Sensing and Imaging (2020) 21:29

https://doi.org/10.1007/s11220-020-00295-2

OVERVIEW / SUMMARY PAPERS

Vehicle Detection and Traffic Estimation with Sensors

Technologies for Intelligent Transportation Systems

Pankaj P. Tasgaonkar1   · Rahul Dev Garg1 · Pradeep Kumar Garg1

Received: 25 February 2020 / Revised: 20 April 2020

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Abstract
The paper reviews the vehicle detection methods using the intrusive and non-intru-
sive sensors. The objective of literature review is to summarize the sensors and tech-
nologies used in vehicle detection and traffic estimation. Integrating these sensors
will give vital information by communicating with the monitoring station related
to the presence of the vehicle on the road. Sensors and communicating technolo-
gies have a widespread application in intelligent transportation systems. The modern
devices and technologies are discussed that determine the vehicle count, classifica-
tion, location, speed, traffic volume, density, traffic estimation. Sensors fusion can
further integrate information from different sources and provides more accuracy.

Keywords  Sensors · GPS · WSN · Intelligent transportation system (ITS) · Vehicle

detection · Vehicle classification

1 Introduction

Sensors and other technologies obtain information related to the physical attributes
of the environment or system. Sensors can vary from small size to bulky one as per
the application. Sensors have a vast number of applications in agriculture, defence,
environment, forest, disaster management, medical, and transportation, etc. [1]. One
of the important uses is in the Intelligent Transportation System (ITS) which con-
sists of electronics, communications, control, and sensing all types of traffic param-
eters to improve efficiency through the intrusive or non-intrusive sensors [7]. The
sensors provide the traffic-related information, like speed, volume, density, individ-
ual classification of the vehicles, and much more.
The road transport has a crucial role in the economic growth. Public transporta-
tion is available almost in all the countries; however, people prefer their motorbikes
or cars for commuting, but it leads to a rise in the number of vehicles and pollution

* Pankaj P. Tasgaonkar
pankajtgr@ce.iitr.ac.in
1
Civil Engineering Department, Indian Institute of Technology, Roorkee, Roorkee, India

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on the road. During peak hours, lots of congestion is faced by the drivers. The com-
muters and goods transport occupy the entire width of way for faster travel to the
destination. The monitoring of road vehicles is possible with the help of modern
devices and technologies [19]. Installations of cameras and various sensors along
the road can detect the presence of vehicles, while a real-time Global Positioning
System (GPS) can track the vehicle throughout the journey [4]. Vehicle detection is
also based on image and video processing by using static and dynamic cameras [78].
The traffic parameters are used to determine the road traffic conditions, i.e.,
whether the road is free for travel or is congested [8]. The traffic congestions may
also be due to rains, snowfall, water-logging, landslides, accidents, oil spill on the
road, big container vehicles blocking the roads, new road lane construction, agi-
tation, strikes, and religious activities. All these events have a massive effect on
road traffic in the city and national highway [44]. The traditional methods of traf-
fic monitoring comprise of the intrusive sensors, like inductive loops, pneumatic
tubes, piezoelectric sensors. However, these equipment are bulky, and maintenance
is quite tricky as road pavement cut is required to install them [19], and the traffic is
adversely affected due to installation work.
A literature study was carried out on vehicle detection, i.e., sensors and technolo-
gies available for detection of vehicles. To determine the papers related with the
congestion on roads, papers based on traffic estimation were referred. Various search
engines e.g. Google Scholar, Science Direct, Microsoft Academic, CORE, etc. have
been used in order to get the research papers most relevant to the current topic.
This paper discusses the popularly available vehicle detection and tracking systems,
including an overview of various sensors and technologies used for vehicle detec-
tion, road traffic monitoring, and management. Numerous case studies using sensor
technologies are also covered. At the end, the readers will get a reasonably good
idea about the selection and use of sensors/technology for a specific application of
ITS. The paper is organised in six different sections. Section 1 covers the introduc-
tory part, while Sect. 2 covers the various vehicle detection systems and their needs.
Section  3 includes several applications of vehicle tracking systems, while Sect.  4
focuses on selected case studies of vehicle detection and traffic estimation. Section 5
presents a comparative study of sensors technologies for transportation system,
while Sect. 6 summarises the review of work.

2 Vehicle Detection Systems

The vehicle detection system gives detailed information about the type of vehi-
cle, whether it is a car, truck, bus, or heavy vehicle. The driver details can also be
saved and shared through the server. The speed and traffic are estimated to avoid
speed violations and traffic congestion by giving alerts to drivers. Drivers get the
alarms so they can avoid congestion or reduce the speed or take the alternate path
available on the route. Present-day sensors, like accelerometers [37, 56], ultrasonic
[24], magnetic sensors [64] are useful in vehicle classification, distance measure-
ment and determining the vehicle speed. The radio frequency identification (RFID)
[69] is frequently being used for tracking the vehicles at certain checkpoints. The

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RFID readers [46] are installed on the roadside to monitor the information related to
the vehicle and compare it to calculate the speed travelled for a particular distance.
The RFID with the combination of hybrid technologies [45], like Geographic Infor-
mation System (GIS) and Wireless Sensor Network (WSN), [72], Global System
for Mobile (GSM) [42], GPS [58] are minimizing the disadvantages of RFID and
increasing the efficiency for many ITS applications. Video cameras [57], High-reso-
lution satellite images [13]; Wang et al., 2013), Unmanned Aerial Vehicles (UAVs)
Aerial images [15], Light Detection and Ranging (LiDAR) [51] based systems are
also used, but these have a higher cost as compared to other methods, like WSN [11,
43], and GIS [5, 14].
The sensor nodes collect the data related to road transport. This sensor is low-
cost and works on battery backup and communicates wirelessly to the devices. This
information will decrease the time required for traveling, pollution in the air, over-
all consumption of fuel, and the congestion on roads [66]. Various types of sensors
have been used world-wide for traffic-related studies. The two broad categories are;
intrusive and non-intrusive sensors, as briefly explained below:

2.1 Intrusive Sensors

These include inductive loops [8] (Marszalek et al. 2018), magnetometers [68, 79],
piezoelectric sensors [8], micro loop probes and pneumatic road tubes. The sensors
are installed beneath the pavement of the road. These sensors provide high accuracy
for vehicle detection [19]; but the disadvantage is that road cutting requires closure
of the way.

