111
1 INTRODUCTION
Maritime accidents have attracted considerable
attentioninthepastdecadesowingtotheenormous
property damage, casualties and serious
environmental pollution. In the recent years, major
accidents have been occurred worldwide, Table 1
shows the major maritime accidents in the recent
years,itcanbeseenthat serious consequences have
beencausedbythesema
ritimeaccidents.
Table1majormaritimeaccidentinrecentyears
_______________________________________________
ShipnameFatalities Shiptype Accidenttype
_______________________________________________
EasternStar 442 passenger foundering
Pinak‐6 48ferryfoundering
Sewol 294 passenger flooding
CostaConcordia 17cruisestranding
Xinmingfa17 8lost container foundering
Jinyouyuan 9 15 oiltanker collision
_______________________________________________
Figure1. Different types of ships involved in maritime
accidents
WhileintheYangtzeRiver,astatisticanalysis of
themaritimeaccidentsiscarriedoutintheperiodof
2009 to 2012. Among all the maritime accidents,
differenttypesofshipsareinvolved.Specifically,the
Emergency Management of Maritime Accidents in the
Yangtze River: Problems, Practice and Prospects
X.P.Yan,B.Wu,D.Zhang&J.F.Zhang
WuhanUniversityofTechnology,Wuhan,China
ABSTRACT:Maritimeaccidentshavereceivedconsiderableattentionsduetotheenormouspropertydamage,
casualtiesandseriousenvironmentalpollution.Thispaperfirstmakesstatisticalanalysisofthedifferenttypes
ofmaritimeaccidentsintheperiodof2012to2014intheYangtzeRiver.Second,theproblemsofemergency
ma
nagementofmaritimeaccidentsarealsoproposedfromtheanalysisofthemajoraccident“EasternStar”..
Afterwards, four practice cases, including decision support for maritime accidents, emergency resource
allocation, emergency simulation system and effectiveness of emergency management, are introduced to
presenttheinsightsgainedfromthesepractices.Last,inordertoaddresstheseproblems,thi
spaperproposes
that an artificial societies, Computational experiments, and Parallel execution (ACP) approach should be
introducedtoestablishan improved managementsystemformaritime accidents in the future, and an ACP
basedmaritimeaccidentemergencymanagementframeworkisproposed.
http://www.transnav.eu
the International Journal
on Marine Navigation
and Safety of Sea Transportation
Volume 11
Number 1
March 2017
DOI:10.12716/1001.11.01.13
112
cargo ship accounts for 52%, and oil tanker ranks
second, which accounts for 13%, while container
ranksthelast,itaccountsforonly5%.Itcanbeseen
that the container ship has a better safety situation
thanothershipsthoughthethroughputsofcontainer
arealsohighinYangtze
River.
Moreover, the distribution of different types of
maritimeaccidentscanalsobeobtained.Thecollision
accidents, which are also the most frequently‐
occurring accidents, rank first with a proportion of
49%.Notundercontrolships,thoughhaven’tcaused
accidents after well handled, it’s also taken
consideration,whichaccountsfor
35%.Moreover,the
groundingaccidentsaccountfor10%.
Figure2.Differenttypesofmaritimeaccidents
Althoughmany accidents have occurred together
withdifferenttypesofaccidentsanddifferenttypesof
ships are involved, there are only a few major
accidents occurred. In fact, only the “Eastern Star”
causedmore than10casualties intheYangtzeRiver
intherecentyears,andlessthanfiveaccidentscaused
more than 3 casualties. This can be seemed that the
emergency management in Yangtze River is good.
However, as the major accident occurred, some
problems may also exist. Therefore, this paper
managestomakeathoroughreviewonthepractices
of emergency management in Yangtze River, and
thenintendsto
discovertheproblemsfromthismajor
accident.Theremainderofthispaperisorganizedas
follows. Section 2 presents the practices of maritime
accident management, Section 3 proposes the
problems of emergency management learned from
the “EasternStar”.The future work of developing a
parallelcontrolandmanagementsystemthatused
to
enhance the emergencymanagement is discussed in
Section4.ConclusionsaredrawninSection5.
