct a system for estimating and

visualizing congestion and collecting user reports about congestion. We use the number of mobile devices as

quantitative congestion measurements obtained from Wi-Fi packet sensors, and user-report-based congestion

as a perceptual congestion measurement collected via our Web service. Base on the obtained quantitative and

perceptual congestion, we investigate the relationship between these values.

1 INTRODUCTION

Congestion measurements and estimates are useful

and important for various applications. Congestion

information can assist congestion avoidance and mit-

igation. It is also important that we grasp the num-

ber of visitors to retail stores for customer analysis

and marketing strategies. Additionally, evacuation

planning for disasters requires congestion information

(Choi et al., 2011).

The extent of congestion is measured or estimated

manually or using sensors such as cameras and Wi-

Fi packet sensors. Vision-based congestion estimates

using cameras have recently been developed, and the

accuracy of these methods improves with each year.

Cameras must be carefully installed in the target area

considering occlusion and blind areas, and the initial

costs tend to be high. Wi-Fi packet sensors estimate

the number of mobile devices (e.g., smartphones and

laptop computers). The number of mobile devices

tends to be proportional to the number of people, so

we can use them to roughly estimate congestion. Wi-

Fi packet sensors cover distances between dozens to

a hundred meters. A Wi-Fi radio wave has a higher

transmittance than visible light, therefore we can in-

stall packet sensors in typical situations without con-

sidering blind areas.

The above-mentioned techniques are aimed at es-

timating quantitative congestion measurements such

as people count, crowd density, and the number of

mobile devices. For customer analysis, the actual

(a) Dining hall (b) Bus stop

Figure 1: Two spots with similar crowd density.

people count and density are useful and important fac-

tors.

Meanwhile, in terms of providing congestion in-

formation to people, qualitative congestion measure-

ments such as a person’s perception are also impor-

tant. Figure 1 shows two spots with almost the same

crowd density. There are few vacant seats in the

dining hall, so we would feel that the dining hall is

crowded. The crowd density at the bus stop is similar

as the dining hall, but the bus stop cannot be consid-

ered crowded. Human perception about congestion

depends on the people count and density and also the

locations characteristics, such as area and seating ca-

pacity.

In this paper, we focus on the relationship between

quantitative and perceptual congestion toward provid-

ing perceptual congestion information. We use the

number of mobile devices as quantitative congestion

measurements obtained from Wi-Fi packet sensors,

and user-report-based congestion as a perceptual con-

gestion measurement collected via our Web service.

We investigate the relationship between these values.

Igarashi, M., Shimada, A., Oka, K. and Taniguchi, R-i.

Analysis of Wi-Fi-based and Perceptual Congestion.

DOI: 10.5220/0006206102250232

In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2017), pages 225-232

ISBN: 978-989-758-222-6

225

Probe request

Wi-Fi packet sensor

Server

• Congestion estimate and forecast

• Congestion visualization

• Accuracy improvement of

congestion estimate by user reports

Visualization

User reports

Figure 2: Overview of our system.

2 RELATED WORK

2.1 Preliminaries

2.1.1 Probe Request and Counting Mobile

Devices

Probe request is a packet (or more precisely, a frame)

deﬁned in IEEE 802.11, which is broadcast by a mo-

bile device with a Wi-Fi function when the device

searches for access points before establishing a con-

nection. The transmitting interval is between several

to several hundred seconds and depends on the device.

The probe request frame includes a MAC address for

the sender, so the receiver can identify the sender and

count the peripheral mobile devices. Additionally, the

receiver can obtain a received signal strength (RSS)

value. The RSS value is lower for larger distances be-

tween the sender and receiver. Therefore, the receiver

can roughly estimate the distance to the sender using

the RSS value.

2.1.2 Wi-Fi Packet Sensor

A Wi-Fi packet sensor is a sensor for collecting probe

request frames. We can make a prototype Wi-Fi

packet sensor using a single-board computer such as

a Raspberry Pi

. Commercial Wi-Fi packet sensor

products have recently become available

. A Wi-

Fi packet can be sent from over one hundred meters

away, so they can cost effectively cover a large area.

2.2 Estimation and Analysis of Crowd

Density and Pedestrian Flow

Fukuzaki et al. analyzed pedestrian ﬂow using Wi-

Fi packet sensors (Fukuzaki et al., 2014). Yaik et

https://www.raspberrypi.org/

http://aibeacon.jp/

al. compared the number of Wi-Fi frames and crowd

counting manually (Yaik et al., 2016). They evalu-

ate the correlation between manual and Wi-Fi frames

counting. Above-mentioned methods used Wi-Fi

probe requests. Xi et al. use Channel State Infor-

mation (CSI) of Wi-Fi for counting crowd (Xi et al.,

2014). Their method outperforms the state-of-the-art

approaches in terms of accuracy and scalability.

Weppner et al. estimated crowd density by Blue-

tooth scan data (Weppner and Lukowicz, 2013).

Schauer et al. proposed a hybrid method for esti-

mating crowd density and pedestrian ﬂow using Wi-Fi

and Bluetooth (Schauer et al., 2014). They estimated

the crowd density (deﬁned as the number of people

per unit area) for each spot using Wi-Fi and Blue-

tooth, and compared the estimated number of people

with the ground truth.

