A method to enhance drivers' hazard perception at night based on "knowledge-attitude-practice" theory.

During nighttime driving, the inherent challenges of low-illuminance conditions often lead to an increased crash rate and higher fatalities by impairing drivers' ability to recognize imminent hazards. While the severity of this issue is widely recognized, a significant research void exists with regard to strategies to enhance hazard perception under such circumstances. To address this lacuna, our study examined the potential of an intervention grounded in the knowledge-attitude-practice (KAP) framework to bolster nighttime hazard detection among drivers. We engaged a cohort of sixty drivers split randomly into an intervention group (undergoing specialized training) and a control group and employed a holistic assessment that combined eye movement analytics, physiological response monitoring, and driving performance evaluations during simulated scenarios pre- and post-intervention. The data showed that the KAP-centric intervention honed drivers' visual search techniques during nighttime driving, allowing them to confront potential threats with reduced physiological tension and ensuring more adept vehicle handling. These compelling findings support the integration of this methodology in driver training curricula and present an innovative strategy to enhance road safety during nighttime journeys.

What can we learn from the AV crashes? - An association rule analysis for identifying the contributing risky factors.

The objective of this study is to explore the contributing risky factors to Autonomous Vehicle (AV) crashes and their interdependencies. AV crash data between 2015 and 2023 were collected from the autonomous vehicle collision report published by California Department of Motor Vehicles (DMV). AV crashes were categorized into four types based on vehicle damage. AV crashes features including crash location and time, driving mode, vehicle movements, crash type and vehicle damage, traffic conditions, and among others were used as potential risk factors. Association Rule Mining methods (ARM) were utilized to identify sets of contributing risky factors that often occur together in AV crashes. Several association rules suggest that AV crashes result from complex interactions between road factors, vehicle factors, and environmental conditions. No damage and minor crashes are more likely affected by the road features and traffic conditions. In contrast, the movements of vehicles are more sensitive to severe AV crashes. Improper vehicle operations could increase the probability of severe AV crashes. In addition, results suggest that adverse weather conditions could increase the damage of AV crashes. AV interactions with roadside infrastructure or vulnerable road users on wet road surfaces during the night could potentially lead to significant loss of life and property. Furthermore, the safety effects of vehicle mode on the different AV crash damage are revealed. In some contexts, the autonomous driving mode can mitigate the risk of crash damages compared with conventional driving mode. The findings of this study should be indicative of policy measures and engineering countermeasures that improve the safety and efficiency of AV on the road, ultimately improving road transportation's overall safety and reliability.

Optimization of colored pavement considering driving behavior and psychological characteristics under dynamic low-visibility conditions related to fog-a driving simulator study.

OBJECTIVE: Colored pavement is commonly used to reduce the road traffic risk and promote road traffic safety, but its performance in foggy environments has not been fully assessed. The goal of this research is to explore the effectiveness and optimization of colored pavement in a dynamic low-visibility environment. METHODS: A driving simulation experiment is conducted. Three road risk sections in which collisions are common, including a long straight section, a sharp bend section, and a long downslope section, are considered, and three forms of colored pavement are used in five different visibility environments. The effectiveness of the colored pavement is explored by collecting and analyzing driving behavior and physiological characteristic data for 30 drivers in the established driving environment, and information is obtained through a subjective colored evaluation questionnaire. Eight evaluation indexes are selected from the perspectives of driving behavior and physiological characteristics, and the gray premium evaluation method is applied to evaluate the effectiveness of different forms of colored pavement considering the influence of visibility. Finally, the optimal colored pavement under various visibility and road alignment conditions is proposed. RESULTS: The results show that reasonably selecting colored pavement can effectively improve drivers' behaviors and physiological characteristics under foggy conditions. For different road alignments and visibility conditions, different forms of colored pavement should be used to ensure road traffic safety. CONCLUSIONS: The findings provide a theoretical reference for the optimization of colored pavement in foggy conditions.