2 PROBELEMSOFMARITIMEACCIDENTS
MANAGEMENT
A major accident, which caused 442 casualties, has
beenoccurredin June1,2015.Afterorganized more
than 200 interviews, the Chinese government has
issued the accident investigation
report. From the
analysis, the key causation factors can be ranked as
wind,shipandhumanerror.Moreover,twoprojects
havealsobeenfundedinthelast2years.Oneproject
istoidentifythemajorhazardsintheYangtzeRiver,
including the human factors, ship condition,
navigational environment, and also
safety
management. Another project is to develop a novel
safetysystemfortheinlandwaterwaytransportation
especially in the Yangtze River. From these two
projects, insights have been gained and many
problemshavebeenaddressedtopromotethesafety
levelintheYangtzeRiver.
However,asthispaperfocuseson
theemergency
management of maritime accidents, only the
problemsrelatedtotheemergencymanagementwill
be introduced in this paper. From the analysis of
“Eastern Star” accidents and previous works, the
significantproblemsthatshouldbeaddressedcanbe
summarizedasfollows.
Thefirstproblemisthetimelimitation.Timeis
the
most important factor for life salvage. When
predicting the trajectory of ship, uncertainties will
increase when the time passes (Zhang et al., 2016).
Moreover,accordingtotheinvestigationofmanyship
flooding accidents, only the people take actions
quicklycanbesaved(Jasionowski2011),forexample,
MVEstoniaand
MVRocknes.In this “Eastern Star”
accident, although 12 people have been saved, but
only2 people were saved after the ship foundering.
That means all the other 10 people have take early
actionsbeforetheshipfoundering.
Thesecondproblemistheresourceconstraint.The
allocation of resources is a
trade‐off between
economicandsafety.Thereareafewoilspillsitesin
China,see(Xiong etal.,2015).Moreover, eveninthe
NanshaIslands,thesearch and rescue abilityis also
restricted(Shietal., 2014).In theYangtzeRiver,the
emergency resources are also constraint, from the
regulationoflocaladministration,intheportarea,the
tug should arrive at the accidental scene in 15 min,
whileintheotherwaterwayarea,andthetugshould
arrive at30 min. In this “EasternStar” accident,the
large‐sized floating crane is far from the accidental
scenes which makes
the search and rescue a little
delay.
The third problem is the cooperation among
involved multiple organizations. As the emergency
responsetomaritimeaccidentsareverycomplexand
experts from different organizations should be
involved in this process, in this “Eastern Star”
accident, more than 10 organizations are invited to
giveopinions,suchasthearmy,theMSA,theSalvage
Bureau.
The last problem is the dynamic feature of the
maritimeaccidents.Themaritimeaccidentmaycause
secondtieraccidentifitisnotwellhandled(Ulusçuet
al., 2009). Moreover, the accident will also develop
into different stage if the
response actions are
different (Mazaheri et al., 2014). Fig. 10 presents a
framework for the accidents development, which
incorporatesboththeriskscenarioandsafetybarrier
(Wu et al., 2017), while Fig. 11 presents the safety
barriersinthemaritimeaccidentdevelopment.Itcan
alsobeinterpretedthat this framework
canconsider
theaccidentdevelopmentbyusingdifferentresponse
actions. In order to address this problem, an
information system should be flexible to define the
navigational environment including the traffic flow,
critical structures, etc., otherwise, the simulation
systemwillbemuchdifferentfromreality.Then,the
calculations and experiments can be
carried out in
this platform. However, in reality,it is very hardto
modelthebehaviorofthetrafficflow.
113
3 PRACTICESOFMARITIMEACCIDENTS
MANAGEMENT
3.1 Decisionsupportformaritimeaccidents
Decision support is to use the historical data and
expert experience to select the best option among
multiple alternatives for accident damage control.
