
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)13488-3
10.1016/j.heliyon.2024.e37457
e37457
Research Article
Road crash dynamics in Malaysia: Analysis of trends and patterns
Mohd Azami Muhammad Fadhirul Anuar fadhirul.anuar@student.usm.my
ab
Misro Md Yushalify yushalify@usm.my
a⁎
Hamidun Rizati rizati@miros.gov.my
c
a School of Mathematical Sciences, Universiti Sains Malaysia, 11800, Gelugor, Pulau Pinang, Malaysia
b School of American Education, Sunway University, Bandar Sunway, 47500, Subang Jaya, Selangor, Malaysia
c Malaysian Institute of Road Safety Research (MIROS), Lot 125-135, Jalan TKS 1, Taman Kajang Sentral, 43000, Kajang, Selangor Darul Ehsan, Malaysia
⁎ Corresponding author. yushalify@usm.my
11 9 2024
30 9 2024
11 9 2024
10 18 e3745712 6 2024
20 8 2024
4 9 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Road crashes represent a significant public health and safety concern globally, and Malaysia is no exception. Understanding the trends and patterns of road crashes is essential for devising effective strategies to mitigate risks and enhance road safety. This study presents a comprehensive analysis of road crash dynamics, focusing on road users, severity patterns and geographical patterns in Malaysia from 2012 to 2022. Data sourced from the Royal Malaysian Police (RMP) are utilized to examine various aspects of road crashes. Road crash trend, geographical patterns, linear trend analysis and K-means clustering are employed to explore patterns of road crash in Malaysia. The findings reveal that motorcycles consistently emerged as the most involved road user. Geographical patterns discovered that Selangor exhibits higher crash number. Linear trend analysis revealed significant upward trends in crash frequency prior to the pandemic, while the number of fatalities resulting from road crash showed a downward trend over the observed period. K-means clustering identified that Selangor recorded high total crashes and high total of fatalities. This study also considers the influence of the Covid-19 pandemic on road crash dynamics, highlighting changes in travel patterns and behaviour. There also have been notable successes, such as the reduction in total fatalities and the effectiveness of targeted interventions via the accomplishments of initiatives of Malaysian Road Safety Plan 2014–2022.

Keywords

Road crash analysis
Road crash trend
Geographical patterns
Linear trend analysis
K-means clustering
==== Body
pmc1 Introduction

Road crashes are a global tragedy with a rising trend pose significant challenges to public safety and well-being. Developing countries account for a significant majority of fatal road crashes, with over 80 percent of such crashes occurring in these countries [1]. The rate of road crashes in developing countries is higher than the global average, despite some measures being taken to reduce deaths [2]. Additionally, low- and middle-income countries are more affected, with 93 percent of road traffic injury-related mortality [3]. Nevertheless, Malaysia has no exception. According to Department of Statistics Malaysia (DOSM), road crashes have emerged as one of the principal causes of death in Malaysia in 2022. With a population over 32 million and a vast road network spanning national highways, state highways, federal roads, and district roads, Malaysia faces unique challenges in managing road safety.

There are various factors influence the road crash such as, aggressive driving behaviour is one significant contributing factor, as it increases the chances of being involved in a traffic collision [4]. Other factors include the condition of road pavements, which can deteriorate over time and require maintenance [5]. Climatic variables, such as weather conditions, can also affect road safety [6]. Additionally, damage to road infrastructure can contribute to crashes, especially if it is not properly maintained [7].

Factors such as inexperience, lack of riding competence, and risky riding behaviour contribute to road traffic crashes among young motorcyclists aged 16–19 years old [8]. Faulty vehicles, uneven roads, and driver behaviour are also significant causes of accidents [9]. Additionally, heavy-goods vehicles (HGVs) pose a hazard due to their size and operation characteristics, with road geometry, lane marking, and traffic system being significant factors in fatal HGV-related crashes [10]. Motorcycle accidents remain a challenge in Malaysia, and integrating motorcycle and car safety technologies is crucial to reducing the rate of accidents [11]. Furthermore, the condition of road infrastructure, assessed using the Pavement Condition Index (PCI) method revealed as an important factor in road accidents [12].

Various factors such as junction type, time, traffic control, weather, light, road geometry, movement, location type, and surface condition have been studied in relation to the occurrence and severity of road accidents. Studies have shown that factors such as weather, time, road characteristics, temperature, and road intersection safety have significant effects on the severity of accidents [[13], [14], [15]]. For example, higher traffic volume and longer traffic waiting time at road intersections have been found to be associated with a higher number of accident cases [16]. Additionally, the type of road traffic accident (collision, crash, or pedestrian running-over) has been found to be influenced by geographical, meteorological, time-related, driver's characteristics, vehicle's features, and road characteristics factors [17].

