Pareto-Optimal Transit Route Planning With Multi-Objective Monte-Carlo Tree Search

Planning ideal transit routes in the complex urban environment can improve the performance and efficiency of public transportation systems effectively. However, finding such routes is computationally difficult due to the huge solution space constituted by billions of possible routes. Considering the limited scalability of exact search methods, heuristic search methods were proposed to boost the efficiency and incorporate flexible constraints. Nevertheless, the existing methods conceal multiple criteria in an objective, and thus evaluating the performance of the generated route becomes challenging due to the lack of comparable alternatives. Inspired by the prior study, we formulate the definition of pareto-optimal transit routes based on multiple criteria. However, extracting these routes remains challenging because: A) the sheer volume of possible transit routes; and B) the sparsity of pareto-optimal routes. We address these challenges by developing an efficient search framework: for challenge A, a random search method is developed based on Monte Carlo tree search where the unproductive solution subspaces are pruned progressively to reduce the search cost; and for challenge B, an estimation method is derived to guide the search process by assessing the value for each solution subspace. The superior effectiveness of our approach in approximating the pareto-optimal transit routes was demonstrated by the comprehensive evaluation based on the real-world data.