Evaluation of driver demand for in-vehicle information: An integrated method combining clustering and multivariate ordered probit model.

INTRODUCTION: The proper execution of driving tasks requires information support. While new technologies have increased the convenience of information access, they have also increased the risk of driver distraction and information overload. Meeting drivers' demands and providing them with adequate information are crucial to driving safety. METHODS: Based on a sample of 1,060 questionnaires, research on driving information demands is conducted from the perspective of drivers. A principal component analysis and the entropy method are integrated to quantify the driving information demands and preferences of drivers. The K-means classification algorithm is selected to classify the different types of driving information demands, including dynamic traffic information demands (DTIDs), static traffic information demands (STIDs), automotive driving status information demands (ATIDs), and total driving information demands (TDIDs). Fisher's least significant difference (LSD) is used to compare the differences in the numbers of self-reported crashes among different driving information demand levels. A multivariate ordered probit model is established to explore the potential factors that influence the different types of driving information demand levels. RESULTS: The DTID is the driver's most in-demand information type, and accordingly, gender, driving experience, average driving mileage, driving skills, and driving style significantly affect the driving information demand levels. Moreover, the number of self-reported crashes decreased as the DTID, ATID, and TDID levels decreased. CONCLUSION: Driving information demands are affected by a variety of factors. This study also provides evidence that drivers who have higher driving information demands are more likely to drive more carefully and safely than their counterparts who do not exhibit high driving information demands. PRACTICAL IMPLICATIONS: The results are indicative of the driver-oriented design of in-vehicle information systems and the development of dynamic information services as a way to avoid negative impacts on driving.

The use of car safety seats for children in China: A questionnaire survey based on the theory of planned behavior.

OBJECTIVE: The purpose of this study was to analyze the psychological characteristics underlying Chinese parents' behaviors in using child car seats and to understand their decision-making processes. Based on the theory of planned behavior (TPB), three extended variables of perceived accident severity, perceived benefits, and perceived barriers were introduced. From the perspective of social psychology, the psychological factors that influence parents' use of child car seats and their interrelationships were explored. METHODS: A questionnaire was designed to collect data, including information on demographic characteristics, basic components of the TPB, and relevant extension variables. Using on online survey, 585 valid questionnaires were collected. Structural equation modeling was used to calibrate the data, and multiple group analysis was performed on the demographic variables. RESULTS: The extended TPB can effectively explain and predict parents' behaviors when using children's car seats. The results of the model show that parents' positive attitudes toward child safety seats (CSSs), others' recognition of their own use and perceptual control of the use of CSSs increase their willingness to use CSSs. Parents' willingness to use has a positive impact on the use of CSSs. Additionally, for the three extended variables introduced, perceived benefit significantly promoted parental intention and behavior to use CSS for children; perceived barriers significantly reduced parental use of CSS; and perceived accident severity had no significant effect on parental use of CSS. CONCLUSIONS: This study established the validity of the extended TPB model in predicting parents' behaviors in using car seats for their children. In addition, the current findings may provide a theoretical basis for policy development to promote CSS use.

Nomophobia, attitude and mobile phone use while riding an E-bike: Testing a dual-process model of self-control.

Using a mobile phone while participating in traffic is a growing safety concern all over the world. However, mobile phone use (MPU) while riding an e-bike has not yet been given enough attention by researchers and practitioners. To fill this gap, this study conducted a preliminary online interview and a questionnaire-based survey in China to study what kinds of MPU behaviors e-bikers commonly engaged in and the prevalence of these behaviors. A conceptual dual-process framework consisting of e-bikers' demographics, e-bike usage pattern, nomophobia, attitude and self-control was also proposed to analyze the psychological mechanism underlying this phenomenon. The preliminary online interview revealed 7 typical types of MPU behaviors performed by e-bikers on the road. Results of the questionnaire survey showed that though the overall frequencies of MPU behaviors were low, nearly 60% of the respondents reported a history of mobile phone use during riding in the last three months. E-bikers' MPU frequencies were significantly impacted by e-bikers' gender, attitude, self-control and information-related nomophobia. Besides, self-control also significantly moderated the predictive effects of information-related nomophobia and attitude on MPU frequencies while ring an e-bike. Fears of being unable to access information on the mobile phone only contributed to MPU at low levels of self-control. In contrast, the protective effect of unfavorable attitude against engagement in the behavior became stronger at high self-control levels. The results not only offer deeper insights into the current situation of MPU among e-bikers in China, but also could facilitate the development of intervention and safety promotion strategies targeting this specific road user group.