Studies have shown that driver behaviour, such as driver inattention and red light running, is a significant contributor to accident occurrence [18]. Other factors that have been found to affect the severity of accidents include driving on the wrong side of the road and vehicle faults [19]. Roadside features, such as the presence of bridges, slopes, and sign supports, have also been found to significantly affect accident severity [20]. Besides, factors such as time of accident, road surface condition, accident type, and vehicle type have been identified as significant in predicting intersection severity [21]. Roadway characteristics, such as traffic control, location type, topography, and roadway divisions, have been found to impact cyclist severity [22].

Furthermore, over 60 percent of fatal accidents occurred on rural roads, with nearly half of all fatalities taking place on federal roads and over a quarter on state roads [23]. Right-turning motorists (RTMs) at unsignalized intersections and identified gap patterns and critical gaps that contribute to serious conflicts [24]. Road Environment Assessment Program (REAP) developed to evaluate road risk factors and identify problematic sections for improvement [25]. The influence of pavement conditions on accidents investigated and found that more than 70 percent of accidents occurred on roads in good condition, suggesting that other factors such as human negligence and environmental conditions may contribute more significantly [26]. Traffic accidents in Pekanbaru are studied and identified accident-prone locations and characteristics, recommending traffic engineering, education, and law enforcement strategies [27].

The importance of road design and driving behaviors in contributing to crashes and fatalities, with poor design identified as a major factor [28]. Insurance claims and crash distribution for cars equipped with Blind Spot Detection (BSD) technology are investigated, finding a reduction in side crashes when BSD was implemented [29]. The issue of conspicuity in heavy goods vehicles (HGVs) and found that while compliance with lighting components was high, accidents still occurred due to inadequate visibility [30]. Temporal trends in road accidents for specific vehicle types in Malaysia have shown various patterns. It is found that the highest road accident ranking based on types of injuries in Johor was wreckage injuries, followed by fatal injuries, minor injuries, and serious injuries [9]. An increase in the number of road traffic crashes and road deaths in Perak is observed, with motorcycles being the most common vehicle involved in fatalities [5].

In Malaysia context, some studies have focused on specific regions or aspects of road safety. For instance, a comprehensive analysis of road traffic crashes in Perak, Malaysia, has been conducted, revealing that from 2011 to 2018, motorcycle-related fatalities were prevalent, particularly among young males, with a significant increase in injury rates in 2020 despite a decrease in the fatality rate [5]. Moreover, in-depth analysis of the severity levels and trends of road accident cases at Johor has been conducted using the Analytical Hierarchy Process (AHP) and Geographical Information System (GIS) provides the insight of the road accidents ranking by the types of injuries at the states road of Johor [9].

Moreover, the types of road users involved in accidents cover a diverse range, including motorcyclists, pedestrians, and drivers of various vehicles. Various studies have been conducted on individual vehicles type, such as, heavy-goods vehicle (HGV) fatal crashes in Malaysia identified road and environmental factors as significant contributor [10]. Additionally, a study analyses the impact of road characteristics on motorcycle crash fatalities involving only with heavy goods vehicles (HGV) in Malaysia has identified key factors such as surface smoothness and slope conditions that contribute to crash severity [31]. Moreover, the analysis of pattern of motorcycle accidents in Malaysia has been deeply discussed and resulted that motorcycle accidents remain a significant issue in Malaysia, with a relatively consistent number of accidents yearly [32]. Despite efforts to improve road safety through campaigns and stricter regulations, the overall trend remains troubling, with a noted rise in fatalities and serious injuries [5,9].

Since the fatalities and injuries are reported data, clustering methods can be effectively applied to observe similar patterns based on geographical locations. In crash analysis, it is crucial to identify these patterns to understand regional trends and risk factors. While several clustering methods have been employed to analyze such datasets, Hierarchical Clustering, struggles with computational complexity and scalability, making it less suitable for large datasets typically involved in crash analysis [33]. Similarly, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), while effective in handling arbitrary shapes and noise, is sensitive to parameter selection and struggles with varying cluster densities [34].

In various studies, K-means clustering has been utilized to analyze road accidents and identify underlying patterns. For instance, a method combining K-means clustering and the Apriori algorithm was proposed to classify accident levels and mine contributing factors efficiently [35]. Another study focused on K-means clustering to group traffic violations based on incident times, revealing distinct clusters with varying violation frequencies [36]. Additionally, a study in India employed K-means clustering to analyze road accidents and develop a prediction model, achieving an 81 percent accuracy rate in predicting accidents based on factors like road conditions and weather [37]. Furthermore, a deep-embedded clustering approach incorporating K-means was introduced to predict optimal ambulance positioning locations, demonstrating superior performance compared to traditional clustering algorithms [38].

The K-means clustering algorithm has demonstrated significant advantages in evaluating crash data across various studies. It effectively categorizes crash incidents into distinct clusters, allowing for the identification of patterns and risk factors associated with different types of crashes. For instance, Tahfim's research highlights the algorithm's ability to enhance the understanding of spatial and temporal distributions of crashes, which is crucial for targeted interventions [39]. Sathiyanathan and Warnajith further emphasize that K-means clustering facilitates the analysis of contributing factors, enabling stakeholders to prioritize safety measures based on data-driven insights [40]. Additionally, the work by Tahfim and Yan illustrates the algorithm's efficiency in processing large datasets, which is essential for real-time crash analysis and response strategies [41]. In addition, K-means clustering serves as a powerful tool for improving crash evaluation methodologies [42]. Overall, K-means clustering proves to be a valuable tool in understanding and addressing road accident scenarios.

