Article citation information:
Bugdol, M.N., Bugdol, M.D., Grzegorzek M.,
Mitas A.W. Road traffic estimation using Bluetooth sensors. Scientific Journal of Silesian University of
Technology. Series Transport. 2017, 96,
15-25. ISSN: 0209-3324. DOI: https://doi.org/10.20858/sjsutst.2017.96.2.
Monika
N. BUGDOL[1], Marcin D. BUGDOL[2], Marcin GRZEGORZEK[3],
Andrzej W. MITAS[4]
ROAD TRAFFIC
ESTIMATION USING BLUETOOTH SENSORS
Summary. The
Bluetooth standard is a low-cost, very popular communication protocol offering
a wide range of applications in many fields. In this paper, a novel system
for road traffic estimation using Bluetooth sensors has been presented. The
system consists of three main modules: filtration, statistical analysis of
historical, and traffic estimation and prediction. The filtration module is
responsible for the classification of road users and detecting measurements
that should be removed. Traffic estimation has been performed on the basis of
the data collected by Bluetooth measuring devices and information on external
conditions (e.g., temperature), all of which have been gathered in the city of
Bielsko-Biala (Poland). The obtained results are very promising. The smallest
average relative error between the number of cars estimated by the model and
the actual traffic was less than 10%.
Keywords:
Bluetooth; ITS; traffic estimation
1. INTRODUCTION
Every year the intensity of road
traffic grows constantly, with this trend unlikely to change in the near
future. People spend more and more time unproductively in vehicles, which
directly result in economic losses of the part of the individual and the entire
economy. The search for an alternative route is very important, not only from
the driver’s point of view but also for managing the traffic in this area.
The existing systems for notifying
an individual driver about traffic jams, which remain the responsibility of
traffic management centres, involve variable message signs installed at
selected routes. This information, although extremely precise, is very often
delayed with respect to the point in time at which the decision about changing
the route should be made. Traffic data are collected, based on sensors mounted
at appropriate points along the road network (e.g., at intersections). For this
purpose, the most commonly used sensors are inductive loops, video detection,
acoustic detection and radio detection [1].
Drivers who are equipped with
cellular phones have access to many different navigation systems, which can
provide up-to-date traffic information on a given road stretch. This allows for
the most time-efficient route to be planned in advance. A major drawback of
such systems is the fact that the data used to determine the traffic situation
are collected from users of these applications. Therefore, the estimation
accuracy is strongly dependent on the number of service users, meaning that
information can be very inaccurate if there are too few users. Furthermore,
some users are concerned about their privacy being invaded by IT corporations,
given that information about their location is sent to location-based services,
which operate mostly in continuous mode recording every position change. The
most significant problem regarding all mobile applications, however, is the
lack of confirmed information about their accuracy. Moreover, the results are
presented in the form of colours, which are not defined in a quantitative way
and may represent different speeds on different types of roads.
There are many studies on the
estimation of vehicle velocity, location and travel route on the basis of
different mobile phone information. The possibility of using data from mobile
phones in local IT systems has been described in [2]. Location data have been
gathered and tests carried out under simulation conditions. The measurements
presented in [3] have been performed on a rather short section of the road (2.5
km), while the mobile phones in question must have been equipped with dedicated
software. In [4, 5], it has been proven that information from the cellular
network can be used to detect traffic congestion, while, in [6], an undefined
wireless vehicle-to-vehicle communication protocol is used to estimate traffic
density. Traffic estimation on the basis of GPS data and speed profile is
presented in [7]. In [8], the same problem is resolved by employing GSM data
gathered in a large database. Noisy data filtering and the estimation of
vehicle localization have also been presented. The detection of dangerous road
events using smartphone sensors has been described in [9].
An important disadvantage of these
systems is that they use mobile networks to collect data and thus require
operators’ approval. Much more flexible are solutions that are based on
generally available communication protocols, such as Bluetooth or Wi-Fi,
available in each new mobile device.
