asri2011-motorcyclist acceptability on road safety policy motorcycle exclusive lane in-libre

8/11/2019 Asri2011-Motorcyclist Acceptability on Road Safety Policy Motorcycle Exclusive Lane in-libre

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FSTPT International Symposium, Pekanbaru, 11-12 November 2011

MOTORCYCLIST ACCEPTABILITY ON ROAD SAFETY

POLICY: MOTORCYCLE EXCLUSIVE LANE IN

MAKASSAR

Arifin AsriDoctoral Student

Graduate School of Civil Eng.

Civil Eng. Depart. of UNHASJln. Perintis K Km10, Tamalanrea

Kampus Universitas Hasanuddin

Makassar, Sul-Sel, 90145

Telp: (0411) 587 [email protected]

Muhammad Isran RamliHead Lector

Transport. System & Eng. Lab.





Lawalenna SamangProfessor

Graduate School of Civil Eng.





Abstract

This paper attempts to describe motorcyclist acceptability on implementation of motorcycle exclusive lane as

a road safety policy in Makassar, Indonesia. The survey was conducted on three main roads as pilot projectlocation of the policy in the city. In order to grasp the motorcyclist acceptability, the multinomial logit model

was adopted. In this regard, motorcyclist acceptability became response variables, while motorcyclist

perception, socio-demographic condition, and trip characteristics were explanatory variables. The results

show that the model was acceptable according to the log-likelihood ratio indicator. Further, motorcyclists

perception to the policy becomes the more significant variable in context the policy implementation. We

expect that the result provides a basis for expansion model in further study in order to grasp motorcyclistperception to traffic safety policies. In addition, the result could become input for policy makers on preparing

and implementing for the policy and others transportation safety policies in the future.

Key Words:Motorcyclist acceptability, road safety, policy, exclusive lane.

INTRODUCTIONIn the two last decades, motorcycle safety constitutes an increasingly significant in many

Asian developing countries. For example, motorcyclists contributed more than 60% of the

road injuries on Malaysian roads (Radin et al., 1996). In Thailand, 76% of the injured

accident victims are either motorcycle drivers or passengers (Hossain, 2006). Especially in

Indonesia, during 2003-2007 there were 70% of road accidents involved with motorcycles

in Bali (Wedagama and Dissayake, 2010a; 2010b).

Responding those situations, many efforts remain to be made. One of the engineering

approaches to overcome the motorcycle accident problem is segregating other road users

from motorized traffic through an exclusive motorcycle lane that is restricted tomotorcyclists with physical barriers and or markings. The effort has been implementing on

Malaysian road (Law and Radin, 2005). In Indonesia, this road safety policy has been tried

to be implemented in many big cities in Indonesia, such as, Jakarta, Yogyakarta, Makassar,

etc. during January in 2007. However, the implementation was only pilot project in order

to introduce the policy to road users. Many reasons why the policy could not be continued

to established yet, such as readiness of the road lane construction, perception and

acceptability of not only for motorcyclists but also for other road users, etc.
mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]


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Regarding the acceptability of the motorcyclist on the exclusive motorcycle lane in order

to reduce motorcycle accident, this paper attempts to describe motorcyclist acceptability on

implementation of the traffic safety policy in Makassar, Indonesia. In this regard, a

multinomial logit model approach is used to describe relationship between acceptabilitymotorcyclist as response variable and some predictor variables.

This paper begins with a review of previous studies and then presents methodological

approach. This is followed by a description of data and model estimation results of the

acceptability motorcyclist on the exclusive lane policy. Finally, the paper provides discussion

related to the result and concludes.

LITERATURE REVIEWPrevious research on motorcycle accident in developing countries has primarily focused on

the issues of the effectiveness of rider equipment safety, i.e. helmet on reducing head

injury severity (Chang, 2005), the investigation of influence factors caused motorcycles

accident and injuries (Wedagama and Dissayake, 2010a; 2010b), medical investigation of

motorcycles accidents (Hossain and Iamtrakul, 2007), causality cost of motorcyclists

slight injury (Widyastuti, 2007), and comfortability of excluxive lane for motorcycle (Law

and Radin, 2005).

