Abstract
Integrating microtransit with fixed-route transit (FRT) can improve travelers’ mobility by leveraging microtransit’s flexibility and FRT’s capacity. However, the high operating costs of microtransit pose a challenge, calling for careful evaluation of the trade-offs between mobility gains and operational costs. This presentation introduces a modeling approach and solution procedure to identify Pareto-optimal designs. The focus is on design parameters of practical interest, namely, fare policies and microtransit fleet size. To explore these trade-offs, a bi-level and bi-objective (i.e., minimize taxpayer subsidy and maximize mobility-based consumer welfare) modeling framework is developed, featuring an agent-based transportation system simulation model at the lower level and a multi-objective Bayesian Optimization (BO) model at the upper level. The modeling and solution approach is applied to Lemon Grove, California (a suburban area in San Diego County). Results reveal a diverse set of solutions along the Pareto frontier, indicating that naive microtransit fare strategies are suboptimal. Notably, Pareto-optimal designs feature a 50–100% discount for microtransit-to-FRT transfers, as well as peak-period fare multipliers between 1.8x and 3.5x to manage time-varying demand effectively.
