Design and development of a framework for large scale distributed Agent-Based simulation systems

This thesis investigates the potential for optimizing urban transportation systems to improve overall traffic liveability through advanced simulation techniques. The research begins with an exploration of smart city concepts, focusing on the challenges of managing complex urban environments. The primary objective is to enhance traffic management systems by leveraging innovative simulation approaches that can accurately model and predict traffic behaviors under various scenarios. A critical component of this work is the development and application of city simulations. These simulations serve as the backbone for testing and validating different traffic management strategies. Initially, standalone simulations were utilized to model traffic flow and assess the impact of various interventions on CO2 emissions and traffic congestion. However, these standalone models often faced limitations in capturing the multifaceted interactions within a city's transportation network, leading to a recognition of the need for more integrated and scalable simulation frameworks. The research then transitions to co-simulation methods, which allow for the integration of multiple simulation tools to provide a more comprehensive analysis of urban traffic systems. This approach addresses the shortcomings of standalone simulations by enabling detailed interaction modeling between different traffic components. The co-simulation framework developed in this thesis is designed to be scalable, ensuring that it can handle large-scale urban environments with high levels of detail and accuracy. A key innovation of this thesis is the scalability assessment of the co-simulation framework. Through rigorous testing and evaluation, the framework was demonstrated to efficiently manage the computational demands of simulating large urban areas. This scalability is crucial for practical applications, where real-time data and dynamic traffic conditions require robust and responsive simulation tools. The results of this research highlight significant improvements in traffic liveability. By implementing optimized traffic management strategies within the co-simulation framework, notable decreases in emissions and enhancements in traffic flow were achieved. These results underscore the potential of advanced simulation techniques to contribute meaningfully to the development of smarter, more sustainable cities. The structure of this dissertation is organized into eight chapters, each addressing a specific aspect of the research. The introduction outlines the context and objectives of the study. Subsequent chapters delve into the technical aspects of simulation and co-simulation methods, including detailed discussions on the development and implementation of the co-simulation framework. The findings are presented with comprehensive analyses of the results, followed by discussions on the implications for urban traffic management and environmental sustainability. In conclusion, this thesis provides a robust framework for the optimization of urban transportation systems through advanced simulation techniques. The innovative co-simulation approach developed herein offers a scalable and effective tool for city planners and policymakers to improve traffic conditions, paving the way for smarter and more sustainable urban environments.