Maya Arlini Puspasari1*
Beryl Putra Sanjaya1Richard J. Hanowski2Hardianto Iridiastadi3Salsabila Annisa Arista1Hasna Hamida Nurkamila1Claresta Yasmine Putri Pribadyo1Ahmad Ghanny1Keishandra Nabila Junistya1- 1Department of Industrial Engineering, Faculty of Engineering, Universitas Indonesia, Depok, Indonesia
- 2Motorcycle Safety Solutions, LLC, Virginia, VC, United States
- 3Department of Industrial Engineering and Management, Bandung Institute of Technology, Bandung, Indonesia
Urban transport research encompasses transport safety, as accident-related fatalities are a significant problem, particularly in developing countries. In Indonesia, motorcycle crashes account for over 70% of all vehicle crashes. These crashes primarily result from behavioral and performance factors associated with the drivers of other vehicles. Most studies on motorcycle riders focus mainly on riding behavior and skills. However, few have examined how distractions influence rider behavior and traffic incidents, particularly when comparing private riders with motorcycle taxi riders. This study aims to develop a model for motorcycle riders by examining the causal relationships between variables through partial least squares structural equation modeling (PLS-SEM). This study also compares differences in driving behavior among age groups, genders, and driver types (private riders and motorcycle taxi riders). The results show that distractions significantly increase both errors and incidents, while risk perception directly influences speeding behavior. Riding errors and the use of protective equipment also make significant contributions to incident occurrence. Chi-square analyses further reveal that male and older riders report more consistent use of protective gear, younger riders exhibit higher levels of speeding and distraction, and taxi riders adopt safer practices compared to private riders. Based on these findings, this study proposes targeted safety strategies that include strengthening rule enforcement, implementing technological systems, conducting regular infrastructure inspections, and promoting public safety campaigns to enhance rider safety.
1 Introduction
The worldwide urbanization rate continues to increase as more people migrate to urban areas (Nations et al., 2018). Consequently, this also increases the demand for transportation, which leads to more vehicle emissions and road accidents. Lower-middle-income countries, like Indonesia, have been reported to have road traffic fatalities two times higher than those in high-income countries (Global Status Report on Road Safety, 2023), and most of them are caused by motorcycle crashes. The prevalence of motorcycles in Indonesia is primarily due to their affordability, as they are often the only feasible transportation option. Moreover, 85% of households in Indonesia own at least one motorcycle and use it as their primary means of transport. The Greater Jakarta Area, encompassing Jakarta, Bogor, Depok, Tangerang, and Bekasi (Jabodetabek), has over 17 million motorcycles, which is 14.87% of Indonesia’s total motorcycle population (Korlantas Polri, 2022). Surges in motorcycle ownership are driven by the need for mobility and the convenience of owning a vehicle (Pujiastutie, 2006). Such an increase in vehicle volume leads to more road conflicts, resulting in crashes and traffic congestion (Ahmed et al., 2021; Hafram and Asrib, 2022). Alarmingly, motorcycle crashes dominate Indonesia’s traffic crash statistics, accounting for over 70% of total crashes involving various vehicles. Jakarta has recorded a significant number of motorcycle crash cases, with approximately 4,507 victims in 2021 (Badan Pusat Statistik, 2024; Indonesian National Police, 2023). Even online motorcycle taxi riders are not exempt from crashes, with 103 reported cases in 2017 due to distractions caused by smartphone usage while navigating customer addresses (Manurung et al., 2019).
Human factors are a major contributor to crash occurrence, particularly in developing countries, where they encompass riders’ behavioral tendencies and cognitive capabilities (Sami et al., 2013). Riding behavior is generally classified into intentional behaviors (deliberate violations such as speeding or stunt riding) and unintentional or cognitive errors (misperception, lapses in attention, or poor hazard anticipation) (Stephens and Fitzharris, 2016; Xing et al., 2020). These two categories stem from distinct cognitive processes (de Winter et al., 2015). Errors are typically unintentional and habitual, often resulting from distraction, inexperience, or cognitive failures related to attention, memory, and perception (Rason et al., 1990; Shi et al., 2010). Violations, by contrast, arise from failures in higher-order cognitive control such as impulse regulation, aggression management, and personality-related factors like emotional stability and conscientiousness (Dahlen et al., 2012; Sani et al., 2017; Wickens et al., 2008). Both mechanisms increase crash risk, violations by elevating exposure to hazardous maneuvers, and cognitive errors by triggering loss-of-control events (de Winter et al., 2015; Elliott et al., 2007. Psychological dimensions, including attitudes and risk perception, further explain how riders decide when to comply with or deviate from safety rules (Wisutwattanasak et al., 2022).
