case-based reasoning and rule-based reasoning for railway incidents prevention

7/21/2019 Case-Based Reasoning and Rule-Based Reasoning for Railway Incidents Prevention

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Case-BasedReasoning and Rule-Based Reasoning for Railway

Incidents

Prevention

Yanping

Cui,

ZhenminTang2,

Haibing

Dai

School

of Traff ic and

Transport,

Beijing

Jiaotong Ud vers ity, Beijing 100044, hina

School of ElectricalEngineering, Beijing Jiaoton g Universi ty,

Beijing

100044,China

Schoolof Computer Science, Inner Mongolia University, Inner Mongolia 010200,China

[email protected], [email protected], [email protected]

2

3

Absfract

This paper presents a knowledge-basedapproach

for preventing railway

operation

incidents

which

helps

ra i lway

operation professionals explore

opportunities

for

dispatch

quality improvement The approachconsists of two processors:

incident analysis and dispatch advisory. Given

a specific

operation

incident description the

proposed

approach

enables

the identification of

underlying

problems in the incident

and

the

suggestion

of

appropriatedispatch strategies that can

be

implemented to

prevent

repetitions

of

similar

incidents

Case Based

Reasoning (CBR)

and

Rule-Based Reasoning

(RBR) are applied

to

generate dispatch strategies

by

adapting

ones applied to previous incidents and/or

using

generalised

rule-based knowledgefor dispatch.

Keywords:

case-based reasoning rule-based reasoning;

railway

incidents preveation

I.

INTRODUCTION

For the

railway service system, safety is the most

important factor

of

all. With the development of science and

technology,

all

kinds of advanced equipments have been

used to promote safety conditions. This has brought two

changes: on the one hand, the accidents caused by

equipments has decreased greatly; on the other hand,

dispatch difficulty caused by

the the

technique progress and

train density has increased. Subsequently,

many

goods loss

and

train

delay are caused by human error.

The effective mhag eme nt of adverse incidents is a major

issue. Many lessons can be learnt from investigating adverse

incidents and taking actions to avoid a repetition. Given a

particuIar incident, it is important

to

find appropriate

strategies that can be implemented for decreasing the

occurrence

of

similar incidents. To

do

this, incidents need to

be

analysed. It is required to understand what incidents result

from and identify factors that contribute to such incidents.

Th e pathways and contributing factors can then

be

viewed as

suggested strategies

to

be

implemented to prevent repetitions

of incidents, for example, recommendations for regulation

or

deregulation

of

dispatcher where appropriate; strategies

to

improve commu nications between operation professionals.

This paper introduces

a

knowledge-based approach

for

preventing railway operation incidents.

A

variety of

knowledge is applied in the approach.

The

ontological

knowledge

is

used for incident analysis. Specific incident

cases and rule-based dispatch knowledge are applied for

dispatch ad vi so q based on Case-Based Reasoning (CBR)

and Rule-Based Reasoning (RBR).

n

intelligent system has

been developed based on the approach, which assists

operation professionals in analysing incidents and

determining relevant measures

to

prevent the occurrence of

such incidents. The system allows dispatchers and other

operation professionals to complete structured incident

reports about any event, which has caused loss and

can

cause

potential loss. When a new incident is reported, a set of

remedy strategies are recommended, which can be

implemented to reduce the chances of similar problems

occurring. In

our

context, operation incident

c a n

be broadly

defined as an unintended event,

no matter

how seemingly

trivial or comm onplace, which could have caused loss during

the train moving. This criterion includes near m i s s where

the loss may have been averted, but the potentid for loss

exists.

11. DESIGN

OF

PREVENTION

STRATEGIES

Designing prevention strategies is concerned with

analysis of the given incident and given an incident

description reported by operation professionals, the

proposed design

approach

identifies

sources

of

underlying

problems and suggests measures for solving these problems.

These measures can be viewed

as

strategies that can be

implemented with

high

potential for decreasing the

occurrence of similar incidents. Furthermore, Rule-Based

Reasoning (RBR), and ontological knowledge base are

applied in the design of prevention strategies.Figure 1 gives

major components of the proposed approach. Two

processors, incident analysis and prevention measures are

core components. The arrows with a single line represent

a

process

low

while mows witb double lines represent

datalinformation flow.

. ..

An incident

deaulpt ion

Firr.1

the components of

systems

h e design approach takes

an

inciaent description as inpur.

First, The processor of incident analysis examines it and

identifies sources of underlying problems involved

by

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applying ontolo gical know ledge about operation incidents.

