Driver distraction by digital billboards? Structural equation modeling based on naturalistic driving study data: A case study of Iran

Assessing the determinants of crash propensity using structural equation modeling: Role of distractions caused by fellow drivers

INTRODUCTION

Aggressive behavior of drivers is a source of crashes and high injury severity. Aggressive drivers are part of the driving environment, however, excessive aggressive driving by fellow drivers may take the attention of the recipient drivers away from the road resulting in distracted driving. Such external distractions caused by the aggressive and discourteous behavior of other road users have received limited attention. These distractions caused by fellow drivers (DFDs) may agitate recipient drivers and ultimately increase crash propensity. Aggressive driving behaviors are quite common in South Asia and, thus, it is necessary to determine their contribution to distractions and crash propensity.

METHOD

Our study aimed to evaluate the effects of DFDs using primary data collected through a survey conducted in Lahore, Pakistan. A total of 801 complete responses were obtained. Various hypotheses were defined to explore the associations between the latent factors such as DFDs, anxiety/stress (AS), anxiety-based performance deficits (APD), hostile behavior (HB), acceptability of vehicle-related distractions (AVRD), and crash propensity (CP). Structural Equation Modeling (SEM) was employed as a multivariate statistical technique to test these hypotheses.

RESULTS

The results supported the hypothesis that DFDs lead to AS among recipient drivers. DFDs and AS were further found to have positive associations with APDs. Whereas, there was a significant negative association between DFD, AS, and AVRD. As hypothesized, DFD and AS had positive associations with CP, indicating that distractions caused by aggressive behaviors leads to stress and consequently enhances crash propensity.

PRACTICAL APPLICATIONS

The results of this study provide a statistically sound foundation for further exploration of the distractions caused by the aggressive behaviors of fellow drivers. Further, the results of this study can be utilized by the relevant authorities to alter aggressive driving behaviors and reduce DFDs.

The role of traffic conflicts in roundabout safety evaluation: A review

The roundabout is one type of at-grade intersection commonly seen in many countries. The evaluation of roundabout safety is usually counted on conflict analysis of the roundabout traffic due to random and limited records of real accidents. This paper surveyed published papers and reports that investigate the role of traffic conflicts in roundabout safety evaluation. It summarized the definitions and observation methods of roundabout conflicts and classified the attributing factors of roundabout conflicts and the countermeasures to control the conflicts. This study found that although unique traffic flow movements at roundabouts create special patterns of roundabout conflicts, the methods of roundabout conflict analysis used in most existing studies were inherited from the studies of highway or cross-intersection conflicts, including conflict definitions, conflict measurements, and thresholds of conflict severity. Special or improper designs of roundabout configurations or basic geometry elements could arouse roundabout conflicts. The most common vehicle-to-vehicle conflicts were entering-circulating conflicts, sideswipe conflicts, and exiting-circulating conflicts. The conflicts among vehicles and vulnerable road users (VRUs) easily evolved into serious collisions, but these conflicts did not get deserved attention in previous studies. Drivers' familiarity with roundabouts also affected road users' safety. Traffic signs and pavement markings were commonly used to control roundabout conflicts, while traffic signals were more effective methods for the roundabouts with uneven distribution of approaching traffic or high traffic volume. Based on the analysis of existing studies, this paper pointed out seven future directions of further research in term of conflict measurement, data collection, infrastructure and access management, geometry, drivers and VRUs, signal control, and vehicle control.

Application of naturalistic driving data: A systematic review and bibliometric analysis

The application of naturalistic driving data (NDD) has the potential to answer critical research questions in the area of driving behavior assessment, as well as the impact of exogenous and endogenous factors on driver safety. However, the presence of a large number of research domains and analysis foci makes a systematic review of NDD applications challenging in terms of information density and complexity. While previous research has focused on the execution of naturalistic driving studies and on specific analysis techniques, a multifaceted aggregation of NDD applications in Intelligent Transportation System (ITS) research is still unavailable. In spite of the current body of work being regularly updated with new findings, evolutionary nuances in this field remain relatively unknown. To address these deficits, the evolutionary trend of NDD applications was assessed using research performance analysis and science mapping. Subsequently, a systematic review was conducted using the keywords "naturalistic driving data" and "naturalistic driving study data". As a result, a set of 393 papers, Published between January 2002-March 2022, was thematically clustered based on the most common application areas utilizing NDD. the results highlighted the relationship between the most crucial research domains in ITS, where NDD had been incorporated, and application areas, modeling objectives, and analysis techniques involving naturalistic databases.

