Published at : 18 Sep 2024
Volume : IJtech
Vol 15, No 5 (2024)
DOI : https://doi.org/10.14716/ijtech.v15i5.5726
Kardina Nawassa Setyo Ayuningtyas | Faculty of Civil and Environmental Engineering, Institut Teknologi Bandung, Jl Ganesha No. 10, Bandung, 40132, Indonesia |
Aine Kusumawati | Faculty of Civil and Environmental Engineering, Institut Teknologi Bandung, Jl Ganesha No. 10, Bandung, 40132, Indonesia |
Safety Husna Pangestika | Faculty of Civil and Environmental Engineering, Institut Teknologi Bandung, Jl Ganesha No. 10, Bandung, 40132, Indonesia |
Istyana Hadiyanti | Faculty of Civil and Environmental Engineering, Institut Teknologi Bandung, Jl Ganesha No. 10, Bandung, 40132, Indonesia |
The iRAP Star Rating
assessment has gained global recognition as an effective strategy for reducing
fatalities caused by road crashes. As part of its road safety initiatives,
Indonesia has also adopted the iRAP Star Rating methodology with the goal of achieving
a 3-Star Rating or higher for over 75% of its road network, with a focus on
prioritizing toll roads. However, it remains crucial to ascertain the
effectiveness of the iRAP Star Rating methodology with regards to safety
performance indicators such as crash and fatality rates and the number of
crashes and fatalities. Unfortunately, there have been limited studies
examining the relationship between safety performance indicators and iRAP Star
Rating, with only a few conducted in Indonesia. Most of these studies only
focused on iRAP Star Rating results and used a limited dataset without
examining correlations to safety performance indicators. Thus, this study aims
to estimate the relationship between the iRAP Star Rating Score (SRS) and safety
performance indicators. The results indicate a positive correlation between SRS
and safety performance indicators, with a higher SRS indicating a higher level
of hazard and correspondingly higher safety performance indicators values.
However, it is important to note that the R2 value was not
particularly high (ranging from 0.2-0.7), suggesting that this relationship may
not be accurately reflected, possibly due to factors unique to the Indonesian
environment and human factors not specifically accounted for in the iRAP
methodology.
Crash rate; Fatality rate; iRAP star rating; Star Rating Score (SRS); Toll road
Li et al. (2024) reviewed the estimation of fatalities
and serious injuries (FSI) saved by iRAP protocols in 74 countries. The study
suggested more extensive applications of iRAP protocols to improve road safety as
it could significantly reduce FSI through its countermeasures. Through before
and after studies using crash data and iRAP protocols and its countermeasures, there
were evidence on reduction of crashes in UK (iRAP, 2011), and reduction of FSI by 50% in Sweden (EuroRap, 2020; Vadeby, 2016; Carlsson, 2009).
EuroRAP (2011) conducted a study on the relationship between
average crash rates and the iRAP Star Rating/Road Protection Score (RPS) in
multiple European countries using various models. The findings from these studies
showed variations, but overall, they suggested that higher Star Ratings were
associated with a decrease in average crash rates or crash cost and vice versa.
Jurewicz and Excel (2016) utilized AusRAP protocols (Austroad, 2024; Metcalfe and Smith, 2005) to calculate the relative risk
of different types of severe crashes and used these results to predict the
frequency of severe crashes in the Australian National Risk Assessment Model
(ANRAM) (ARRB, 2015). A validation study conducted by Ambros, Borsos, and Sipos (2017) on rural roads in Hungary using a state-of-the-art empirical Bayes
approach (Hauer et al., 2002; Hauer, 2001)
confirmed the
relationship between increasing Star Rating and decreasing crash frequencies.
However, the study also found that the Star Rating had a minor influence and an
unexpected positive relationship with crash frequency.
There were only limited studies
regarding the relationship between iRAP Star Rating and crash rate/frequency in
Indonesia. Hadi (2015) conducted a
study on a 28-km length of a national road segment in Central Java, Indonesia,
and found that segments with higher Star Ratings were associated with lower
crash rates. However, the study only considered Star Ratings between 1 to 3, as
no segments with Star Ratings 4 or 5 were included in the study. Another study
by Rahmita and Malkamah
(2020) examined
the relationship between crash rate, number of crashes, Star Rating Score
(SRS), and Star Rating band for a 10.6 km segment of National Road in Java
Island. The study showed a weak correlation (R2 less than 0.07)
between the SRS and Star Rating band with crash rate and number of crashes.
Nevertheless, none of the studies explored the relationship between iRAP Star
Rating and crash severity, despite the fact that iRAP aims to evaluate road
infrastructure features that impact crash likelihood and severity.