Indeed, various factors discussed in the literature, yet the holistic overview on the various type of road users with number of road fatality trend is underreported. While some studies have explored crash patterns in specific regions, a comprehensive analysis covering all states in Malaysia is still needed. Notable gap pertains to the latest trends in the road crashes number and geographic variations’ patterns in road crashes trends across Malaysia states is important to provide more comprehensive and up-to-date insights that are crucial for developing targeted and effective road safety policies. Therefore, this study aims to examine the trends in the number of road accidents and explore the geographical patterns of road accidents across Malaysian states. Linear trend analysis is also conducted to investigate the overall trajectory of road accidents and death over time. K-means clustering as an unsupervised machine learning algorithm is carried out to identify distinct clusters based on similarities in the number of road crashes and fatalities across different states in Malaysia.

This paper is structured to facilitate the research process and findings as follows: In section 2, research methodology presented. Next, data analysis comprises on road accident trend, geographical patterns, linear trend analysis and K-means clustering analysis discussed in section 3. Section 4 presents the conclusion and future recommendation.

2 Methodology

2.1 Data background

The primary dataset for this study is sourced from the Royal Malaysian Police (RMP), covering the years 2012–2022 associated with the number of crashes for each road user i.e., bicycle, bus, car, four-wheel drive, lorry, motorcycle, pedestrian, van, and others. A state-specific dataset spanning from 2012 to 2022 is utilized to analyze trends in the number of crashes. The primary dataset is derived from the POL27 road accident form used by the RMP for recording all road accidents across Malaysia. This form collects 91 variables for each accident case, covering factors related to the road environment, vehicles, and road users. The POL27 form is designed to be completed efficiently by traffic officers, who simply circle the appropriate values for each variable, which are systematically categorized into different sections. Then the collected data is stored in a database for analysis, retrieval, and further processing [43]. The visual sequence in Fig. 1 effectively portrays the entire process of accident data handling, from the initial event to data storage and analysis.Fig. 1 Primary Data Collection Stage *This images were generated using artificial intelligence (AI) and are intended solely for visualization purposes. They are not based on actual events or real data but are designed to illustrate the process described in the study.

Fig. 1

2.2 Data preprocessing

Prior to analysis, the datasets undergo preprocessing procedures to ensure consistency and reliability. This involves consolidating all relevant data into a unified format, typically a CSV (Comma Separated Values) file. In this study, the data preprocessing is conducted to facilitate further analysis using R scripts. For instances, the initial dataset was distributed across 16 categories by state, including separate entries for the Federal Territories of Putrajaya and Labuan. The dataset was reviewed, and cases for Putrajaya were reassigned to Kuala Lumpur, while Labuan was reassigned to Sabah, thus aligning the dataset with the 14 states of Malaysia. Following this combination, the dataset was compared with Statistical Report Road Accident Malaysia (2022) published by the RMP to ensure consistency and accuracy.

2.3 Data visualization

Data visualization is adopted for the exploration and interpretation of trends and patterns within the dataset. In this study, the ggplot2 package, a popular data visualization library in the R programming language, is utilized to create various visual representations of the data. Specifically, line graphs are employed to observe the road fatality trend from 2012 to 2022 for different type of road users which consist of bicycle, bus, car, four-wheel drive, lorry, motorcycle, pedestrian, van, and others. The observation then magnified on the visualization for each individual trend of road user to observe the detail fluctuation on the number of crashes. To analyze geographical patterns, line graphs were used to visualize trends in road crashes across all states in Malaysia from 2012 to 2022. Given the large scale of the data, some minor fluctuations in crash numbers were not clearly visible. To address this, separate line graphs with individual scales for each state were created to better highlight state-specific trends.

2.4 Linear trend analysis

Linear trend analysis involves fitting linear regression models of the data to identify and quantify any relationships present for number of crashes, and number of fatalities, observed from 2012 to 2022 with a total of 4,864,410 number of crashes and 62,728 number of fatalities. The trends are then compared to the observation by excluding year 2020 and 2022 due to the Covid-19 pandemic outbreak forced Malaysia to enforce movement control orders and travel restrictions. The linear model constructed as follows:(1) Yi=β0+β1X+ε

where:

Yi represents the dependent variable; Y1 is number of crashes and Y2 is the number of fatalities,

X represents time in years,

β0 is the intercepts coefficient,

β1 is the slopes coefficient,

ϵ represents the error term.

2.5 K-means clustering

K-means clustering analysis is conducted to further explore patterns within the dataset. The objective of this analysis is to partition the data into distinct clusters based on similarities in the number of crashes and number of fatalities for each state. To determine the optimal number of clusters (k) for the dataset, the elbow method is employed.

