Thesis

With the continuous advancement of buses' connectivity levels, urban roads witness a mixed traffic flow comprising both connected buses and non-connected private cars. Leveraging the advantages brought by the connected environment and fully integrating the macroscopic state of mixed traffic flow with the microscopic trajectories of connected buses, the connected bus speed and traffic signals can be jointly optimized, significantly enhancing the refined management of urban road traffic. Presently, research predominantly considers either macroscopic traffic states or microscopic vehicle trajectories, lacking an integrated macro-microscopic traffic flow model that comprehensively represents the state of mixed traffic flow and bus driving behaviors. Moreover, existing methods mainly focus on connected bus speed guidance and transit signal priority control, neglecting the interactive influences among traffic signals, connected buses, and non-connected private cars. Therefore, this thesis combines traditional and advanced connected detection methods to estimate the state of mixed traffic flow and constructs an integrated macro-microscopic model. Building upon a thorough consideration of the mutual interactions between connected buses and non-connected private cars in mixed traffic flow, this thesis aims to maximize the overall efficiency of mixed traffic flow by jointly optimizing the link-level connected bus guidance speed and the intersection-level signal control scheme.

For the purpose of improving the sustainability of the transportation system, this project presents a sustainable traffic management and control strategy for urban intersections by optimizing traffic signals. The methodology applied in this project links the macroscopic models with the microscopic models, in which the fuel consumption model and the environment model are on the basis of the traffic model. In the corresponding sustainable traffic signal control problem, the optimization framework combines average travel time, fuel consumption and pollutant emissions and the algorithm with the limited computational budget is applied to collectively and dedicatedly solve the optimization problems by various combinations of indicators in the objective function. Then, the solutions to the above problems are applied to an empirical study of the Yangon coastal network via Aimsun. The results indicate that the proposed methodology yields better performance than the existing signal control scheme in transport sustainability and evidently, outperforms more significantly while accompanying the offset optimization. So, the proposed methodology can provide a strategic reference for a more sustainable transportation system.