DEVELOPMENT OF A CHATBOT FOR TRAFFIC ARBITRATION WITH AN INTEGRATED REWARD SYSTEM BASED ON DIGITAL COINS

Abstract

This paper presents the results of developing a specialised chatbot aimed at optimising daily operations within traffic arbitration teams. Traffic arbitration teams face numerous challenges, including communication difficulties, excessive workload from routine tasks, unstructured workflows, and the lack of convenient progress tracking tools. The proposed software solution addresses these challenges by incorporating a staff motivation mechanism based on an internal virtual currency – coins, combining task distribution, progress tracking, analytics integration, and motivational incentives in a single Telegram-based interface. The chatbot architecture follows asynchronous programming principles using the Aiogram framework, which supports finite-state machine models essential for multi-step user dialogues. The document-oriented MongoDB database management system was chosen for its flexibility in storing user profiles, task histories, coin transactions, and referral data. The server-side component is deployed on cloud platforms such as Render or Railway, providing automatic updates and secure webhook handling. The study provides functional and structural models of the system, including UML class and component diagrams, as well as flowcharts of the main algorithms. A comparative analysis of existing similar solutions, such as ZeyBot, Everbot, Standuply, and Karma Bot, is performed, justifying the choice of the technology stack. The testing results confirm the stable operation of the developed application under load conditions close to real-world usage scenarios, with an average response time of less than half a second under a load of one hundred simultaneous users. Special attention is paid to information security issues, including HTTPS encryption, Telegram ID-based authentication, database access restrictions, and logging of administrative actions. The prospects of integrating artificial intelligence elements, such as intelligent traffic management and natural language processing, are also discussed.