How do driving behavior and attitudes toward road safety vary between developed and developing countries? Evidence from Iran and the Netherlands

INTRODUCTION

The rates of road traffic injuries and fatalities in developing countries are significantly higher than in developed countries. This study examines the differences in driving behavior, road safety attitudes, and driving habits between a developed country (the Netherlands) and a developing country (Iran), which bear major differences in terms of crash involvement per population.

METHOD

In this context, this study assesses the statistical association of crash involvement with errors, lapses, aggressive driving incidents, and non-compliance with traffic rules, attitudes, and habits. Structural equation modeling was used to evaluate data obtained from 1,440 questionnaires (720 samples for each group).

RESULTS

The results revealed that more insecure attitudes toward traffic-regulation observance, negative driving habits, and risky behaviors, such as traffic rule violations act as influential factors of crash involvement. Iranian participants showed a greater likelihood to get involved in violations and driving habits with a higher level of risk. In addition, lower levels of safety attitudes toward traffic-regulation observance were observed. On the other hand, Dutch drivers were more likely to report lapses and errors. Dutch drivers also reported safer behavior in terms of unwillingness to engage in risky behaviors such as violations (speeding and no-overtaking). The structural equation models for crash involvement based on behaviors, attitudes, and driving habits were also evaluated for their accuracy and statistical fit using relevant indicators.

PRACTICAL APPLICATIONS

Finally, the findings of the present study point out the need for extensive research in some areas to foster policies that can effectively enhance safer driving.

Public perceptions of vehicular traffic emissions on health risk in Lagos metropolis Nigeria: A critical survey

The perception and behavior of the public is key in reducing Traffic-related air pollution health burdens which has become an increasingly alarming problem in many cities across the globe. The study assessed the perception of the public about vehicle traffic emissions and the health hazard associated with them in Lagos, Nigeria using structured questionnaires. Multivariate statistical analysis and structural equation modeling were performed to determine the factors that were associated with the participant's perception of traffic air pollution and the health risks it presents. The findings revealed the majority (78.9%) of the respondents were aware of the haze air pollution from vehicles and its adverse effects on health. The regression model showed a significant relationship between age, education status, employment status, road proximity, vehicle ownership and air pollution awareness (P < 0.05). However, the structural equation model SEM revealed that age, gender, marital status, education, employment status, and road proximity showed statistical significance (p < 0.05) and indicated a linear relationship to vehicular emissions perception. The findings suggest the need to increase public education for all ages and especially for roadside residents on the effects of prolonged exposure and long-term effects of transport-related air pollution and associated risk. The result is applicable in many developing cities, especially in Sub-Saharan Africa.

Investigating the impact of environmental and temporal features on mobile phone distracted driving behavior using phone use data

Mobile phone distracted driving (MPDD) is one of the most significant and common factors in distraction-affected crashes. In previous studies, MPDD has been described as a self-selected behavior that affects driving performance, rather than a multidimensionally impacted behavior. In this study, the researchers hypothesized that external environmental features significantly impacted MPDD and tested this hypothesis by structural equation modeling (SEM). Three external latent variables (road, operation, and control factors) were measured at different times during weekdays in urban areas of Texas by integrating a large number of mobile phone sensor data and roadway inventory data. A structural model was developed to test the relationship between the latent variables and the rate of drivers involved in MPDD (MPDDR) on the roadway during different time periods. Finally, the data summary and model results revealed significant temporal effects. Standardized estimates from the SEM results revealed the positive impact of roads factors in the morning peak that broader shoulders, wider medians, and smaller curve radians were correlated with higher MPDDR in the morning peak hours; the negative impact of operation factors that higher average annual daily truck traffic (truck AADT) were associated with lower MPDDR significantly. And the impact of control factors on MPDDR is positive. In other words, the road segments with a large number of traffic signals in urban areas had a higher MPDDR than those without traffic signals. These findings could assist transportation and legislation agencies in the development of appropriate countermeasures or enforcement tactics and implement them effectively to reduce the occurrence of MPDD. In addition, this study provides a novel perspective close to the actual consideration of drivers about using mobile phones while driving, in the context of MPDD research, rather than comparing driver groups and vehicle performance.

