Search Results (54)

Electric bike (e-bike) accidents have emerged as a significant road safety concern in recent years. Employing a mixed-methods approach, this study seeks to elucidate the mechanisms underlying e-bike accidents and to develop an e-bike safe system aimed at enhancing e-bike safety and accident prevention. Quantitative analysis was employed to identify key components and their relationships through an event-based examination of a structured accident dataset using a Bayesian network. Complementing this, qualitative methods—including observations and interviews—were conducted to gain deeper insights into how riders interact with other components within the system. This study was carried out in Guangzhou, a metropolitan city with an increasing use of e-bikes and e-bike-related accidents. The key findings of this study are as follows: 1. The safe system of e-bike safety comprises critical components, including infrastructure (roads and facilities), e-bikes, riding behavior, individual riders, and other road users. 2. E-bike accidents predominantly result from dysfunctions of the safe system. The alteration of one component influences other components, which may, in turn, provide feedback to the original component. 3. While riders’ mistakes play a role, the interactions between riders and other components also contribute to the accidents. 4. At the individual rider level, barriers to safe riding include a lack of safety knowledge, low penalties for violations, and high opportunity costs associated with safe riding behaviors. Deficiencies in infrastructure, regulations, and law enforcement contribute to violations and risky riding practices. This study contributes to the current body of accident studies by developing an e-bike safe system. Full article

The paper applies the prototype willingness model (PWM) and incorporates components of the theory of planned behavior (TPB), along with deterrence factors, to understand the behavioral intentions, willingness, and recidivism behaviors of individuals penalized for drunk driving. It explores psychological and social factors influencing repeat offenses, focusing on attitudes, subjective norms, prototypes, and deterrence. The PWM outlines two pathways—reasoned (based on intentions) and social reactive (based on willingness). The model helps predict risky behaviors like drunk driving. Thirteen hypotheses are proposed in this study to examine how various factors, such as attitudes, subjective norms, and deterrence, influence willingness, intentions, and behavior. Surveys were conducted among individuals attending road safety classes after being penalized for drunk driving. A total of 1156 individuals participated in the survey, with 855 valid responses collected. The results indicate that behavioral willingness had a stronger impact on recidivism than intention. On the other hand, subjective norms did not significantly affect the intent to reoffend, but attitudes, deterrence, and PBC did. The findings suggest that focusing on behavioral willingness, deterrence, and educational interventions could help reduce repeat drunk driving offenses. The paper offers insights for policymakers to improve prevention strategies, by focusing on the psychological motivators of repeat offenders. Full article

Background: Food delivery motorcyclists face unique risks that often lead to risky riding behaviors. Thailand, with one of the highest rates of motorcycle-related injuries and fatalities globally, has seen a surge in food delivery services following the COVID-19 pandemic, increasing the number of motorcyclists on the roads. Delivery motorcyclists are especially vulnerable due to frequent exposure to traffic congestion, time pressures, and adverse weather. This study aims to identify key health beliefs and external factors contributing to risky riding behaviors among food delivery motorcyclists in Thailand. Methods: The study surveyed 2000 food delivery motorcyclists across five regions in Thailand, employing the Motorcycle Rider Behavior Questionnaire and the Health Belief Model. Structural equation modeling was used to analyze the relationships between health beliefs and risky riding behaviors. Results: The analysis revealed that health motivation, perceived susceptibility, perceived severity, perceived benefits, and cues to action were negatively associated with risky riding behaviors. Conversely, perceived barriers positively influenced these behaviors. Fatigue and aggressive riding were significant predictors of increased risky behaviors at the 0.001 level. Conclusions: Addressing individual health beliefs and external factors like fatigue and aggression is essential for reducing risky riding behaviors and preventing severe injuries. Full article

Yaoundé, the capital of Cameroon, is one of the cities in the country most affected by road traffic crashes. Despite the measures taken by authorities, the human factor remains a major cause of these crashes. This study aimed to evaluate the measures taken to reduce the risk-taking behaviors of collective taxi drivers in Yaoundé. A survey of 144 collective taxi drivers was conducted to gather information on their driving habits, adherence to, and perceived effects of safety regulations. The study revealed the following prevalence of risky driving behaviors among collective taxi drivers: 41.33% for impaired driving; 67% for speeding, 62% for disobeying traffic lights, 68.86% for distraction; and 67% for risky maneuvering on the road. Significant associations were found between risk perceptions and involvement in risky driving behaviors. Associations were also established between the frequency of police inspections and involvement in risky behaviors, between the participation in training programs on safety issues and using poorly maintained vehicles, and between the frequency of awareness campaigns and poor maneuvering on the road. To address these issues, it is essential to strengthen preventive measures on risk factors, raise awareness on a large scale and on a regular basis, and strictly enforce the existing regulations. Full article

