IoT System for Monitoring and Managing Public Transport Data
IHOR ZAKUTYNSKYI, LEONID SIBRUK, ANZHELIKA KOKARIEVA
Radio Electronic Devices and Systems Department,
National Aviation University,
Lubomyr Huzar Avenue, 1,
UKRAINE
Abstract: - Public transport has a significant role in the economy and modern city development. Today, public
transport systems face many problems which should be solved, like real-time monitoring, data management,
passengers flow optimization and road accident prediction. The Internet of Things (IoT) is a promising
technology for the development of the modern public transport management system. IoT systems combine a
wide range of technologies, such as sensors, edge devices, and cloud computing. Also as well as many
communication infrastructures which can be applied to develop robust and automated public transport systems.
The transfer of information from devices to the cloud is the most important part of an IoT system. All devices
should use network standards and protocols to allow physical objects to interact with each other and the cloud.
Information transfer from IoT devices to the cloud is only possible if devices are securely connected to a
communication network. Network protocols and standards are policies that comprise certain rules that define
communication between two or more devices over the network. This article aims to evaluate the possibilities of
using IoT for monitoring and managing public transport data in modern Ukrainian realities. Specifically, in this
study, we analyze general industry protocols and standards that are used by IoT devices and meet requirements
like bandwidth, latency, and power consumption. This work describes a specific device that transmits
information from a transport unit to the cloud. Lastly, this paper proposes an IoT system architecture for the
public transport data monitoring and management system.
Key-Words: - IoT, Public Transport, M2M communication, Data analytics, Intelligent transport systems, Smart
City.
Received: June 12, 2022. Revised: February 9, 2023. Accepted: February 28, 2023. Published: March 15, 2023.
1 Introduction
Public transportation refers to shared passenger
transportation services such as buses, trolleybuses,
trains, ferries, and express transportation like the
metro. Design of an intelligent real-time Public
Transportation Monitoring and Management System
based on IoT systems, depending on several
technologies, allows retrieving data from any
transport node and managing and control of the
transportation network.
IoT protocols are a crucial part of the IoT
technology stack without them, the hardware would
be rendered useless as the IoT protocols enable it to
exchange data in a structured and meaningful way.
IoT protocols and connectivity help in transferring
pieces of data from which useful information can be
extracted for the end-user and thanks to it, the whole
deployment becomes economically profitable,
especially in terms of IoT device management, [12].
Wireless protocol selection plays a major role in the
above system design. The following are the critical
parameter for the selection of the wireless protocol
requirement to make it affordable, scalable, and
efficient according to system needs:
Effective Radio Distance/Data Rate.
Operating Frequency Band.
Network Deployment Models.
Security Features Supported.
2 Literature Review
The development of intelligent transport systems is
an important problem today. Over the past decade,
many studies have been conducted on this topic, and
many systems have been implemented in different
cities and countries around the world. For example,
in this paper, [7], the authors proposed an IoT
tracking system for public buses. Also, a lot of
studies are dedicated to the development of tools for
drivers. For example, this paper, [8] considers an
IoT navigation system for bus drivers. Also, a lot of
studies are dedicated to the smart management of
public transport. In [8] the authors develop a
framework that allows the transport administration
to manage resources more effectively.
[9], [10] provide a detailed analysis of research on
the use of IoT for public transport systems. Overall,
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most of the reviewed papers consider the
transmission and visualization of public transport
data, without analysis and training of models based
on this data. In this paper, we propose a system
architecture that can save, transmit, analyze, and
build predictions based on public transport data.
Our study was written to evaluate the possibilities of
using it for monitoring and managing public
transport data in modern Ukrainian realities. This
research focused on the idea of automated
management of city bus routes without drivers. The
idea has been put forward since 2010.
Unfortunately, full implementation in world practice
was not achieved.
3 Methods and Materials
This research combines analytical and experimental
methods. It also includes the best practices of
specific IoT systems realizations, which are
described in the chapter "Literature review".
For each service is selected a set of possible
technologies which are analyzed and compared by
performance tests. The performance tests are
executed on device emulators, which allows an
effective compare the capabilities of the
technologies.
Also, we analyze the feasibility and complexity of
implementation into existing transport.
4 Results
4.1 System Requirements
The system can be divided into two main parts -
data collection and data processing. The purpose of
this paper is to analyze and select stable and secure
communication channels between the above parts.
For data collection and transmission (Fig. 1)
developed special modules, that are installed
directly in the vehicle (Vehicle module). This
module collects and builds packets for transmitting
the following data: current geolocation data,
information from passenger validators, and vehicle
service data (from CAN bus).
Fig. 1: Data transmission system structure
Source: Created by the authors
Data should be transmitted to the gateway in
conditional real-time mode, with the maximum
delay time equal to 1 min.
