Revista Científica ‘‘INGENIAR”: Ingeniería, Tecnología e Investigación. Vol. 8 Núm. (16) 2025. ISSN: 2737-6249

Application of macrotexture and friction parameters in pavement management for road safety.

DOI: https://doi.org/10.46296/ig.v8i16.0303

APLICACIÓN DE PARÁMETROS DE MACROTEXTURA Y FRICCIÓN EN LA

GESTIÓN DE PAVIMENTOS PARA LA SEGURIDAD VIAL

APPLICATION OF MACROTEXTURE AND FRICTION PARAMETERS IN

PAVEMENT MANAGEMENT FOR ROAD SAFETY

1

2

Moreno-Ponce Luis Alfonso ; Solórzano-Villegas Lucy Elizabeth ;

3

4

Carvajal-Rivadeneira Daniel David ; Álvarez-Álvarez Martha Johana

1

Universidad Estatal del Sur de Manabí. Jipijapa, Ecuador.

Correo: luis.moreno@unesum.edu.ec. ORCID ID: https://orcid.org/0009-0000-9026-1009

2

Universidad Estatal del Sur de Manabí. Jipijapa, Ecuador.

Correo: lucy.solorzano@unesum.edu.ec. ORCID ID: https://orcid.org/0000-0002-9903-5304

3

Universidad Estatal del Sur de Manabí. Jipijapa, Ecuador.

Correo: daniel.cavajal@unesum.edu.ec. ORCID ID: https://orcid.org/0000-0002-5288-5483

4

Universidad Estatal del Sur de Manabí. Jipijapa, Ecuador.

Correo: martha.alvarez@unesum.edu.ec. ORCID ID: https://orcid.org/0000-0002-9879-0367

Resumen

El estudio abordó mejoras en la seguridad vial mediante la integración de parámetros de

macrotextura y fricción en un Sistema de Gestión de Pavimentos (PMS). El objetivo fue

desarrollar y validar un marco que armonizara mediciones heterogéneas mediante el Índice

Internacional de Fricción (IFI), estableciera umbrales operativos y produjera una priorización

orientada al riesgo de intervenciones dirigidas a la pérdida de resistencia al deslizamiento. Se

aplicaron procedimientos de adquisición estandarizados (perfilometría 2D/3D para MPD/ETD y

mediciones de fricción continua/puntual), junto con controles de QA/QC, un flujo de trabajo ETL,

conversión a IFI (ASTM E1960), estratificación por clase funcional y clima, y derivación de

umbrales mediante análisis ROC/percentiles; la priorización se realizó con un esquema

multicriterio (condición, exposición, coste y beneficio esperado). Los resultados mostraron series

de IFI trazables por segmento, umbrales diferenciados por tipología de carretera y una lista

jerarquizada de secciones críticas consistente con los registros de exposición y accidentes, lo

que permitió una programación más efectiva de los tratamientos de conservación. Se concluyó

que la integración propuesta permitió tomar decisiones reproducibles y auditables, enfocando los

recursos donde el impacto esperado en la reducción del riesgo es mayor; se recomendó la

calibración local de coeficientes y la incorporación progresiva de métricas 3D y series de tiempo

para fortalecer la predicción y el monitoreo operativo.

Palabras clave: Macrotextura; Fricción; IFI; Gestión del pavimento; Seguridad vial.

Abstract

The study addressed improvements in road safety by integrating macrotexture and friction

parameters into a Pavement Management System (PMS). The objective was to develop and

validate a framework that harmonised heterogeneous measurements via the International Friction

Index (IFI), established operational thresholds, and produced a risk-oriented prioritisation of

interventions targeting loss of skid resistance. Standardised acquisition procedures were applied

(

2D/3D profilometry for MPD/ETD and continuous/spot friction measurements), together with

QA/QC controls, an ETL workflow, conversion to IFI (ASTM E1960), stratification by functional

class and climate, and threshold derivation using ROC/percentile analyses; prioritisation was

performed with a multicriteria scheme (condition, exposure, cost, and expected benefit). The

results showed traceable IFI series by segment, differentiated thresholds by road typology, and a

ranked list of critical sections consistent with exposure and crash records, enabling more effective

Información del manuscrito:

Fecha de recepción: 08 de julio de 2025.