2.2 Non‑intrusive Sensors

These are installed above ground, and include video cameras [78], microwave radar
[210), LiDAR [36], ultrasonic [27, 35] and hybrid sensor technologies, like passive
infrared [47]. These sensors are installed beside the roadside or overhead of the lane.
These devices detect the presence, speed, classify the types of vehicles, lane cross-
ing, etc. The most significant disadvantage is that they are power-hungry devices,
and their performance deteriorates during fog, heavy snow, or rains.
The vehicle tracking system consists of grouping GPSs, RFID tags, readers, and
sensors. The GPS is a tracking device that determines the exact location of a vehicle
[20]. The collected data from the sensor/device is sent to the server through GSM/
General Packet Radio Service (GPRS) module installed in the GPS unit. The reader
reads the information from the RFID tag, and GIS traces the location of the vehicle.
The GPS works on the constellation of satellites in the space that computes the posi-
tion on the Earth [39]. It has an enormous number of applications in transportation,
like (1) vehicle fleet management and monitoring, (2) data collection and mapping
of the transport infrastructure, (3) incident management and monitoring, and (4)
vehicle navigation systems.

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3 Applications of Vehicle Tracking Systems

The GPS tracking is a vital technology in day-to-day life. Many organizations and
services require real-time monitoring of the vehicles. It is a way to find out the loca-
tion of objects, persons or vehicles, its source, and destination. The tracking devices
trace the path of the vehicle. The vehicle tracking system provides detailed infor-
mation regarding latitude, longitude, and direction of flow of vehicle from source
to destination. If a vehicle is stolen or damaged during an accident; in those cases,
tracking devices can provide vital information about the vehicle through the server
to the monitoring station. The tracking may be in the form of Barcode, Quick
response (QR) code, RFID based, or GPS based tracking. Some of the important
applications of the tracking system are as follows:-

3.1 Tracking Criminals

Police officers can install a GPS tracker for the suspected vehicle. The device can
monitor every move and direction of the vehicle. The GPS devices track the vehicles
continuously, and can help police to track any stolen or missing vehicles [39] and
criminals. In developed countries, along with GPS tracking, video surveillance from
the helicopters keeps a track of the criminals, informs the chasing vehicles to follow
the direction so that they catch them quickly.

3.2 School Buses

The tracking devices installed in the school buses will give the current location and
the distance from the school to home and vice versa [41]. This information is valu-
able as regards to child safety in the vehicle. Schools, as well as parents with the app
installed on their smartphone can track the status of the school buses. Cameras are
also installed in school buses for real-time video surveillance.

3.3 Bus Rapid Transit Systems (BRTS)

In BRTS, a separate lane is allocated, especially for the city buses. It gives priority
to city buses at intersections, and diversions, The city buses can interact with each
other to reduce the delays [21]. The display board indicates the information about
all the bus running on the route, its predicted time of arrival, and the last stop to the
passengers.

3.4 Cabs

Commuters use private cars for a shorter or outstation journey from companies, like
OLA, UBER, MERU, etc. These offers this facility with various types of cars, like
mini, micro, sedan, prime, etc., based on the economy and comfort of the passengers.

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Three to four passengers on the same route can share the car through pool options.
The mobile application on the smartphone gives data of nearby vehicles from the
source and estimated time of arrival using the GPS [32]. It indicates the shortest and
fastest route from source to destination after hiring the car. The application displays
the information of the driver, like his name, contact number, photo, vehicle number,
mobile number. In case of an emergency, relatives can also get the details.

3.5 Fleet and Traffic Management

For fleet management, the organizations keep the database of the information and
track the transport vehicles traveling throughout the journey. It keeps the informa-
tion related to the driver, types of goods, its capacity, halt record, etc. It consists of
database information, telematics, and GPS tracking [39]. It can help prevent delays
in delivery, fuel consumption, reduce road accidents, track and recover lost vehicles.
In case of traffic regulation, the system would provide information to drivers
about taking alternate route or release the traffic at regular intervals, as the system
is continuously tracking the vehicles during accident or landslides or falling of the
vehicle on the road or traffic jam due to heavy rush in a festival.

3.6 Smart Cities

The system will provide reliable navigation and traffic management for the drivers
and commuters of the vehicle. It will mitigate traffic congestion with the help of
Internet of Things [17]. The monitoring system collects the data and then commu-
nicates to the central server. The information is related to weather, disasters, traffic
congestion, diversion, bridges, flyovers, etc.

4 Some Case Studies

A large number of studies have been carried out world-wide on vehicle detection
and traffic estimation using sensors, electronic devices, cameras, etc. These case
studies have been grouped as per sensors/devices used for traffic estimation, vehicle
tracking and traffic management. Some of the popular sensors and technologies used
are discussed as follows:

4.1 Accelerometers

Accelerometers with the combination of magnetometers detect the traffic conditions.

Accelerometers detect the vehicle axles while magnetometers check vehicle entry,
exit, and approximate speed. Ma et al. [37] developed a system and tested at Inter-
state 80 at Pinole, CA, USA. They installed high-resolution cameras on the high-
way that give the video and images while a commercial weigh-in-motion system
records the axle counts, spacing, and weight of every truck. The results obtained
include vehicle classification, axle spacing, and vehicle counts. The results during

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congestion, were found to be 99% accuracy using the prototype automatic vehicle
classification system.
Rivas et al. [56] developed an algorithm to detect the presence of cars, their travel
direction, and speed. The piezoelectric accelerometers have the same working prin-
ciple as Micro-Electromechanical Systems (MEMS), i.e., to examine the peak levels
and frequency of vibrations of vehicles through this sensor. A radar and video-cam-
era was used to validate the results with that of accelerometer data.