For each value of k, the K-means clustering algorithm calculates the Within-Cluster Sum of Squares (WSS), which measures the sum of squared distances between each data point and its assigned centroid. The formula for WSS as follows:(2) WSS=∑i=1k∑j=1ni‖xij−ci‖2

where:

k is the number of clusters,

ni is the number of data points in the ith cluster,

xij is the jth data point in the ith cluster,

ci is the centroid of the ith cluster,

||.|| denotes the Euclidean Distance between a data point and its centroid.

The methodology process, encompassing all key steps and procedures, is thoroughly summarized in the detailed flowchart presented in Fig. 2.Fig. 2 Methodology flowchart.

Fig. 2

3 Findings

3.1 Road fatality trend

Analysing road fatality data spanning from 2012 to 2022 reveals significant trends across various type of road users, providing insights into the complex dynamics influenced by mobility patterns, government interventions, and the ongoing impact of the Covid-19 pandemic.

Motorcycle takes center stage with a remarkable consistency in reporting a substantial number of fatalities, consistently exceeding 4000 cases annually as depicted in Fig. 3. However, a notable deviation occurs in 2019, continuing through 2020 and 2022, wherein the number of fatalities showed below the 4000 cases. Crucially, these years align with the implementation of Malaysia's diverse movement control orders and travel restrictions, strategic responses to the Covid-19 outbreak. This suggests a tangible correlation with the restrictive measures enforced during this period. Intriguingly, 2022 presents a distinct shift as the number of fatalities resurges, surpassing 4000 cases.Fig. 3 Road fatality trend by the type of road users (2012–2022).

Fig. 3

In tandem, car assumes a prominent role, securing the second-highest position in the hierarchy. The number of fatalities within this category exhibits fluctuations, typically oscillating between 1000 and 1500 cases. Mirroring the trend observed in the motorcycle, there is a discernible decline from 2019 to 2022, with figures dropping below the 1000 cases. However, a noteworthy deviation occurs in 2022, where the number of fatalities experiences a subsequent increase. This resurgence of the number of fatalities for motorcycle and car may signify the gradual relaxation of restrictions and a potential return to pre-pandemic levels of mobility and road activity.

Pedestrian unveils a consistent trend, reporting fatality figures slightly over and under 500 from 2012 to 2016. A gradual decrease is observed until 2020, followed by relative stability until 2022. In addition, the remaining road users exhibits a distinct trend, marked by fluctuations in fatality numbers. The individual trend of road fatalities for each type of road users presented in Fig. 4.Fig. 4 Individual trend of the road fatalities (2012–2022).

Fig. 4

The graph in Fig. 4 presents the number of road fatalities among various types of road users in Malaysia from 2012 to 2022. For bicyclists, there was an initial decrease in fatalities until 2014, followed by a peak in 2015, and a general downward trend until 2021, with a slight increase in 2022. Bus category fatalities showed a steady decline from 2012, dropping significantly by 2020, and then a slight rise in 2022. Car fatalities generally decreased over the years, with minor fluctuations, peaking in 2015 and 2017, reaching a low in 2021, and increasing again in 2022.

The four-wheel drive category exhibited fluctuations from 2012 to 2016, and drop afterward, then a rising trend from 2019 to 2022. Lorry fatalities experienced a decrease from 2012 to 2021, with a slight uptick in 2022. Motorcyclist fatalities followed a general decline from 2012, with the lowest point in 2021, before increasing in 2022. The "Others" category saw a similar pattern of fluctuating numbers but with a consistent decrease until 2021 and a slight rise in 2022.

Pedestrian fatalities consistently declined from 2012 to 2021, hitting a low point in 2021, and then experienced an increase in 2022. Lastly, van fatalities showed fluctuations over the years, with a significant rise in 2022.

The implications of Covid-19 measures on the logistics and transportation sector likely contribute to the observed patterns within this category. In contrast, other type of road users demonstrate a comparatively stable scale of fatalities, highlighting a resilient consistency amid the more fluctuating trends witnessed in the motorcycle, car, pedestrian, and lorry categories.

3.2 Geographical patterns

Geographical patterns of road crashes are examined across Malaysian states reveals visible patterns and disparities, with Selangor emerging as the dominant in the number of crash occurrence. Over the period spanning from 2012 to 2022, Selangor consistently registers a remarkably high incidence of crashes, exceeding 100,000 cases as shown in Fig. 5. Selangor's status as the most populous state in Malaysia [44]. With an estimated population exceeding 6.5 million, likely contributes significantly to its high incidence of road crashes. The dense urban landscape of Selangor, characterized by extensive road networks and heavy traffic congestion, amplifies the risk of crashes, especially considering the influx of commuters and motorists from neighbouring states.Fig. 5 Road crashes trend by state in Malaysia (2012–2022).