Fecha de aceptación: 18 de septiembre de 2025.

Fecha de publicación: 27 de octubre de 2025.

438

Moreno-Ponce et al. (2025)

programming of preservative treatments. It was concluded that the proposed integration enabled

reproducible and auditable decisions, focusing resources where the expected impact on risk

reduction is greatest; local calibration of coefficients and the progressive incorporation of 3D

metrics and time series were recommended to strengthen prediction and operational monitoring.

Keywords: Macrotexture; Friction; IFI; Pavement management; Road safety.

1. Introduction

of vehicle control (Spitzhüttl et al.,

020).

2

Road

safety

is

severely

The problem is exacerbated on road

compromised under wet-pavement

conditions, where the loss of

adhesion between the tyre and the

road surface significantly increases

crash risk. Various studies have

shown that approximately 20% of

traffic accidents occur during rainfall,

with pavement skid resistance being

sections

with

unfavourable

surfaces, or

geometry,

worn

insufficient drainage, where the

incidence of skid-related crashes is

even higher (Li et al., 2025;

Pitaksringkarn

et

al.,

2018).

Evidence shows that decreasing

friction values are directly correlated

with increased wet-pavement crash

rates, and that improving adhesion

can reduce such incidents by up to

a

determining factor in the

occurrence of such incidents (fawzy

et al., 2024; Fwa, 2017). The

reduction in friction, caused by the

presence of water and the diminution

of surface macrotexture, limits

vehicles’ ability to brake and

manoeuvre safely, especially at

higher speeds or in emergency

situations (Cai et al., 2024; Najafi et

68% (Lyon et al., 2018; Pardillo &

Jurado, 2009). However, the

management and assessment of

skid resistance present challenges,

since traditional methods do not

always consider variables such as

rainfall intensity, vehicle speed, or

the irregular distribution of water over

the surface (Cai et al., 2024; Fwa,

al., the

accumulation of water on the

carriageway can generate

2017).

In

addition,

2

017). Therefore, it is essential to

hydroplaning, in which contact

between tyre and pavement is

completely lost, resulting in total loss

establish appropriate macrotexture

and friction parameters within

Pavement Management Systems

Revista Científica ‘‘INGENIAR”: Ingeniería, Tecnología e Investigación. Vol. 8 Núm. (16) 2025. ISSN: 2737-6249

Application of macrotexture and friction parameters in pavement management for road safety.

(

PMS) in order to identify and treat

The integration of these frameworks

and parameters into Pavement

Management Systems has evolved

towards multicriteria models that

consider not only structural and

functional condition, but also safety,

sustainability, and vehicle operating

costs (Khahro et al., 2021). Recent

experiences show that incorporating

indicators such as IFI and PCI into

PMS enables the prioritisation of

interventions based on the risk of

loss of adhesion and crash

occurrence, optimising resource

allocation and improving road safety

high-risk sections as a priority and

thus reduce crash frequency

associated with loss of adhesion on

wet pavements (McCarthy et al.,

2

022; Pitaksringkarn et al., 2018).

The development of regulatory and

methodological

pavement

frameworks

for

management

and

evaluation has been led by

international organisations such as

PIARC, TRRL, and ASTM. PIARC

(World Road Association) has

promoted global guidelines for

assessing safety and surface

performance of pavements, while the

United Kingdom’s TRRL (Transport

and Road Research Laboratory) has

been a pioneer in research on the

relationship between macrotexture,

friction, and crash occurrence,

(Abu Dabous et al., 2020; Loprencipe

et al., 2017). Moreover, the current

trend is towards automation and

digitalisation of data collection, as

well as the application of multicriteria

analysis

methodologies for

critical sections,

prioritising

establishing

widely

adopted

integrating friction, texture, traffic,

and sustainability variables in the

comprehensive management of road

networks (Abu Dabous et al., 2020).

measurement methods and safety

thresholds. For its part, ASTM has

standardised procedures such as

ASTM E274 for friction measurement

and ASTM E965 for macrotexture, as

well as the use of the Pavement

Scientific evidence shows that

reduced friction on wet pavements

increases both the probability and

severity of crashes, hence managing

Condition

Index

(PCI)

for

of

comprehensive

assessment

pavement condition (Loprencipe et

al., 2017).

