Estimating congestion in train cars
by using BLE Signals
Eigo Taya∗† , Yuji Kanamitsu∗† , Koki Tachibana∗ ,Yugo Nakamura‡ , Yuki Matsuda∗†§ , Hirohiko Suwa∗† , Keiichi Yasumoto∗†∗ Nara Institute of Science and Technology, Nara, Japan,

Email: {taya.eigo.td3, kanamitsu.yuji.ky4, tachibana.koki.ti0, yukimat, h-suwa, yasumoto}@is.naist.jp.
† RIKEN Center for Advanced Intelligence Project AIP, Tokyo, Japan.
‡ Kyushu University, Fukuoka, Japan, Email: yugo@arakawa-lab.com.
§ JST PRESTO, Tokyo, Japan.

Abstract—In many of the world’s major cities, commuter trains
provide vital transportation support and thus play an essential
role in our daily lives. Therefore, it has become necessary toestimate the degree of congestion in each train car, both toimprove passenger comfort levels and, more recently, to prevent
worsening the COVID-19 pandemic infection rate. However, it
is difﬁcult to estimate the degree of congestion within a train
without violating passenger privacy. The same issues are true
for busses, which is noteworthy because we have previously
developed and evaluated a system that can estimate the degreeof congestion within a bus while protecting passenger privacy
by using Bluetooth Low Energy (BLE) signals. In this paper, we
report on our efforts to extend that system to railway use, whichwere conducted on actual trains in cooperation with Kintetsu
Railway Co., Ltd. During this trial, we collected BLE signals
and used the data to estimate congestion levels in each car
using an ML regression model. The results show that the mean
absolute error (MAE) and the mean absolute percentage error(MAPE) could be estimated at accuracy levels of 5.56 and 0.27,
respectively.
Index Terms—BLE, Train, Public transportation, Congestion
estimation, Machine learning

I. I NTRODUCTION
In many of the world’s major cities, commuter trains
provide vital transportation support for commuters and thus
play an essential role in our daily lives. However, commuter
rush hours and weather-related passenger surges can cause
train congestion levels to rise past those where comfort iscompromised. Because of this, it is well known that many
passengers decide which train they will take based not only on
its on-time operation and fare but also on its comfort level [1].
Accordingly, train operators often collect and provide information on congestion levels to prevent users from abandoning
trains due to their lack of comfort. Another factor requiringconsideration is the COVID-19 pandemic, which has beenongoing since November 2019 and was still causing severe
economic, sanitary, and social problems for human society
as of December 2021 [2]. However, even though people are
striving to reduce contact with others to avoid spreading theinfection, they still use trains to sustain daily activities such ascommuting to work or shopping, so knowing train congestion

levels in advance will permit them to schedule journeys anduse trains with less risk of spreading the infection. This is aparticularly urgent problem since trains are among the mostwidely used public transportation systems and tend to be themost congested at speciﬁc times. Our research group has previously proposed a congestion Bluetooth Low Energy (BLE)
estimation system for local busses that protects passenger
privacy [3]. In this system, BLE signals from the mobile
devices of bus passengers are ﬁrst detected, after which theBluetooth Device Address (BD Address) included in the BLE
signal, and the received signal strength indication (RSSI), areprocessed. In our method, passenger numbers are deﬁned as
the number of addresses satisfying the threshold value for theobtained BLE signal RSSI and a dataset that combines BLE
data and bus-speciﬁc information. That study also discussed amethod for estimating passenger numbers on busses using amachine learning (ML) model.
In this paper, we report on adapting the above system totrains by estimating the degree of congestion in each traincar. To accomplish this, we ﬁrst carried out a data collection
experiment on operational trains in cooperation with Kintetsu
Railway Co., during which the passenger numbers estimatedfrom the collected data showed a threshold-based estimation
result with a mean absolute error (MAE) and a mean absolute
percentage error (MAPE) of 7.27 and of 0.27, respectively.
In contrast, the ML-based estimation resulted in a MAE of
5.56 and a MAPE of 0.27. These results indicate that theestimation accuracy levels improved for the car, especially athigh passenger levels.
The contributions of this research are as follows:
1) We constructed a device that collects BLE signals emitted
by passenger mobile terminals and collected BLE data on
actual trains in operation.
2) We constructed an ML-based model to estimate thecongestion in train car and found that the model was
sufﬁciently accurate for use in actual operation.

