A reinforcement-learning dispatch controller for semi-on-demand transit feeders serves 16% more passengers than a fixed route in simulation, with the RL layer adding a small 2.4% gain over a nominal zonal rule.
Flexing service schedules: Assessing the potential for demand-adaptive hybrid transit via a stated preference approach,
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Semi-on-Demand Transit Feeders with Shared Autonomous Vehicles and Reinforcement-Learning-Based Zonal Dispatching Control
A reinforcement-learning dispatch controller for semi-on-demand transit feeders serves 16% more passengers than a fixed route in simulation, with the RL layer adding a small 2.4% gain over a nominal zonal rule.