Factors affecting drivers' off-road glance behavior while interacting with in-vehicle voice interfaces

In-vehicle voice-based interfaces have been massively embedded in modern vehicles as a countermeasure to visual-manual distractions. However, limited data are available regarding the actual visual demands imposed on the driver when interacting with such an interface. How those factors that are associated with the drivers themselves affect their visual behavior when interacting with an in-vehicle voice-based interface remains understudied. This study focused on investigating factors affecting drivers' off-road visual behavior while interacting with a voice-based interface. A secondary data analysis leveraging structural equation modeling was performed based on data collected from a large-scale field experiment wherein participants drove a vehicle on the highway and performed a series of tasks using an in-vehicle voice interface. The results indicate that while voice-based interfaces are designed to help reduce drivers' visual demands, drivers can still direct their eyes off the road for a prolonged period of time during the interaction and exhibit potentially risky visual behavior. In addition, individual-level factors can exert influence on drivers' off-road visual behavior in such a way that older drivers and drivers with higher trust in technologies are associated with more long eyes-off-road glances when interacting with a voice-based interface. To promote the general public's adoption of in-vehicle voice interfaces and support safe interactions, we recommend relevant research and system design consider drivers' trust in, perceptions, and previous usage of similar technologies, as well as their individual characteristics such as age.

Causal mediation analysis of the impacts of distracted driving on crash injury risks

Distracted driving can pose great risks to road traffic safety. Although there is a rich body of literature devoted to identifying the statistical association between distracted driving and crash risks, few are available to examine the causal effect mechanism of distracted driving. Thus, the study attempts to conduct the causal mediation analysis to reveal the impact mechanism of distracted driving on crash injury risks, in which various hazardous driving actions are used as the mediators between driver distraction and crash injuries. Sensitivity analysis is also carried out to validate the underlying assumption of causal mediation analysis. The analytic results indicate that 1) distracted driving can lead to a higher likelihood of hazardous driving actions such as failing to yield, disobeying traffic control devices, driving left of lane center, and failing to stop in assured clear distance, 2) both the driver distraction and hazardous actions are the contributory factors to the severe crash injuries, and 3) distracted driving is identified to have significant mediation effects on crash injury risks. The study confirms the causal mediation effects of distracted driving on crash injury risks, which can serve to propose specific safety countermeasures to mitigate the crash injury risks.

A study of the factors affecting driving risk perception using the Bivariate Ordered Probit model

This paper aims to examine the key factors influencing driving risk perception in Iran. We conducted separate surveys for two groups of Iranian drivers, namely passenger car drivers and truck drivers. In order to assess driving risk perception, respondents were asked what they think about their Probability of Having a Road Accident (PHRA) and if they eventually have an accident as a driver, what they think about the Probability of it being Fatal or causing Severe Injury (PFSI). A Bivariate Ordered Probit model, which considers the possible correlation between PHRA and PFSI, was developed to explain the observed driving risk perception using type of vehicle, driving experience, socio-demographic information, and driving behaviour. According to the results, vehicle type, vehicle age, driving experience, sleep quality, at-fault accidents over the past three years, vehicles safety-related equipment, and education level have significant effects on driving risk perception (p-value < 0.05). In addition, this paper compares the driving risk perception of truck and passenger car drivers. The results show that truck drivers have a higher perception of PHRA and PFSI compared with passenger car drivers (p-value < 0.05). The results may convince policy-makers to consider the characteristics of the two categories of drivers when designing regulations.

Deterring illegal smartphone use while driving: Are perceptions of risk information associated with the impact of informal sanctions?

Advertising campaigns have used threat appeals to increase the perceptions of risk associated with dangerous driving behaviours. Building on recent research that suggests increasing the pertinence of informal sanctions (sense of guilt, fear of peer disapproval, or fear of hurting oneself and others) decreases the likelihood of illegal smartphone engagement while driving, this study aimed to determine if perceptions of risk information, that is, how effective drivers believe the information is in deterring their illegal use, is associated with perceptions of informal sanctions. An online survey of 1027 Victorians who self-reported illegal smartphone use while driving asked participants to rate the personal deterrent effectiveness of five types of risks associated with illegal smartphone use while driving. A hierarchical linear regression model explored the associations between the most effective risks and each informal sanction. Female drivers were more likely than male drivers to experience guilt, fear of social loss and fear of physical loss when using their smartphones illegally. More frequent illegal smartphone engagement while driving was associated with decreased impact of informal sanctions. Female drivers were more likely than male drivers to perceive each risk as effective. The most effective piece of information was, "Contributes to 16% of Victorian road deaths and serious injuries annually"; it was also the least known (32.7% males, 26.7% females). This message, alongside, "Increases risk of a crash comparable to those associated with driving under the influence of alcohol or cannabis" and, "A 2 s glance at your phone while driving at 50 km/h effectively means driving blind for 27 m", were associated with stronger perceptions of guilt, fear of peer disapproval, or fear of hurting oneself and others when thinking about using a smartphone illegally while driving. These results suggest tailoring and targeting these specific risk information may be effective in deterring illegal smartphone use while driving.

