An Intelligent Transportation System for Air and Noise Pollution

Management in Cities

Mariam Osama Zaky and Hassan Soubra

Department of Computer Science and Engineering, German University in Cairo, Cairo, Egypt

Keywords:

IoT, ITS, Smart Cities, Air and Noise Pollution, Vehicle Routing.

Abstract:

Air quality and noise levels in urban cities have become major environmental concerns worldwide. Road

vehicles are a primary source of air pollution in urban cities and they also are a considerable source of noise

pollution. Undeniably, air and noise pollution are hazardous to human health. Managing pollution levels has

become an absolute priority in order to reduce the anthropogenic impact on the environment. In this paper, we

propose an Intelligent Transportation System-ITS for monitoring and managing air and noise pollution caused

by road vehicles in cities. The system proposed dynamically routes a vehicle using, on the one hand, its

particle emissions and noise indicators; and on the other hand, a city’s pollution levels and deﬁned thresholds.

The system proposed in this paper could be used for pollution based road tolls or taxes.

1 INTRODUCTION

Road vehicles, commonly known as cars, allow ease

of mobility and hence their usage has become es-

sential for people. Due to the dramatic growth in

the world’s population and car ownership, hence, the

number of cars roaming the roads have increased.

Currently the world population is increasing with a

rate of 1.05%, which is estimated to be 81 million

people per year (Worldometer, 2020). And based on

the statistics done by the International Organization

of Motor Vehicle Manufacturers (OICA), sales of ve-

hicles have increased from 66 million units in 2005 to

91 million in 2019, including both passenger cars and

commercial vehicles (IOMVM, 2019).

The increase in the number of vehicles on the

roads has many consequences on the environment

and notably on people. First off, there is an obvi-

ous increase in trafﬁc congestion: rush hours have

become irregular and, hence, unpredictable. Trafﬁc

congestion is related to the difference between the

road trafﬁc performance and its actual condition. In

other words, congestion happens when the number

of vehicles present on a particular road at the same

time exceeds its capacity. Generally, trafﬁc conges-

tion has negative effects on the economy because of

time wastage, but it also has a negative effect on

fuel consumption -which in turn causes gas emis-

sion and air pollution, noise pollution, and ﬁnally on

vehicle wear and tear and on road safety (C P and

Karuppanagounder, 2018). The delays caused by con-

gestion make drivers waste a lot of time leading to

late arrivals to work and possibly missing important

meetings; and more importantly delays in delivery of

goods often causing customer dissatisfaction. Hence,

trafﬁc congestion impacts the economy.

Trafﬁc congestion makes cars stop and start many

times, leading to more fuel consumption than cars that

travel without stopping. Based on a study done in

Slovenia in 2018, it has been found that fuel consump-

tion during acceleration was 2.65 times more than the

average fuel consumption (Jereb et al., 2018).

In addition, the number of road trafﬁc accidents

is highly correlated to the number of cars (Figure

1). The World Health Organisation stated that around

1.35 million people die yearly because of road acci-

dents, and many more suffer from dangerous injuries

(WHO, 2020).

Moreover, the environment, and in consequence

humans, are also affected. The increase in usage of

Figure 1: Relationship between trafﬁc volume and accident

frequency (Retallack and Ostendorf, 2020).

Zaky, M. and Soubra, H.

An Intelligent Transportation System for Air and Noise Pollution Management in Cities.

DOI: 10.5220/0010403403330340

In Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2021), pages 333-340

ISBN: 978-989-758-513-5

333

cars and their omnipresence led to extremely high

pollution rates inside cities. For example, The Min-

istry of State for Environmental Affairs of Egypt esti-

mates that vehicle emissions represent about 26% of

total pollution caused by suspended particulate mat-

ter (PM10) in Greater Cairo, 90% of carbon monox-

ide (CO) and 50% of nitrogen oxides (NOx) (Abou-

Ali and Thomas, 2011). Transportation systems -

including cars- are also responsible for around 25% of

Carbon Dioxide (CO2) emissions in the world (BBC,

2020). CO2 is one of the Greenhouse Gases. Green-

house Gases have an extremely dangerous effect on

the ozone layer of our planet. They lock in the heat

causing climate change, notoriously also known as

Global Warming (USEPA, 2018). In 2018, trans-

portation was considered the largest source of Green-

house Gases in the United States with a contribu-

tion of 28.2% of the emissions as shown in Figure

2 (USEPA, 2018).

