Andreassen and Artificial Neural Network Models Development for Fatality Prediction with Accessibility Aspect on Regency Area Cluster in West Java Province, Indonesia

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International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 10, October 2013)

32 Andreassen and Artificial Neural Network Models

Development for Fatality Prediction with Accessibility Aspect

on Regency Area Cluster in West Java Province, Indonesia

Supratman Agus

1

, Bambang Riyanto

2

, Pinardi Koestalam

3

1_{Civil Engineering Departement, Indonesia University of Education, Indonesia.} 2_{Civil Engineering Departement, Diponegoro University, Indonesia.} 3_{Engineering Departement, Bhayangkara Surabaya University, Indonesia.}

Abstract— In order to predict fatality victim in most

countries including Indonesia, two variables are used and these cover population and motor vehicles numbers. Those variables are not actually fit with Indonesian condition because of its population density, longest road infrastructures, highest number of motor vehicles, and vastest areas compared to those ASEAN countries. Republic of Indonesia Traffic Ordinance number 22 Year 2009 stated that fatality data must be supplied with hospitals’ data. However, the data reported by Republic of Indonesia Police has not been in accordance to the law indicating under-reporting. Therefore, the main purpose of the study was to design a multivariable fatality prediction model by developing Andreassen and Artificial Neural Network models in order to gain accurate fatality data fit with Indonesian condition, especially in the Province of West Java. Prediction model was designed by using population data year 2007-2010 from ten areas of regency in the province of West Java, Indonesia. Model validation test result with error model test Mean Absolute Percent Errors criteria (MAPE), Mean Absolute Errors (MAE), and Root Mean Square Errors (RMSE) revealed three main results: (1) Three variables Artificial Neural Network with two hidden layers (ANN3-2HL) prediction model was the best model used to predict actual fatality numbers in ten regencies in West Java Province, Indonesia. (2) Actual fatality number occurred in ten regencies in West Java Province, Indonesia, was 1607 people in year 2010. This number was 49.91 % higher than fatality data that was reported by Republic of Indonesia Police, that is, 1072 people. (3) Andreassen Prediction Model (1985) was not fit to be used in Indonesia because of its population density, longest road infrastructure and vastest areas. It was recommended that ANN3-2HL prediction model can be considered as the newest model used in road safety study in Indonesia.

Keywords-- Fatality prediction model, multivariable, model Andreassen, Artificial Neural Network (ANN), Indonesia

I. INTRODUCTION

Accurate number on fatality traffic accident victims has not been fully identified in Indonesia, especially in West Java Province and in fact fatality data reported by Republic of Indonesia (RI) police derived from that of accident scenes but has not yet provided with any data from victims died in hospital (hospital’s data). This fact indicates under-reporting data, implying that many traffic accident victims have not been in record and this is not in line with RI Traffic Ordinance number 22 Year 2009 stating that fatality data must also be supplied with hospital’s data. Road safety study requires accurate number on fatality data for different purposes and sides including researchers, police, road planner, educators, statisticians, communication experts, lawyers and related institutional organization. Inaccurate data reflects road safety unreliability.

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These multi-variables include accessibility aspect and numbers of motor vehicles and population. The study is expected to find out the newest fatality prediction model in line with Indonesian condition as well as to be widely applied in areas that have the same characteristics as those in Indonesia.

A.Traffic Accident Fatality Record in Indonesia

Police of Republic of Indonesia is entrusted by RI Traffic Ordinance number 2 Year 2009 section 233 stating that every traffic accident must be recorded in traffic accident form that constitutes a forensic data.

Traffic accident data is managed by RI Police and must be supplied with data from hospitals.

The intended data refers to data of traffic accident fatality, serious injury and slight injury victims.

International Road Traffic and Accident Database (IRTAD, 1998) defines fatality as traffic accident victims fell dead or died within 30 days since the accident occurred. Therefore, section 233 of RI Traffic Ordinance number 22 implies that RI police and hospitals staffs are accountable for recording highly accurate traffic accident data and assisting those interested parties.