Thisisthemostimportanttoolformaritimeaccidents
management in practice and much attention
has
focusedonthisresearchfield(Calabreseetal., 2012;
Jasionowski 2011; Wu et al., 2015a; Krohling et al.,
2011). For instance, the knowledge‐based system,
proposed by Calabrese (et al. 2012), can assist to
handle the dangerous events and accidents
effectively. Jasionowski (2011) presented a decision
supportsystemto
helpthecrewmembersinmaking
decisions for ship flooding crisis management.
Krohling & Campanharo (2011) proposed a Fuzzy
TOPSIS method decision support for oil spill
management. Ölçer & Majumder (2006) presented a
case‐baseddecisionsupportsystemforfloodingcrises
onboardships.
Regarding the decision support for maritime
accidents, a
very important step is to discover the
multiplealternatives.IntheYangtzeRiver,forthenot
undercontrolships,therearefouralternatives,which
aretugassistanceoperation;beachingoranchoringin
the outer limit of the fairway; anchoring in nearby
anchorage; immediate anchoring in fairway,
respectively(Wuetal.,
2016).Whileforthegrounded
ships,theoptionsareself‐refloating,waitingforhigh
water,runagroundatfullspeed,andtugassistance,
and physical/mechanical, chemical, biological
technologiesarewidelyusedforoilspillaccidents(Li
et al., 2016). For the collision ships, the options are
pushing with dead slow speed,
run aground,
immediatelyanchoring(Ma&Shen2008;Xue,2013).
Figure10.Frameworkofaccidentdevelopment
Figure11.Two‐phasebarriersystemformaritimeaccidents
114
Figure1Three‐leveldecisionsolutionfornotundercontrol
ships
Figure2.Decisionsupportframeworkforselectionofsafety
controloptions
The multiple‐layer decision framework is always
introducedformaritimeaccidentshandling.Takethe
notundercontrolshipsforexample(Wuetal.,2016);
the developed three‐layer decision framework is
showninFig.1. In this decisionframework, thefirst
level is the alternative level, which are the available
options used
for emergency response. The second
levelisthe attributelevel. Traditionally, this level is
usedtofacilitatethedecision‐makingprocessandthe
decision‐makeronlyhastomakedecisionsintermsof
evaluation on these attributes. The last level is the
influencingfactorslevel,wheretheinfluencingfactors
are
always indentified from the historical data and
expertexperience.
In practice, after identifying the influencing
factors,inordertoselectthebestoptionformaritime
accidents, there are two problems need to be
addressed.Also takethenot undercontrolships for
example, and the process for decision support is
shown
in Fig.2. The first problem is to obtain the
weights of the attributes. As the weights of the
attributes are always obtained from the expert
judgments, which may have different preference
formats,forexample,intervalnumbers,crispvalues,
fuzzy numbers, incomplete information, a method
should be proposed to integrate these
different
formats. For example, the linear programming
method is used for the not under control ship.
Another problem is to integrate the influencing
factorstoobtaintheattributevalues.Inthisprocess,
theFuzzylogic(Wuetal.,2016;Mokhtarietal.,2012),
Bayesian network (Davies & Hope 2015), and
Evidential
reasoningarewidelyusedmethods(Yang,
2001;Yang&Xu2002).
3.2 Resourceallocationformaritimeaccidents
One significant reason whymaritime accidents pose
high risk is owing to the harsh navigational
environment caused by the offshore activities. This
makes the resources for emergency response is
constraint and the majority
of existing researches
focusedonhowtoallocatetherestrictedresourcesfor
emergencyresponse.Forexample,Cunha(etal.2014)
intended to manage the contaminated marine
marketableresourcesafteroil spill.Lehikoinen(etal.
2013)proposedanoptimizedmodel foroil recovery
inthegulfoffinishusingBayesiannetwork.Siljander
et al. (2015) used the cost distance module in the
geographicinformationsystemforsearchandrescue
planning.Garrettetal.(2017)proposedadynamicoil
spill response planning model by considering the
accidentdevelopmentinthearctic.
