Decentralized Public Transport Management System Based on Blockchain Technology ^†

Abstract

The development of intelligent transportation systems (ITSs) is penetrating many economies around the globe. This paper presents three key innovations in the field of intelligent transportation systems, as follows: (1) a novel tokenization approach where each vehicle is represented as a macro-token subdivided into 500,000 micro-tokens for precise condition monitoring, (2) a comprehensive mathematical model for vehicle state assessment incorporating multiple operational factors, and (3) the GDEPZ method for optimizing data transmission via satellite communication. These innovations enable the autonomous control of technical conditions, transparent fleet management, and efficient data processing in hard-to-reach areas. Various researchers in both industry and academia are looking into more efficient management methods for both vehicles and related data processing aspects. A vast trend related to the latter is the distributed data processing of transmitted data. This article discusses approaches to the use of blockchain technology in ITSs. It explores the use of blockchains in modern transport industries. In particular, the paper proposes a novel approach to the maintenance of public transportation vehicles and buses. The specificity of the proposed approach is the autonomous control of technical conditions using information systems. When using blockchain technology, building a transparent vehicle fleet management system is possible. The specificity of the proposed approach lies in data processing. Within the organization, confidence in data increases, the possibility of manipulating transportation is eliminated, and the decision-making chain is reduced. As a result, the system can manage itself. This also helps to increase the service life of vehicles, makes it possible to predict their malfunctions, and improves the quality of data on their technical conditions.

1. Introduction

The maintenance of corporate machinery, e.g., public transport, is designed to follow well-established country-specific approaches, yet it is heavily pushed forward by research and development progress [1]. These rules are primarily due to the regulations that related public health institutions set to avoid potential injuries in operation [2]. Each vehicle must be qualitatively serviced and not allowed to operate under unacceptable conditions. Moreover, public transport is a conveyor with a fast turnover of vehicles.

As a common practice, vehicles travel along established routes with the intention of returning to the vehicle park at night. As this process is continuous, the intervals between the return of the vehicle from the route and its exit to the line are very short; thus, this should be taken into account when organizing the process of maintenance, repair, or the recharging of public transport as part of the ITS ecosystem [3].

The above limitations lead to attempts to maintain public transportation equipment in accordance with the manufacturer’s regulations. This involves replacing and checking all technical components of the vehicle with a certain periodicity of mileage or time. Since routes are commonly fixed, this allows one to predict the next maintenance date and make such a maintenance plan.

The main disadvantage of this conventional approach is its usability only for periodic maintenance cases, as well as the need to consider a particular vehicle’s operating conditions (e.g., weather, geography, road quality, and passenger traffic). Also, this approach does not consider the breakdowns and malfunctions that may occur—these must be eliminated outside the plan [4]. Therefore, equipment may undergo repairs only after a breakdown. Preventive maintenance, unfortunately, is impossible, because the maximum transportation efficiency is squeezed out of one vehicle as a single unit. The presence of an imperfect maintenance system in the manufacturer’s regulations, without considering the individual characteristics of transport vehicle operation and repairs after breakdowns, leads to the main problem.

Recent studies on distributed systems have highlighted the growing importance of efficient data management and sharing mechanisms. As Gorbunova et al. [5] demonstrate, traditional centralized systems can no longer guarantee the required level of availability and reliability due to the growing number of involved nodes, complicated heterogeneous architectures, and increasing task loads. This makes blockchain-based solutions particularly relevant for transportation systems that require a high availability and reliable data exchange.

Therefore, the following set of research questions arise:

• RQ1: Is it possible to improve the efficiency of public transport maintenance and repair with the help of modern technologies?
• RQ2: Could their application effectively ensure the uninterrupted and safe transportation of passengers?
• RQ3: Will such a system be an effective management tool for transport organizations?

Our approach to blockchain implementation in transportation systems focuses on the following three key aspects: secure data management through a private blockchain architecture, efficient vehicle condition monitoring via tokenization, and optimized data transmission for remote areas. This technical foundation enables automated maintenance decisions while addressing common blockchain implementation challenges.

The key contributions of this paper are as follows:

• The development of an innovative tokenization approach for vehicle condition monitoring, where each transport unit is represented as a macro-token subdivided into 500,000 micro-tokens, enabling the precise tracking of vehicle resource consumption and maintenance needs.
• The creation of a mathematical model for vehicle state assessment that incorporates multiple operational factors, including mileage, passenger load, weather conditions, and maintenance history, providing a comprehensive framework for condition monitoring.
• The introduction of the GDEPZ method for optimizing data transmission via satellite communication, specifically designed for vehicles operating in remote areas with limited connectivity.

These innovations collectively address the challenges of modern transportation system management, providing a foundation for autonomous vehicle maintenance decision making and efficient fleet management.

While blockchain technology offers significant advantages for transportation systems, we acknowledge its common implementation challenges, such as latency, scalability, and energy consumption. Our approach addresses these challenges through the following three key design decisions: (1) utilizing a private blockchain architecture that significantly reduces computational overhead and energy costs compared to public networks, (2) implementing the GDEPZ method for data optimization that minimizes transmission latency, and (3) designing a scalable token-based system that can efficiently handle up to 1500 vehicles with a 25 TPS network capacity. These design choices enable practical implementation while maintaining the benefits of blockchain technology.