TABLE I

TRAFFIC ACCIDENT VICTIMS DATA IN INDONESIA IN 2007-2010

Year Numbers of

Accident

Victims Number

Number of motor vehicles (million unit)

Number of Population (million)

Fatality Serious injury

Slight

injury Total

2006 87.020 15.726 33.282 52.310 101.318 50,4 222,2

2007 48.508 16.458 20.180 45.860 82.498 57,8 225,6

2008 59.164 20.188 23.440 55.722 99.350 65,3 228,5

2009 59.991 18.471 22.789 59.960 101.220 71,5 231,4

2010 63.787 18.371 24.225 60.310 102.906 78,95 234,2

Total 314.024 129.389 174.932 346.257 647.578 Percentage (%) 19,980 27,013 53,469 100

Source: RI Police, Statistics Department and RI Communications Ministry (2010)

The results of study and analysis of transportation experts in Indonesia and international institutions showed that Indonesia faces serious problem with recording numbers of traffic accident victims bearing the assumption of unrecorded and unreported victims. RI Communications Department (2004) stated that RI Police records victims’ number died in the scene, however, RI Health Ministry and hospitals do not report fatality number within 30 days after the accident to the RI Police. On the other hand, RI Jasa Raharja Insurance (AJR) company only records cases based on claims filed by victim’s family. Recording process grouped by each institution results in different data information filed for the same accident.

Relevant to this, the Law defines institutions that are directly responsible for Indonesia road safety namely: (1) Public Works Ministry; (2) RI Police; (3) Health Ministry; (4) Communications Ministry; (5) National Education Ministry; and (6) Jasa Raharja Insurance Company (AJR). In 2007, Indonesian Transportation Society reported that appointed institutions in the country to record traffic accident victims are not well cooperated. Each institution works in solitude and ignores partnership, results in poor road safety management system and high rate of traffic accident.

Traffic accident victims data reported by RI police is unreliable viewed from its low number of accident victims and therefore is not factual.

In countries where road safety is prioritized, information collection of traffic accident as data base is significant and highly reliable. In Indonesia, traffic accident victims in year 2007-2010 can be seen from the Table I.

II.CONTEXT AND REVIEW OF LITERATURE

A.Road Fatality Data in ASEAN countries

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TABLE II

DATA FATALITY IN ASIA

Country Population Number of

Vehicle

Fatality Reported China 1.336.317.116 145.228.994 96.661

India 1.169.015.509 72.718.00 105.725

Indonesia 231.626.978 63.318.522 16.548

Japan 127.966.709 91.378.636 6.639

Cambodia 14.443.679 154.389 1.668

Malaysia 26.571.879 16.825.150 6.282

Singapore 4.436.281 851.336 214

Thailand Vietnam 63.883.662 87.375.196 25.618.447 22.926.238 12.492 12.800

Source : WHO (2009)

B.Role of Fatality Data in Road Fatality Study

In road safety study, traffic accident fatality data is primary data. Fatality data accuracy is needed to obtain relevant study results with factual data in the field. The study result is useful for the settlement of strategic policy, among others are law enforcement, road safety management system to reduce traffic accident risks, black spot maintenance, road safety action planning program and program evaluation setting before the implementation, ongoing and in the future.

According to the study conducted by RI Land Transportation Safety Directorate (2008), inaccurate data on traffic accidents victims both qualitatively and quantitatively cannot be regarded as sound source when analysing and planning general policy for road safety maintenance system in the country. If the road safety is conducted using data input resulted in low accuracy, the output of the study will not show factual condition and it will not attain target to maintain an expected condition, that is, road safety maintenance system preventing the increase on road fatality victims.

C.Andreassen Prediction Model

Andreassen model (1985) is a model derived from Smeed (1949, 1955) model that reflects some weak points including non universal and particular use only (Broughton, 1988; Oppe, 1991; and Ameen & Naji, 2001). These Smeed shortcomings are seen from equation parameter of α and β that keeps changing on different location and time. From Smeed equation, Andreassen model adjusts intercept and gradient parameters. Anderassen model requires Constanta C, coefficient M1 and M2 by searching the value of α, βand γ usingdouble linier regression analysis. Thus, general form of Andreassen equation is as follows.

