Lund University, Sweden Lund University, Sweden https://www.tsr.international/#:~:text=Traffic%20Safety%20Research%20(TSR)%20is,Traffic%20safety%20(ICTCT)%20association. Traffic Safety Research 2004-3082 e000056 10.55329/gzqy4926 Research article Examining the relationship between road service quality and road traffic accidents: a case study on an expressway in Malaysia Conceptualization, Methodology, Writing—original draft, Writing—review & editing. Zahidy Aniq Asyranie

is a civil engineering researcher. He has done this research as part of his MSc in civil engineering final research thesis in Universiti Teknologi Petronas, Malaysia.

 1 Supervision, Validation, Writing—review & editing. Sutanto Muslich Hartadi

is a civil engineering researcher and the Director of Technology Exploitation & Delivery at Universiti Teknologi Petronas.

 1 https://orcid.org/0000-0001-5336-827X Writing—review & editing. Sorooshian Shahryar shahryar.sorooshian@gu.se

is an industrial management researcher at the University of Gothenburg, Sweden. He is also an honorary professor of Saito University College, Malaysia.

 2 3 D'Agostino Carmelo 4 Pappalardo Giuseppina 5 Universiti Teknologi Petronas Malaysia University of Gothenburg Sweden Saito University College Malaysia Lund University Sweden University of Catania Italy 11 9 2024 8 25 9 2023 25 7 2024 2024 Abstract

Malaysia's economic prosperity is overshadowed by a concerning rate of 19 daily road fatalities. This study aimed to investigate road users' perceptions of road service quality (RSQ) and its association with road traffic accidents (RTAs) on an expressway in Malaysia. A questionnaire-based approach collected data from respondents comprising bikers, motorists, bus operators, and truck drivers. Descriptive analysis indicated that, except for motorcyclists, most road user groups rated the overall RSQ of the expressway as poor. Statistical analysis revealed significant variations in perceptions of road surface among road user categories. Pearson correlation analysis demonstrated strong positive relationships between road surface, road drainage, road maintenance, and RTAs. No significant relationships were found between road furniture, rest areas, and RTAs. Multiple regression analysis revealed that road maintenance, road surface, and road drainage accounted for 7.6% of the variance in RTAs, highlighting their importance as predictors. The Relative Importance Index analysis identified ten influential factors on RTAs, including permanent wave, poor workmanship, water pounding, road settlement, repeated construction, invisible road markings, insufficient traffic signs, potholes and bumps, insufficient street lighting, and oily road surfaces. These findings provide policymakers with valuable insights to enhance road safety regulations and develop effective strategies for improving RSQ and reducing RTAs.

 Keywords Malaysia road service quality (RSQ) road traffic accidents (RTA) user perception Introduction

Road networks are often referred to as the ‘lifeline‘ for national economic growth (Berg et al., 2015), as they facilitate the free flow of products, services, and people, contributing to the development of economically stagnant areas (Mohmand et al., 2017). Additionally, roads play a crucial role in connecting various modes of transportation, including airports, train stations, and ports, thereby enhancing accessibility and promoting national integration in Malaysia, a multi-racial developing country (Anor et al., 2012; Othman et al., 2016). However, the reliability of roads in Malaysia, from the perspective of the public, is hindered by the high occurrence of road traffic accidents (RTAs) and related fatalities and injuries (Hamid et al., 2023). In fact, RTAs rank as the country's fourth highest cause of death overall, with the number of fatal and nonfatal accidents on the rise (Pillary et al., 2022). RTAs have emerged as a significant public health concern and an obstacle to national prosperity, both in emerging and wealthy nations (Samsuddin & Masirin, 2016). Therefore, it is imperative to provide a safe and efficient road network with a high level of service, as RTAs have a detrimental impact on public health and the economy (Tehrani et al., 2015).

RTAs are responsible for a significant number of deaths and injuries worldwide each year (WHO, 2015). In Malaysia, the Ministry of Transport reports an average of 19 deaths per day due to RTAs (2014). Despite efforts to enhance roadway safety, the rate of RTAs continues to increase (ASEAN, 2016). Therefore, comprehending the root causes of RTAs is crucial for developing effective preventative measures. Despite previous research on RTAs, there has been a notable lack of focus on roadway factors (Adanu & Jones, 2017; Amedorme & Nsoh, 2014; Belwal et al., 2015; Bhatti et al., 2010; Bokaba et al., 2022; Chen, 2010; Kelarestaghi et al., 2017; Mohanty & Gupta, 2015). Understanding how different aspects of roads influence RTA rates is crucial for effective preventative initiatives (Infante et al., 2023). While previous studies have primarily concentrated on human factors, highway factors have been overlooked. Besides, research has shown that various road management systems, including safety management systems, may complement one another (Cafiso et al., 2021; Putrajaya, 2006). Thus, this research aims to contribute to the existing body of knowledge by investigating the connection between RSQ and RTAs from the perspective of drivers. The study investigates drivers' perceptions of RSQ, its correlation with RTAs, and its ability to predict RTAs. Research questions, thus, are: (1) What is the perception of drivers regarding RSQ, including road surface, drainage, furniture, rest areas, and maintenance? (2) Is there a correlation between the level of RSQ and the occurrence of RTAs? (3) Can RTAs be predicted based on the RSQ?

By analyzing drivers' perspectives on RSQ in Malaysia, this study aims to enhance our understanding of the causes of RTAs and guide industry professionals and legislators towards effective solutions. Evaluating the quality of road services through the lens of road users is essential, as they are critical stakeholders in any road system (Zeithaml et al., 1990). The results of this investigation will be valuable to policymakers, industry practitioners, and researchers seeking to implement better road safety measures. Furthermore, the findings of this study have the potential to deepen our knowledge of RTAs in Malaysia and other developing nations, enabling more effective efforts to address this significant public health concern. Ultimately, this research aims to shed light on the factors contributing to RTAs in Malaysia and aid in the development of effective preventative policies.

 Literature review Role of road infrastructure in economic development: empirical evidence and global perspectives

Investment in road infrastructure has long been recognized as a crucial driver of economic expansion due to its ability to strengthen economic chains, accelerate development, and support regional growth objectives (Crescenzi et al., 2016; Magazzino & Mele, 2021). Improved economic growth, increased productivity, job creation, and enhanced well-being for local residents are positive outcomes of road infrastructure investments (Hlotywa & Ndaguba, 2017; Porter, 2014; Sun et al., 2018). Consquently, the total length of paved roadways in a country is often used as a measure of its development level (Aldagheiri, 2009).Leung (2006) demonstrates the multiple ways in which investments in road infrastructure contribute to economic growth and societal well-being, as depicted in Figure 1.

Transportation and economic growth (<xref id="x-b811e7a7bd69" rid="R242220031442878" ref-type="bibr">Leung, 2006</xref>)

The relationship between road infrastructure and economic change is influenced by various factors, including the competitive structure of regional markets, technological and institutional shifts in transport, communications, and production systems, as well as the overall condition of the transport network (Lakshmanan & Chatterjee, 205). Table 1 categorizes the impacts of transport infrastructure upgrades based on time (short-term, long-term) and space (local, regional, global) as classified by Lakshmanan and Chatterjee (205).

