The presentation deals with the Importance of resilience in transportation systems: factors that influence its relevance, the trade-off between robustness and efficiency, and the relation of resilience and evacuation management.

1. Introduction to Transport Resilience
Towards useful insights, models and tools
Delft University of Technology
Part 1
Serge Hoogendoorn, Adam Pel, Louise Klingen, Elgard van Leeuwen, Niels van Oort, Maaike Snelder

2. Defining Resilience
Robustness vs Resilience
Resilience
Systemperformance
100%
50%
0%
t0 t1
Recovery timeRobustness
Vulnerability
Model shows difference robustness and resilience
Relevant aspects: disturbance probability, impact of disturbance, and time it takes to recover
Unlike reliability… vulnerability, robustness and resilience deal with large disruptions

3. Social and Economic Impacts
Relevance of robustness and resilience in Transportation
Cost of disruptions for heavy rail around 400-500 kEuro (KiM)
Disturbances cause of large share of traffic congestion, societal cost around 1 billion Euro (NL)
68%
19%
3%
7%2%0%0%1%
Too high demand
Accidents
Roadworks
Incidents
Other
Events
Weather
Capacity reduction
Cost of disruptions in Stockholm PT around 650 kEuro
Extra travel time
0-10
10-20
20-35
35-50
50-100
>=100
No data
Number of incidents

4. Trends affecting mobility
Increasing importance of resilience
Amongst trends are many related to resilience
Increased probability disruption
Increased impact
Increasing reliance on ICT
Increasing system complexity
( )
( )

5. Increasing disruption probabilities
Increasing importance of Resilience
Extreme weather conditions (snow, storm, heavy rain) occur more frequently
Higher flood risk levels (weather and sea-level rise)
Increased risk bush fires due to drought
mm/day
Average time between events (in years)
Scaled GEV fit 1951
Scaled GEV fit 2014
Observed 2014
Frequency of extreme days
(more than 130 mm rainfall)
is about 5 time higher in 2014
Source KNMI (www.knmi.nl)

6. Increased impacts of disruptions
Increasing importance of Resilience
Urbanisation and densification
High regular loads on transport networks
Active Traffic Management has focussed on optimising infrastructure use
Is there a relation between robustness and efficiency?
Recovery time is impacted since emergency services have hard time to get to disturbance location

7. Trade-off Efficiency and Robustness
Increasing importance of Resilience
Example trade-off for PT networks (RandstadRail)
Driver scheduling to reduce cost substantially
Complex (efficient) schedules are however less robust
0
100
200
300
400
500
600
Additional driver
scheduling cost
Operator robustness
benefits
Operator and
traveler robustness
benefits
EUROSX1000
Figure shows that investing in simpler driver schedules works
In general: resilience “comes at a price” - what is it worth?

8. Introduction into Transport Resilience
Towards useful insights, models and tools
Ill-predictability and its implications
Part 2
Disaster and Evacuation Management as a Case

9. Threat Warning Impact Recoil Rescue
Post-
trauma
Resilience and Disasters
Looking at extreme disruptions
Resilience
Systemperformance 100%
50%
0%
t0 t1
Recovery timeRobustness
Projection of phases of Leach on bathtub model
Note: model used by Leach to identify victim behaviour

10. Gather
information
Judge
situation
Rational
behaviour
Verify perception
Stereo-
typical
behaviour
Blocking
Hypo / hyper
activity
paralysis
10-15%
70-80%
10-15%
VR experiment to model behaviour
Large variation in behaviour
Three different groups
Impact disaster, information and others
Importance of ill-predicability
Leaders, followers and blockers

11. Chaotic examples of herding
Importance of ill-predicability
Coping strategy and task execution under stress
Task participants was to finish puzzle and evacuate
Prominent role of herding
Example showing case where first to finish created havoc and others followed
Resulting behaviour is highly uncertain

12. Role of uncertainty
Importance of ill-predicability
Uncertainty also relevant for other disasters (e.g. bushfires)
Question if current scenario-based approaches are suitable
Occurence of disruption and its dynamics are highly stochastic
Growth of breach during flooding and resulting flood dynamics highly uncertain
Location of breach is also very uncertain, multi-location breaches possible

13. Our solution: real-time decision support
Dealing with ill-predicability…

14. Regional case study North-Holland
Assessing feasibility of combining real-time flood modeling & evacuation DSS
First prototype providing insight into evacuation capacity
In coöperation with:
Waterschappen
Veiligheidsregio’s
Provincie Noord-Holland
SOS Flooding
Pilot 2018

15. Optimal and robust guidance
Considering system characteristics in resilience engineering
Example shows EVAQ model for the Walcheren peninsula
Large improvement of number of evacuees due to optimisation (42000 to 81000 evacuees in 6 hours)
Robust schemes include flood dynamics / behaviour uncertainty with limited impact (5-20%)

16. Si Vis Pacem? Para Bellum!
Use of tools for disasters and less dramatic disturbances
First successful trials real-time prediction of multi-modal traffic operations during evacuation
Tooling has been used for dealing with non-evacuation disturbances (e.g flooding of part of the networks)
First experiences with (real-time) optimisation of busses when part of the metro system failed

17. Introduction to Transport Resilience
Main take-aways
…probability of disturbances is increasing
…transport system has become more complex, reliance on ICT is growing
Resilience has become a very relevant topic, because…
Importance of ill-predicability in developing tooling
Limited use of classical scenario-based approaches
Need for real-time decision support during different phases of disaster
… focus has been on improving utilisation, possibly at the expense of robustness

18. UMO Urban Mobility Lab AMS Living Lab
DiTT Lab (data analysis and simulation)
PT Lab
Traffic Flow
Theory and
Management
Automated
Transport
Active Mode
Lab
Rail Traffic
Lab
Freight and
Logistics Lab
Transportation Resilience Lab
Traffic and Transportation Safety
Smart Mobility

19. Reliability Vulnerability Robustness Resilience
Description Probability of
serviceability
Susceptibility of
serviceability loss
Ability to maintain
serviceability
Ability to maintain
and recover
serviceability
Disturbance
relevance
Probability of
occurrence of…
Not withstand the
effects of…
Withstand the effects
of…
Withstand and if
necessary recover
from…
Probability
relevance
Main focus – Indicates
proximity to perfect
performance
Facilitating – Indicate
chance of function loss
Facilitating – Indicate
chance of function
loss
Facilitating – indicate
recovery ability
Effect relevance N/A Quantification of effects Quantification of
effects
Quantification of
effects
General application Both locally & on
network
Mainly on network
level, but also locally
applicable
Mainly on network
level, but also locally
applicable
Mainly local, but also
applicable on network
level
Indicators Indicators related to
travel time distribution
(e.g. standard
deviation, skewness,
buffert time index,
missery index)
Focus on resistance (the
ability to avoid going
into a state of
congestion):
- Delay caused by a
disturbance
Focus on resistance
(the ability to avoid
going into a state of
congestion):
- Delay caused by a
disturbance
Focus on recovery:
- LPIR
- Time to recover
- Delay caused by a
disturbance
What is resilience?
Robustness vs Resilience
Unlike reliability… vulnerability, robustness and resilience deal with large disruptions
Resilience also includes recovery to desired service level
Visualisation using the so-called “bathtub model”