Global Optimization of Emergency Evacuation Assignments

Journal Article · Sun Jan 01 04:00:00 EST 2006 · Interfaces

DOI:https://doi.org/10.1287/inte.1060.0251· OSTI ID:1003725

Han, Lee ^[1]; Yuan, Fang ^[1]; Chin, Shih-Miao ^[2]; Hwang, Ho-Ling ^[2]

---

1. University of Tennessee, Knoxville (UTK)
2. ORNL

Conventional emergency evacuation plans often assign evacuees to fixed routes or destinations based mainly on geographic proximity. Such approaches can be inefficient if the roads are congested, blocked, or otherwise dangerous because of the emergency. By not constraining evacuees to prespecified destinations, a one-destination evacuation approach provides flexibility in the optimization process. We present a framework for the simultaneous optimization of evacuation-traffic distribution and assignment. Based on the one-destination evacuation concept, we can obtain the optimal destination and route assignment by solving a one-destination traffic-assignment problem on a modified network representation. In a county-wide, large-scale evacuation case study, the one-destination model yields substantial improvement over the conventional approach, with the overall evacuation time reduced by more than 60 percent. More importantly, emergency planners can easily implement this framework by instructing evacuees to go to destinations that the one-destination optimization process selects.

• Research Organization: Oak Ridge National Laboratory (ORNL)
• Sponsoring Organization: ORNL work for others
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1003725
• Journal Information: Interfaces, Journal Name: Interfaces Journal Issue: 06 Vol. 36; ISSN 1526-551X; ISSN 0092-2102
• Country of Publication: United States
• Language: English

Related Subjects

29 ENERGY PLANNING, POLICY, AND ECONOMY
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION
EMERGENCY PLANS
EVACUATION
FLEXIBILITY
OPTIMIZATION
ROUTING
TRAFFIC CONTROL
dynamic traffic assignment
emergency planning
evacuation
intelligent transportation system
microscopic simulation
network optimization