Constructing the behavioral sequence of the takeover process-TOR, behavior characteristics and phases division: A real vehicle experiment.

Autonomous driving will still use human-machine co-driving to handle complex situations for a long term, which requires the driver to control the vehicle and avoid hazards by executing appropriate behavioral sequences after takeover prompts. Previous studies focused on the division of static behavioral indicators and major phases in the initial phase of takeover, while lacking the construction of behavioral sequences based on the dynamic changes of behavioral characteristics during the takeover process. This study divides the takeover process in a detailed manner and investigates the impact of audio types on the behavioral sequence at each phase. 20 professional drivers performed the NDRT in autonomous driving mode on real roads, and after receiving audio prompts, they took over the vehicle and performed hazard avoidance maneuvers. The results show that the behavioral characteristics could construct the behavioral sequence of different phases, with the dynamic characteristics of the takeover operation change. In addition, different types of audio prompts will affect the timing of the takeover operation and its driving performance. Choosing different audio prompts or combinations can help improve the effect of taking over the vehicle. This study helps to provide guidance on the design of human-machine interaction for behavior optimization at different phases, so that guiding the driver to take over the vehicle safely and effectively.

Investigation of intervention methods based on different leading roles in family regarding child road safety education: An experimental study.

Children are vulnerable to traffic injuries due to their low hazard perception. Previous studies have indicated that both parents and children have potential leading roles in safety education within the family. However, the effects of different leading roles (parents or children) on the hazard perception of children have not yet been revealed, and interactive education with children in the leading roles could also be an important means to enhance children's road safety. To fill this gap, based on constructivism theory and the Feynman learning method, this study proposed two methods of enhancing children's hazard perception based on different leading roles in the family regarding child road safety education and investigated the effectiveness of the above intervention methods. Thirty sets of parents and children participated in this experiment and were divided into three groups. The children received one of three different road safety interventions: a constructivism intervention, a parental guidance intervention or a children's Feynman learning intervention. Compared with the constructivism intervention, the hazard perception of children who received road safety educations within the family were significantly improved; these children were more sensitive to potential traffic hazard factors and presented better visual search patterns. A comparative analysis showed that the two educational interventions based on different leading roles in the family regarding child road safety education were not identical with respect to effectiveness. The children's Feynman learning intervention had a more significant effect than the parental guidance intervention. The findings of this study provide insightful information for safety education researchers, governments, educators and families with children.

Diagnostic analysis of environmental factors affecting the severity of traffic crashes: From the perspective of pedestrian-vehicle and vehicle-vehicle collisions.

OBJECTIVES: Traffic crashes under low-visibility conditions are frequent and serious. The aim of this study was to investigate how the road environment affects the severity of pedestrian-vehicle and vehicle-vehicle collisions under low-visibility conditions. METHODS: The injury severity of pedestrian-vehicle collisions and vehicle-vehicle collisions under low-visibility conditions was set as the dependent variable and divided into 2 categories: "killed or severe injury collision" and "slight injury collision." Ten variables, including environment conditions, road traffic facility status, collision characteristics, and road attributes, were selected as independent factors according to the existing research and the traffic collision data set. Based on 656 valid pedestrian-vehicle collisions and 1,430 valid vehicle-vehicle collisions under low-visibility conditions, 2 random parameter logit models were established to evaluate the impacts of influencing factors on the severity of pedestrian-vehicle collisions and vehicle-vehicle collisions, in which the effect of unobserved heterogeneity was accounted for. RESULTS: The results show that visibility, presence of a roadside protection, road type, road pavement condition, and road alignment were significant factors affecting the severity of pedestrian-vehicle collisions. In addition, the presence of a median divider, location of the collision, road type, road surface condition, road pavement condition, and road alignment were significant factors affecting the severity of vehicle-vehicle collisions. Furthermore, the injury severity of both pedestrian-vehicle collisions and vehicle-vehicle collisions under low-visibility conditions on highways, poor road pavement, and non-straight-line sections was more likely to be fatal or serious. CONCLUSION: These results have implications for the design of more effective strategies to reduce casualties from traffic crashes under low-visibility conditions.