1 2

M M

F C V P (1)

FeVP (2)

In which:

F = fatality prediction number C = Constanta

V = Number of Motor vehicles P = Number of population

M1 = coefficient degree of number of motor vehicles M2 = coefficient degree of number of population

D. Artificial Neural Network Model

ANN model has been oftentimes implemented in many field of science to predict (William dan L.Yan, 2008). It is a model instrument of non-linier statistical data that can be used to model a complex relationship between input and output to seek patterns. There are three types of ANN model namely Multi Layer Perceptron (MLP), Radial Basis Function (RBF), and Kohoren Network (KN). In terms of prediction problems, MLP model is the most used model to map out a set of input data to become a set of output data by applying non-liner activation function. In MLP, both independent and dependent variables have three levels of metric and non-metric measures. MLP can also be called as forward network or back-propagation because the information moves in one direction that is from input layer towards hidden layer and then towards output layer (see Figure 1).

Figure 1 Multi Layer Perception (MLP) ANN prediction Model

Activation function in hidden layer is:

 Hyperbolic tangent : _{( )} _{tanh( )}

c c

e e

Y c c

e e



 

 (3)

 Sigmoid :

( )

1

c

Y c

e







(4)

Activation function in output layer is:

 Identity :

Y c

( )



c

(5)

 Softmax :

₍

₎

k

j c k _c j

e

Y c

e



[image:3.612.391.495.454.539.2]

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 Hyperbolic tangent : _{( )} _{tanh( )}

c c

e e

Y c c

e e



 

 (7)

 Sigmoid : _{( )} 1

1 c

Y c

e 

 (8)

E. Model Validation Test

Model validation test is conducted in order to find the best model and several developed prediction models. This study uses three types of error model test criteria, namely Mean Absolute Percent Errors (MAPE), Mean Absolute Errors (MAE), and Root Mean Square Errors (RMSE) with the following formulas:

1

100

j j

j

o t MAPE

n o

  

 

 _  _

 



(9)

1

j j

MAE t o

n





 (10)

2 1

j j

RMSE t o

n





 (11)

The best prediction model is the one having the smallest difference from actual fatality data. This is taken by fatality number data by RI police is added with survey results on victims data of traffic accident died in the hospital.

III. METHODOLOGY

A. Study Area Location

The location of the study was on the cluster of regency administrative areas in West Java Province, Indonesia, consisting of ten regencies, namely: (1) Bandung and West

Bandung Regencies; (2) Indramayu Regency; (3) Cianjur Regency; (4) Ciamis Regency; (5) Majalengka Regency; (6) Subang and Purwakarta Regency; (7) Sumedang

[image:4.612.86.230.292.377.2]

Regency; (8) Garut Regency; (9) Kuningan Regency; and (10) Karawang Regency. The width of study area location is 59. 43% of 38.783,13 km2 of West Java Province and 42.88% of the total of population number which is 43.806.653 in year 2010. Figure 2 shows the study area location in Java Island, Indonesia.

Figure 2 Study Area Location in Java Island, Indonesia TABLE III

RESEARCH VARIABLE AND INPUT DATA

Research Variable Total input data of all study areas (per-year) Input variable

2007 2008 2009 2010 Andreassen ANN

Population (Million) 19,964 20,319 20,623 20,859 √ √

Vehicle (million/unit) 2,246 2,387 2,732 3,217 √ √

Accessibility (road length

ratio to that of area width) 0,558 0,559 0,560 0,565 × √

Factual fatality *) 1781 916 855 1072 √ √

*) RI Police Data + Survey results from Hospitals

B. Research Variables and Input Data

The analysis of variables relation on fatality numbers concluded that independent input variables on each model were used in this study (see Table III). In Andreassen model, two variables were used including population and motor vehicles numbers. Whereas, in ANN model, four variables were used covering population and motor vehicles numbers as well as accessibility (road length ratio to that of area width).

