Route Choice Behaviour and Optimal Traffic
Management during Evacuation
BIN Tokyo lecture Dr. ir. Adam Pel

A Traffic Engineer’s Perspective…
Quantitative modelling and simulation
Establishing models that are predictively valid…

Transport and Traffic Modelling• Developing theory and models that can
predict network traffic operations in caseof an evacuation• Conditional on disaster dynamics,
information and evacuation instructions,traffic management, etc.
• Taking into account all relevant
behavioural aspects and uncertainties
thereinUsage of models• Evacuation potential and planassessment• Optimisation of instructions, evacuation
management and control

A Traffic Engineer’s Perspective…
Quantitative modelling and simulation…and use them to optimise evacuation instructions

Example application: model
based optimisation ofevacuation instructions• Describe optimisation problem
as a bi-level problem• Optimisation approachschedules and routes such that
all critical links are fully used(assuming high compliance)• Reduction of computational
complexity by innovative fixedpoint problem formulation forfast computation of optimal
solutionsEvacuation Traffic Dynamics and Travel Behaviour
To evacuatie or not to evacuate, that is the question…
…but this is hard to determine if your models are not predictively valid!

• Many evacuation model studies hampered by severe limitations in predictive
validity of models used, and hence outcomes should be used with caution• Typical (behavioural) aspects to consider in modelling:•

Travellers are not familiar with situation and hence have limited expectations
regarding prevailing traffic conditions•

Driving behaviour changes due to stress, emotion, driving task attention loss,
weather conditions, etc., causing large changes in road capacity•

Infrastructure may be affected substantially (flooded tunnels) or useddifferently (lane reversal)•

Adaptation of decision-making mechanisms affecting travel behaviour• Large role of heterogeneity in behaviour and high levels of uncertainty (inbehaviour and in conditions) further complicate modelling tasks

Part I: Empirical Observations & Experimental Findings
BIN Tokyo lecture Dr. ir. Adam Pel

Evacuation Response Rate
Empirical observations for hurricane response
Gulf Shores, Alabama evacuation hurricane Ivan 2004…

•

Similar response patterns observed forvarious ‘predictable’ hazards•

Differences lie in temporal scale•

Typically approximated by Sigmoid or
Weibull curveOther factors influencing behaviour…
Results of web-based survey show impact of different attributes
Towards modelling behavioural aspects of evacuation…

Major behavioural differences per phase
T H R E ATWARNING

IMPACTRECOVERYRESCUE• Threat and warning phase characterised
by denial (in general) to reduce ‘feeling of
discomfort’• Denial includes response to warnings, but
may be improved by relevant information• Impact phase is characterised by disbelief,
denial, often due to overload of sensory
information• Although necessary, evacuation is difficult
due to (mental, emotional) state of evacuee
But there are also big differences inbehaviour between evacuees…

Insights from Survival Psychology
Behaviour Dynamical Framework in case of Calamities
John Leach (1994) - Survival Psychology

G AT H E R
I N F O R M AT I O N

o
r
t
S

JUDGE
S I T U AT I O N

R AT I O N A L
BEHAVIOUR

r
u
o
i
v
a
h
STEREOebsrnei
VERIFY PERCEPTION
T
Y
P
I
C
A
L
n
s
n
n
a
o
BEHAVIOUR
i
m
t
c
e
a
v
r
i
e
t
t
a
n
g
i
e
g
n
n
BLOCKING

HYPO- ANDH Y P E RACTIVITYP A R A LY S I S

Insights from Survival Psychology
Heterogeneity in Behaviour
Leaders, followers, and blockers…

10-15%

r
o
e
itiv

s
o
p
in

70-80%

10-15%

Studying Evacuation and Travel Behaviour• Environment appears immersive according to participants responses• Preliminary data analysis shows impacts of information and interaction effects• Sequential choice modelling predicting decision to stay or leave based on
different attributes (earthquake, news bulletin, number of people seen leaving)• Probability to leave is determined by stochastic utility of staying:U i (t) = ASC + β1 ⋅nleaving (t) + β2 ⋅ earthquake + β3 ⋅news(t) + ε
with ASC = 0.80, β1 = −0.43, and β 3 = −0.60• Importance of herding is supported by results of questionnaires showing that
more then 30% indicated to be influenced by others• Basis for more advanced model development