Fig. 5

Similarly, Johor exhibits a comparable trajectory, mirroring Selangor's trend with a recorded figure surpassing 75,000 crashes within the same timeframe. Johor's position as the third most populous state in Malaysia, estimated to be around 4 million people coupled with its strategic location bordering Singapore, exposes it to substantial vehicular traffic and transport-related challenges. The rapid pace of urbanization and industrialization in both Selangor and Johor further magnify these issues, as expanding urban centres attract increased vehicular activity and commercial transport operations.

The dominant states, in terms of crash occurrence, include Selangor, Johor, Kuala Lumpur, Pulau Pinang, and Perak. While these states showcase considerable variations in crash rates, others demonstrate relatively less fluctuation, with accident tallies remaining below 50,000 cases. Discernibly, Perak maintaining its consistent position following Pulau Pinang from 2012 to 2021, before advancing to the fourth position in accident rankings by the year 2022. An obvious reduction in trend is observed from 2019 to 2021, coinciding with the implementation of movement restrictions amid the Covid-19 pandemic.

According to Fig. 5, Perlis consistently reports the lowest crash occurrences throughout the years spanning from 2012 to 2022. However, the number of crashes in Fig. 6 shown that there is an evidence of fluctuation in Perlis. Nevertheless, it is in the small scale within 1600 to 2300 number of cases.Fig. 6 Road crashes trend by individual state in Malaysia (2012–2022).

Fig. 6

Fig. 6 magnified the trend of the number of road crashes in various states of Malaysia from 2012 to 2022. Johor shows a gradual increase in crashes from 2012 to 2018, followed by a decline in 2019 and a sharp increase again in 2022. In Kedah, the number of crashes rises steadily from 2012 to 2017, with a slight dip in 2020, and then increases significantly in 2022. Kelantan experiences a minor increase over the years, with a drop in 2020 and a significant rise in 2022. Kuala Lumpur maintains a relatively stable trend with minor fluctuations, peaking in 2019, then decreasing, and slightly increasing again in 2022.

In Melaka, crashes increase steadily until 2019, then decrease, but surge sharply in 2022. Negeri Sembilan sees a gradual increase until 2018, followed by a decline in 2020 and a notable rise in 2022. Pahang exhibits a slight decline until 2019, then a significant decrease in 2020, with a sharp rise in 2022. Perlis shows minor fluctuations throughout the years, with a decrease in 2020, followed by an increase in 2022.

Perak has a consistent rise in crashes until 2018, followed by a decrease in 2019 and 2020, but a significant rise in 2022. Pulau Pinang shows a steady increase over the years with a slight dip in 2020 and then a rise in 2022. Sabah experiences minor fluctuations, with a decrease in 2020 and an increase in 2022. Sarawak shows a generally stable trend with a minor decrease in 2019 and an increase in 2022.

Finally, Selangor has the highest number of crashes among the states, with a consistent increase from 2012 to 2018, a decline in 2019 and 2020, and a sharp rise in 2022. Terengganu maintains a relatively stable trend with slight fluctuations, ending with a rise in 2022.

3.3 Linear trend analysis

The linear regression analysis was conducted to investigate the relationship between the number of road crashes and the year, as well as between the number of fatalities and the year, covering the period from 2012 to 2022. The model was further examined by excluding the years 2020 and 2021 to account for the impact of the Covid-19 pandemic. Table 1 summarizes the output of the linear regression analysis. Each individual model is further detailed in the following subsection.Table 1 Summary of linear models.

Table 1Model	Variable	Estimate	Std error	t value	p-value	R-squared	
Road Crashes (2012–2022)	Intercept	450700	12170000	0.037	0.971	1.265e-06	
Year	20360	6036	0.003	0.997		
Road Crashes (2012–2019 and 2022)	Intercept	−21303551	4128289	−5.16	0.00131	0.7996	
Year	10821	2048	5.285	0.00114		
Road Fatality (2012–2022)	Intercept	405162.9	121656.9	3.33	0.00879	0.5443	
Year	−197.77	60.32	−3.279	0.00955		
Road Fatality (2012–2019 and 2022)	Intercept	194934.5	53926.71	3.615	0.00857	0.6353	
Year	−93.4	26.75	−3.492	0.0101		

3.3.1 Trend in number of road crashes

Based on Table 1, the linear model estimated an intercept of 450700 and a coefficient of 20360 for the year and presented in Equation (3). However, neither of these coefficients was statistically significant, with p-values more than 0.05 i.e., 0.971 and 0.997 respectively. Additionally, the model's performance was poor, as indicated by a low adjusted R-squared value of −0.1111 and a non-significant F-statistic (p = 0.997). These results suggest that the linear model failed to adequately capture any underlying trends in the number of crashes over the observed time period. Consequently, there is insufficient evidence to support the presence of a linear trend in the frequency of crashes from 2012 to 2022 based on the available data. Fig. 7 presents the graphical presentation of the linear trend.(3) Y1 = 450700 + 20360X

Fig. 7 Linear trend of road crashes (2012–2022).

