How to Determine Road Maintenance Priorities? An Analysis Using a

Probabilistic Approach

Melchior Bria, Priska G. Nahak and Anastasia H. Muda

Department of Civil Engineering, State Polytechnic of Kupang, Adisucipto Street, Kupang, NTT, Indonesia

Keywords: Road Maintenance, Probabilistic Approach, Binary Logistic.

Abstract: The provision of limited maintenance budget for basic infrastructures often lead to unrepair and dilapidation

of the untargeted roads, and this tends to be wasteful. Presently, treatment priority is one of the alternatives

that have been carried out. This study aims to discuss determining road maintenance priorities based on a

probabilistic approach, namely the probability of a road section being proposed in the maintenance program.

The samples used were obtained from the Bakunase-Oenesu and Soe-Fatumnasi roads. The variables utilized

were the pavement structure, average daily traffic, accessibility, economic benefits, time travel, and asphalt

recycling technology. The binary logistic analysis showed that the condition of the pavement structure and

the average daily traffic were variables that had significant effect on road maintenance. There is a greater

chance for the Soe-Fatumnasi road section than the Bakunase-Oenesu being drafted for maintenance. A

probabilistic approach can be applied in determining roads for maintenance programs. This method will be

more appropriate if applied to planning at the first level. In making road maintenance decisions, policymakers

need to pay attention to various factors to make their decisions right on target.

1 INTRODUCTION

In road maintenance planning, obstacles are often

encountered while producing a predictive model for

it conditions, mainly due to errors in the visual survey

and lack of knowledge about essential factors

affecting degradation (Zhao B, Soga K and Silva E

2019). When this continues, it affects the road's

serviceability until it reaches its economical age and

this increases costs for users (Salih J, Edum-Fotwe F

and Price A 2016) (Mikolaj J, Remek L and

Margorinova M 2019), also impacting the residents

and the environment around the area negatively

(Freitas E, Silva L and Vuye C 2019). Furthermore,

highway repair is closely related to budget

availability, and it is necessary to carry out an

appropriate assessment (Muda A, Dumin L and

Nahak P 2019), which include the deterioration of this

area and its sections, such as inspection of the

pavement, shoulders, drainage channels, and

subgrade (Bhuva C R, Patel B and Kanam M 2019)

(Tchemou G, Minsili L S, Mokotemapa A M, Eko R

M and Manguelle J H 2011). This implies that there

is need for right decision making when planning road

maintenance (Siswanto H, Supriyanto B, Pranoto,

Putra Y A M and Huda A S 2018). Consequently,

various efforts have been made by government both

technically and managerially, these involved carrying

out routine checks periodically, and conducting a

priority analysis for sustainability. However, the

sustenance budget is often not followed by useful

methods, therefore, it does not produce a good result

(Susanna A, Crispino M, Giustozzi F and Toraldo E,

2017). Maintenance is an essential part of traffic

management (Ma J, Cheng L and Li D, 2018), and

when planning, it is necessary to consider various

aspects before making decisions, including the

performance and strength of the pavement structure

(Bazlamit S M, Ahmad H S and Al-Suleiman T I,

2017), traffic load, age, road level, and non-technical

aspects, such as resources, disposition, and

bureaucratic formation (Li H, Ni F, Dong Q and Zhu

Y 2018) (Hayat E and Amaratunga D 2014). Besides,

due to limited natural and financial resources,

handling critical highways needs to be performed

with a good strategy by relying on renewable

materials and choosing the right construction method

(Susanna A, Crispino M, Giustozzi F and Toraldo E

2017).

One of the techniques is planning a proper

maintenance program. Studies on organizing have

been carried out, with a priority approach in making

1018

Bria, M., Nahak, P. and Muda, A.

How to Determine Road Maintenance Priorities? An Analysis Using a Probabilistic Approach.

DOI: 10.5220/0012028700003575

In Proceedings of the 5th International Conference on Applied Science and Technology on Engineering Science (iCAST-ES 2022), pages 1018-1022

ISBN: 978-989-758-619-4; ISSN: 2975-8246

decisions using multicriteria analysis (Ahmed S,

Marcelino P, Liem F, 2019), based on road structural

data (Bazlamit S M, Ahmad H S and Al-Suleiman T

I 2017). However, the use of a probabilistic approach

in planning maintenance programs has never been

carried out. To fill this gap, this paper aims to discuss

determining road maintenance priorities based on the

probability approach, namely the chance of a road

section being included in the maintenance program.

For this reason, the considerations that will be used in

the analysis are not only on the structural aspects of

roads but also considering the economic value of

roads and the application of green construction

principles.

