American Journal of Industrial and Business Management, 2013, 3, 740-745
Published Online December 2013 (
Open Access AJIBM
Decision Support Technology Research of Emergency
Qing Wang1,2#, Yuanchun Huang2, Yuelei He2, Zhigang Liu2, Hua Hu2, Aiqin Sun1,2
1The College of Business Administration, Shanghai University of Engineering Science, Shanghai, China; 2The College of Urban
Railway Transportation, Shanghai University of Engineering Science, Shanghai, China.
Received November 12th, 2013; revised December 11th, 2013; accepted December 16th, 2013
Copyright © 2013 Qing Wang et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In accor-
dance of the Creative Commons Attribution License all Copyrights © 2013 are reserved for SCIRP and the owner of the intellectual
property Qing Wang et al. All Copyright © 2013 are guarded by law and by SCIRP as a guardian.
This paper focuses on the problem about how to efficiently process the emergency of rail transit and guarantee the low-
est accident loss in a short period of time, which is the urban rail transit management policy that makers are faced with,
and which develops a high integrated system with strong information based on contingency plans to give the decision
aid of urban rail transit emergency events. The paper uses formal methods to present the emergency plan, generate the
emergency disposal plan, meet the requirements of on-site emergency disposal, and it realizes the modernization of ur-
ban rail transit emergency management which has an important significance. Finally, taking a subway fire as an exam-
ple, it describes the practicality of the auxiliary decision system.
Keywords: Emergency; Urban Rail Transit; Contingency Plans; Auxiliary Decision System
1. Introduction
With the expansion of rail transit network scale, re-
quirements of emergency response system have also been
improved. Sudden events are unpredictable and often
cause great damage due to the inadequate preparation,
which requires the staff to make th e correct response in a
very short period of time [1]. How to minimize the losses
in a short time is the most important problem for man-
agement decision makers.
Emergency work flow is mainly about the accident
information report and processing instructions issued.
The paper makes the command center as the object, to
establish the emergency disposal decision aid system.
2. Key Support Technology Demand
The core of emergency disposal of urban rail transit net-
work emergencies is to enable decision makers to control
the accident monitoring information of the scene, and
making use of the characteristic information of events
according to the emergency response plan of the emer-
gency disposal program dynamically generates solutions
[2]. This article will focus on the following two aspects
to research the emergency disposal of urban rail transit
network aided decision su pport technology [3].
2.1. Emergency Plans of Collaborative
Integration Digital Model—Digital Model
for Emergency Plan
Multidimensional collaborative digital modeling of con-
tingency plans refines plans to every process, every step,
and comb out the relationship between various agencies,
position, du ties, personnel, process and resources, so that
form an organic, interconnected and linkage of the over-
all [4].
The plan model of each dimension is not an isolated
individual, but an interrelated, overall coordination. The
relationship among the various dimensions is shown in
Figure 1 [5]. According to the above model, the paper
explains the plan to a multi-dimension, multi-perspectiv e
plan model.
*This research was supported by Shanghai science and technology key
roject (Project No.: 11170501400), the People’s Republic of China.
#Corresponding author.
Decision Support Technology Research of Emergency Disposal 741
Figure 1. Emergency multidimensional integration model.
2.2. Generation Technology Based on Plan
The design idea of emergency disposal plan is as follows:
1) find a similar emergency event list feature information
to match the sudden incident; 2) if the case library has
storage solu tions correspondin g to the event, then extrac-
tion solution, and modification in proper way to form the
eventual solution; 3) if th e case base does not exist in th e
corresponding disposal scheme, using the number of
emergency plan to find proper events [6]; 4) after acquire
the solution, extract the handing steps to generate the
final solution draft; 5) amendments by the user of the
draft, and check the correctness of the modified, disposal
the final plan; 6) send the plan to the scene, the scene of
the accident deal with the accident according to the solu-
tion, disposal and real-time feedback accident, thereby
circulating the process until the end to achieve dynamic
adjustments to the disposal plan; 7) after the accident, the
user should to judge whether the value of the emergency
disposal plan, if valuable, store the emergency event to
the relative case library to enrich the library [7]. In ac-
cordance with the above ideas, to achieve the above
process, the first to solve the following two problems.
Digital Alarm Emergencies: according to the accident
and its influence degree and other basic information,
automatic judging accident levels, providing disposal
measures guiding the disposal of the accident scene to
ensure the network safety operation, and automati-
cally generate alarm record. Alarm template includes
accident situation, accident level and alarm record.
The accident situation mainly reflects the basic in-
formation of accident and the degree of influence [8].
Information form contains the occurrence time, dura-
tion, accident types, locations, detail place, location
trips, casualties, expected running time, passenger
flow and controllability to fully reflect the basic ac-
cident information, impact degree and scope. Acci-
dent level is used to reflect the degree of accident.
According to the degree of harm, rail transit emer-
gency may be caused by the propagation range, in-
fluence of size, the casualties and property losses,
from high to low is divided into special major (grade
I), major (grade II), large (grade III), general (grade
IV) .
Improved Event Similarity Measure Model: in the
field of CBR, there are kinds of ev ent similarity algo-
rithm based on the nearest neighbor algorithm; the
nearest neighbor algorithm (K-Nearest Neighbor Al-
gorithm, KNN) is the most commonly used [9]. Since
KNN algorithm requires complete information of
search condition, the paper introduces the structural
similarity, local attribute similarity and attribute al-
ternative conceptu al on the basis of KNN algorithm to
improve it [10]. The model is as follows. Assuming
that matches the target event
and event
are described by m attributes,
,, ,
a ,, ,
b and ,,aaa
the attribute weights 3,4, ,
im calcula- , 1,2,wi
tion methods of similarity
expressed as follows.
,1 ,
SIMw simab
 