Most of those previous studies have used logit model approach in order to describe those

issues. For example, Wedagama and Dissayake (2010a; 2010b) developed logistic

regression in case of multinomial logit model to describe the influence of accident related

factors on road fatalities in Bali, Indonesia. As well as, Chang (2005) used the multinomial

logit model to analyze effectiveness of mandated motorcycle helmet in Taiwan.

Due to the several similarities on behavioral characteristic of motorcyclist in developing

countries, those past studies have provided valuable empirical insights and analysis

methodologies for this study.

METHODOLOGYMultinomial Logit (MNL) Model

Multinomial logit model or logistic regression modelis one of model approaches to

represent relationship between response (dependent) variable (Y) that categorical and one

or more predictor variables (X) that not only categorical but also continual. When the

dependent variable consist of more than two category, i.e. Y = 1 (success) and Y = 0(otherwise), then multinomial logit model model could be applied.

Furthermore, the categories of the dependent variable result in Y follow the Bernoulli

distribution. The probability function of the Ywith parameter is stated as below:

( ) yyyYP == 11)( (1)Wherey = 0, 1


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Then, probabilities of each categories are P(Y=1) = and P(Y=0) = 1 withE(y) = , for

01.

Generally, probability of the logistic regression that deal with npredictor variables could

be formulated as follows (Ramli et al., 2010):

( ))...(

)...(

22110

22110

1 nn

nn

xxx

xxx

e

exyP

++++

++++

+= (2)

Where xn is a vector of observed variables that represent relevant attributes to dependent

variable, Y.nis parameter ofxnthat should be estimated, and 0

is a specific constant of

the model.

This study uses the Multinomial Logit (MNL) model to grasp the relationships between

acceptability level of motorcyclist as response variable and some identified factors.

Variable specification

Specification of variables that taking account in this research is shown by Table 1. The

table shows that the response variable (motorcyclist acceptability) is categorized into 4

categories, i.e. very accepted, accepted, abstain, and un-accepted. Furthermore, the

predictor variables that considered in this study include safety perception, age, education,

income, origin place of trip, and destination place of trip.

Table1 Variable and Its Attitude

Variable Type Variable Title Attribute/Attitude Variable

1. Motorcyclistacceptability

Y

0 = Very Accept;

1 = Accept;2 = Abstain

3 = Un-accept

2. Safety perceptionSafperc1

Safperc2

Safperc3

1 = Feel safe; 2 = Otherwise

1 = Abstain; 2= Otherwise

1 = Un-feel safe; 2 = Otherwise

3. Age (years old) X1. 12 2. 12 15 3. 15 18

Ag4. 18 25 5. 25 55 6. 55

4.Education

Educ1

Educ2

Educ3

Educ4

Educ5

1 = University; 2 = Otherwise

1 = Senior high school; 2 = Otherwise1 = Junior high school; 2 = Otherwise

1 = Elementary school; 2 = Otherwise

1 = Training; 2 = Otherwise

5. Income (Rp.1x106

X) 1.0.5 2. 0.5 1.0 3. 1.0 1.5

In4. 1.5 2.0 5. 2.0

6. Origin place

Origplc1

Origplc2

Origplc3

Origplc4

1 = Home; 2 = Otherwise

1 = School; 2 = Otherwise

1 = Office; 2 = Otherwise

1 = Shopping place; 2 = Otherwise

7. Destination place

Destplc1

Destplc2

Destplc3

Destplc4

1 = Home; 2 = Otherwise

1 = School; 2 = Otherwise

1 = Office; 2 = Otherwise

1 = Shopping place; 2 = Otherwise


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Parameters estimation of the multinomial logit model

This study adopted maximum likelihoodtheory in order to estimate the parameter values of

the multinomial logit model. The procedure to estimate maximum likelihood value

involves development of a joint probability density function of the observed sample, calledthe likelihood function, through estimation of parameter values which maximize the

likelihood function. The likelihood functionin case Tobservation facejcategories results isdefined as follows (Koppelman and Bhat, 2006):

( ) ( )( ) jtTt jj

jtPL

= (5)

Where jtis chose indicator (=1 if jis happen by observation tand 0, otherwise) and Pjt

is

the probability when the observation tgive eventj. The solution in order to maximize the

log-likelihood function is the second derivation of the function with respect to . In thisstudy, the parameters values of the model are estimated by using statistical package

software, i.e. SPSS Version 16.0.

