从常规城市交通中识别隐藏的高风险状态
Shiyan Liu1, Mingyang Bai1, Shengmin Guo2
1School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China.
PNAS nexus
|March 13, 2025
概括
在城市交通等复杂系统中识别隐藏的高风险状态对于风险管理至关重要. 这项研究使用最大模型来发现未观察到的危险状态,为系统故障提供早期预警信号.
科学领域:
- 复杂系统科学 复杂系统科学
- 网络科学 网络科学
- 城市动态 城市动态
背景情况:
- 识别隐藏的高风险状态对于防止复杂系统中的系统崩至关重要.
- 在城市交通等系统中,高维度和非线性相互作用使得从经验数据中检测这些状态具有挑战性,因为大多数状态仍然未被观察到.
研究的目的:
- 开发一种方法来识别大规模复杂系统中隐藏的高风险状态,特别是城市交通.
- 通过定位未被观察到的关键状态,为风险管理提供早期预警知识.
主要方法:
- 从城市交通动态推断底层交互网络,使用最大模型.
- 构建系统的能源格局,以可视化和定位潜在的高风险状态.
- 利用能源格局来识别高概率过渡到危险最小值的状态.
主要成果:
- 成功推断了交互网络,并为城市交通系统构建了能源景观.
- 找到隐藏的高风险状态,这些状态在经验数据中不存在.
- 这些已识别的状态作为有效的风险信号,表明潜在的系统故障.
结论:
- 最大模型和能源景观方法可以有效地识别复杂系统中未被观察到的高风险状态.
- 该方法为城市交通和潜在的其他大型系统的风险管理提供了有价值的早期预警信号.
- 调查结果为积极的风险管理策略提供了见解,以减轻系统故障并降低恢复成本.
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