Fig. 7

The trend in Fig. 7 obviously captured the sharp drop in the number of road crashes on 2020 and it continue dropping on 2021. This disruption evidently caused by the Covid-19 pandemic. Thus, the analysis further examined by excluding the years 2020 and 2021, focusing solely on the trend observed from 2012 to 2019 and 2022. A different picture emerges regarding the number of road crashes as demonstrated in Fig. 8.Fig. 8 Linear trend of road crashes (2012–2019, 2022).

Fig. 8

The linear model fitted to this subset of data reveals a statistically significant positive trend in the number of road crashes over time. The intercept of the model is estimated to be −21303551, indicating the baseline number of road crashes in the year 2012. The year coefficient is estimated to be 10821, suggesting an average increase of 10821 road crashes per year over the observed period. The model is presented in Equation (4).(4) Y1 = −21303551 + 10821X

Both coefficients are highly statistically significant, with p-values well below the threshold of 0.05 i.e., 0.00131 and 0.00114 respectively. The model exhibits a high goodness-of-fit, with a multiple R-squared value of 0.7996, indicating that approximately 79.96 percent of the variance in the number of road crashes can be explained by the linear trend observed from 2012 to 2019 and 2022. The adjusted R-squared value of 0.771 confirms the robustness of the model, adjusting for the number of predictors. Overall, these results strongly suggest a significant upward trend in the frequency of road crashes in Malaysia from 2012 to 2019 and 2022.

3.3.2 Trend in number of fatality

The linear trend analysis is also conducted on the number of fatality over the same period of time. The model estimated an intercept of 405162.86, representing the baseline number of fatality in the initial year of observation. Additionally, the coefficient for the year was estimated to be −197.77, indicating an average decrease of 197.77 fatality per year over the observed period. The model depicted in Equation (5).(5) Y2 = 405162.86 − 197.77X

Both coefficients of intercept and year were found to be statistically significant at 0.05 i.e., 0.00879 and 0.00955 respectively, suggesting a meaningful relationship between the year and the number of fatality. The model exhibited a relatively high goodness-of-fit, with a multiple R-squared value of 0.5443, indicating that approximately 54.43 percent of the variance in the number of fatality could be explained by the linear trend observed over the years. The adjusted R-squared value of 0.4937 further confirmed the robustness of the model, adjusting for the number of predictors. Overall, these results suggest a significant downward trend in the number of fatality in Malaysia over the observed period, with an average annual decrease of approximately 198 fatality. Fig. 9 depicts the linear trend of number of fatality.Fig. 9 Linear trend of fatality (2012–2022).

Fig. 9

However, this analysis may not fully account for the influence of the Covid-19 pandemic on road safety. During the Covid-19 pandemic, movement restrictions, lockdowns, and other measures have led to changes in traffic patterns, reduced travel, and altered road usage behaviours [45]. These changes could have had complex effects on road safety, with potential reductions in traffic-related fatalities due to less travel, but also possible shifts in risk factors such as speeding, impaired driving, and distracted driving.

Upon excluding the years 2020 and 2021 from the analysis, the linear regression model fitted to the data on fatality resulting from road crashes revealed a less pronounced trend compared to the analysis that included all years from 2012 to 2022. The model estimated an intercept of 194934.51, representing the baseline number of fatality in the initial year of observation. Additionally, the coefficient for year was estimated to be −93.40, suggesting an average decrease of 93.40 fatality per year over the observed period from 2012 to 2019 and 2022. The model is presented in Equation (6).(6) Y2 = 194934.51 − 93.40X

Evidently, the model is still showed a negative trend and the result was statistically significant at the 0.05 significance level for coefficients of intercept and year at 0.00857 and 0.01010 respectively. This model improved its performance with a multiple R-squared value of 0.6353, indication that approximately 63.53 percent of the variance in the number of fatality could be explained by the linear trend observed over the years. The adjusted R-squared value of 0.5832 confirmed the robustness of the model after adjusting for the number of predictors. Overall, these results strongly suggest a significant downward trend in the frequency of road fatality in Malaysia from 2012 to 2019 and 2022 as shown in Fig. 10.Fig. 10 Linear trend of number of fatality (2012–2019, 2022).

Fig. 10

The Covid-19 pandemic caused significant disruptions in the road crash and fatality trends for 2020 and 2021. Movement restrictions and lockdowns led to a decrease in number of crashes and fatalities, which skewed the overall trends. This makes it challenging to interpret long-term patterns and assess the true impact of interventions over the entire study period with the presence of the irregularity data years due to pandemic. Moreover, the linear regression models showed poor fit when including 2020 and 2021, with non-significant p-values and low R-squared values. This suggests that the pandemic's influence introduced variability that the model couldn't adequately capture, affecting the accuracy of trend predictions. Despite these challenges, by utilizing the linear regression, the trend thrivingly captured upward trends in road crashes and downward trends in fatalities from 2012 to 2019 and 2022, by excluding the years 2020 and 2021. This indicates that despite pandemic-related disruptions, there are underlying trends that can be effectively pinched with appropriate data handling.