4.2 Ultrasonic Sensors

Odat et  al. [47] used six Passive infrared(PIR) thermopiles Melexis MLX 90614
attached to the system through SMBus. The ultrasonic rangefinder MaxBotix MB
7066 and two passive infrared sensors arrays in combination classified the vehicle
types, detected the presence, and estimated their speed. The sensing device inter-
faced with the microcontroller through serial communication. They measured
the temperature in the adjacent area. The ARM Cortex M4 with a frequency of
168 MHz, and an internal Lithium battery of 8 Ah was used as energy source. The
passive Infrared (PIR) sensors detected the infrared radiation emitted by any vehicle.
The ultrasonic sensors emitted the sound waves and echoes back after recognizing
the target. They used a dynamic Bayesian Algorithm that integrates data from dif-
ferent sources in spatial as well as temporal domain. The proposed model having a
combined interface of the sensors gave 99% accuracy for vehicle detection, with a
mean error of 5 kph in vehicle speed estimation, and a mean error of 0.7 m in vehi-
cle length estimation. The decentralized approach was found to reduce the energy
utilization and increase its lifetime.
Jo et al. [26] used ultrasonic sensors module NRF04 from Devantech and MicaZ
from Crossbow which detected the vehicles in multiple lanes. The ultrasonic sensors
obtained road traffic data with WSNs. This information is sent to the client–server,
using a routing protocol for decreasing the power consumption and transmission
delay. The vehicle detection algorithm involves segmentation; vector signal condi-
tioning, vector extraction, and pattern matching. The routing protocol consisted of
power profiling as well as the discovery phase, synchronization, and data collection.
Ultrasonic sensors measured the distance data of the vehicle from two lanes out of
the bidirectional four lanes road. The vehicle detection algorithm gave an error rate
of less than 2%, and routing protocols saved about 92% of the communication mod-
ule power.

4.3 Magnetic Sensors

A magnetic sensor recognizes the change in the magnetic field. All vehicles con-
sist of some amount of metals. It creates a magnetic disturbance concerning the
Earth’s magnetic field. Zhu and Yu [79] developed an algorithm for detecting the
cars in parking and determining the speed using WSNs. The proposed algorithm
includes state-machine detection and cross-correlation detection. At the same time,
cross-correlation gives the speed between the signals of two sensors along the road.

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Experimental results showed that the parking algorithm had an accuracy of 99.65%
for entry and 99.44% for an exit, while the speed determination had an accuracy of
92%.
Taghvaeeyan and Rajamani [64] used magnetic sensor network along the road-
ways of the Minnesota which measured the traffic in the immediately adjacent lane.
Figure 1 shows the arrangement of sensors, which includes four three-axis Aniso-
tropic Magnetic-Resistive (AMR) sensor placed on the side of the road. The system
measured speed, number of vehicles, and differentiated the type of vehicle that was
passing through a nearby lane. Signal processing techniques computed faster with
the cross correlation techniques. There results show that the maximum error of the
speed estimated is less than 2.5% over the entire range of 5–27 m/s (11–60 mi/h).
The algorithm developed by Vancin et al. [67] gives the traffic status of the road
in four conditions; no traffic, mild traffic, heavy traffic, and very-heavy traffic. The
magnetic sensors obtained immediate, real-time, and novel solutions as a vehicle
detection system. The sensor nodes are installed at the start and the finish of the road
to obtain the number of vehicles per hour for a day for traffic analysis. The vehi-
cle classification algorithm classified the vehicle, like cars, mini-buses, buses, and
trucks by obtaining magnetic signature length with an accuracy rate of 95%.
Velisavljevic et al. [68] installed a set of 32 magnetometer sensor nodes on a pub-
lic road of the area of Bicester, Oxfordshire, UK. Magnetic sensors generated a mag-
netic signature of the vehicle. Magnetic signatures determined the vehicle speed,
length by extracting the position at the start and the end. The time delay between
the two graphs determined the vehicle speed. Video recording and magnetic loop
devices validated the results.

4.4 GPS Receivers

The exact location, i.e., latitude, longitude and altitude is determined with the GPS.
The GPS uses the basic working principle of trilateration to get relative position of

Fig. 1  Magnetic sensor collects data of the vehicle

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objects. The GPS receivers are also in built in the smart phone or a mobile computer
attached to the car. It can track the movement of the trucks, how the driver delivers
the goods timely to its proper destination. Fleet management systems, smartphones,
ambulances usually have GPS receivers for tracking or finding the locations [58].
The GPS is a satellite-based system that uses satellites orbiting the globe to measure
and compute its position on the Earth. It is also known as Global Navigational Sat-
ellite System (GNSS). At least four satellites are required to be tracked by the GPS
receiver to improve the accuracy. The Global Positioning System in vehicle track-
ing systemsis used that provide information such asthe location coordinates, speed,
time,This GPS receiver is used today in many applications, like smartphones, cabs,
fleet management, etc. [4]. The GPS uses Geofencing; whenever a vehicle enters
a boundary, location-based services gets activated by the application. The GSM/
GPRS module is useful for maintaining the communication between the computer
and GSM/GPRS system installed in the vehicle with a SIM module. A GSM/GPRS
module assembles a GSM/GPRS modem with, so that it can be easily interfaced
with a computer or a microprocessor/microcontroller-based system interfaces with
a GSM/GPRS module through standard communication interfaces, like RS-232
(serial port), USB, etc.

4.5 RFID

The RFID identifies the object and tracks the tag with a reader. The data related to
any vehicle is stored electronically on the card. The RFID tags can be active tags or
passive tags. Passive tags don’t have their power; they get it from the adjacent RFID
reader. Active tags have their battery source; they can operate at a longer distance
[45]. The RFID technology improves the location accuracy with the arrangement
of the RFID tags and the vehicles that carry RFID readers along with them. The
GPS accuracy minimizes when the traffic is under the tunnels, basement parking, or
dense areas. Figure 2 shows the view of the positioning using active RFID [75]; the