macrotexture

and

friction

as

operational variables in PMS is

critical for prioritising interventions

440

Moreno-Ponce et al. (2025)

with a road-safety criterion. Reviews

and empirical studies maintain that

with schemes based solely on

traditional structural or functional

condition (Kumar et al., 2023).

improving

skid

resistance

is

associated with substantial accident

reductions, and that the friction–

accident relationship persists after

Objective. To develop and validate

an

integration

framework

for

macrotexture and friction parameters

within a PMS oriented towards road

safety, which: (i) harmonises

measurements via IFI to ensure

inter-equipment comparability; (ii)

establishes operational thresholds

and risk maps by functional class and

climatic setting; and (iii) implements

a multicriteria prioritisation scheme,

including condition, risk, cost, and

expected benefit, for scheduling

controlling

operation,

for

geometry

example

and

with

continuous devices such as SCRIM

and seasonal correction

methodologies (Fwa, 2017).

In parallel, the PIARC, TRRL, and

ASTM

frameworks

have

consolidated protocols to measure

and harmonise texture and friction,

for example ASTM E274 for locked-

wheel testing and the International

Friction Index (IFI). This enables

inter-equipment and inter-network

comparability, which is indispensable

for integrating thresholds and risk

maps into asset management (ASTM

International, 2020; PIARC, 2009).

Methodologically, recent literature

recommends combining texture

metrics (MPD/ETD) and friction

preservative

and

corrective

treatments, with traceability to

PIARC, TRRL, and ASTM standards

and recent practices in safe

pavement management.

The article is structured into four

parts: Conceptual and Regulatory

Framework, which sets out the

foundations of macrotexture and

friction, IFI, and PIARC, TRRL, and

ASTM guidelines; Materials and

Methods, describing integration into

PMS, including data acquisition and

quality control, conversion to IFI,

indicators and thresholds, risk

(F60/BPN or continuous) with

exposure to risk (traffic, climate,

speed), and employing multicriteria

prioritisation approaches within PMS,

which have shown improvements in

transparency and efficiency of

preservative programming compared

mapping,

and

multicriteria

prioritisation; Results, presenting the

441

Revista Científica ‘‘INGENIAR”: Ingeniería, Tecnología e Investigación. Vol. 8 Núm. (16) 2025. ISSN: 2737-6249

Application of macrotexture and friction parameters in pavement management for road safety.

application at network and project

levels, validation of the safety

module, sensitivity analysis, and the

intervention plan; and Conclusions,

which synthesise contributions and

recommendations for institutional

implementation in Latin American

contexts.

parameters into a functional

assessment module that enables

the identification of sections with

adhesion deficits and the

prioritisation of their treatment.

The methodological structure

was

designed

to

ensure

coherence, traceability, and

compatibility with institutional

PMS

inspection

and

2. Materials and Methods

maintenance

procedures,

without modifying its operational

architecture.

2

.1. Methodological design and

integration into the PMS

2

.2. Data sources and data

The study applied a quantitative and

applied approach, aimed at

acquisition

integrating macrotexture and friction

parameters within the Pavement

Management System (PMS) for road

Primary and secondary data were

used and integrated into

a

georeferenced database linked to the

PMS.

safety

purposes.

The methodology was organised into

three main stages:

Primary data:

•

Macrotexture: determined by

D/3D profilometry in

•

Measurement: acquisition of

texture and friction data at

network and project levels using

standardised equipment.

2

accordance with ISO 13473-1,

and by the volumetric sand patch

method at

verification points.

Surface friction: obtained using

water-filled wheel devices

ASTM E1911 / EN 13036) and,

(ASTM

E965)

Conversion

and

harmonisation: standardisation

of results to the International

Friction Index (IFI) to ensure

•

(

comparability

between

at specific locations, with the

British Pendulum Tester (ASTM

E303).

equipment and conditions.

•

Integration into the PMS:

incorporation of the converted

442

Moreno-Ponce et al. (2025)

Secondary data:

using the harmonisation equations

established by PIARC–TRRL:

These included annual average daily

traffic (AADT), percentage of heavy

vehicles, operating speed (V85),

climatic conditions (precipitation and

temperature), road geometry, and

records of crashes on wet

pavements.