Impacts of investments in transportation (<xref id="xref-fc0259d48a6f46a3b32c943a7b33a283" rid="R242220031442947" ref-type="bibr">Lakshmanan & Chatterjee, 205</xref>)

Attributes

Short-term

Long-term

Very long-term

Types and forms of effects

Reduced congestion

Shorter travel times and lower vehicle operating costs

Rising demand and output

Logistical reorganization

Inventory cost reduction

Local and regional growth

Larger markets for products, labours, and services

Export expansion

Entry and exit of firms

Regional/national integration

Structural and developmental effects

Promotion of globalization processes

Global distribution and production

Global flows of goods, services, capital and knowledge

Underlying processes and contextual factors

Increased competition

Supply and demand forces

Monopolies may emerge

Economic of scale

Agglomeration

Cumulative causation

Endogenous growth

Confluence of technical and organisational or institutional changes in transport, communication, and production sectors

Description and measurement of effects

Benefit-cost analysis

New economic geography theory

Notion of gains from trade

Computable general equilibrium models

Economic history analysis

According to HE (2016), investing in roads can have positive effects on the economy through direct and indirect means. Direct effects include investment in capital, while indirect effects result from savings in transportation costs. Four primary techniques contribute to these effects:

Capitalizing on opportunities to enhance performance, stimulate agglomeration economies, increase competitiveness, and maximize the benefits of international trade and investment

Boosting exchange rates between countries

Encouraging domestic and international investment by businesses and builders alike

Reducing unemployment rates and increasing job availability.

Transportation investments have been shown to have a positive economic impact, particularly in underdeveloped nations. Hlotywa and Ndaguba (2017) analyzed the relationship between road transport investment and Gross Domestic Product (GDP) growth in South Africa using panel data from 1990 to 2014. They found a positive correlation between road transport investment and economic growth in South Africa, with the explanatory variables accounting for approximately 86.7% of the variance in economic growth. Similarly, (Shabani & Safaie, 2018) examined the impact of Iran's road and rail networks on the country's provincial economies. Their research revealed that investments in road and railway infrastructure not only directly boosted provincial economies but also had indirect positive effects on neighboring provinces, indicating the presence of spatial spill-over effects.

The effects of improving road infrastructure on GDP and competitiveness within the European Union (EU) were studied by Ivanova and Masarova (2013) in Slovakia. Their time series and correlation study demonstrated that increased spending on roads and attracting Foreign Direct Investment (FDI) both contributed to higher GDP and improved competitiveness. Additionally, Peter et al. (2015) investigated the impact of road transportation on GDP growth in Nigeria. Through econometric analysis of primary and secondary data, they found a long-term connection between road transport infrastructure and economic growth. Similarly,Saidi (2016) examined the influence of road transport on economic development and FDI attraction in Tunisia. Econometric modeling revealed that roads contribute to attracting FDI, which, in turn, fosters long-term economic growth.

The studies cited above underscore the importance of roads in promoting economic growth. They provide evidence of the positive effects of investing in road transport infrastructure on productivity, trade, investment, and GDP expansion. These findings support the argument that national investment in road infrastructure is crucial for enhancing regional connectivity and boosting national economies.

 RTAs in Malaysia: current situation

Despite the important contribution of road infrastructure development to the nation's growth, the rapid development of road infrastructure has led to a rising number of fatalities and injuries caused by road traffic accidents (RTAs). According to Pembuain et al. (2020), the number of RTAs would be increased as the infrastructure risk increases unless it was designed and constructed to accommodate all aspects of safety for its users. Today, RTAs issue representing a major challenge to public health and national growth worldwide especially in developing countries, and Malaysia is one of those countries.

Various road safety policies and plans have been forwarded to account for the significant reduction in RTAs. However, it seems does not prove to be successful in reducing RTAs, as the number of RTAs is still alarming, although a number of policies have been implemented. Like many other developing countries, the situation of RTAs in Malaysia is not much different. Figure 2 illustrates the historical data of RTAs and road deaths in Malaysia spans from 2015 to 2022. It can be seen that the number of RTAs has increased from 489 606 in 2015 to 521 466 in 2016, recording 6 706 people deaths in 2015 before accelerating to reach all time high of 7 152 people deaths in 2016. The number of RTAs increased steadily to 533 875 in 2017, 548 875 in 2018, and finally peaked to reach all time high of 567 516 in 2019. Within this period, however, the number of deaths has shown a decreasing trend, with figures dropping to 6 740 in 2017, 6 284 in 2018, and further dropped to 6 167 in 2019. In 2020 and 2021, the number of RTAs has decreased to 418 237 and 370 286, respectively. Following the decreasing of the number of RTAs, the number of deaths has also decreased to 4 634 in 2020 and reached a lower peak of 4 539 in 2021.

Despite a notable decrease in both RTAs and deaths in 2020 and 2021, the figures do not accurately represent the current situation in Malaysia. It was due to a global phenomenon, and the primary reason for its occurrence was the Movement Control Order (MCO) which was enacted by a number of countries as a reaction to the COVID-19 pandemic (OECD, 2020). It can be observed when the number of RTAs has increased significantly by 47.3% from a total of 370 286 in 2021 to 545 588 in 2022. The number of deaths has also recorded an increasing of 34.0% from a total of 4 539 in 2021 to 6 080 in 2022. Hence, the current situation concerning the RTAs in Malaysia remains unacceptable and requires for an improvement. It could not be denied that the existing road safety efforts may have mitigated the situation from getting worse when considered in the context of the increasing global population and rapid motorization that has taken place over the same period (WHO, 2023), nevertheless more efforts need to be made.

Number of RTAs and deaths from 2015 to 2022 (<xref id="x-ce47cc5ca6e3" rid="R242220031691624" ref-type="bibr">DoSM, 2023</xref>)

On the basis of the current circumstances, it is important to point out that the Malaysian aspirations of halving deaths from RTAs by the year 2030, as outlined in the Malaysia Road Safety Plan 2022–2030 (Putrajaya, 2022), will not be accomplished unless effective mitigation measures are developed. As a consequence to this, the agenda of the Decade of Action for Road Safety 2021–2030 with a target of halving at least 50% of deaths and injuries from RTAs by 2030 (WHO, 2021), is unlikely to be achieved. This gives rise to the following question: have the formulated policies and plans related to road safety in Malaysia taken into consideration all of the causation factors of RTAs?

Road safety plans and initiatives in Malaysia: strategies, implementation, and results

The government of Malaysia has implemented road safety strategies and programs to reduce the number of road traffic deaths (Putrajaya, 2006; Putrajaya, 2014). The Malaysian Road Safety Plan 2006–2010, outlined by the (Putrajaya, 2006), aimed to implement road safety plans, set new targets, provide education in schools, conduct media campaigns, establish road safety audit units and departments, and implement blackspot treatment programs. However, the targets for 2010 were not achieved, as the overall number of RTAs and fatalities from 1997 to 2016 exceeded the set targets. This indicates a need for more effective implementation of the measures outlined in the Malaysian Road Safety Plan 2006–2010.

