Porównaj metody

	Lifeline Interdependency Analysis	Evacuation Time Estimation Modeling
Dziedzina	Disaster Studies	Disaster Studies
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	2001	2008
Twórca	Steven M. Rinaldi, James P. Peerenboom & Terrence K. Kelly	Michael K. Lindell (EMBLEM2); regional evacuation modeling tradition
Typ	Network model of coupled critical-infrastructure systems and cascading failure	Behavioral-and-network model of mass evacuation duration
Źródło pierwotne	Rinaldi, S. M., Peerenboom, J. P., & Kelly, T. K. (2001). Identifying, understanding, and analyzing critical infrastructure interdependencies. IEEE Control Systems Magazine, 21(6), 11-25.	Lindell, M. K. (2008). EMBLEM2: An empirically based large scale evacuation time estimate model. Transportation Research Part A: Policy and Practice, 42(1), 140-154.
Inne nazwy	Critical Infrastructure Interdependency Analysis, Infrastructure Interdependency Modeling, Lifeline Network Analysis	Evacuation Time Estimate Modeling, ETE Modeling, Mass Evacuation Modeling
Pokrewne	3	3
Podsumowanie	Lifeline interdependency analysis studies how critical infrastructure systems — power, water, natural gas, telecommunications, and transportation — depend on one another, so that a disaster striking one can cascade into others. The foundational framework, set out by Steven Rinaldi, James Peerenboom, and Terrence Kelly in 2001, classifies the couplings among infrastructures into physical, cyber, geographic, and logical interdependencies and characterizes how disruptions propagate across them. Modeled as coupled networks, the lifelines and their dependency links allow analysts to simulate cascading failure: a power outage stops water pumping and telecommunications, transport disruption delays restoration, and feedbacks amplify the impact. The analysis estimates the system-wide consequences of disruptions and informs restoration sequencing, making it central to understanding why disasters disable far more than the directly damaged components and to planning resilient, recoverable infrastructure.	Evacuation time estimate (ETE) modeling predicts how long it will take to move an at-risk population to safety, a quantity central to emergency planning for hurricanes, floods, wildfires, nuclear plants, and other hazards. The method joins two ingredients: a behavioral component describing when households decide to leave — the mobilization or 'loading' curve, grounded in warning-response research such as the Protective Action Decision Model — and a transportation component describing how fast the road network can carry them away. Michael Lindell's EMBLEM2 exemplifies the empirically based approach, letting emergency managers compute ETEs from a modest set of route, behavioral, and scope parameters and even update them in real time as a hazard approaches. By combining human departure timing with network capacity, ETE modeling tells planners when to issue evacuation orders and where congestion will bind, turning evacuation from guesswork into quantified logistics.