Naturally, inhaling the particles from vehicles’

emissions also have a harmful, and sometimes deadly,

effect on human health and other living beings. A re-

port of the government of Canada (2019) states that

the exposure to air pollution causes lung related dis-

eases, such as asthma and allergies, in addition to

heart related diseases e.g. hypertension, heart attack,

and heart failure. Moreover, the report concluded

that more than 14,000 premature deaths per year in

Canada can be linked to air pollution (Canada, 2019).

Air pollution is one part of the equation, the sec-

ond part lies in the amount of noise pollution. There

are several sources of noise pollution in a vehicle:

noise could stem from its engine, especially if it is

old and does not get proper maintenance; squealing

brakes; different resonating parts; etc. In addition, a

car has human controlled noise sources such as: the

probable excessive use of the car’s horn, the use of

high-volume radio and music systems, etc.

According to the conclusions of the Environmen-

tal Burden of Disease report by the World Health Or-

ganization, noise pollution is ranked the second en-

vironmental threat in Europe after the air pollution

(WHO, 2011).

Noise pollution has several negative impacts on

human health. Noise causes emotional and behav-

ioral stress. An exposure to a sudden loud noise may

cause severe damage to the eardrum and may lead to

hear loss. It can also cause headaches, high blood

pressure, and heart failure (Subramani et al., 2012).

Moreover, noise is the major cause of sleeplessness

and sleep disturbance; psychological disorders; hy-

peractivity and learning difﬁculties in children; as

well as fatigue and reduced productivity (Wokekoro,

2020). The European Environment Agency estimates

that around 10,000 people die prematurely every year

because of their exposure to noise pollution (EEA,

2020).

Thus, noise stemming from cars should also be

managed and reduced as much as possible.

Road trafﬁc cars are hence considered to be the main

contributor to both air and noise pollution. Approv-

ingly, the World Health Organization (WHO) states

that both air and noise pollution are the most harmful

environmental problems (EEA, 2019).

In this study we aim at monitoring and minimiz-

ing car air and noise pollution inside urban cities. Our

approach is to implement an IoT system consisting

of a network of sensors embedded inside the car to

monitor, in real-time, its particle emissions and noise

level. An additional network of sensors embedded in

the city’s infrastructure is also used in our system in

order to obtain its pollution levels in real-time.

This paper is divided as follows: section 2

presents the literature review. While section 3 de-

scribes the pollution ITS proposed, section 4 dis-

cusses the experiment process and the results. Finally,

a conclusion follows in section 5.

Figure 2: Sources of Green-house Gases in the United

States (USEPA, 2018).

2 LITERATURE REVIEW

In this section, the literature review is presented and

discussed. Several research works on how to monitor

and control vehicles pollution are summarized in this

section.

(Kumar, 2017) claims that cars’ emissions cannot

be 100% prevented unfortunately. Nevertheless, they

can be monitored and controlled in order to reduce

them as much as possible to decrease their harmful

effects. The amount of harmful emissions is directly

proportional to the car’s age and usage. Also, they

depend on whether the car gets maintenance properly

and on a regular basis or not.

VEHITS 2021 - 7th International Conference on Vehicle Technology and Intelligent Transport Systems

334

Noise is measured in Decibels-dB using sound sen-

sors or sound level meters. However, measuring noise

levels produced from a speciﬁc source is challenging,

because the noise signals produced from the intended

source are affected by the background and the sur-

rounding noises. Hence, the noise sensors read inac-

curate values (Subramani et al., 2012).

