Driver Age Groups Research Articles

Revisiting Older Drivers' Risks of At-fault Motor Vehicle Collisions in Japan.

In Japan, older drivers have been encouraged to surrender their driving licenses for traffic safety, despite the potential adverse social and health outcomes of driving cessation. We reconsidered such policies and social pressure by comparing the risk of at-fault motor vehicle collisions (MVCs) across the age groups of drivers. Using the national data of police-reported MVCs that occurred between 2016 and 2020, we examined the number of at-fault MVCs per licensed driver (MVC rate) and the number of fatally and non-fatally injured persons per at-fault MVC by the sex and age groups of at-fault drivers. The MVC rate of older drivers was higher than that of middle-aged drivers but lower than that of young drivers. The number of injured persons among the collided counterparts (collided car occupants, motorcyclists, bicyclists, and pedestrians) per MVC caused by older drivers was not greater than that by drivers in other age groups. In fatal MVCs caused by older drivers, drivers themselves or their passengers tend to be killed rather than their collided counterparts. Overall, the results were mostly consistent between male and female drivers. The risk of at-fault MVCs increased with the advancing age of drivers after middle age; however, this risk among older drivers did not exceed that among young drivers, without posing a high risk of injuries to their collided counterparts.

Meta-Feature-Based Traffic Accident Risk Prediction: A Novel Approach to Forecasting Severity and Incidence

This study aims to improve the accuracy of predicting the severity of traffic accidents by developing an innovative traffic accident risk prediction model—StackTrafficRiskPrediction. The model combines multidimensional data analysis including environmental factors, human factors, roadway characteristics, and accident-related meta-features. In the model comparison, the StackTrafficRiskPrediction model achieves an accuracy of 0.9613, 0.9069, and 0.7508 in predicting fatal, serious, and minor accidents, respectively, which significantly outperforms the traditional logistic regression model. In the experimental part, we analyzed the severity of traffic accidents under different age groups of drivers, driving experience, road conditions, light and weather conditions. The results showed that drivers between 31 and 50 years of age with 2 to 5 years of driving experience were more likely to be involved in serious crashes. In addition, it was found that drivers tend to adopt a more cautious driving style in poor road and weather conditions, which increases the margin of safety. In terms of model evaluation, the StackTrafficRiskPrediction model performs best in terms of accuracy, recall, and ROC–AUC values, but performs poorly in predicting small-sample categories. Our study also revealed limitations of the current methodology, such as the sample imbalance problem and the limitations of environmental and human factors in the study. Future research can overcome these limitations by collecting more diverse data, exploring a wider range of influencing factors, and applying more advanced data analysis techniques.

News coverage of older drivers' fatal car crashes: is it over-represented?

Japan's stringent licensing policies for older drivers have not been questioned, possibly due to negative perceptions of older drivers potentially influenced by media coverage of their car crashes. We examined whether older drivers' fatal crashes are over-represented in the news articles. To examine the news coverage of fatal crashes that occurred between January 2016 and December 2020, we extracted driver- and crash-related data from articles reporting fatal crashes in the two best-selling newspapers, Yomiuri and Asahi. We obtained the corresponding data of police-reported fatal crashes during the same period. We calculated the proportion of newspaper-reported fatal crashes to police-reported fatal crashes by at-fault driver's age group and crash characteristics. Of 12,987 police-reported fatal crashes, 5,888 (45%) and 2,909 (22%) crashes were reported in Yomiuri and Asahi newspapers, respectively. Excluding 2,098 crashes where at-fault drivers or their ages were not identifiable, Yomiuri reported 39%, 35%, and 31%, and Asahi reported 20%, 16%, and 14% of fatal crashes caused by drivers aged <30 years, 30-69 years, and 70 years or older, respectively. Crashes that caused more fatalities or killed children tended to be reported regardless of at-fault drivers' age groups. Compared with young and middle-aged drivers, older drivers' fatal crashes involving child fatalities were more reported, whereas their single fatal crashes ending in their own deaths were less reported. Older drivers' at-fault fatal crashes were not over-represented in the news coverage of overall fatal crashes, and their crashes killing themselves were under-reported.