Innovative Deployment of VR environments
Studying Herding Behaviour in Evacuation Decision Making
Towards modelling behavioural aspects of evacuation…

Studying Evacuation and Travel Behaviour• Environment appears immersive according to participants responses• Preliminary data analysis shows impacts of information and interaction effects• Sequential choice modelling predicting decision to stay or leave based on
different attributes (earthquake, news bulletin, number of people seen leaving)• Probability to leave is determined by stochastic utility of staying:U i (t) = ASC + β1 ⋅nleaving (t) + β2 ⋅ earthquake + β3 ⋅news(t) + ε
with ASC = 0.80, β1 = −0.43, and β 3 = −0.60• Situation is ambiguous: People see others as source of information!
• Importance of herding is supported by results of questionnaires showing that
more then 30% indicated to be influenced by others

Innovative Deployment of VR environments
Studying Herding Behaviour in Evacuation Decision Making
Towards modelling behavioural aspects of evacuation…

Expected Behaviour Adaptation
E F F E C T E N O P C A PA C I T E I T I N F R AEmpirical and Experimental observations at Strategic level
A
F
H
A
N
K
E
L
I
J
K
V
A
N
E
M
O
T
I
E
E
N
A
F
L
E
I
D
I
N
G
Findings for route choice behaviour during evacuation•

Excess travel demand
and unknown network
conditions lead to astrong need for (traffic)
information•

Lack of information
impairs rerouting,which creates bias
towards familiar routes
and motorways(perceived as reliable)•

Generally low
compliance•Self-organisation fails

Expected Behaviour Adaptation
E F F E C T E N O P C A PA C I T E I T I N F R AEmpirical and Experimental observations at Operational level
A
F
H
A
N
K
E
L
I
J
K
V
A
N
E
M
O
T
I
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E
N
A
F
L
E
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D
I
N
G
…using remote sensing and driving simulator experiments

Characteristic

Incident

Fog

Emergency

Free speed

-

-

+

Max acceleration

0

-

+

Min headways

+

-

-

•

Due to distraction,
capacity per lane
reduces with 30-50%•

Changes further include
increased headway,
reduced speed, andincreased reaction time•

Driver simulator
experiments show
impacts on driving
behaviour forevacuation situations•

More aggressive
behaviour, unstableflows, faster = slower

Summary so far…
Behavioural aspects of Evacuation Modelling and Management
Insights from empirical observations and experiments…

!
Insights:• Changes in driving behaviour lead to
reduction in efficiency• Dynamically adaptive choice
behaviour responding to uncertain
and changing conditions• Prominent role for information• Strong interactions andheterogeneity among individuals• Empirics and experiments allow
quantification of behaviour!

Part II: Modelling Evacuations
BIN Tokyo lecture Dr. ir. Adam Pel

Models in evacuation studies• DYNEV• DynaSMART• OREMS• DynaMIT• PARAMICS• DynusT• CORSIM• TransCAD• VISSIM• INDY• INTEGRATION• EVAQ

Models used in Evacuation Studies
Realism in modelling paradigms differs strongly!
Evidently, a model should be predictively valid within the intended application area

population
data

Trav
el D
ema
(C ho
nd
ice
Mo d
ellin
g)
evacuation choicemode choiceevacuation

Netw
o rk
Supp
(Tra
ly
ffic
Flow
Mo d
elli
ng )

hazarddisruptions

infrastructure &transit network

departure time choicecontrolplanmeasuresdestination choicetraffic simulation

evacuationroute choice(driver behaviour)

times

Traf
fi c
assi
(C ho
gnm
ice
ent
Mo d
ellin
g)

simulation loop
traffic information& route guidance

traffic flows

Modelling efforts
Bounded Rationality Framework of EVAQ
Quantifying behavioural aspects in mathematical and simulation models

Demand modelling
t

t+1

t+2

p(t )

1 − p(t )evacuatestayp(t + 1)

1 − p(t + 1)

evacuatestayp(t + 2)

Traffic assignment

Traffic Operations

1 − p(t + 2)

:

:

:

:

Sequential probability
function:

Hybrid route choicemodelling:

Advanced queuing
modelling:• Travellers decide to stay orleave each time period• Pre-trip route choice based on
expectations or compliance toinstructions• Speeds and capacities are
dependent on road conditions
and weather conditions andcontrol measures• Decision is based on
characteristics of disaster,household, instructions,information, etc.