In compliance with United Nations
Sustainable Development Goals (UN SDGs), one of the road safety targets stated
in the Indonesian National Road Safety Plan 2021-2040 is to achieve a minimum 3-Star Rating, using
iRAP, for more than 75% of Indonesia’s road network (Central
Government of Indonesia, 2022). The primary focus is on
enhancing road safety on toll roads, given their higher crash rates compared to
other road types in the country. To achieve this goal, it is crucial to
understand the relationship between crash data (such as the number of crashes,
severity, crash rates, and fatality rates) and the iRAP Star Rating Score
(SRS). This understanding will help determine the effectiveness of the iRAP
assessment in Indonesia, which has distinct driving behavior compared to other
countries. The purpose of this study is to assess toll road safety using iRAP,
and estimate the relationship between iRAP Star Rating, crashes, and their
severity levels in Indonesia toll road case study.
The iRAP methodology assesses the road safety in a
proactive manner and identify road infrastructure deficiencies towards
providing some countermeasures to improve road safety by eliminating highrisk
roads (Uddin and Raihan, 2023; iRAP, 2021; Hoque et al.,
2010).
iRAP Star
Rating is generated using ViDA online platform for every 100-meter road segment
(iRAP,
2020). Road attributes required in ViDA to
calculate the SRS include roadside severity (distance and type of side object),
vehicle flow, speed limit, operation speed, sight distance, intersection, pave
shoulder, access point, number of lanes and lane width, grade, road condition,
delineation, and the existence of shoulder rumble strip, curvature, traffic
calming, service road, street lighting (iRAP, 2022). Only vehicle Star Rating was calculated in this research, as
pedestrians, motorcyclists, and cyclists are not permitted to use the toll
road. In addition to road attributes, the number of fatalities for each crash
type is also an input in Vida for fatality estimation.
Figure 1 Study Locations
The roadside hazards in ViDA were coded according to the type of
roadside object which is part of “Severity” in formulation (1), along
with its distance from the edge line to the object per 100m road segment. If
there were variations in road attributes within a coding segment for the length
of 100m, the attribute with the highest risk would be coded for the segment. In
the case of choosing the roadside object with the highest risk, iRAP has
compiled a list of 62 roadside hazards, ranked according to their relative
risk, to assist in identifying the objects with the highest risk. This list can
be found in iRAP (2022). The top-ranked
hazard on the list is the cliff, regardless of its distance from the edge line.
In contrast, wire rope barriers located more than 10m from the edge line are
considered to have the lowest risk. However, it is important to note that these
rankings were based on research from around the world and may not necessarily
reflect the specific environment of Indonesia.
Formulation (1) highlights the importance of operating speed as a factor
influencing the crash type scores. This formulation considers operating speed
as a combined risk factor without separating the effects of speed on the
likelihood of a crash occurring and the severity of a crash. Higher operating
speeds lead to higher crash-type scores, indicating a higher relative risk of
serious injury and fatality for road users. The likelihood component of the formulation
(1) takes into account several road attributes, including lane width, number of
lanes, curvature and its quality, delineation, shoulder rumble strip, road
condition, grade, skid resistance, intersection type, and its quality, the
presence of street lighting, sight distance, and traffic calming. On the other
hand, the severity component considers the roadside object and its distance,
paved shoulder width, median type, intersection type, and access points to properties.
The relationship between each attribute is based on previous studies collected
by iRAP in each iRAP methodology factsheet (iRAP, 2014). Figure 2 illustrates the formulation (1) and its attributes in more
detail.
SRS is
apportioned to Star Rating bands to determine the Star Rating for every 100
meters of the road as in Table 1 based on iRAP. Star Rating Bands are not
equally distributed. Thus, the lower the Star Rating, the greater the variance
between SRS. A higher SRS indicates a more hazardous road. On the opposite, the
lower the Star Rating band, the more dangerous the road. Star rating band 1 is
the most dangerous, while 5 is the safest. A star rating band is commonly used
to describe the star rating on the map or table because it is simpler and
easier to understand (iRAP, 2024). On the other hand, SRS is the calculation for the relative risk of
death and serious injury without any simplification. The distribution of SRS
into Star Rating bands for vehicle occupants is as shown in Table 1:
Figure 2 SRS equation in more detail
(formulation 1) (iRAP, 2014)
3.1. Star Rating Bands
on Indonesia Toll Road
The star
rating band was calculated using Vida based on the formulation (1), which
consider the risk factor based on inputting variable for iRAP (road attributes
and fatality estimation). It describes the level of road safety from 1 (highest
risk) to 5 (safest road). The Star
Rating bands for Indonesia Toll Roads in the case study can be seen in Table 2
and Figure 3.