퐹(60) = 푎 + 푏 × 퐹_푖

_퐹_푅₌_퐹₍₆₀₎_×_푒−푐(푆−ꢀꢁ)

where 퐹(60)is the friction corrected

to 60 km/h, ꢂis the measurement

speed, and 푎, 푏, ꢃare the calibration

coefficients for each device.

Sampling

was

conducted

by

The the

unification

obtained with different devices and

under different environmental

conversion

enabled

homogeneous segments defined

according to functional class and

climatic setting. Each measurement

was georeferenced using GNSS,

with density proportional to the road’s

level of criticality.

of

measurements

conditions, ensuring inter-network

comparability.

IFI values were linked to the PMS

inventory through a unique spatial

identifier, forming the basis for the

risk analysis and prioritisation

Daily calibration routines, statistical

variability control (coefficient of

variation

and

z-scores),

and

technical acceptance criteria were

applied in accordance with QA/QC

protocols.

described

in

the

following

subsections.

2

.4. Construction of indicators

Data were integrated into a relational

database, normalised in units and

test conditions, and prepared for

conversion to IFI.

and risk classification

With the normalised values of

macrotexture (MPD/ETD), friction

(IFI), and functional condition (PCI),

a road-risk matrix was prepared to

identify sections with a higher

probability of loss of adhesion on wet

pavements.

2.3. Normalisation

and

parameter conversion

The

values

obtained

for

macrotexture (MTD or MPD) and

friction (BPN, FN) were converted to

the International Friction Index (IFI)

The procedure comprised three

phases:

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Revista Científica ‘‘INGENIAR”: Ingeniería, Tecnología e Investigación. Vol. 8 Núm. (16) 2025. ISSN: 2737-6249

Application of macrotexture and friction parameters in pavement management for road safety.

•

Definition of thresholds:

critical friction values were

established based on PIARC–

•

Surface friction (IFI)

Pavement condition (PCI)

Annual average daily traffic

TRRL

recommendations,

classifying sections into three

risk levels:

(AADT)

•

Unit intervention cost

o High risk: IFI < 0.35

Expected benefit in risk

reduction

o Medium risk: 0.35 ≤

IFI < 0.45

Each criterion was weighted through

normalised linear weighting,

o Low risk: IFI ≥ 0.45

assigning greater weight to friction

and traffic due to their direct

influence on operational safety. The

result was a Priority Index (PI) that

enables the ranking of sections

•

Integration of variables:

texture, friction, and drainage

indicators were related to

traffic and climatic exposure

using simple weightings,

obtaining a composite risk

index (SRI).

according

to

the

need

for

intervention.

Operational

validation

of

the

•

This process provided a

structured indicator base

interoperable with the PMS

conservation and planning

modules, under verifiable

road safety criteria.

procedure was performed by

verifying the coherence of the

identified critical sections with

historical records of crashes on wet

pavements and with the annual PMS

programme.

This

comparison

ensured the technical consistency of

the safety module and its applicability

to decision-making for preventive

and corrective maintenance.

2

.5. Multicriteria prioritisation

and operational validation

Priority sections were selected using

multicriteria analysis model

compatible with the PMS

a

environment. The factors considered

were:

444

Moreno-Ponce et al. (2025)

3. Results and discussion

measures of central tendency and

dispersion.

3.1 General characterisation of

Table 1 presents the descriptive

statistics of the mean values of MTD

surface parameters

The initial characterisation made it

possible to establish the variability of

the recorded macrotexture and

surface friction parameters in the

evaluated road network.

(

Mean Texture Depth), MPD (Mean

Profile Depth), and BPN (British

Pendulum Number), classified by

functional road type. The results

reflect a general trend of higher

roughness on primary roads and

lower friction values on urban

sections, due to surface wear

conditions and accumulated heavy

traffic.

The data obtained at network and

project levels were processed

statistically to determine their

representative

behaviour

using

Table 1. Descriptive statistics of macrotexture and friction by functional class.

Functional class

Primary

MTD (mm)

0,74

MPD (mm)

0,82

BPN

63

Std. Dev.

