Evacuation Network Optimization: Models, Solution Methods and Applications

On finding the most effective ways to minimize the traffic congestion and disaster threat over an urban or regional evacuation network, the focus of this study is to develop a set of analytical tools and computational methods for seeking optimal allocation of existing network capacity and connectivity. The core problem posed in this text is a network optimization problem with regard to two lane-based planning strategies: lane reversal on roadway sections and crossing elimination at intersections. These strategies supplement one another by increasing capacity in specific traffic directions and creating an interruption-free traffic environment throughout the network. The joint consideration of these strategies greatly increases the problem complexity and combinatorial effect. A Lagrangian-relaxed, tabu-based solution method has been developed to solve this otherwise intractable problem, which takes advantage of Lagrangian relaxation for problem decomposition and complexity reduction and whose algorithmic design is based on the principles of tabu search metaheuristic. The requirement of emergency vehicle assignment is also incorporated into the above modeling and solution framework, which creates a bi-objective evacuation network optimization problem. A lexicographic optimization approach is developed to identify the Pareto-optimal set of routing and network solutions for scenario analysis and decision making. The set of evacuation planning models and solution methods have been tested and evaluated with both numerical examples and an evacuation case study in Monticello, Minnesota with varying network settings and conditions. The evaluation results prove the applicability, reliability and robustness of the developed methodology in both theoretical and practical network circumstances and provide useful insights and directions for further research.

Author dc:creator

• Xie, Chi

• Evacuation planning
• Lane reversal
• Crossing elimination
• Network optimization
• Discrete and combinatorial optimization
• Multi-objective optimization
• Lagrangian relaxation
• Tabu search

Language dc:language.iso

en_US

Handle dc:identifier.uri

https://hdl.handle.net/1813/10869

OAI identifier oai:identifier

oai:ecommons.cornell.edu:1813/10869