2.1 Air Pollution

A sensor node was implemented by (Miralavy et al.,

2019) on the car’s exhaust system to monitor its emis-

sions. They then sent the sensed data along with the

car’s information to a base station that is controlled

by the authorities. If the car’s pollution level exceeds

a certain limit, the authorities should warn the car

owner to ﬁx it. And if the problem persists, the car

owner would be charged.

(Kundu and Maulik, 2020) implemented a system

which detects pollutant vehicles using deep learning

techniques on real-time images of the vehicles. These

images are captured from the infrastructure or from

neighboring vehicles. They used the Inception-V3

model, which is an image recognition model provided

by Google. After training and testing their model,

they achieved 97% accuracy in the unknown testing

data set. This model depends on the shape and color

of the vehicle’s exhaust. However, some pollutant

emissions are transparent and cannot be seen or de-

tected by images. This model will not be able to de-

tect such types of emissions.

Another study (Muthumurugan, 2018) focused on

measuring light, air, noise, and thermal pollution of

vehicles by connecting suitable sensors to a micro-

controller. When the sensors read data exceeding the

allowed limit, the vehicle will send a message to the

control room within the area, through a GSM module,

and displays the message to the vehicle’s owner with

the phone number of the nearest service center, so ap-

propriate service is provided to ﬁx possible issues .

An MQ-135 gas sensor was used by (Reshi et al.,

2013) to sense NOx, Benzene, and CO2. And an MQ-

7 sensor to measure CO. Then they sent the data to

the server using GPRS (in the form of SMS), based

on 2G/3G communication networks. The data gets

uploaded then to a database and is used to inform

or alert the car owner about the respect of pollution

thresholds.

(Dhingra et al., 2019) implemented a sensor net-

work in speciﬁc locations around the city. These sen-

sors collect data about the air pollution level. Firstly,

they collect the data from the sensors that are con-

nected to an Arduino board. The Arduino board sends

then these readings to a cloud platform to store them.

The Arduino board uses a Wi-Fi module to connect to

the internet. When the driver enters the source and the

destination of a the journey, the system gets the route

between them using Google Maps Routing API, then

predicts the pollution level of the entire route and send

warnings to the user if the pollution level is too high,

so the driver can reroute their car. The system also

keeps track of the history of the predictions.

Another study by (Guanochanga et al., 2019) im-

plements a wireless network with gateway nodes that

have internet access and sensor nodes. The sensor

nodes send the air pollution measurements to the cor-

responding gateway node. Then, the data is sent to

a cloud server via the gateway node. After that, it

will be published on a web page that is available for

the users and accessible using web browsers or smart-

phones.

2.2 Noise Pollution

Measuring trafﬁc noise is complicated because it is in-

ﬂuenced by many attributes: Trafﬁc density, vehicles

velocity, trafﬁc ﬂow, road surface type and condition,

vehicle mass, tires, road inclination, etc. All these

attributes are not constant. Therefore, trafﬁc noise

power constantly varies in time and space (Prezelj and

Murovec, 2017).

(Afsharnia et al., 2016) used a TES sound meter

to measure the trafﬁc noise level in the city of Bir-

jand in Iran. the objective of this study is to com-

pare the noise pollution level in Birjand with national

standard-levels. The TES sound meter is used to mea-

sure daily sound levels at several stations and during

four different time periods: morning, noon, evening

and night. The average results of the measurements is

78.1db in the morning, 82.25db in the noon, 81.21db

in the evening, and 81.01db in the night. The study

concluded that generally morning has lower noise lev-

els than noon, and evening is also quieter than night.

In (Fiedler and Zannin, 2015) researchers aimed

to examine the environmental impact of road trafﬁc

noise in the city of Curitiba, Brazil. Their object is

the main urban trafﬁc hubs. They used B&K 2238

and B&K 2250 sound analyzers for noise level mea-

surements, and predictor 8.11 software for acoustic

map calculations. They measured the noise level in

232 different points. 171 of these points showed noise

levels exceeding 65db which is the maximum sound

level people can hear safely and which is extremely

dangerous. The researchers introduced three hypo-

thetical scenarios in attempt to reduce the noise lev-

els. The ﬁrst scenario simulates reducing the current

total number of vehicles by 50%. The second one

simulates reducing 50% of the heavy vehicles in the

An Intelligent Transportation System for Air and Noise Pollution Management in Cities

335

trafﬁc hubs. Finally, the third one simulates a 56%

increase in the total number of vehicles in trafﬁc over

the next 10 years. The results of the ﬁrst two scenar-

ios showed a decrease of 3db of the calculated noise

level. On the other hand, the third scenario resulted in

a 3db increase in the noise level.

