Taxi scheduling method and system based on deep reinforcement learning

A technology of reinforcement learning and scheduling methods, applied in the field of artificial intelligence, can solve problems that are not interpretable, cannot reflect the impact of historical decisions, and do not consider the road network structure.
CN111862579AActive Publication Date: 2020-10-30SHENZHEN UNIV
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
SHENZHEN UNIV
Publication Date
2020-10-30

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Abstract

The invention relates to a taxi scheduling method and system based on deep reinforcement learning. The system comprises an area construction module, a demand prediction module, a vehicle scheduling module and a simulator. The method comprises the steps of S1 forming a regional network; S2 predicting the number of orders that will appear in any area at any time; S3 calculating the total vehicle supply of one area; obtaining a demand / supply state of each area; and S4 inputting the states of the area where any idle vehicle is located and the neighbor area into a trained taxi scheduling model to obtain a scheduling strategy of the vehicle, and determining whether the vehicle is continuously left in the local area or scheduled to the neighbor area specified by the system. According to the invention, idle taxis are dispatched, the success rate of order matching is increased, the waiting time of passengers is reduced, and the utilization rate of taxis is improved.
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Description

technical field

[0001] The present invention relates to the technical field of artificial intelligence, and more specifically, to a taxi dispatching method and system based on deep reinforcement learning. Background technique

[0002] The emergence of large-scale modern ride-hailing platforms will greatly benefit our daily travel, allowing passengers to book travel plans in advance and match available taxis with ride requests in real time. Although such a system can serve millions of ride requests and tens of thousands of taxis in the city every day, there will still be a large number of requests that cannot be served every day due to the lack of available taxis nearby for some passengers. On the other hand, in other places, there may be a large number of idle taxis looking for passengers, causing a waste of taxi resources. The imbalance between supply and demand of taxis among different geographic locations in cities is common, which will seriously reduce the efficiency of...

Claims

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