An analysis tree is derived, which contains details of what

happened and how they happ ened in the given incident. The

processor of prevention advisory then recommends

appropriate prevention strateg ies based on information in the

analysis

tree.

In doing this, specific incident cases and

generalised rule-based know ledge about dispatch are used

based on case-based reasoning and rule-based reasoning.

Three types of knowledge are applied in the proposed

approach. The ontological knowledge is used for incident

analysis.

It

provides guidelines for analysing incidents in

order to identify types, features and possible causes of

underlying problems. The incident case base contains

a

number

of

specific operation incidents, which provides

contextual information about problems

and

situations in

incidents. Each incident cas e consists of an incident profile,

analysis information about underlying problem s and relevant

suggestions that can be used to avoid a repetition. The

rule-based knowledge for prevention is used to generate

resolvents if n o matched incid ent case is available.

Some effort

has

been made in

risk

incident monitoring and

disposing in railway domain. Our work differs from the

above studies

in

that it provides detailed contextual

information abou t specific processes and situations that can

lead to adverse outcomes. Such information is then used to

make specific recommendations for improvement. The

incidents

are

managed in terms of identifying sources

of

problems and offering suggestions.

The next two sections respectively describes two

processors; incident anaIysis and prevention advisory

in

terms of relevant process and knowledge components.

HI. INCIDENT ANALYSIS

When

an

incident is reported, an incident profile is

completed. The incident profile includes the incident's

information, incident outcom es and a general description of

the incident. The processor of incident analysis investigates

the information of the given incident profile to identify types

of problems involved in the incident, relevant features and

possible causes. This is done based upon ontological

knowledge using structured fixed - choice responses. The

processor provides a set of choices in levels of Problem Type

(FT),

Problem Feature (PF) and Possible Causes (PC)

according to the ontological knowledge for incidents. The

user responses by selecting one or more from the choices.

As a result, an analysis tree is derived for the given incident.

It captures analysis information in a hierarchy. Th e analysis

tree represents types, features and possible causes

of

underlying problems in the given incident.

The processor of incident analysis applies ontological

knowledge about incidents to identify problem types,

features and possible causes. Ontological knowledge

represents domain knowledge for incident analysis. It

provides guidelines for identifying F'roblem Types

(F Ts),

Problem Features PFs) and Possible Causes

(PCs)

in the

incident. This know ledge is formulated in a hierarchical and

categorical ontology based

on

relationships among PTs,

PFs

and PCs. A PT s described by

a

number of

PFs.

each of that

is further related to

a

set of PCs.

The Figure 2 gives some of ontological knowledge for

dispatch incidents. The problem-type layer show s the type

of problem, eg com mand problem, judgemen t problem, etc.

Th e problem feature layer represents the categories of

problem featufes found

for

a particular problem type. For

example, for command problem, the problem features

include incorrect comm and etc. The problem-cause layer

shows the possible causes

found

from stu dies that are linked

to each prob lem feature.For example, the possible causes for

incorrect command'' include wrong receiver, receiver 's

misunderstanding etc.

.....

udgement

ommand

\\

\\

l r r e c t

......

missed delayed

\

1

command \

Limitad

\ \

Wrong __ _ Recwiver ' s eqer ienca

' ' .'

reCBiVel misunderstanding

Fig.2 T h e

hierarchy

ofE

F G d

Pc

n

the generalised%alysi

rules.

Th e processor of incident analysis contains three actions:

(1)

Identifying types of underlying problems;

(2) for each problem, identifying relevant features related

(3) for each featur e, identifying possible causes.

Th e output of the incident analysis is an analysis tree for

the given incident. The analysis

tree

contains information

about what problems are involved and why they happen.

Moreover, each branch in the tree provides an underlying

problem and its features and possible causes, which can be

used for the processor of prevention advisory to suggest

appropriate prevention strategies.

to the problem;

IV. PREVENTION

ADVISORY

The processor of prevention advisory applies CBR and

rules-based reasoning to generate preventions based on

information represented on the analysis tree.

First,

it

searches through the incident case base. In the generation

of

preventions, CBR and RBR are applied in complementary

manner. It

allows

specific incident cases to be used directly

when relevant and the generalised advisory process, incident

cases rules

to

be used when no matched case is available. So,

two types of knowledge are applied in the prevention

know ledge for preventions.

A. Representation o incident

cases

A

case in the system represents information related to a

specific operation incident. An operation incident can be

defined as an unexpected event, which has caused

loss

or can

have potential bad effect

to train

operation. n operation

incident involves one

or

more underlying problems (eg.,

human errors and preventable system problem s), each of that

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implies sources of potential loss. An underlying problem can

be represented by a number of features that can be further

related

to

some possible causes.

