# Decentralized Public Transport Management System Based on Blockchain Technology +
## 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:  
1. Operational efficiency: The tokenization system enables predictive maintenance planning, reducing unexpected breakdowns and operational costs.
2. Data management: The GDEPZ method optimizes data transmission, making the system viable even in areas with limited connectivity.
3. 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.  
Conceptualization, methodology, investigation, S.T. and M.K.; validation, M.K. and L.V.; writing--original draft, S.T.; writing--review and editing, S.T. and M.K.; research funding, L.V. All authors have read and agreed to the published version of the manuscript.  
The publication was prepared within the framework of the Academic Fund Program at HSE University. Grant No. 23-00-035 \"Overcoming the limitations of the interaction of cyber-physical systems in heterogeneous networks of the remote Internet of things using machine learning methods\".  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.