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arXiv AI By Sheng Hong, Tianyu Yue, Yang You, Zhengnan Lv, Xu Tang, Jing Hu, Hongwei Yin

A Resilience Recovery Method for Complex Traffic Network Security Based on Trend Forecasting

Read the original on arXiv AI →

The paper introduces a resilience recovery method for complex traffic networks that relies on trend forecasting. It develops a SIRD‑R fault propagation model incorporating risk values and constructs a resilience model combining real‑time and overall resilience. Using LSTM networks, the method forecasts network resilience and proposes a recovery strategy, with experimental results confirming its effectiveness and scalability across diverse traffic networks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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