 
The ,
means structural similarity between
, it describes the impact of missing data on event
attributes similarity computing, ,
represents the
degree of substitutability between
im ab means the local similarity in the properties
of events between
1) The Computation of Similarity
The calculation process of structure similar ity between
target events and events are as follows. Assume that the
target event set is 0
, all non empty attribute events set
is 1
, 0
and 1
intersection marked
, and merger
recorded as ; the weights of all attributes of set U
and respectively U
and U
, and the definition of
structural similarity between
is as follows:
2) Calculation of Local Similarity
The local similarity refers to events in each attribute
similarity, emergency attribute value type can be divided
into Continuous type, classification, fuzzy numbers or
fuzzy interval types, different data types should have
corresponding property local similarity measure. Sym-
bolic attributes: in urban rail transit traffic accident, ac-
cident types, locations, the nature of the accident and
other attributes are symbolic attributes. Their similarity is
calculated as follows:
ii ii
sim abab
The ii
represents the property value between the ,ab
target event
Open Access AJIBM
Decision Support Technology Research of Emergency Disposal
Determined attribute similarity algorithm: the simi-
larity method based on Hamming distance formula of
evolution is calculated as follows.
 
,1 ,1
max min
iiii ii
sima bdista b
 (4)
The calculation method of fuzzy number type attribute
similarity: fuzzy number default here is a convex fuzzy
set. If 1234ii ii
aa and 1234ii ii
, we aa bbbb
can use Graded Mean Integration-repre sentation Distan ce
to calculate the similarity.
 
iii i
ii ii
ii ii
sim abp ap b
 
3) Calculate The Difference ,
Between The Tar-
get Event
and Event
Variability of response is the difference between the
degree of each unit in the overall difference, the greater,
the more difficult to be replaced. It is calculated as fol-
wsimab X
 