The results indicate that 67.25% of the toll road network achieved a
3-star or better rating. Among the network, only 0.19% achieved a 5-star
rating, indicating the lowest risk, while 2.54% of the network received a
1-star rating, representing the highest risk. The majority of the network
achieved a 2-star or 3-star rating, with 30.21% and 47.56% of the network
falling into these categories, respectively. In order to improve road safety,
it is important to focus on the specific areas with lower star ratings,
particularly Star Ratings 1 and 2. A detailed observation of these areas can
help identify the best countermeasures to address the safety issues. The Safer
Roads Investment Plan (SRIP) in iRAP recommends a number of road safety
measures for these segments, including adding a central median barrier, and
roadside barrier, improving delineation, and adding shoulder rumble strips in
several segments.
Figure 4 Wirerope barrier median (a) and
wide median (b) in Cipali toll road
3.2. Number of Crashes, Crash Rates, Fatalities,
Fatality Rates, and Star Rating Score
Analysis of the number of
crashes per 100m segment shows that most segments had no crashes, with some
having one crash and only a few with two or three crashes due to the
consideration of only one year of crash data. The data were then combined into
a 1km segment to allow for more variation in the number of crashes. The SRS for
the 1km segment was the average SRS for the ten-100m segment.
The number of crashes was
in the range between zero to four, except for one segment, which had ten
crashes. The segment was KM 91-92B of the Cipularang toll road, which is one of
the blackspot locations in the Indonesian toll road network. The corresponding
crash rate for this segment was 79.8 crashes per 100 million vehicle-km. Figure
5 presents the distribution of the number of crashes and the crash rate for
each of the SRS.
Figure 6 Distribution of the number of
fatalities vs SRS (a) and fatality rate vs SRS (b)
3.3. Relationship Between SRS and Number of Crashes, Number of
Fatalities, Crash Rate, and Fatality Rate
To
establish the relationship between SRS and the number of crashes, fatalities,
crash rates, and fatality rates, the data in Figure 5 and Figure 6 were grouped
according to the number of crashes. For each number of crashes, the number of
fatalities for all segments with a certain number of crashes was summed up, and
the SRSs were averaged. Therefore, for example, road segments with only one
crash had a cumulative fatality of 39 and an average SRS of 10.912.
Grouping
the data based on the number of crashes allowed for a more accurate analysis of
the data distribution. Nonetheless, the data point representing ten crashes on
the Cipularang Toll Road was excluded from the analysis due to its outlier
status. The resulting groups of data are thus presented in Table 4.
Table
4 Crash Data in Groups
Number of Crashes |
Crash Rates |
Number of Fatalities |
Fatality Rates |
Average SRS |
0 |
0 |
0 |
0 |
6.468 |
1 |
13.33 |
39 |
3.41 |
10.912 |
2 |
28.30 |
23 |
5.54 |
13.416 |
3 |
46.66 |
14 |
10.51 |
13.430 |
4 |
54.68 |
14 |
34.98 |
12.770 |
Figure 8 Relationship
between number of fatalities vs SRS (a) and fatality rates vs SRS (b)
This study has revealed that the SRS has positive relationship with the
number of crashes, number of fatalities, crash rate, and fatality rate. These
findings are in accordance with the theory that a higher SRS value implies a
more hazardous road segment. Therefore, SRS can be used as a proxy to measure
the hazard level of a segment relative to the other segments. Reducing the SRS
or improving the iRAP Star Rating band can lead to a decrease in the likelihood
of road crashes and fatalities.
Nonetheless,
it should be noted that the R2 values obtained from the graphs were
not high, specifically for the relationships between the SRS and the number of
crash rates as well as the number of fatalities. As a result, the obtained
results may not fully and accurately represent the existing relationships. It
is possible that the unique environment and driver behavior in Indonesia differ
from those of other countries used as the basis for establishing the iRAP
methodology. Additionally, there may be other factors related to crashes that
were not considered in the methodology, particularly those related to human
factors. These factors could have contributed to the lower R2 values
observed in this study.
Road
safety assessment using iRAP Star Rating in Indonesia toll roads show that
approximately 67.25% of toll road in the case study had a 3-star rating band or
better and met the international standard set by the UN SDGs target. This study
has revealed that the SRS has positive relationship with the number of crashes,
number of fatalities, crash rate, and fatality rate. These findings are in
accordance with the theory that a higher SRS value implies a more hazardous
road segment. However, it should be noted that the R2 values for
each relationship were not high, suggesting that the existing relationships may
not be accurately reflected. This may be attributed to the Indonesian road
environment and driver behavior, which were not specifically considered in the
iRAP methodology. Further research is required to investigate these potential
factors and their impact on the relationship between the SRS and various safety
performance indicators in Indonesia. It is highly recommended that the iRAP
methodology be adapted to the Indonesian environment to achieve more accurate
results. Additionally, the findings of this research indicate that iRAP Star
Rating can serve as a suitable proxy for assessing road safety conditions, in
this case, safety improvements should be prioritized for segments with a star
rating less than 3.
This research was supported by P2MI ITB 2021
research grant. The authors would like to thank PT Jasa Marga and PT Lintas
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