In the Yangtze River, the harsh navigational
environment should be taken into
consideration for
resourceallocation,andoneimportantproblemisto
allocateenoughresourcesbeforeaccidentoccurs. For
example, in the bridge area, where the navigable
waterway is reduced due to the construction of the
bridge,theresourceallocationshouldbeenhanced.In
ourpreviouswork(Wuetal.,2013),weproposed
a
risk‐based approach to allocate the patrol marine
vessels in the Yangtze River, which can be used for
emergency response to maritime accidents to make
both the bridge and ship safe. The as Low as
reasonablypracticable(ALARP)isintroducedinthis
model,andtheprincipleofdefiningthe
relationship
betweenrisklevelandresourceallocationisshownin
Table1.
Table1. Relationship between risk level and resource
allocation
_______________________________________________
Risklevel Riskbasedresourceallocation
_______________________________________________
Negligible Riskcanbeignoredandnoresourceshould
beallocated
Acceptable Theriskisacceptableandtraditional
maintenanceshouldbecarriedout
ALARP Riskisrelativehighandadditional resources
shouldbeallocated
UnacceptableTheriskistoohightoconstructsuchcritical
infrastructure
_______________________________________________
Figure3. Resource allocation prediction using case based
reseaning
115
Another important problem is to allocate the
resources after accident occurs. One practice in the
YangtzeRiveristousethesimilarandexistingcases
to predict the required resources in the new case,
whichisknownasthecasebasedreasoningmethod
(Deng et al., 2014). The principle of
this method is
shown in Fig. 3. Once the new accident occurs, the
similar cases will be retrieved and the associated
solution will be given by using this case based
reasoning method. However, considering the
distinguishing character of the new case, some
revision on the recommended solution may also be
carried out, which is the final solution for the new
accident.
3.3 Emergencymaritimesimulationsystem
Maritimesimulationsystemiswidelyusedowingto
the distinguishing advantages of immersive,
intuitiveness, low‐cost and interactive for training.
Currently, there are some acknowledged simulation
systemsfortrainingthecrewssuchasKongsbergand
Transas.However,thesesimulationsystemsmanages
to promote the ability of ship maneuvering, while
only a few attention have been focused on the
emergencyresponsetoshipsaftermaritimeaccidents.
Varela (et al. 2007) proposed a virtua l environment
for the ship damage control; moreover, they also
presented a simulation system
for the ship flooding
recently(Varelaetal.,2014).
Different from the simulation system used for
training the crews, Wu et al. (2014) proposed a
simulation system for training the staffs in the
maritimesafety administration,whoareinchargeof
maritime safety in the Yangtze River. The system
architecture of
this system is shown in Fig. 4. The
system involves five components, the accident
evolution and intervention logic, accident virtual
environment, emergency training simulator,
hardware‐in‐the‐loop and human‐in‐the‐loop. In this
system,differenttypesofmaritimeaccidentsaswell
as the virtual environment of accident development
can be simulated, and the involved multiple people
can carry out the accident drills in this system.
Moreover, the effectiveness of emergency response
performancecanalsobecarriedoutinthissimulation
system(Guietal.,2016).
Figure4. System architecture of emergency maritime
simulationsystem
Moreover,onethingneedto be mentioned is the
softwaresystem.Astherearesomesystemsforearly
warmingandaccidenthandlingintheYangtzeRiver,
these systems areall simulated in order to establish
an immerse environment, the cooperation among
multiple person is shownin Fig.5 (Yan et al., 2015).
They are automated identification system (AIS),
Closed Circulate Television (CCTV), Vessel Traffic
Servicesystem(VTS),decisionsupportsystem(DSS)
andsearchandrescue(SAR)system.
Figure5. Multiple person involved emergency maritime
simulationsystem
The simulation software used for calculating the
consequencesisalsoimportantforthemanagementof
maritime accidents. That’s because when the
consequences can be predicted, the response actions
can be adjusted according to the predicted
consequences. Although some developed models
such as Bayesian network can also be used for
predicting the
consequences using historical data
(Zhangetal.,2013;Zhangetal.,2016),thesimulation
software should be better since it predict the
consequence with better accuracy. The associated
simulation software for different types of maritime
accidentsisshowninTable2.