II. R ELATED W ORK
There are currently numerous research types and a wide variety of different approaches to congestion estimates [4]–[12].
In this section, we introduce research on estimating passenger
numbers and BLE signal-based congestion estimations, bothof which are particularly relevant to our study.

Their method appropriately aggregates the data obtained fromuser devices, and estimates crowd density levels without theneed to install new sensors. However, the above methods
require a mechanism to encourage user involvement since theestimation accuracy depends primarily on the number of users
participating in the sensing process.

A. Estimating passenger numbers

C. Positioning of this research

Camera-based systems have been proposed as the simplest
way to estimate passenger numbers [13]–[15]. For example,
Song et al.proposed a system that counts passenger numbers
using surveillance camera video footage [13]. However, theircamera-based estimation system records passenger faces andthus could potentially be used to track individuals. This is
an important issue because it is not desirable for a public
transportation system, such as a train system, to routinely
collect information that may violate passenger privacy.
Several other studies have proposed using Wi-Fi signals
to estimate congestion levels [16]–[19]. For example, Handte
et al.proposed estimating passenger numbers on a Wi-Fiequipped bus by counting the number of passenger mobile
terminal media access control (MAC) addresses connected toan access point. In this system, the error for of the estimatednumber of passengers was 5.1, but the accuracy decreased
when the bus became crowded. In addition, estimating congestion using Wi-Fi signals collects the MAC address for eachpassenger, which may also be used to identify individuals.
Methods aimed at detecting passengers using infrared sensors have also been proposed [20]–[24]. For example, todetect passenger numbers at an airport, Bauer et al.proposed
combining an infrared sensor with a mat-type pressure sensor[20]. However, infrared sensors count people by detectingchanges in signal intensity levels, so the estimation accuracyof that system tends to be poor when people loiter near thesensor or when a large number of people rapidly pass thesensor. Such situations frequently occur in the case of trains(e.g., leaning against the doorway, getting on and off a train
at stations), which means that infrared sensors are not suitablefor use under those conditions.

As described above, there are numerous studies on estimating passenger numbers, but only a few both ensure privacy
protection and can be practically used in transportation systems. In contrast, our research group has previously proposed a
BLE signal-based congestion estimation system for busses thatcan more thoroughly protect passenger privacy [3]. Therefore,
this study extends the application of that system to trains andreports on our attempt to estimate railway passenger numbers
without compromising their privacy.

B. Estimating congestion using BLE signals
With the widespread use of smartphones, many studies
have proposed congestion-based estimation schemes using
wireless communication technologies such as BLE [25]–[29].
For example, Umeki et al.proposed a system to estimate thecongestion levels in sightseeing spots by focusing on RSSI
intensity levels, which vary signiﬁcantly depending on thenumber of people present [27]. In that study, they installed a
BLE signal broadcaster and a receiver at a sightseeing spot
and estimated the degree of congestion by observing andevaluating the RSSI intensity distribution at three levels: ”low,””medium,” and ”high.”
Separately, Weppner et al.proposed a method for estimating crowd densities that works by aggregating the numberof mobile signals detected from nearby user-carried BLE
terminals moving in a monitored environment [28], [30].

III. DATA C OLLECTION S YSTEM
A. System overview
A photograph of the sensing device used in our implemented
system is shown in Figure 1. As can be seen, the system isimplemented on a Raspberry Pi single-board computer andconsists of a Bluetooth dongle, a global positioning system(GPS) module, and a network module to enable Long-Term
Evolution (LTE) communication.
1) Bluetooth dongle: To protect passenger privacy while
estimating congestion levels on public transport systems, oursystem detects the BLE signals generated by passenger mobile
terminals. BLE is a power-saving communication standard
among Bluetooth-standard short-range wireless communication devices. When active, each BLE device continuously
broadcasts its availability data in an attempt to connect withother BLE devices. The dongle attached to the system collects
BD addresses and RSSI signal strengths from the signals sentby the passenger BLE devices in order to identify the devices.
However, since BD addresses change randomly at regularintervals, passenger privacy is protected.
2) GPS module: To estimate the congestion level, it isnecessary to identify at which station sections the system hascollected BLE signals. In theory, since public transportation
systems operate according to established timetables, it ispossible to map BLE signals to the station sections using
the time information. However, as public transport is often
delayed by external factors such as weather and accidents,
it is challenging to identify BLE signals related to station
sections using only time information. Therefore, the system
uses a GPS module to obtain the train’s position, so that it
can map BLE signals to station sections even if the transport
service is delayed.
3) Network module: Since many public transportation systems do not have network access points like Wi-Fi in every car,we use a network module that can be attached via a Universal
Serial Bus (USB) connector to monitor the system remotely
and collect real-time data.