• En-route information may leadto adaptation of route choiceduring trip

• Inclusion of spillback, capacity
drop, etc.

Modelling efforts
Bounded Rationality Framework of EVAQ
Quantifying behavioural aspects in mathematical and simulation models

Modelling efforts
Bounded Rationality Framework of EVAQ
Quantifying evacuation travel demand…

Demand modelling
t

t+1

t+2

p(t )

1 − p(t )evacuatestay• Binomial Logit model is sequentially applied
over time to simulate evacuation decisionsp(t + 1)

1 − p(t + 1)

evacuatestayp(t + 2)

1 − p(t + 2)

:

:

:

:

Sequential probability
function:• Travellers decide to stay orleave each time period• Decision is based on
characteristics of disaster,household, instructions,information, etc.

• Utility functions are estimated for hurricane
evacuation (post-hurricane survey) andwildfire evacuation (stated-choice experiment)• Typically, explanatory variables include
instantaneous conditions and sociodemographic characteristics• Dynamically updated to account for changes
in disaster conditions, evacuation order, traffic
conditions, etc.

Modelling efforts
Bounded Rationality Framework of EVAQ
Quantifying evacuation traffic assignment…

Traffic assignment• Multinomial Logit model is applied to route
choice decisions at origins and intersections• Utility functions may include (information on)
traffic conditions, disaster conditions, andbias towards motorways, familiar routes, andinstructed routes

Hybrid route choicemodelling:• Dynamically updated to account for changes
in network disruptions, traffic management,
and traffic information• Pre-trip route choice based on
expectations or compliance toinstructions• Choice coefficients determine individuals’
willingness to comply with instructions• En-route information may leadto adaptation of route choiceduring trip

• Incorporates destination choiceExample case studies
Impacts of instructions, information, and compliance
Modelling departure time choice in case of emergencies• Evacuation of Rotterdam area in case of emergency• EVAQ used to assess different evacuation
instructions and scenarios
InstructedSouthward, little
information, highcompliance

Instructed SouthEast, moderate
information, lowcomplianceExample case studies
Impacts of instructions, information, and compliance
Modelling departure time choice in case of emergencies•

To improve traffic conditions: control travel
demand (phasing) and extend network
supply (peak lanes, hard shoulders,
contraflow)•

To accelerate evacuation: increase
bottleneck capacity (ramp metering,
contraflow across onramps)•

Traffic information improves traffic
conditions and fulfil travellers’ desire to be
informed•

However, traffic regulation is more effective
than self-organisation

Intermediate Remarks…
Modelling Evacuation Choice Behaviour and Traffic Management
The adequacy of evacuation models…

!
Insights:• Models need to account for dynamics• Models need to capture effects of
information and compliance• Simulation of traffic management
measures allows for planning• Multi-modality is key• Check for sensitivity towards
uncertain parameters• Model calibration and validation!

Part III: Optimal Traffic Management for Evacuation
BIN Tokyo lecture Dr. ir. Adam Pel

Traffic management•

Contraflow: reversing lane direction shows
up to 1.5 - 1.7 increase in outbound traffic
flows

•

Crossing elimination: preventing conflicts atintersections yields 40% lower travel times•

Adapted signal timings: align with mobility
management strategy

Mobility management•

Sheltering in place•

Phased evacuation: regulate network inflow
by departure time windows•

Route guidance: distribute flows over
network to avoid capacity drop and spill backMobility and Traffic Management Measures
Deploying measures to control the evacuation
Towards an integrated network management strategy for evacuations…

Importance of Regulating Network Inflow
Performance reduction due to capacity drop & spill back
Perimeter control prevents under- or over-load of transport network…

Optimisation of Mobility Management
Bi-level model-based optimisation framework
Mathematical optimisation of evacuation phasing and route guidance