The Motorcycle Rider Behavior Questionnaire (MRBQ) was suggested by Elliott et al. (2007) as a valuable measurement tool for several behavioral types that may increase the risk of motorcycle crashes. The original MRBQ comprises 43 items, each representing a particular riding activity that falls under one of the four primary behavioral categories: errors, stunts, speed violations, and use of riding equipment. Errors refer to unintentional mistakes; stunts are driving behavior that is deliberately carried out as a form of “showing off” to other people; violations mean the driver tends to violate traffic rules and procedures intentionally; and use of riding equipment refers to the use of safety equipment when driving, such as helmet, protective jacket, gloves, boots, and protective body armor (Elliott et al., 2007). Violations, which are deliberate breaches of traffic rules, and errors, which denote unintentional mistakes while driving, are strongly influenced by a rider’s driving style and skills (de Winter et al., 2015; Özkan et al., 2006). In Vietnam, more than 95% of road traffic crashes are attributed to traffic violations, including the use of smartphones while driving, not wearing a helmet while riding a motorcycle, and disregarding traffic regulatory signs (Truong et al., 2016).
The rising usage of in-car information systems and communication technology has contributed to the growth of research on driver distraction and inattention in recent decades (Truong et al., 2016). Distractions impair driving performance, which increases the risk of traffic crashes (Arevalo-Tamara et al., 2022; Sundfør et al., 2019; Wundersitz, 2019). Researchers have explored distraction and driving behavior among car drivers (Alhomoud et al., 2022; Arevalo-Tamara et al., 2022) and truck drivers (Hanowski, 2011); however, studies investigating distraction among motorcycle riders are underexplored. Most studies related to motorcycle riders focus solely on riding behavior and skills, but few examine the role of distraction. Research on the impact of distractions on motorcycle riders primarily examines the usage of cellphones, the prevalence, and related variables (Gupta et al., 2022; Nguyen et al., 2020). In contrast, Ledesma et al. (2023) reported that using a map navigation system, listening to music or the radio, and adjusting vehicle devices were the most distracting activities, not cellphone usage (Ledesma et al., 2023). Moreover, in the riding behavior sections, different results are reported by previous studies. According to (Elliott et al., 2007), traffic errors and violations are predictors of collisions, with traffic errors being the more potent predictor (Elliott et al., 2007). Stephens et al. (2017) stated that the likelihood of being involved in a near-crash is increased by speeding violations and errors related to motorcycle control (Stephens et al., 2017). By contrast, Özkan et al. (2012) reported that the performance of stunts, not traffic errors, is an accurate predictor of crashes (Özkan et al., 2012). This finding was supported by (Sakashita et al., 2014), who found a correlation between stunts and police-recorded crashes (Sakashita et al., 2014). These studies present inconsistent findings concerning which aspects of riding behavior contribute to increased crash risk and which sources of distraction impair riding performance. Moreover, the simultaneous examination of riding behavior and distraction remains an underexplored area in current research.
In Indonesia, motorcycle riders can generally be classified into two types. The first group consists of private motorcycle riders, who primarily use their vehicles for personal purposes such as commuting to school, work, or other daily activities. The second group comprises online motorcycle taxi riders, who rely on motorcycles as their primary source of income, providing transportation and delivery services. Due to prolonged exposure to traffic, weather, and air pollution, motorcycle taxi riders face greater occupational health risks, with back pain, vision problems, fatigue, and headaches frequently reported (Berrones-Sanz, 2018; Diaz Olvera et al., 2016). These risks would contribute to the riders’ behavior and distraction, which underlines the importance of comparing private and taxi riders.
The novelty of this study lies in several aspects. First, this study conducts modeling that integrates distraction, behavior, and incidents in the Greater Jakarta Area, thereby contributing to improved road safety in Indonesia. Second, this study explicitly compares private riders and motorcycle taxi drivers, a topic rarely addressed despite its high relevance in developing countries like Indonesia, where motorcycles are widely used for both private mobility and work. Finally, this study addresses inconsistencies in previous research by simultaneously examining the roles of errors, violations, stunts, and protective equipment in crash risk, as well as the effects of distraction.