5,8

CV (%)

9,2

Secondary

Collector

Urban

Average

0,59

0,52

0,45

0,58

0,66

0,61

0,54

0,66

67

71

76

69

6,5

7,1

8,2

6,9

9,7

10,0

10,8

9,9

Note. Mean values obtained from homogeneous network and project measurements.

Figure 1 shows the comparative

distribution of MTD and BPN values,

evidencing moderate dispersion in

macrotexture

and

a

slight

concentration of friction values

between 60 and 75 units, an

acceptable range for in-service

surfaces. This behaviour indicates

functional

uniformity

between

sections and an absence of

significant outliers within the ranges

expected by international standards.

445

Revista Científica ‘‘INGENIAR”: Ingeniería, Tecnología e Investigación. Vol. 8 Núm. (16) 2025. ISSN: 2737-6249

Application of macrotexture and friction parameters in pavement management for road safety.

Figure 1. Comparative distribution of mean MTD and BPN values.

Note. Prepared with field data processed using descriptive statistics.

The joint analysis of the parameters

shows that the mean macrotexture

equations established by National

Academies of Sciences (2009) y

PIARC (1995).

(

0.58 mm) and the average friction

BPN = 69) lie within the operational

(

he purpose of this process was to

range recommended by PIARC

1995) for flexible pavements under

ensure

comparability

between

(

equipment and road networks,

allowing the results to be expressed

on a unified scale of friction corrected

to a standard speed of 60 km/h.

wet conditions.

The differences observed between

functional classes reflect only the

expected behaviour by road type and

traffic volume, without implying

structural deficiencies or significant

loss of adhesion

The conversion was performed using

the calibration coefficients a, b, and c

for

each

device

employed,

determined according to reference

tests and fitting curves established in

international regulations. Table 2

summarises the values adopted for

each measuring device.

3.2 Conversion and

harmonisation of parameters

Once the macrotexture (MTD/MPD)

and friction (BPN) values had been

obtained, they were converted to the

International Friction Index (IFI), in

accordance with the harmonisation

446

Moreno-Ponce et al. (2025)

Table 2. Calibration coefficients used in conversion to IFI.

Measuring device

Base

parameter

BPN

a

b

c

Reference speed

(km/h)

British Pendulum Tester

0.045 1.125 0.045

60

(

BPT)

Laser profilometer

SCRIM or water-filled

wheel

MPD

FN

0.030 1.080 0.050

0.000 1.000 0.040

60

Note. Normalised parameters according to National Academies of Sciences (2009) and PIARC

1995).

(

unique spatial identifier, maintaining

traceability by section, date, and

The conversion was executed by

applying

the

harmonisation

equipment

type.

equations:

Figure 2 shows the relationship

between macrotexture (MTD) and

퐹(60) = 푎 + 푏 × 퐹_푖

퐹푅 = 퐹(60) × 푒⁻^푐⁽^푆⁻^ꢀ^ꢁ⁾

퐹(60)represents

friction

(IFI)

obtained

after

where

friction

conversion, which evidences an

inversely proportional behaviour

typical of asphalt surfaces: as

macrotexture increases, friction

tends to stabilise, maintaining an

operational range suitable for road

safety.

corrected to 60 km/h, ꢂis the test

speed, and 퐹푅is the harmonised IFI

value.

Subsequently, the data were

integrated

into

the

PMS

georeferenced database using a

Figure 2. Relationship between MTD and IFI after parameter conversion and harmonisation.

Note. Representative linear fit of the inverse macrotexture–friction trend.

447

Revista Científica ‘‘INGENIAR”: Ingeniería, Tecnología e Investigación. Vol. 8 Núm. (16) 2025. ISSN: 2737-6249

Application of macrotexture and friction parameters in pavement management for road safety.

The linear correlation analysis

between both parameters yielded a

This stage made it possible to

establish the spatial distribution of

zones with greater susceptibility to

coefficient

of

determination

R^2=0.82, indicating a strong and

consistent association between the

variables, confirming the validity of

the harmonisation and the quality of

the normalised data.

skidding

on

wet

pavement,

information essential for technical

prioritisation within the PMS.

