An Event-Driven Scheme of Scenario Deduction for Railway

Emergency

Zhenhai Zhang

, Xiaozhen Yin and Zhilong Xu

School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China

* Zhenhai Zhang (1983—), Male, Linzhou city, Henan province, associate professor, Post-doctoral researcher, interested in

cenario deduction

or railwa

emer

enc

Keywords: Railway transportation, emergency information, scenario deduction, event-driven model.

Abstract: Railway emergency management is currently transforming from plan management to scenario management.

Firstly, an event-driven model of scenario deduction for railway emergency was established. Then, according

to the evolution process of scenarios for railway emergency, the emergency scenarios were divided into initial

scenario, middle scenario and final scenario, among which the mutual relationships were analyzed.

Furthermore, using Bayesian network inference algorithms, the specific process of scenario deduction for

railway emergency was elaborated. Finally, taking train derailment accidentals for example, the states of node

variables in scenario network were deduced. The scenario deduction results are in line with the real thing,

which prove the proposed scheme feasible and effective.

1 INTRODUCTION

Railway has played an important role to ensure the

smooth operation of economy and social stability.

Due to the extremely high requirements of high-speed,

high-density and heavy-load railway, the

management tasks of railway transportation are

increasingly arduous. Thus, the possibility of

unexpected events is also increasing. At present, all

kinds of railway emergency plans are improved and

published, such as, flood protection, fire accidents,

network and information security incidents and so on.

Combined with geography, physiognomy, climatic

characteristics in every railway administration,

emergency plans have been consummated, which

conforms to the characteristics and requirements of

the region to make the operation program specific.

Foreign railway companies also use the safety

management system to provide dangerous source

management, emergency investigation, information

of track quality and standard degree and so on. It

provides effective information for the timely

prevention and timely setting of emergency plans for

railway accidents. By comprehensively analyzing the

relevant data, it can provide auxiliary decisions for

the maintenance, supervision, accident prediction,

and emergency rescue of the railway line.

The evolution of emergencies itself has strong

dynamic, complexity and uncertainty, it determines

the problems that emergency decision of the

emergencies are also uncertain and complex(Wang, et

al, 2011). At present, the decision-making method

based on experience depends on the current

information and experience which has been analyzed.

Therefore, it has strong subjective randomness, and

the incomplete analysis of information may seriously

affect the decision-making results of decision-makers,

so it is difficult to ensure the correctness of decision-

making results. The emergency decision-making

method of current emergency has begun to change

from subjective decision-making based on

experience to scientific objective decision-making

based on comprehensive data analysis. It combines

the expert knowledge and mathematical model.

In the field of emergent evolution and emergency

decision-making research, She Lian put forward the

theory of traffic disaster early warning management,

Huo Ran had done a lot of careful research on the

construction of fire emergency scenario and the

analysis of incident evolution. Jiang Hui made a

preliminary analysis of the concept and connotation

of the scenario evolution in the real time decision

making of a rare emergency, divided the evolution of

the event stage. According to the limitation of the

traditional “prediction - response” and the

characteristics of various stages of unconventional

emergencies, he put forward corresponding measures

and countermeasures. The research of scenario

Zhang, Z., Yin, X. and Xu, Z.

An Event-Driven Scheme of Scenario Deduction for Railway Emergency.

In 3rd International Conference on Electromechanical Control Technology and Transportation (ICECTT 2018), pages 125-130

ISBN: 978-989-758-312-4

125

inference process is an important means of

emergency decision support for railway emergency.

This paper will establish an event driven model of

railway emergency scenario inference, study the

function of event driven by analyzing of the process

of deduction.

2 SCENARIRESPONSE MODE OF

RAILWAYEMERGENCY

The scenario coping model is a solution, which is

made by the decision maker. They analyze the

existing information and data, integrate them,

complete the scenario construction, analyze the

situation by the experience information, evaluate

the evolution of scene situation and then obtain the

corresponding solution according to the evaluation

results(Li, et al, 2012). The starting point of the

scenario coping model is that the scenario is not static,

but dynamic. As the emergency process goes on, the

content of scenario element description also changes.

Emergency decision makers can predict the evolution

of events and trends based on scenarios. According to

the results of the prediction, the corresponding

measures are taken to control the development of the

events, and change the direction of a sudden event to

a good aspect.

3 UNCERTAINTY OF SCENARIO

DEDUCTION

After the failure of the early warning of the railway

incident, the event scene will enter the evolutionary

stage. Aim at the dynamic characteristics of the event,

decision makers must make real time decisions on the

basis of the process of scene evolution. The current

state of the situation changes with uncertainty, so it

will inevitably make the scenario deduction and the

evolutionary path uncertain. The situation of railway

emergencies is a process of dynamic evolution. The

process of evolution is shown in Fig. 3.1, which may

change almost at any time. The key to influencing the

decision-making subject's important decision is the

state of the railway emergencies’ scenario factors.