Reader Location Estimation Information to Driver

RFID tags

GIS

Fig. 2  Active RFID positioning scheme

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tags were installed on one lane of the road. The reader reads the information from
these cards. The location and speed are determined by tag readings and provided
to the GIS-based navigation system. Nearby vehicles get the data through wireless
communication. The traffic status, vehicle collision, and shortest route information
are thus determined.
The RFID technology can be integrated with WSN to develop an intelligent bus
tracking system [21]. The integration is done in two ways. In the first one, with
the wireless facility, RFID reader transmits data to and from the reader. The RFID
reader behaves like a sensor node; it reads the tag ID of a vehicle and sends it to the
host application with an ad-hoc network. In other case, motion sensor detects the
change in the direction of the object. The hybrid technique with WSN and RFID
offers more benefits as there is more noise during the measurement of Received Sig-
nal Strength Indicator (RSSI), while WSN has a good range [12]. On the other hand,
RFID technology has the advantage of providing precise information with high fre-
quency (HF) and good range for Ultra High Frequency (UHF). The combination of
indoor positioning and tracking system increases the positioning accuracy and avail-
ability. Figure 3 shows the architecture of the hybrid positioning system, where the
sensor node collects the information along with RFID [72].
The UHF and HF tags have their own RFID reader, while the sensor nodes of
WSN communicate through IPV6. The positioning algorithm utilizes the Kalman
filter. The information passes through the local area network to the database server.
Xiong et  al. [72] evaluated the performance of the proposed tracking system, first
through simulations and then through real experiment deployment.
Nafar and Shamsi [42] used the RFID and GSM together with IEEE802.15.4 to
create a vehicle identification (ID) system. They used the tag and kept a fixed wait-
ing time. Later, a pseudo-random generator was used to add a random number to
provide the security. Initially, scanning is done by RFID to avoid collision of the
packets. It becomes difficult for the third party to recognize the tag ID as random
numbers are mixed with its information.
The Internet of Things (IoT) approach tracks the vehicle with GPS, GSM, and
RFID together. Prinsloo and Malekian [52] designed an antenna, modulation
scheme, and software for vehicle tracking. The database server stored and trans-
mitted the information with the Graphical User interface (GUI). The PHP scripting

Sensor Node RFID tag with High Frequency RFID tag with Ultra High Frequency

Gateway HF Reader UHF Reader

Local Area Network

Database Engine

Applications

Fig. 3  Hybrid RFID-WSN systems

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language created a database server and GUI with Microsoft.NET. A position estima-
tion algorithm predicted the location. The GSM technology transmitted information
to the cloud for storage and processing. The GUI helped display the information on
a Google Maps interface.

4.6 RADAR & LIDAR

The Radio Detection and Ranging (RADAR) works on the principle of Doppler’s
effect as the moving body approaches nearer, frequency increases; and as it goes
away, frequency decreases. The radar gun targets directly towards any vehicle and,
the device displays the speed on it. Fang et al. [16] used a system that uses a K-band
Gun transceiver as radio front and DSP chip as a signal processing unit. It was oper-
ated in the K-band and utilized digital signal processing. Hough Transform classi-
fies and extracts spatial data. The accuracy was found to increase for detecting and
classifying the vehicle. It has less cost as compared to the other traffic sensors. This
device is efficient for researchers as well as traffic police. Continuous-wave radar
detects and classifies the types of vehicles.
Luo et al. [36] used LiDAR point clouds that build up a 3D picture of the vehi-
cle in Canada. The Kalman filter increased the accuracy with the Hungarian algo-
rithm. MATLAB software was used to provide vehicle tracking solution. The results
gave the average run time as 70 ms per frame, while vehicle travels at fix speed of
100 km/h.

4.7 Cameras

It provides more advantages as it gives pictorial data. Suresh et  al. [63] found
that video recording is useful for traffic analysis, as it determines the relationship
between the speed volume and density. They fixed the cameras above highway lanes.
The static cameras are used for recognizing the license plate from captured images.
Ozbay and Ercelebi [48] detected the license plates of the vehicles using image pro-
cessing algorithms.
Zhou et al. [78] worked on cameras which record the road traffic in different cli-
matic conditions. They collected 2009 samples, in which 1218 samples belonged to
vehicles, and the remaining photos had shadows, fog light, and solid commotion.
The samples are divided into two sections; the preparation set which incorporates
263 vehicles and 246 non-vehicles that utilized in SVM-based classifiers, and the
other set is used as the testing set to inspect the presentation of classifiers. For the
estimation of the background, they proposed an improved algorithm by isolating the
pictures into numerous little non-covered areas. The vehicle part can be found from
the fields if there is some power change between the present picture and the founda-
tion. All classified results are merged into a parallelogram that gives the state of
information of every vehicle.
Schoepflin and Dailey [57] used the algorithm in three phases to adjust roadside
traffic cameras that were introduced along the street side to track the vehicles with a
traffic speed sensor. The algorithm determines the camera position for the roadway

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utilizing the movement and edges of the vehicles. The calibrated cameras with cam-
era position algorithms define the lane boundaries and endpoints of the road. The
image coordinates transform into real-world coordinates; and distance divided by
the inter-frame time determines the speed.
Zhang et al. [76] experimented using a video camera vehicle detection and clas-
sification framework for gathering traffic information using computer vision algo-
rithms. Detection of shadows and removing it from successive images of the vid-
eos were carried out extracting the background images. They derived background
images from a video sequence, detected the presence of a vehicle, and calculated
pixel-based vehicle lengths for classification. Pixel length classifies the types of
vehicles in real-time images as well as video processes through a plug and play
system. The system was tested at three different locations under various traffic and
environmental conditions. The accuracy for vehicle detection obtained was above
97%, and the total truck count error was lower than 9% for all three tests.

