Bi-level Programming for Resilience Restoration of Commuting Corridor Based on Deep Reinforcement Learning
In order to realize the scientific design of motor bus transferring scheme in the commut-er corridor,the resilience recovery process of commuting corridor is regarded as a bi-level pro-gramming in which the resilience is improved through continuous exploration and iteration of ground bus transferring scheme in complex environment.The deep reinforcement learning algo-rithm is introduced to form the upper level planning,and the value function neural network is used to fit the response function of emergencies and travelers'cluster behavior to the adjustment of ground bus transferring scheme.The decision-making objective is achieved by training the transferring schemes.In the lower level planning,the cellular neural network model is intro-duced to simulate the cluster travel choice behavior under the background of data intelligence.The case study shows that this method can effectively improve the resilience of the commuter cor-ridor,and the cluster behavior will have a negative impact on the resilience recovery.
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