4) Calculation of Event Feature Attribute Weight
This paper uses the attribute hierarchy model (Attrib-
ute Hierarchical Model AHM) [11] to calculate the
weight of the event’s attribute value. AHM is a method
to calculate the relative weight of an attribute, the attrib-
ute weights can be adapted to the calculation in the ab-
sence of input information, to meet the weight loss or
error the operator filled in information of computing at-
tribute in emergency situation.
The AHM method is based on the improved analytic
hierarchy process AHP (Analytic Hierarchy Process),
according to the Table 1 show, mode on the relative im-
portance of each attribute was scoring assessment by
experts in rail transit safety.
Table 1. Attribute important degree evaluation.
x is * important
than y equally slightlymore much
more very much
Fraction 1 2 3 4 5
After require the relative importance ij between at- a
tributes i and a
a, the property transfers ij between u
the important degrees were calculated by formula (7).
The transfer of property represents the important degree
of attributes and
0.5,1, ,
ij ij
Transfer important degree was calculated by the use of
formula (8). The weight of each attribute n means
the number of attributes of the event C.
3. Implementation of Emergency Aided
Decision Support Technology
3.1. Digital Template Emergency Plan to Take
Station Fire Plan as an Example
The emergency plan of city subway station fire accident
is modeled by the emergency plan of multidimensional
digital model proposed in the previous paper, and estab-
lished digital template. The template also includes five
dimensions of organizational, resource, process, function
and information. Table 2 shows the metro station fire
accident contingency plan digital template, since the
original template format is too wide to show completely
in this page, here just intercepted a part template.
3.2. Emergency Case
The following emergency treatment takes a subway fire
accident as an example, to introduce the process of deci-
sion making by using the technology of emergency dis-
posal. The background for a car fire smoke, informed the
station integrated control, integrated control officer to
report the line dispatching immediately; another train
reach to the station platform, the fourth compartment fire
can’t be controlled, 7 passengers suffered serious burns,
the comprehensive control center of the line command
station buckle car, which is expected to break 1 - 4 h, and
report to the command center, and report to the co mmand
center, launched the emergency plan.
Event Alarm: After the occurrence of unexpected
events, information station integrated control officer
reported in the alarm interface according to the situa-
tion of emergency, and share its to the OCC, enter-
prises total harmonic comma nd center TCC.
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Decision Support Technology Research of Emergency Disposal
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Table 2. Fire emergency plan digital template (part).
Organization Personnel Emergency processSub process Task The process of task
Report line dispatching Report
Report the ring tone Control officer Reporting process
Notify the adjacent line
Report group 1
Notify this line
Report 119 Report
Report group 2 Report the emergency center
Report on duty station Report
Report station
Report the production office
Report group 3
Report to the police
Continue to report Report
The scene station
command center
The final report Report
Table 3. Event attribute.
The property 1
C 2
C 3
C 4
C 5
Accident type A1 Transfer station Non transfer station Transfer stationSection Transfer station
The location A2 A B C D E
Accident properties A3 Fire Flood Large passengerCatenaries power outages Extraterrestrial
The specific location A4 Orbit range Near the railway NA Orbit range Orbit range
Happened time A5 16:20 14:20 07:50 18:15 08:25
Accidents level A6
The number of casualties A7 2 NA NA 1 1
Interruption of operation time A8 0.5 - 1 h 3 - 10 h 1 - 3 h 1 - 3 h 0 - 0.5 h
Passenger flow A9 Large Small Large Large Medium
Controllability A10 Strong Weak Medium Medium Strong
Matching Emergency Plan: According to the alarm
information, get the target event, its attributes: {acci-
dent, a transfer station, fire, train accident, 17:35,
grade II, 7, 1 - 4 h, the big, strong}. Calculate the
event similarity, as shown in Table 3. First of all, us-
ing the above formula to calculate the weight of each
attribute of the event, as shown in Table 4; and then
one by one to calculate structural similarity, event at-
tribute similarity and different local event property
substitutability between the settlement results as
shown in Table 5.
According to the calculation result of the global simi-
larity by formula (1), the similarity of event 1
C and
target event is 0.738507 . Define the event and target
event to match most, at the same time, to search for the
corresponding emergency plan for the digital modeling,
and get digital template contingency plans such as shown
in Table 2.
According to the login information, system can require
attention to the relevance information in the emergency.
This will ensure that the disposal personnel can get the
information they need in the process to dispose of acci-
dents, improve the efficiency of emergency. Through the
above process, we found that the decision by the emer-
gency disposal technology can effectively ensure all lev-
els of staff to get their goal in the shortest time, improve
efficiency, reduce the reaction time, and contribu te to the
minimum loss of the accident, so that the rail transit sys-
Decision Support Technology Research of Emergency Disposal
Table 4. Attribute weights calculation.
Property A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 Weight
A1 0 0.70 0.20 0.50 0.70 0.30 0.30 0.25 0.50 0.20 0.118889
A2 0.30 0 0.10 0.20 0.50 0.10 0.10 0.15 0.15 0.10 0.162222
A3 0.80 0.90 0 0.90 0.90 0.30 0.75 0.75 0.80 0.70 0.048889
A4 0.50 0.80 0.10 0 0.90 0.30 0.75 0.75 0.80 0.70 0.117778
A5 0.30 0.50 0.10 0.20 0 0.10 0.10 0.10 0.20 0.10 0.164444
A6 0.70 0.90 0.70 0.80 0.90 0 0.70 0.70 0.80 0.50 0.053333
A7 0.70 0.90 0.25 0.70 0.90 0.30 0 1 0.75 0.30 0.092222
A8 0.75 0.85 0.25 0.70 0.90 0.30 0.50 0 0.70 0.30 0.093333
A9 0.50 0.85 0.20 0.50 0.80 0.20 0.25 0.30 0 0.20 0.122222
A10 0.80 0.90 0.30 0.80 0.90 0.50 0.70 0.70 0.80 0 0.068889
Table 5. Calculation results of event and target event similarity.
The attribute of local similarity 1
C 2
C 3
C 4
C 5
C Weight
A1 1 0 1 0 1 0.124628
A2 1 0 0 0 0 0.102512
A3 1 0 0 0 0 0.063865
A4 0 0
NA 1 1 0.124582
A5 0.845 0.932 0.568 0.954 0.631 0.118135
A6 1 1 0.75 0.75 0.25 0.069671
A7 0.135
NA NA NA 0.135 0.120473
A8 0.425 0.322 1 1 0.387 0.121925
A9 1 0 1 1 0 0.064216
A10 1 0 0 0 1 0.089992
Property substitutability 0.959851 0.960240 0.952419 0.899305 0.953287
Structural similarity 1 0.896456 0.802031 0.869856 1
Event similarity 0.738507 0.178978 0.310633 0.319864 0.391386
tem can return to normal operation as soon as possible.
4. Conclusion and Research Prospect
This paper analyzes the characteristics of Chinese urban
rail transit network operation and designs road network
emergency response decision su pport system. Meanwhile,
the key techniques involved are studied; the emergency
command system architecture under the condition of
network operation of urban rail transit is proposed; the
emergency disposal work flow in this command system
through the investig ation is deduced.
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