4.8 Satellite and Aerial Images

Remote sensing systems provide useful images for desired urban/suburban infra-
structure, e.g., Landsat MSS and TM, IKONOS, World-View, Radarsat, etc. [23].
Razakarivony and Jurie [54] used machine learning techniques and implemented a
protocol to generate and check the results. Aerial vehicles captured images for rec-
ognizing the road vehicles. The database of vehicles consisted of various classes,
such as camping car, car, pick-up, tractor, truck, van, and other vehicles.
Cao et al. [9] used satellite images to implement transfer learning for recogniz-
ing the vehicles on the highway. They found that with the low pass filter, accuracy
increases in aerial and satellite images. Vector maps and satellite image coordinates
through ArcGIS and MapInfo software were utilized.
Zhang et al. [74] used the Conventional Neural Network (CNN) and Multilayer
Perceptron (MLP) to test aerial photography and satellite sensor dataset in urban as
well as rural areas of Southampton, UK. The sample data was gathered and divided
for training and testing purposes. The CNN-MLP combined algorithm gave a better
performance as compared to individual methods.
Geospatial information provided by optical remote sensing can be used for rec-
ognizing an object. Zhang et  al. [77] used two datasets in optical remote sensing
images to evaluate the performance of their proposed model. Intensity gradients
represented the appearance with a rotation-invariant feature, and the support vector
machine detects the geospatial objects.
Traffic estimation was done by Reinartz et al. [55] based on single-car measure-
ments with the time series images achieved from the airborne sensor. Geocoding
gave the speed and traffic-related parameters from the image sequences. The photos
taken from different heights and angles provided information about traffic analysis,
while the traditional and this method compared the image for reconstruction.
The UAVs are ubiquitous as these are cheaper and have vast applications. Instead
of using single UAV, Elloumi et al. [15] used multiple UAVs for detecting the road
traffic. The UAV camera provided real-time images and videos data from various

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positions. The control station monitored the traffic information with an algorithm
that generated the trajectory of UAV moving data point location.
The camera in the air monitors the remote areas which have less accessibility. It
can capture several vehicles. It considers two scenarios; one standstill and the other
dynamic. For both cases, there are different approaches for realizing the existence
of vehicles, a pixel-level video foreground detector during standstill scenarios. In
dynamic case, the images are registered initially, and then the pictures detect the
moving vehicle. For changing the resolution, the training samples get updated.
Xiang et al. [71] utilized the multi-threading method for the analysis of traffic data.

4.9 Geographic Information System

The GIS gathers, manages, and analyzes the data. The integrated information is in
the form of maps and 3D representation. It analysis the position spatial informa-
tion, and arranges the data in graphical format. Derekenaris et al. [14] merged the
data from GIS, GPS, and GSM technologies in Greece for routing the path of ambu-
lances. In an unlikely accident, the ambulance reaches the incident place using the
shortest path. The navigation system provided the nearest hospital; and the smallest
distance from the available routes. Maps showed the path with the data structures.
Aloquili et al. [5] determined the petrol/diesel level, vehicle speed, latitude, and
longitude using GPS, GIS, GPRS and tracking software. They considered the fac-
tors, like traffic congestion, and topography of the region. Algorithm developed was
assigned to choose the shortest way between the starting point and the end station.
Security was given through Geofencing methods, while Dijkstra’s and Kruskal’s
algorithms were applied to compute weights depending on the proposed cost func-
tion. The GIS represented the real-time position of the vehicles which was useful for
road condition analysis.

4.10 Inductive Loops

The inductive loops are actually under the road surface of city roads or highways.
The inductance of the coil is going to vary whenever any (metallic) vehicle passes
through it. Marszalek et al. (2018) used inductive loop approach to recognize every
passing car nearby. The instrument measured the impedance of the circuit. It con-
tained the resistive and inductive magnetic part for identifying vehicle axles pass-
ing through it. A total of 4000 vehicles were captured from the video footage with
usual road conditions, and the axle was detected with greater accuracy. The results
achieved were approximately 71.8% accurate with a lifted axle, and 98.8% for other
vehicles.

4.11 Smart Phones/Wireless Devices

Mobile phones with android operating system recognize the existence and types of
vehicles. The radio signal power is checked frequently for road vehicle congestion,
and traffic data can further reduce the emissions from fuel, duration of the journey.

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Lewandowski et al. [33] used the random forest technique for checking and getting
more approximation. They realized three types of vehicles; semi-trucks, trucks, and
personal cars.
Li et al. [34] used bluetooth, Wi-Fi, mobile beacons together to give traffic infor-
mation and differentiate between types of vehicles along the road at Wisconsin. The
data server obtained all the information related to smartphones, and the distributed
sensor network followed a single object. It coordinated between routing protocols
and Constraint Satisfaction Problems (CSP) algorithms. The networking algorithms
developed were geographic centric. In space–time cells, the cells get divided into
less area and permitted the message through the entire network. Nodes managed the
signal processing and communication in the whole area. Each activated node runs
an energy detection algorithm whose output sample at an apriori constant rate esti-
mates objects; it executes the method of recognizing energy.
Mohan et al. [40] used sensors, like accelerometer, microphone, GSM radio, and
GPS in smartphones in Bangalore to check the road traffic conditions. They detected
honks, braking, potholes, and bumps, and the Nericell sensors monitored the path of
vehicles and road conditions. The system collected the data together from the smart-
phone and the server.
Alexander et al. [3] proposed a system for sensors to get that battery recharged
through solar for observing the change in the state of vehicles. In remote locations,
there is an interruption of electricity or no power. In that scenario, Zigbee nodes
created the network. Every node consisted of an IR sensor that realizes some move-
ment. In the star arrangement, one node acts as manager and others as slaves in the
network. The sensor is attached to a power unit that can give energy continuously
from the sun.
In tunnels or dense areas, GPS does not work efficiently, so along with roadside
units (RSU) in vehicle inertial navigation system (INS) was proposed by Zarza et al.
[73]. The locus circle increased accuracy. The unit along the path gave mobile sig-
nals, with the data obtained from the navigation system. After linearization, the least
square method estimated the vehicle position for the given range and varying speed
and INS estimation errors. It obtained the required locus circles. Lots of simulations
were carried out to check the error rate.

4.12 Sensors Fusion

Kim et al. [29] used two different sensors which were placed together to realize the
object by having perception from 3D and light amplification. For robust tracking of
the objects, the period renewal method synchronizes the oldness of the object deter-
mined. Initially, the sensor processes the data for accurate tracking of the obstacles.
Data processing algorithms were designed for, such as re-ordering of object data,
object age, and time delay update. A period renewal method was developed to pre-
dict the progression time of the laser scanner. Kalman filter calibrated the informa-
tion from light amplification and compared for identical results from a 3D device for
a period of 1 ms and 66 ms, respectively. Statistical methods were used to check the
results of the sensor’s data.