This study aims to design a model that correlates distraction and rider behavior with self-reported traffic incidents and assesses behavioral differences among motorcycle riders, which are analyzed and visualized using partial least squares structural equation modeling (PLS-SEM). This study also compares differences in driving behavior among age groups, genders, and driver types (private riders and motorcycle taxi riders). Structural equation modeling enables the examination of complex relationships between dependent and independent variables using path models, which depict the hypotheses and interconnections between variables (Hair et al., 2011; Hair et al., 2022). This study seeks to address critical issues related to motorcycle safety by understanding the impact of human factors on driving behavior. Practical strategies for reducing motorcycle crashes and enhancing road safety are developed by analyzing the relationships between various variables.
2 Materials and methods
2.1 Research object and subject
Data collection involved distributing online questionnaires to motorcycle riders aged between 17 and 59 years. All participants were required to possess a valid driving license (SIM C) before completing the questionnaires. This research was conducted in the Greater Jakarta Area, which comprises Jakarta, the capital and metropolis of Indonesia, as well as Bogor, Depok, Tangerang, and Bekasi, its surrounding suburbs. The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Research and Community Engagement Ethical Committee of the Faculty of Public Health Universitas Indonesia (protocol code Ket-613/UN2.F10. D11/PPM.00.02/2023).
The questionnaires used in this study were as follows: the MRBQ, which assesses various aspects of motorcycle rider behavior while riding, namely, errors, speed violations, stunts (dangerous actions on the road), and use of riding equipment (Elliott et al., 2007); the Road Distractions Scale (Useche et al., 2018), which captures distractions experienced while riding, and the Risk Perception Scale (Useche et al., 2018), which measures riders’ understanding of road risks and their experiences with crashes or incidents while riding within the past year.
A total of 211 respondents (148 males and 63 females) participated in this study. This number surpassed the minimum of respondents required for PLS-SEM, as determined by using the inverse square root method (Kock and Hadaya, 2025). The participants were categorized into two age groups: young (17–25 years old) and middle-aged (26–59 years old). This age category is based on established developmental theory, which distinguishes emerging adulthood (approximately ages 18–25) from established adulthood (ages 26 and above) (Arnett, 2000). Furthermore, according to Arevalo-Tamara et al. (2022) and Arafa et al. (2020), there are three age groups of motorcyclists: those aged 17–25, those aged 26–59, and those aged 60 and above. Respondents in this study ranged in age from 17 to 59. Among the 211 motorcycle riders, 65.88% (139 respondents) were in the young group, and 34.12% (72 respondents) were in the middle-aged group, respectively. The respondent recruitment process is an open online link distributed to students and workers living in the Greater Jakarta area. The characteristics of the respondents are in Table 1.
Table 1. Demographic characteristics of respondents.
2.2 Model and research hypotheses
This conceptual model design is based on a study by Arevalo-Tamara et al. (2022), who identified several variables that can influence driver behavior using the Driving Behavior Questionnaire model: error, violations, distractions, risk perception, and incidents (Arevalo-Tamara et al., 2022). This study focused specifically on motorcyclists, given the significant prevalence of motorcycles in Indonesia, and the model was further developed based on the MRBQ (Elliott et al., 2007) to assess the behavior of motorcycle riders on the road. Seven latent variables were used in this study: the Road Distraction Scale (RDS); the Risk Perception and Regulation Scale (RSRP); errors (ER); speed violations (SV); stunts (S); use of riding equipment (EQ); and incidents (I). Figure 1 illustrates the conceptual and PLS-SEM models, where circles denote the latent variables and rectangles represent the indicators. The arrows indicate the connections between the latent variables and the indicators, as well as between each latent variable. Based on the model, 14 research hypotheses (Table 2) were developed (Arevalo-Tamara et al., 2022; Elliott et al., 2007). The codes used for each latent variable are shown in Table 3.
Figure 1. (a) Conceptual model (b) PLS-SEM model.
Table 2. Research hypotheses.
Table 3. Code used for each latent variable.
2.3 Data collection
The data collection process involved determining the types and methods of data collection, developing conceptual models and hypotheses based on a literature review, designing questionnaires, and collecting responses. The data processing phase consisted of assessing the validity and reliability of the questionnaires, specifying the model, conducting structural equation modeling (SEM) analysis, which included evaluating the measurement model (outer model) and the structural model (inner model), and performing a chi-square test for further study.