Moreover, a study by (Ballesteros et al., 2015) fo-

cused on ﬁguring out the noise source of a pass-by

car. The authors wanted to prove that Beam forming

is able to identify the noise sources of a moving car

and get more insight to the mechanisms of the gener-

ated noise. They used a planar 56-microphone array

with 28 additional microphones, located on 8 exter-

nal arms attached to the center array, they also limited

the measurement area with two light barriers. From

the generated noise source maps, they concluded that

the noise is mainly located near the center of the car

tread, and it is slightly louder in the front tires than in

the back ones.

A study by (Desarnaulds et al., 2004) in Sweden

stated that when a car’s speed is reduced from 50km/h

to 30km/h, its noise decreases by 2 to 4dB. Similarly,

in Delft and in Oslo respectively, two studies (Lopez-

Aparicio et al., 2020) (den Boer and Schroten, 2007)

analyzed the effect of reducing the trafﬁc speed limit

on the trafﬁc noise levels. Both studies showed that

reducing trafﬁc speed limit has an effective result in

reducing the noise pollution caused by cars.

After analysing the scientiﬁc literature, and to our

best knowledge, there are no intelligent transportation

systems that aim at reducing air and noise pollution in

urban cities via routing decisions based on predeﬁned

city entry-exit points in a addition to pollution indi-

cators, ﬁxed thresholds, and real-time pollution level

readings from both the vehicle and the city sides.

3 PROPOSED POLLUTION

MANAGEMENT ITS

The system proposed measures, in real-time, the cur-

rent air and noise pollution levels of the car and of the

city using IoT sensors embedded in both the car and

the infrastructure. Using the car’s current location and

its destination, the system determines the most efﬁ-

cient route according to the measured pollution levels

and their effect on the ongoing city pollution levels.

The system is divided into two layers: software and

hardware. The software layer includes two different

parts: the server side which is managed by an admin;

and the user side which informs the driver about the

pollution levels and the route. The hardware part con-

sists of a network of wireless sensors embedded in the

car and in the city’s infrastructure.

3.1 Software Implementation

3.1.1 Server Side

The server is considered to be the back-end of the

application. It is responsible for deﬁning the city’s

information. The city information includes: its bor-

ders, the geographical locations of its different en-

trance and exit points, its pollution thresholds, and its

name e.g. see Figure 3.

Figure 3: Representation of a city showing its borders and

its different entry-exit points as deﬁned on the server.

The city’s information will then be used to set the

routing rules for the vehicles entering and exiting the

city, based on the origin-location and destination of

the user. The Vehicle’s origin and destination points

play a role in deciding whether it will be subjected to

the routing technique proposed to reduce the pollution

rate inside the city or not. If the Vehicle’s origin and

destination points are inside the boundary of one of

the deﬁned cities in the database, the vehicle’s pollu-

tion levels are then taken into consideration with that

city pollution thresholds and current pollution rate.

Next, the system generates two routes: 1-a pollution

optimized route, which cares for reducing the amount

of pollution produced inside the city; 2-and the over-

all shortest path based on Google Maps routing API.

The system also calculates the total distance that the

car will travel in both routes. The two routes are then

displayed to the user through an application.

The back-end is connected to a database contain-

ing the information about the registered cars. Each

registered car gets an identiﬁer that represents a

unique ID (UID) for the car, and has: a model infor-

mation about its make and year of production, a plate

number, and a toll credit. When retrieving the data of

a car, the server also gets the nominal average amount

of gas emission of that car type. The server retrieves

this information from the ofﬁcial website of the U.S

government for fuel company using the car’s model

and year of production (Fueleconomy.gov, 2020).