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LiDAR, camera and RADAR can gather the information together for the dynamic
movement of an entity and checking for its directions. Chavez-Garcia and Aycard
[10] collected the data in different driving scenarios and classified, like pedestrian,
bike, car, and truck. A clustering method identified the dynamic things off the recog-
nized area, and their procedure provided data on the observable shape and the close-
ness of the moving vehicle. The classifier was found to have a significant influence
on the feature and speed as it checks the car position frequently.

5 Comparative Study of Sensors in Road Traffic

During the literature review, it was observed that various sensors, approaches, tech-
niques, and input data are used by authors for several applications, including vehicle
surveillance, traffic count, traffic classification, traffic management, etc. The selec-
tion of sensors is as per the feasibility of applications and areas. Therefore, a com-
parative study has been made to provide an insight to the readers and give some idea
about the suitability of sensors and approaches for a specific application. It is given
in Table 1.
Each sensor has its own way of working as well as its cost varies. Table 2 sum-
marizes the pros and cons of the traffic sensors. The results obtained from vari-
ous approaches proposed by different authors can provide the vital information for
selecting the devices and technology. Table  3 presents a brief comparison of the
equipment used along with the methodology, results, and end applications.
The sensors/technologies provide their strength and weakness in the road trans-
portation applications. The inductive loop offers the best accuracy for vehicle count
[8], and the high-frequency excitation model provides better vehicle classification.
The major weakness of this sensor is lane closure required for maintenance and
installation of the inductive loop each time as the pavement cut is essential. Ultra-
sonic sensors are cost-effective as they are used frequently for recognizing vehicle.
Detection of vehicles is done either from the upper to lower part or from sideways
inclined manner [24]. Lane violation detection, over height vehicle detection, dis-
tance estimation is possible with the ultrasonic sensors. Environment conditions
have less impact on ultrasonic sensors [35]. The magnetic sensors are less suscepti-
ble to stresses of traffic. They can’t sense any stopped vehicles without any interfac-
ing of the sensors and the software [68]. Microwave Radar provides the most signifi-
cant advantage by measuring the vehicle speed and direction [2]; the disadvantage
it offers is that it cannot detect stopped vehicles on the road. The passive infrared
sensor measures the speed while it is quite sensitive to bad weather [47]. Video cam-
eras detect various lanes, and many vehicles, the only thing is the accuracy depends
on the view and location of the camera. Lousy weather conditions affect the qual-
ity of images/videos. The maintenance/calibration of the camera is required periodi-
cally [57]. Smartphones offer more advantages as multiple sensors integrate with the
android based operating system [40]. It can provide additional traffic and environ-
mental related information to the user as well as the control station. The disadvan-
tage is, there can be interference in the ISM frequency bands of operation.

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 Sensing and Imaging

Table 1  Working principle and applications of sensors technologies for road traffic

Sensors/technology Working principle Application

Accelerometers Vehicle wheel creates vibrations Vehicle classification

(2020) 21:29

Ultrasonic sensors Echoes back signal after detection Vehicle detection

Magnetic sensors Change in the magnetic field Vehicle detection, classification
RFID Detection by Reader with Radio signal Vehicle detection
Infrared Sensors Emission of IR Vehicle detection, speed estimation
LIDAR Light reflects back Vehicle detection and tracking
RADAR Gun EM wave reflects back Speed estimation
Inductive Loops Vehicle induces eddy currents Traffic detection
Video Cameras video recording Vehicle detection, traffic detection
Satellite/aerial images Images captured through satellite or any aircraft. Traffic detection
GIS It captures, analyzes, and represents the data. The vehicle tracking system, Navigation system
Wireless Device/smart Phones Sending of the beacon signal. Vehicle classification
Page 15 of 28 
29

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Page 16 of 28

Table 2  Advantages and disadvantages of sensors/devices

Sensors Technologies Advantages Disadvantages

Accelerometers Vehicle axles are detected Sensitive to any vibrations in the environment
Robust in any weather conditions
Ultrasonic sensors Multiple lanes are detected Sensors are on one side and objects are on another side
Magnetic sensors Robust in any weather conditions To be installed very nearer the lane
RFID Low power, low cost If the card is out of range, cannot read the tag
Infrared Sensors Along with vehicles, bicycle, as well as pedestrians, are detected very much sensitive to light and weather conditions
LIDAR/RADAR Can work in any condition Installation is difficult and costly
Inductive Loops It is flexible in design For installation and maintenance, cutting of road pavement
It provides all traffic-related parameters
Video Cameras Exact details of vehicles are detected and are stored Requires more memory for processing
Satellite/Aerial Images It covers a larger geographical area There can be distortion in images
It has a high spatial resolution Image quality affects adversely under adverse weather conditions.
Sensing and Imaging

GIS It offers a quick collection of data Systems and tools are quite complex
It gives better prediction and analysis
(2020) 21:29
 Table 3  Comparison of the sensors and methodology
References Sensors/Source Hardware, Software, and Methodology Results Application
algorithm

Ma et al. [37] Accelerometer Accelerometer and Mag- The tire of the vehicle cre- Axle spacing, vehicle count The vehicle classification
netometer, weight in ates a force that creates system, vehicle detection
Sensing and Imaging

motion system, a vibration on the road

Moving average filter pavement.
Rivas et al. [56] Piezoelectric acceleration The algorithm Time vs. acceleration, vehi- Vehicle detection, speed
sensor, Data Acquisition Checks the vibrations level cle detection, displace- estimation, and vehicle clas-
module, Arduino Uno is higher than ± 0.5 mg ment, Travel direction sification.
Microcontroller in the data of each sensor.
(2020) 21:29

Vehicle detection algo-

rithms
Odat et al. [47] Ultrasonic sensors ARM Cortex microcon- Cross-correlation and wave- Temperature, error in Speed Estimation, Observe
troller, six PIR thermo- let transform estimate temperature, Road vehicles scenarios and
piles, time delay urban flash floods
Dynamic Bayesian Net-
works,
Gaussian Mixture Models
Jo et al. [27] WSN, PLC microcontroller, Sensors measure the Distance, Average utiliza- Traffic information
Control module, voltage distance, and algorithm tion of energy based
regulator, converts distance data to on packet transmission
Routing protocol, vehicle traffic data periods, vehicle detection
detection algorithm count, an Error rate
Page 17 of 28 
29