According to the whole emergency management

process of railway emergencies(Rong, et al,2012),

this paper divides the situation of railway

emergencies into the initial scenario, intermediate

scenario and ending scenario. The initial scenario is a

description of the state of the scenario factors when

the railway incident occurs. For a specific railway

emergency, the initial situation is determined. The

middle situation is caused by the influence of the

initial situation through various scene elements or the

interference from the outside world. The impact of

different factors will lead to the evolution of the

scenario path. The ending scenario is the state of the

system after the emergency response of the railway

emergency. The evolution of railway emergency

scenarios may be affected by different factors, which

may lead to multiple evolution paths. Therefore, there

may be many situations when the scene ends.

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iii

xxx

22221

112

22221

11211

MOMM

⎟

⎠

⎞

⎜

⎝

⎛

⎟

⎠

⎞

⎜

⎝

⎛

Figure 3.1: Process of scenario evolution.

4 EVENT-DRIVEN SCENARIO

DEDUCTION MODEL

4.1 Elements of event-driven model

The emergency response was immediately activated

after the incident occurred. Emergency response

events belong to the driving elements of the scenario,

which is the emergency task that the emergency

subject needs to implement in order to meet the

emergency target, or the action of the emergency

object, which will inevitably lead to the change of the

state of the emergency situation. Therefore, we can

indicate an emergency response event by a group of

seven elements:

{

}

Event = ID, Name,Subject, Object, Action, De

cription, Previous

Among them,

is the only sign of the event;

ame

is the name of the event;

Subject

and

Object

represent the subject and the object of the event

respectively;

ction

is the action of the subject for

the object;

escription

is the description of the

event;

revious

is the set of predecessor nodes for

the execution of the event.

Scenario inference is a discrete visualization

process of the emergency entity state and behavior in

the specified space and time, which is the main line

of the implementation process of the emergency task.

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126

The emergency scenario inference process includes a

series of scenarios. The scenario inference uses an

event driven model. An event can be considered a

function that maps time to a Boolean value, that is:

{

}

:, Event t True False→

other

at t happensevent if

)(

⎩

⎨

⎧

Flase

True

4.2 Construction of the scenario model

In the scenario model of the railway emergencies, the

change of the state of the event scenario has a great

influence on the evolution of the situation, and there

is a strong causality between them. Therefore, the

deduction process can be expressed through the

Bayesian network. According to the analysis of the

evolution of railway emergencies, the sub-elements

of scenario

are used as network node variables to

construct Bayesian network for scenario deduction.

The node of Bayesian network in

is divided into

three categories according to its effect on the process

of inference, that is, the scenario input variable, the

state analysis variable and the scenario output

variable.

(1) Scenario input variables: Describes the input

elements for the situation analysis, which are the

determined variables in scenario

. Scenario input

variables mainly include the variety of factors that

cause the situation or the collection of various

measures in the process of situation response

{

}

Pp im=≤≤

, the set of state variables of the

event scenario itself

{

}

IS is k l=≤≤

, and the set of

response variables that control the events to change

{

}

CR JR=

,among them

{

}

CR cr j n=≤≤

{

}

Rjr ef=≤≤

(2) State analysis variables: Describes the state of

the elements require scenario analysis. The

development trend of the situation element state can

be obtained through the analysis of the state of the

event itself and the state of the disaster situation. In

this paper, the state analysis variables are divided into

two parts: the state variable set

{

}

IS is k l=≤≤

and

the event state of the disaster

{

}

ES es q t=≤≤

(3) Scenario output variables: Describe the next

possible occurrence of Scenario

, including the

affected external environment state and the loss state

of the event scenario. Let

{

}

OS os w h=≤≤

be the

set of affected external environment state variables,

and

{

}

FS fs z x=≤≤

be the loss variable set caused

by event scenario, among them

{

}

SOSFS

The key to determining the impact of the event's

scenario input variables, state analysis variables, and

scenario output variables is how to combine specific

railway emergencies to collect event information and

domain expert knowledge. Based on the analysis of

the evolution of railway emergencies, the three-tier

Bayesian network structure of "scenario input layer -

state analysis layer - scenario output layer" is further

constructed according to the causal relationship

between the nodes, as shown in Figure 4.1.

Figure 4.1: Scenario deduction model.

5 CASE ANALYSIS

Taking the derailment accident of a train as an

example to make empirical analysis .When the freight

train reached about 200m before the exit of a tunnel,

the vehicle pulled behind the locomotive suddenly

derailed and caused the leakage of the liquefied

petroleum gas tanker. So train drivers take braking

measures. The locomotive and 1-37 vehicles parked

in the tunnel and 38-55 vehicles parked out of the

tunnel. The leakage of liquefied petroleum gas spread

rapidly and burn, that caused casualties in the tunnel,

and the line interrupted.