Single-vehicle roadway departure crashes at rural two-lane highway curved segments: A diagnosis using pattern recognition

Curved segments account for a disproportionately high proportion of fatal and serious injury crashes, with most of these crashes occurring on rural two-lane (R2L) highways. During the 10-year period from 2008 to 2017, a total of 1 234 fatal single-vehicle roadway departure (SV-RwD) crashes occurred on R2L roads in Louisiana, out of which 635 (51.5 %) crashes occurred on curved segments. Therefore, it is critical to investigate the causes of SV-RwD crashes, specifically those that occur on curved segments. This study aimed to investigate the ‘association knowledge’ of the factors contributing to SV-RwD crashes on R2L curved segments in Louisiana using fatal and injury crash data collected from 2008 to 2017. The study utilized Cluster Correspondence Analysis (CCA), a robust joint dimension reduction and clustering method for handling high-dimensionality and multicollinearity of crash data, to achieve this objective. Based on the cluster validation measures, the study identified five clusters with specific traits, including alcohol-impaired male drivers with no seatbelt usage, young (15–24 years old) female drivers’ crash involvement in cloudy weather conditions, animal-involved crashes in rainy weather conditions, crashes occurring on hillcrest locations under cloudy weather conditions, and crashes in the dark with the presence of streetlights and higher traffic volume. Furthermore, young (15–24 years) female drivers were identified in most clusters, implying that this specific age group of female drivers requires special consideration when dealing with SV-RwD collisions on R2L curved segments. To improve safety on R2L curved segments, policymakers can use the findings of this study to develop targeted countermeasures.

Analyzing the Effectiveness of Imbalanced Data Handling Techniques in Predicting Driver Phone Use

Distracted driving leads to a significant number of road crashes worldwide. Smartphone use is one of the most common causes of cognitive distraction among drivers. Available data on drivers’ phone use presents an invaluable opportunity to identify the main factors behind this behavior. Machine learning (ML) techniques are among the most effective techniques for this purpose. However, the potential and usefulness of these techniques are limited, due to the imbalance of available data. The majority class of instances collected is for drivers who do not use their phones, while the minority class is for those who do use their phones. This paper evaluates two main approaches for handling imbalanced datasets on driver phone use. These methods include oversampling and undersampling. The effectiveness of each method was evaluated using six ML techniques: Multilayer Perceptron (MLP), Support Vector Machine (SVM), Naive Bayes (NB), Bayesian Network (BayesNet), J48, and ID3. The proposed methods were also evaluated on three Deep Learning (DL) models: Arch1 (5 hidden layers), Arch2 (10 hidden layers), and Arch3 (15 hidden layers). The data used in this document were collected through a direct observation study to explore a set of human, vehicle, and road surface characteristics. The results showed that all ML methods, as well as DL methods, achieved balanced accuracy values for both classes. ID3, J48, and MLP methods outperformed the rest of the ML methods in all scenarios, with ID3 achieving slightly better accuracy. The DL methods also provided good performances, especially for the undersampling data. The results also showed that the classification methods performed best on the undersampled data. It was concluded that road classification has the highest impact on cell phone use, followed by driver age group, driver gender, vehicle type, and, finally, driver seatbelt usage.

Traffic Accident Traits and Driver Characteristics Implication on Road Accidents using Descriptive Analysis: A Cross Sectional Study in Sulaymaniyah, Iraq

The current study focuses on disclosing the types and causes of traffic events in the Sulaymaniyah governorate and their association with driver implications. The study was conducted between September 2019 and August 2020 in cooperation with the General Directorate of Traffic. A total of 573 traffic accident forms were directly collected at the site of crush events. The result shows that the collision type of Road Traffic Accidents (RTAs) is the most frequent type, accounting for 64.6% of the total. Higher incidence of RTAs was recorded in pickup, taxi, and motorcycle automobiles than in private cars. The analysis showed that the driver's faults are responsible for 81.4% of RTAs followed by road issues, mechanical car faults, and environmental factors which were responsible for 15%, 2.4%, and 1.2%, respectively. The most important driver factors accountable for RTAs were overspeeding, low level of education, gender, young age, and alcohol intake. Driver age group 19-33 is highly associated with RTAs with the peak occurring at the age of 21 years.