Heuristic optimisation algorithm(ant colony, particle swarm)

traffic flows, travel times

departure time windows& route guidance

Evacuation transport model(EVAQ)• Assume disaster dynamics are known, and willingness to comply is known• Upper level: set time windows for neighbourhoods and route guidance at intersections(to maximise arrival rates)• Lower level: evaluate evacuation process according to plan and compute traffic flows
and travel times

varying(des+route(compliance(varying(dep.-me(compliance(varying(dep.-me(&(des+route(compliance(

More self-organization•

Optimal integrated evacuation
planning has high potential•

For optimal evacuation planning:
non-compliance strongly reduces
the efficiency monotonically, but
non-linearly (threshold values)•

Route guidance still efficient for
low compliance levels if network
accumulation is below critical•

Mobilising and metering traffic
inflow into network is essential!

More regulation

Investigating the Impact of Compliance
When regulation becomes more efficient than self-organisation
The importance of evacuation phasing and route guidance…

Including Uncertainty in Optimisation
Robust optimisation of evacuation instructions
Dealing with limited knowledge and other sources of uncertainty

Anticipate on uncertain compliance levels• Efficiency always increases upon anticipatingcompliance correctly when optimising• Low compliance level show large
improvements• Low compliance with anticipation
outperforms high compliance without
anticipation• Instructions optimised on lower compliancelevel appear less sensitive!
Robust optimisation framework allows to
include uncertainty in disaster dynamics

Investing in Enforcement

ENFORCEMENT FRAMEWORKWhere to send the military to…

An example of impacts of increasing compliance• Where to deploy

limited resources forhigher compliance

Minimum

Maximum• Compare and test

different strategies

Cost

680

1.560

Performance

63.19578.023

Arrivals

92.387114.194• First results show

considerable
improvement with(limited) levels of
enforcement• Strategic deployment

of enforcement canthus be beneficial!

Conclusions on Evacuation Management…
The importance of human response behaviour
Towards a behaviour-robust network management strategy

Insights:• Integrated mobility and traffic
management has high potential• Not anticipating human response
yields inefficient evacuation plans• Evacuation plans can be adapted toaccommodate non-compliance• Mobilisation and metering of travel
demand is essential• Much uncertainty in evacuation asks
for robust optimisation!

“The%Netherlands%is%a%bathtub.%A%six4lane%
motorway%is%of%no%use%when%ﬂooded.”%!–!Melanie!Schultz,!Minister!of!I&E!!
(Volkskrant,!26!Jan!2013)!

Conclusions…
To evacuatie or not to evacuate, that is the question…
…lessons from 7 years of evacuation modelling and management• Survival psychology shows caution is needed
when relying on self-reliance:• People tend to fall back on ‘known behaviour’

NUL-FASE

VOORBEREIDINGFASE

HOOFDDOEL PER FASE

* Algemene, brede
communicatierijksoverheid: wat
te doen bij crisis?
* Voorbereiding(o.a. aanpassingen
op wegennet)* Opstellen
operationele
scenario’s door
veiligheidsregio’s* realisatie vanaantal (no regret)maatregelen zoals
koppelen VRI’s opvluchtroutes

DOEL: INFORMEREN EN VOORBEREIDEN
RWS-maatregelen:• routes inventariseren en knelpunten van deze routes in beeld
brengen: waaraan ontbreekt het? Zijn er alternatieven?
• bepaling voorkeursvluchtroutes, gebaseerd op de mogelijke
vluchtrichtingen• bebordingsplannen opstellen en realiseren• aanpassing belijning (indien wenselijk / noodzakelijk)• afstemming met veiligheidsregio’s ivm netwerkbenadering (dusHWN en OWN samen!)• aanschaf mobiele camera’s• Indien nodig en noodzakelijk: wensenlijst opstellen van
aanvullende maatregelen (zie toolkit)• Voorkeuren mbt routes inbrengen in (lopende) planstudies

KADER

MOGELIJKE OPTIES PER KADEROMSTANDIGHEDENSOCIAAL

FYSIEK* doorstroom
vergroten* instroom
beperken* spreiding over
netwerk* tijdelijke
bebording plaatsen* inzet extra
mankracht ter
ondersteuning
maatregelen* check ofcalamiteitenstandin VRI’s ingesteld isMaatregelen overige partijen:• VRI’s OWN aanpassen (indien mogelijk)• shelters prepareren / inrichten• plaatsen van aanvullende bebording op OWN• weghalen eventuele bottlenecks op OWN (denk aan rotondes
tijdelijk aanpassen of vluchtheuvels verwijderen, maar ook:
plaatsen van varkensruggen om bepaalde straten af te sluiten)

