BLECE: BLE-based Crowdedness Estimation
Method for Restaurants and Public Facilities
Yuki Matsuda † , Kentaro Ueda , Eigo Taya , Hirohiko Suwa † , Keiichi Yasumoto †Nara Institute of Science and Technology, Nara, Japan,
Email: {yukimat, ueda.kentaro.ug2, taya.eigo.td3, h-suwa, yasumoto}@is.naist.jp.
† RIKEN Center for Advanced Intelligence Project AIP, Tokyo, Japan.

Abstract—The crowdedness in various places in the city, suchas public transportation, restaurants, and public facilities, ishigh-demand information for not only general people but also
municipalities and companies. However, it is not easy to acquirecomprehensive data because existing services of crowdedness
measurement separately collect and provide data in different
ways, although there are many services. This study aims toestablish the universal method of crowdedness estimation, which
is robust to various environments, by scanning BLE (Bluetooth
Low Energy) signals emitted from mobile devices owned bygeneral people. In this paper, we focus on restaurants and public
facilities with different types, conditions, and sizes and propose amethod of crowdedness estimation by fusing data obtained fromother numbers of BLE scanners depending on each space. As aresult, we confirmed that models trained with the same feature
set for each space show a practical performance. Additionally, we
explore the technical challenges when implementing the system
in a new space through detailed analysis.
Index Terms—Smart City, IoT, Crowdedness Estimation, Bluetooth Low Energy

I. I NTRODUCTION
The level of crowdedness at various places in an urban
environment is one of the most anxious information for people
visiting and staying in a city. This demand covers a wide
range of places including public transportation, restaurants,
and public facilities. From the perspective of the whole society,there is also a need to preserve cities safe and comfortable forall by leveling congestion (i.e., avoiding excessive crowding inpublic spaces). From both perspectives, it is essential to know
the level of crowdedness across various places.
In recent years, various congestion estimation methods
have been studied. For example, methods using GPS logs
from smartphones [1], [2], cameras and LiDAR [3]–[8],smartphone-equipped sensors [9]–[11], and radio waves suchas Wi-Fi and BLE [12]–[20] have been proposed. Some ofthese methods are commercialized as actual services. However,existing congestion measurement services collect data andprovide information in different ways at different locations
and by other providers, making it difficult for users to obtain
crowdedness information across various places.
This study aims to establish a universal crowdedness estimation method independent of location and environment.
This study was supported in part by JST PRESTO under Grant No.
JPMJPR2039 and JST COI-NEXT under Grant No. JPMJPF2115.

To achieve this aim, we are exploring a technique basedon scanning BLE (Bluetooth Low Energy) signals, which
are emitted from smartphones and other electronic devices,from the environmental side, and estimating the level ofcrowdedness using the data of the received signals. In recent
years, people have tended to bring/wear/use multiple devices
such as smartphones, smartwatches, smart tags, and smart
locks. These devices communicate via BLE when exchanging
data in most cases. Hence, we can assume that people usuallyturn on the BLE of their devices. So far, we have proposeda method for estimating the number of passengers in a carof public transportation, namely fixed-route buses, and trains,and have confirmed the effectiveness of the method [15], [16].
In this paper, we focus on the crowdedness in closed spaces
such as restaurants and public facilities, which differs fromthe target of our previous work. Compared with previous
work, people can enter and leave the space anytime forindoor spaces. Hence, the problem of increasing the numberof addresses will be expected to arise. We created the inthe-wild datasets for around 10 days in actual restaurants
and public facilities, which have different types, conditions,and sizes (four locations in total) by using other numbers of
BLE scanners. Then, we built and evaluated a crowdedness
estimation model. The results showed that our model built foreach space using XGBoost Regressor performs with the mean
absolute error (MAE) of 4.89, the mean absolute percentage
error (MAPE) of 84.0%, and the root mean square error(RMSE) of 6.34 in the worst case. These results indicatethat a certain crowdedness level can be estimated by usinga model with common features. Additionally, we explore thetechnical challenges when implementing the system in a newspace through detailed analysis.
Our contribution is three-fold:1) we proposed a BLE-based crowdedness estimation
method for public facilities and restaurants based on
fusing data from a different number of BLE scanners,2) we evaluated the performance of the proposed methodwith in-the-wild datasets in four public spaces whichhave different types, conditions, and sizes, and3) we provided a discussion of the technical challenges
when distributing the proposed method to a new space
through detailed analysis.