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336

3.1.2 User Side

The second element of the system’s Software is the

user application.

Figure 4: User Application.

Through this app, the user enters the car’s UID

to be able to open a map, as shown in Figure 4, that

contains the car detailed information, including its

model, plate number, average amount of produced

emissions in Grams per Kilometer, and the current

credit. This credit represents the amount of money the

user precharged to pay future pollution related tolls or

taxes. The application also shows the car’s real-time

emission data from the embedded sensors. The ap-

plication allows the user to choose the desired origin

and destination points, so the server can generate the

routes based on the routing algorithm implemented,

see Figure 6.

3.2 Hardware Implementation

To measure in real-time pollution levels of both the

car and the city, different sensor nodes were used.

Each sensor node includes different types of sensors

connected to an Arduino board and a Node MCU. For

air pollution data, different gas sensors are used: an

MQ-7 to measure Carbon Monoxide and an MQ-135

to measure Nitrogen Oxides and Carbon Monoxide.

For noise pollution data, a sound sensor is used to

measure the decibel value of the noise pollution. The

sensor nodes, as implemented in Figure 5, are embed-

ded in the car, as well as in the city infrastructure.

Figure 5: The hardware circuit used in measuring air and

noise pollution.

3.3 Routing Algorithm

The routing algorithm proposed takes into considera-

tion the car’s origin and destination points, car’s cur-

rent pollution level, the city’s current pollution level

and the city’s pollution preset thresholds.

The system provides the user with two possible

routes: 1-the pollution optimized route, which adds

into consideration the amount of emissions and noise

the car produces, and the distance that the car will

travel inside the city. The algorithm tends to minimize

the distance travelled inside the city, if the pollution

levels are high. 2-the ’normal’ shortest path based on

Google Map’s Routing API, which takes into consid-

eration the time, distance, and trafﬁc.

The algorithm ﬂows as follow: the user enters the

origin and destination points, the system checks if any

of the points is inside the boundary of a deﬁned city

on the server side. Next, it takes the real-time readings

of the pollution of the car and the concerned city. Fi-

nally, it generates the possible routes. Figure 6 shows

an overview of the routing algorithm and Algorithm 1

shows an example of exiting a city in details.

When the pollution levels are high, the algorithm

works as follows: If the destination point is inside a

deﬁned city, in order to minimize the distance trav-

elled inside the city, it checks all the possible entry

points and calculates the distance between each one

of them and the destination point. It takes the nearest

entry point to the destination and adds it as a ”way

point”; this is from where the user should enter the

city. The route is then generated by connecting the

shortest path from the origin point to the selected en-

try point and the shortest path from the entry point

and the destination point together.

If the origin point is inside a deﬁned city, then it

checks all the possible exit points and calculates the

distance between each of them and the origin point.

The nearest exit to the origin point becomes a ”way

point” on the route and then the full route is generated.

Lastly, if both origin and destination points are in-

side a deﬁned city, it compares the direct route from

the origin to the destination and a route that exits the

city and re-enters it. This is done by adding the dis-

tance travelled inside the city from the origin to its

nearest exit point and from the nearest entry point to

the destination. If this exit and reenter route’s segment

is shorter inside the city than the direct path from the

origin to the destination, it would be chosen as the

pollution optimized route.

Naturally, when the pollution levels are below the

threshold, the routing algorithm generates only the di-

rect shortest path, without any concerns about the dis-

tance travelled inside the city.

An Intelligent Transportation System for Air and Noise Pollution Management in Cities

337

Figure 6: Overview of the ITS Routing Algorithm.

Algorithm 1: Routing Algorithm to get out of the city.