In response to international initiatives to improve road safety, the Government of Malaysia established the Road Safety Plan of Malaysia 2014–2020. The Ministry of Transport Malaysia developed a five-point plan to reduce the number of road traffic accident-related fatalities, setting both long-term and short-term goals (Ultimate Outcomes and Midterm Outcomes) (Putrajaya, 2014). Although progress has been made, the goals of the Road Safety Plan of Malaysia 2014–2020 have not been fully achieved. Recent data indicates that the number of fatalities in RTAs did not decrease as expected between 2015 and 2019, despite a temporary slowdown in trends starting in 2016. The overall number of RTAs continued to rise. While the 2020 COVID-19 outbreak resulted in a significant reduction in RTAs due to the movement restriction order imposed by the government, further research into the root causes of RTAs in Malaysia is warranted.

RTAs have multifaceted causes, with human error, particularly reckless driving, being a major contributor. Driver-related factors such as speeding, lack of attention, and unsafe driving maneuvers are often responsible for accidents (Abdelfatah, 2015). Additionally, the condition of the roads, their design quality, and maintenance play a significant role (Naveen & Kumar, 2016). Vehicle-related issues, such as mechanical problems, also contribute to a substantial number of accidents (Jaiswal & Bhatore, 2016). It is important to consider the interactions between individuals, roads, and vehicles when addressing RTAs, as neglecting any of these factors can hinder accident prevention efforts. Further research is needed to identify the root causes of RTAs in Malaysia and develop effective solutions to improve road safety.

The systems theory of accident causation provides a useful framework for understanding the factors contributing to accidents. According to Firenze (1978), accidents occur when human beings, technology, and the natural world interact, with risks determined by the interplay of these factors. Errors are viewed as breakdowns of the entire system, encompassing hidden flaws and vulnerabilities (Elslande & Fouquet, 2007). Molinero et al. (2009) emphasize the significance of human error within the framework of rules and regulations, highlighting that RTAs are systemic failures resulting from the need for drivers to navigate machines and a constantly evolving environment. Understanding RTAs through the lens of the systems theory of accident causation is vital, as it acknowledges the roles played by humans, technology, and the surrounding environment.

 Malaysian studies on RTAs

Considerable research has been conducted on the causes of RTAs in Malaysia.Abdullah and Zamri (2010) examined the relationship between RTAs and population, registered vehicles, and road length. Their findings revealed a significant association between RTAs and these variables, with registered vehicles exhibiting the highest correlation. Najib et al. (2012) utilized the Analytic Hierarchy Process to identify the primary contributors to RTAs in Malaysia. Their research identified several causes, including speeding, inattentive driving, poor road conditions, roadside obstacles, and inadequate vehicle maintenance. Junus et al. (2017) explored how economic, climatic, and calendar characteristics varied across regions in Malaysia and influenced RTAs. The results demonstrated that the consequences of RTAs vary by location due to local factors. (Darma et al., 2017) investigated how road-related issues contribute to fatalities resulting from automobile accidents, highlighting the role of poorly lit streets, steep shoulder drop-offs, and potholes.

Understanding the mindset and behavior of drivers is crucial for addressing RTAs.Redhwan and Karim (2010) investigated the knowledge, attitudes, and behaviors of Malaysian college students regarding RTAs. They identified speeding, lack of awareness of road laws, and violation of road rules as primary causes of accidents. Ismail et al. (2015) developed a model to assess driver engagement in RTAs based on criteria such as frequent overtaking, tailgating, and high speeds. Mohamad et al. (2017) examined the role of young drivers in speeding, highlighting the influence of factors such as experience, driving time, circumstances, and road conditions. Sultan et al. (2016) studied young motorcyclists in Malaysia, emphasizing impatience, carelessness, and risky driving behaviors as significant factors.

The studies mentioned above contribute to our understanding of the causes of RTAs in Malaysia. They highlight the importance of road and environmental factors, as well as vehicle-related issues, while emphasizing the role of human behavior. It is evident that RTAs rarely have a single cause but rather result from the complex interaction of multiple factors. By comprehending the intricate relationships among individuals, machines, and their surroundings, efforts can be directed towards reducing accidents and mitigating environmental hazards. Nevertheless, to the best of the authors knowledge, the assessment of the correlation between RSQ and RTAs appears to have been overlooked.

 Development of measurement constructs: assessing RSQ and RTAs

For assessing the RSQ and RTAs, the measurement constructs utilized in this study were developed by drawing upon relevant sources. The following literature provides insights into approaches for evaluating the quality of road services and the occurrence of RTAs. The Quality Assessment System for Completed Road Work, developed and published by the Construction Industry Board Malaysia in 2011 (CIDB, 2011), offers a third-party technique for assessing the quality of recently completed road projects. The standard comprises five elements of road works service quality: road surface, slope and retaining structure, drainage, bridges and other structures, and traffic and road furniture. Table 2 provides an overview of the defect groups assessment used to standardize the quality of road construction projects.

Groups of defects evaluating the standard (<xref id="x-ba5d73d1856b" rid="R242220031693597" ref-type="bibr">CIDB, 2011</xref>)

Component

Element

Defect groups

Road surface

Carriageway, shoulder and verge, median

Riding quality

Safety

Aesthetic

Slope and retaining structure

Slope, retaining structure, slope drainage, above ground services, slope furniture

Crack and damage

Joint, filler, and gaps

Functionality

Finishing

Installation

Drainage

Drain, drain cover, sump, pipe, box culvert

Crack and damage

Joint and gap

Functionality

Finishing

Bridge and other structures

Above ground bridge (road bridge, pedestrian and motorcycle bridges)

Special structures (underpass/vehicular box culverts, tunnel)

Crack and damage

Joint and gap

Functionality

Visibility

Finishing

Installation

Traffic and road furniture

Marking, traffic calming devices, traffic sign, electrical devices, pedestrian facilities, kerb, traffic barrier, delineators

Crack and damage

Functionality

Visibility

Finishing

Installation

Tortum et al. (2012) developed a model that identified road failure as a major cause of accidents in Turkey. The model highlighted road pits, wheel traces, soft shoulders, loose material, permanent waves, lack of road signs, and road settlement as contributors to road failure. Chang et al. (2015) created the ‘Assessment Framework of RSQ from the Point of View of Road Users’ in Taiwan. The framework encompasses safety, surface, construction, management, and facilities and landscaping as the five pillars of road quality service. This framework allows for an assessment of which aspects of RSQ are most important to different demographics of drivers. Table 3 presents the Users' Perceptions of the Quality of Road Services Assessment Framework.

Road service quality (RSQ) (<xref id="xref-b70318401be24b46a3ad6d20a26815ef" rid="R242220031442899" ref-type="bibr">Chang et al., 2015</xref>)

Dimension

Measurement

1.

Road safety

Unsafe conditions due to manhole covers

Slipping due to traffic marking

Insufficient street lighting

Road sign safety

2.

Road surface

Potholes and bumps on pavements

Subgrade slippage

Damaged manhole covers

Drainage failure

Defective gutters

3.

Road construction

Insufficient information on construction works

Notice of construction duration

Poor backfilling after construction

Repeated construction

4.

Road management

Curtailed or narrow lanes

Encroachment on right of way

Insufficient parking space

Large traffic volume

5.

Road facilities and landscaping

Disorder/dirty roads

Untidy roadside landscaping

Poor installation of street lighting

Landscape image

Zuna et al. (2016) developed the Toll RSQ model in Indonesia using an artificial neural network methodology. The model combines the SERVQUAL and Minimum Service Standards frameworks to analyze customer satisfaction with toll road services. Table 4 summarizes the dimensions and features of the Toll RSQ model.