II. R ELATED W ORK
Research on crowdedness estimation in public spaces has
been tackled for various spaces, with various approaches.
Here, we explain them categorized into three groups: urban
spaces (i.e., wide areas of the city), mobility spaces (e.g.,
public transportation), and indoor/outdoor spaces (e.g., public
facilities, restaurants). In the following, a literature review ofrelated studies and the position of this study will be provided.
A. Crowdedness Estimation in Urban Spaces
Several telecommunication companies provide crowdedness
information of the city by using their customer’s connection
status. There are several services, Yahoo! Map Congestion
Radar [1] provided by Yahoo Japan and Kompreno [2] byAgoop. These services collect and visualize location information using GPS-equipped mobile devices such as smartphones,
with permission from users of applications provided by eachcompany. These services estimate the population at a certain
mesh, such as 125m or 250m square, but it is difficult to derive
the degree of crowdedness in a specific space.
In the research area of computer vision, estimating crowdedness and people flow by image analysis using cameras isgetting attention for a long time [3], [4]. Sindagi et al. [3]proposed a method for estimating crowd density and count byfusing multiple CNN components which incorporate global
and local contextual information of a crowd image. Liu etal. [4] proposed an end-to-end trainable deep architecture,
named Context-Aware Network, that combines the features
obtained from multiple receptive field sizes for counting
people in crowded scenes.

C. Crowdedness Estimation in Indoor/Outdoor Spaces
There are studies and services using the technology of laser
imaging, detection, and ranging (LiDAR). Yamaguchi et al. [7]proposed a LiDAR-based estimation method of individuals’
and human crowds’ locations and behavior, named Hitonavi.
SICK AG [8] provides a system for counting the number ofpeople who leave or enter an area by using 3D-LiDAR.
Also, the methods using several sensors equipped on mobile
devices are proposed [9]–[11]. Kannan et al. [9] proposed
participatory crowdedness estimation system where soundsignal emitted from a smartphone is received by anotheruser’s smartphone, and its acoustic characteristics is analyzed.
Nishimura et al. [10] proposed a method for estimating thesmoothness of pedestrian flows using accelerometer data andambient sound data collected by smartphones. Moustafa etal. [11] proposed crowdedness estimation method in railway
stations using motion sensors of a smartphone for analyzing
passenger’s behavior and the microphone for capturing ambient sound characteristics.
The crowdedness estimation methods using Wi-Fi and BLE
are also studied [17]–[19]. Umeki et al. [17] used the BLE
emitter and receiver for counting people who pass throughbetween devices, by observing the RSSI fluctuation causedby people. Weppner et al. [18] proposed the people density estimation method by using people’s mobile devices
for scanning neighbor BLE devices. Takahashi et al. [19]combines an overhead camera and a Wi-Fi scanner that detects
probe requests from people’s mobile devices for estimating
crowdedness of bus stations.

B. Crowdedness Estimation in Mobility Spaces

D. Position of this study

The methods for estimating the crowdedness using cameras
installed inside a car of public transportation are proposed [5],[6]. Song et al. [5] proposed a system for counting the numberof passengers using images from surveillance cameras. Although these approaches directly recognize images of people
to be observed and thus enable estimation with relatively highaccuracy, the installation and analysis of cameras are likely tocreate restrictions on the locations where they can be installedfrom the viewpoint of social acceptability.
The methods using radio waves, such as Wi-Fi and BLE, arealso proposed [12]–[16]. Handte et al. [12] proposed a method
for estimating the number of passengers on routed busses
by counting the number of MAC addresses of passengers’
mobile devices that connect to the Wi-Fi access point equippedwith a bus car. Hydayat et al. [13] used GPS and WiFi scanner which detects MAC addresses of individual bus
passengers, for estimating the number of passengers. Maekawa
et al. [14] proposed a method based on scanning surrounding
Bluetooth signals by the passengers’ smartphones. In ourprevious work [15], [16], we proposed a method for estimating
crowdedness on buses and trains by scanning BLE signals
emitted from passengers’ mobile devices using scanners in
the environment and building models with the signal reception
strength and its combinations.