In [10], the suitability of Bluetooth
technology is examined in terms of the provision of IT services. This study
tested parameters, such as maximum connection range, delays in connection
set-up and the influence of vehicle speed on communication quality. The results
prove that this technology can be employed in traffic monitoring. In [11, 18],
the employment of Bluetooth in vehicle-to-vehicle communication was studied in
order to increase road safety. In [12], the system for car tracking using
Bluetooth devices is limited to vehicles that travel at 70 km/h at most, which
is insufficient for traffic monitoring on high-speed roads. In [13], an
analysis of the influence of different Bluetooth data aggregation methods on
the accuracy of travel time estimation is presented. A complex system using
Bluetooth data for traffic monitoring in a large city is presented in [14],
which proves that this kind of technology can be successfully employed within
IT services.
In this work, a novel system for
road traffic estimation has been developed. It consists of several modules,
while its main task is to provide information to drivers about the road traffic
situation, which is evaluated on the basis of the number of detected Bluetooth
devices, as well as weather conditions and time parameters.
2. METHODOLOGY
The whole system consists of a
filtration module, data archiving, statistical analysis of historical data, and
traffic estimation. Its task is to provide short- and long-term forecasts of
road traffic using Bluetooth sensors, which have been developed in the course
of another part of the project.
2.1. Filtration
Bluetooth sensors are distributed
according to the user’s needs. Each of them gathers signals from all Bluetooth-enabled
devices within its range. The first step is the filtration of the acquired data
in order to find measurements that could corrupt the subsequent calculations.
The following road users have been
identified:
·
Objects that are still or
moving at low speed (e.g., pedestrians or cyclists)
·
Cars or trucks
·
Buses
The residence time of the device
within the wireless user detector (WUD) served as the basis for the
classification.
Data filtering should take place at
the local station to reduce the amount of transmitted information to the
central repository and thus shorten the processing time to determine the
parameters of traffic on the road network. In addition, a smaller amount of
transferred data decreases the minimum technical characteristics of certain elements
of the infrastructure, which directly affects the price of the system.
The following indications are used
in formulas and diagrams:
ArrVeh – array containing the last 20
residence times of vehicles within the Bluetooth sensor
ArrVehArch – array containing aggregated
archival residence times of vehicles within the Bluetooth sensor
ArrNoVehArch – array containing aggregated
archival number of occurrences of Bluetooth devices that have been filtered
E(t)
– value of the moving average of the residence time of vehicles within the
Bluetooth sensor
α – coefficient of the moving average
tVehBt – time period in which the
Bluetooth device has been within the sensor range
Archived data are stored in the
database table archival_data,
containing average residence times for a given sensor. In each record in this
array, information is stored on the type of day and time given to the nearest 5
min for which these averages have been calculated. In addition, the number of
Bluetooth devices registered in the time period, which are classified as
belonging to pedestrians, cyclists or other persons whose
position does not change significantly over time (e.g., in buildings), is saved
and stored in a data archive.
Current data are
stored in the ArrVeh array, which has
a “last in, last out” (LILO) structure. The length N of array ArrVeh can be modified depending on the individual needs at each
location. Taking into account the results of the tests, however, it should be
presumed that such modifications will certainly be unnecessary or, in some
cases, performed only a few times.
The ArrVehArch
array contains historical data, including tVehBt, which refers
to the times of residence of vehicles with active Bluetooth devices in the
range of the WUD sensor, aggregated every 5 min. First, these data are obtained
during the observation period, but this table should be updated periodically.
The ArrNoVehArch array can be optionally completed after the
observation period. In this case, it includes the aggregated number of devices,
which, at a given time, were recorded by the sensor, but did not belong to
moving vehicles.
The filtering algorithm
for Bluetooth devices is as follows:
2.
It is then checked
whether the ArrVeh array is completed. If not, tVehBt should
be added to the queue. This step is carried out only in the preliminary phase
of the system work.
3.
Value E(t) is equated
to 0:
Such
a situation takes place only once, after inserting the first N
records into the ArrVeh array. Therefore, like step 2,
this is only required in the first phase of the system work. If the value
before going to step 1.