VOORBEREIDINGSTIJDVOORBEREIDINGWAARSCHUWINGSFASE* doorstroom
vergroten* instroom
beperken* spreiding over
netwerk* inzet extra
mankracht ter
ondersteuning vanmaatregelen* VRI’s opvluchtroutes incalamiteitenstandschakelen

GEEN VOORBEREIDINGSTIJD< 48 UUR> 48 UURGEBIEDSKENMERKEN

NOORD
OOST
ZUID
WEST

AANTAL
MOTORVOERTUIGEN< 10.000> 10.000 EN < 50.000> 50.000BESCHIKBAARHEIDWEL BEDIENINGS-MOGELIJKHEDENGEEN BEDIENINGS-MOGELIJKHEDENSOCIAAL

FYSIEK

Maatregelen overige partijen:• plaatsen van aanvullende bebording op OWN• shelters openstellen / gebruik faciliteren• weghalen eventuele bottlenecks op OWN (denk aan rotondes
tijdelijk aanpassen of vluchtheuvels verwijderen, maar ook:
plaatsen van varkensruggen om bepaalde straten af te sluiten)• VRI’s aanpassen (indien mogelijk)

wel beperking
geen beperking

VOORBEREIDINGSTIJDGEEN VOORBEREIDINGSTIJD< 48 UUR> 48 UURGEBIEDSKENMERKEN

NOORD
OOST
ZUID
WEST

AANTAL
MOTORVOERTUIGEN< 10.000> 10.000 EN < 50.000> 50.000BESCHIKBAARHEIDWEL BEDIENINGS-MOGELIJKHEDENGEEN BEDIENINGS-MOGELIJKHEDENSOCIAAL

DOEL: EVACUATIE BURGERS -> ALLES UIT DE KAST
RWS-maatregelen:• spitstroken zijn open
• VRI’s HWN (aansluitingen snelwegen op OWN) aanpassen• extra capaciteit inschakelen (oa dubbel door de bocht envluchtstrookgebruik) zoals opgenomen in het VCE(Verkeerskundig Concept Evacuatie)• indien noodzakelijk: knooppunten (deels) afsluiten conform VCE• inzet mankracht om evacuaties verkeerskundig te begeleiden• bepaalde toe- en afritten afsluiten (bv bij toepassen reversed
laning) om doorstroom op vluchtroute te bevorderen• fysieke maatregelen treffen om ongewenst gedrag tevoorkomen. Bv plaatsen van barriers bij toe- en afritten

wel personeel
geen personeel

OMSTANDIGHEDEN

Verkeersmanagementmaatregelen
uitvoeren:

wel beperking
geen beperking

DOEL: EVACUATIE BURGERS -> PREPARATIE
RWS-maatregelen:• spitstroken tijdig openen• indien nodig: testen of calamiteitendoorsteken ‘werken’ en dus
beschikbaar zijn voor het geval dit nodig is• mobiele maatregelen plaatsen (oa camera’s en barriers)• tijdelijke belijning, markering, bebording aanbrengen• maatregelen treffen voor extra capaciteit (oa dubbel door debocht en vluchtstrookgebruik) zoals opgenomen in het VCE(Verkeerskundig Concept Evacuatie)• indien noodzakelijk: knooppunten (deels) afsluiten conform VCE• indien nog niet gedaan: VRI’s HWN (aansluitingen snelwegen op
OWN) aanpassen• toepassen maatregelen om reversed laning in te kunnen zetten

wel personeel
geen personeel

OMSTANDIGHEDEN

Verkeersmanagementmaatregelen
uitvoeren:• Importance of good (=relevant!) informationand training for professionals• First ‘succes’ in ‘action plan’ (handelingsplan)
Traffic Information and Management
Rijkswaterstaat

ACTIES

Maatregelen overige partijen:• Opstellen operationele scenario’s / draaiboeken door
veiligheidsregio’s• Aanschaf mobiele camera’s• Koppelen / herprogrammeren VRI’s (calamiteitenstand)• Shelters aanwijzen en indien wenselijk inrichten (denk ook aan
bv aanbrengen bebording of speciaal logo ter herkenning)

