Monte Carlo Tree Search-based intersection signal optimization model with channelized section spillover

Signal optimization has received significant attentions from the research community. However, during peak hours, the performances of existing models still have room for improvements, especially when channelized section spillover (CSS) occurs. The evolution of CSS is dynamic in nature, not only due to the interactions between traffic flow of different movements, but also because existing CSS influences the queuing process in a channelized section and contributes to new CSS formation at the next cycle. Neglecting such spatial-temporal interaction in current traffic signal optimization procedures may lead to suboptimal results. A Monte Carlo Tree Search-based model is proposed to solve the intersection optimization problem (named MCTS-IO) with explicit modeling of CSS dynamic evolution. The model works in a rolling horizon way. At each decision point, MCTS-IO simulates the intersection by selecting a sequence of phases, and progressively updates the relative preferences of the phases. After all simulations are performed, the phase with the best policy PI is selected. Both the PI and decision space can be customized, which ensures algorithm flexibility. The method is tested against Synchro results with both stable and variable demand, which demonstrates the proposed model is always able to find a solution better than Synchro.