Risky driving behaviors of young novice drivers and related crashes pose not only a threat to road safety but also a significant challenge to sustainable development. The high rate of traffic crashes involving young drivers leads to substantial losses in human capital, productivity, and labor, which are irreparable and place a strain on societal resources. Addressing the safety issues of this vital group in society is crucial for promoting the long-term social and economic sustainability of communities. Despite this, studies have lacked a comprehensive explanation of the safety issues surrounding young novice drivers. Therefore, the aim of this study is to identify factors affecting the safety of young novice drivers in Iran, a developing country with a significant young population. The study uses a qualitative approach that is built on grounded theory to reveal the model. The statistical population of the study is specialists, experts, and researchers in the field of road safety who are spread across Iran. The data collection phase was realized through the conduction of semi-structured interviews with 36 of them, and the analysis process was continued until complete theoretical saturation was achieved. The research data were then analyzed using the coding method based on the systematic design of Strauss and Corbin’s grounded theory. Finally, the categories were organized into seventeen subcategories within the six dimensions of the paradigm model: main category (three subcategories), causal factors (three subcategories), contextual factors (three subcategories), strategies (two subcategories), environmental conditions (three subcategories), and consequences (three subcategories). These findings not only provide a deep understanding of the safety issues faced by young novice drivers but also offer strategies that can contribute to improving road safety, thereby fostering social sustainability. Enhancing driver safety is crucial for reducing the economic and social costs of road crashes, which aligns with the broader goals of sustainable development by preserving human capital and societal well-being. Full article

(1) Background: This study explored the challenges faced by schoolchildren while commuting to school, particularly the hazards associated with poor road conditions, exposure to high-speed traffic, and traffic-related air pollution (TRAP). (2) Methods: The research focused on community perspectives gathered through four focus group discussions (FGDs) involving parents and teachers from two primary schools in Blantyre City. Employing qualitative analysis with NVivo, themes, sub-themes, and codes were developed collaboratively within the research team. (3) Results: The community identified road safety risks, emphasizing concerns about different actors’ risky behaviors, poor infrastructure (such as sidewalks, crossings, and signals), and personal safety issues. Proposed solutions for road safety involved educational initiatives for students, infrastructure enhancement, and enforcing stricter penalties. The study revealed a lack of awareness about air pollution among participants, which they often associated solely with unpleasant odors. Suggestions for addressing air pollution centered on educational interventions related to waste disposal and hygiene practices. (4) Conclusions: This research underscores the community’s adeptness at identifying road safety issues and proposing viable interventions. However, it highlights the need for enhanced education and awareness regarding air pollution. The paper advocates for community engagement to comprehensively address these challenges, fostering community cohesion, and empowering community members to advocate for change. Full article

This study derived effective driving behavior indicators to assess the driving safety of autonomous vehicles (AV). A variety of operation design domains (ODD) in urban road networks, which include intersections, illegal parking, bus stop, bicycle lanes, and pedestrian crossings, were taken into consideration in traffic simulation analyses. Both longitudinal and interaction driving indicators were investigated to identify the driving performance of AVs in terms of traffic safety in mixed traffic stream based on simulation experiments. As a result of identifying the appropriate evaluation indicator, time-varying stochastic volatility (VF) headway time was selected as a representative evaluation indicator for left turn and straight through signalized intersections among ODDs related to intersection types. VF headway time is suitable for evaluating driving ability by measuring the variation in driving safety in terms of interaction with the leading vehicle. In addition to ODDs associated with intersection type, U-turns, additional lane segments, illegal parking, bus stops, and merging lane have common characteristics that increase the likelihood of interactions with neighboring vehicles. The VF headway time for these ODDs was derived as driving safety in terms of interaction between vehicles. The results of this study would be valuable in establishing a guideline for driving performance evaluation of AVs. The study found that unsignalized left turns, signalized right turns, and roundabouts had the highest risk scores of 0.554, 0.525, and 0.501, respectively, indicating these as the most vulnerable ODDs for AVs. Additionally, intersection and mid-block crosswalks, as well as bicycle lanes, showed high risk scores due to frequent interactions with pedestrians and cyclists. These areas are particularly risky because they involve unpredictable movements from non-vehicular road users, which require AVs to make rapid adjustments in speed and trajectory. These findings provide a foundation for improving AV algorithms to enhance safety and establishing objective criteria for AV policy-making. Full article