_______________________________________________
Accidenttype Simulation References
software
_______________________________________________
Collisionand GRACAT (Friis‐Hansenand
groundingSimonsen2002)
ANSYS (Montewkaetal.,2014)
fireFDS(SuandWang2013)
Oilspill OILMAP (KrohlingandCampanharo
2011)
Lifesalvage HECSALV ABS
_______________________________________________
Figure6.OilspillsimulationusingOILMAP
In Yangtze River, these software have also been
introduced for the management of martiime
accidents.Fig.6showstheoilspillsimulationinthe
116
usingOILMAPwhileFig.7 showsthe simulationof
firedevelopmentusingFDS.
Figure7.FiresimulationinengineroomusingFDS
3.4 Effectivenessofemergencymanagement
Effectiveness analysis is to discover whether the
management is effective or not so that counterpart
measures can be carried out to enhance the
management. When the international safety
management (ISM) code was introduced, the
effectiveness of it was also conducted both in UK
(Bhattacharya et al.,
2012) and in Greece (Tzannatos
and Kokotos 2009). Similar work for analyzing the
effectiveness of safety management was also carried
outinthegulfofFinlandbyusingBayesiannetwork
(Hänninen et al., 2014) and also on‐board ships
(AkyuzandCelik2014).
IntheYangtzeRiver,theMSAhave
usedthefive
index (i.e. incidents, graded accidents, shipwreck,
causalities, economic loss,) method to measure the
effectiveness of emergency management. In order to
obtain a comprehensive result, Zhang et al. (2014)
used a generalized Belief Rule Base (BRB)
methodologytoevaluatetheperformanceoftheMSA
intheYangtzeRiverby
usingsearchandrescuedata.
The framework of this approach is shown in Fig. 8,
where the MSA performance is evaluated by using
thesafetysituationandcostattributes.
However, as the abovementioned method cannot
take the navigational environmental factors into
consideration, Wu et al. (2015b) proposed a data
envelopment
analysis (DEA) based method to
evaluatetheeffectivenessofemergencymanagement.
In the DEAmethod, the navigational environmental
factors are treated as the inputs, while the accident
dataastheoutputs.Theprincipleofthismethodisto
maximize the relative efficiency subject to a linear
inequalityconstraintthattheweighted
outputsareno
more than the weighted inputs. The inputs and
outputs are shown in Fig. 9, which is also the
developedDEAmodel.
Figure8.FrameworkforMSAperformanceassessment
Figure9.TheproposedDEAmodellayout
4 FUTURETRENDOFMARITIMEACCIDENTS
MANAGEMENT
Fromtheaboveanalysisofthepracticesandproblems
oftheemergencymanagementintheYangtzeRiver,
much attention has been attracted to the micro
emergencytechnologies.However,Macro‐simulation
system, which can reappear the maritime accidents
development, should also be incorporated into the
maritime emergency simulation system. This is
different from the virtual simulation system (micro
simulation) that manages to promote the ability of
search and rescue in the virtual environment, the
macro simulation system manages to promote the
ability of emergency management especially the
resourceoptimizationanddecisionsupportinthereal
environment,i.e.thesafetymanagementsystem.The
reasonwhythisisimportantit’sthat theemergency
managementisapartofthesafetymanagementand
theemergencymanagementshouldbecarriedoutin
therealenvironment.
Artificial societies, Computational experiments,
and Parallel execution (ACP) is widely used in the
safety engineering.
The principle of ACP is to
establish one or more virtual environment based on
onerealenvironment. This method has been widely
usedinthe transportation engineering.For example,
the Asian Games in 2010 has introduced this for
traffic management (Xiong et al., 2013), Ning et al.
(2011)usedit
forhigh‐speedtrainmanagement,while
Duanetal.(2011)useditforpublichealthemergency
management.