2 METHOD

The research location was in West Timor, East Nusa

Tenggara Province. The samples were the roads of

Bakunase-Oenesu in Kupang Regency and Soe-

Fatumnasi in South Central Timor. The main concept

of this study was to identify the chances for a section

to be included in the road maintenance program,

because in one budget year, the available repair funds

were very limited, below what was needed.

Therefore, to strengthen the related technical

agencies' proposals, the planning approach with this

probability concept was used as a guide in proposing

maintenance for a particular road section. The

considerations used to measure how many

opportunity was accepted or not were based on these

main criteria, namely the condition of the pavement

structure (X1), average daily traffic (X2),

accessibility (X3), economic benefits (X4), time

travel (X5) and use of asphalt recycling technology

(X6). The dependent variable was the sustenance of

the Bakunase-Oenesu and Soe-Fatumnasi roads.

The approach used to obtain the value of the

formulas above was quantitative subjective; that is,

the respondent made an assessment based on the

information they had on the distributed

questionnaires. Therefore, they understood the

problems involved in road maintenance activities and

were responsible for the problems, including

budgeting. Summarily, they are the policymakers in

relations to construction works. In this study, the

number of respondents were 150 from technical

agencies at the provincial to district levels.

The analytical method used to ascertain the

probability of road maintenance was binary logistic,

and because the questionnaire questions were

qualitative, each response gave a certain nominal

scale, as in Table 1.

Table 1: Variables and scales used.

No. Variable Res

onse

1 The Pavement

Structure

(0) slightly damaged,

(1) moderate, (2)

heavil

dama

2 Average daily traffic

(0) low, (1) moderate,

(2) hi

3 Accessbility

(0) low, (1) moderate,

(2) hi

4 Economic Benefits

(0) low, (1) moderate,

(2) hi

5 Travel Time

(0) slow, (1) moderate,

(2) normal

6 Application of

recycling technology

(0) is not urgent, (1)

important and ur

7 Maintenance of roads

(0) not yet feasible, (1)

feasible

Based on the variables above, the following model

was formed:

The basic form of binary logistics:

𝑝=

exp(𝛽



+𝛽



𝑋)

1+exp (𝛽



+𝛽



𝑥)

𝑒





 





1+ 𝑒













From this basic form, then used as a binary logistic

equation model tested in this study are:

𝐿𝑜𝑔



𝑃/

(

1−𝑃

)



= 𝛽



+ 𝛽



(

𝑋



)

+𝛽



(

𝑋



)

+𝛽



(

𝑋



)

+𝛽



(

𝑋



)

+𝛽



(

𝑋



)

+𝛽



(𝑋



)

is a constant; β

is the regression coefficient of each

variable; Xi is the independent variable. P is the

probability that the road will be included in the

maintenance program; (1-P) is the chance that the

road is not included in the maintenance program.

The model evaluation included the following

tests: (1) goodness of fit, concerning the level of

significance 0.05; (2) simultaneous variable effect

based on the chi-square significance value; (3)

determination using the Negelkerke R-square criteria,

and (4) the significance of the variables in the

equation. Because two dependent quantities were

compared, each section's analysis was carried out

separately for the Bakunase-Oenesu and Soe-

Fatumnasi roads.

3 RESULT AND DISCUSSION

In modeling the probability between two alternative

options, using a discrete approach, the model needed

to meet the test conditions. Table 2 describes the

results of the model test in the form of the goodness

How to Determine Road Maintenance Priorities? An Analysis Using a Probabilistic Approach

1019

of fit test, simultaneous and determination test. The

goodness of fit test and the simultaneous test are

based on a significance value of α = 0.05, while the

determination test shows the magnitude of the

influence of the independent variables in the model.

Table 2: Result of model test.

Model

The goodness of

fit test

Model

simultaneous

test

Determination

test

Maintenance

of Bakunase-

Oenesu road

1. The chi-square

significance

value 0.211 >

α = 0.05

The chi-square

significance

value on the

omnimbus test is

0.000 < α = 0.05

The

Negelkerke R-

square value is

0.406

2. The percentage

on the

classification

test is 80.2%.

Maintenance

Soe-

Fatumnasi

road

3. The chi-square

significance

value 0.912 >

α = 0.05

The chi-square

significance

value on the

omnimbus test is

0.000 < α =

0.05.

Nilai

Negelkerke R-

square sebesar

0.564

4. The percentage

of the

classification

test is 81.7%.

First, to assess the goodness of fit of the standard,

the terms used were the chi-square significance value.

From Table 2 , it was observed that the two models

met the requirements where the chi-square value of

the Hosmer and Lemeshow test was more than 0.05.

These indicated that the model was used to predict the

observations made.

