Aggregate-level demand management in evacuation planning

A risk-based method for planning of bus–subway corridor evacuation under hybrid uncertainties

“…5. In detail, when the time waiting for shuttle bus requires [22,24] min, [1650,1800] persons would be transferred to subway by shuttle bus (Option A), and [1590,1742] persons would be evacuated by routine bus directly (Option B) during the first hour. It is indicated that the above evacuation pattern is the same as that when ΔWST 7 1 equals to [8,10] …”

“…Thus, the corresponding uncertainties associated with the evacuation-planning-based modeling efforts are far beyond the capabilities of conventional optimization techniques [22]. Significant contributions have been made by many researchers in addressing system uncertainties [23][24][25][26][27][28][29]. For example, Janacek [25] focused on the uncertainty in the vehicle evacuation time in the road network due to unpredictable circumstances, and proposed an approach for tackling the uncertainty based on the theory of fuzzy sets.…”

“…Yao [26] considered evacuation via surface transportation networks in an uncertain environment, and developed a robust optimization approach based programming model for dealing with demand uncertainty, where hard constraints were guaranteed within an appropriate uncertainty set. Bish and Sherali [24] provided an aggregate-level modeling framework that offered strategic flexibility and utilized a lexicographic objective function that represented a hierarchy of relevant evacuation-based goals; meanwhile, the models with and without congestion in relation to tractability, normative optimality, and robustness under demand uncertainty were compared in the study. However, less attention has been paid to those complexities in traffic corridor incident based evacuation modeling.…”

Elementary modelling and behavioural analysis for emergency evacuations using social media

“…and only theK i have to be estimated. This suggest that equations (10)(11) are less likely to apply if evacuations are subject to considerable uncertainties regarding congestion. Over-confidence in assessing congestion may thus be a serious problem in real-world evacuations and there may be interesting parallels here with over-confidence in financial markets during bubbles (Fry, 2012).…”

Emergency Evacuation of Hazardous Chemical Accidents Based on Diffusion Simulation

“…Siddiqui et al (2012) [27] and Verma et al (2012) [28] studied the evacuation problem of hazardous materials, in which Siddiqui et al (2012) [27] proposed a computational fluid dynamics-(CFD-) based model for indoor risk assessment that considered accidental release of a sustained, small, undetected leak of a dense toxic gas (chlorine) in an industrial indoor environment. Li et al (2012) [29], Najafi et al (2013) [30], and Bish and Sherali (2013) [31] studied largescale regional evacuations caused by hurricanes, wildfires, or earthquakes. They studied the demand-based strategies of aggregate-level staging and routing to structure the evacuation demand, both with and without congestion and then designed two heuristics.…”