To address the above research problems, this article proposes a model for organizing a maintenance and repair system for public transport vehicles based on Distributed Ledger Technology (DLT). The proposed system utilizes a token whose maximum value corresponds to the exploitation resource of a vehicle, i.e., 100%. This can further decrease or increase during certain operational or maintenance events. These correspond to the transactions of the actual transport vehicle’s lifetime stages. The system is designed as a private blockchain, which could be integrated within an organization or into one geographical area, e.g., a city. Moreover, the proposed system can store the complete, immutable history of the maintenance and operation of the vehicle in DLT.

This article offers a foundation for the future of unmanned transport. In an unmanned system, the importance of smart contracts increases many times, since there is no decision-making driver. This model allows one to manage the technical conditions of vehicles by taking into account only objective factors with which the vehicle operates—routes, stops, weather conditions, and passenger traffic. The model needs to take into account the current requirements of local legislation, which mandate daily inspections before going on the line.

In addition to unmanned vehicle applications, there are problems with obtaining up-to-date data on technical conditions, for example, in hard-to-reach regions. In particular, for this purpose, the article suggests using satellite communications to improve the quality and stability of the system.

The rest of the paper is organized as follows. Section 2 provides an overview of the blockchain application in the transportation segment. In Section 3, a model of the system operation and the data transmission mechanism are proposed. Section 4 presents the technical components of the model, and Section 5 elaborates on the direction of future research.

2. Related Work and Background

To address the first research question, we executed a systematic literature review and analyzed sources focusing on the application of blockchain technology and smart contracts in the transportation field. Two hundred and four sources were analyzed, a quarter of which formed the basis of this work. The works were collected from 2019 to 2022, as this research direction is quite young.

A general understanding of blockchain technology in the context of intelligent applications in Industry 4.0 is given in a comprehensive study [6]. It reveals the issue of blockchain security for applications in agriculture, healthcare, logistics and supply chains, business, tourism, hospitality, and energy. One of the main advantages of blockchains is the possibility for the secure transmission of information in systems.

Delving into the use of blockchain in supply chains, it is important to mention one study [7], which presents, in detail, the classification of distributed registry technology in operations and supply chain management. In addition to the literature review, this study also offers a full-fledged architecture demonstrating the capabilities of blockchain technology in the transport sector. This further indicates the untapped potential of this technology. Thus, based on the number of literature reviews by researchers and the diverse sources considered, the problem of introducing blockchain technology in transport is urgent.

Recent advances in maintenance optimization have demonstrated the effectiveness of mathematical approaches for complex technical systems. Kechagias et al. [8] developed a comprehensive semi-Markov decision process model that allows organizations to determine the optimal maintenance policies by considering both preventive and condition-based maintenance options. Their approach provides a mathematical framework for analyzing equipment deterioration and maintenance scheduling while incorporating time constraints and cost optimization. This mathematical foundation is particularly relevant for blockchain-based maintenance systems, as it offers rigorous methods for decision making in maintenance management.

Recent studies have demonstrated the effectiveness of survival analysis techniques in predictive maintenance applications. As highlighted by Softic and Hrnjica [9], survival analysis provides a robust statistical framework for estimating machinery failure probabilities and identifying the significant factors affecting equipment lifespan. Their research showed how these techniques can be particularly valuable when integrated with existing maintenance systems, offering insights into both immediate failure risks and long-term reliability patterns.

The integration of blockchain technology with maintenance planning has shown significant potential for optimizing resource utilization and improving system reliability. Recent research by Al-Refaie et al. [10] demonstrated how blockchain-based maintenance systems can effectively manage and schedule maintenance activities while minimizing costs and maximizing resource utilization. Their approach provides a framework for integrating maintenance optimization with blockchain technology, offering insights into how decentralized systems can enhance maintenance planning efficiency.

2.1. Intelligent Transport Systems and Urban Logistics

Blockchain technology is widely applicable in developing ITSs and autonomous driving vehicles. The fundamental factor for their operation is the security and integrity of data exchange between the actors of the system [11]. In the case of DLT, the risk of a single point of failure is significantly reduced. As another solution, deep learning can detect intrusions in ITSs using various elements of the Internet of Things (IoT) [12].

Additional conventional ITS issues also include poor scalability, difficulties with maintaining the quality of service, and problems with reliability and confidentiality [13]. Blockchain-based solutions can solve these problems within the Internet of Vehicles (IoV) framework [14]. However, the use of blockchain in the IoV also brings a minor set of limitations—the speed and size of the transaction. These factors also need to be optimized for transmissions over the wireless medium [15]. A system that considers these limitations is presented in one work [16]. Considering the realities of the modern world, information exchange between vehicles is increasing with the development of modern cellular technologies beyond 5G [17]. The improvement of transmission rates requires ITSs to organize access control only for trusted nodes [18].

Blockchain technology is also used in conventional road transport, which directly participates in any logistics chain. The entire logistics system is constantly faced with an unstable environment, uncertain schedules, and a high information uncertainty. Blockchains can be used for coordinated work in supply chains involving road transport [19]. In addition to controlling the chain from above, it is important to understand the information about the actual conditions and positions of each vehicle in the system. Reference [20] offers the GOLIATH model, a real-time platform for exchanging data between vehicles. This continues the topic of using blockchains in intelligent transport systems. Finally, the automotive industry needs to conduct recall campaigns cost-effectively. Each such campaign is associated with a huge array of data that could be stored in a blockchain for the transparency and greater efficiency of the entire process [21]. Such systems can be developed on the open Ethereum blockchain.