Take-Over Intention during Conditionally Automated Driving in China: Current Situation and Influencing Factors.

Drivers' take-over intention is important for the design of the automated driving systems and affects the safety of automated driving. This study explored the influence factors on drivers' take-over intention during conditionally automated driving, examined the correlations among factors through path analysis, and established a take-over intention model. A questionnaire survey was conducted in Hefei, China, and a sample of 277 drivers was obtained. Our study shows that the average take-over intention of those aged under 20 is lower than that of the older age groups. In the positive emotions (PE) scenarios, the take-over intention of aged 31-40 is significantly higher than that of the other age groups. Education and occupation have a significant influence on the take-over intention. The perceived ease of use (PEofU) and perceived usefulness (PU) of automated driving are significantly negatively correlated with drivers' take-over intention in the road conditions (RC) and climate conditions (CC) scenarios. In addition, through path model analysis, our study shows that trust in the safety of autonomous vehicles (AVs) plays an important role in drivers' take-over intention. Technology acceptance, risk perception and self-efficacy has indirectly correlated with take-over intention through trust in the safety of AVs. In general, drivers with lower technology acceptance, lower self-efficacy and higher risk perception are less likely to trust automated driving technology and have shown stronger intention to take-over the control of the vehicles.

Effects of the Spatial Structure Conditions of Urban Underpass Tunnels' Longitudinal Section on Drivers' Physiological and Behavioral Comfort.

To investigate the physiological and behavioral comfort of drivers traversing urban underpass tunnels with various spatial structure conditions, a driving simulator experiment was conducted using 3DMAX and SCANeRTM studio software. Three parameters, including the slope, slope length, and height of a tunnel, were selected as research objects to explore the optimal combination of structural parameters in urban underpass tunnels. The heart rate (HR), interbeat (RR) interval, speed, and lane centerline offset value were collected for 30 drivers. Then, a measurement model of the relationship among HR, RR interval, speed, lane centerline offset value, and structural parameters was established by using partial correlation analyses and the stepwise regression method. On this basis, a structural constraint model based on the drivers' physiological and behavioral comfort thresholds was also constructed. The results show that the driver's HR, RR interval, speed, and lane centerline offsets are significantly related to the tunnel height, slope, and slope length. More importantly, this paper not only analyzed the effects of various structural parameters on drivers' physiology and behavior but also proposed an optimized combination of structural parameters based on drivers' physiological and behavioral comfort. It can reasonably improve tunnel design in China, ensure tunnel traffic safety, and seek the maximum comfort of the driver in the driving process.

The trade-off between safety and time in the red light running behaviors of pedestrians: A random regret minimization approach.

Pedestrian safety has been a major concern in Hong Kong, where walking is an important access mean to urban transportation services and pedestrian-vehicle conflicts are prevalent. Red light running violation of pedestrians is a leading cause of pedestrian-vehicle crashes at the signal intersections. It is necessary to examine the possible factors including personal characteristics and road environments that affect the propensities of red light running violation of pedestrians. Therefore, effective traffic control and enforcement measures can be implemented to deter against the red light running behaviors of pedestrians. This study attempts to examine the roles of trade-off between safety and time, as well as situational features and personality traits, in the red light running behaviors of pedestrians using a stated preference survey method. Then, a regret-based panel mixed multinomial logit model is established for the association measure between propensities of red light running violation and possible factors, with which the effects of unobserved heterogeneity and correlation in the choices between different scenarios of the same person are considered. Results indicate that the choice decision of pedestrians are more sensitive to a reduction in time loss, as compared to the equivalent increase in safety risk. In addition, the safety versus time trade-off may vary between pedestrian groups. Furthermore, presence and type of another violator also significantly affect the propensities of red light running violation. Such findings are indicative to effective policy interventions that can deter against the red light running behaviors of vulnerable pedestrian groups. Therefore, overall pedestrian safety level can be improved in the long term.