Measurements and characteristics of Toll RSQ (<xref id="xref-6afe5137a4914299ab54dfb2786160d0" rid="R242220031442892" ref-type="bibr">Zuna et al., 2016</xref>)

Dimension

Attribute

1.

Information

Accuracy of information

Information board and signage

2.

Accessibility

Toll gates

Performance of toll gates officer Hospitality of toll gates officer

Honesty of toll gates officer

3.

Reliability

Smoothness of road surface

Road lighting

4.

Mobility

No traffic congestion

5.

Safety and security

Driving safety

Security from crime

6.

Rest area

Rest area facilities

7.

Responsiveness

Accident handling

Road maintenance

Call centre

Fast response of emergency unit

To measure the state of roads in India, researchers Pilaka et al. (2017) created the road service indices. There are eighteen different indications of service built into the indices' four road service standards, as illustrated in Table 5.

Indicators of roadway service (<xref id="xref-6ce638a4d208423386751c0bbfae5a8b" rid="R242220031442876" ref-type="bibr">Pilaka et al., 2017</xref>)

Road service parameter

Service indicator

1.

Quality of road

Smoothness

Roadway markings

Shoulder condition

2.

Safety of road

Pedestrian crossing facilities

Signs and signals

Lighting at the junctions

3.

Security and emergency services of the road

Highway police patrolling

Ambulance for accidents victims

Crane facility for vehicle breakdown

Telephone booth for emergency call

4.

Road user amenities

Restaurants

Canteens

Petrol pumps

Auto service centers

Medical aid

Parking lots

Public toilets

Rest house for drivers and travellers

Pilaka et al. (2017) created road service indices to measure the condition of roads in India. The indices include four road service standards with eighteen different indicators. Table 5 illustrates the service indicators and standards incorporated in the road service indices.

While these models provide valuable insights, they may not directly align with measuring drivers' satisfaction with improvements made to the East Coast Expressway Phase 2 (ECE2) from their perspective. Some elements, such as ‘slope and retaining structure’ and ‘bridge and other structures’ require technical knowledge and physical on-site inspections for accurate assessments. However, drivers can provide valuable input regarding road surface, drainage, traffic volume, and road furnishings. The measurement constructs developed for this study (Table 6) were based on expert opinion and existing literature.

Measurement constructs

Dimension

Component

Variable

1.

Road surface

Carriageway

Potholes and bumps on carriageway

Permanent wave

Road settlement

Water pounding

Improper cross fall

Oily road surfaces

Shoulder

Soft shoulders Insufficient shoulder space

Untidy shoulder

Level gap between shoulder and road surface

2.

Road drainage

Drain and sump

Too close to the shoulder

Damaged manhole covers

Not functioning

3.

Road furniture

Marking

Dirty road marking

Invisible road marking

Traffic sign

Unclear traffic signs

Street lighting

Insufficient street lighting

Poor installation of street lighting

Guardrail

Defective guardrail

Too close to carriageway

Inconsistently aligned to carriageway

4.

Rest area

Rest area location

Uncomfortable for rest

Inappropriate location

5.

Road maintenance

Maintenance works

Fast repair of defects Insufficient traffic signs

Poor workmanships

Repeated construction

Previous studies on RTAs have primarily focused on objective measurements derived from secondary data. However, subjective measurements have also been used to assess drivers' feelings of safety and the likelihood of accidents on the road. This study incorporates subjective measurements, including RTA occurrence rates, response times, accident management efficiency, and drivers' overall perceptions of expressway safety.

By investigating the connections between RSQ and RTAs, this study aims to address the under-recognized and under-studied relationship between road factors and RTAs. While previous road safety policies have primarily focused on human factors, it is evident that a comprehensive approach considering all potential causes of RTAs and their interactions is necessary to effectively address the alarmingly high rate of accidents (Konlan & Hayford, 2022). The literature emphasizes the importance of road conditions, environmental factors, and vehicle variables in causing RTAs. A significant proportion of reported accidents can be attributed to combinations of road, human, and vehicle factors. Therefore, road safety measures should encompass all potential causes and their interactions. Effective road safety measures can only be developed by addressing the underlying causes of RTAs, as highlighted by the World Health Organization (WHO, 2015).

 Methodology

With the knowledge gained from the literature review section, this section outlines the methodology employed in this study to address the research objectives. It is organized into four sub-sections, each focusing on a specific aspect.

Research design

The research design is guided by a conceptual framework that illustrates the interconnected nature of RSQ and RTAs. The proposed conceptual framework, Figure 3, shows how various factors, such as road conditions, drainage systems, furnishings, rest stops, and upkeep, contribute to the overall quality of road services. The study aims to determine the relationship between RSQ and RTAs and quantify this connection. The research design aligns with postpositivism, as it seeks to isolate and evaluate the factors that contribute to the results. A quantitative approach is employed to gather empirical data and analyze the relationship between RSQ and RTAs.

Conceptual framework Research location

The was conducted on the East Coast Expressway Phase 2 (ECE2), one of the newly completed road infrastructure projects in Malaysia. The ECE2 is 184 kilometres long, dual-direction, four-lane was built at a cost of RM4.1 billion. The expressway has attracted public concern and government attention due to the substantial number of RTAs occurred shortly after it opened to the public. Within six months (July 2015 to December 2015) after opened to the public, it was recorded 963 cases of RTAs with a death of 26 peoples, 53 peoples suffered from serious injuries, and another 173 victims' minor injuries. The cases have resulted in overall increasing of number of RTAs in Malaysia for the year 2015 (Putrajaya, 2016). The RTAs most frequently occurred along the KM200 to KM400. Table 7 summarized the number of RTAs, deaths, and injuries occurred on the ECE2 from 2016 to 2021.

Number of RTAs, deaths, and injuries on the ECE2 (2016–2021) (<xref id="x-4795b4eba05b" rid="R242220031700365" ref-type="bibr">Putrajaya, 2023</xref>)

2016

2017

2018

2019

2020

2021

RTA

955

737

772

721

579

522

Deaths

16

20

17

20

10

9

Serious injuries

48

56

58

82

36

32

Minor injuries

176

125

165

133

96

65

Throughout the mitigation efforts that have been done, the number of RTAs in 2016 has decreased to 955 cases with 20 deaths, 56 serious injuries, and 125 minor injuries, and further decreased to 737 cases with 26 deaths, 53 serious injuries, and 173 minor injuries in 2017. However, the number of RTAs was increased to 772 cases, with 17 deaths, 58 serious injuries, and 165 minor injuries before decreased to 721 cases in 2019 with 20 deaths, 82 serious injuries, and 133 minor injuries. In 2020 and 2021 where the Movement Control Order (MCO) has been enforced saw the number of RTAs has decreased to 579 and 522 cases, respectively. As a result, the number of deaths has decreased to 10 in 2020 and nine in 2021. Consequently, the number of serious injuries has also decreased to 36 in 2020 and 32 in 2021. Furthermore, the number of minor injuries recorded a decreasing to 96 in 2020 and further decreased to 65 in 2021.

Since RTAs occurred in unacceptable rate considering the length, and traffic volume compared to other highways, the ECE2 is an interesting case research. Moreover, the physical conditions of the highway are expected to be of acceptable quality due to its newly opened. For example, the quality of road surface in term of wear and tear result from normal wear or aging should be not the issued.