117
Figure12. Framework of parallel emergency management
system
The proposed framework of parallel emergency
management system based on ACP approach is
shown in Fig. 12. First, artificial societies are
established according to the real world, which
includes the system modeling tools and system
support tools.The artificial societies should be
establishedowingtothecomplexityofrealworldand
thedynamic featureof maritime accidents.Second,
thecomputationalexperimentsarecarriedoutonthe
artificial societies to discover the accident
development mechanism. Third, the parallel control
and management can be carried out to enhance the
emergencymanagement.
5 CONCLUSIONS
The contribution of this paper is to summarize the
problems
of maritime accident management,
including the time limitation, resource constraint,
multiple organization cooperation and dynamic
development, are proposed by learning from the
“EasternStar”majoraccidentinthispaper.Moreover,
thepracticesofmaritimeaccidentmanagementinthe
Yangtze River, which includes decision support,
resource allocation, emergency simulation and
effectiveness analysis,
are also presented. Last, an
artificial societies, computational experiments, and
parallelexecutionbasedframeworkisalsoproposed.
However, although some practices have been
carried out in the Yangtze River and some insights
have been gained; the problems of emergency
management of maritime accidents should also be
carefully addressed owing to
the occasionality and
uncertainty of maritime accidents. Moreover,
different perspectives of public safety management
shouldbeintroducedtothemarineengineering,such
as the above‐mentioned ACP approach and also the
hierarchicaltasknetworkplanning.
ACKNOWLEDGEMENT
The research presented in this paper wassponsored
by a grant from the Key Project
in the National
Science & Technology Pillar Program (Grant
No.2015BAG20B05), grants from National Science
Foundation of China (Grant No. 51609194), grants
fromthespecialfundsofHubeiTechnicalInnovation
Project (Grant No. 2016AAA055) and the
Fundamental Research Funds for the Central
Universities(WUT:2017IVA103).
REFERENCES
Akyuz, E., & Celik, M. (2014). A hybrid decision‐making
approach to measure effectiveness of safety
management system implementations on‐board ships.
SafetyScience,68,169‐179.
Bhattacharya,S.(2012).TheeffectivenessoftheISMCode:
Aqualitativeenquiry.MarinePolicy,36(2),528‐535.
Calabrese,F., Corallo,A., Margherita, A.,
& Zizzari, A.A.
(2012).Aknowledge‐based decisionsupportsystemfor
shipboard damage control. Expert Systems with
Applications,39(9),8204‐8211.
Cunha,I.,Neuparth,T.,Moreira,S.,Santos,M.M.,&Reis‐
Henriques,M.A. (2014).Management of contaminated
marinemarketableresourcesafteroilandHNSspillsin
Europe.
Journalofenvironmentalmanagement,135,36‐44.
Davies,A.J.,&Hope,M.J.(2015).Bayesianinference‐based
environmental decision support systems for oil spill
response strategy selection. Marine Pollution Bulletin,
96(1‐2),87.
Deng, S.C., Wu, Q., Shi, B., Chen, X.Q., Chu, X.M. (2014).
Prediction of resource for responding
waterway
transportation emergency based on case‐based
reasoning[J].Chinasafetysciencejournal,24(3):79‐84.(In
Chinese)
Duan,W.,Cao,Z.,Wang,Y.,Zhu,B.,Zeng,D.,Wang,F.Y.,
...&Wang,Y.(2013). AnACPapproachtopublichealth
emergency management: using a campus outbreak of
H1N1
influenza as a case study. IEEE Transactions on
Systems,Man,andCybernetics:Systems,43(5),1028‐1041.
Eleye‐Datubo,A.G.,Wall,A.,Saajedi,A.,&Wang,J.(2006).
Enabling a powerful marine and offshore decision‐
support solution through bayesian network technique.
RiskAnalysis,26(3),695‐721.
Friis‐Hansen, P., &
Simonsen, B. C. (2002). GRACAT:
software for grounding and collision risk analysis.
MarineStructures,15(4),383‐401.
Garrett,R.A.,Sharkey,T.C.,Grabowski,M.,&Wallace,W.
A. (2017). Dynamic resource allocation to support oil
spill response planning for energy exploration in the
Arctic. European Journal of Operational Research,
257(1),272‐286.