Reasonable driving speed limits based on recognition time in a dynamic low-visibility environment related to fog-A driving simulator study.

To determine a reasonable speed limit and ensure traffic safety in a dynamic low-visibility environment with fog, a driving simulator study was conducted. A total of 31 young participants were recruited, and each completed 5 driving simulator trials under varying visibility conditions and speed levels during the daytime. The combined coupling effect of the visibility and driving speed on drivers' recognition times was explored, and a quantitative model of the recognition time, visibility, and driving speed was established. A determination method and suggested value of a reasonable driving speed limit in dynamic low-visibility conditions were proposed based on the stopping sight distance model. The results show that there are significant differences in the recognition times of drivers under different visibility and speed conditions. The reasonable driving speed limit values in dynamic low-visibility conditions should be based on visibility changes. When the stopping sight distance is 75 m and the visibility is less than 35 m, the speed limit should be 20 km/h. When the visibility is between 35 m and 60 m, the speed limit should be 30 km/h. When the visibility is between 60 m and 140 m, the speed limit should be 50 km/h. When the visibility is greater than 140 m, the speed limit should be 60 km/h. These research results can provide a theoretical reference for the formulation of a VSL in a dynamic low-visibility environment related to fog and reduce crash risk in conditions of inadequate visibility in fog.

Research on intervention methods for children's street-crossing behaviour: Application and expansion of the theory of "behaviour spectrums".

Due to immaturity in their physical and cognitive development, children are particularly vulnerable to road traffic injuries as pedestrians. Child pedestrian injury primarily occurs in urban areas, with a significant share at crosswalks. The aim of this study is to explore whether an intervention programme based on the theory of "behaviour spectrums" can improve the street-crossing skills of primary school children. Children were recruited near a local primary school through invitation letters and were randomly divided into two groups: a control group (n = 10, no intervention) and an experimental group (n = 10, intervention). The children in the experimental group received 30-45 min of training. The child participants were asked to wear an eye tracker and performed a crossing test in a real-world street environment; in this test, they were required to successively pass through an unsignalised intersection, an unsignalised T-intersection and a signalised intersection on a designated test route. A high-definition camera was used to record the children's crossing behaviour, and the Tobii Pro Glasses 2 eye tracker was used to derive indicators of the children's visual behaviour in the areas of interest (AOIs) in the street. The evaluation was conducted on children's crossing behaviour in the control group (which received no intervention) and the experimental group (tested at two time points after the intervention: children tested immediately after the intervention and children retested one month after the intervention). The results showed that compared with the control group, the children in the experimental group no longer focused on the small area around the body (e.g., the zebra crossing area) and the area in front of the eyes (e.g., the sidewalk area), which increased their visual attention to the traffic areas on the left and right sides of the zebra crossing; thus, unsafe crossing behaviour was reduced in the experimental group. Compared with the experimental group immediately after the intervention, the intervention effect on some indicators showed a significant weakening trend in the retest of the experimental group one month later. Overall, the results show that an intervention programme based on the theory of "behaviour spectrums" can improve children's crossing skills. This study provides valuable information for the development and evaluation of intervention programmes to improve children's street-crossing skills.

The extended theory of planned behavior considering heterogeneity under a connected vehicle environment: A case of uncontrolled non-signalized intersections.