4.
The condition is
checked:
If this is fulfilled,
the analysed device belongs to a pedestrian, cyclist or a person in a
stationary vehicle. The record should be inserted into ArrNoVehArch,
before going back to step 1.
5.
The condition is
tested:
If it is fulfilled, the
record must be classified as incorrect and deleted, before going to
step 1.
6.
If conditions 4
and 5 have not been met, the last entry from the ArrVeh array should then be removed, the remaining records
should be shifted by one position and a new tVehBt should be added in the first place.
7.
On the basis of
data from the ArrVeh array, the moving average E(t) is
computed. It is a weighted average with exponential weights, the value of which
is calculated according to the following formula:
where:
α – coefficient of the moving average
C(t) –
value of the element with index t
E(t - 1) – weighted average
from t - 1 periods
Coefficient α of the moving average is calculated using the following formula:
where:
N – array length
8.
Bus detection
should be performed.
2.1.1. Bus detection
Bus detection should
proceed as follows:
1.
For each analysed time tVehBt, it should be checked whether, in
less than tBUS seconds, at
least k3 MAC addresses of
Bluetooth devices occurred. If this condition is not fulfilled, then tVehBt does not belong to a device
inside a bus; otherwise, proceed to step 2.
2.
It should be checked whether
all MAC addresses from step 1 have stopped being visible in a time interval
shorter than tBUS seconds.
If that condition is fulfilled, all these MAC addresses belong to devices
travelling in a bus; otherwise, tVehBt
does not belong to a device in a bus.
2.1.2. Data update
Data update should
be performed daily using only those tVehBt
values that have been registered when there was no traffic congestion. For each
aggregation time period, the mean value of the time when the vehicle is within
the WUD range (Earch)
should be updated according the formula:
2.2. Models
Models should be
individually constructed for each sensor. Their correction can take place any
time, but it is not recommended to carry this out more frequently than the
database update. The resulting linear models take the following form:
where:
a – coefficients matrix
v – input data
2.2.1. Adding a new variable
The procedure should
begin with the addition of a new variable containing information about the
temporal distance from the peak hours: dist_rush_h. In order to compute
it, the peak hours for a given location must first be determined. The
average number of vehicles for each time interval between 6 am and 10 am must
be calculated; the one for which the maximum is reached must be indicated at
the morning rush hour mrh. A similar procedure is performed when evaluating the
afternoon rush hour arh, but the averages are calculated for points in time
between 2 pm and 6 pm. Next, a new variable should be added in each record in
the database:
where:
h – hour in the given record
2.2.2. Variables selection
The selection of
variables is performed independently using two algorithms: Hellwig’s
method [15] and stepwise forward selection [16]. If both methods give the same results, there is no need
to assess the quality of fit of the model to empirical data [17].
Let k denote the number of potential exogenous variables. After adding dist_rush_h, there are m variables:
Since, in contrast to Hellwig’s
method, the stepwise forward selection is a popular tool, only the latter will
be described below. In Hellwig’s method, all possible subsets of
potential variables are analysed, with the best combination selected on the
basis of integral capacity indicators H. The higher the value
of the integral capacity indicator for a given variable combination, the better
is this combination in terms of the built model.
First, the number L
of all possible combinations is computed:
All possible variable
combinations must be determined (from m one-element
combinations to one m-element combination). Next, the
individual information capacity h must be evaluated:
where:
k – combination number
mk – number of variables in the k-th combination
j – variable number in the given combination
The following step is to
compute the integral capacity for the given combination:
The best variable combination kmax is chosen as follows:
2.2.3. Building the model
For both sets of
variables, selected with the methods mentioned above, two models are built,
that is, with and without the constant term (maximum of four models; the number
can decrease if two methods give the same results). The values of the model
parameters ai are estimated using the
least-squares method:
where:
a
– parameters matrix
x
– exogenous variables
y
– reference data
The model for which the
highest adjusted coefficient of determination
where:
yt – actual number of vehicles at
moment t
n – number of measurements (records)
w – number of exogenous variables
2.2.4. Predicting the number of cars
The estimation of the
number of vehicles is conducted every 5 min on the basis of historical data,
aggregated in 5-min intervals, as well as the current data collected from the
sensors and subjected to filtration.
