This document summarizes a PhD project that aims to optimize evacuation route instructions considering uncertainty and human compliance behavior. The project develops a more efficient computational method using a fixed-point approach that decomposes the complex optimization problem into simpler sub-problems involving route guidance optimization, traffic flow simulation, and an approximated behavior model. A case study applies the method to hypothetically evacuate 120,000 residents from a flood in Walcheren over 6 hours, showing the fixed-point approach maintains solution quality while substantially reducing computational time compared to an undecomposed approach.

1. PhD research & reporting tips
Olga Huibregtse
Masterclass T&P/TIL, 29-9-2011
Delft
University of
Technology
Challenge the future

2. PhD Project
Robust optimization of evacuation measures
• Typical for evacuations:
• Demand
• Behavior
• High congestion level
• Measures
• Demand: departure time instructions, etc.
• Supply: reversing lanes, etc.
• Uncertainty:
• The impact of the hazard
• The behavior of the people
• Etc.
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3. Part of the PhD project
That I will present today
• Method to optimize route instructions considering uncertainty and
compliance behavior
• Computationally heavy when considering uncertainty and behavior
• Can we develop a more efficient method?
• Research is carried out together with:
• Gunnar Flötteröd & Michel Bierlaire (EPFL)
• Andreas Hegyi & Serge Hoogendoorn (TU Delft)
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4. Optimization of route guidance
State-of-the-art
• Discussion: System-optimal (SO) or user-equilibrium (UE)
• SO: unrealistic, results in non-compliance
• UE: low effectiveness from system point of view
• Importance of system optimality and behavior of people
• Considering route choice behavior:
• usual approach: modeled on node level (compliance rate)
• In reality, route decision depends on total trip, thus behavior should be
considered on route level but this is computationally expensive
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5. Considering behavior on route level
Relatively complex problem
• High number of routes, thus high number of decision variables
• 3X3 square:
• 16 turning fractions
• 6 routes
• 10X10 square:
• 324 turning fractions
• 48,620 routes
• An efficient method is needed to solve the problem
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6. Efficient approach
To optimize route guidance
• Optimization of route guidance, constrained by:
• Traffic flow simulation model
• Behavior model describing route choice
• Fixed-Point Approach: decomposing the problem into simpler
problems which are iteratively solved
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7. Fixed-point approach
Sub-problems and their simplifications
Sub-problems Decision variable: Constraint: traffic Constraint:
route advice flow model behavioural
model
1. Optimization Turning fractions  Full compliance
of turning
fractions
2. Optimization  Fixed time- 
of route advice dependent link
travel times
3. Approximation of behaviour: adapting bounds on turning
fractions
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8. Case study
Hypothetical flood of Walcheren
• 120,000 residents
• 6 hours to evacuate
• Route choice model
• Traffic flow model
• Optimization method
• Undecomposed approach
• Fixed-Point Approach
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9. Results
The FPA is substantially faster, while maintaining
the quality of the solution
3
Performance: -0.30%
Test
2
Performance: -0.05%
1 Fixed-Point Approach
Performance: +4.92% Undecomposed Approach
0 50 100 150 200 250 300
Computational time (hours)
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10. And finally
Some reporting tips
• Tips in general:
• Follow the usual setup from introduction to conclusions
• Think about the motivation
• Compact
• Audience
• Etc.
• How my writing skills improved over the years:
• Practice
• Reviewing other papers
Tip: carefully read reports or papers from other people
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