13
 Table 3  (continued)
29  

References Sensors/Source Hardware, Software, and Methodology Results Application

algorithm

13
Zhu and Yu [79] Magnetic Sensor Magnetic sensor, Determines the normalized Vehicle arrival, depar- Recognizing Vehicle, speed
State machine detection and cross-correlation between ture, determining speed approximation
cross-correlation, parking two signatures, Avelom- accuracy.
Page 18 of 28

recognition algorithm, eter tests for accuracy.

vehicle speed detection
algorithm
Sifuentes et al. [61] A magnetic sensor, optical LDR Sensor wakes upon Power consumption, mag- Vehicle detection
sensor, wireless sensor arrival or departure, and netic signature
node, Magnetic Sensor detects
Vehicle detection algorithm a change in Earth’s mag-
netic field on the arrival
of the vehicle.
Taghvaeeyan and Rajamani A magnetic sensor, XBee The distance between the Speed measurements of Vehicle Counting, Classifica-
[64] wireless module, Mag- two sensors is 0.9 m that GPS, speed estimation tion, and Speed Measure-
netic field model determines the speed with error, Magnetic signature ment
cross-correlation.
Sensing and Imaging
(2020) 21:29
 Table 3  (continued)
References Sensors/Source Hardware, Software, and Methodology Results Application
algorithm

Ning et al. [46] RFID Modules of power, clock, Microcontroller on the Vehicle position Track vehicle Location
LED, RFID, GPS, GSM arrival of the vehicle,
Sensing and Imaging

sensing and core control- reads RFID tag data

ling, through the reader. The
Microsoft Visual Studio algorithm predicts naviga-
2008, Microsoft SQL tion.
Server 2005 LAND-
MARC Algorithm
(2020) 21:29

Xiong et al. [72] RFID readers, Wireless Montecarlo simulations give RSSI, CDF, Positioning Indoor Tracking
Sensor nodes, tracking performance as errors
ContikiOS, Kalman filter root mean square of error
position.
Prinsloo and Malekian [52] RFID Reader Module, GPS Design of RF Circular Antenna Inductance, Vehicle location system
and GSM module, antenna with LC tank cir- regulated supply, and
Database system with PHP, cuit, Kalman filter gives interrogator distance
GIS the prediction
Pendor and Tasgaonkar [49] RFID, Cloud, Internet of The vehicle has passive Graph of Distance Vs. Time Real-time velocity monitoring
things, Ethernet module RFID tags; RFID reads and approximate velocity of a vehicle.
the information of speed
and timestamp and sends
it to the cloud through the
Ethernet module.
Page 19 of 28 
29

13
 Table 3  (continued)
29  

References Sensors/Source Hardware, Software, and Methodology Results Application

algorithm

13
Wen [69] RFID RFID, Infrared sensors The flooding algorithm Data of owner, vehicle, Traffic management
JSP, JAVA, MySQL determines the speed Toll fees, vehicle speed
and the traffic on the
Page 20 of 28

road. The server gets

the information and
forwards it to all the
centers in the city
Ji and Sprou [25] GIS Cell phones, PDA, Measurement module Location estimation, Dynamic location computa-
Laptops, record the moving location error, direction, tion
Accelerometers, GPS mobile’s characteris- speed
receivers, compass, tics. Wi-Fi and GSM
Log distance path-loss networks provide
model, GIS Localization
Aloquili et al. [5] GPS/GPRS modem, To provide an optimal Driver details, Tracking vehicle location tracking
Dijkstra’s and Kruskal’s path between source path of vehicles, Device system
algorithms, Geofencing, and destination from ID, Distance
VB 6, Microsoft Access real-time information
database, like travel time, traffic
lights, JAM factor, and
topography
Sensing and Imaging

Soni [62] M680 Vehicle Counter, GIS integrates the traffic Vehicle Count, Vehicle Real-Time Road Traffic
Inductive loops data from inductive Classification, Analysis
Twitter, HTML, CSS, loops and the tweets
JAVA script, PHP, from the social media
MongoDB, platform
Pritee and Garg [53] Dijkstra’s algorithms, Dijkstra’s algorithms Finding the Shortest path analyzing road conditions
(2020) 21:29

PostgreSQL, PostGIS, select the network and

QGIS build the graph
 Table 3  (continued)
References Sensors/Source Hardware, Software, and Methodology Results Application
algorithm

Reinartz et al. [55] Remote Airborne cameras, Sobel operator detects the Detections of cars, traffic Traffic monitoring
sensing NAVTEQ database, vehi- edges of road images, congestion
Sensing and Imaging

cle detection algorithms that determines vehicle

arrival or departure
Mao et al. [38] WSN Light sensor, 40 sensor iLight analyses the mov- Distance estimation Indoor passive tracking
nodes, and Base station, ing patterns i.e. height based on changes in
Probability-based and speed of 5 persons, RSSI and Link Quality
sensor informs change in Indicator(LQI)
(2020) 21:29

environment to sink node

Wenjie et al. [70] Vehicle unit, roadside To configure the transport Mean speed of Traffic Traffic control system
unit, intersection unit, network, optimization network
Minimum travel time opti- of traffic parameters like
mization algorithm length, lane numbers,
and speed
Tan et al. [65] Mobile sensors, static 75 sensor nodes detect Receiver operating char- Target detection
sensors, Defense vehicles at the acteristics, Detection
Greedy scheduling intersection. To get simu- probability
algorithm, Movement lation results, Training of
scheduling algorithm, data set in the algorithm,
that takes real data traces
for target detection
Sharp et al. [60] Berkeley Mica2Dot Nodes near evader senses Route time, Evader GPS Vehicle detection and
motes, 100 sensor and calibrates events. position, Intruder tracking
nodes, Magnetometer, Selection of Leader tracking
autonomous robot, an and aggregation of
ultrasound transceiver data, Robot chases the
Page 21 of 28 