From the reference data, the average
percentage of vehicles with an active Bluetooth transmitter for the moment tc and
for the moment
Using the model, the
estimated number of cars
where:
Bluetooth and weather
sensors were distributed in 40 places in Bielsko-Biala, Poland. After a preliminary
observation period of two months, four locations were chosen for reference data
acquisition. These data were collected in a time period of one month. The
stored aggregated information included the number of Bluetooth devices, date,
starting hour of the aggregation interval, average air temperature, average
road temperature, average humidity, average air pressure, average salinity,
freezing point, dew point, water film, average precipitation, and the number of
Bluetooth devices registered by the sensor (after filtration).
For filtration purposes,
parameters were set on the basis of the authors’ observations as follows:
Yet, these can be
modified for each new location if data obtained during the observation period
indicate the need to do so.
Outlier elimination was
performed using the Dixon Q test. As a result, four records were deleted from
the database, since incomplete data were not considered.
The prepared reference
data, combined with the according input data, were randomly divided into the
training set and the test set in proportions of 80/20, 85/15 and 90/10.
Modelling was then performed on each training set, with the resulting models
evaluated on appropriate test sets. For every sensor and proportion, this
procedure was repeated 10 times, after which the results were averaged. Table 1
presents the obtained relative errors for the selected sensors.
Tab. 1
Relative errors for sensors
|
Training set percentage |
Test set percentage |
Relative error [%] |
|||
|
Sensor 1 |
Sensor 2 |
Sensor 3 |
Sensor 4 |
||
|
80% |
20% |
16.4% |
14.8% |
15.4% |
15.2% |
|
85% |
15% |
13.1% |
12.2% |
13.2% |
10.6% |
|
90% |
10% |
8.6% |
8.2% |
7.7% |
8.6% |
For all analysed
sensors, the results exhibit a similar characteristic. The relative error
decreased from about 15-16% to about 8-8.5%, while the share of the training
set changed from 80% to 90%. When the test set included 15% of all available
data, the relative error was approximately 12-13%.
The strongest influence
on the relative error involved aggregation periods where there was a small
number of cars, given that, in this case, even a small absolute error could
have been the source of a large relative error.
In this paper, a novel
road traffic estimation system using Bluetooth sensors has been presented. Data
collected by Bluetooth sensors, supplemented with data describing the external
conditions, serve as a basis for the assessment of the number of cars passing a
sensor location in a given time period.
The obtained results are
very promising. The smallest average relative errors (less than 10%) occurred
when 90% of the collected data served as the training set, which leads to the
conclusion that the length of the observation period has a significant impact
on the accuracy of the prognosis. If there is a possibility to repeat the
observation period, then the model should be retrained and recalculated in
order to provide the best possible estimations.
Acknowledgements
The system described has been developed as
a part of the Multimodal System of Road Traffic Monitoring project, founded by
the National Centre for Research and Development in Poland, in the context of
the INNOTECH-HiTech programme.
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Received 11.05.2017; accepted in revised form 15.08.2017
Scientific Journal of Silesian
University of Technology. Series Transport is licensed under a Creative
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[1] Faculty of Biomedical
Engineering, The Silesian University of Technology, Roosevelta 40, 41-800
Zabrze, Poland. E-mail: monika.bugdol@polsl.pl.
[2] Faculty of Biomedical Engineering,
The Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland. E-mail:
marcin.bugdol@polsl.pl.
[3] Pattern
Recognition Group, University of Siegen, Hoelderlinstr. 3, D-57076 Siegen,
Germany. E-mail: marcin.grzegorzek@uni-siegen.de.
[4] Faculty of Biomedical Engineering, The Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland. E-mail: andrzej.mitas@polsl.pl.