As mentioned above, although there have been many studies
on crowdedness estimation, there has not been sufficient verification of its applicability to different environments. This study
aims to establish a universal crowdedness estimation methodthat can be applied across various public spaces, including
mobility and indoor/outdoor spaces. The purpose of this paper
is to evaluate and discuss whether crowdedness estimation is
feasible in indoor spaces, especially restaurants and public
facilities with different conditions, by applying the method
proposed by the authors previously [15], [16] which uses BLE
scanners placed in the environment.
III. DATA COLLECTION
A. Data collection system
For data collection, a device with the same functionality as
those used in previous studies by the authors [15], [16] will be
used (hereafter referred to as BLECE node). The setup of thedevice and its installation example are shown in Figure 1. The
BLECE node operates based on the Raspberry Pi 4 Model B,
and BLE data acquired using a Bluetooth 4.0+EDR/LE Class
1 compatible USB adapter (BUFFALO, BSBT4D105BK) will
be uploaded to a cloud server via mobile network using LTE
compatible USB dongle (PIXELA, PIX-MT100).

TABLE I: Overview of target spaces and collected data
Space ID

Type of space

Capacity

A *a
B *b
C *c
D *d

Cafe
Restaurant
Restaurant
City Library

25
50
50
-

Shape of
space
SquareSquareRectangle
L-shaped# of BLECE
nodes
1
1
2
3

Label datacollection method
SurvCam*e
SurvCam*e
SurvCam*e
Manual*f# of Labeldata samples
556
895
1398
316

Data collection period *g
Dec. 15, 2021 – Mar. 15, 2022
Dec. 23, 2021 – Mar. 15, 2022
Nov. 24, 2021 – Mar. 15, 2022
Jan. 5, 2022 – Mar. 29, 2022

*a nijiiro*cafe (cafe & restaurant), https://nijiirocafe.com/*b Tonmasa (Japanese pork cutlet restaurant), https://tonmasa.com/*c Marukatsu Ikoma (Japanese pork cutlet restaurant), https://marukatsu912.com/*d Ikoma City Library, https://lib.city.ikoma.lg.jp/*e A surveillance camera is placed at entrance.
*f A person in charge manually counts number of people.
*g In this paper, we used nondeletional and continuous 10 days data from data collection period for building models.

Space A (cafe)Space B (restaurant)

Raspberry Pi 4

Kitchen

KitchenReception

BLE dongle(BSBT4D105BK)

Reception

LTE dongle
Entrance(PIX-MT100)(a) Setup of BLECE node(b) Installation example of BLECE nodeSpace C (restaurant)

Entrance

Space D (library)

Fig. 1: Overview of BLECE node
B. Overview of target spaces and collected data
In order to verify the possibility of estimating congestion in
public facilities and restaurants of different types, conditions,and sizes, the fixed spaces shown in Table I were used for datacollection. This study was approved by the Ethical Review
Committee for Research Involving Human Subjects at Nara
Institute of Science and Technology (Approval No.: 2020-I16).
As shown in Table I, the target spaces A–D have different
business types and capacities, and shapes of space. The required number of BLECE nodes placed differs based on these
conditions. Figure 2 shows the shape and seating arrangementof each space, as well as the placement of BLECE nodes
and entrance/exit cameras (the red ⌥ indicates the locationof BLECE nodes and the green N indicates the location ofsurveillance cameras). For example, three BLECE nodes havebeen placed in space D because it is a city library in frontof a main station and has a large area and the floor plan is
L-shaped. The data collected are timestamps, the Bluetooth
Device Address (hereinafter referred to as BD address) of eachscanned device, and the received signal strength (RSSI) of theBLE. Since this study is targeting indoor spaces, information
specific to mobility spaces, such as location information androute numbers, which have been utilized in previous studies [15], [16], are excluded. The BLE scan interval is 15
seconds (10 seconds for scanning and 5 seconds for sending
data and waiting time). To reduce installation costs, we tolerate

Kitchen

Entrance

Entrance

Librarian's
office

Rcpt
Rcpt

Entrance

BLECE node

Surveillance camera

Space ASpace BSpace CSpace D

Fig. 2: Floor plan of each target space, locations of BLECE
nodes and surveillance cameras, and photos of each target
space. Note that the scale and aspect ratio of space and thelocation of tables and seats are not precise necessarily.