C. Population and Sample of Study Area

[image:4.612.327.558.314.462.2]

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Table IV demonstrates samples of class A and B hospitals classification on each study area, while Table V illustrates criteria specifications of the hospitals.

D. Survey Technique of Fatality Data in the Hospitals

Traffic accident victims’ data died in the hospital was done by using document analysis of medical record each patient has. This was referred to RI laws Number 14 Year 1993 regarding Road Traffic and International Road Traffic and Accident Database (IRTAD 1998) stating that the length of nursing care is 30 days maximally after the accident occurred. Figure 3 illustrates stages of patient medical record document analysis in the hospital.

TABLE IV

CITIES AND HOSPITALS POPULATIONS/SAMPLE

Regencies area Rumah Sakit Sampel

West Bandung 1. West Bandung Regional Hospital Indramayu 2. Indramayu General Hospital Cianjur 3. Cianjur General Hospital Ciamis/ Banjar city 4. Ciamis General Hospital Majalengka 5. Majalengka General Hospital Subang- Purwakarta 6. Subang General Hospital Sumedang 7. Sumedang General Hospital

Garut 8. Garut General Hospital

Kuningan 9. Kuningan General Hospital Karawang 10.Karawang General Hospital

TABLE V HOSPITAL CLASSIFICATION

Source: RI Laws Number 44 Year 2009 Regarding hospital

Figure 3 Stages of Fatality Data Survey in the Hospital

E. Prediction Model Development Strategy

Model development was conducted by using two approaches including the application of Andreassen (1985) and Artificial Neural Network (ANN) from Haykin (1994) fatality prediction models in which each model is used by adjustment to areas characteristics and road transportation infrastructure condition in West Java Province, Indonesia. In developing Andreassen model prediction, two stages were conducted by firstly using equation form of Andreassen (1985) model using two variables, that is, population and motor vehicles numbers and secondly by using multi-variables Andreassen model, by basing it on Andreassen general equation model multiplied by accessibility factor as a correlation model to fatality numbers so that fatality prediction was equal to

( .

[image:5.612.327.567.141.272.2]

On the other hand, ANN model was developed by using one and two hidden layer shown in Figure 4 below.

Figure 4 ANN Model with One and Two Layers No Medical Service

facilities type

General Hospital Classification based on Medical Services Facilities Ability Class

A

Class B

Class C

Class D 1. Emergency

services √ √ √ √

2. General services √ √ √ √

3. Basic specialists

services min 4 min 4 min 4 min 2 4. Medical

supporting specialists

5 4 4 ---

5. Sub-specialists

services 12 8 --- ---

6. Other types of

[image:5.612.47.292.307.622.2] [image:5.612.327.563.522.642.2]

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In order to get the best prediction model, calibration/ validation test was conducted. Each prediction model was mathematically tested by using the criteria of Mean Absolute Percent Errors (MAPE), Mean Absolute Errors

(MAE), and Root Mean Square Errors (RMSE). The best prediction model is the one that has the smallest error difference.

Fatality Prediction Model Development Procedure

Fatality Prediction Model Development Procedure was designed based on different types of input data from all variables. Afterwards, model development was conducted by using general Equation from Andreassen (1985) and Artificial Neural Network (ANN) model developments from Multy Layer Perception (MLP) form. Fatality prediction model development procedure is shown in Figure 5.

[image:6.612.78.507.240.708.2]

Figure 5 Fatality Prediction Model Development Process

FATALITY PREDICTION MODEL VALIDITY

ANALYSIS AND RECOMMENDATIONS OF FATALITY PREDICTION MODEL

DEVELOPMENT STUDY

ROAD FATALITY PREDICTION MODEL DEVELOPMENT

TWO AND THREE ANN PREDICTION MODEL

Hyperbolic tangent : ( (

Sigmoid : (

1 2 3 4 5 )

(

1 ,

1,...,3

1

n n n n n n

n a x b x c x d x e x k

H

n

e

     





1 1 2 2 3 3 4 4 5 5

( )

