Towards a secure urban traffic network

Abstract

Traditional Intelligent Transport Systems (ITS) face several critical limitations such as single points of failure due to centralized control and data. These limitations result in ITS solutions that are often inefficient and unreliable in meeting modern transportation demands. Recent research highlights blockchain technology as a promising solution to these challenges. By decentralizing control, securing data immutably, and enabling transparent, distributed decision-making, blockchain can strengthen ITS against failures and manipulation while improving responsiveness to dynamic traffic conditions. This dissertation presents the design, development, and implementation of a decentralized traffic management system that integrates blockchain technology with a traffic simulation. A locally hosted Ethereum Virtual Machine (EVM)-compatible blockchain (via Ganache) is connected to the Aimsun Next 23 simulation platform using Python APIs, enabling real-time, bidirectional communication between the simulation and the blockchain network. The system uses three smart contracts: one to log vehicle counts using event emissions, another to manage actuated traffic light logic based on real-time traffic data and another to establish priority to emergency vehicles. This decentralized approach enables tamperproof data logging, distributed control, and programmable traffic responses. It also supports the logging and management of emergency events, such as simulated lane closures, without relying on centralized control. Four simulations were conducted to evaluate the system’s functionality, ranging from basic traffic data logging to full actuated signal control under emergency conditions. The results demonstrate that integrating blockchain technology into ITS frameworks is favourable, leading to several benefits such as transparency, resilience, and dynamic traffic management capabilities. The dissertation concludes that the prototype meets its objectives. Future work could explore deployment on scalable platforms like Polygon and the application of this work to other traffic networks.