(1) Scenario profile

The event is simplified as follows: the freight

trains in the tunnel are derailed for some reasons and

cause fire and explosion

{

}

SPISSIS=

. The drive

event is

PIS

and the current state of the event is

SIS

At present, the emergency management department

should take a variety of emergency plans and

different emergency plans lead to different evolution

paths of event scenario. Thus, the Bayesian network

structure of the event scenario evolved is shown in

Figure 5.1. In view of the current situation,

An Event-Driven Scheme of Scenario Deduction for Railway Emergency

127

emergency management departments may take a

variety of contingency measures, and different

emergency measures will lead to the evolution of

event scenarios in different directions.

Figure 5.1: Instance of scenario deduction.

(2) Node probability assignment

First, we need to determine the probability of each

situation and the probability of not happening. In this

case, the expert specifies the conditional probability

of the node according to the knowledge experience

and the changing process of the event scenario, and

constructs the conditional probability table of the

event scenario. The conditional probability of each

scenario is shown in Table5.1.

(3) Scenario deduction

Using Bayesian reasoning algorithm, we start

from the top scenario node in Figure 5.1, and

gradually calculate the state probability of the follow-

up critical scenario to track the change process. The

state probability for the event

1es

is calculated as

follows:

() ( )( ) ( )( )

11/ 1/P es P es S True P IS True P es IS false P IS false== =+ = =

Similarly, the probability of the state of the other

critical scenarios is calculated from the conditional

probability table, and the results are shown in Table

5.1.In the process of reasoning, we simplify the

process of case scenario evolution, and the path of

scenario evolution only takes into account two

cases(Shu, 2012).Considering the actual situation of

the evolution of the event scenario, the above

reasoning results are basically consistent with the

actual situation, which proves that the event driven

railway scene scenario deduction method is effective

and feasible. However, there are a variety of

responses to the same scenario, so there are many

ways of the evolution of the situation. Therefore, in

practical applications, we must conduct a

comprehensive and m-path reasoning analysis for

specific events, and make the evolution of event

scenarios more realistic and systematic.

Table 5.1: Probability of critical scenario state of events.

True

0.1*1+0*0=0.1

False

0.9*1+1*0=0.9

1es

True

0.9*1+0*0=0.9

True

0.3*0.9+0*0.1=0.27

False

0.1*1+1*0=0.1

False

0.7*0.9+1*0.1=0.73

2es

True

0.95*0.9+0.2*0.1=0.875

True

0.5*0.875+0*0.125=0.4375

False

0.05*0.9+0.8*0.1=0.125 False 0.5*0.875+1*0.125=0.5625

3es

True

0.8*0.875+0.1*0.125=0.7125

True

0.9*0.7125+0.3*0.2875=0.7275

False

0.2*0.875+0.9*0.125=0.2875

False

0.1*0.7125+0.7*0.2875=0.2725

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6 COMPARISON OF RELEVANT

STUDIES

At present, the methods applied to scenario deduction

are mainly based on contingency plans and case-

based reasoning. These two methods can help to

realize the scientific decision of emergency response

of railway emergency, and can assist decision makers

to make emergency decisions to a certain

extent(Cheng, et al, 2012). They can use the existing

knowledge to form a more scientific and auxiliary

decision-making knowledge and improve the

scientific nature of emergency decision-making.

However, due to the differences and application

characteristics between the different methods, there

still have some limitations in practical applications,

as shown in Table 6.1. Among them, ☆ is preferred,

□ is medium, and △ is poor.

Through the comparison of the three kinds of

deduction models, studying the applicability of them

in the emergency response of railway emergencies,

and theoretically analyzing the differences between

them, we can conclude that: The event-driven

deduction model is superior to the other two methods

in terms of scope, flexibility and the level that need

expert participation. It has great development

potential.

7 CONCLUSIONS

Starting from the time law of scenario evolution of

emergency events, the emergency scenarios were

divided into initial scenario, middle scenario and final

scenario, among which the mutual relationships were

analyzed. The paper explicates how the event-driven

model can play a role in the scenario deduction for

railway emergency, and makes assumptions about the

process of scenario deduction of events. Then, an

event-driven model of scenario deduction for

railway emergency was established. In addition,

taking train derailment accidentals for example, the

Bayesian network of scenario was set up and

analyzed to further deduce the states of node

variables in scenario network. The deduction results

accord with the practical situation. The proposed

scheme has been proved to be feasible and effective

for railway emergency.

Table 6.1: Comparison of three models.

Project

Proposition - based

deduction model

Derivation Model

Based on Case - based

Reasoning

Event - driven

deduction model

Applicable scope

△

□ ☆

Flexibility

△

□ ☆

Processing speed

△

□ □

Targeted ☆ ☆ □

Field expert Participation

△ △

□

ACKNOWLEDGEMENTS

This research was supported by the National Natural

Science Foundation of China(Grant No.61763025),

China Postdoctoral Science Foundation funded

project (Grant No.167306) and the Post doctoral

Special Foundation of Lanzhou Jiaotong

University(Grant No.2017003).

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