Three questionnaires covering rider demographics and characteristics were used to collect data. The MRBQ was used to explore motorcycle riders’ behavior; it contained 36 questions relevant to motorcycle riders’ behavior in Indonesia (Sakashita et al., 2014). The RDS was administered to assess the influence of distractions while driving, and the RSRP was used to elucidate the riders’ understanding of road risks (Useche et al., 2018). The final question posed to the respondents concerned road incidents experienced by riders in the past year. Additional data were collected and validated to support the development of strategy recommendations. These data were evaluated alongside the results of the hypothesis analysis, chi-square test, and literature review, incorporating input from relevant professionals.
Experts were involved in the validation process to ensure the content validity of the questionnaire prior to its distribution. They assessed whether the questionnaire items accurately reflected the content being measured, checked whether the items were relevant and representative, and identified any critical aspects that may have been overlooked. These experts were from various institutions, including associate expert researchers from the National Research and Innovation Agency and academics who have extensive experience in research related to transport safety.
2.4 PLS-SEM processing
SmartPLS 4 was used to process the PLS-SEM data, which involved model specification, evaluation of the measurement model (outer model), and evaluation of the structural model (inner model). SEM processing was performed on the data of 211 respondents. The number of respondents exceeded the minimum number of samples required for PLS-SEM, as determined using the inverse square root method (Kock and Hadaya, 2025). A measurement model and a structural model were implemented as evaluation methods. The measurement model consisted of four steps as listed below.
2.4.1 Determining the indicator reliability value
The indicator reliability was used to determine the relationship between each indicator and the construct variables, as seen in Table 4. If the outer loading value falls within the range of 0.4–0.7, it is recommended to remove it to enhance the validity and reliability of the model. Meanwhile, the loading indicator value of <0.4 must be removed from the measurement model [29]. The Stunts indicator was removed because it had a low reliability value compared to other indicators.
Table 4. Outer loading factors.
2.4.2 Determining the internal consistency and convergent validity value
The internal consistency of the model was evaluated using Composite Reliability (CR), and values greater than 0.7 indicate good reliability. A convergent validity test was performed by calculating the average variance extracted (AVE) value, with AVE >0.5 indicating validity (Hair et al., 2022), and the variables that did not meet the criteria were eliminated (Table 5).
Table 5. Composite Reliability and Average Variance Extracted value.
2.4.3 Determining the discriminant validity value
A discriminant validity test was conducted using the Fornell–Larcker criterion (Table 6) and the heterotrait–monotrait (HTMT) ratio (Table 7). The model also met the Fornell–Larcker criterion, which states that the value of each variable that is the same must be higher when compared to a different variable, and all variables satisfied the required HTMT ratio (below 0.90). Thus, all variables were valid.
Table 6. Fornell-locker criterion.
Table 7. Heterotrait–monotrait (HTMT) ratio.
The structural model was used to evaluate the extent of the relationship between one construct variable and others within the model. The structural model consisted of three steps. The first one was testing the correlation strength between two or more independent variables within the model. A multicollinearity test was conducted to identify any multicollinearity issues within the model. Multicollinearity can lead to errors in parameter estimation, rendering specific parameters statistically insignificant (Grewal et al., 2004). The multicollinearity test was based on the variance inflation factor (VIF), with VIF >5 indicating the presence of multicollinearity in the model (Becker et al., 2015; Mason and Perreault, 1991). The multicollinearity test results, obtained using SmartPLS, showed that all VIF values were under 5, indicating the absence of multicollinearity among the model indicators (Table 8). The second step was assessing the path coefficient. The third step was to determine the coefficient of determination (R2), which indicates the proportion of variance in the dependent variable that is explained by each independent variable (Raithel et al., 2012). The model fit with an SRMR value of 0.079, which met the standard threshold of 0.08.
Table 8. VIF values.
3 Results
3.1 Analysis of causal relationships in hypotheses
The significance of the causal relationships in the hypotheses was tested. The relationship significance was used to select a decision: rejection of H0 or failure to reject H0. The importance of these relationships was tested using SmartPLS. This test was conducted via bootstrapping with a resampling number of 5,000 subsamples. Testing was also performed under two-tailed conditions at a 5% significance level. The results of the significance test for the relationship between the 14 hypotheses in the motorcycle rider model (Table 9) are as follows. Five hypotheses show a significant relationship between variables. According to these findings, road distractions have a significant influence on errors and incidents (H1 and H5). Furthermore, risk perception directly affects speed violations (H7), errors significantly affect incidents (H11), and the use of riding equipment affects incidents (H14).
Table 9. Significance test on hypotheses about motorcycle riders.
