About me
Optimal Fare Policy and Fleet Sizing for Integrated Fixed-route Transit and Microtransit - Integrating microtransit with fixed-route transit (FRT) presents a great opportunity for transit agencies to improve mobility and accessibility by leveraging the flexibility benefits of microtransit and the capacity benefits of FRT. However, microtransit services are quite expensive for transit agencies to offer. Although the mobility vs. cost trade-off is well-established in the literature and practice, prior research fails to optimize (or even analyze) fare structures, fare levels, and other transit design parameters to ensure Pareto-efficient outcomes.
To address the optimal fare policy and microtransit fleet sizing problem, we propose a bi-level modeling framework. At the upper level, we use a multi-objective Bayesian Optimization (BO) algorithm to jointly optimize the fare structure and fleet size, considering (i) minimizing transit subsidies and (ii) maximizing mobility logsum as a measure of consumer welfare—two inherently conflicting objectives. At the lower level, we use a simulation-based modeling framework with endogenous mode choice, multimodal transit path choice, and microtransit performance to model the travelers’ response to the transit agency’s fare structure and fleet size decisions. The bi-level model returns Pareto-optimal solutions, providing key insights into the trade-offs between the conflicting objectives.
Case studies on two real-world road networks of San Diego (dense urban area) and Lemon Grove (suburban area) reveal clear insights. Notably, the total subsidy is driven primarily by the microtransit fleet size, with fare parameter changes having a smaller impact. High FRT fares persist in all Pareto-optimal solutions in Lemon Grove, suggesting that in suburban areas, travelers along FRT corridors benefit the most from FRT and are willing to pay high fares. In contrast, microtransit fares vary along the Pareto-frontier, balancing fleet availability and user demand.
Our study—i.e., our methodological approach and case study findings—provide valuable insights to transit agencies considering microtransit investment and its integration with FRT.
