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Demand response type bus scheduling method

A technology of demand response and scheduling method, which is applied in the direction of neural learning method, data processing application, biological neural network model, etc. It can solve the problems of difficult model migration and generalization, complicated modeling and solving process, etc., and achieve simplified line planning and The effects of scheduling processes, reducing computational complexity, and shortening response time

Active Publication Date: 2020-05-05
TONGJI UNIV
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AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a demand-responsive bus dispatching method in order to overcome the defects of complex modeling and solution process and difficult model migration and generalization in the above-mentioned prior art

Method used

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  • Demand response type bus scheduling method
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  • Demand response type bus scheduling method

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Embodiment

[0046] This embodiment provides a demand-response bus dispatching method, such as figure 1 shown, including the following steps:

[0047] 1. Step 1: Define the operation mode of demand-responsive public transport, including the organizational form of lines, stations, and vehicles (demand-responsive public transport).

[0048] The operation mode of Demand Responsive Public Transit (DRPT) is the "fixed line + demand response station" operation mode, which is different from the conventional bus system with fixed timetable and fixed stations. Carry out capacity adjustments in real time, demand-response buses run fixed routes, and provide stop services at demand-response stations according to the needs of passengers Passengers will take the bus according to the assigned result, not necessarily the vehicle that arrives first. .

[0049] 2. Step 2: Use the deep reinforcement learning algorithm Deep Q-Network (DQN) as the main scheduling algorithm, that is, build a demand-response ...

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Abstract

The invention relates to a demand response type bus scheduling method. The method comprises the following steps: S1, defining an operation mode of a demand response type bus; S2, based on the operation mode, establishing a demand response type bus deep reinforcement learning model; S3, obtaining training data, and training the demand response type bus deep reinforcement learning model by using thetraining data to obtain a demand response type bus deep reinforcement learning optimization model; and S4, scheduling the demand response type bus based on the demand response type bus deep reinforcement learning optimization model. Compared with the prior art, the modeling and solving process is simple, and the model is easy to migrate and generalize.

Description

technical field [0001] The invention relates to the field of demand-response sharing / public transportation, in particular to a demand-response bus dispatching method. Background technique [0002] Demand-responsive public transport is a new type of public transport system with high flexibility and cost-effectiveness. It organically combines the characteristics of conventional public transport and demand-responsive services, and provides a good solution for various public transport travel needs. solution. Demand-responsive buses do not have fixed routes or timetables, and adjust capacity in real time according to passenger needs to provide passengers with demand-responsive services. Demand-response customized buses have been widely used in my country. According to the survey, by the end of 2016, more than 30 cities across the country had opened customized bus lines. With the development of social economy, people's travel needs are becoming more and more personalized and div...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06N3/08
CPCG06Q10/06315G06N3/08Y02T10/40
Inventor 马万经吴冕钟志华俞春辉王玲
Owner TONGJI UNIV
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