Profiling drivers to assess safe and eco-driving behavior - A systematic review of naturalistic driving studies

Road accidents and vehicular emissions are two significant issues related to road transportation, affecting both human life and the environment. Prior research suggests that driver behavior is a crucial factor in the majority of road crashes and is a significant factor influencing fuel consumption and vehicle emission. Significant improvement in driving behavior can be achieved by providing feedback to drivers about their driving behavior. An increasing interest among researchers to identify risky and non-economical driving maneuvers has led to the development of driver behavior profiling, i.e., rating/categorizing drivers into different categories based on how they drive. To get an insight into different parameters and methodology adopted by researchers for categorizing drivers into different categories, this paper presents a systematic review of studies on driver behavior profiling. In the present paper, PRISMA approach was adopted to shortlist the most relevant studies for systematic review out of 1231 initial studies, which were extracted using the relevant keywords. The findings from our study suggest that the selection of parameters for profiling the driver will depend on the application of the profiling scheme, type of device used for extracting data, and importance of parameter in rating criteria. Further, the findings suggest that significant improvement in driving behavior can be achieved by providing feedback to the drivers about their driving behavior and by implementing usage-based insurance schemes. It is also suggested that future studies shall focus on using smartphone devices for the collection of driver data as smartphones are nowadays easily accessible to everyone.

A Scoping Study on Driver’s Perspective of Distracting Factors

Distracting activities while driving are common and can result in errors that threaten road users’ safety. The main objectives of this study were to investigate drivers’ perspectives of the factors contributing to distraction, determine the relative rank of types of distractions, recognize the road factors and environmental effects that make distractions more dangerous, and identify the most effective measures to reduce driver distractions. A survey was conducted to assess Jordanian drivers’ experiences with distracted driving, and what solutions they believed could be implemented to solve the problems. The study’s outcomes revealed that drivers perceive visual distractions as the most dangerous, followed by cognitive, manual, and auditory distractions, respectively. It was also found that “mobile phone texting or dialing” was ranked the top most dangerous visual and manual distracting factor. “Baby is crying or kids are fighting in the back seat” was perceived by all demographic groups as the riskiest auditory factor. Regarding cognitive distraction, four factors were perceived as the most serious, of which “Baby is crying”, “Driving while angry or sad or agitated”, “Talking on a cell phone—even a hands-free one” and “Conversing with passengers” were determined to be the top four distracting factors. The results also revealed that drivers believe that “laws and enforcement” is the most effective measure to reduce distractions while driving.

Structural equation modeling for identification of patient safety antecedents in primary care

Background

Patient safety is defined as an activity that minimizes and removes possible errors and injuries to patients. A number of factors have been found to influence patient safety management, including the facilities available in the practice, communication and collaboration, education regarding patient safety and generic conditions. This study tested a theoretical model of patient safety interventions based on safety antecedents.

Methods

Medical professionals were surveyed using a questionnaire developed by Gaal et al. The results were analyzed with SPSS 20 and AMOS. A hypothetical model of direct and indirect effects on patient safety in a primary care environment was created and analyzed using structural equation modeling (SEM).

Results

SEM proved to be an effective tool to analyse safety in primary care. The facilities in the practice appear to have no significant influence on patient safety management in the case of female respondents, those below mean age, those who are not GPs (general practitioner) and respondents not working in counselling centres.

Conclusions

The integrated safety model described in the study can improve patient safety management.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12875-021-01533-6.

Analyzing traffic violations among motorcyclists using structural equation modeling

In developing countries, the motorcycle is one of the main modes of transportation, especially in the downtown area. Motorcyclists' safety is a common issue since they have more vulnerability for severe crashes. Thus, it is necessary to decrease the probability of encountering high-risk situations. The main reason for motorcycle crashes is human errors in the form of traffic violations. This article aims to identify the role of seven factors on traffic violations, including motorcyclist characteristics, road conditions, environmental circumstances, riding skills, cycling statuses, motorcycle age and previous negative experiences such as fines and accidents. The structural equation modeling was applied to evaluate the impact of these factors pertinent to motorcyclists' crashes. To assess these factors, 600 motorcyclists have been interviewed in Kerman, Iran. The results indicated that motorcyclists' characteristics are the most effective factor in violation commitment. Since most motorcyclists are young, with low income and education, it is necessary to pay more attention to their training and education before giving a cycling license. In addition, those with more previous crashes and violations are more susceptible to committing violations. This relates to the lack of enough control and enforcement in developing cities; also, it shows that the current traffic fines are not deterrent enough. Finally, considering the results of this research can help to minimize traffic violations among motorcyclists, which is a step towards safer roads.

