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国际学术期刊
Risk-based spatial zone determination problem for stage-based evacuation operations
发布时间:2014-3-2110:57:8来源:作者:Yu-Ting Hsu, Srinivas Peeta   

Yu-Ting Hsua,
Srinivas Peetab,



Highlights


•We propose evacuation risk as a time-dependent measure.
•Evacuation risk captures evolving disaster and traffic conditions simultaneously.
•Evacuation risk zone encompasses the population with the highest evacuation risk.
•Evacuation risk zone seamlessly enables resource prioritization in operations.
•The concept of evacuation risk zone can be applied to different types of disasters.



Keywords

Disasters; Evacuation operations; Evacuation risk; Evacuation risk zone



Abstract

This study seeks to determine risk-based evacuation subzones for stage-based evacuation operations in a region threatened/affected by a disaster so that information-based evacuation strategies can be implemented in real-time for the subzone currently with highest evacuation risk to achieve some system-level performance objectives. Labeled the evacuation risk zone (ERZ), this subzone encompasses the spatial locations containing the population with highest evacuation risk which is a measure based on whether the population at a location can be safely evacuated before the disaster impacts it. The ERZ for a stage is calculated based on the evolving disaster characteristics, traffic demand pattern, and network supply conditions over the region in real-time subject to the resource limitations (personnel, equipment, etc.) of the disaster response operators related to implementing the evacuation strategies. Thereby, the estimated time-dependent lead time to disaster impact at a location and the estimated time-dependent clearance time based on evolving traffic conditions are used to compute evacuation risk. This time-unit measure of evacuation risk enables the ERZ concept to be seamlessly applied to different types of disasters, providing a generalized framework for mass evacuation operations in relation to disaster characteristics. Numerical experiments conducted to analyze the performance of the ERZ-based paradigm highlight its benefits in terms of better adapting to the dynamics of disaster impact and ensuring a certain level of operational performance effectiveness benchmarked against the idealized system optimal traffic pattern for the evacuation operation, while efficiently utilizing available disaster response resources.



Article Outline

1. Introduction
2. Evacuation risk zone and evacuation risk assessment
2.1. Evacuation risk zone and stage-based framework for evacuation operations
2.2. Evacuation risk assessment
2.2.1. Literature review of risk in evacuation problems
2.2.2. Evacuation risk measure


3. Evacuation risk zone determination
3.1. RSZDP formulation
3.1.1. Clearance time estimation
3.1.2. Contiguity-related constraints
3.1.3. Summarized RSZDP formulation

3.2. Solution method

4. Numerical experiments
4.1. Objectives of experiments
4.2. Experimental setup
4.3. Evacuation operation deployment scenarios
4.4. Performance measures
4.5. Results and insights
4.5.1. Sensitivity analyses of ERZ-based deployment at different resource levels
4.5.2. Experiment results under varying disaster impact dynamics


5. Concluding comments
Acknowledgments
References



Figures

   

Fig. 1.

Stage-based framework for evacuation operations with ERZ-based deployment.


Fig. 2.

Conceptual flowchart of the iterative process to solve the RSZDP.


Fig. 3.

Non-contiguous ERZs due to disaster characteristics and geographical features.


Fig. 4.

Example of a potential non-contiguous ERZ solution.


Fig. 5.

Illustration of the decision tree of the branch-and-bound algorithm.


Fig. 6.

The Borman Expressway network.


Fig. 7.

Operational performance under Scenario A (16,800 vehicles for one unit of demand level).


Fig. 8.

ERZ patterns derived for the demand level of 33,600 vehicles under Scenario A (ERZ[1/2] in solid line and ERZ[1/3] in dashed line).


Fig. 9.

Sensitivity analysis on resource level (67,200 vehicles under Scenario A).


Fig. 10.

ERZ patterns derived for the demand level of 33,600 vehicles with ERZ[1/3].



Tables


Table 1. Operational performance comparison.

Table 2. Comparison of ERZ-based deployment at different resource levels.

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