With the emergence of connected vehicle (CV) technology, there is a doubt whether CVs can improve driver intentions and behaviors, and thus protect them from accidents with the provision of real-time information. In order to understand the possible impacts of the real-time information provided by CV technology on drivers, this paper aims to develop a model which considers the heterogeneity between drivers with the aid of the extended theory of planned behavior. At the uncontrolled non-signalized intersections, a stated preference (SP) questionnaire survey was conducted to build the dataset consisting of 1001 drivers. Based on the collected dataset, the proposed model examines the relationships between subjective norms, attitudes, risk perceptions, perceived behavioral control and driving intentions, and studies how such driving intentions are simultaneously related to driver characteristics and experiences in the CV environment. Furthermore, driver groups which are homogenous with respect to personality traits are formed, and then are employed to analyze the heterogeneity in responses to driving intentions. Four key findings are obtained when analyzing driver responses to the real-time information provided by CV technology: 1) the proposed H-ETPB model is verified with a good fitness measure; 2) irrespective to driver personality traits, attitudes and perceived behavioral control have a direct and indirect association with driving intentions to accelerate; 3) driving intentions of high-neurotic drivers to accelerate are significantly related to subjective norms, while that of low-neurotic drivers are not; 4) elder high-neurotic drivers, and low-neurotic drivers who have unstable salaries or ever joined in online car hailing service have a strong intention in accelerating. The findings of this study could provide the theoretical framework to optimize traffic performance and information design, as well as provide in-vehicle personalized information service in the CV and CAV environments and assist traffic authorities to design the most acceptable traffic rules for different drivers at an uncontrolled non-signalized intersection.

Drivers' car-following behaviours in low-illumination conditions.

Low illumination is a special driving condition that negatively affects drivers' vision, information acquisition (IA) ability, distance recognition and risk perception. This study evaluated drivers' car-following behaviours and characteristics using questionnaire-based research conducted among 214 drivers in Hefei. In this study, exploratory factor analysis (EFA) was used to determine the factor structure of the scale, and the internal consistency of all factors was good. The results show that low illumination strongly influences drivers' following behaviour and that they tend to choose safe and conservative ways to follow leading vehicles. Street lights are beneficial, aiding drivers' IA and their grasp of surrounding or distant environments. Myopic drivers performed worse in car following when driving in a low illumination environment, regardless of the presence of street lights. Drivers with astigmatism performed worse when street lights were present. Drivers who reported more aberrant behaviours were more aggressive when driving and tended to adopt shorter following distances at night. Practitioner summary: This study developed two scales to evaluate drivers' car-following behaviours and characteristics. A total of 214 drivers participated in the survey. Low illumination strongly influences drivers' following behaviour. Drivers who reported more aberrant behaviours were more aggressive when driving and tended to adopt shorter following distances at night.

Exploring Factors Influencing the Risky Cycling Behaviors of Young Cyclists Aged 15-24 Years: A Questionnaire-Based Study in China.

Road traffic crashes are the leading cause of death for young people, among whom cyclists account for a higher percentage of injuries and deaths than any other road users. This study aimed to examine the factor structure of the Young Cyclist Behavior Questionnaire (YCBQ) and investigate the relationships among demographic characteristics, cycling use-related variables, perceived risk, perceived cycling skills, and risky cycling behaviors among young people. A sample of 448 cyclists (mean age of 20.37 years) completed the questionnaire. Exploratory factor analysis, confirmatory factor analysis, and structural equation modeling were utilized. The YCBQ had a clear factorial structure, items with high factor loadings, and good internal consistency. The five-factor structure included traffic violations, impulsive behaviors, ordinary violations, distractions, and errors. Risky cycling behaviors could be explained by gender, age, perceived risk, and perceived cycling skills, with the model explaining 37% of the variance. Gender had the greatest impact on risky cycling behaviors; male individuals were more likely to engage in risky behaviors. Young cyclists with higher levels of perceived risk had lower probabilities of engaging in risky cycling behaviors. Cyclists with lower scores on perceived cycling skills were more likely to report engaging in risky cycling behaviors. Age significantly explained risky behaviors; the younger the cyclist was, the higher his or her risky behaviors score. This research provides a theoretical foundation for the prevention of risky behaviors among young cyclists. Regarding intervention design, attention to the identified gender differences, the need to strengthen the ability to perceive risk, and the importance of road safety education for young cyclists may promote safer cycling.