Are Double-Lane Roundabouts Safe Enough? A CHAID Analysis of Unsafe Driving Behaviors

This study investigated the nature and causes of unsafe driving behavior at roundabouts through an on-road study. Four urban double-lane roundabouts with different layouts were selected for an on-road study. Sixty-six drivers (41 males and 25 females) aged 18–65 years took part in the study. Unsafe behaviors observed during the in situ survey were divided into three different categories: entry unsafe behaviors, circulation unsafe behaviors, and exit unsafe behaviors. Three chi-square automatic interaction detection (CHAID) analyses were developed in order to analyze the influence of roundabout characteristics and maneuvers on unsafe behaviors at double-lane roundabouts. The results confirmed the awareness that double-lane roundabouts are unsafe and inadvisable. More than half of unsafe driving behaviors were found to be entry unsafe behaviors. Moreover, the entry radius was found to be the geometric variable most influencing unsafe driving behaviors.

Analyzing driver behavior under naturalistic driving conditions: A review

For a decade, researchers working in the area of road safety have started exploring the use of driving behavior data for a better understanding of the causes related to road accidents. A review of the literature reveals the excellent potential of naturalistic driving studies carried out by collecting vehicle performance data and driver behavior data during normal, impaired, and safety-critical situations. An in-depth understanding of driver behavior helps analyze and implement pre-crash safety measures - the development of enforcement policies, infrastructure design, and intelligent vehicle safety systems. The present paper attempts to review the naturalistic driving studies that have been undertaken so far. The paper begins with an overview of different methods for collecting unobtrusive driver behavior data during their day to day trip, followed by a discussion of various factors affecting driving behavior and their influence on vehicle performance parameters. The paper also discusses the strategies mentioned in the literature for improving driving behavior using naturalistic driving studies to enhance road safety. Some of the major findings of this review suggest that i) driver behavior is a major cause in the majority of the road accidents ii) drivers generally reduce their speed and increases headway as a compensatory measure to reduce the workload imposed during distracting activity and adverse weather conditions iii) mobile phone has emerged as a potential device for collecting naturalistic driving data and, iv) improvement in driving behavior can be achieved by providing feedback to the drivers about their driving behavior. This can be done by implementing usage-based insurance schemes such as pay as you drive (PAYD), pay how you drive (PHYD), and manage how you drive (MHYD). While a considerable amount of research has been done to analyze driving behavior under naturalistic conditions, some areas which are yet to be explored are highlighted in the present paper.

Assessment pedestrian crossing safety using vehicle-pedestrian interaction data through two different approaches: Fixed videography (FV) vs In-Motion Videography (IMV)

A significant portion of pedestrian accidents occurs in the outskirts areas due to the high vehicle speed and lack of safety facilities for pedestrians. Behavioral study on drivers and pedestrians is the key to better understand the causes of pedestrian accidents in order to develop safety models. Despite numerous studies on pedestrian safety based on various roads, outskirt areas have not been considered. Hence, the present study focuses on evaluating the safety of pedestrian crossing in urban and outskirt areas and to determine the differences of drivers and pedestrians' behaviors between these areas through data based on fixed videography (FV) and in-motion videography (IMV). These approaches may lead to an exact analysis of the behavioral differences of road users behaviors from the perspective of pedestrians (FV data) and drivers (IMV data) in urban and outskirts roads. Accordingly, behavioral studies were conducted at urban and outskirts sites through FV as well as IMV using the behavior of 29 participants in the same roads in Babol city, Iran. The gap acceptance model using linear regression and pedestrian crossing probability model using logistic regression for both approaches showed similarity on results in both urban and outskirts roads. Furthermore, behaviors of pedestrians crossing and drivers' yielding on urban and outskirts roads were very similar. Vehicle speed, the distance of vehicle to pedestrian at the possible collision point, size of pedestrian groups, and waiting time before crossing were the most important behavioral differences of pedestrian for choosing a gap acceptance and probability of crossing on various sites through two different approaches. The inference of the models obtained in this study will lead to a better understanding of the behavior of road users for studies on advanced driving assistance systems (ADAS).