Dynamic Occupancy Rate for Shared Taxi Mobility-on-Demand Services through LSTM and PER-DQN

As an effective public transportation system, a Shared Taxi Mobility-on-Demand (STMoD) provides passengers with door-to-door shared taxi service. This study proposes a dynamic occupancy rate rebalancing approach with centralised dispatching for STMoD systems to equalise taxi supply in response to passengers' demands in a city. The occupancy rate changes dynamically since the passengers' demand varies during the time, as predicted using a Long Short-Term Memory (LSTM) machine learning algorithm. The zone, weekday, time, and holidays are used as effective parameters to train the LSTM model. The occupancy rate increases in peak hours and decreases in off-peak hours to balance the number of passengers and the number of idle taxis in the corresponding zones. Then, the taxi transferring procedure applies to the remaining imbalanced zones, balancing the request and response in the whole city. The proposed approach adjusts the drivers' incomes to increase the number of taxis earning money and decrease the idle taxis without income. Also, it reduces passenger waiting time. Taxis learn to follow the shortest paths to pick up and drop off passengers using the Prioritised Experience-Deep Q Network (PER-DQN) reinforcement learning algorithm. Using the New York City passenger demand data in Manhattan, we simulated and compared the STMoD performance with the classic shared taxi system in an agent-based simulation environment. The evaluation results showed a a 28.18% improvement in the balance ofmoney earned by taxis compared to the classic shared taxi scenario. Also, the number of idle taxis decreased by 38%, and the passenger waiting time significantly reduced by 22.69%.