3.4 K-means clustering analysis

K-means clustering analysis is carried out on the number of crashes and fatalities across all states in Malaysia. The elbow method was utilized to determine the optimal number of clusters. Upon plotting the WSS against the number of clusters (k), the plot revealed an elbow point at k = 2 as in Fig. 11, suggesting that two clusters would provide meaningful partitioning of the data. The WSS results for each respective number of cluster as depicted in Table 2 and Fig. 11.Fig. 11 Elbow Method for optimal k.

Fig. 11

Table 2 Within-Cluster Sum of Square (WSS).

Table 2Number of Clusters	Within-Clusters Sum of Squares (WSS)	
1	360.00000	
2	102.75460	
3	72.58402	
4	54.57178	
5	33.35237	
6	22.70957	
7	19.76680	
8	13.55382	
9	11.12106	
10	10.19335	

The WSS values indicate the compactness of the clusters formed by the K-means algorithm for each year. Lower WSS values suggest better clustering results, as they imply that the data points within each cluster are closer to their respective cluster centroids. Two distinct clusters were identified. Cluster 1: Characterized by a center of (−0.273, −0.289), this cluster encompasses regions or time periods with below-average incidents of fatalities and crashes. Cluster 2: With a center of (2.155, 2.277), represents areas or time periods experiencing above-average occurrences of both fatalities and crashes.

Based on Fig. 12, Cluster 1, represented in red includes primarily the state of Selangor and majority of Johor, which shows a high number of total crashes and a relatively high fatality count. Selangor's data points are notably clustered in the upper right quadrant of the plot, indicating a significant number of both crashes and fatalities over the observed period. Johor, on the other hand, is plotted below Selangor in terms of the number of crashes but shows a similarly high number of fatalities.Fig. 12 Cluster Analysis of states based on total fatality and total crashes at k = 2.

Fig. 12

Cluster 2, represented in blue encompasses the rest of the states, including two data points of Johor, Kuala Lumpur, Pulau Pinang, Perak, and others. This cluster displays a broader spread across the graph, with most states having lower total crashes and fatalities compared to Selangor. However, Johor stands out within this cluster, showing higher values in both total crashes and fatalities than most other states in the same cluster.

The prominence of Selangor in Cluster 1 is magnified each year from 2012 to 2022, as shown in Fig. 13. It is clear that Selangor consistently belongs to Cluster 1. However, Johor is in the same cluster as Selangor from 2012 to 2019 and again in 2022, but it does not appear in Cluster 1 during 2020 and 2021, which coincide with the Covid-19 pandemic years.Fig. 13 Cluster Analysis of states based on total fatalities and total crashes at k = 2 for each year.

Fig. 13

This analysis suggests that Selangor is an outlier with significantly higher road crash incidents and fatalities, indicating a possible area of concern for road safety and traffic management efforts. The rest of the states, while having varying degrees of crashes and fatalities, do not reach the levels seen in Selangor, highlighting the unique traffic dynamics of this state. This information is crucial for directing resources and interventions where they are most needed and understanding regional traffic safety challenges which can aid in developing targeted strategies for different regions. This segmentation helps in understanding areas with above-average and below-average incidents, guiding more focused safety measures.

4 Discussion

The findings from this study, in conjunction with insights from the Ministry of Transport Malaysia (MOT). The MOT is responsible for the development and implementation of policies and programs related to land, aviation, and maritime transportation. The ministry aims to enhance the efficiency, safety, and effectiveness of the country's transportation systems, supporting economic growth and public welfare. The MOT has developed the Malaysian Road Safety Plan (Pelan Keselamatan Jalan Raya Malaysia (PKJRM) 2014–2020).The PKJRM 2014–2020 aimed to address road safety issues and implement strategies to reduce road traffic accidents and fatalities. Over this period, the plan successfully achieved a reduction in road accidents annually. This was accomplished through a variety of measures, including stricter enforcement of traffic laws, public education campaigns, and improvements in road infrastructure. This plan is a strategic framework targeting a reduction in road crashes and fatalities by 50 percent by 2030. Building on the successes and lessons learned from the 2014–2020 plan, PKJRM 2022–2030 sets a more ambitious target. It aims to reduce road traffic fatalities by 50 percent by the end of 2030, in line with the United Nations' Decade of Action for Road Safety 2021–2030. The PKJRM 2022–2030 emphasizes ten key safety aspects, including effective monitoring and evaluation, safer motorcycle riding, speed management, and safe work travel. It also incorporates a multi-pronged approach involving discipline enhancement, advocacy, enforcement, and various road safety campaigns.

4.1 Trends in road crashes

The analysis of road crash trends spanning from 2012 to 2022 revealed significant patterns across different road user categories. Prior to the Covid-19 pandemic, Malaysia witnessed a concerning increase in the frequency of crashes from 2012 to 2019, as reported in Utusan Malaysia on February 11, 2018. Notably, motorcycle crashes remained consistently high throughout the study period, with a deviation observed in 2020, coinciding with Covid-19 pandemic-related movement restrictions. This decline underscores the impact of mobility patterns on road activity and emphasizes the need for ongoing vigilance in road safety efforts. The MOT's bulletin highlights a concerted effort to address road safety concerns, aligning with the objectives of PKJRM 2014–2020. The bulletin indicates a successful reduction in road crash fatalities, attributing it to various programs and initiatives implemented under PKJRM 2014–2020.