1

m H m H m H m H m H K

Y

e

     





2 AND 3 VARIABLE ANDREASEEN MODEL

F = C*(V)M1_{* (P)}M2

In F = ln C + M1 ln V + M2 ln P Y = α + β X1 + γ X2

α, βand γ are taken from double linier regression analysis by SPSS software

α = ln C, thus C = eα

, β = M1, thus M1 = β,

γ = M2, thus M2 = γ

therefore :

F = C* ( V ) M1_{* ( P)}M2

with ( β( γ Standardized, Normalized and Analysis of Research Data

Variable X 2

Factual Fatality Data

INPUT DATA

Number of Vehicle

Pasinger Car

Pick Up

Bus

Truck

Population

Age

Sex

Accessibility (road length ratio to

that of area width)

Nasional road

Province road

City road

Local road

Police of

State Hospital

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IV. RESEARCH RESULTS AND DISCUSSION

A. Fatality Prediction Model Development Result

Table VI and Table VII illustrates the summary of research on Andreassen 1985 and ANN MLP type model developments with one and two hidden layer and multi-variable year 2007-2010 in line with areas characteristics and road transportation infrastructure condition in West Java Province, Indonesia. Andreassen model application (FA) was done based on Andreassen general equation form with two variables input data namely population and motor vehicles numbers. Meanwhile, Multivariable Andreassen model prediction was conducted by input data referring to inter-variables relation analysis. Those inter-variables relation were relation between driver behavior and fatality number by using two step cluster, between accessibility variable relation (road length ratio to area width) and fatality number, between mobility relation (road length ratio to 1000 population number) and fatality number, relation between population and motor vehicles number and fatality number. Relation inter-variable analysis was also conducted to ANN model development.

From error model test validation result using three types of criteria namely Mean Absolute Percent Errors (MAPE), Mean Absolute Errors (MAE), and Root Mean Square Errors (RMSE), it was found that three variables Artificial Neural Network (ANN) prediction model with two hidden layers (ANN3-2HL) was the best model occupying the smallest error value compared to other model predictions that was MAPE = 13.17; MAE=16.94 and RMSE = 34.39. From the best ANN 3-2HL prediction model, it was found out that fatality number prediction occurred in all administrative areas in West Java Province is 1607 in year 2010.

Table VII illustrates fatality data under-reporting comparison from each prediction model development on data fatality from Police of RI. In Table VII it was found that 60.26 % of factual fatality data and 46.91 % of ANN3-2HL model fatality prediction number was not recorded in RI police report.

Figure 6 illustrates Artificial Neural Network 3 variable model equation formula and form with two hidden layers (ANN3-2HL) taken from fatality prediction model development result in the study.

TABLE VI

PREDICTION MODEL VALIDATION TEST RESULT IN REGENCY AREAS IN WEST JAVA PROVINCE

No Regencies Name Factual Fatality

(Police+ Hospital)

Prediction Model Development and Actual Fatality Prediction Number (FA)

Andreassen 1985

(FAM) Andreassen

3 variabel

ANN3- 1HL

ANN3- 2HL

1 Bandung and West Bandung Regencies 332 281 298 245 275

2 Indramayu Regency 276 141 196 197 164

3 Cianjur Regency 180 143 233 181 172

4 Ciamis Regency 151 140 157 178 155

5 Majalengka Regency 52 108 114 137 121

6 Subang and Purwakarta Regencies 192 170 204 203 179

7 Sumedang Regency 129 101 104 116 119

8 Garut Regency 168 148 143 170 160

9 Kuningan Regency 119 97 120 99 127

10 Karawang Regency 119 170 157 170 135

Total 1718 1499 1726 1696 1607

MAPE 17,01 14,73 17,41 13,17

MAE 24,06 18,67 20,89 16,94

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TABLE VII

COMPARISON BETWEEN PREDICTION MODEL FATALITY DATA NUMBER OF REGENCIES IN WEST JAVA PROVINCE AND RIPOLICE REPORT

Fatality Data in 2010 Fatality Number that was not recorded in RI Police

Police Hospital Factual Fatality

(Police+Hospital

(PA) Andreassen

1985

(PAM) Andreassen

3 variabel

ANN3- 1HL

ANN3- 2HL

1072 646 1718 427 654 624 535

Percentage (%) 60,26% 39,83% 61,01% 58,21% 49,91%

Form and Formula of ANN3-2HL Fatality Prediction Model for Regencies Cluster in West Java Province Indonesia

Input Variable:

1.