Data Collection

This study used data from a survey result that conducted by Indonesian Society of

Transportation, Branch of South Sulawesi in January 2007. The survey was carried out in

order to measure motorcyclists perception and their acceptability on a pilot project

implementation of a road safety policy, namely exclusive lane for motorcycle in Makassar,

South Sulawesi, Indonesia.

The survey was conducted at three primary roads, i.e. Yani Street, Sudirman Street, and

Pettarani Street, where the pilot project of the policy had been socialized by traffic police

and local government of the city. The survey took two hours duration in order to survey

randomly 500 motorcyclists on each the road. On the survey, the surveyor stopped themotorcyclists who were passing on the exclusive lane for motorcycle, to the road side, then

interviewed them about their socio demography, origin-destination trip, and perception and

acceptability on the exclusive lane policy.The location of three streets where the survey

conducted is shown by Figure 1.

Sudirman Street

Yani Street

Survey Location:

Makassar Map

Pettarani Street

Figure1 The Location of Road Side Survey


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RESULT AND DISCUSSIONData Description

The data description related to characteristic of motorcyclist is provided in Figure 2. While

acceptability level of motorcyclist related to their characteristics is shown by Figure 3.

8

8

3

3

0

8

8

7

0

0

49

99

6

1

0

236

350

58

28

2

10

25

6

4

0

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%100%

University

Senior High Schoo l

Junior High Schoo l

Elementary School

Training

Percentage of Each Age Category (%)

EducationLevel

= 55

25

73

24

8

0

66

217

37

18

1

99

123

9

3

1

59

52

3

2

0

53

20

2

1

0

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

University

Senior HighSchool

Junior HighSchool

ElementarySchool

Training

Percentage of Each Income Level in IDR. x 10^6 (%)

EducationLevel

= 2.0 a. Education Level vs. Age Category b. Education Level vs. Income Level

132

55

114

102

52

7

11

13

104

11

86

48

91

2

35

35

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Home

School

Office

Shopping

Percentage of Each Origin Trip (%)

DestinationTrip

Home School Of f ice Shopping

146

264

48

24

1

58

110

11

3

0

98

111

16

5

1

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

University

Senior HighSchool

Junior HighSchool

ElementarySchool

Training

Percentage of Safety Perception (%)

EducationLeve

l

Feel safe Abstain Feel un-safe

Figure2 Motorcyclist Characteristics on the Study Locationc. Destination Trip vs. Origin Trip d. Education Level vs. Safety Perception

14

23

7

3

0

153

284

43

20

1

47

72

6

5

0

88

106

19

4

1

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%100%

University

Senior High Schoo l

Junior High Schoo l

Elementary School

Training

Percentage of Each Acceptability Level (%)

EducationLevel

Very Accept Accept Abstain Un-accept

0

0

1

1

45

1

0

11

27

82

381

31

1

5

4

22

98

3

1

4

13

50

150

10

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

= 55


AgeCategory(Year

s)


a. Education Level vs. Acceptability b. Age Category vs. Acceptability

7

19

9

7

5

69

183

148

56

45

17

58

30

16

9

37

79

48

37

17

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

= 2.0


IncomeLevel(IDR.x10^6)


39

379

31

34

5

70

71

36

3

52

28

148

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Very Accept

Accept

Abstain

Un-accept

Percentage of Safety Perception (%)

Acceptabilityofthe

Policy

Feel safe Abstain Feel un-safe c. Income Level vs. Acceptability d. Safety Perception vs. Acceptability

Figure3 Acceptability of Motorcyclist to Variety of Its Characteristics


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Figure 2a shows that the motorcyclist majority in the study location has age 25 55 years

olds for all each education level. In aside, Figure 2a shows that dominant of the

motorcyclist has income IDR 0.5 1.0 x 106for all each education level. However, there is

also significantly motorcyclist that has income IDR 1.0 1.5 x 106on university and senior

high school level, and less than or equal IDR 0.5 x 106

on the other education levels.

Furthermore, Figure 2c shows that amount large of motorcyclist as respondent have homeas their origin trip for all destination trip categories. Also, there is significant number of

motorcyclist having office as their origin trip. In addition, Figure 2d shows that the

majority of motorcyclist feel safe as impact of the exclusive lane policy implementation.

However, a significant number of the motorcyclist fells unsafe even the policy has been

implemented.