The delay and throughput of vehicle communication are important in urban environments. One work [22] outlines that a vehicle-for-hire system with a driver moderates supply and demand, with the ability to predict them and manage the entire system in real time due to a high transmission rate. Secondly, a similar task is the forecasting and management of urban logistics systems [23]. Thirdly, there is a traffic light system in every city. The operation of this system depends on many factors, and its safe and reliable functioning keeps order and tranquility in cities. Such fundamental objects in traffic management should be maximally protected and resistant to attacks. A blockchain-based approach can facilitate adaptive traffic light management [24].

2.2. Safety and Implementation Challenges

Transportation safety is an important element in transport and logistics. The topic of safety is central in transport, since any transportation is always associated with an increased danger to people. A blockchain bundle with the IoT can transfer personal data in industry, services, transport, and healthcare [25]. This study’s results allow for using a digital signature algorithm with an elliptic curve to ensure data integrity. Another sub-task in security matters is to reduce the cost of processing information. Together with increased privacy and safety in transport systems, it is possible to achieve a 10% reduction in processing costs and a 50% reduction in fraud detection problems [26]. In addition, secure models in a secure Mist network provide 81% less latency when transmitting information on local nodes compared to other solutions [27].

To enable blockchain security in ITS management, researchers mainly offer custom versions of systems and algorithms [28,29]. The first includes an additional element of deep learning, and the second consists of a Fog/Edge computing architecture combined with deep learning.

Also, it is worth talking about integrational limitations in the same section. There are two main types of obstacles preventing the introduction of blockchain technology into existing business processes, as follows: (1) insufficient business awareness and (2) a lack of knowledge of the potential of blockchain technology to improve future supply chains [30].

2.3. Supply Chain Financing

In this subsection, the focus will be on the smart contracts used in logistics supply chains. The simplest is the conclusion of a smart contract between a manufacturer and a buyer [31]. The use of smart contracts reduces the risks of non-payment and non-delivery for each of the parties, obliging them to conduct business as transparently as possible. Moreover, using smart contracts reduces the cost of paperwork and software for tracking products. A more serious advantage is the possibility for better avoidance of fraud and the manipulation of prices/added value at all stages of the logistics chain [32].

There are three main levels in the financing of supply processes using blockchains, as follows: (1) co-financing and joint operational solutions, (2) the choice of financing mode, and (3) improving the efficiency of financing [33]. All three coupled together are necessary to develop an optimal strategy for finding solutions for a specific supply chain and counterparties. However, in addition to the search for a theoretical optimum, an important aspect of the operational management of supply chain financing could be solved by, e.g., game theory [34].

The last element in the financing of supply chains is related to cryptocurrency contracts, with the main advantage being ample opportunities for optimizing commissions at all stages of the supply chain [35]. This topic has yet to be sufficiently studied due to the lack of clear legal norms governing payments in non-conventional currencies.

2.4. Related Works Summary

Based on the literature study, some general trends and key features of blockchain implementation in transport management systems were identified. First, such systems emphasize a system’s security based on their transparency. All transactions and phases of the logistics process are recorded in DLT. Secondly, blockchains are presented as a more secure enabler for open transport structures, such as intelligent transport systems. All system participants have access to complete ledger information, which increases the reliability and coherence of the system.

Despite these advantages, integrating modern technology still requires tremendous efforts due to, e.g., high entry barriers and no obvious benefits from implementation. In addition to focusing on security and transparency, introducing smart contracts into logistics processes reduces transaction costs. In an economic sense, this plays a significant role only in large and long logistics chains. In contrast, using blockchains and smart contracts in the transportation processes of small- and medium-sized businesses primarily entails costs.

To answer the questions raised and suggest a scenario for solving the problem of using blockchains in small- and medium-sized companies, it is necessary to study the technological process of developing and implementing blockchains in transport systems. Then, it is essential to propose simplifications of this process to maintain the key advantages of blockchain technology. To specify the task, a model of smart contracts will be used at a public transportation company, where a contract is set between the vehicle owner’s company and a subcontractor, i.e., a maintenance/repair company. The entire chain of repairs and maintenance of the vehicle will be stored in the blockchain. It will always be possible to transparently track the causes of emergency situations.

Comparing blockchains to other encryption algorithms, it becomes evident that blockchains stand out as a unique and highly suitable solution, especially in the realm of public transportation [36]. While traditional encryption methods ensure data security, blockchains provide an additional layer of confidence and transparency. In the public transportation sector, where efficient coordination and safety for millions of passengers are paramount, blockchains can serve as an ideal tool. Their decentralized nature and the impossibility of altering existing records guarantee reliability and protection against fraud. This makes blockchains an appealing solution for improving public transportation management systems, ensuring schedule accuracy, and enhancing passenger safety in this critical domain [37].

To provide a structured overview of the existing research and enhance the visibility of key findings in blockchain applications for transportation systems, we present a comprehensive summary in

Table 1. Summary of blockchain applications in transportation systems.

Application Domain	Key Features	Implementation Challenges	References
Intelligent Transport Systems	Security and data integrity	Scalability	(Jha et al., 2022) [11]
	Real-time data exchange	Network latency	(Abdel-Basset et al., 2021) [12]
	Decentralized trust
Safety Management	Transparent monitoring	Integration costs	(Chen et al., 2022) [25]
	Secure data sharing	System complexity	(Kumar et al., 2021) [28]
	Fraud prevention
Supply Chain and Logistics	Asset tracking	Implementation barriers	(Reddy et al., 2021) [19]
	Smart contracts	Technology adoption	(Maffiola et al., 2021) [20]
	Process automation

. This table synthesizes the main application domains, their key features, implementation challenges, and corresponding references from the literature review. The summary demonstrates the diverse applications of blockchain technology in transportation while highlighting common challenges across different domains.