that each BLECE node does not synchronize the timing of
scanning BLE devices.
The label data of crowdedness, i.e., the number of people
staying in space at a certain moment, has been collected indifferent ways depending on the requests from the managers
of each space. In spaces A, B, and C, a surveillance camera(equivalent to the BLECE node configuration with the additionof a Raspberry Pi Camera Module) was installed at theentrances and exits of the spaces. Then, label data every
five minutes were acquired by performing posterior annotation
through counting manually. The difference in the number oflabel data samples for each space is due to the different
opening hours. An example of the installation of a surveillancecamera and an example of the captured images are shown in
Figure 3. When installing the surveillance cameras, the datacollection has been conducted in consideration of the privacyof general people, referring to the Camera Image Utilization
Guidebook Ver. 2.0 [21] formulated by the Ministry of Economy, Trade and Industry (METI), the Ministry of Internal
Affairs and Communications (MIC), and IoT Acceleration
Consortium (ITAC). Specifically, captured movies taken by
surveillance camera have been processed on the edge side sothat specific individuals cannot be identified, and the noticeof this experiment has been provided through the authors’
website 1 and posters put in each space during the datacollection periods (from one month before starting to endingof the experiment). In space D, an investigator staying in thespace has patrolled periodically (about once every 15 minutes)
the space and collects the label data by visually counting thenumber of people.
The data collection period is shown in Table I. Due to atechnical problem, there are several missing data, hence, inthe following sections, we used non-deletional and continuous
10 days data of each space (Spaces A, B, and C: January 19
to February 3, 2022; Space D: March 15 to March 29, 2022).
IV. C ROWDEDNESS ESTIMATION MODEL
Using the datasets collected in the previous section, we
evaluate the feasibility of estimating crowdedness in various
indoor spaces.
A. Preprocessing
In this paper, the number of BLECE nodes differs due to thedifferent space sizes of target spaces A to D. Therefore, as apreprocessing step, the data scanned by multiple BLECE nodes
are integrated and converted into a format that is independentof the number of BLECE nodes. As mentioned above, the
BLECE nodes perform BLE scans with a measurement cycleof 15 seconds, but the acquisition timing among the BLECE
nodes is asynchronous because the scans are initiated atarbitrary timings.
The data integration procedure is as follows. First, the mostrecent N samples before time t of the label data are obtainedfor each BLECE node, and the pairs of data to be integrated
1 https://www.iopt.jp/exp/blece-vol1

Example of captured movie

Fig. 3: Installation example of surveillance camera
are determined by considering the time-adjacent data as having
been obtained at the same time. Next, the union set (OR) of
the BD addresses in the data to be integrated will be taken. For
duplicate data that are caused by a device being scanned bymore than one BLECE node, the one with the highest RSSI
(stronger signal) is retained. This process is equivalent to a
virtual superposition to the same location of BLECE nodes
that are actually installed in different locations.
B. Feature extraction
BLE signals can not be scanned from all devices in thespace at any given time, but only when the device sends a
BLE advertising packet while the BLECE node is performinga scan (10 seconds in the BLECE node configuration in thispaper, as mentioned above). Also, if the number of devices inthe space is large, scan omissions may occur. Therefore, it isnecessary to mitigate these effects by providing the model withfeatures based on multiple samples of data. On the other hand,
many of the devices currently in the markets incorporate analgorithm that irregularly randomizes BD addresses to protect
the privacy of the owner. In other words, there is a risk ofan unnecessary increase in the number of BD addresses if thesamples used for calculating features of estimation models aretaken from too long time range. In mobility spaces such as
fixed-routed buses, people do not move in and out of spaces
while driving between bus stops. Our previous method utilize
this condition as a cue that the person was staying in the space.
In contrast, people can enter and leave the space at any time
for indoor spaces such as a restaurant, hence, the problem of
increasing the number of addresses will be expected to arise.
In order to construct a model that does not depend on
the location or environment while taking into account theconditions specific to an indoor space, we employ additionalfeatures derived by varying the range (time width) of pastsample acquisitions. Table II shows a list of features and theirdetails. Since it is supposed that the number and demographics
of visitors in restaurants and public facilities will be changed
depending on the time of day and the day of the week, we also
employed features of the hour (time of day) and is weekday(weekday flag). In total, 42 features are extracted.