Risk thresholds were defined in

accordance with PIARC (1995) and

the Guide for Pavement Friction

(2009), adopting three operational

ranges based on friction corrected to

3.3 Classification and distribution

of risk by sections

Once the friction values had been

converted to the International Friction

Index (IFI) scale, the road network

sections were classified according to

their risk level associated with loss of

adhesion.

6

0 km/h (F60). Table 3 shows the

classification applied and the

percentage distribution of the

sections evaluated in each category.

Table 3. Classification of risk level according to normalised IFI values.

Risk

category

High risk

Medium risk

Low risk

Total

IFI friction range

Percentage of sections

Cumulative length

(%)

22.8

46.3

30.9

100.0

(km)

15.4

31.2

20.8

67.4

IFI < 0.35

0.35 ≤ IFI < 0.45

IFI ≥ 0.45

—

Note. Thresholds defined according to PIARC (1995) and NCHRP (2009).

The results indicate that almost half

of the sections (46.3%) fall within the

medium-risk range, which reflects

acceptable surface conditions but

with potential for degradation under

heavy traffic or prolonged rainfall.

vehicles, while the remaining 30.9%

present safe conditions and

adequate friction for operation on wet

pavement.

Figure 3 shows the percentage

distribution of risk by functional class,

evidencing a higher concentration of

critical sections on secondary and

urban corridors. The graphical

representation makes it possible to

The 22.8% classified as high risk

corresponds mainly to urban and

collector roads with reduced texture

and a high percentage of heavy

448

Moreno-Ponce et al. (2025)

visually identify the segments

requiring preventive maintenance

due to adhesion deficits.

Figure 3. Percentage distribution of road risk according to IFI friction categories.

Note. Prepared from the risk classification in Table 3.

The analysis of Figure 3 shows a

heterogeneous functional distribution

of risk, consistent with the

constructive diversity and surface

ageing of the network.

Priority Index (PI) were constructed,

both aimed at decision-making within

the PMS.

The procedure was supported by a

weighted multicriteria model, which

assigns relative weights to each

variable according to its influence on

Taken together, the results provide

an

objective

basis

for

the

construction of the composite risk

indicators presented in the following

subsection, aimed at technical

prioritisation within the PMS.

road

safety

and

pavement

preservation.

Table 4 presents the weighting

matrix used, in which surface friction

obtained the highest relative weight

(0.30), followed by pavement

functional condition (0.25) and

annual average daily traffic (0.20).

Cost and benefit factors were

included with lower values due to

their complementary nature.

3.4

Composite indicators and

technical prioritisation

From the results of friction (IFI),

surface

condition

(PCI),

and

a

exposure to traffic (AADT),

composite risk index (SRI) and a

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Revista Científica ‘‘INGENIAR”: Ingeniería, Tecnología e Investigación. Vol. 8 Núm. (16) 2025. ISSN: 2737-6249

Application of macrotexture and friction parameters in pavement management for road safety.

Table 4. Weighting matrix applied for the calculation of the Priority Index (PI).

Technical criterion

Surface friction (IFI)

Functional condition (PCI)

Traffic (AADT)

Brief description

Tyre–pavement adhesion level

Surface condition and maintenance

Annual average daily traffic

Cost-to-treated-length ratio

Impact on risk reduction

—

Relative weight

0.30

0.25

0.20

0.15

0.10

1.00

Unit intervention cost

Expected benefit

Total

Note. Weights defined according to functional and operational incidence on road safety.

The normalised values of each

criterion were integrated by simple

linear weighting, obtaining a Priority

Index (PI) for each section of the

network. This index enabled the

ranking of segments with higher

operational risk and optimised

intervention scheduling in the PMS.

Figure 4 comparatively represents

the percentage contribution of each

criterion to the PI. A dominance of

friction and pavement condition is

observed, which together contribute

more than 50% of the model’s overall

influence, validating its technical

coherence

objectives.

with

road-safety

Figure 4. Percentage contribution of the criteria to the Priority Index (PI).

Note. Prepared from the relative weights in Table 4.

The analysis of the final index shows

approach makes it possible to

allocate preservative maintenance

resources to sections with greater

that prioritisation based on friction,

condition, and exposure enables a

more

efficient

operational

adhesion maintaining

deficits,

classification than that supported

solely by structural parameters. This

traceability of the criteria and

450

Moreno-Ponce et al. (2025)

reproducibility of the procedure

within the PMS.