Developing a new system should be accompanied by comparing its key characteristics with existing processes. The main goals of blockchain technology for the maintenance of public transport are the following:

• Reductions in maintenance costs and downtime due to the introduction of a more honest mechanism for monitoring vehicle resources;
• Improving the fault tolerance of the overall transportation system through the predictable and realistic management of expectations for vehicle capabilities;
• Increasing the level of data analytics and confidence in the framework of transport system management.

3. System Design and Implementation

This section elaborates, in detail, on a model that allows for monitoring vehicle conditions and maintaining them in working condition.

Understanding how various processes in the studied areas will be improved through blockchain technology or smart contracts is essential.

We first highlight the main categories in the integration of blockchain technology and supply chain management, as follows: (1) accounting and administration, (2) trust, (3) data platform, (4) compatibility, and (5) interference elimination [38]. Working on these categories together may lead to comprehensive improvements in supply chain management.

The study shows that the coexistence of these technologies strengthens component connections, decision rationalization, automation, and process integration. Considering the human factor in using new technologies in the supply chain is also necessary. A study on how managers make decisions about the use of blockchains in supply chains allows us to conclude that the problem of transparently calculating implementation efficiency results remains unresolved [39].

Evidently, blockchains in supply chains reduce costs and increase the level of security, but at the same time, there is still a risk of cyber-attacks that is difficult to calculate and cannot be predictably managed [40]. Despite all the difficulties, the use of blockchains not only makes supply chains more efficient, but also increases their overall stability [41].

Conclusions regarding positive expectations from blockchain technology can be drawn based on its introduction by Brazilian companies into the supply chain [42]. One can focus on pallet management by switching to the unit level in the company hierarchy. Using a blockchain to control operations in pallet logistics affects the sustainable development of the logistics industry [43]. Finally, while it is impossible to fully implement new technology into existing supply chains, it is permissible to turn to the Lean approach. Even with this approach, blockchains can increase the efficiency, reliability, and transparency of supply chains [44].

3.1. Token-Based Vehicle Monitoring Model

The proposed model was collaboratively developed in conjunction with a public transportation organization specializing in passenger bus services. Each bus was conceptualized as a macro-token subdivided into 500,000 micro-tokens. This numerical value was empirically derived and is represented as the variable Q(T) (Quality) within the model. The specific numerical assignment of Q(T) may vary depending on the type of transportation and the particular transportation task, but for urban public transportation buses, it stands at 500,000.

Unlike traditional fleet management systems that rely on fixed maintenance schedules and human decision making, our tokenization approach provides a dynamic, real-time assessment of vehicle conditions. Each operational parameter directly affects the token value, creating a comprehensive digital representation of the vehicle’s state. This innovative approach transforms abstract wear-and-tear concepts into measurable, actionable metrics, enabling automated, data-driven maintenance decisions.

The value of Q(T) is subject to fluctuations over the course of a bus’s operation. In collaboration with the transportation organization, empirical data were collected over a three-month period to determine how the bus token could be depleted or replenished. Consequently, the token depletion rates are outlined in

Table 2. Token value reduction rules.

Metric	Description	Token Change, Units
One mileage kilometer	Every kilometer reduction.	−4
One passenger	Each transported passenger reduction.	−3
One stop	Each stop reduction.	−35
Temperature conditions	Working outside the target temperature conditions (below −20 and above 25 degrees Celsius), reduction according to the delta temperature formula from the border * 200. The average daily temperature is taken.	[−200; −4000]
Precipitation amount	Precipitation in the form of snow or rain reduction	−1000
A traffic accident	A blocking incident requiring a return to the park for inspection and recovery. An accident is understood as any emergency situation involving other road users or not.	−300,000
One change of drivers	Each shift for a new driver (including the first exit per shift) reduction.	−750
One engine hour	Every hour of engine operation reduction.	−100

, while the token replenishment rates are detailed in

Table 3. Token value increase rules.

Metric	Description	Token Change, Units
Routine maintenance	General inspection of the bus. Some units are checked according to the regulations every 30,000 km, most of them every 15,000 km	200,000
Replacing the coolant	Every 30,000 km	20,000
Replacement of fuel purification filters	Every 30,000 km	20,000
Oil and oil filter replacement	Every 15,000 km	20,000
Oil change in the gearbox	Every 60,000 km	20,000
Oil change in the rear axle crankcase	Every 60,000 km	20,000
Replacement of brake pads	Every 60,000 km	40,000
Rubber replacement	Every 6 months	10,000
Washing	Washing a vehicle	1000
Repair after an accident	Restoration work after any accident	300,000

. The data in these tables were verified using a mathematical model of the operation of a fleet of buses, and are considered to be reliable for this particular type of public transportation.

Let the reduction indicators be denoted as R (Reduce), ranging from $R_1$ to $R_n$, with their values specified separately for each type of transport due to variations in maintenance cycles and requisite indicators between a bus and, for instance, a transport boat.

In this context, the token increase indicators, I (Increase), take on values from $I_1$ to $I_m$, with their values also contingent on the specific type of transportation.

Theorem 1.