Network
Module1{"timestamp": 1628662870,"lat": 34.693779,"lng": 135.782786,"ble": [{"addr": "72:12:4f:67:44:92","rssi": -54},{"addr": "1a:b0:4f:e5:94:e4","rssi": -87
},
],

2
3
4
5

Raspberry Pi

6
7
8
9
10
11
12
13
14
15

}

GPS Module
BLE Module

Fig. 3. Scan data excerpt.
Fig. 1. System sensing device.
TABLE I
L ABELING DATA EXCERPT.
Station A

Station B

Station C
・・・

label

Investigator

Time
15:13
15:15
15:21
15:25
15:32
15:36

Start
Nara
Shinomiya
Yamatosaidaiji
Gakuenmae
Ikoma
IshikiriEnd
Shinomiya
Yamatosaidaiji
Gakuenmae
Ikoma
Ishikiri
Fuse

Type
Local
Local
Express
Express
Express
Express

Order
3
3
5
5
5
5

Num
33
38
47
49
38
35

15sec.

DeviceD. Feature extraction

scan
Time

Fig. 2. Sensing process.

Since the BLE data collected by the system in a single
scan may contain signals from terminals outside the car, weaggregate all the data scanned in each station section and build
two features. First, we assume that the same BD address canbe detected multiple times within a station section when the
BLE terminal and system sensing device are in the same car.
Hence, the frequency F of signals generated by a single BD
terminal is calculated as follows:

The system collects the BLE signal, which requires thepassenger’s mobile device to turn on Bluetooth while moving.
However, Bluetooth is becoming more widespread with thedevelopment of BLE devices such as wireless earphones andsmartwatches and contact tracing technology. We, therefore,
assume that the system can collect a sufﬁcient number of BLE
signals to estimate the number of passengers.

ndetected
× 100
(1)
Nscan
where Nscan is the detection count in a station section andndetected is the number of times that the same BD address is
detected. In addition, the average of the RSSI Smean in station
sections is calculated as follows.

Section 1

Section 2

B. Assumption

C. Sensing process
In our system, we estimate passenger numbers on thecars while the train moves from station to station(section) inorder to provide an indicator of their congestion levels. An
overview of the data sensing and labeling process is shownin Figure 2, where it can be seen that the system detects
BLE signals from the surrounding area every 15 seconds andsends that data to the data server. The sent data includes
timestamp, latitude, longitude, and BLE data collected fromseveral terminals (Figure 3). In our experiment, an investigator
carrying a sensing device boarded a car of an operating train
and manually recorded passenger numbers while the device
simultaneously collected sensing data (Table I).

F (%) =

Smean =

1ndetected∑

ndetected

i=1

S(i)

(2)

where S(i) is the RSSI of the i-th detection in a station
section. Next, we adopt the BD address count above a certain
threshold as a station section feature, using the values obtainedby the above equations 1, 2.
IV. E STIMATION AND E VALUATION
A. Experiment environment
We then collected BLE data on trains in operation using
the system described in station section III. More speciﬁcally,
we collected data from 381 station sections (with overlaps) in
Nara Prefecture. There were two restrictions to our actual train
experiment: (1) no power supply could be used for the sensing

TABLE II
F EATURE LIST.

Device
Seat

Door

Seat

Door

Door

Seat

Door

Seat

Domain
BLE
Seat

Door

Seat

Door

Seat

Door

Seat

Door

Seat

Train

1 Threshold is set S

means :-30
to -100 or higher，F :0 to
100or higher

Fig. 4. Device and investigator location in the cars.