Background/Objectives: Life expectancies have increased in most countries, leading to a higher accident rate among older drivers than their younger counterparts. While numerous studies have analyzed the decline in cognitive abilities and physical limitations as contributing factors, there are other considerations. For instance, younger male drivers tend to take more risks than younger female drivers. However, there is a lack of research and evidence regarding the role of gender in risk-taking among individuals over 65. Given this gap, our current study aims to investigate the relationship between gender and risk propensity in this particular age group. The primary goal was to determine if driving experience affects the gender gap in risk attitude; Methods: We studied risk behavior in both car drivers and pedestrians. Our sample included 200 individuals (101 women), all over 65, with the same weekly driving times. After a brief demographic and anamnestic interview, they completed the Driver Road Risk Perception Scale (DRPS) and the Pedestrian Behavior Appropriateness Perception Scale (PBAS) questionnaires. They also provided information about traffic violations and road crashes; Results: Our research revealed that older male drivers continue to tend to risky behavior, highlighting the need for targeted interventions to improve risk awareness, especially among older men; Conclusions: Our findings suggest that road safety messages should specifically target male drivers as they are less likely to view responsible driving actions, such as observing speed limits, as desirable. Full article

While macroscopic traffic flow models adopt a fluid dynamic description of traffic, microscopic traffic flow models describe the dynamics of individual vehicles. Capturing macroscopic traffic phenomena accurately remains a challenge for microscopic models, especially in complex road sections. Based on a macroscopic network flow model calibrated to real traffic data and new rules for the acceleration and merging behavior on the on-ramp, we propose a microscopic model for on-ramps. To evaluate the performance of the new flow-based model, we conduct traffic simulations assessing speeds, accelerations, lane change positions, and risky behavior. Our results show that, although the proposed model exhibits some limitations, its performance is superior to the Intelligent Driver Model in the evaluated aspects. While the Intelligent Driver Model simulations are almost free of conflicts, the proposed model evokes a realistic amount and severity of conflicts and therefore can be considered for safety analysis. Full article

Road–rail intermodal transportation (RRIT) leverages the advantages of multiple transport modes and is crucial for addressing the current issue of imbalanced development in the transportation sector. However, passengers’ behavior in choosing RRIT remains unclear, and it is necessary to optimize travel service quality through analyzing RRIT choice behavior based on user perceptions. This study designed a stated preference experiment that included both direct and multi-modal travel options. A hybrid choice model considering attitude variables was constructed, and four latent attitude variables—convenience, economy, comfort, and riskiness—were extracted to analyze their impact on intercity travel mode choice behavior under conditions of ticket booking uncertainty. The results revealed that the ticket booking success rate is a critical factor in travelers’ decision-making. Passengers tend to choose travel options with higher ticket booking success rates, even if it entails a slight increase in the ticket prices for the high-speed rail to high-speed rail transfer option. The attitude variables significantly influence intercity travel mode choice behavior, with travelers generally exhibiting a preference for risk avoidance in their travel options. Moreover, there are differences among various groups of travelers in their preferences and demands for the convenience, economy, and comfort aspects of travel options. These research findings can enhance our understanding of the key factors influencing the selection of RRIT services, thereby supporting RRIT designers and planners in improving service quality and facilitating the future growth of RRIT. Full article

When analyzing road safety across cultural boundaries, driver behavior is a crucial component to consider. Given that driver behavior directly affects both the likelihood of accidents and the severity of their implications, it is crucial to comprehend and analyze it. The present study examined the differences in dangerous, aberrant, and positive driving behaviors across China and Pakistan. The effects of these behaviors on road traffic accidents were also considered. In the study, 1253 respondents completed a questionnaire package consisting of the Dula Dangerous Driving Index (DDDI), Aberrant Driving Behavior Questionnaire (DBQ), Positive Driving Behavior Scale (PDBS), and items related to demographics. Generalized linear models were utilized to compare and determine the factors responsible for dangerous driving behaviors. Mean scores for DBQ and PDBS items were compared. Finally, binary logistic regression models were used to find the factors responsible for traffic accidents across both countries. The results indicated that aggressive and risky driving predicted traffic accidents in both countries, followed by errors and violations, which also predicted traffic accidents significantly. Positive driving behaviors predicted accidents negatively in both samples. Furthermore, it was found that Chinese drivers compared to Pakistani drivers are less aggressive and risk-taking and commit fewer violations and errors while driving. To increase road safety in Pakistan, traffic laws must be strictly enforced uniformly, and violations must result in severe penalties, i.e., demerit points or cancellation of a driver’s license. Programs for road safety awareness and education must be expanded. Employing national culturally concentrated road safety strategies may be a more effective way to encourage safe driving behaviors. Full article