Let:

• ${Q\left(T\right)}_t$—the value of the quality token;
• $R_i$—the reduction metric;
• $I_j$—the increase metric;
• $a_{t_1}$—the reduction ratio;
• $b_{t_2}$—the increase ratio;
• The formula for calculating the current state of a vehicle’s quality, used to determine the required maintenance, is as follows:

$${Q\left(T\right)}_t=\left(\sum _{i=1,t_1=0}^{i=n_1,t_1=m_1}{R}_i{a}_{t_1}\right)+\left(\sum _{j=1,t_2=0}^{j=n_2,t_2=m_2}{I}_j{b}_{t_2}\right)$$

(1)

Based on the results of several interviews with representatives of transport companies, the basic quantitative values for buses were obtained. They may vary depending on the vehicle model and the operation of a particular route. Still, this accuracy is sufficient for primary modeling. In the basic scenario provided for by maintenance regulations, checkups and repairs should be performed every 15,000 km. This is the most common interval for buses. From interviews with representatives of the transport company, for 15,000 km, the average bus undergoes the following:

• It will stay operational for 40 days;
• It will be capable of transporting 20,000 people;
• It will have 80 driver shifts;
• It will have an operational time of 600 h;
• It will make, at minimum, 1600 stops.

In this scenario, the vehicle consumes 300,000 mini tokens from one maintenance to the next maintenance. Therefore, it must replenish the macro token during maintenance. At the same time, accidents, precipitation, temperature, and changes in proportions to increase any of the parameters (e.g., mileage), unlike the normal operational scenario, bring the vehicle closer to routine maintenance. Then, it is performed earlier, considering the actual operation of the bus.

A private blockchain with information covering all transactions made per vehicle is one approach to implementing such a system. This works because, as a rule, transport is managed by one company in one geographical location.

The automated recording of all transactions in the blockchain allows one to use the most transparent and complete data to manage repairs and maintain analytics for future periods based on already occurring transactions, as shown in Figure 1.

3.2. Data Collection and Transmission

To operate the system and maintain the technical conditions of a bus, it is necessary to obtain actual information about its operation, e.g., the manufacturing year and fuel type, as well as the features that the manufacturer lay down. All this information may be obtained through the internal CAN bus from the vehicle’s onboard computer. Part of the analytics takes place directly on the vehicle, so various messages can light up on the driver’s dashboard.

Information from existing sensors, as well as from additionally installed ones, is sent to the onboard computer. From there, it must be taken into the system using the cellular network [45]. Further, information about technical conditions when entering the blockchain is stored in the transaction history of the vehicle.

The amount of transmitted data depends on the following:

• The type of vehicle;
• The number of IoT sensors per vehicle;
• A variety of options for the transmitted information;
• The frequency of data transmission.

All information is transmitted in the form of time series. On average, a bus may contain up to 20 IoT sensors covering all the following main technical nodes: engine, suspension, turbine, fuel system, cooling system, etc. The primary task is to collect information from them, aggregate it, and transfer it to the cloud. For one value, it is enough to allocate 16 characters, that is, 128 bits of information. Accordingly, for one bus, 2560 bits are transmitted every minute. This frequency is sufficient for analyzing technical conditions, even when discussing autonomous transport. This is enough, since the model does not use complex sensors like a Lidar or a camera.

For one bus per day, this volume is 3,684,400 bits, and for a system with 500 buses, this is already 1,843,200,000 bits per day for analysis. Honestly, this is a tiny amount to analyze. This volume must be efficiently processed and stored. For this, it is important to determine the compression algorithm and the structure of the blockchain. The actual amount of data will depend on the specific situation, and this calculation is indicative. Possible types of data and their transmission frequencies are presented in

Table 4. Data types.

Source	Data Type	Data Transmission Frequency
Engine speed, vehicle speed	Word	One per minute
Temperature of engine oil, antifreeze	Integer	One per minute
Check engine	Boolean	One per 30 min
The mileage of a vehicle	Integer	One per day
Wear of brake pads and discs	Byte	One per day
Number of stops, passengers, drivers	Word	One per day
Engine running time	Datetime	One per day
Vehicle location	Geometry	One per minute

.

Rush hours and the day of the week affect the stored data volumes. The morning and afternoon hours are the densest. The average distribution of passengers for one bus is shown in Figure 2. This distribution is taken from the empirical data of a public transport company. The graph shows peaks in the morning and evening hours. However, the actual distribution may differ between specific conditions and routes, and also strongly depends on the day of the week.

3.3. Proposed System Description

The blockchain for the proposed system should have several characteristics. However, it is necessary to consider the specifics of the application in the transport sector when developing it.

While both private and consortium blockchains offer advantages for transportation systems, our choice of a private blockchain architecture is justified by several key factors specific to public transportation management, as follows:

• Operational Control: Private blockchains allow for complete control over network parameters and transaction validation, which is crucial for maintaining the required transaction processing capacity.
• Data Privacy: Public transport organizations need to maintain control over sensitive operational data while still ensuring transparency within their organization.
• System Efficiency: Our implementation requires processing a specific number of vehicle tokens, which is more efficiently managed within a private blockchain structure.
• Geographical Scope: Since the system is designed to operate within one geographical area, a private blockchain provides sufficient decentralization while maintaining operational efficiency.

The main characteristics and justification of these values are presented in

Table 5. Main blockchain characteristics.