Gui, H., Wang T.F., Wang, Y., Wu, Q. (2016). An
Effectiveness Evaluation of Waterway Transportation
EmergencyResponsePerformanceBasedonaWorkflow
Model. Journal of Transport Information and Safety,
34(3),116‐125.(InChinese)
Hänninen,M.,Banda,O.A.V.,&Kujala,P.(2014).Bayesian
networkmodel of
maritime safetymanagement. Expert
SystemswithApplications,41(17),7837‐7846.
Jasionowski, A. (2011). Decision support for ship flooding
crisismanagement.OceanEngineering,38(14),1568‐1581.
Krohling,R.A.,&Campanharo,V.C.(2011).FuzzyTOPSIS
for group decision making: A case study for accidents
withoilspillinthe
sea.ExpertSystemswithApplications,
38(4),4190‐4197.
Lehikoinen, A., Luoma, E., Mäntyniemi, S., & Kuikka, S.
(2013).OptimizingtherecoveryefficiencyofFinnishoil
combatingvesselsintheGulfofFinlandusingBayesian
118
networks.Environmentalscience&technology,47(4),1792‐
1799.
Li, P., Cai, Q., Lin, W., Chen, B., & Zhang, B. (2016).
Offshore oil spill response practices and emerging
challenges.MarinePollutionBulletin,110(1),6‐27.
Ma, Y.D., Shen Z.P. (2008). Study on the maritime
emergency response to collision ships. Navigation
Technology
,4:21‐23.(InChinese)
Mokhtari, K., Ren, J., Roberts, C., & Wang, J. (2012).
Decision support framework for risk management on
sea ports and terminals using fuzzy set theory and
evidential reasoning approach. Expert Systems with
Applications,39(5),5087‐5103.
Montewka,J.,Ehlers,S.,Goerlandt,F.,Hinz,T.,
Tabri,K.,&
Kujala, P. (2014). A framework for risk assessment for
maritimetransportationsystems—Acasestudyforopen
sea collisions involving RoPax vessels. Reliability
Engineering&SystemSafety,124,142‐157.
Mazaheri,A., Montewka, J., & Kujala, P. (2014). Modeling
the risk of shipgrounding—a literature review from a
riskmanagementperspective.WMUJournalofMaritime
Affairs,13(2),269‐297.
Ning, B., Tang, T., Dong, H., Wen, D., Liu, D., Gao, S., &
Wang,J.(2011).Anintroductiontoparallelcontroland
management for high‐speed railway systems. IEEE
Transactions on Intelligent Transportation Systems, 12(4),
1473‐1483.
Ölçer,A.
İ.,&Majumder,J.(2006).ACase‐basedDecision
Support System for Flooding Crises Onboard Ships.
QualityandReliabilityEngineeringInternational,22(1),59‐
78.
Shi,W.,Su,F.,&Zhou,C.(2014).Atemporalaccessibility
model forassessing the abilityof search and rescuein
Nansha Islands, South China Sea.
Ocean & Coastal
Management,95,46‐52.
Siljander, M., Venäläinen, E., Goerlandt, F., & Pellikka, P.
(2015). GIS‐based cost distance modelling to support
strategic maritime search and rescue planning: a
feasibilitystudy.AppliedGeography,57,54‐70.
Su,S.,&Wang,L.(2013).Threedimensionalreconstruction
of the
fire in a ship engine room with multilayer
structures.OceanEngineering,70,201‐207.
Tzannatos,E.,&Kokotos,D.(2009). Analysisofaccidentsin
Greek shipping during the pre‐and post‐ISM period.
MarinePolicy,33(4),679‐684.
Ulusçu, Ö. S., Özbaş, B., Altıok, T., & Or, İ.
(2009). Risk
analysisofthevesseltrafficinthestraitofIstanbul.Risk
Analysis,29(10),1454‐1472.
Varela, J. M., & Soares Guedes, C.. (2007). A virual
environment for decision support in ship damage
control.IEEEcomputergraphicsandapplications,27(4),58‐
69.