Secondly, the simultaneous results based on the

omnimbus test (Table 2) show that the chi-square

significance level was less than 0.05 in both. These

proved that all the variables that make up the model

- X

) had a simultaneous influence on road

maintenance. In other words, at least one of the

quantities had a significant effect on road

maintenance.

Thirdly, from Table 2, it was also observed that

the negelkerke R-square value of the two was far

above 0.05. These results proved that the

independent explain the dependent variable's

instability by 40.6% in the Y

model and 56.4% in the

model.

Fourth is a partial effect test to see the effect of

each variable. In Table 3, there are constant values for

each variable (β) and significance (Sig) as well as the

odd ratio value (Exp (β)) of the Y

model. The results

showed that in the Bakunase-Oenesu road

maintenance model, there were two quantities that

partially have a significant effect, namely X

and X

because the value was <0.05. Variable X

, which was

positive, indicated that the greater the pavement

structure's damage, the greater the chance for the

Bakunase-Oenesu road section to be included in the

road maintenance program. The magnitude of this

opportunity, observed from the odds ratio, was 8,425

times higher than if the damage does not increase.

Likewise, with average daily traffic, it has 1,923

chances of being included in the program when it

increases than when constant or depreciates.

Table 3: Result of Y

Model: maintenance of the Bakunase-

Oenesu road.

Variable

S.E. Wald Df Sig.

Exp(β)

2.131 .561 14.430 1 .000 8.425

.654 .290 5.097 1 .024 1.923

.150 .309 .235 1 .628 1.162

.126 .304 .172 1 .678 1.134

.096 .407 .056 1 .814 1.101

.439 .449 .955 1 .328 1.551

Constant -4.385 1.354 10.496 1 .001 .012

Similar to Table 3, Table 4 also describes the

constants of each variable in the Y2 model, the

significance value and the odd ratio value of each

variable. In contrast to the Y

model, it was observed

that the magnitude of the influence of the X

on the

was positive by 20,980, greater than if the road

section does not experience additional damage. For

, it also had a positive effect with an odds ratio of

2.475.

Table 4: Result of Y

model: maintenance of Soe-

Fatumnasi road.

Variable B S.E. Wal

df Si

Exp (β)

3.044 .702 18.811 1 .000 20.980

.906 .340 7.093 1 .008 2.475

.291 .353 .680 1 .410 1.338

.597 .385 2.407 1 .121 1.817

.797 .489 2.663 1 .103 2.220

-.442 .457 .934 1 .334 .643

Constant

-6.161 1.714 12.917 1 .000 .002

The analysis results showed that in the Y

and Y

models, the variables that had significant effect are

the condition of the pavement structure and average

daily traffic. These results confirmed the general

trend that had occurred, where pavement conditions

are the major basis for making road maintenance

iCAST-ES 2022 - International Conference on Applied Science and Technology on Engineering Science

1020

decisions (Bazlamit S M, Ahmad H S and Al-

Suleiman T I 2017, Li H, Ni F, Dong Q and Zhu Y

2018). It means that policymakers have not fully

developed various criteria in assessing the feasibility

of a road section being included in the repair program.

Economic benefits and accessibility have not yet

received attention. It becomes a serious problem

when faced with the choice where many roads are in

the same condition, and such decision are not easy to

make. In this study, two roads were assessed since

they have a strategic role as a route to a tourist area.

On the Soe-Fatumnasi road, apart from this function,

it is also the only access from the interior to urban

areas. However, this study showed that the large

probability of this road segment compared to

Bakunase-Oenesu is only affected by pavement

damage and average daily traffic. Therefore,

policymakers need to determine the direction of the

maintenance program, in order to pay attention to

various criteria, and to obtain the best decision from

the several alternatives. Besides, sustainability issues

need to be considered, for example, by rewiring

asphalt pavements (Liem F, Nenobais O, Bria M and

Mata A 2019) in maintenance work.

However, in this study, the assumption of

respondents was that they fully understand the

maintenance and condition of the two sample roads.

This certainly has weaknesses because respondents

did not fully understand these problems. Moreover,

data collection was carried out by distributing

questionnaires and there was no dialogue. Therefore,

the data obtained did not completely reflect the actual

conditions.

4 CONCLUSION

Determining road maintenance priorities based on a

probability approach using the logistic regression

model, results in a different assessment form

compared to the priority determination based on

ranking or weight. The logistic regression showed

how possible it is for a road section to be included in

the maintenance program. The benchmark is the

amount of opportunity from an alternative, and it is

more appropriate when applied to planning at the first

level.

ACKNOWLEDGMENTS

The authors express their gratitude to the State

Polytechnic of Kupang, for funding this research.

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