(a)

Data linking, where IFI, PCI,

and

AADT

indicators

were

georeferenced using

segment identifier.

a

unique

3

.5

Integration into the PMS

Safety Module

(b)

Normalization,

which

The integration of macrotexture and

converted all variables to a 0–1 scale

friction

Pavement Management System

PMS) was carried out through the

parameters

into

the

to ensure comparability.

(

(c)

And Operational validation,

priority indices were

Safety Module, designed to support

data-driven decision-making for road

safety interventions.

where

compared with official crash records

to verify correlation consistency.

This module consolidates the

information obtained in previous

The Table 5 presents the final

prioritization of road segments

according to the Priority Index (PI)

generated by the multicriteria model.

Segments classified as “Very High”

and “High” priority correspond to

those with reduced skid resistance

stages

(IFI

conversion,

risk

classification, and prioritization) and

links it with the functional and spatial

structure of the road network.

The

integration

process

was

and

high

traffic

volumes,

organized into three stages:

representing the most critical points

for immediate maintenance.

Table 5. Final prioritization of road segments integrated into the PMS Safety Module.

Segment

ID

Functional

class

IFI (– PCI (– AADT

Priority Index

(PI)

Priority

level

)

S–03

S–07

S–11

S–15

Urban

0.32

0.38

0.41

0.46

0.55

0.60

0.70

0.78

8900

7200

5600

9100

0.83

0.74

0.63

0.52

Very High

High

Medium

Medium–

Low

Collector

Secondary

Primary

S–18

Urban

0.52

0.81

4300

0.39

Low

Note. Classification based on weighted priority index (PI).

The

PMS

Safety

Module

these

the structure of the module,

emphasizing the flow of data

automatically

updates

classifications when new survey data

are imported. The Figure 5 illustrates

between

the

Survey

Layer

(macrotexture and friction), the

451

Revista Científica ‘‘INGENIAR”: Ingeniería, Tecnología e Investigación. Vol. 8 Núm. (16) 2025. ISSN: 2737-6249

Application of macrotexture and friction parameters in pavement management for road safety.

Analytical Layer (risk and priority

models), and the Decision Layer

(maintenance programming and

safety evaluation).

Figure 5. Structural scheme of the PMS Safety Module.

Note. Integration of macrotexture, friction, and condition indicators for safety-oriented

management.

The PMS Safety Module enables

traceable and reproducible

prioritization, aligning maintenance

programming with safety

PMS makes it possible to identify and

prioritise sections with higher risk

due to loss of adhesion under wet

conditions, in line with the literature

that links available friction with crash

probability, especially on wet

performance rather than solely with

surface condition. This integration

directly fulfills the study’s main

objective by embedding texture and

friction-based parameters into the

carriageways.

framework

The

was

proposed

based on

standardised measurements, IFI

harmonisation, and the construction

of thresholds and risk maps, which is

consistent with reference guides and

syntheses for friction management in

road networks (National Academies

of Sciences, 2009).

PMS

architecture,

the

thereby

transforming

management

system into a safety-driven decision

platform consistent with PIARC’s

Safe System approach.

Discussion

Regarding the metrological basis,

harmonisation via IFI resolves

comparability between equipment

and speeds, a problem documented

The findings confirm that the

systematic integration of

macrotexture and friction into the

452

Moreno-Ponce et al. (2025)

since the international experiment

coordinated by PIARC and embodied

in ASTM E1960. The combined use

of a texture descriptor (MPD/ETD or

MTD) and a friction metric corrected

to 60 km/h (F60) improves inter-

network traceability and the transfer

of operational thresholds. This

texture parameters and friction are

integrated within statistical or

multiscale frameworks. This supports

the decision to combine IFI with

texture and exposure indicators

(traffic, climate, speed) to derive

composite indices and reproducible

prioritisation at network and project

level. Advances in 3D scanning and

its correlation with friction, especially

with BPN and continuous measures,

reinforce the adoption of hybrid

approach

aligns

the

practice

presented with the PIARC–ASTM

technical consensus and with the

evidence that texture conditions the

sensitivity of friction to speed

schemes and

for

diagnosis

(PIARC, 1995).

monitoring (Kumar et al., 2023).

