Examining driver distraction in the context of driving speed: An observational study using disruptive technology and naturalistic data

A comparative analysis of distracted driving behavior among drivers of different income levels: A case study in huainan, China

This research aims to investigate the differences and causes behind distracted driving behavior among drivers with varying income levels. A comparative survey of 1121 drivers in Huainan City, China, was conducted, including 562 drivers from high-end communities representing the high-income group, and 559 drivers from general communities representing the low-income group. Employing social norms, risk perception, and experience as independent variables, the study further examines the role of in-group bias as a mediating variable, with distracted driving behavior serving as the dependent variable, through the construction of two structural equation models for analysis. The study found that among the high-income driver group, in-group bias significantly mediates the impact of social norms, risk perception, and experience on distracted driving behavior; however, this mediating effect is less pronounced in the low-income driver group. This finding is crucial for understanding the potential distracted driving behaviors induced by in-group bias within the high-income driver group and for effectively promoting driving safety. In summary, this research provides new insights into reducing distracted driving behavior among the high-income driver group, thereby enhancing road safety.

"It's okay because I'm just driving": an exploration of self-reported mobile phone use among Mexican drivers

Introduction

Technological advancements have the potential to enhance people's quality of life, but their misuse can have a detrimental impact on safety. A notable example is the escalating issue of distracted driving resulting from the use of mobile phones behind the wheel, leading to severe crashes and injuries. Despite these concerns, both drivers' usage patterns and their risk-related associations remain scarcely documented in Mexico. Therefore, this descriptive study aimed to examine the mobile phone usage of Mexican drivers, its relationships to risk awareness and near-miss/crash involvement, and the self-reported underlying reasons for this behavior.

Methods

This cross-sectional study utilized a sample of 1,353 licensed Mexican drivers who took part in a nationwide series of interviews regarding their onboard phone use settings.

Results

A significant percentage of drivers (96.8%) recognize using a mobile phone while driving as high-risk behavior. However, only 7.4% reported completely avoiding its use while driving, with 22.4% identified as high-frequency users. Frequency was also found positively associated with the self-reported rate of near-misses and crashes. Furthermore, qualitative data analysis highlights the emergence of a 'sense of urgency' to attend to phone-related tasks in response to daily demands and life dynamics, offering a potential explanation for this behavior.

Conclusion

The results of this study suggest common patterns of onboard mobile use among Mexican drivers concerning driving situations and associated risks. This underscores the need for increased efforts to discourage onboard phone use in the country.

The effects of distraction on younger drivers: A neurophysiological perspective

Distracted driving remains a significant cause of traffic accidents globally, including in Australia. However, many younger drivers still admit to using a phone while driving. A simulated driving study investigated the neurophysiological effects of visual, auditory, and higher-order cognitive (i.e., requiring the use of executive functions) distraction on young drivers. In total, 24 young adults aged 18-25 years completed four 8 min simulated driving sessions while concurrently engaging in various distractor tasks. Neurophysiological arousal was measured via EEG. Additionally, subjective workload and objective driving performance were assessed. Frontal beta and gamma power exhibited their highest levels during tasks involving higher-order cognitive and visual demands. The higher-order cognitive condition was rated as the most mentally demanding. In comparison, the visual condition had the most significant impact on both the standard deviation of speed and standard deviation of lateral positioning. This study has significant implications for all road users, particularly those aged 18-25 years, and it reinforces the importance of not using a phone while driving.

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.

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.

Temporal analysis of cellphone-use-involved crash injury severities: Calling for preventing cellphone-use-involved distracted driving

With the popularity of smartphones and the increasing dependence on cellphones, cellphone-use-involved distracted driving has become a global traffic safety concern. Calling, texting, or watching videos while driving could have harmful impacts on driving abilities and increase crash-injury severities. To investigate the temporal stability and the heterogeneity of cellphone-involved crash injury severity determinants, a series of likelihood ratio tests and random parameters logit models with heterogeneity in means and variances are estimated. Cellphone-involved single-vehicle crash datasets of Pennsylvania from 2004 to 2019 are utilized. Marginal effects are also applied to investigate the impact of explanatory variables on injury severity outcomes. The results indicate an overall temporal instability of cellphone-involved crashes across different periods. However, driving without seatbelts and overturns are observed to produce relatively stable and positive influence on the increased injury severities of cellphone-involved crashes. Besides, it is noteworthy that a combination of cellphone usage with risky driving behaviors (aggressive driving, alcohol- or drug-related driving, speeding, or fatigue driving) significantly increase driver injury-severities. This finding highlights the necessity of identifying drivers with multiple risk-taking behaviors and enacting laws to prohibit these drivers from using cellphones while driving. Applications of smartphones provide another feasible approach to prevent using cellphones while driving. Insights and suggestions of this study would be valuable to mitigate the negative outcomes of cellphone-involved crashes and prevent the crashes caused by cellphone-involved distracted driving in the future.