Characteristic	Value	Background
Network capacity	Twenty-Five Transactions Per Second (TPS)	The number of buses in 1 city with a population of up to 1 million is up to 1500 units. It is necessary to receive the status of transactions and technical conditions at least once every 60 s. This means that the network bandwidth is sufficient at the level of 25 TPS.
The number of tokens	1500 tokens	We divide each token into 500 thousand parts.
Type of tokens	Stablecoins	Each token is provided with 1 real vehicle
Token Standard	ERC-721	Each asset (vehicle) is unique and not interchangeable. There is no task to exchange with other tokens since the solution works completely in a private blockchain.
Issue of tokens	Possible	When expanding the participants using the proposed system.

.

An Ethereum-based blockchain will be used to implement the described system as a universal solution in the industry.

To implement these characteristics effectively, we develop a three-layer blockchain integration architecture that ensures seamless interaction between the token-based monitoring system and the blockchain infrastructure.

Table 6. Blockchain integration architecture components.

Architectural Layer	Components	Functionality
Smart Contract Layer	ERC-721 token contract State management contract Maintenance trigger contract Access control contract	Vehicle token management Value modification handling Automated maintenance scheduling Permission management
Consensus and Validation Layer	PoA consensus mechanism Transaction validation Node configuration Real-time updates	25 TPS throughput optimization Private network operation Token value validation System performance monitoring
Data Management Layer	Maintenance history storage Token value transactions IoT data integration Event logging	Immutable record keeping Value modification tracking Sensor data processing Maintenance event recording

presents the key components and functionality of each architectural layer.

This architectural approach enables automated maintenance decisions through smart contracts while maintaining the system’s performance requirements. When a vehicle’s token value reaches predetermined thresholds, the system leverages this architecture to trigger maintenance scheduling, update the vehicle status, notify stakeholders, and record all events in the blockchain, ensuring data integrity and operational efficiency.

In this model, one of the key tasks is to transfer information from the vehicle, namely from the IoT sensors to the cloud server.

Placing messages from the sensors in SBD packets with subsequent compression can be accomplished by applying the compression algorithm (operator) to messages placed in the container for the SBD package [46].

Theorem 2.

Let:

• First, we define the SBD packets as having a size V and a set of n messages of sizes $a_1$, … $a_n$. It is necessary to find an integer number of containers B and partition the set {1,…,n} into B subsets $S_1$, … $S_B$ such that we obtain the following:

$${\sum }_{\left({\forall }_i\in S_k\right)}{a}_i\le V,\forall k=\{1,\dots ,B\}$$

(2)

Further, each of the subsets of messages $S_1$, … $S_B$ is compressed by the compression operator $A_z$: $A_{z{(S}_1)}$, …, $A_{z{(S}_B)}$, thus, the solution would be considered better if B is minimal. The minimum B is further denoted as OPT.

• V—the volume of the container in bytes;
• n—the number of messages from sensors in the buffer;
• $A_i$—the volume of the i-th message in bytes;
• B—number of containers;
• $Y_i$—equals 1 if container i is used or 0 otherwise;
• $X_{\left\{i,j\right\}}$—equals 1 if message j is placed in container I;
• $A_z$—is an operator for compressing messages placed in a container.
• The task is to minimize $B={\sum }_{\left(i=1\right)}^n{Y}_i$ under the set constraints.
• The total size of messages placed in the container and then compressed by the compression operator cannot exceed the size of the container:

$$\begin{array}{c}{A}_z:\left({\displaystyle \sum _{n,i=1}^{n,j=1}}{a}_j{X}_{i,j}\right)\le V{Y}_i,i\in \left\{1,\dots ,n\right\}\end{array}$$

(3)

All n messages must be placed in containers as follows:

$$\begin{array}{c}{A}_z:\left({\displaystyle \sum _{n,i=1}^{n,j=1}}{X}_{i,j}\right)=1,{\forall }_j\in \left\{1,\dots ,n\right\}\end{array}$$

(4)

where $Y_i\in \left\{\mathrm{0,1}\right\},{\forall }_i\in \left\{1,\dots ,n\right\},{X_{i,j}\in \left\{1,\dots ,n\right\},\forall }_j\in \left\{1,\dots ,n\right\}$, where $Y_i=1$ and if container i is used and $X_{i,j}$ if the message is placed in container i.

3.4. Description of the Operation of the Compression Algorithm

Compression algorithms are commonly used to reduce data size for more efficient transmission and storage. In the context of IoT sensors, the amount of data generated can be substantial. Thus, the use of compression algorithms can be particularly important. The theorem being discussed, Theorem 3, provides a mathematical formula for determining the amount of data that can be placed in a container for sending, given various volumes and compression ratios. Specifically, the theorem defines the relationships between the amount of data placed in the container (V₁), the volume of primary data from the IoT sensors (V₂), the volume of serialized primary data from the IoT sensors (V₃), the amount of uncompressed data placed in the container (V₄), the volume of compressed data placed in the container (V₅), and the compression ratios (K₁, K₂, and K₃) associated with serialization, container placement, and the compression operator, respectively. One can optimize compression algorithms for IoT data transmission and storage by understanding these relationships.

Theorem 3.