Figure 3a shows that majority of motorcyclist of the each education level accepted the

implementation of exclusive lane policy. However, there was significant number of the

motorcyclist that un-accepted the traffic safety policy. Similarly, majority of motorcyclists

in each age and income level categories accepted the policy as shown by Figure 3b and

Figure 3c respectively. In addition, Figure 3d shows that the safety perception ofmotorcyclists on the policy is in line with their acceptability level.

Table 2 provides additional information on the mean, standard deviation, skewness, and

kurtosis of the explanatory variables.

Table2 Descriptions of Variable Data

Variable TypeVariable

TitleMean Std. Deviation Skewness Kurtosis

Percept_Lane

Feel_Safe Safperc1 1.461 0.499 0.157 -1.980

Abstain Safperc2 1.797 0.403 -1.478 0.186Un-feel_Safe Safperc3 1.742 0.438 -1.109 -0.771

Age XAg 36.433 8.809 -0.693 0.395

Income XIn 1.102 0.521 0.826 -0.342

Education

University Educ1 1.663 0.473 -0.691 -1.527

Senior_High_School Educ2 1.459 0.499 0.166 -1.977

Junior_High_School Educ3* 1.916 0.277 -3.011 7.084

Elementary_School Educ4* 1.964 0.186 -5.012 23.173

Training Educ5* 1.998 0.047 -21.130 445.491

Origin

Home Origplc1 1.550 0.498 -0.202 -1.963School Origplc2* 1.907 0.290 -2.815 5.937Office Origplc3 1.722 0.448 -0.993 -1.016

Shopping_Place Origplc4 1.820 0.384 -1.671 0.795

Destination

Home Destplc1 1.577 0.494 -0.312 -1.907

School Destplc2* 1.916 0.277 -3.011 7.084Office Destplc3 1.728 0.445 -1.025 -0.952

Shopping_Place Destplc4 1.779 0.415 -1.347 -0.185

Note: * The variable was removed due to its data do not follow a normal distribution


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Model Estimation Result

According to normal distribution test of data at significant level 95%, the skewness and

kurtosis values should be in the range -2.58 2.58 to assess a data group has normal

distribution, we removed 5 variables on Table 2 (i.e.: education-3 (Junior high school),education-4 (elementary school), education-5 (training), origin place-2 (school), and

destination place-2 (school)) from the model estimation.

Calibration and validation of the multinomial logit model in order to estimate and assess

the parameters values of the logit model for probability of motorcyclist acceptability were

conducted in view of statistics. There were two kinds of statistical test which conducted,

i.e. significant test (i.e. pvalue) in order to evaluate contribution of each variable itself to

the model, and goodness of fit statistic test in order to validate the goodness of fit of the

model. Table 3 provides the parameters values and statistical indicators of the model.

Table3 Result calculation of parameters valuesVariables

of model

Parameter values of each acceptability categories

1. Very accept 2. Accept 3. AbstainB Sig. Exp(B) B Sig. Exp(B) B Sig. Exp(B)

Constant 77.14 5E-701 0.000 6.77 0.0241 0.000 3.10 0.375 0.000

Safperc1 -4.07 2E-101 0.017 -3.47 0.0001 0.031 -1.61 8E-071 0.199

Safperc2 -1.88 0.0141 0.153 -1.71 0.0001 0.181 -2.38 2E-151 0.093

Safperc3 0.00 -b - 0.00 -

b - 0.00 -

b -

Age 0.05 0.0271 1.053 0.01 0.336 1.012 0 0.901 1.002

Income 0.09 0.819 1.089 0.17 0.431 1.186 -0.09 0.748 0.918

Educ1 0.6 0.294 1.825 0.05 0.883 1.056 -0.3 0.521 0.742

Educ2 0.36 0.475 1.436 -0.27 0.426 0.764 -0.32 0.455 0.723

Origplc1 1.84 0.0051

6.268 0.57 0.1312

1.777 0.63 0.1522

1.874Origplc3 0.63 0.308 1.874 0.79 0.0461 2.208 0.7 0.1252 2.011

Origplc4 0.49 0.460 1.635 0.16 0.704 1.177 0.4 0.425 1.494

Destplc1 -15.09 8E-2441 0.000 0.23 0.546 1.263 0.5 0.260 1.649

Destplc3 -15.37 1E-2121 0.000 0.04 0.925 1.039 0.34 0.457 1.407

Destplc4 -16.06 0.0001 1 E-07 -0.41 0.325 0.665 -0.05 0.917 0.951

Number of observation 896

Likelihood ratio,2

:

- Cox and Snell 0.390

- Nagelkerke 0.438

- McFadden 0.224

Hit Ratio

(%) 66.7Note:1Significant at 95%, 2

Significant at 80%b

The reference category is Un-accept on exclusive lane policy

This parameter is set to zero because it is redundant

As shown in Table 3, the "very accept" category on motorcycle acceptability model had 8variables that produced statistically significant parameters (i.e.: constant, safety perception-

1 (feel safe), safety perception-2 (abstain), age, origin place-1 (home), destination place-1

(home), destination place-3 (office), and destination place-4 (shopping place)). The

"accept" category on the model had only 5 variables that produced statistically significant


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parameters (i.e.: constant, safety perception-1 (feel safe), safety perception-2 (abstain),

origin place-1 (home), and origin place-3 (office)); however, the origin place-1 (home)

variable has only significant level at 80%. The "abstain" category on the model had only 4

variables that produced statistically significant parameters (i.e.: safety perception-1 (feelsafe), safety perception-2 (abstain), origin place-1 (home), and origin place-3 (office)); 2 of

these 4 variables (i.e. origin place-1 (home) and origin place-3 (office)) produced onlystatistically significant level on 80%.

Furthermore, Table 3 shows that the motorcyclist acceptability model has enough good

overall statistical fit with McFadden pseudo-2value in the 0.2-0.4 range (0.2 is minimum

value to assess that a model is enough good (Alviansyah et al., 2005; Ramli et al., 2010)).

As well as, the Cox and Snell pseudo-2value and Nagelkerke pseudo-

2

value in the 0.2-

0.4 range and 0.5-0.7 range respectively. To provide additional insight, Table 3 also shows

the hit ratio value, correct percentage between observed data and predicted model, more

than 60% that indicated the model enough good to predict the motorcyclist acceptability.

Generally, perception of motorcyclist on the road safety policy became the moresignificant variable in order to accept the policy than other variables such as age and

education. It means that a road safety policy have to be more socialized in order to build

positive perception on the policy, before the policy is established as permanent road safety

policy.

CONCLUSIONThis paper has evaluated motorcyclist acceptability on a road safety policy, i.e.

implemented exclusive lane for motorcycle. The safety policy has been implemented as

pilot project in some main roads in big cities in Indonesia including Makassar, in SouthSulawesi Province.The policy was tried to be adopted by traffic police and government in

order to increase motorcyclist safety, irrelevant to decrease the motorcyclist accident.

This study used data from a survey result that conducted on three primary roads, i.e. Yani

Street, Sudirman Street, and Pettarani Street, where the pilot project had been implemented

in the city. The survey investigated socio demography, origin-destination trip, and

perception and acceptability of motorcyclists who were passing on the exclusive lane.

The study adopted a multinomial logit model approach in order to grasp relationship

between response variable, i.e. motorcyclist acceptability, and some predictor variables

such as socio demography variable, and motorcyclist perception on the policy. In thisregard, there are four categories of motorcyclist acceptability that considered, i.e. very

accepted, accepted, abstain, and un-accepted.

The calculation result based on the model approach showed that the model was acceptable

according to the log-likelihood and hit ratio indicators. Furthermore, perception of

motorcyclist on the road safety policy became the more significant variable in order to

implementation of the policy.


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Finally, we expect that the model provide a basis to develop anexpansion model in further

study, such as structural model of motorcyclist perception and acceptability on the road

safety policies, etc. Also we hope that this study should become input to the policy makers

on preparing and implementation not only for the policy but also for others transportationsafety policies in the future.

ACKNOWLEDGEMENTWe would like to express our thanks and appreciation to Mr. Muhammad Idris and his surveyor team in the

Survey Institute of Transportation and Environmental (SITE) that supported the survey activities in this

research. We also many thanks to Indonesian Transportation Society, South Sulawesi Branch and Traffic

Unit of Indonesian Police in Makassar City which allow us to access and utilized the survey data for thepurpose of this paper. Without their cooperation, this paper would not be possible to be arranged.

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