3.2 Chi-square analysis by gender
Chi-square analysis shows a significant relationship between gender and the use of riding equipment (Table 10). Male riders reported higher and more consistent use of protective equipment compared to female riders, who were more concentrated in the lower usage categories.
Table 10. Chi-square test results (gender).
3.3 Chi-square analysis by age
Based on the chi-square test results, age has a significant influence on speed violations and the use of riding equipment (Table 11). Young riders are more likely to exceed speed limits and tend to use fewer riding equipments, such as protective pants, shoes, jackets, body protectors, reflective clothing, headlights, and gloves compared to middle-aged riders (Figure 2). Moreover, young riders are more frequently distracted while riding, particularly when using GPS (Figure 3). These risky behaviors, which include speeding, reduced use of protective equipment, and increased susceptibility to distraction, highlight a pattern among younger riders. Despite reporting a higher level of risk perception compared to middle-aged riders, they demonstrate a gap between their awareness and their actual safety practices.
Table 11. Chi-square test results (age).
Figure 2. Use of riding equipment based on age.
Figure 3. Trends of distraction across age categories.
3.4 Chi-square analysis by type of rider categories
According to the chi-square test results, four behavioral variables—error, speed violations, stunts, and use of riding equipment—were measured on a Likert scale from “never” to “always,” showing significant differences between private and motorcycle taxi riders (Table 12). Private riders reported higher frequencies of speeding, and occasional stunts, whereas taxi riders reported lower frequencies of these risky behaviors. Moreover, Figure 4 illustrates the differences in riding equipment usage between private and taxi riders, showing that taxi riders tend to use more complete protective gear than private riders. Figure 5 presents the distribution of distraction sources, highlighting the trend of distraction across both rider types. For distraction caused by text messages, measured on a scale from “not distracting” to “very distracting,” private riders overwhelmingly perceived text messaging as “very distracting,” while taxi riders showed a more distributed response across the scale, although many also considered it distracting. Finally, for risk perception, assessed on a scale from “strongly disagree” to “strongly agree,” both groups generally agreed or strongly agreed with risk-related statements, but private riders were more concentrated in the highest category, suggesting a stronger overall perception of risk compared to taxi riders.
Table 12. Chi-square test results (rider type).
Figure 4. Use of riding equipment based on type of rider categories.
Figure 5. Trends of distraction across the type of rider categories.
4 Discussion
This study aimed to develop a model that correlates distraction and rider behavior with traffic incidents, assess behavioral differences among riders, and determine safety strategies for motorcycle riders on the road. The results of this research indicate that distraction has a significant impact on errors (H1) and traffic incidents (H5). This study also supports the fact that risk perception directly affects speed violations (H7). Driving errors (H11) and the use of riding equipment (H14) affect incidents. These findings have the potential to significantly influence the development and implementation of motorcycle safety measures and policies, ultimately contributing to a safer road environment for all users.
The hypothesis tests confirm that distraction significantly increases the likelihood of rider errors. The results show that text messages (RD 2), phone calls (RD 1), and road conditions (RD 6) are the most distracting factors compared to other factors (Figures 3, 5). Interestingly, road conditions emerged as a dominant variable, likely due to Indonesia’s relatively poor road infrastructure, which contributes substantially to riding errors and incident risk. While smartphone-related distractions remain a major concern (Manurung et al., 2019), this study suggests that other forms of distraction (e.g., navigation use, surrounding traffic, and environmental factors) can have equally detrimental effects. This finding aligns with (Ledesma et al., 2023), who found that map navigation, listening to music, and adjusting devices can be very dangerous because they continuously divide visual and cognitive attention. Wierwille et al. (2002) emphasized that common multitasking behaviors such as calling, eating, and drinking impair driver performance. Collectively, these results reinforce that distraction is a multifaceted phenomenon, spanning visual, auditory, and cognitive dimensions, and that its impact depends on the attentional resources it consumes.
This study demonstrates that distraction is a significant contributor to traffic incidents, consistent with prior research indicating that distraction is a major factor in both fatal and non-fatal crashes. Distractions may increase road traffic accident risk by 12.8 times (Dingus et al., 2016) and according to the results of certain studies conducted in the United States, it has been observed that approximately 40% of drivers who have been involved in vehicular collisions were distracted (Klauer and Tech, 2025).
Additionally, it is well established that motorcyclists’ perception of risk has a significant impact on their likelihood of committing speeding violations. Motorcyclists are often perceived as fast and risk-prone road users. It is not uncommon to see motorcyclists driving faster than cars, frequently overtaking, and squeezing into small gaps in traffic (Cheng et al., 2015).