Table 1. Vehicle module data description
Data
Description
Size
Geolocation data
NMEA packages
The maximum
length of each
packet is
restricted to 255
bytes.
Passengers data
Information
about the number
of successful
validations at the
stop. The
package should
contain the stop
ID and passenger
count. Data is
received from
the RS232
interface.
128 bytes.
Vehicle service
data
Information
about the
vehicle's
technical
condition (for
example, battery
level, etc.).
Depends on the
vehicle type.
The maximum
length of each
packet is
restricted to 256
bytes.
Source: Created by the authors
According to Figure 1, we also have two
communication types: Device-Gateway
communication and Gateway - API communication.
Device to gateway communication. It is the
telecommunication connection between the Vehicle
module and gateway nodes. Gateways are more
powerful computing nodes than IoT devices. They
have two main functions: to consolidate data from
devices and route it to the relevant data system (API
in our case); to analyze data and, if some problems
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are found, return it to the device. For this connection
type, we should consider IoT network connectivity.
Gateway – API communication. It is the data
transmission from a gateway to the appropriate data
system – API Service.
4.2 IoT network Connectivity
IoT connectivity technologies provide the network
infrastructure and communication capabilities
required by IoT devices to collect, transport, and
exchange data over the internet and are remotely
monitored and controlled.
IoT connectivity covers a range of communication
technologies:
Low-power wide-area network (LPWAN)
technologies, such as LoRa.
Cellular technologies, including 2G, 3G, 4G, and
5G
Short-range wireless technologies, such as Wi-Fi,
Bluetooth, Zigbee, and many others
Satellite technologies, such as VSAT, BGAN, and
the newest satellite-based LPWAN
The IoT network technologies specification are
presented in Table 2.
Table 2. IoT network technologies specifications
Distance
Frequency
Rate
Power
cons.
~10 km
Cellular
10
Mbps
High
~ 100 m
2.4 Ghz
1,2,3
Mbps
Low
~ 100 m
2.4, 5 Ghz
54
Mbps
Medium
~ 100 m
2.4 Ghz
250
kbps
Low
~ 5km
sub-GHz
50
kbps
Low
Source: Created by the authors, based on, [1], [2], [4],
[5]
Since data needs to be transmitted in real time over
long distances, the most suitable technologies are –
Cellular connectivity and LoRa.
Cellular networks are based on open, global industry
standards, use licensed spectrum, and are always
operated by wireless network providers.
A cellular network or mobile network is a
communication network where the link to and from
end nodes is wireless. The network is distributed
over land areas called "cells", each served by at least
one fixed-location transceiver (typically three cell
sites or base transceiver stations). These base
stations provide the cell with the network coverage
which can be used for transmission of voice, data,
and other types of content. A cell typically uses a
different set of frequencies from neighboring cells,
to avoid interference and provide guaranteed service
quality within each cell, [1]. In our case, we should
review two generations of cellular networks – GSM
(2G, 3G) and LTE (4G, 5G).
GSM is the second generation of Cellular
networks. 3G and 2G Coverage is currently
available in many cities around the world. With
General Packet Radio Service (GPRS), 2G offers a
theoretical maximum transfer speed of 40 kbit/s (5
kB/s). With EDGE (Enhanced Data Rates for GSM
Evolution), there is a theoretical maximum transfer
speed of 384 kbit/s (48 kB/s), [2].
LTE (Long-term evolution) - is standard for high-
speed data transmission. It delivers download
speeds of around 100 Mbps (theoretically could be
raised to 150 Mbps) and upload speeds of around 50
Mbps. The main LTE advantage over 2G and 3G is
that it is a promising technology that increases
coverage in the world, and LTE will provide
continuity for networks in the long term. Also, LTE
provides special technology for IoT devices - LTE-
M or LTE CAT-M1. LTE-M is a reduced LTE
version designed especially for battery-powered
devices. In this case, IoT devices can
transmit/receive data over cellular networks with
more power-efficient.
LoRa (from "long-range") is a physical
proprietary radio communication technique, [4]. It is
based on spread spectrum modulation techniques
derived from chirp spread spectrum (CSS)
technology, [5]. The technology covers the physical
layer, while other technologies and protocols such
as LoRaWAN (Long Range Wide Area Network)
cover the upper layers. It can achieve data rates
between 0.3 kbit/s and 27 kbit/s, depending upon the
spreading factor, [6]. For the LoRaWAN network,
we need to provide a special Gateway
(corresponding to base stations in a cellular
network) (Fig. 2).
Fig. 2: LoRaWAN Network components.
Source: Created by the authors, based on, [4]
In our case, LTE is the preferred system because of
the already existing infrastructure and high data
transfer rates.