device, and (2) no sensing device could actually be installedin the trains. As explained above, our investigators carried asensing device equipped with a mobile battery onto the car tocollect data while simultaneously visually checking passenger
numbers in order to obtain the actual passenger counts. This
experimental environment ensured that the sensing device was
located at the same position as the investigator who sat in the
middle of the car where its interior could be observed easily,
as shown in Figure 4.
B. Evaluation methodology
We estimate passenger numbers in each station section withthe data collected in the IV-A section. Two methods are used
for the estimation: one is based on the BLE signal threshold,and the other is based on an ML regressor.
1) Threshold estimation: As described in the III-D section, we calculate the average RSSI (Smean ), and occurrence
frequency (F ) for all BD addresses detected in each station
section. Then, BD addresses above a particular value are set
as valid addresses, and the total number of these addresses is
set as the estimated value.
2) ML regressor estimation: Table II shows the ML features
used. Here, we consider the average RSSI or occurrence
frequency of a BD address to be essential indicators thatindicate the address is present in the car. Therefore, the numberof addresses at each incremental threshold is included in the
BLE features. We also include the number of scans in the
BLE features since the BD address count for each thresholdis considered to affect the number of scans a device performs
within a station section. Additionally, since information suchas train operation times and types: e.g., local, limited express,
or express, is considered vital for estimating congestion levels,
such as commuting rush hour trafﬁc and transit times, information speciﬁc to the train being observed is included in the
train features.
Next, we constructed a train dataset (TD) with BLE features
and a dataset containing all features (TD+ ) and then used themto compare accuracy levels with and without train information. Each model estimates passenger numbers in one station
section for these datasets using the Random Forest (RF) andLightGBM (LGBM) regression models. The estimation results
were then evaluated by leave-one-out cross-validation, and the
Optuna Framework was used to adjust the hyperparameters ofeach model [31].

FeatureBD address count 1
Number of scans
Departure hour
Operation type
Station id
Order of cars

TABLE IIIR ESULT OF THRESHOLD ESTIMATION .
MethodALL BD Addresses
Smean ≥ −50
Smean ≥ −55 ∩ F ≥ 40

MAE
283.98
9.29
7.27

MAPE
13.67
0.33
0.27

C. Result1) Threshold Estimation: First, we explain the results when
passenger numbers are deﬁned by the total BD address countobtained in each station section. The graph in Figure 5 plots
the total BD address count (Estimated value) and passenger
numbers (True value) on the vertical and horizontal axes,
respectively. Note that the blue line in the graph shows theideal state when there is no difference between the trueand estimated values. This graph shows that the total BD
address count is signiﬁcantly larger than the actual number ofpassengers. Accordingly, we then calculated the absolute error
between each station section’s true and estimated values andthen averaged them over the total number of station sections
to obtain the MAE and MAPE values, which were 283.98,
and 13.67, respectively (Table III).
Next, we describe the estimation results obtained by setting
thresholds for the average RSSI (Smean ) and occurrence
frequency (F ) of BD addresses. Table III shows the MAE and
MAPE values when the appropriate thresholds are set, while
Figure 6 shows a graph plotting the true and estimated values(number of valid BD addresses). Here, the threshold is set toan empirical value. We also show a graph plotting the trueand estimated values for the number of valid BD addresses
in Figure 6. From Table III, it can be seen that estimating
passenger numbers with threshold results in smaller MAE and
MAPE values, and thus more accurate estimations. Compared
to Figure 5, it can be seen that the estimation is much closer
to the true value. In contrast, the estimation accuracy is low
in the station section where the true value is more than 50
people.
2) Estimating ML regressor estimation: Table IV shows
cross-validated MAE and MAPE values. Here, it can be seenthat the model with TD+ achieves the best performance, with
RF MAE and MAPE values of 5.85 and 0.28, respectively, andLGBM MAE and MAPE values of 5.56, of 0.27, respectively.
The graph plotting the true and estimated values obtained by

TABLE IV
P ERFORMANCE OF E ACH M ODEL FOR E ACH DATASET

2500

Estimated value

2000

Model
RF
LGBM

1500
1000

0
0

20

40

60

80

True value

Features

500

100

100

100

80

80

Estimated value

Estimated value

Fig. 5. Measured values (BD address counts) versus true values (passenger
counts) obtained from raw data.

60
40
20

MAE
5.96
6.07

TD
MAPE
0.28
0.29

TD+
MAE MAPE
5.85
0.28
5.56
0.27
785

add_freq_10
add_freq_20
add_freq_30
add_freq_80
add_rssi_70
add_rssi_55
add_freq_40
add_freq_0
add_rssi_45
add_rssi_65
add_freq_60
start
add_freq_70
end
add_rssi_60
add_freq_50
add_freq_90
add_rssi_50
add_rssi_40
add_rssi_75
order_of_vehicle
add_freq_100
add_rssi_80
scan_num
dep_houradd_rssi_85
type_suburban
add_rssi_35
type_express
type_local
type_rapidexpress

724
650
591
558
543
540
521
510
493
480
473
472
471
463
454
442
436
434
432
407
394
365
292
255
145
91
67
45
44
39
0

100

200

300

400
500
Feature importance

600

700

800

Fig. 8. Feature importance value.