Data collection

The data was collected from road users who stopped at Rest and Service Areas (RSAs) located each two at Perasing, Paka, and Ajil. These RSAs accommodated road users driving from Kuantan to Kuala Terengganu, as well as from Kuala Terengganu to Kuantan and located along the KM200 to KM400 of the expressway. This approach allowed for convenient access to a diverse range of road users and facilitated the collection of representative data. The self-administered questionnaire with delivery and collection type is used as the primary data collection instrument, consisting of three parts: general information, ECE2 service quality, and overall service quality perception. Prior presentation (Zahidy et al., 2019) of exploratory factor analysis of the variables served as the foundation for this study's survey. The questionnaire items are measured on a five-point Likert scale to capture respondents' experiences and opinions. An abbreviated copy of the final questionnaire is provided in Appendix A . A convenience quota sampling method is utilized to gather data from primary groups of road users include motorcyclists, car drivers, bus drivers, and truck drivers. The research study considered recommendations on sample size in correlation studies.Salkind (2017) advises a minimum of 30 individuals per group for investigating group differences, while Bujang and Baharum (2017) suggest a minimum of n ≥ 29 to detect a reasonably high correlation coefficient. Since there was no comprehensive list of road users available, a convenience quota sampling method was employed during the sampling process.

Prior to the main survey, however, a pilot study with 24 participants is conducted to refine the questionnaire, assess its face validity, and ensure understandability. The content validity of the questionnaire is established through expert reviews, while construct validity is inferred from existing literature. The reliability of the questionnaire is measured using Cronbach's alpha coefficient. Data screening is performed to validate input, fill in missing values, test for normality, identify outliers, and verify linearity.

Analysis of data

The data analysis is conducted using SPSS Version 26.0. Descriptive statistics are employed to assess how respondents rate different aspects of the ECE2's road quality. One-way analysis of variance (ANOVA) is used to determine if there are statistically significant differences in mean ratings among different groups of drivers. Correlation analysis is conducted to examine the relationship between RSQ variables (road surface, drainage, furnishings, rest areas, and road maintenance) and RTAs. Multiple regression analysis is employed to calculate the coefficient of determination (R2) and assess the relative importance of the RSQ variables in predicting RTAs. The Relative Importance Index (RII) is utilized to rank RSQ elements based on their likelihood and impact on RTAs:

RII= ∑W(A*N)

where:

W is the weight given to each factor of independent variables by the respondents from 1, 2, 3, 4, and 5 based on the five-point Likert scales (1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree and 5 = strongly agree);A is the highest weight (i.e. 5 in this case);N is the total number of respondents.

The methodology ensures that valid and reliable data is collected and analyzed using appropriate statistical techniques. By employing a combination of quantitative methods, the study aims to provide insights into the relationship between RSQ and RTAs on the ECE2.

 Results

The measurement tool used in this study was evaluated for reliability using Cronbach's alpha coefficient analysis. The results indicate that all items achieved Cronbach's alpha coefficients greater than 0.7, indicating high reliability. Among the dimensions of RSQ, road furniture exhibited the highest reliability (0.887), while road maintenance demonstrated the lowest (0.739). The constructs of road furniture, road surface, road traffic accident, and road drainage showed high levels of reliability with coefficients of 0.887, 0.834, 0.829, and 0.810, respectively. The reliability of rest area and road maintenance was considered acceptable with coefficients of 0.765 and 0.739, respectively. Additionally, the inclusion of all variables contributed to the reliability, as slight changes in Cronbach's alpha were observed upon removing any variable.

The validity of the measurement instrument was assessed through content validity and construct validity. Content validity was achieved by conducting a comprehensive literature review and involving academicians, industry professionals, and participants in a preliminary investigation. Their input was used to improve the questionnaire, ensuring that the items were unambiguous, relevant, and precise. Construct validity was established using principal component factor analysis with varimax rotation. The Kaiser-Meyer-Olkin measure of sampling adequacy was found to be 0.68, indicating satisfactory intercorrelations among the items. The Bartlett's test of sphericity confirmed the suitability of the data for further analysis. The factor analysis revealed that the eigenvalues accounted for 69.9% of the variance, surpassing the suggested threshold of 60% for adequate explanation of variance.

To answer research question 1, a descriptive analysis was conducted to assess the perception of different groups of respondents regarding the overall service quality of the ECE2. The results, presented in Table 8, indicate that perceptions varied among the different road user groups. For motorcyclists, 42.3% perceived the overall RSQ as good, while 14% viewed it as poor. Among motorists, 43.9% perceived poor RSQ, and 22.0% considered it good. Among bus operators, 56.1% perceived poor RSQ, and 19.5% perceived it as good. For truck drivers, 45% perceived poor RSQ, and 22.5% perceived it as good. The analysis also conducted a one-way analysis of variance (ANOVA) to determine if there were statistically significant differences in perception between road user groups for each RSQ dimension. The results showed that only road surface exhibited a statistically significant difference in perception between groups (F = 3.008, p < 0.05), while the other dimensions did not show significant differences.

Overall perception on RSQ

Respondents

Scale

Frequency (N)

Percentage (%)

Motorcyclists

(N = 52)

Very poor

1

1.9

Poor

14

26.9

Acceptable

15

28.8

Good

22

42.3

Very good

-

-

Motorists

Very poor

1

2.4

Poor

18

43.9

Acceptable

13

31.7

Good

9

22.0

Very good

-

-

Bus operators

Very poor

-

-

Poor

23

56.1

Acceptable

10

24.4

Good

8

19.5

Very good

-

-

Truck drivers

Very poor

-

-

Poor

18

45.0

Acceptable

13

32.5

Good

9

22.5

Very good

-

-

To answer research question 2, correlation analysis was conducted to examine the relationship between RSQ dimensions and road traffic accidents (RTAs). The results, shown in Table 9, indicate that road surface, road drainage, and road maintenance exhibit significant positive correlations with RTAs. Road maintenance showed the highest correlation (r = 0.280, p < 0.05), followed by road surface (r = 0.226, p < 0.05), and road drainage (r = 0.159, p < 0.05). Road furniture and rest area, on the other hand, did not show significant correlations with RTAs (r = 0.129, p > 0.05; r = 0.111, p > 0.05, respectively). These findings suggest that deficiencies in road maintenance, road surface, and road drainage contribute to a higher risk of RTAs.

Pearson Correlation

Variables

RS

RD

RF

RA

RM

RTAs

RS

r

1

p

–

RD

r

.424**

1

p

.000

–

RF

r

.359**

.399**

1

p

.000

.000

–

RA

r

.299**

.359**

.252**

1

p

.002

.000

.001

–

RM

r

.422**

.082

.211**

.104

1

p

.000

.284

.005

.174

–

RTAs

r

.226**

.159*

.129

.111

.280**

1

p

.003

.036

.090

.144

.000

-

Means

3.017

2.172

2.759

2.101

3.322

3.009

SD

.714

.987

.743

.955

.909

.720

Road surface (RS); road drainage (RD); road furniture (RF); rest area (RA); road maintenance (RM)

** Correlation is significant at the 0.01 level (2-tailed).

* Correlation is significant at the 0.05 level (2-tailed).

