ABSTRACT. Redesigning multimodal transit network can utilize shared autonomous vehicles (SAVs) as feeders to enhance service efficiency and coverage. This paper presents an optimization framework for the joint multimodal transit frequency and SAV fleet size problem, a variant of the transit network frequency setting problem. The objective is to maximize total transit ridership (including SAV-fed trips and subtracting boarding rejections) across multiple time periods under budget constraints, considering endogenous mode choice (transit, point-to-point SAVs, driving) and route selection, while allowing for strategic route removal by setting frequencies to zero. Due to the problem's non-linear, non-convex nature and the computational challenges of large-scale networks, we develop a hybrid solution approach that combines a metaheuristic approach (particle swarm optimization) with nonlinear programming for local solution refinement. To ensure computational tractability, the framework integrates analytical approximation models for SAV waiting times based on fleet utilization, multimodal network assignment for route choice, and multinomial logit mode choice behavior, bypassing the need for computationally intensive simulations within the main optimization loop. Applied to the Chicago metropolitan area’s multimodal network, our method illustrates a 33.3% increase in transit ridership through optimized transit route frequencies and SAV integration, particularly enhancing off-peak service accessibility and strategically reallocating resources.

ABSTRACT. In this study, we combine a microscopic traffic simulation with the eFLIPS scheduling approach to analyze bus electrification in part of Kyoto's bus network. We compare two scenarios -- solely depot charging and terminus fast charging -- to estimate vehicle requirements, charger allocation, and peak power loads. The microscopic simulation provides detailed trip-level energy consumption, while eFLIPS optimally assigns trips and simulates charging events. Results show terminus charging demands extra vehicles but uses smaller onboard batteries, whereas depot charging lowers peak electric loads. The method yields rapid insight into operational and economic trade-offs and can adapt to larger networks or additional constraints.