TABLE II: List of features
Feature name*a

Details

all num T sec

Total number of devices (BD addresses)
scanned in the past T seconds.
Total number of unique devices (BD addresses) scanned in the past T seconds.
Percentage of unique devices among
BD addresses scanned in the past T
seconds (ratio of unique number andtotal number).
Total number of unique devices whose
RSSI is more than threshold S among
BD addresses scanned in the past T
seconds.

unique num T sec
unique ratio T sec

unique num T sec Sdb

hour
is weekday

Time of day (0–23)
Weekday flag
(weekday: 1, weekend/holiday: 0)*a T indicates the time range (15, 30, 45, 60 seconds) over which pastsamples are referenced, and S indicates the threshold ( 60 to 90 dB, in
5 dB increments) when referring to samples with high RSSI values.

C. Evaluation and results
We built a crowdedness estimation model using the extracted features for each space and evaluated them. Three typesof machine learning models were used: SVR (Support Vector
Regressor, RBF kernel), RFR (Random Forest Regressor),and XGBR (XGBoost Regressor). The hyperparameters ofeach model were optimized by GridSearchCV in scikit-learn.
To evaluate the models, leave-one-day-out cross-validation
was used, and MAE (Mean Absolute Error), MAPE (Mean
Absolute Percentage Error), and RMSE (Root Mean Squared
Error) were used as evaluation indices.
The evaluation results are shown in Table III. Overall, themodel constructed by XGBR showed relatively high performance. For Space A, the range of the number of people visiting
this space is too small within the data set collected in this paper(maximum label data was 12 people), hence, we will exclude
Space A in the subsequent analysis and discussion.
The results of the model constructed by XGBR for spaces
B, C, and D (scatter plots of the label data and estimateddata) are shown in Figure 4. The scatter plots show that eachestimation model captures the general trend in the crowdednessof the space. Also, we have confirmed the performance ofthe models did not differ significantly for different numbersof BLECE nodes. In Spaces B and C, both cases show large
errors in MAPE of 51.9% and 58.1%, respectively. This might
be due to the fact that there were many samples with a small
number of people (less than 10 people) in the data set, as canbe seen from Figure 4a and Figure 4b.
Next, the feature importance (top 20 features) of the models
constructed by XGBR for the spaces B, C, and D are shownin Figure 5a, 5b, and 5c, respectively. The results show thatthe most promising BLE-related features are the total numberof BD addresses in the most recent sample (all num 15sec),
the percentage of unique BD addresses in a wide time range(unique ratio T sec, range of T = 30
60sec), and the

TABLE III: Evaluation results of crowdedness estimation
model for each space
Space ID
A
B
C
D

Model
SVR
RFR
XGBR
SVR
RFR
XGBR
SVR
RFR
XGBR
SVR
RFR
XGBR

MAE
(# of people)
1.36
1.44
1.49
3.97
3.75
3.61
4.51
4.09
4.04
5.47
4.91
4.89

MAPE
(%)
61.1
71.2
69.6
91.0
90.0
84.0
80.1
77.5
70.9
27.5
25.5
24.0

RMSE
(# of people)
1.97
2.01
2.06
5.40
5.00
4.59
6.15
5.59
5.62
6.99
6.33
6.34

number of uniques BD addresses with strong signal strength
in a narrow time range (unique num T sec Sdb, range ofT = 15 30sec and S > 70db). For the total number of BD
addresses and the number of unique BD addresses, the data inthe short term and/or data with strong signal strength tend tocontribute effectively because they directly and roughly reflect
the number of people. In contrast, for the percentage of uniqueBD addresses, the data in the long term tend to contribute
effectively because they could explain how fluid the location
is. These indicate that it is effective to provide features withdifferent time ranges which are newly employed in this paper.
Also, the time of day was shown to be a strong cue.
V. C ONCLUSION
This study aims to realize a method for estimating thecrowdedness in various spaces including mobility spaces andindoor/outdoor spaces based on the BLE signals emitted frommobile devices such as smartphones owned by general people.
In this paper, we build and evaluate crowdedness estimation
models for four indoor spaces (restaurants and public facilities) with different types, conditions, and space sizes. As
a result, it was shown that the models built for each space
using common features achieve a certain level of crowdedness
estimation performance regardless of the different numbers of
BLECE nodes. However, we also confirmed the performanceof congestion estimation is not yet enough when the numberof people in the space is less than 10 people. In the future,
we will explore more effective data integration methods andfeature extraction methods to improve performance and realize
a universal crowdedness estimation method.
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Fig. 4: Evaluation result of crowdedness estimation model with XGBR(a) Space B(b) Space C(c) Space D

Fig. 5: Top 20 important features for crowdedness estimation model with XGBR
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