Abstract
In this project, we carried out simulation and emulation of an urban traffic signalling system. We first built a simulator that can randomly generate networks and car routes to test how different signal scheduling choices affect the total distance travelled by cars in a given period. Based on this, we built an emulator to search for optimal scheduling using Bayesian optimisation. To overcome the problem of exploding search space without sacrificing flexibility or descriptiveness, we introduced four different scheduling schemes and conducted experiments to compare their performances. Results show that a preset scheduling gives the best convergence limit under reasonable number of iterations in most cases. We believe that the combination of Bayesian optimisation with traffic planning offers some novel insights and has much more potential to be discovered. With sufficient research effort, this area would bring great benefit to city planners and the general public, with potential applications in other areas that involves network traffic controls.
