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- W4200344498 abstract "Public transit systems play an important role in the alleviation of traffic congestion in urban road networks. The same vehicle type and a fixed departure timetable are usually applied to a bus route in the conventional public transit systems. They fail to cater to the time-varying travel demand or the diversified characteristics of transit passengers. To this end, this study proposes a demand-responsive public transit (DRPT) system consisting of a fixed bus route and demand-responsive stops with multiple vehicle types. The vehicle types of dispatched buses and the ride-matching schemes are optimized to serve transit passengers in real-time. Due to the non-convexity, Deep Q-Network (DQN), a reinforcement learning (RL) algorithm, is applied to the dynamic dispatching problem in the proposed DRPT system. The numerical studies validate the advantages of the proposed DRPT system and the RL-based dispatching algorithm." @default.
- W4200344498 created "2021-12-31" @default.
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- W4200344498 date "2021-12-14" @default.
- W4200344498 modified "2023-09-26" @default.
- W4200344498 title "Reinforcement Learning Based Demand-Responsive Public Transit Dispatching" @default.
- W4200344498 doi "https://doi.org/10.1061/9780784483565.038" @default.
- W4200344498 hasPublicationYear "2021" @default.
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