TinyOS, flooding algo- evader after interception

rithm, the landmark planning
29

routing algorithm

13
 Table 3  (continued)
29  

References Sensors/Source Hardware, Software, and Methodology Results Application

algorithm

13
Khakpour et al. [28] Vehicular Adhoc Network Clustering, flooding, Clustering reduces data Cluster head lifetime, Target tracking
VANET TinyOS simulator, Ns2 propagation and facili-
tates network manage-
Page 22 of 28

ment
Kumar et al. [30] Video Cameras Data Set from Video Artificial Neural Network Traffic volume Traffic flow prediction
cameras predicts traffic flow
using the record of
traffic volume, speed,
density, and time
Sharma et al. [59] Video Cameras Differential morphology Vehicle Detection accu- Vehicle detection
Morphological Images closing profile extracts racy
vehicle information with
image processing
References Sensors/Source Hardware, Software and Methodology Results Application
algorithm

Kumar and Kushwaha Video Cameras Video Camera, Comparision of vehicle Detection Accuracy in Vehicle detection and Speed
[31] Open CV, JAVA, MySQL position in the succes- Morning, Afternoon, Estimation
sive frames evening, cloudy day
Anandhalli and Baligar Raspberry Pi, The algorithm detects Accuracy of Front View, Vehicle recognizing at run
Sensing and Imaging

[6] Open CV, C++, Kalman the number of vehicles Side View, Rearview, time and Tracing
Filter, on the road. HSV color Top view, Multi-View
domain differentiates the
vehicles, Kalman filter
tracks it
(2020) 21:29
 Table 3  (continued)
References Sensors/Source Hardware, Software and Methodology Results Application
algorithm

Hostettler and Djuric [22] Sensor Accelerometer, Mag- Sampling Importance Position, speed, mean Vehicle tracking
Fusion netometer particle filter processes estimation error
Sensing and Imaging

Particle filtering, Kalman with Rao-Blackwelli-

filter, 100 Monte Carlo zation method the data
simulations of accelerometer and
magnetometers
Kim et al. [29] Laser Scanner, stereo The algorithm determines Object matching Vehicle detection
vision the time delay of the
(2020) 21:29

Kalman filter sensor. Kalman Filter

predicts the data of
the Laser scanner and
matches with stereo
vision data
Planas et al. [50] Temperature, Pressure, GPS gives vehicle posi- Vehicle ID, Address and Monitoring of dangerous
Vehicle inclination, On- tions to OBT. Informa- GPS coordinates goods
Board Terminals, User tion is sent to the server,
Monitoring Terminals Occurance of events,
software, Decision sup- school transportation is
port software modules, retrieved from external
risk knowledge platform sources.UMT software
displays Geo-referenced
information
Page 23 of 28 
29

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Sensors technologies provide a different aspect of collecting vehicle traffic data.

Accelerometers recognized larger size of vehicles approximately as compared to the
smaller size of vehicles [7]. Ultrasonic sensor system identified the improper traffic
rule violation of the vehicles. Liu et al. [35] achieved an accuracy of more than 90%.
Magnetic sensors provided good accuracy for vehicle entry and exit. These sensors
describe the types of vehicles based on the magnetic signature. The accuracy rate is
more than approximately 99% was achieved for a vehicle at a standstill [79]. RFID,
if used with other technologies, like GPS, WSN, and INS. These sensors provide
more benefits considering the application is indoor area or tunnels [45]. Unmodu-
lated continuous wave radar is cost-effective as compared to other kinds of sensing
devices [16]. The LiDAR provides a more magnificent view and 3D perception of
the objects with a 360º scanning feature. Data processing is also less cumbersome. It
works in any complex environment when multiple vehicles are to be detected [36].
The detector with the inductive loop determines the vehicle length (Marszalek et al.
2018).
The best way to recognize the vehicles that have storage facility is with the cam-
corders that can capture pictures as well as take videos throughout the day. The
detection is best if it is located at the best view angle to cover a larger area of road
lanes. During the unlikely climate scenarios, working is going to get deteriorated
[78]. Algorithms can detect vehicles without training data in the satellite images [9].
Multi-temporal data sets are readily available to the researchers frequently with sat-
ellite images. Computer vision approaches require machine learning and training of
data for analysing remote sensing images [54]. In GIS environment, georeferencing
provides secure tracking of the vehicles carrying the goods and commuters on the
road. It determines the shortest route which is very useful in traffic management [5].

6 Conclusion

In this study, we have summarized the literature review on vehicle detection, track-
ing, and traffic estimation with sensors and technologies. We have provided a survey
of vehicle detection techniques so that readers can have a fair idea of selecting the
sensor for road traffic monitoring and management. Smart sensors installed at the
intersections of the road would help avoid traffic congestion. Emergency vehicles
may be given the highest priority, and their waiting time is minimized. Along with
WSN research studies on traffic control and management, RFID, Zigbee, Vehicular
Ad hoc Networks (VANET), bluetooth devices, cameras, and infrared signals have
been used [43]. Three basic things are always considered sensing, traffic control, and
safety. Sensors gather the traffic data and provide it to the control station. The traffic
control station manages the traffic congestion, while the security is active during the
traffic jams and road rules violation.
Modern technologies offer electronic toll control at toll plazas. Fast tags vehicles
have a separate lane and waiting time decreases. Service roads are for local traffic as
it separates from transport. Two-lane highways are to be converted to 4-lanes, while
4-lanes are widening to 6 lanes. Proper grade separator, median openings, footpath
for pedestrians are also required. Separate roads as per the speed, like slow-moving,

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Sensing and Imaging (2020) 21:29 Page 25 of 28  29

medium moving, and fast-moving vehicles are needed to avoid obstructions and
accidents [18].
Railway level crossings on NH network should not interfere with highways;
over-bridge or under-bridge constructed gives a safer and faster journey. Ring road
and link roads make the intercity travel better as proper connectivity is available in
the city [18]. Bypass roads can separate the NH from entering the central city and
reduce traffic congestion. However, road widening, development of the road infra-
structure depends upon the environment, and land acquisitions, and financial budget
aspects.
Vehicle detection, classification, and tracking utilize the state of art technologies.
The location-based services provide the vehicle count, speed, volume, density to the
driver as well as the monitoring station. Various mathematical models estimate the
road traffic. Sensors/technologies offer a higher demand in the ITS. It covers a vast
number of applications that can minimize the traffic congestion issues with the inte-
grated data from sensor fusion. The smart cities will have the transportation sector
connected to the IoT.

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