60
40

V. D ISCUSSION
20

A. Estimation threshold

Figure 5 shows that the total BD address count detectedin the station section is much larger than the true value.
(a) Smean ≥ −50(b) Smean ≥ −55 ∩ F ≥ 40 This may be due to the detection of BLE devices outside
the car and the fact that the BD address changes over time.
Fig. 6. Values estimated by LGBM (BD address count) versus true values
This indicates that by setting appropriate thresholds for the(passenger count).
average RSSI and the occurrence frequency of BLE signals,
we can signiﬁcantly improve estimation accuracy levels, as
shown in Figure 6. On the other hand, as shown in Figure
LGBM using TD+ shown in Figure 7 references the feature 6 (b), the estimated value is much lower than the true value
importance values in Figure 8. Here, it can be seen that the for the interval where the true value is more than 50 people.
occurrence frequency feature (add fre x) makes a particularly This is believed to be due to the characteristics of BLE
signals, which are attenuated signiﬁcantly when they encounter
important contribution to the estimation.
obstructions. Therefore, in a crowded environment, the signals
are attenuated by human bodies, and it is assumed that the100
100
signals from some terminals cannot be detected. In particular,
80
80
since the devices were situated near the center of the carin our experiment, they experienced difﬁculties detecting the60
60
terminals of passengers at the car ends.
40

20

40

60

True value

80

0
0

100

Estimated value

Estimated value

0
0

40

20

40

60

True value

80

100

B. ML regressor estimation

In this study, we improved the accuracy of the model by
including train-speciﬁc features in addition to the BLE-related
0
0
0
20
40
60
80
100
0
20
40
60
80
100 features used to estimate passenger numbers per car. The
True value
True value
cross-validation results show that the best-performing model(a) TD(b) TD+
has a MAE of 5.56 and a MAPE of 0.27. Comparing theresults with the threshold estimation in Figure 6, it can be seenFig. 7. Values estimated by LGBM (number of BD addresses) versus truethat the accuracy of our model has been improved, especiallyvalues (number of passengers)
for the station section where the true value is greater than 50
individuals. The feature importance values in the model with
20

20

the best accuracy, shown in Figure 8, indicate that there arehigh values related to relatively small frequency values of 1030%. When detecting BLE terminal signals, we found thatthe device could detect the signal from a nearby terminal witha high threshold while more distant terminal signals could
only be detected with low thresholds. Therefore, the small
frequency value is considered to have contributed signiﬁcantly
to signal determination for distant terminals.
VI. C ONCLUSION
This paper reports on the collection of BLE data from operating trains as part of efforts to estimate passenger numbers
while protecting their privacy. To deal with the varying numberof devices in the train and passengers carrying multiple BLE
devices, we applied an estimation method that sets thresholds
for BLE data and uses an ML model to estimate passenger
numbers. The results show that passenger numbers can be
estimated with an MAE accuracy of about 5.56. Our future
work will include the collection of additional data. Our experimental environment only includes data from the hoursof 13:00 to 19:00, which do not include the morning rush
hour. Accordingly, it will be necessary to collect data undervarious conditions in order to achieve a high level of accuracy
before our system can be put into practical use. We also
made careful efforts to protect the privacy of passengers byusing BLE addresses, which are better than video images for
producing congestion estimations. However, since a detailed
analysis of the addresses in the BLE data could be used to
identify individuals, it will be necessary to design a moreprivacy-aware system that discards the addresses as soon as
the feature construction process has been completed.
ACKNOWLEDGMENT
This study was funded in part by the Japan Science andTechnology Agency (JST)’s Strategic Basic Research Program,
Precursory Research for Embryonic Science and Technology(PRESTO) grant number JPMJPR2039. We would like tothank Kintetsu Railway Co., Ltd. for their cooperation in thedata collection process of this study.
R EFERENCES[1] D. van Lierop, M. G. Badami, and A. M. El-Geneidy, “What inﬂuences
satisfaction and loyalty in public transport? a review of the literature,”
Transport Reviews, vol. 38, no. 1, pp. 52–72, 2018.
[2] W. H. Organization, “Coronavirus disease (covid-19) pandemic.”
https://www.who.int/emergencies/diseases/novel-coronavirus-2019, accessed: 2021-12-01.
[3] Y. Kanamitsu, E. Taya, K. Tachibana, Y. Nakamura, Y. Matsuda,
H. Suwa, and K. Yasumoto, “Estimating congestion in a ﬁxed-routebus by using ble signals,” Sensors, vol. 22, no. 3, 2022. [Online].
Available: https://www.mdpi.com/1424-8220/22/3/881[4] L. Del Pizzo, P. Foggia, A. Greco, G. Percannella, and M. Vento, “Aversatile and effective method for counting people on either rgb or
depth overhead cameras,” in 2015 IEEE International Conference on
Multimedia & Expo Workshops (ICMEW). IEEE, 2015, pp. 1–6.
[5] S. D. Pore and B. Momin, “Bidirectional people counting system invideo surveillance,” in 2016 IEEE International Conference on Recent
Trends in Electronics, Information & Communication Technology (RTEICT). IEEE, 2016, pp. 724–727.