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IoT data protocols
There are several protocols proposed for M2M/IoT
communication, with a focus on mentioned
constrained environments. The most frequently
adopted protocols are MQTT (Message Queue
Telemetry Transport), and protocols based on HTTP
– CoAP or REST API, [3]. In this paper, we need to
test the performance of the above protocols and
compare them for our system scenarios. Results can
help determine which protocol should be used. After
a brief description of MQTT and HTTP protocol,
testing results are presented.
4.3 MQTT
MQTT is a machine-to-machine Internet of Things
connectivity protocol for use on top of the TCP/IP
protocol stack which was designed as an extremely
lightweight broker-based publish/subscribe
messaging protocol for small code footprints (e.g.,
8-bit, 256KB RAM controllers), low bandwidth and
power, high-cost connections and latency, variable
availability, and negotiated delivery guarantees. In
the hub and spoke model of the Message-Oriented
Middleware messaging server forwards messages
from sensor devices to monitor devices. In such
architecture, a device whose main task is to
continuously produce and send data to the server is
defined as a publisher. The MQTT broker collects
messages from publishers and examines to whom
the message needs to be sent. On the other side,
every device which had previously registered its
interests with a server will keep receiving messages
until the subscription is disabled.
Fig. 3: MQTT Publish/Subscribe architecture
Source: Created by the authors, based on, [3]
Using this (Fig. 3) architecture, publishers and
subscribers do not need to know each other, which
is one of the major advantages of this protocol.
Devices that send data need not know who are the
clients that are subscribed for receiving data and
conversely. Further to this, the publishers and
subscribers do not need to participate in the
communication at the same time and do not need to
be familiar with each other. It is intended for
devices with limited power and memory
capabilities, where the network is expensive, has
low bandwidth, or is unreliable. One of the key
requirements of an Internet of Things concept is low
network bandwidth used to send data and minimal
device resource requirements.
4.4 HTTP
HTTP (HyperText Transfer Protocol) was invented
as a component of the World Wide Web to transfer
documents. It is most familiar to us as one of the
enabling technologies that allow web browsers to
work. HTTP clients can make requests: GET, PUT,
DELETE, and POST, to name the most common
(Fig. 4).
Fig. 4. HTTP Request/Response
Source: Created by the authors
In our case, the vehicle module will be sent a POST
request with data described in Table 1. The data
type will be separated by an appropriate HTTP path
(Tab. 3). Moreover, the HTTP request for location
of the data is presented in Table 3.
Table 3. HTTP request for location data
Metho
d
Path
Data
POST
/API/v1/loca
tion
$GPGGA,092750.000,5321.6
802,
N,00630.3372,W,1,8,1.03,61.
7,M,55.2,M,,*76
POST
/API/v1/batt
ery
{ level: 75 }
Source: Created by the authors
Table 4 shows the main differences between the
MQTT and HTTP.
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Table 4. MQTT / HTTP Comparison
MQTT
HTTP
Architecture
Publish/subscrib
e
Request/response
Underlying
Protocol
TCP/IP
TCP/IP
Security
method
TLS +
username/passw
ord
TLS +
username/password
Messaging
Mode
Asynchronous,
event-based
Synchronous
Message
queuing
The broker can
queue messages
for disconnected
subscribers
-
Message
Size
256MB
maximum
No limit
Content
type
Binary
Text
Message
distribution
One to many
One to one
Data
validation
Software
implementation
Software
implementation
Source: Created by the authors based on, [3]
For the performance test, we develop a program that
simulated a Vehicle module, which runs both an
MQTT client and an HTTP client, and then
measures the response time and tracks the packets
sent over the wire. The test results are shown in
Table 5.
Table 5. MQTT / HTTP Performance test
MQTT
Bytes
HTTP Bytes
Establish connection
5572
2261
Disconnect
376
0
For each message
published
388
3285
The sum for 1
message
6336
5546
The sum of 10
messages
9829
55,460
The sum of 100
messages
44,748
554,600
Source: Created by the authors, based on, [11]
As MQTT was designed for IoT solutions, it fits
many more IoT scenarios than HTTP. The only case
where HTTP might be a valid choice is to connect
devices that already have an HTTP client installed
to a provider which has an HTTP option. But then
only for low-volume data transmission, and without
the option of sending control commands to the
device. According to the above results, we can use
MQTT for data transmission between the physical
device (Vehicle module) and the Cloud gateway,
and for the microservice communication, we can use
REST API, [11].
4.5 IoT Network Structure
The proposed IoT network is shown in Figure 5.
Information from physical devices is transmitted in
real-time through cellular networks, according to the
MQTT protocol. Between Cloud microservices, data
is transmitted via HTTP.