The link between friction deficit and

crashes has been reported in studies

using continuous equipment, for

example SCRIM and DWW, which

In management and prioritisation,

recent literature recommends

multicriteria approaches, for example

AHP or TOPSIS and fuzzy variants,

which weight safety, condition, traffic,

and costs, showing improvements in

support

investigation

and

intervention levels by site category

and geometry. In particular, analyses

of major corridors show that

decreasing friction increases the risk

of wet skidding and that targeted

transparency

and

efficiency

compared with strategies based

solely on surface or structural

condition. The proposal of a Priority

Index that privileges friction and

exposure is congruent with these

friction-improvement

programmes

reduce crashes, results that are

consistent with the use of thresholds

and risk maps in the PMS (Parry &

Viner, 2005).

recommendations

and

with

experiences of integrating friction

and texture into PMS safety modules

(

Marcelino et al., 2019; Xu et al.,

From a modelling perspective, state-

of-the-art reviews indicate that

prediction of skid performance

improves when three-dimensional

2

024).

In terms of safety impact, empirical

evidence suggests that increases in

453

Revista Científica ‘‘INGENIAR”: Ingeniería, Tecnología e Investigación. Vol. 8 Núm. (16) 2025. ISSN: 2737-6249

Application of macrotexture and friction parameters in pavement management for road safety.

friction translate into crash reduction,

with particularly clear effects in wet

climates. Modelling and before–after

approach presented is alignable with

international standards, scalable,

and auditable in PMS, offering a

practical pathway to incorporate

studies

have

reported

crash

modification functions (CMF) and

investigation levels based on

measurable friction, which supports

the inclusion of thresholds and alert

rules in the PMS. Thus, the proposed

safety module is consistent with the

practice of directing preservative

maintenance towards sections with

the greatest expected contribution to

risk reduction (McCarthy et al., 2021,

adhesion-related

safety

into

conservation programming astm

(ASTM International, 2015; National

Academies of Sciences, 2009).

4. Conclusiones

1

.

The systematic integration of

and friction

macrotexture

harmonised by IFI made it possible to

construct operational thresholds and

reproducible risk maps, enabling

transparent identification of critical

sections due to loss of adhesion on

wet pavements within the PMS.

2022).

Finally, limitations and future lines

are acknowledged. First, local

calibration of IFI and thresholds by

functional class and climate remains

necessary for different aggregate

gradations and operating speeds.

2.

The

ETL–IFI–SRI–

prioritisation workflow demonstrated

technical feasibility and traceability.

The derived indicators, that is friction

corrected to 60 km/h, MPD or ETD

texture, exposure, and geometric

context, were integrated into PMS

operational dashboards with QA/QC

criteria and data versioning.

Second,

seasonality,

that

is

temperature and precipitation, and

drainage variables can modulate risk

and should be tagged for intra and

inter seasonal comparisons. Third,

the integration of time series and

advanced 3D metrics can increase

predictive power and decision

stability. Fourth, the availability and

quality of crash data condition the

robustness of thresholds derived by

ROC analysis. Nevertheless, the

3

.

Multicriteria

weighting friction, condition, traffic,

cost, and expected benefit,

prioritisation,

generated a Priority Index consistent

with safety evidence and with the

454

Moreno-Ponce et al. (2025)

annual

programme,

favouring

temperature and precipitation, and

drainage conditions that are not

always captured.

preservative maintenance where the

impact on risk reduction is greatest.

4.

IFI harmonisation resolved

8.

Future lines: incorporate time

comparability between equipment

and between corridors or seasons, a

necessary condition for scaling

adhesion management to network

level and for interannual comparison

in contexts with climatic variability.

series and 3D texture metrics for

more robust predictive models;

explore non-linear and machine-

learning

models

to

capture

interactions between texture, friction,

and climate; strengthen seasonal

correction and interoperability with

geometry and HFST modules; and

formalise local CMFs to quantify

safety benefits.

5.

Operational validation through

comparison with crash records and

functional coherence between road

classes showed concordance with

the prioritised sections, reinforcing

the usefulness of the safety module

as decision support in maintenance.

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