[6] P. G. Kannan, S. P. Venkatagiri, M. C. Chan, A. L. Ananda, and L.-S.
Peh, “Low cost crowd counting using audio tones,” in Proceedings ofthe 10th ACM Conference on Embedded Network Sensor Systems, 2012,
pp. 155–168.
[7] A. B. Chan, Z.-S. J. Liang, and N. Vasconcelos, “Privacy preserving
crowd monitoring: Counting people without people models or tracking,”
in 2008 IEEE Conference on Computer Vision and Pattern Recognition.
IEEE, 2008, pp. 1–7.
[8] T. Nishimura, T. Higuchi, H. Yamaguchi, and T. Higashino, “Detecting
smoothness of pedestrian ﬂows by participatory sensing with mobile
phones,” in Proceedings of the 2014 ACM International Symposium
on Wearable Computers, ser. ISWC ’14. New York, NY, USA:
Association for Computing Machinery, 2014, p. 15–18. [Online].
Available: https://doi.org/10.1145/2634317.2642869[9] Y. Wada, T. Higuchi, H. Yamaguchi, and T. Higashino, “Accurate
positioning of mobile phones in a crowd using laser range scanners,”
in 2013 IEEE 9th International Conference on Wireless and Mobile
Computing, Networking and Communications (WiMob), 2013, pp. 430–435.
[10] M. Fu, P. Xu, X. Li, Q. Liu, M. Ye, and C. Zhu, “Fast crowd density estimation with convolutional neural networks,” Engineering Applicationsof Artiﬁcial Intelligence, vol. 43, pp. 81–88, 2015. [Online]. Available:
https://www.sciencedirect.com/science/article/pii/S0952197615000871[11] G. Li, C.-J. Chen, W. Peng, and C. Yi, “Estimating crowd ﬂow andcrowd density from cellular data for mass rapid transit,” 2017.
[12] K. Nagao, D. Yanagisawa, and K. Nishinari, “Estimationof crowd density applying wavelet transform and machinelearning,” Physica A: Statistical Mechanics and its Applications,vol. 510, no. C, pp. 145–163, 2018. [Online]. Available:
https://EconPapers.repec.org/RePEc:eee:phsmap:v:510:y:2018:i:c:p:145163[13] H. Song, X. Liu, X. Zhang, and J. Hu, “Real-time monitoring for crowd
counting using video surveillance and gis,” in 2012 2nd International
Conference on Remote Sensing, Environment and Transportation Engineering. IEEE, 2012, pp. 1–4.
[14] H. Takayanagi, T. Sano, and H. Watanabe, “A study on the pedestrianoccupied territory in the crossing ﬂow: The analysis with pedestrian
territory model,” Journal of Architecture and Planning (Transactions of
AIJ), vol. 66, no. 549, pp. 185–191, 2001.
[15] H. Su, H. Yang, and S. Zheng, “The large-scale crowd density estimation based on effective region feature extraction method,” in Asian
Conference on Computer Vision. Springer, 2010, pp. 302–313.
[16] M. Handte, S. Foell, S. Wagner, G. Kortuem, and P. J. Marrón, “Aninternet-of-things enabled connected navigation system for urban bus
riders,” IEEE internet of things journal, vol. 3, no. 5, pp. 735–744,
2016.
[17] S. Jackson, A. Lesani, and L. Miranda-Moreno, “Toward a wiﬁ system
for trafﬁc monitoring in different transportation facilities,” in Transportation Research Board 93rd Annual Meeting, 2014.
[18] A. Hidayat, S. Terabe, and H. Yaginuma, “Wiﬁ scanner technologies
for obtaining travel data about circulator bus passengers: case study inobuse, nagano prefecture, japan,” Transportation Research Record, vol.
2672, no. 45, pp. 45–54, 2018.
[19] ——, “Estimating bus passenger volume based on a wiﬁ scanner survey,” Transportation Research Interdisciplinary
Perspectives, vol. 6, p. 100142, 2020. [Online]. Available:
https://www.sciencedirect.com/science/article/pii/S2590198220300531[20] D. Bauer, M. Ray, and S. Seer, “Simple sensors used for measuringservice times and counting pedestrians: strengths and weaknesses,”
Transportation research record, vol. 2214, no. 1, pp. 77–84, 2011.
[21] H.-b. Qian and H. Han, “The applications and methods of pedestrian
automated detection,” in 2010 International Conference on Measuring
Technology and Mechatronics Automation, vol. 3, 2010, pp. 806–809.
[22] H. Yang, K. Ozbay, and A. Bartin, “Investigating the performance ofautomatic counting sensors for pedestrian trafﬁc data collection,” inProceedings of the 12th World Conference on Transport Research, vol.
1115, 2010.
[23] R. Greene-Roesel, M. C. Diogenes, D. R. Ragland, and L. A. Lindau,“Effectiveness of a commercially available automated pedestrian counting device in urban environments: Comparison with manual counts,” UC
Berkeley: Safe Transportation Research and Education Center, 2008.
[24] U. Scheunert, H. Cramer, B. Fardi, and G. Wanielik, “Multi sensor based
tracking of pedestrians: a survey of suitable movement models,” in IEEE
Intelligent Vehicles Symposium, 2004, 2004, pp. 774–778.