Varela,J.M., Rodrigues,J.M.,&
SoaresGuedesC. (2014).
On‐board decision support system for ship flooding
emergencyresponse.ProcediaComputerScience,29, 1688‐
1700.
Wu, B., Yan, X.P., Wang Y., Sun, X., Guo Z.X. (2013).
Maritime patrol resource configuration based on
navigational risks in bridge area. Navigation of China,
36(4),82‐85.(InChinese)
Wu, B., Yan, X.P., Wang, Y., & Wei, X. (2014). Maritime
emergencysimulationsystem(MESS)‐avirtualdecision
support platform for emergency response of maritime
accidents.2014InternationalConferenceonSimulationand
Modeling Methodologies, Technologies and Applications
(SIMULTECH),155‐162.
Wu, B., Yan, X.P., Wang, Y., & Zhang, J. F.
(2015a).
Emergency Group Decision‐Making with Multi‐
divisional Cooperation for Inland Maritime Accident.
In:A. Weintrit, T. Neumann (Eds), Information, Com‐
municationandEnvironment,MarineNavigationandSafety
ofSea Transportation,CRC Press/Balkema,London, UK,
165‐171.
Wu, B., Wang, Y., Zhang, J., Savan, E. E., & Yan, X.P..
(2015b).
Effectiveness of maritime safety control in
different navigation zones using a spatial sequential
DEA model: Yangtze River case. Accident Analysis &
Prevention,81,232‐242.
Wu, B., Yan, X.P., Wang, Y., & Soares Guedes, C. (2016).
Selection of maritime safety control options for NUC
shipsusing a hybrid group decision
‐making approach.
Safetyscience,88,108‐122.
Wu,B.,Yan,X.P.,Wang,Y.,& Soares,GuedesC.(2017).An
Evidential Reasoning ‐ Based CREAM to Human
Reliability Analysis in Maritime Accident Process. Risk
Analysis,DOI:10.1111/risa.12757.
Xiong,S.,Long,H.,Tang,G.,Wan,J.,&Li,H. (2015).The
management
inresponse to marineoilspillfromships
inChina:Asystematicreview.Marinepollutionbulletin,
96(1),7‐17.
Xiong, G., Dong, X., Fan, D., Zhu, F., Wang, K., & Lv, Y.
(2013). Parallel traffic management system and its
applicationtothe2010asiangames.IEEETransactionson
IntelligentTransportation
Systems,14(1),225‐235.
Xue B. (2013). Discussion on the emergency response to
collisionships.NavigationTechnology,2013,4:18‐20.(In
Chinese)
Yan,X.P.,Wu,B.,Wang,Y.,Wei,X.Y.(2015).Overviewof
Development and Current Progress in Maritime
Simulation Research. Journal of System Simulation, 2015,
27(1):
13‐28.(InChinese)
Yang, J. B. (2001). Rule and utility based evidential
reasoningapproach for multiattribute decision analysis
under uncertainties. European Journal of Operational
Research,131(1),31‐61.
Zhang, D., Yan, X. P., Yang, Z. L., Wall, A., & Wang, J.
(2013). Incorporation of formal safety assessment and
Bayesian
networkinnavigationalriskestimationofthe
Yangtze River. Reliability Engineering & System Safety,
118,93‐105.
Zhang, J., Yan, X.P., Zhang, D., Haugen, S., & Yang, X.
(2014).Safety managementperformanceassessmentfor
Maritime Safety Administration (MSA) by using
generalizedbeliefrulebasemethodology.SafetyScience,
63,157‐167.
Zhang, J., Teixeira, Â. P., Guedes Soares, C., Yan, X.P., &
Liu,K. (2016). Maritime transportation risk assessment
of Tianjin Port with Bayesian belief networks. Risk
analysis,36(6),1171‐1187.
Zhang, J., Teixeira, Â. P., Guedes Soares, C., & Yan, X.P.
(2017).Probabilisticmodellingof thedrifting trajectory
of an
object under the effect of wind and current for
maritimesearchandrescue.OceanEngineering,129,253‐
264.