The Effects of Dynamic Complexity on Drivers' Secondary Task Scanning Behavior under a Car-Following Scenario

The user interface of vehicle interaction systems has become increasingly complex in recent years, which makes these devices important factors that contribute to accidents. Therefore, it is necessary to study the impact of dynamic complexity on the carrying capacity of secondary tasks under different traffic scenarios. First, we selected vehicle speed and vehicle spacing as influencing factors in carrying out secondary tasks. Then, the average single scanning time, total scanning time, and scanning times were selected as evaluation criteria, based on the theories of cognitive psychology. Lastly, we used a driving simulator to conduct an experiment under a car-following scenario and collect data on scanning behavior by an eye tracker, to evaluate the performance of the secondary task. The results show that the relationship between the total scanning time, scanning times, and the vehicle speed can be expressed by an exponential model, the relationship between the above two indicators and the vehicle spacing can be expressed by a logarithmic model, and the relationship with the total number of icons can be expressed by a linear model. Combined with the above relationships and the evaluation criteria for driving secondary tasks, the maximum number of icons at different vehicle speeds and vehicle spacings can be calculated to reduce the likelihood of accidents caused by attention overload.

Inclusion of phone use while driving data in predicting distraction-affected crashes

INTRODUCTION

Given the tremendous number of lives lost or injured, distracted driving is an important safety area to study. With the widespread use of cellphones, phone use while driving has become the most common distracted driving behavior. Although researchers have developed safety performance functions (SPFs) for various crash types, SPFs for distraction-affected crashes are rarely studied in the literature. One possible reason is the lack of critical distracted behavior information in the commonly used safety data (i.e., roadway inventory, traffic, and crash counts). Recently, the frequency of phone use while driving (referred to as phone use data) is recorded by mobile application companies and has become available to safety researchers. The primary objective of this study is to examine if phone use data can potentially predict distracted-affected crashes.

METHOD

The authors first integrated phone use data with roadway inventory, traffic, and crash data in Texas. Then, the Random Forest (RF) algorithm was applied to assess the significance of the feature - phone use while driving - for predicting the number of distraction-affected crashes on a road segment. Further, this study developed two SPFs for distraction-affected crashes with and without the phone use data, separately. Both SPFs were assessed in terms of model fitting and prediction performances.

RESULTS

RF results rank the frequency of phone use as an important factor contributing to the number of distraction-affected crashes. Performance evaluations indicated that the inclusion of phone use data in the SPFs consistently improved both fitting and prediction abilities to predict distracted-affected crashes. Practical Applications: The phone use data provide new insights into the safety analyses of distraction-affected crashes, which cannot be achieved by only using the conventional roadway inventory and crash data. Therefore, safety researchers and practitioners are encouraged to incorporate the emerging data sources in reducing distraction-affected crashes.

Investigating Visitor Activity on a Safari Drive

Despite increasing studies focusing on the visitor experience in zoological collections, minimal attention has been paid to visitor activity when driving through safari parks. The dwell time of visitors at exhibits within a traditional zoo setting has offered a good method to measure exhibit and species popularity, but studying visitors on a safari drive offers a unique set of challenges, with factors such as road length skewing a basic dwell time measurement. Therefore, the current study proposes that average speed offers a robust means to investigate visitor activity on a safari drive. Average speed was found to be significantly different depending on species exhibited, with primates and felids eliciting slower speeds and bovids and cervids faster speeds. This result broadly mirrors that of traditional zoo studies where primates elicit longer dwell times. Future safari drive studies could help inform decisions made on a safari drive for aspects such a collection planning, drive layout and exhibit design. Harnessing tracking technology, e.g., GPS, alongside more diverse methodologies, such as questionnaires and multi-institutional approaches, would further allow more robust conclusions to be drawn.

Speeding through the pandemic: Perceptual and psychological factors associated with speeding during the COVID-19 stay-at-home period

During the COVID-19 stay-at-home period there were observed increases in both the percentage of cars engaged in extreme speeding, and the percentage of cars traveling below the speed limit. These changes have been attributed to unusually low traffic volume during the stay-at-home period. We develop a novel theoretical account, based on existing empirical research, of perceptual and psychological processes that may account for changes in speeding behavior under low traffic volume conditions. These include impaired ability to accurately perceive and control speed due to change in visual information, decreased salience of certain norms about socially appropriate speeds, lower perceived risk of speeding, and increased boredom leading to risk-taking behaviors. Further, we consider that individual attitude functions may account for the observed split in speeding behavior.