1. Population Number (P)

2.

2. Motor Vehicles Numbers (V)

3.

3. Accessibility Aspect (A)

Neural Network Artificial Model with 3 variables and 2 Hidden Layers

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International Journal of Emerging Technology and Advanced Engineering

[image:9.612.85.504.98.566.2]

40

Figure 6 ANN3-2HL Fatality Prediction Model

V. DISCUSSION

From the Table VI and Table VII, it was found out that ANN3-2HL model calibration test result has the smallest MAPE, MAE and RMSE error valued and is the lowest compared to two variables Andreassen Prediction Models (PA) and multivariable Andreassen prediction Model Development (PAM). MAPE value is = 27,39; while MAE is = 47,17 and RMSE is = 64,09, explaining that ANN3-2HL prediction model has the lowest error value compared to other model validation test result.

ANN3-2HL model validation test result showed that actual fatality prediction number in 2010 in ten regency areas in West Java Province is 1607. This number is higher as many as 535 or 49.91 % of fatality data indicated not reported by RI police (under-reporting). This number is higher condition and is in accordance with Asian Development Bank, stating that in Indonesia there are still many under-reporting cases viewed from the report of RI police.

Components that are firstly calculated:

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ANN3-2HL fatality prediction model was developed by using three variables namely motor vehicles nimber, population number and accesibility. In order to see the third variable influence, independent variabel importance table and normalized importance figure were used to illustrate it. Normalized importance figure showed that motor vehicles number variable contributes the biggest influence that is 0.523 or 52.3%. The second biggest variable was attributed to accessibility, that is, 0.28 or 28% and the third variable is population number as many as 0.197 or 19.7%. Therefore, in ANN3-2HL prediction model, the most influential variables sequence is motor vehicles number, accessibility and population number.

Model validation test result phenomena above implies that variables used in this study comprisingmotor vehicles number and accessibility (road length ratio to area width) is precise.

VI. CONCLUSION

Based on fatality prediction model development and discussion above, it can be concluded that:

1. Three variable Artificial Neural Network model prediction with two hidden layers is the best prediction model to predict died victims resulted from traffic accident in the cluster of regency administrative areas in West Java Province Indonesia.

2. General equation formula of ANN3-2HL model for fatality prediction resulted from traffic accident in the cluster of regency administrative areas in West Java Province, Indonesia is illustrated below.

37 295 '

' 1.483 2.855 (2 :1) 2.818 (2 : 2)

F F

F H H

 

   

With:

 0.546 0.992 ' 0.939 ' 0.405 '

1 (1:1)

1 V P A

H

e    

 

0.389 0.986 ' 0.244 ' 0.864 '

1 (1: 2)

1 V P A

H

e   

 

 0.018 0.57 (1:1) 0.618 (1:2)

1 (2 :1)

1 H H

H

e   

 

0.417 0.368 (1:1) 2.042 (1:2)

1 (2 : 2)

1 H H

H

e  

 

95864 1028600 0.1249

' ; ' ; '

786146 3825577 1.4342

V P A

V   P   A  

3. Fatality number in 2010 resulted from traffic accident in the cluster of regency administrative areas in West Java Province, Indonesia is 1606. Fatality prediction number is 49.91 % higher that that fatality data reported by RI police that is 1072.

4. Andreassen (1985) prediction model with two variables is not fit with Indonesian condition because of its population density, highest number of motor vehicles, longest road infrastructure and vastest area in ASEAN countries.

REFERENCES

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[17] Lembanran Negara RI Nomor 153 tahun 2009. Undang-undang

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