UC Riverside

As urban traffic congestion and environmental concerns continue to escalate, there is a growing need for innovative approaches to optimize the performance of signalized intersections. This dissertation presents a comprehensive investigation into the co-optimization of vehicle trajectories and traffic signal timing at isolated signalized intersections. The primary objective is to propose a novel co-optimization framework that integrates eco-trajectory planning and traffic signal timing optimization to improve transportation efficiency and reduce environmental impacts, which we call Eco-friendly Cooperative Traffic Optimization (ECoTOp).Central to this research are the functional building blocks, including the creation of an Innovation Corridor testbed in Riverside, CA, a Speed Advisory Tablet App, Lane-level GNSS Applications, and the integration of a refined MOVES emission model. These innovative components enable accurate and efficient data collection, advanced traffic control, and precise emission estimation, elevating the ECoTOp approach to new levels of effectiveness and sustainability. Building upon this foundation, the methodology is established, encompassing the development of hybrid co-optimization algorithms and the utilization of a simulation platform, SUMO, for evaluating various experimental scenarios. The case studies are pivotal in this dissertation, investigating the ECoTOp approach across diverse scenarios with varying traffic volumes, connected and automated vehicle (CAV) penetration rates, and mixed vehicle types. Comparative analyses between ECoTOp and individual strategies are conducted to lay the groundwork for future development of an adaptive optimization strategy. Moreover, the ECoTOp approach is extended to incorporate electric vehicles (EVs), with energy estimated using MATLAB's EV model. Furthermore, the dissertation explores the concept of an adaptive optimization strategy capable of dynamically selecting the most suitable optimization approach based on real-time traffic conditions and environmental considerations. The findings of this dissertation demonstrate the potential of the proposed co-optimization approach in enhancing traffic flow and reducing emissions. By comparing the ECoTOp with the individual optimization strategies, this research serves as a precursor for an innovative adaptive optimization strategy that can pave the way for more efficient and sustainable transportation systems in the future. The dissertation contributes valuable insights into the field of traffic signal optimization and vehicle trajectory optimization, encouraging further research and applications in intelligent transportation systems.