Errors also significantly influence incidents, suggesting they act as a mediator between distraction and traffic incidents. This highlights the mechanisms through which distraction escalates crash risk. It is consistent with the previous studies that claimed that errors significantly affect traffic crashes (Hu et al., 2020; Sakashita et al., 2014; Zhang et al., 2019). However, these results slightly differ from earlier studies, which have highlighted the relationship between violations and stunts toward traffic crashes (Arevalo-Tamara et al., 2022; Özkan et al., 2012; Sumit et al., 2021). The model shows that errors often occur when riders slip due to poor road conditions or a helmet fogging up, or when they focus excessively on the road ahead while turning, which can nearly cause collisions. This is consistent with previous research, which stated that the highest incidence of incidents was caused by lane changing in dense traffic (Aupetit et al., 2016). The current findings indicate that errors are the only variable affecting the risk of traffic incidents, surpassing other MRBQ variables such as speed violations, stunts, and riding equipment.
The use of riding equipment also influences incidents. Helmets and protective clothing serve different functions, but both play crucial roles in reducing injury severity. Helmets, which are used to protect the head, have been proven to effectively reduce head injuries by 69% and deaths by 42% (Cheng et al., 2015), while the use of protective clothing when riding a motorcycle can reduce soft-tissue injuries, such as abrasions and lacerations (Erdogan et al., 2013). In this study, headlights, jackets, and gloves were the most frequently used riding equipment, whereas pants and body protectors were less commonly utilized. The frequent use of headlights may be attributed to the Automatic Headlight On (AHO) feature, which automatically activates when the engine starts and supports Indonesia’s Safety Riding Program under Traffic and Street Transport Law mandating daytime headlight use (Association of Indonesia Motorcycle Industry, 2011). Additionally, middle-aged riders were found to use protective equipment more consistently than younger riders, while motorcycle taxi riders showed greater compliance compared to private riders. This pattern may be linked to the Minister of Transportation Regulation No. 12 of 2009, Article 4(L), which requires public motorcycle drivers to wear reflective jackets with identification, long pants, shoes, gloves, and to carry raincoats (Menteri Perhubungan Republik Indonesia, 2019).
In addressing discrepancies from the results of previous studies, this study found that errors in riding had a greater influence on the occurrence of incidents than violations, stunts, and riding equipment. This is related to the study’s model, which included variables related to distraction and risk perception. Distractions are suspected to be a more influential variable in error occurrence, as they reduce driver focus, thereby increasing the number of errors on the road. Specifically, our study concludes that distraction serves as a critical factor that has been underexplored in previous studies. Distraction affects rider attention and cognitive resources, which directly increases the likelihood of committing riding errors. While violations, stunts, and inadequate riding equipment certainly contribute to traffic incidents, their effects may be overshadowed by riding errors, as the more immediate impact of attention deficits.
The chi-square analysis revealed that gender has a significant impact on the use of protective equipment. Male riders reported higher and more consistent use of helmets, gloves, jackets, boots, and body armor compared to female riders, suggesting that women may be less consistent in adopting safety gear and therefore more vulnerable in crashes. This disparity may partly result from the unequal gender distribution in our sample, with female riders representing only 34.12% of respondents, which may potentially bias the observed correlation. Supporting this, a study in Pakistan found extremely low helmet use among female pillion riders as one of the riding equipment that must be used, despite awareness of its protective benefits (Saeed et al., 2014) Barriers included discomfort, unwanted attention, concerns about appearance, and poorly designed helmets. Strategies to increase helmet use among female riders included media campaigns, distributing helmets, stricter law enforcement through fines, and endorsements by religious leaders regarding social norms and cultural barriers (Khan et al., 2023).
Age also plays a significant role in riding behavior. Younger riders were more likely to commit speed violations, especially those under 30, who tend to exhibit more reckless behavior and a sense of invincibility. Their limited riding experience often combines with risk-taking tendencies, which leads to higher rates of speeding and traffic violations (Islam, 2021). At the same time, older riders consistently reported greater use of protective gear. This finding is consistent with Naderpour et al. (2023) who observed that older motorcyclists are generally more safety-conscious than younger ones.