These works considered the number of people or

the number of devices, but not the peoples perception

of the congestion.

3 SYSTEM FOR CONGESTION

ESTIMATE AND COLLECTING

USER REPORTS

In this section, we describe our system for estimating

and visualizing the congestion, and collecting user re-

ports.

3.1 System Overview

Figure 2 gives an overview of this system. We capture

probe request frames and store tuples of the received

time, location ID, and MAC address to the database.

Then, we calculate the extent of the congestion us-

ing the data stored in the database. Our Web service

plots a time series of the congestion for each location.

This service has a function for receiving user reports

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Big Sand 1F Big Sand B1F

QASIS

Learning

Space

Bus stop

(Nishitetsu)

Bus stop

(Showa)

Dining hall: Big Sand 1F

Past Forecasted

Congestion

Figure 3: Web service for visualizing congestion.

about the congestion. The details of the system are

described in following subsection.

3.2 Probe Request Capturing and

Filtering

We used Wi-Fi packet sensors located in various loca-

tions to capture probe request frames. Wi-Fi packets

can be received when the receiver is several hundred

meters away from the sender. In this study, we esti-

mated the congestion at dining halls and bus stops in a

university campus. We ﬁltered out packets with weak

signal strengths (under -80dB) so that we only col-

lected data from close devices. The received time t of

the packet, place ID (sensor ID) p, and MAC address

m are stored to the database D

D ← D ∪{(t, p, m)} (1)

3.3 Congestion Degree based on Probe

Requests

We deﬁne the congestion degree c(t, p) for each

time and place using probe request data without prior

Actually, we store hash values to the database instead

of MAC addresses because of privacy issues.

Please report about congestion.

Crowded

Medium

Vacant

Submit

Figure 4: User report form.

knowledge of the location as

(t, p) =

|{m |(t

, m, p

) ∈ D, t − 180 ≤ t

≤ t, p

= p}| (2)

c(t, p) =

(t, p)

α max

(t, p))

, (3)

where c

(t, p) is the number of unique MAC address

observed during the three minutes. We obtain c(t, p)

by normalizing c

(t, p). The value α is determined

empirically (in this paper, α = 0.75).

3.4 Visualizing Congestion

We developed a Web service to visualize the extent of

the congestion. Figure 3 plots the congestion degree

calculated using Eq. 3. The red line represents the

congestion during the last 30 minutes and the green

line represents the forecasted congestion. Readers can

browse other locations using the upper tabs.

3.5 User Report

We collected user reports as perceptual congestion

measurements. Figure 4 shows the form for report-

ing the congestion degree on our website. There are

ﬁve radio (option) buttons. Users can only select one

radio button. After a user pushes the submit button

in the form, the selected item, time, and location are

submitted to the server.

Analysis of Wi-Fi-based and Perceptual Congestion

227

Table 1: Location of Wi-Fi packet sensors.

ID Location Floor Type Purpose

1 Dining hall A GF Indoor Breakfast, lunch and dinner

2 Dining hall B GF Indoor Breakfast, lunch and dinner

3 Learning space 3F Indoor Learning

4 Dining hall C B1 Indoor Lunch

17 Bus stop A N/A Outdoor Returning home

18 Bus stop B N/A Outdoor Returning home

2. Dining hall B (GF)

1. Dining hall A (GF)

4. Dining hall C (B1)

3. Learning space (3F)

18. Bus stop B

17. Bus stop A

50m

Figure 6: Wi-Fi packet sensors on a campus.

4 ANALYSIS OF CONGESTION

AND USER REPORTS

In this section, we analyze the congestion and user

reports obtained using our system.

4.1 Operation of Our System

We installed six Wi-Fi packet sensors on an university

campus (Figures 5 and 6). Table 1 shows the installed

spot, whether or not the spot is located indoors. We

have been operating these packet sensors since Jan-

uary 2016.

Figure 5: Wi-Fi packet sensor.

We have been operating our Web service for vi-

sualizing congestion and recording user reports since

July 2016. We received over three hundred user re-

ports about congestion via our Web service during the

ﬁrst four weeks.

4.2 Time Series of Congestion

Figure 7 shows the congestion calculated using Eq. 3

for a typical week. We can see that the dining halls

(IDs 1, 2, and 4) have a steep peak around 12:00 be-

cause of lunchtime. The congestion at the bus stops

(IDs 17 and 18) tends to ﬂuctuate intensely. This is

because busses arrive every 5 to 15 minutes to col-

lect passengers. Both bus stops are mainly used to go

home, so peak congestion occurs around the evening.

4.3 Correlation Analysis of User

Reports

We analyzed the correlation between user reports and

the congestion recorded using Wi-Fi packet sensors.

Figure 8 shows the scatter diagrams and correlation

coefﬁcients for the user reports and Wi-Fi-based con-

gestion for each location. The correlation coefﬁcients

for locations 1, 3, and 4 are over 0.6, so we can

say that the quantitative and perceptual congestion of

those spots have moderate correlations. Meanwhile,

the correlation coefﬁcient for location 2 is less than

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