Online calibration of an integrated framework for information‐based evacuation operations

This study seeks to online calibrate the parameters of aggregate evacuee behavior models used in a behavior‐consistent information‐based control module for determining information strategies for real‐time evacuation operations. It enables the deployment of an operational framework for mass evacuation that integrates three aspects underlying an evacuation operation: demand (evacuee behavior), supply (network management), and disaster characteristics. To attain behavior‐consistency, the control module factors evacuees' likely responses to the disseminated information in determining information‐based control strategies. Hence, the ability of the behavior models to predict evacuees' likely responses is critical to the effectiveness of traffic routing by information strategies. The mixed logit structure is used for the aggregate behavior models to accommodate the behavioral heterogeneity across the population. An online calibration problem is proposed to calibrate the random parameters in the behavior models by using the least square estimator to minimize the gap between the predicted network flows and unfolding traffic dynamics. Background traffic, an important but rarely studied issue for modeling evacuation traffic, is also accounted for in the proposed problem. Numerical experiments are conducted to illustrate the importance of the calibration problem for addressing the system consistency issues and integrating the demand, supply, and disaster characteristics for more efficient evacuation operations. Copyright © 2016 John Wiley & Sons, Ltd.