The system (Fig. 1) is distributed and consists of the
following modules:
1. Vehicle module – the device that collects and
transmits data through the cellular network to the
Cloud. This device is integrated with the onboard
computer, a ticket validator, and has a GPS
module.
2. MQTT Broker – a broker that receives messages
from Vehicle modules (publishers) and transmits
them to the clients (subscribers). API Gateway is a
service that stores and manages stored data.
3. The API Gateway is the primary interface that
provides access to stored data, analytics, and
neural network-based prediction results. API
Gateway also provides a Public API for third-
party integrations.
4. ML (Machine learning) Service is a service for
building neural network models based on stored
data.
5. Analytics Service – a service for building data
analytics.
6. Management Dashboard – control panel and data
visualization. Allows tracking of the location of
vehicles, the number of passengers, technical
conditions, etc. in real-time.
7.
Fig. 5: Data flow structure
Source: Created by the authors
5 Discussion
The proposed system architecture is easy to
integrate into and cost-efficient. The main
advantage is real-time data transmission and
processing. In this paper, the data transmitting is
considered for Geolocation, Passenger, and Vehicle
Service data. But the above dataset can be
supplemented with any data, and this will not affect
the overall system architecture. Also, the system is
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distributed and consists of separate microservices
that are independent of each other. Therefore, we
can add, remove, and scale each microservice
without affecting the overall performance of the
system. To implement the above system, it is
necessary to install modules (Vehicle modules) in
the vehicle. Each module will be integrated with an
onboard computer and ticket validator. For the
Cloud services deployment can be used
infrastructure as code systems such as Terraform or
CloudFormation.
As for the implementation of automated and
robotic programs, they are implemented in cities
with a fairly high density of buildings and a high
population density (primarily Asian cities: Tokyo,
Hong Kong, Singapore), [13], [14], [15] at the same
time, in countries with a high level of GDP per
capita. Therefore, due to the clear organization of
traffic, such systems are effective and viable,
because they allow optimizing the movements of
transport and passenger flows, [15]. At the same
time, they minimize expenses for the organization.
As for European, primarily Ukrainian cities, such
projects are only discussed, analyzed, and individual
elements are implemented, [16]. There are quite a
few reasons: it requires a complete reconstruction of
the existing infrastructure and bringing highways up
to international standards. As for the organizational
aspects, Ukrainian cities still cope with numerous
passenger flows due to the potential that was laid in
the Soviet period through the concept of subways
and electric transport. Today, from a practical point
of view, this problem is not relevant due to a
significant reduction of existing routes and diversion
to the main city routes and the saturation of cities
with their electric transport and their road transport.
The main demographic condition is the reduction in
the number of cities due to hostilities and
displacement and migration flows of the Ukrainian
population. Directly in the theoretical aspect, this
topic is relevant from the medium-term perspective.
Therefore, in the article, the scientific justification
of the idea of automated movement of transport is
carried out.
However, the idea itself may not be realized due
to the innovative implementations of Chinese
innovators, who successfully shape the movement
of passengers in densely populated cities at the
expense of motor drones and autodromes.
The advantage of the conducted research is that
it reflects a unique approach to the architectural
solution of the software complex, which is simple
and relatively cheap, compared to other theoretical
developments.
6 Conclusions
In this paper, we analyzed transmitting technologies
and protocols which can be used for the city
monitoring and management system. Based on the
analysis and performance tests, we chose suitable
technologies and proposed the IoT network
architecture.
The proposed architecture allows to quickly
implement effective collection, monitoring, and
analysis of public transport data. Implementation of
the above system will allow the monitor and
therefore respond in time to potential problems in
emergencies, problems with the technical condition,
etc.
For data transmission, the system uses publicly
available cellular networks, which do not require
additional infrastructure.
The idea of introducing complexes for city
vehicles is quite realistic. From a technical point of
view, it is easily implemented, which is proven by
research, because the city's IT infrastructural
saturation system is quite significant and diverse
(cameras, sensors, sound and signal traffic lights,
barriers, etc.). Thus, the bandwidth of information
channels is quite significant, which allows for
quickly and fully implementing technical solutions.
From an economic point of view, such a decision is
relatively cheap and low-cost.
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Conflicts of Interest
The authors declare no conflicts of interest.
Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
Formal analysis, Leonid Sibruk; Investigation,
Anzhelika Kokarieva; Methodology, Leonid Sibruk;
Resources, Ihor Zakutynskyi; Software, Leonid
Sibruk; Validation, Anzhelika Kokarieva; Writing—
original draft, Ihor Zakutynskyi; Writing—review
and editing, Leonid Sibruk.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
This research received no external funding.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.en
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DOI: 10.37394/23202.2023.22.25
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