Distraction is a significant concern, particularly for younger riders, who are especially susceptible to GPS-related distractions while riding. Key cognitive skills required for safe riding, such as visual scanning, hazard anticipation, and managing in-vehicle distractions, are not yet fully developed in younger riders, which makes multitasking on the road more difficult (Cassarino and Murphy, 2018). This result then aligns with Ledesma et al. (2023), who reported that younger riders are more prone to engaging in distracting activities and committing riding errors. What makes this even more striking is that young riders in our study also reported a higher level of risk perception compared to middle-aged riders, demonstrating a gap between their awareness and their actual safety practices. Additionally, the Chi-square analysis showed a significant relationship between age and risk perception (p = 0.041), indicating that younger riders reported higher perceived risk compared to middle-aged riders. It suggests that young riders have a relatively good understanding of safety, but it is not consistently applied in their daily practices. This reinforces the observed gap between young riders’ awareness and their actual safety practices.
Rider type also shapes riding behavior. Private riders, who primarily use motorcycles for daily commuting or leisure, report higher frequencies of speeding, occasional stunts, and lower use of protective gear. In contrast, taxi riders who depend on motorcycles for work tend to adopt safer practices. A case study in South China confirmed this pattern: motorcycle taxi drivers displayed safer behavior than non-occupational riders (Wu and Loo, 2016). This study also found that private riders are more easily distracted by text messages than motorcycle taxi riders. It is also in line with findings from NHTSA, which identified texting as the most alarming distraction (National Highway Traffic Safety Administration, 2025). According to the NHTSA, taking 5 seconds to send or read a text while riding at 55 mph is equivalent to traveling the length of a football field with your eyes closed. However, the risk perception of private riders is generally higher than that of taxi riders, suggesting that their understanding of road risks is more comprehensive, despite making more frequent errors and violations on the road.
This study contributes to the development of a novel model by combining various variables, specifically in the context of motorcycle riders in a developing country. Most of the previous research has applied SEM to examine distraction and behavior among car and truck drivers (Alhomoud et al., 2022; Arevalo-Tamara et al., 2022; Hanowski, 2011; Yan et al., 2022), while research adopting PLS-SEM to investigate distraction and motorcycle riders’ driving behavior in developing countries remains considerably underexplored.
In addition to its methodological contribution, this study also offers practical contributions for road safety interventions in Indonesia and other developing countries with high motorcycle usage. This research proposes strategies specifically tailored to address the empirical relationships identified between distraction and rider behaviors. The approach that can be implemented includes the use of technology, inspections, enforcement of rules, and safety campaigns (Anastasiadou and Kehagia, 2025; Andrey et al., 2001; Cardoso et al., 2007; Foundation for Traffic Safety, 2013; Kumphong et al., 2019; New South Wales Government, 2025; Özkan et al., 2006; Patil and Shiurkar, 2020; Rodrigues et al., 2015).
The use of an Intelligent Transportation System (ITS) to provide riders with information about road conditions, visibility, and other relevant details through variable message signs can help reduce the risk of riding errors. The ETLE (Electronic Traffic Law Enforcement) system can also be expanded to roads frequently used by motorcyclists to monitor speeding and detect mobile phone use, thereby reducing traffic violations and distractions (Sutandi, 2021). Furthermore, technological improvements for motorcycles could be installed, such as collision avoidance systems and collision warning systems, for example, RideHawk, a system that will alert motorcyclists when it detects potential hazards (Motorcycle Safety Solutions, 2024). Equipping motorcycles with these systems can enhance safety by giving riders more time to react to potential hazards (Abdul Rashid et al., 2019).
For infrastructure, regular road safety inspections can be conducted to prevent traffic crashes caused by unfavorable road conditions. A reduction in the frequency and magnitude of accidents, as well as a decrease in potential accident costs, are the most significant benefits derived from road safety inspections (Elvik, 2006; Traffic and Transport Theory and Practice, 2016). These inspections assess the quality of traffic signs, road markings, road surface, and the adequacy of road visibility.
Last, the government can also collaborate with nonprofit road safety organizations to develop campaigns that address aggressive motorcycle rider behavior and emphasize the importance of wearing appropriate riding equipment. In short, enhancing monitoring and enforcement on city road systems, implementing technological systems, managing infrastructure, and initiating campaigns to raise public awareness of safety are identified as key practical implications for increasing rider safety.
5 Conclusion
This study developed a conceptual model linking distraction, driving behavior, and traffic incidents among motorcycle riders in Indonesia. The findings indicate that road distractions significantly increase both errors and incidents, while risk perception has a direct influence on speeding behavior. Riding errors and the use of protective equipment were also found to contribute significantly to the occurrence of incidents. Chi-square tests further reveal that male and older riders reported greater use of protective gear, while younger riders showed higher tendencies toward speeding and distraction. Additionally, taxi riders are more likely to engage in safer practices compared to private riders.