To answer research question 3, multiple regression analysis was performed to examine the effect of RSQ dimensions on RTAs. The regression coefficients and standardized beta coefficients (β) were used to determine the unique contribution of each dimension. The results, presented in Table 10, indicate that road surface (β = 0.167, p = 0.037), road drainage (β = 0.109, p = 0.039), and road maintenance (β = 0.186, p = 0.005) were statistically significant predictors of RTAs. Road furniture (β = 0.006, p = 0.945) and rest area (β = 0.026, p = 0.664) did not significantly contribute to the regression model. The regression equation provides a predictive model for RTAs based on the defects in road maintenance, road surface, and road drainage:

y=1.937+0.186·x1+0.167·x2+0.109·x3

where:    

y is level of RTAs;x1 is defects of road maintenance;x2 is defects of road surface;x3 is defects of road drainage.

Standard multiple regression of RSQ dimensions on RTAs

Varialbes

RTAs

RS

RD

RF

RA

RM

B

β

(Constant)

1.937

RTAs

RS

.226

.167*

.192

RD

.159

.424

.109*

.178

RF

.129

.359

.399

.006

.006

RA

.111

.229

.359

.252

.026

.034

RM

.280

.422

.082

.211

.104

.186*

.235

Means

3.009

3.017

2.172

2.759

2.101

3.322

SD

.720

.714

.987

.743

.955

.909

*p < .05R = .321*; R2 = .103; Adjusted R2 = .076; F(5, 168) = 3.854

Additionally, RII technique was used to rank the RSQ factors according to their importance in relation to RTAs. The results presented in Table 11 indicate that factors such as permanent wave, water pounding on pavement or shoulder, road settlement, and insufficient street lighting were identified as significant contributors to RTAs. The ranking provides insights into the factors that have the highest impact on RTAs and can guide interventions and improvements to enhance road safety.

RII for the effect of RSQ on RTAs

Variable

RII (%)

Rating

Degree of effect

Group

Overall

A.

Road surface

1.

Potholes and bumps on carriageway

65.5

4

8

HE

2.

Permanent wave

80.8

1

1

VHE

3.

Oily road surfaces

60.0

5

10

HE

4.

Road settlement

73.2

3

4

HE

5.

Water pounding on pavement or shoulder

73.4

2

3

HE

6.

Improper cross fall

53.9

6

13

ME

7.

Soft shoulders

47.7

9

19

ME

8.

Insufficient shoulder space

46.0

10

21

ME

9.

Untidy shoulder

51.4

8

17

ME

10.

Level gap between shoulder and road surface

51.5

7

16

ME

B.

Road drainage

11.

Too close to the shoulder

44.3

1

24

ME

12.

Damaged manhole covers

42.6

3

27

ME

13.

Not functioning

43.4

2

25

ME

C.

Road furniture

14.

Dirty road marking

59.3

3

11

ME

15.

Invisible road marking

67.9

1

6

HE

16.

Unclear traffic signs

51.8

5

15

ME

17.

Insufficient street lighting

65.4

2

9

HE

18.

Poor installation of street lighting

56.7

4

12

ME

19.

Defective guardrail

49.3

6

18

ME

20.

Guardrail too close to carriageway

46.7

7

20

ME

21.

Guardrail inconsistently aligned to carriageway

44.4

8

23

ME

D.

Rest area

22.

Uncomfortable for rest

38.3

3

28

LE

23.

Inappropriate location

43.3

2

26

ME

24.

Too far between one another

44.8

1

22

ME

D.

Road maintenance

25.

Fast repair of defects

52.6

4

14

ME

26.

Insufficient traffic signs

65.7

3

7

HE

27.

Poor workmanships

76.4

1

2

HE

28.

Repeated construction

70.9

2

5

HE

The findings of this study highlight the significance of RSQ dimensions, particularly road surface, road drainage, and road maintenance, in predicting and understanding RTAs. The results emphasize the need for targeted interventions to address deficiencies in these areas to improve road safety on the East Coast Expressway.

Last, but not least, Table 12 present a detailed overview of the demographic characteristics of the respondents, providing information about their profiles, such as gender, age, occupation, and driving experience. This information helps contextualize the responses and understand potential variations based on demographic factors.

Demographic of respondents (N = 174)

Frequency (N)

Percentage (%)

Group

Motorcyclists

52

29.9

Motorists

41

23.6

Bus operators

41

23.6

Truck drivers

40

23.0

Gender

Male / Female

138 / 36

79.3 / 20 7

Age

Below 20

14

8.0

21–30

45

25.9

31–40

56

32.2

41–50

45

25.9

51 and above

14

8.0

Education

Secondary school

119

68.4

High school

7

4.0

Diploma

14

8.0

Bachelor's degree

29

16.7

Master's degree

5

2.9

Occupation

Student

3

1.7

Government

6

3.4

Private

58

33.3

Teacher/Lecturer

7

4.0

Driver

80

46.0

Businessman

6

3.4

Self-employed

14

8.0

Average distance travelled per year

< 20 000 km

43

24.7

20 001–40 000 km

45

25.9

40 001–60 000 km

20

11.1

60 001–80 000 km

31

17.8

80 001–100 000 km

32

18.4

> 100 000 km

3

1.7

Frequency travel along ECE2

Every day

50

28.7

1 time/week

12

6.9

2–3 times/week

15

8.6

4–6 times/week

59

33.9

1 time/month

30

17.2

More than 1 time/month

8

4.6

Reasons for travelling along ECE2

School

2

1.1

Work

132

75.9

Transportation

3

1.7

Business

5

2.9

Holidays

10

5.7

Return to hometown

21

12.1

Other

1

0.6

Experienced in accidents in ECE2

Yes / No

18 / 156

10.3 / 89 7

Caused of accident in ECE2

Human factors

5

2.9

Road factors

7

4.0

Vehicle factors

4

2.3

Environment factors

2

1.1

Finally, the results of this study demonstrate the reliability and validity of the measurement tool and provide valuable insights into the perception of overall service quality, the relationship between RSQ dimensions and RTAs, and the predictive power of RSQ dimensions in relation to RTAs. The findings contribute to the understanding of factors influencing road safety on the East Coast Expressway and can guide interventions to improve RSQ and reduce the occurrence of RTAs.

 Conclusion

This study examined the relationship between RSQ and RTAs on a selected expressway in a developing country, Malaysia, providing valuable insights into the factors influencing RTAs. The findings highlight the significance of RSQ in predicting and understanding RTAs, shedding light on factors beyond human error and vehicle defects.

The results revealed that the majority of road users perceived the RSQ of the expressway as inadequate, with the exception of motorcyclists. This distinction can be attributed to the differences in vehicle size and mechanical systems among the road user groups. The correlation analysis confirmed significant positive relationships between road surface, road drainage, road maintenance, and RTAs. Road maintenance emerged as the most significant predictor of RTAs, followed by road surface and road drainage. Additionally, the study identified the ten most influential factors on RTAs, ranging from permanent wave and poor workmanship to inadequate street lighting and slick road surfaces. These findings provide valuable guidance for addressing specific areas that contribute to RTAs and improving road safety.

By focusing on the causal relationship between RSQ and RTAs, this study challenges the assumption that accidents are solely caused by human error and vehicle defects. It emphasizes the importance of considering RSQ alongside other factors in road safety initiatives and underscores the need for public participation in shaping transportation systems. The study contributes empirical evidence to the existing literature on road safety and informs the formulation of policies aimed at reducing RTAs.