In the

system,

an

incident

case consists of

a

number of subcases. Each subcase

represents information to

an

underlying problem and

relevant preventions to it.

An incident case in the system contains

an

ncident profile;

analysis information

of

underlying problems,

which describes what happened and why they

happened; and

measures which can be implemented to solve

each problem in the incident.

Incident Profile

Incidents time:

Incidents

Place:

Incidents Category:

Incident description:

Preventions:

1)

2)

Analysis information of

underlvinp;problems

Problem Type:

Problem Features: . . .

Possible Causes:

Figure

3

shows

the

representation of an incident case,

which consists of subcase 1.1, subcase 1.2 etc.

The

ncident

profile provides

(1)

the incidents information (eg., time,

place, reiatvie profession als, etc.) (2) outcomes of incident

in terms of imm ediate consequen ce and potential

loss,

and

(3) a contextual description of the incident. Each subcase

provides analysis information and appropriate preventions

for an underlying problem. The analysis information of

underlying problems

is

represented by a number

of

parameters: type

of

the problems; features

of

problems;

and possible causes to the problems. This information

describes details

of

what happened and why they happened.

Associated with each underlying problem, a

set of

relevant

preventions is represented

in

the subcase. For

example,

a

judgement problem in the subcase 1.2 is featured by

missed or delayed etc. It occurred due to possible causes

such as the operators limited experience and doze on duty

etc.

B. Rule bused knowledge f o r preventions

The rule-based knowledge about preventions is used to

generate relevant preventions when no relevant case is

available and CBR is not appropriate. Given aFT PF and a

PC particular typ e

of

prevention can be derived. The form

of prevention rules

is

IF PT(i)

and PFQ)

and

PC

(k)

THEN

a set of relevant

preventions.

If a matched subcase can be found based on problem type,

features and possible causes, the preventions in this subcase

are

adapted as preventions to

the

problem.

In

the current

system, the adaptation process is simply taking previous

preventions as ones for the new problem. This is because we

only consider exact matching between subcases and the new

problem description.

If

no relevant subcase is available, t he

generalised prevention rules are applied to generate

preventions using RBR.

The

algorithm f or prevention advisory includes

For each branch in the analysis tree

Retrieving subcases using information

of

the branch;

If a subcase is

available

Then adapting its preventions;

Else applying generalised rules to generate preventions;

Com bining preventions generated for each problem.

Once a set of prevention strategies is generated for the

given incident profile, the process updates the incident case

base by adding a

new

incident case that consists of

the

input

incident profile, information

from the

analysis tree and

suggested prevention strategies.

V.

CONCLUSIONS

A knowledge-based approach for incident prevention

strategies

was

described. The approach consists

of

two major

processors: incident analysis and p revention advisory . Given

a specific incident description, the proposed approach

investigates

it

by identifying what underlying. problems

happened and why they happened in the incident based on

ontological knowledge on incidents. Such analysis

information is then used

as

a basis

for

designing appropriate

preventions. The prevention advisory process is

based

on

adapting ones applied to previous incidents and/or using

generalised rule-based knowledge.

This paper has presented

an

approach for incident

prevention and so me relevant

issues

for application of CBR

and

RBR.

Our further considerations include an automatic

process of incident an alysis, retrieval of relevant incident

cases according

to

a flexible attribute order, and linking

prevention measures w ith relevant on-line sources. First, the

current incident

analysis

is conducted based on interaction

between the system

and

users through fix-choice responses.

This can be improved by an automatic analysis process.

When

an

incident is reported, the con textual description of

the incident can be captured in a well-structured form at. Th e

proposed approach can then analyses the contents

of

the

incident description to identify types, features an d possible

causes o underlying problems. Second, in the retrieval of

incident cases, a fixed attribute order is currently used to

specify the importance order for attributes used in the query.

To

provide previous incidents that are more relevant to

users query, it is important to allow users to specify the

importance order for attributes used in the query. Third, the

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suggested preventions in the current

approach

only provide

strategies that can be im plemented to avoid the occurrence of

similar incidents. It will be useful to

provide some

references

and guidelines

associated

with the suggested preventions.

These reference and guidelines can be directly from on-line

sources. Additionally, i n order for users to have a better

understanding of the prevention measures, it i s essential

to

offer

some

scenarioslexamples when preventions

are

recommended.

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case-based reasoning and rule-based reasoning for railway incidents prevention

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