Psychological predictors of seat belt usage among university student passengers and an examination of the differences between the front and rear seats.

Objective: This study aimed to analyze the cognitive psychological process and to understand the decision-making process involved in university student passengers' seat belt usage behavior and the difference between front and rear seat belt use. Based on the Theory of Planned Behavior (TPB), two extended variables, namely, perceived accident severity and safety consciousness, were introduced. From the perspective of social psychology, we explored the psychological factors influencing university student passengers' seat belt usage behavior and the relationship between these factors.Methods: The reliability and validity of 544 valid questionnaires were tested, and the differences were analyzed in the scores of the TPB variables between university student transportation majors and other majors. Finally, structural equation models were established to compare the factors affecting seat belt usage in the front and rear seats.Results: The results show that the TPB could effectively explain and predict university student passengers' seat belt usage behavior. Significant differences were observed in the TPB variables, the extended variables and the behavioral intention scores between the university students majoring in transportation and those with other majors. In addition, significant differences were observed between front and rear seat belt usage. All psychological factors had significant positive effects on the seat belt usage behavior of the university student passengers, and all factors directly or indirectly affected seat belt usage behavior.Conclusions: The present research adds further empirical evidence regarding the ability of the TPB to predict seat belt usage behavior. In addition, the current research results may provide a theoretical basis for policy-making aiming to improve university students' seat belt usage.

Maximum acceptable speed based on physiological performance for different alignments of rural roads under the influence of visibility in fog-a driving simulator study.

Objectives: The goals of this research are to: (i) investigate whether the driver's physiological performance has a significant impact on driving speed choice, considering the visibility level under fog conditions and the horizontal and vertical alignment of a rural road; (ii) quantify the relationships among these variables; (iii) calculate the maximum acceptable speed that meets the driver's physiological tolerance threshold.Methods: A driving simulator study was conducted. A total of 30 participants were recruited, and each completed 5 driving simulator trials under varying visibility levels, radii of curvature and vertical grades. A multiple linear regression model was established to measure the association between normalized heart rate, driving speed, visibility, radius of curvature and gradient.Results: The results indicated that driving speed, visibility, radius of curvature and gradient all significantly affected the normalized heart rate. For instance, an increase in driving speed and vertical grade and a reduction in the radius of curvature are related to an increase in normalized heart rate. Moreover, an acceptable speed model that considered the driver's normalized heart rate was constructed, given the visibility level and horizontal and vertical alignment.Conclusions: The study shows a significant correlation between the drivers' physiological performance and his or her driving speed choice under the influence of visibility in fog. Given visibility conditions and road alignment parameters, we can calculate the maximum acceptable speed that meets the driver's physiological requirements. Such findings should be indicative of real-time traffic management that could reduce the crash risk under inadequate visibility conditions in fog.

The association between musculoskeletal disorders and driver behaviors among professional drivers in China.

The main objective of this study is to explore correlations between the severity of musculoskeletal disorders (MSDs) and aberrant driving behaviors among professional taxi drivers. Questionnaires were administered to 162 taxi drivers in a Chinese city. Drivers with more severe MSDs reported more general and dangerous error behaviors and negative moods. Interestingly, MSDs affect drivers' error behaviors through negative moods. The study also examined the effects of age, driving experience, traffic accidents, mood states, safety awareness and driving skills on aberrant driving behaviors. The results showed that age and driving experience were significant predictors of aberrant driving behaviors. Anger was a significant predictor of aggressive violations and dangerous errors. Additionally, drivers who reported higher levels of safety awareness also reported fewer aggressive violations, and drivers with higher levels of driving skills reported fewer dangerous error behaviors.