The theoretical and methodological implications of this study contribute to the newly developed conceptual model, which links distraction and driving behavior with traffic incidents, and deepens the understanding of their complex interactions, particularly in the context of motorcycle use in a developing country. This study combined variables from the Motorcycle Rider Behavior Questionnaire, Road Distractions Scale, and Risk Perception and Regulation Scale to produce a new conceptual model. The practical implication of this study is its formulation of strategies based on established hypotheses; these measures are targeted interventions designed to increase road safety, especially for motorcycle riders in Indonesia and other developing countries with high motorcycle usage. These strategies include strengthening rule enforcement, implementing technological systems, conducting regular infrastructure inspections, and promoting safety campaigns to enhance riding safety. These insights have practical value in the design of evidence-based interventions for mitigating road crashes among motorcycle riders.
This study has several limitations that may have affected the results. First, the research model did not consider other factors, e.g., fatigue and exhaustion, which could have influenced the outcomes. Second, this study focused solely on motorcycle riders in Greater Jakarta and may therefore not accurately represent the behavior of all riders in Indonesia. The sample composition was dominated by males (70.14%) and younger riders (17–25 years old; 65.88%), which may have further contributed to demographic bias. In addition, this study employed an online questionnaire, which has the potential for common-method bias and a tendency towards social desirability. Future research should explore the influence of factors not addressed in this study, include a larger and more demographically balanced sample, and explore methodologies other than questionnaires to enhance understanding of the topic.
Data availability statement
The original contributions presented in this study are included in the online repository. It can be found on Figshare (DOI: 10.6084/m9.figshare.30316363) in this link: https://figshare.com/articles/dataset/From_Motorcycle_Taxi_to_Private_Bikes_How_Distraction_and_Riding_Behavior_Influences_Traffic_Incidents_in_Greater_Jakarta/30316363?file=58592905.
Ethics statement
The studies involving humans were approved by The Research and Community Engagement Ethical Committee Faculty of Public Health Universitas Indonesia (Number Ket- 613/UN2.F10. D11/PPM.00.02/2023). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
Author contributions
MP: Funding acquisition, Writing – original draft, Resources, Formal Analysis, Conceptualization, Supervision, Methodology. BS: Software, Writing – original draft, Investigation, Formal Analysis, Methodology, Data curation, Conceptualization. RH: Writing – review and editing, Validation. HI: Writing – review and editing, Validation. SA: Project administration, Writing – review and editing, Formal Analysis. HN: Data curation, Writing – review and editing, Formal Analysis. CP: Visualization, Data curation, Writing – review and editing. AG: Writing – review and editing, Visualization, Data curation. KJ: Writing – review and editing, Data curation, Visualization.
Funding
The author(s) declared that financial support was received for this work and/or its publication. This research was funded by the Directorate of Research and Community Service (DRPM) Universitas Indonesia [grant number NKB-524/UN2. RST/HKP.05.00/2023, 2023].
Acknowledgements
The authors thank the Directorate of Research and Community Service (DRPM) Universitas Indonesia for funding this research [grant number NKB-524/UN2. RST/HKP.05.00/2023, 2023].
Conflict of interest
Author RH was employed by Motorcycle Safety Solutions, LLC.
The remaining author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
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Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/ffutr.2025.1721997/full#supplementary-material
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Keywords: riding behavior, motorcycle, distractions, traffic incidents, PLS-SEM
Citation: Puspasari MA, Sanjaya BP, Hanowski RJ, Iridiastadi H, Arista SA, Nurkamila HH, Putri Pribadyo CY, Ghanny A and Junistya KN (2026) From motorcycle taxi to private bikes: how distraction and riding behavior influence traffic incidents in greater jakarta. Front. Future Transp. 6:1721997. doi: 10.3389/ffutr.2025.1721997
Received: 10 October 2025; Accepted: 15 December 2025;
Published: 07 January 2026.
Edited by:
Juan de Oña, University of Granada, SpainReviewed by:
Ting Lei, Texas Southern University, United StatesMohammad Mehdi Oshanreh, University of Washington, United States
Copyright © 2026 Puspasari, Sanjaya, Hanowski, Iridiastadi, Arista, Nurkamila, Putri Pribadyo, Ghanny and Junistya. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Maya Arlini Puspasari, bWF5YWFybGluaUB1aS5hYy5pZA==