Driver behavior plays a pivotal role in ensuring road safety as it is a significant factor in preventing traffic crashes. Although extensive research has been conducted on this topic in developed countries, there is a notable gap in understanding driver behavior in developing countries, such as Pakistan. It is essential to recognize that the cultural nuances, law enforcement practices, and government investments in traffic safety in Pakistan are significantly different from those in other regions. Recognizing this disparity, this study aims to comprehensively understand risky driving behaviors in Peshawar, Pakistan. To achieve this goal, a Driver Behavior Questionnaire was designed, and responses were collected using Google Forms, resulting in 306 valid responses. The study employs a Fuzzy Analytical Hierarchy Process framework to evaluate driver behavior’s ranking criteria and weight factors. This framework assigns relative weights to different criteria and captures the uncertainty of driving thought patterns. Additionally, machine learning techniques, including support vector machine, decision tree, Naïve Bayes, Random Forest, and ensemble model, were used to predict driver behavior, enhancing the reliability and accuracy of the predictions. The results showed that the ensemble machine learning approach outperformed others with a prediction accuracy of 0.84. In addition, the findings revealed that the three most significant risky driving attributes were violations, errors, and lapses. Certain factors, such as clear road signage and driver attention, were identified as important factors in improving drivers’ risk perception. This study serves as a benchmark for policymakers, offering valuable insights to formulate effective policies for improving traffic safety. Full article

By applying the “safety atmosphere” measurement theory to Chinese management companies engaged in road transportation, a corporate and individual safety risk assessment system was established that is consistent with the management and cultural climate in China, thereby reducing the driving safety risk of truck drivers. The system realizes the safety risk assessment of enterprises, fleets and individuals in the form of test scales by constructing a structural model of the enterprise safety atmosphere, including the management, communication, and supervision of enterprises, fleets, and individuals. The safety climate was modeled using a two-level framework, at the organizational level and fleet level, and three dimensions of test items for each level were obtained by exploratory factor analysis. The three dimensions of safety management, safety supervision, and safety priority at the organizational level, and the three dimensions of positive communication, safety awareness, and self-discipline at the fleet level, respectively, passed a valid factorial test (p < 0.01). Finally, the validity of the system evaluation results was verified by relying on the actual in-vehicle monitoring data and accident records of the corporate transportation fleet. The results show that the total test scores at the organizational level and the fleet level are significantly correlated with their driving risk behaviors, and both are linearly and negatively correlated with the number of accidents per thousand kilometers. This indicates a high degree of consistency between the system’s test results and actual risky accidents. Full article

Background: Promoting cycling, walking and other ‘active’ transportation means continues to be a shared guideline in urban planning, closely aligned with the Sustainable Development Goals of the United Nations. Nonetheless, young cyclists’ safety figures and their potential contributors, including behavioral issues, remain an ongoing concern for researchers, practitioners, and policymakers. Aim: This study aimed to analyze both risky and protective riding patterns of young Russian cyclists in relation to cycling safety factors using the Cycling Behavior Questionnaire (CBQ). Methods: This study used the data provided by 374 young Russian cyclists, aged M = 21.6 (SD = 4.8) years, who responded to an electronic survey on cycling behavior and safety outcomes. Results: Beyond demographic characteristics, the key road rule knowledge and risk perception of young cyclists were significantly associated with their self-reported cycling behaviors. Although both traffic violations and riding errors were negatively correlated with safety incidents, predictive analyses show that errors (though not violations) play a predictive role in young cyclists’ safety-related incidents. Conversely, positive cycling behaviors have been shown to be significant reducers of riding crash likelihood. Conclusion: The results of this study support the idea that addressing key issues such as risk perception, road rule knowledge and protective riding habits, while also targeting risky behaviors on the road, could enhance cycling safety outcomes. Additionally, these findings offer valuable insights into understanding the factors contributing to riding risks and crashes among young cyclists, particularly in the context of the increasing need to promote safer and more sustainable urban mobility in a country with a still young cycling culture. Full article

Cyclists are at a higher risk of being involved in accidents. To this end, a safer environment for cyclists should be pursued so that they can feel safe while riding their bicycles. Focusing on safety risks that cyclists may face is the main key to preserving safe mobility, reducing accidents, and improving their level of safety during their travel. Identifying and assessing risk factors, as well as informing cyclists about them may lead to an efficient and integrated transportation system. Therefore, the purpose of this research is to introduce a risk index that can be adapted to different road areas in order to measure the degree of how risky these areas are for biking. Cyclists’ behavior and demographics were integrated into the risk index calculation. The methodology followed to obtain the risk index composed of four phases: risk factor identification, risk factor weighting, risk index formulation, and risk index validation. Nineteen risk factors are categorized into four major groups: facility features, infrastructure features, cyclist behavior, and weather and traffic conditions. Full article