Input: Car’s ID uid, Start Point start, Destination Point dest,

Car’s Pollution Level carPoll, City’s Pollution Level cityPoll,

City’s Pollution Threshold cityT hresh

Output: Routes R1, R2

1: if (start inside city) then

2: if (carPoll + cityPoll > cityT hresh) then

3: for Gate g in cityExitGates do

4: Distance d = Distance between start and g

5: Add Gate g with the minimum d to the Route R1 Way

Points

6: end for

7: else

8: R1 = Shortest Path

9: end if

10: R2 = Shortest Path

11: end if

12: return R1 and R2

4 TESTING AND RESULTS

4.1 Testing Scenarios

To test the functionality of the system, six different

scenarios were simulated. While the ﬁrst three sce-

narios allow testing the system in the case of high pol-

lution levels, the next three scenarios allow testing the

system with low pollution levels.

• Scenario 1: Origin point is not inside a deﬁned

city, destination point is inside a deﬁned city, and

the car’s pollution level is high enough to make

the city’s pollution level higher than the thresh-

olds (Figure 8).

• Scenario 2: Origin point is inside a deﬁned city,

destination point is not inside a deﬁned city, and

the city’s pollution level will exceed the thresh-

olds (Figure 9).

• Scenario 3: Origin and destination points are in-

side the city, and the car’s pollution level is high

(Figure 10).

• Scenario 4: Origin point is not inside a deﬁned

city, destination point is inside a deﬁned city, and

the car’s pollution level is low (Figure 11)

• Scenario 5: Origin point is inside a deﬁned city,

destination point is not inside a deﬁned city, and

the car’s pollution level is low (Figure 12).

• Scenario 6: Origin and destination points are in-

side a city, and the car’s pollution level is low

(Figure 13).

4.2 Results

This section presents the simulation output of the sys-

tem using the six testing scenarios.

For each scenario, in addition to the routes, mis-

cellaneous information about each route is also pro-

vided to the user. This information includes: the total

distance, total emissions, total fuel consumed, as well

as the distance travelled and emissions produced in-

side the city; e.g. ﬁgure 7.

Figures 8 to 13 show the outcome of the six different

testing scenarios.

5 CONCLUSION

Because air quality and noise levels in urban cities

have become major environmental concerns world-

wide, managing road vehicles which are considered

a primary source of air pollution and also a consid-

erable source of noise pollution in urban cities be-

comes crucial. In this paper, an IoT based Intelligent

Transportation System-ITS for Air and Noise pollu-

tion management in cities is proposed. Our ITS pro-

posed uses real-time pollution data to route cars based

on the measured particle emissions and noise levels.

Our system is divided into two layers: software and

hardware. The software layer includes two different

parts: the server side which is managed by an admin

and where cities are deﬁned with boundaries, entry

and exit points, have air and noise pollution thresh-

olds; and the user side which informs the driver about

the pollution levels and the possible routes: city-

pollution optimized and ”normal” shortest route. Our

system aims at helping in keeping a city’s pollution

VEHITS 2021 - 7th International Conference on Vehicle Technology and Intelligent Transport Systems

338

Figure 7: Information provided to the user about the routes

given by the system.

Figure 8: Scenario 1, origin point is outside the city, desti-

nation point is inside the city, with high pollution levels.

Figure 9: Scenario 2, origin point is inside the city, destina-

tion point is outside the city, with high pollution levels.

level under a predeﬁned threshold. To verify the fea-

sibility of our approach, six different test scenarios

were simulated and their outcomes were veriﬁed for

one deﬁned city. In the future, we plan to test our

Figure 10: Scenario 3, origin point is inside the city, desti-

nation point is inside the city, with high pollution levels.

Figure 11: Scenario 4, origin point is outside the city, desti-

nation point is inside the city, with low pollution levels.

Figure 12: Scenario 5, origin point is inside the city, desti-

nation point is outside the city, with low pollution levels.

systems on more cities having different conﬁgurations

and further validate our approach.

An Intelligent Transportation System for Air and Noise Pollution Management in Cities

339

Figure 13: Scenario 6, origin point is inside the city, desti-

nation point is inside the city, with low pollution levels.

ACKNOWLEDGEMENTS

We would like to thank Omar Mokbel and Ramy

Mansour, GUC Computer Engineering students-now

graduates- for their precious contribution to the devel-

opment of the prototype used in this study.

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