Let:

• V₁—the amount of data placed in the container to be sent in bytes;
• V₂—the volume of primary data from the IoT sensors in bytes;
• V₃—the volume of serialized primary data from the IoT sensors;
• V₄—the amount of uncompressed data placed in the container;
• V₅—the volume of compressed data placed in the container;
• K₁—compression ratio for the serialization of primary data from the IoT sensors (conversion to binary format);
• K₂—compression ratio when placing data in a container;
• K₃—the compression ratio in the data container after applying the compression operator (algorithm).
• Then, the amount of data placed in the container for sending can be determined as follows:

$$\begin{array}{c}{V}_1=V_2\left(K_1\ast K_2\ast K_3\right)\end{array}$$

(5)

$$\begin{array}{c}{V}_3=V_2\ast K_1\end{array}$$

(6)

$$\begin{array}{c}{V}_4=V_3\ast K_2\end{array}$$

(7)

$$\begin{array}{c}{V}_5=V_4\ast K_3\end{array}$$

(8)

$$\begin{array}{c}{V}_1=V_5=V_4\ast K_3=\left(V_3\ast K_2\right)\ast K_3=\left(\left(\left(V_2\ast K_1\right)\ast K_2\right)\ast K_3\right)=V_2\ast K_1\ast K_2\ast K_3\end{array}$$

(9)

In conclusion, Theorem 3 provides a formula for determining the amount of data that can be placed in a container for sending, given various volumes and compression ratios. This theorem highlights the importance of using compression algorithms for IoT data transmission and storage, where the volume of data generated can be substantial. By optimizing the use of compression algorithms, one can improve the efficiency and effectiveness of IoT systems.

Theorem 4.

Let:

• L—the useful amount of data from IoT sensors in bits, with a 1-bit data transfer rate/sec;
• C—the transfer rate of containers (SBD packets);
• T—the data transfer time.
• Then, we obtain the following:

$$\begin{array}{c}L=C\ast T\end{array}$$

(10)

3.5. Data Encoding and Packaging Method

Satellite communication is a critical element for a model that collects vehicle data and transmits them to the blockchain for further analysis. It allows data transfer from vehicles in hard-to-reach regions and along highways, where it is impossible to use a traditional communication network, such as mobile or cable networks.

With satellite communication, public transport vehicles operating in remote or hard-to-reach places will be able to transmit data about their technical condition, which could indicate potential malfunctions, delays in the schedule, and passenger safety concerns.

However, satellite communication may face limitations in data transmission speed and cost. Here, the GDEPZ method aims to optimize the amount of data transmitted via satellite communications—a bottleneck regarding transmission speed and cost.

Thus, satellite communication and optimized data transmission methods, such as GDEPZ, are necessary for successfully operating a model that collects vehicle data and transmits them to the blockchain for further analysis. It provides reliable communication with vehicles operating in hard-to-reach places and allows for the efficient use of satellite communication resources.

The GDEPZ method aims to optimize the amount of data transmitted over a satellite communication line, which is a bottleneck in terms of speed and cost, as shown in Figure 3.

4. Results and System Evaluation

4.1. Comparison with Traditional Management Systems

Table 7. Comparison with traditional management system.

Parameter for Comparison	Traditional Service Management System	Service Management Using Blockchain
Process Holder	Chief mechanic or head of a transport company	The rules are written in the private blockchain
Accounting of the order and time of service provision	Manually based on the manufacturer’s instructions	Automatically based on the actual operating conditions of the vehicle
The number of errors in the process of organizing maintenance	Constantly, as it depends on the human factor	It is reduced as the blockchain is used, as the system is calibrated as it is filled with data
Transportation safety	Depends on the decency of the staff	Depends on the calibration of the system, the level increases over time
The speed of making a decision about the need for maintenance	Several days	Instantly
Managing repair and maintenance history	Centralized, depends on specific people	Decentralized, can be transferred to a new management
Maintenance costs	Not controlled. Identified upon the fact of breakdowns or according to the maintenance regulations	Seeks to reduce by rationalizing the repair plan

provides the main system parameters and compares the standard approach with the proposed solution.

4.2. Decision-Making Scenarios

The system’s automated decision-making capability is illustrated through the following scenarios:

Scenario 1: Critical Token Depletion. When a bus’s token value drops below 150,000 (30% of maximum), the system automatically conducts the following:

• Schedules maintenance within the next 48 h.
• Adjusts route assignments to minimize additional wear.
• Notifies maintenance crews with specific component diagnostics.

Scenario 2: Adverse Weather Conditions. During extreme weather (below −20 °C or above 25 °C), the following occurs:

• The token depletion rate is dynamically adjusted.
• Maintenance thresholds are automatically lowered.
• Additional diagnostic checks are scheduled.

Scenario 3: Operational Anomalies. When detecting unusual patterns (excessive stops and rapid token depletion), the following occurs:

• Immediate diagnostic procedures are initiated.
• Real-time alerts are sent to operations management.
• Preventive maintenance is scheduled based on pattern analysis.

4.3. Performance Analysis

This article [47] explores a method for encoding, packaging, and compressing data in heterogeneous Internet of Things (IoT) networks using satellite communication channels. A crucial aspect of this research is the reduction in the cost of transmitting messages via satellite communication channels, which is significant for data collection in areas with underdeveloped infrastructure. The authors developed a method that combines multiple data encoding and packaging methods to enhance data transmission efficiency.

• The method reduces the data volume with a common linguistic feature ranging from 30 to 2048 bytes by 10–17 times (4.6 times due to serialization and 2.13–3.6 times due to the authors’ proposed archiving method);
• The cost reduction in data transmission leads to an increased economic efficiency of satellite communications for organizing data transmission networks in remote areas lacking telecommunication infrastructure;
• The method can be further refined and generalized for satellite IoT scenarios using various combinations of data transmission technologies, encoding formats, and container sizes.