4.2 Geographical patterns

Geographical analysis revealed disparities in road crash rates across Malaysian states, with Selangor and Johor emerging as crash-prone areas. Factors such as population density, extensive road networks, and heavy traffic congestion contribute to higher crash rates in these states. The MOT's report confirms these findings, indicating a focus on crash hotspots through targeted interventions and infrastructure improvements. Additionally, Sabah experiencing a notable reduction in road crash fatalities, as reported by Berita Harian on December 21, 2017. Despite this progress, certain areas within Sabah, such as Tawau, Keningau, and Lahad Datu, continued to report higher fatalities. The success of the Road Safety Campaign in Sabah and the successful reduction in the Death Index per 10,000 vehicles, as outlined in the MOT bulletin provides the insight on the effectiveness of such targeted approaches in crash-prone areas. The targeted interventions are important and will improve road safety awareness in reducing crashes.

4.3 Linear trend analysis

The linear trend analysis shows that the temporal dynamics of road crashes and fatalities. While no significant linear trend was observed in the overall dataset. Excluding the years 2020 and 2021 revealed an upward trend in crashes in 2012–2019 and 2022. This reveals the importance of considering external factors, such as the Covid-19 pandemic, in analyzing road safety trends. The significant downward trend in fatalities resulting from road crashes over the observed period, as highlighted in the MOT's report, further emphasizes the effectiveness of road safety initiatives and interventions. Furthermore, the significant decrease in fatalities, as reported by MalaysiaGazette on February 10, 2018, suggests the effectiveness of initiatives aimed at reducing road crash fatalities. The success of Operation Selamat 2017 in reducing fatalities despite an increase in crashes highlights the importance of proactive policing efforts during peak travel periods.

4.4 K-means clustering analysis

K-means clustering identified distinct clusters based on similarities in crash and death rates across Malaysian states. Areas experiencing above-average occurrences of crashes and fatalities were identified which Selangor and Johor consistently positioned in the cluster 1 for high total crashes and high total of fatalities whereas the other states have been classified in the cluster 2. These findings align with the MOT's emphasis on collaborative efforts and support from various government agencies, nongovernmental organizations, the private sector, and the community at large.

5 Conclusion

In conclusion, this study reveals both significant successes and challenges in analyzing road crash data in Malaysia from 2012 to 2022. The Covid-19 pandemic notably disrupted road crash and fatality trends for 2020 and 2021, leading to a decrease that skewed long-term patterns and complicating the assessment of intervention impacts. The linear regression models struggled to fit the data during the pandemic years, reflecting the variability introduced by the pandemic. However, successes include identifying a persistent high risk of motorcycle fatalities and capturing underlying trends in road crashes and fatalities by excluding pandemic years. The study also highlighted geographical patterns and successfully used K-means clustering to identify distinct crash and fatality clusters, providing valuable insights for targeted interventions and resource allocation in high-incidence states such as Selangor.

Nevertheless, this study still appears to have limitations. Although the trend of the number of crashes and fatalities are discussed, the dataset spans only from 2012 to 2022, providing a total of eleven dataset. This sample size is significantly below the recommended minimum threshold to use more powerful tool such as ARIMA model which generally recommended to have a minimum sample size of 50 data. The future research can consider evaluating and examining the trend of the number of crashes and fatalities in monthly dataset. Future research could explore the use of machine learning algorithms to analyze more extensive datasets, enabling the identification of significant factors contributing to road crashes.

Data availability statement

The data that support the findings of this study were provided by the Royal Malaysian Police (RMP) under a confidentiality agreement. Due to the sensitive nature of the data, it is not publicly available. Data may be accessible from the authors upon reasonable request and with permission from the Royal Malaysian Police.

CRediT authorship contribution statement

Muhammad Fadhirul Anuar Mohd Azami: Writing – original draft, Formal analysis. Md Yushalify Misro: Writing – review & editing, Conceptualization. Rizati Hamidun: Writing – review & editing, Data curation.

Declaration of generative AI and AI-assisted technologies in the writing process

During the preparation of this work the author(s) used Pixlr and ChatGPT in order to generate image in Fig. 1 and concise explanations. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

The research was supported by two grants: the Research University Team (RUTeam) Grant Scheme (1001/PMATHS/8580071) from Universiti Sains Malaysia and the Early Career Research Grant (GRTINECR-DADTP-01-2024) from 10.13039/501100010798 Sunway University . The authors would like to express their gratitude to 10.13039/501100004595 Universiti Sains Malaysia , 10.13039/501100010798 Sunway University , and the Malaysian Institute of Road Safety Research (MIROS) for their support throughout the research project.
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