[25] L. Schauer, M. Werner, and P. Marcus, “Estimating crowd densities
and pedestrian ﬂows using wi-ﬁ and bluetooth,” in Proceedings ofthe 11th International Conference on Mobile and Ubiquitous Systems:
Computing, Networking and Services, ser. MOBIQUITOUS ’14. Brussels, BEL: ICST (Institute for Computer Sciences, Social-Informatics
and Telecommunications Engineering), 2014, p. 171–177. [Online].
Available: https://doi.org/10.4108/icst.mobiquitous.2014.257870[26] A. Lesani and L. Miranda-Moreno, “Development and testing of a realtime wiﬁ-bluetooth system for pedestrian network monitoring, classiﬁcation, and data extrapolation,” IEEE Transactions on Intelligent
Transportation Systems, vol. 20, no. 4, pp. 1484–1496, 2018.
[27] K. Umeki, Y. Nakamura, M. Fujimoto, Y. Arakawa, and K. Yasumoto,“Real-time congestion estimation in sightseeing spots with ble devices,”in 2018 IEEE International Conference on Pervasive Computing andCommunications Workshops (PerCom Workshops). IEEE, 2018, pp.
430–432.
[28] J. Weppner and P. Lukowicz, “Bluetooth based collaborative crowd
density estimation with mobile phones,” in 2013 IEEE international
conference on pervasive computing and communications (PerCom).
IEEE, 2013, pp. 193–200.
[29] V. Kostakos, T. Camacho, and C. Mantero, “Towards proximity-based
passenger sensing on public transport buses,” Personal and ubiquitous
computing, vol. 17, no. 8, pp. 1807–1816, 2013.
[30] J. Weppner and P. Lukowicz, “Collaborative crowd density estimation
with mobile phones,” Proc. of ACM PhoneSense, 2011.
[31] T. Akiba, S. Sano, T. Yanase, T. Ohta, and M. Koyama, “Optuna: A nextgeneration hyperparameter optimization framework,” in Proceedingsof the 25th ACM SIGKDD International Conference on Knowledge
Discovery & Data Mining, ser. KDD ’19.
New York, NY, USA:
Association for Computing Machinery, 2019, p. 2623–2631.