IRIDIUM 9602 SBD Satellite Transceiver:

• The maximum size of the sent message is 340 bytes;
• The maximum size of the received message is 270 bytes.

IRIDIUM CORE 9523 Satellite Transceiver:

• The maximum packet size for transmission is 1960 bytes (SBD);
• The maximum packet size for reception is 1890 bytes (SBD).

Depending on the amount of data being compressed, the compression ratio varies.

This approach is assumed to be used, as it has proven its effectiveness in data compression [48]. Satellite communication, in turn, allows the vehicle to be tracked more accurately at any point.

The use of compression technology is extremely important for a large number of vehicles connected to the system. With an increase in their number, the amount of transmitted data increases, as shown in Figure 4.

4.4. Limitations and Challenges

The proposed model exhibits several limitations that require future mitigation. One notable constraint pertains to its validation exclusively on buses, which confines its applicability to a single category of public transportation. For watercraft in remote areas or alternative bus models designed for similar locales, the model necessitates adjustments in distinct parameters that describe the operation of these vehicles. Despite the model’s universal quality token formula, parameters differ among vehicle models, necessitating human intervention at this juncture for calibration.

Additionally, another significant limitation relates to the technical peculiarities of components and systems in different modes of transportation. The data transmission from buses is resolved through one method, collecting data from a specific set of components. In the case of waterborne transportation, unique features come into play, including distinct braking systems, the absence of wheels, and other differentiating factors.

5. Discussion and Future Perspectives

5.1. Future Research Directions and Application in Unmanned Transport

As noted earlier, there is no immediate need for a model of the transport system’s operation with vehicles at the enterprise. Further application of the model is possible in a system with unmanned vehicles. The transition to unmanned transport contains numerous technological, legal, and infrastructural challenges. Still, the conceptual advantages of modeling such a system can be obtained already.

Vehicles travel along pre-set routes. Each vehicle has specific anchor points, as follows:

• Parking place (depot);
• Place of service;
• Stopping points along the route;
• Route from the depot to the beginning of the route;
• Route from the end of the route to the depot;
• Route between the depot and the service station;
• Route between each stop and the service station.

The vehicle can independently carry out trips along pre-set routes and independently go for repairs and maintenance.

In this model, not only is the mechanic excluded from the decision-making chain, as in Section 3, but so is the driver. The driver no longer influences the decision to continue working or to go for maintenance or repair. Thanks to distributed registry technology and integration with the compression algorithm, it is possible to create a closed public transport system for a specific geographical region in the future.

Future research may center on the standardization of the model and the execution of experiments across diverse forms of public transportation.

5.2. System Advantages

The introduction of such a system has several advantages, as follows:

• Reduction in labor costs.
• Reduction in administrative management costs.
• Improvement in the quality of driving equipment on the route.
• Increase in the speed of decision making in the transport system.
• Reduction in maintenance and repair errors.
• Improvement in the reliability and service life of vehicles.

For this model, improving the quality of transport organizations’ management systems is fundamental. The necessary information about transport processes is safely stored in the blockchain; thanks to the compression algorithm, data transmission is faster, and infrastructure costs are reduced. At the same time, the blockchain can still be private, for example, for one organization or city.

6. Conclusions

The paper discusses the use of blockchain technology and IoT sensors to optimize the process of maintaining vehicles. Methods of data compression based on compression algorithms have been proposed, which reduce the costs for storing and transmitting data. The article also presents a model of a transport management system using blockchain technology and IoT sensors. In the future, this will reduce labor costs and improve the quality of equipment, as well as increase the reliability and service life of vehicles. The use of this model can significantly improve the management of transport processes and reduce the costs for their maintenance.

By leveraging modern technologies like blockchains, satellite communication, and data compression algorithms, it is possible to significantly improve the efficiency of technical maintenance and repair in the public transportation sector. These innovations enable the continuous operation of public transportation, thus contributing to passenger safety. Vehicles can be promptly serviced, and all malfunctions can be rectified, even in remote or hard-to-reach areas. This addresses RQ1. Utilizing modern technologies can mitigate transportation challenges in remote or hard-to-reach areas. Through satellite communication and data compression algorithms, it is feasible to enhance the monitoring of transportation vehicles, improving the accessibility and reliability of transportation infrastructure. This addresses RQ2. Implementing the proposed management model serves as an effective tool for transportation organizations. This model allows for the mechanization of technical maintenance and repair processes, ensuring up-to-date data on the condition of transportation assets. Decisions regarding the necessity of repairs are made based on objective indicators, such as macro-tokens and the actual wear and tear of the vehicles, rather than relying on regulations or subjective opinions. This promotes more efficient resource management and a reduction in operational costs.

The practical implementation of the proposed innovations demonstrates significant improvements in the following three key areas:

• Operational efficiency: The tokenization system enables predictive maintenance planning, reducing unexpected breakdowns and operational costs.
• Data management: The GDEPZ method optimizes data transmission, making the system viable even in areas with limited connectivity.
• Decision making: The mathematical model provides objective criteria for maintenance decisions, eliminating subjective factors and improving overall fleet reliability.

One of the essential elements of this model is the use of satellite communications, which allows for receiving data from vehicles operating in remote and hard-to-reach areas. An optimization method for the volume of transmitted data based on compression algorithms has also been proposed, which reduces data transmission and storage costs.