From an industry perspective, this study highlights the significance of maintaining a design and maintenance culture that ensures roads meet their intended lifespan, thereby reducing accidents and operational costs. Emergency maintenance is also emphasized as a crucial aspect in addressing defects in key road components, minimizing the risks of RTAs for road users. Furthermore, the study suggests enhancing the delivery and implementation methods for road infrastructure projects based on road users' perceptions of poor RSQ.

However, it is important to consider the assumptions and limitations of this study. The representativeness of the respondents from specific road user groups should be taken into account, along with the reliance on self-reported experiences for data accuracy. The findings are specific to the case expressway and may not be universally applicable. Future research can expand on this study by investigating additional expressways and regions, refining measurement constructs, and increasing sample sizes to enhance the external validity of the findings. Conducting research in diverse geographical settings would also contribute to testing the generalizability of the findings.

In conclusion, this study provides valuable insights into the causal relationship between RSQ and RTAs, highlighting the need for comprehensive approaches to road safety that consider RSQ alongside human and vehicle factors. The findings can guide policymakers and industry professionals in enhancing road safety measures, improving infrastructure quality, and ultimately reducing the incidence of RTAs.

 Declaration of competing interests

The authors report no competing interests.

 Availability of data

The detailed report is available on reasonable request to the Information Resource Centre, Universiti Teknologi Petronas, Malaysia.

 Acknowledgement

The authors are grateful to M. Napiah and Z. A. Hamid for their assistance with this study. Additionally, the authors are thankful to Universiti Teknologi Petronas for housing the first author's complete MSc thesis, which is the full version of this research.

 Extract from questionnaire

Considering your actual experience as a road user of this highway, indicate the extent to which you agree or disagree with the service quality as the following statements:

Variables

Strongly disagree

Disagree

Neutral

Agree

Strongly agree

Road surface:

Potholes and bumps

Permanent wave

Oily road surfaces

Road settlement

Water pounding

Improper cross fall

Soft shoulders

Insufficient shoulder space

Untidy shoulder

Shoulder and carriageway drop-off

Road drainage:

Too close to the shoulder

Damaged manhole covers

Not functioning

Road furniture:

Dirty road marking

Invisible road marking

Unclear traffic signs

Insufficient street lighting

Poor installation of street lighting

Guardrail too close to carriageway

Guardrail inconsistently aligned

Rest area:

Uncomfortable for rest

Inappropriate location

Too far between one another

Road maintenance:

Fast repair of defects

Insufficient traffic signs

Poor workmanships

Repeated construction

Road traffic accident:

Frequently occurred

Occurred in unacceptable rate

Fast response of emergency unit

Effective of accident handling

Safe for driving

References Berg C N Deichmann U K Liu Y Harris S Transport policies and development World Bank Group 2015 http://documents.worldbank.org/curated/en/893851468188672137/Transport-policies-and-development Policy Research Working Paper WPS 7366 Mohmand Y T Wang A Saeed A The impact of transportation infrastructure on economic growth: Empirical evidence from Pakistan Transportation Letters: The International Journal of Transportation Research 2017 9 2 63 69 10.1080/19427867.2016.1165463 Othman M R Jeevan J Rizal S The Malaysian intermodal terminal system: The implication on the Malaysian maritime cluster International Journal of e-Navigation and Maritime Economy 2016 4 46 61 10.1016/j.enavi.2016.06.005 Anor N Ahmad Z Abdullah J Hafizah R N Road network system in Port Klang, Malaysia and impacts to travel patterns Procedia - Social and Behavioral Sciences 2012 35 629 636 10.1016/j.sbspro.2012.02.130 Hamid M T A Fauzi R Rasiah R Public Perception of Road Traffic Accident in Selected Countries of Asia: A Systematic Review on Malaysia and Thailand. Available at SSRN 4121322 Social Science Research Network 2023 10.2139/ssrn.4121322 Pillary Y Abdullah Y Ala R Road death statistics Kerma Emrooz 2022 96 11 2 3 Samsuddin N Masirin M I M Assessment of road infrastructures pertaining to Malaysian experience. MATEC Web of Conferences 2016 47 1 7 10.1051/matecconf/20164703010 Tehrani S S Falls L C Mesher D Road users’ perception of roughness and the corresponding IRI threshold values Canadian Journal of Civil Engineering 2015 42 4 233 240 10.1139/cjce-2014-0344 WHO Global status report on road safety 2015 World Health Organization 2015 https://iris.who.int/handle/10665/189242 ASEAN ASEAN regional road safety strategy. Association of Southeast Asian Nations 2016 https://asean.org/book/asean-regional-road-safety-strategy-2/ Bhatti J A Sobngwi-Tambekou J Lagarde E Salmi L-R Situational factors associated with road traffic crashes: A case-control study on the Yaoundé-Douala road section, Cameroon International Journal of Injury Control and Safety Promotion 2010 17 4 215 222 10.1080/17457301003728510 Chen G Road traffic safety in African countries - status, trend, contributing factors, countermeasures and challenges International Journal of Injury Control and Safety Promotion 2010 17 4 247 255 10.1080/17457300.2010.490920 Amedorme S K Nsoh S N Analyzing the causes of road traffic accidents in Kumasi Metropolis International Journal of Engineering Innovation and Research 2014 3 6 894 899 Belwal R Belwal S Al Quraini A Road traffic accidents (RTAs) and road safety in Oman: An analysis of people’s perception towards the causes Advances in Transportation Studies 2015 XXXVII 105 118 Mohanty M Gupta A Factors affecting road crash modeling Journal of Transport Literature 2015 9 2 15 19 Kelarestaghi K B Zhang W Wang Y Xiao L Hancock K Heaslip K P Impacts to crash severity outcome due to adverse weather and other causation factors. Advances in Transportation Studies Advances in Transportation Studies 2017 43 31 42 Adanu E K Jones S Effects of human-centered factors on crash injury severities Journal of Advanced Transportation 2017 1 11 Bokaba T Doorsamy W Paul B S Comparative study of machine learning classifiers for modelling road traffic accidents Applied Sciences 2022 12 2 828 828 Infante P Jacinto G Santos D Nogueira P Afonso A Quaresma P Silva M Nogueira V Rego L Saias J Gois P Manuel P R Prediction of Road Traffic Accidents on a Road in Portugal: A Multidisciplinary Approach Using Artificial Intelligence, Statistics, and Geographic Information Systems. Information Information 2023 14 4 238 238 10.3390/info14040238 Cafiso S Montella A D'Agostino C Mauriello F Galante F Crash modification functions for pavement surface condition and geometric design indicators Accident Analysis & Prevention 2021 149 105887 105887 10.1016/j.aap.2020.105887 Putrajaya Road Safety Plan of Malaysia 2006-2010 Ministry of Transport Malaysia 2006 Zeithaml V A Parasuraman A Berry L L Delivering quality service Simon and Schuster New York, USA 1990 Crescenzi R Cataldo M Di Rodríguez-Pose A Government quality and the economic returns of transport infrastructure investment in European regions Journal of Regional Science 2016 56 5 555 582 10.1111/jors.12264 Magazzino C Mele M On the relationship between transportation infrastructure and economic development in China Research in Transportation Economics 2021 88 100947 100947 10.1016/j.retrec.2020.100947 Porter G Transport services and their impact on poverty and growth in rural sub-Saharan Africa: A review of recent research and future research needs Transport Reviews 2014 34 1 25 45 10.1080/01441647.2013.865148 Hlotywa A Ndaguba E A Assessing the impact of road transport infrastructure investment on economic development in South Africa Journal of Transport and Supply Chain Management 2017 11 1 12 Sun J Li Z Lei J Teng D Li S Study on the relationship between land transport and economic growth in Xinjiang Sustainability 2018 10 1 135 10.3390/su10010135 Aldagheiri M The role of the transport road network in the economic development of Saudi Arabia WIT Transactions on the Built Environment 2009 107 275 285 10.2495/UT090251 Leung J A literature review of theory and evidence in transportation and economic growth New Zealand Association of Economists Conference 2006 Wellington, New Zealand Lakshmanan T R Chatterjee L R Economic consequences of transport improvement ACCESS 205 26 https://www.accessmagazine.org/wp-content/uploads/sites/7/2016/07/Access-26-06-Economic-Consequences-of-Transport-Improvements.pdf HE Better journeys, better conversations Highways England Company Limited 2016 https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/490538/S150470_Customer_Service_Strategy.pdf PR101/15 Shabani Z D Safaie S Do transport infrastructure spillovers matter for economic growth? Evidence on road and railway transport infrastructure in Iranian provinces Regional Science Policy & Practice 2018 10 1 49 64 10.1111/rsp3.12114 Ivanova E Masarova J Importance of road infrastructure in the economic development and competitiveness Economics and Management 2013 18 2 263 274 10.5755/j01.em.18.2.4253 Peter S Rita E Edith M The impact of road transportation infrastructure on economic growth in Nigeria International Journal of Management and Commerce Innovations 2015 3 1 673 680 https://www.researchpublish.com/upload/book/The%20Impact%20of%20Road-2205.pdf Saidi S Impact of road transport on foreign direct investment and economic growth: Empirical evidence from simultaneous equations model Journal of Business Management and Economics 2016 7 2 64 71 Pembuain A Priyanto S Suparma L B The effect of road infrastructure on traffic accidents Advances in Engineering Research 2020 186 147 153 OECD Road safety annual report – Covid-19 and road safety International Transport Forum 2020 https://www.itf-oecd.org/sites/default/files/docs/irtad-road-safety-annual-report-2020_0.pdf WHO Road traffic injuries World Health Organization 2023 https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries 20240902 DoSM Vital Statistics Malaysia 2022 Malaysia's official national statistics organisation 17 10 2023 https://open.dosm.gov.my/publications?page=1&search=Vital 20240902 Putrajaya Malaysia Road Safety Plan 2022-2030 Ministry of Transport Malaysia 2022-2030 2022 WHO Global Plan: Decade of Action for Road Safety 2021-2030 World Health Organization 2021 Putrajaya Road Safety Plan of Malaysia 2014 – 2020 Ministry of Transport Malaysia 2014 Abdelfatah A Traffic fatality causes and trends in Malaysia Malaysia Sustainable Cities Program: Working Paper Series 2015 https://scienceimpact.mit.edu/traffic-fatality-causes-and-trends-malaysia Naveen N Kumar M Impact of roadway condition, traffic and manmade features on road safety International Journal Scientific and Engineering Research 2016 7 6 1011 1013 Jaiswal A Bhatore A Effect on road safety by roadway condition, traffic and manmade features International Journal of Latest Trends in Engineering and Technology 2016 7 3 41 48 10.21172/1.73.006 Firenze R J The Process of Hazard Control Kendall Hunt New York 1978 Elslande P Van Fouquet K Analyzing human functional failures in road accidents TRACE 2007 Report D5.1 Molinero A Evdorides H Naing C Kirk A Tecl J Barrios J M Simon M C Phan V Hermitte T Accident causation and pre-accidental driving situations. Part 3 TRACE 2009 Report D2.3 Abdullah L Zamri N Ranking of the factors associated with road accidents using correlation analysis and fuzzy TOPSIS Australian Journal of Basic and Applied Sciences 2010 4 2 314 320 Najib L Abdullah L Abdullah I Salleh Z Weights of road accident causes using analytic hierarchy process ARPN Journal of Science and Technology 2012 2 2 39 44 Junus N W M Ismail M T Arsad Z Rahman R A Malaysia road accidents influences based on structural time series analysis Applied Mathematics and Information Sciences 2017 11 4 1029 1039 10.18576/amis/110409 Darma Y Karim M R Abdulah S An analysis of Malaysia road traffic death distribution by road environment Sādhanā 2017 42 9 1605 1615 10.1007/s12046-017-0694-9 Redhwan A A Karim A J Knowledge, attitude and practice towards road traffic regulations among university students, Malaysia The International Medical Journal Malaysia 2010 9 2 29 34 Ismail A Yi G S Zain M K I M Study on drivers’ behaviour relationships to reduce road accidents in Puchong, Selangor Darul Ehsan Jurnal Kejuruteraan 2015 27 81 85 Mohamad F F Mohamad J A S Abdullah Do young Malaysian drivers involve in speeding behaviour: An investigation at selected accident hotspots in peninsular Malaysia Journal of Advanced Research in Social and Behavioural Sciences 2017 9 1 75 83 Sultan Z Ngadiman N I Kadir F D A Roslan N F Moeinaddin M Factor analysis of motorcycle crashes in Malaysia Planning Malaysia Journal 2016 4 135 146 10.21837/pm.v14i4.154 CIDB Quality assessment system for completed road work Kuala Lumpur: Construction Industry Development Board Malaysia 2011 CIS 19:2011 Tortum A Çodur M Y Kılınc B Modeling traffic accidents in Turkey using linear regression analysis Iğdır University Journal Institute Science and Technology 2012 2 3 69 78 Chang J Liu P Chen G The service quality of roads in Taiwan from the point of view of road users and drivers Journal of Marine Science and Technology 2015 23 3 344 352 10.6119/JMST-014-0327-4 Zuna H T Hadiwardoyo S P Rahadian H Developing a model of toll road service quality using an artificial neural network approach International Journal of Technology 2016 7 4 562 570 10.14716/IJTECH.V7I4.2612 Pilaka N Rao O R S Samanta P K An analysis of roadway service indicators: A comparative study of six toll roads in Pune Region The IUP Journal of Operations Management 2017 16 3 7 22 Konlan K D Hayford L Factors associated with motorcycle-related road traffic crashes in Africa, a Scoping review from 2016 to 2022 BMC public health 2022 22 1 649 649 10.1186/s12889-022-13075-2 Putrajaya Malaysian Highway Authority Annual Report 2016 Ministry of Transport Malaysia 2016 http://www.llm.gov.my/documents/PublishDoc/pub_1483412202.pdf Putrajaya Malaysian Highway Authority Annual Report 2021 Ministry of Transport Malaysia 2023 https://www.llm.gov.my/publication/yearbook Zahidy A A Sutanto M H Napiah M Hamid Z A Measuring the relationships of road service quality and road traffic accidents International Journal of Innovative Technology and Exploring Engineering 2019 9 1 719 725 Salkind N J Exploring research 8 Pearson New Jersey 2017 Bujang M A Baharum N A simplified guide to determination of sample size requirements for estimating the value of intraclass correlation coefficient: A review Archives of Orofacial Sciences 2017 12 1 1 11