DREIGINGSFASE• Evacuation can be improved by improving
instructions and plans, and is necessary
because shelter-in-place is not always the best
optionHANDELINGSPLAN RIJKSWATERSTAATVOORBEREIDING• Development of adequate dedicated models forevacuation assessment is needed and possible

wel personeel
geen personeel

FYSIEK

wel beperking
geen beperking

VOORBEREIDINGSTIJDGEEN VOORBEREIDINGSTIJD< 48 UUR> 48 UURGEBIEDSKENMERKEN

NOORD
OOST
ZUID
WEST

AANTAL
MOTORVOERTUIGEN< 10.000> 10.000 EN < 50.000> 50.000BESCHIKBAARHEIDWEL BEDIENINGS-MOGELIJKHEDENGEEN BEDIENINGS-MOGELIJKHEDENRoute Choice Behaviour and Optimal Traffic
Management during Evacuation
BIN Tokyo lecture Dr. ir. Adam Pel

Our current research• Modelling multi-modal urban
emergencies (software development)• Time-dynamics and social interactions
in choice behaviour (experiment &theory development)• Changes and correlations inlongitudinal and lateral driving
behaviour (driving simulator study)• Optimal integrated network
management for emergencies (control
strategy & algorithm development)• Calibration of choice models!
(experiments & model validation)

Route Choice Behaviour and Optimal Traffic
Management during Evacuation
BIN Tokyo lecture Dr. ir. Adam PelReferences
H. Fu, A.J. Pel, S.P. Hoogendoorn [2014] Compliance-robust evacuation management under intersection control constraints. IEEE
Transactions on ITS (in press)
J.P.T. van der Gun, A.J. Pel, B. van Arem [2014] An activity-based methodology for simulating urban transportation networks during
emergencies. Proceedings of NWO-NSFC seminar, Shanghai, China (in press)
Y. Yuan, A.J. Pel, S.P. Hoogendoorn [2014] The transition between normal and emergency driving behaviour during evacuation and its
implications for traffic flow operations and traffic management. Under review for IEEE ITSC, Qingdao, China!
H. Fu, A.J. Pel [2013] Optimal traffic management to ensure emergency evacuation compliance. Proceedings of 13th IEEE ICNSC, Paris,
France, pp. 532-537
P. Murray-Tuite, B. Wolshon [2013] Evacuation transportation modeling: An overview of research, development, and practice. Transportation
Research Part C 27:25–45
A.J. Pel, S.P. Hoogendoorn, M.C.J. Bliemer [2012] A review on travel behaviour modelling in dynamic traffic simulation models forevacuations. Transportation 39(1):97-123
H. Tu, A.J. Pel, H. Li, L. Sun [2012] Travel time reliability during evacuation. Transportation Research Record 2312:128-133
A.J. Pel, M.C.J. Bliemer, S.P. Hoogendoorn [2011] Modelling traveller behaviour under emergency evacuation conditions. European Journal ofTransport and Infrastructure Research 11(2):122-193
A.J Pel, S.P. Hoogendoorn, M.C.J. Bliemer [2010] Impact of variations in travel demand and network supply factors for evacuation studies.
Transportation Research Record 2196:45-55
A.J. Pel, S.P. Hoogendoorn, M.C.J. Bliemer [2010] Evacuation modelling including traveller information and compliance behaviour. Procedia
Engineering 3:101-111
A.J. Pel, O.L. Huibregtse, S.P. Hoogendoorn, M.C.J. Bliemer [2010] Optimizing evacuation instructions while anticipating traveler compliancebehavior. Proceedings of 13th IEEE ITSC, Madeira, Portugal, pp. 462-467
O.L. Huibregtse, S.P. Hoogendoorn, A.J. Pel, M.C.J. Bliemer [2009] A generic method to optimize instructions for the control of evacuations.
Proceedings of 12th IFAC CTS, Redondo Beach, USA, pp. 217-223

Route Choice Behaviour and Optimal Traffic
Management during Evacuation
BIN Tokyo lecture Dr. ir. Adam Pel