Urban Network Resilience Analysis and Equity Emphasized Recovery based on Reinforcement Learning

This paper introduces an equity emphasized re-covery planning method for urban traffic networks based on a data driven approach. An integrated evaluation index is proposed to assess equity in territorial accessibility during hazards recovery, which brings the variance in accessibility between communities as a penalty term into the overall accessibility. Taking the improvement of the integrated index as the reward function, the equity emphasized recovery control strategy is designed with a reinforcement learning algorithm to determine the recovery priority of the affected links. To test the performance of the proposed approach, a simulation environment with reference to the San Francisco Bay Area was constructed. Experiment results indicate that, compared with the explicit strategies, the proposed recovery strategy is able maintain a more equitable approach during the reconstruction process.