Driver monitoring and passenger interaction system using wearable device in intelligent vehicle

Intelligent transport systems (ITS) explore the benefits of wearable devices to monitor driver health and aid in emergencies and advertising. It integrates multitasking, automatically finding a tourist spot, ordering food, and smartly turning on and off a vehicle. In a medical emergency, passengers can access nearby medical facilities, which are not available in the current ITS. To sort out this deficiency, develop a wearable device for autonomous vehicle driver health and passenger interaction system. As part of the embedded system, the wearable gadget is affixed to the vehicle driver and can control the vehicle's motor operation, accelerometers, and gyroscopes. Drivers can travel more comfortably and engage co-passengers by directing promotion and advertisement. The experiment determines which age group of drivers is best suited for handling multiple services simultaneously.

Intentions and willingness to engage in risky driving behaviour among high school adolescents: evaluating the bstreetsmart road safety programme

ObjectiveTo investigate the impact of a road safety programme on adolescents’ willingness to engage in risky behaviour as probationary drivers, adjusted for covariates of interest.MethodThe bstreetsmart is a road safety...

Effects of traffic sign design and personal characteristics on the usability of traffic signs

BACKGROUND: Previous researchers examined the effects of either drivers’ personal characteristics or traffic sign design features on the usability of traffic signs. Their research indicated a connection existed between personal characteristics and usability and between design features and usability. OBJECTIVES: The focus of this study was to investigate which personal characteristics of drivers and which features of traffic sign design affect traffic sign usability the most. METHODS: The study was conducted in three stages. In the first stage, the participants filled out a questionnaire designed to record each driver’s personal characteristics. In the second stage, a System Usability Scale (SUS) was used to evaluate the subjective usability of traffic signs. The SUS had 10 statements that participants (N = 386) scored on a 5-point Likert-type scale from strongly agree to strongly disagree. In the third stage, these participants assigned from 0 to 100 points to 20 signs based on participant perception of the five design features of familiarity, concreteness, simplicity, meaningfulness, and semantic distance. RESULTS: The results showed that four of the five personal characteristics studied (age, education level, possession of a driving license, and formal driving experience) correlated significantly with traffic sign usability. The exception was gender, which did not correlate significantly. Additionally, it was found that the five traffic sign design features correlated in varying degrees of significance with each other and with specific traffic signs. CONCLUSIONS: Traffic sign usability depends mostly both on driver education level and age group and on the design feature of meaningfulness. These findings have implications for how drivers should be trained and how signs should be designed.

Self-Assessed Driving Skills and Risky Driver Behaviour Among Young Drivers: A Cross-Sectional Study.

The first few years of driving is a critical period when driving skills develop and the driving style is established. While the actual driving skills improve during the first few years of driving, a novice driver’s view of himself/herself as a safe and/or skilful driver also develops rapidly. The aim of this study was to investigate self-evaluated driver safety and perceptual-motor skills among different age groups of young drivers, along with the relationships between self-evaluated skills and driving behaviour. The sample consisted of a stratified random sample of 18–25-year-old drivers from the Finnish driving licence register. The questionnaires, which included the Driver Skill Inventory (DSI), Driver Behaviour Questionnaire (DBQ) and background information, were completed and returned by a total of 1,058 participants. While female drivers assessed their safety skills to be higher than their perceptual-motor skills, the opposite was true for males. In both sexes, perceptual-motor skills increased, and safety skills decreased with experience. Perceptual-motor skills correlated negatively with safety skills, lapses and errors, but positively with aggressive and ordinary violations. Safety skills correlated negatively with all DBQ variables. Safety orientation seems to be most clearly reflected in deliberate aberrant driving behaviours. Sex differences were observed in the development of behaviours and skills, perceptual-motor skills only increased with age among males, while safety skills decreased through experience among both men and women. Results showed that driving experience was strongly related to both driving style (violations, errors) and the drivers’ view of their skills (safety orientation), highlighting the importance of the first few years of driving.

Criterion for assessing the causes of road accidents according to a specific age group of drivers in Poland based on road incidents occurred in 2020

This research analyses the main causal factors that lead to road accidents. Particular focus has been on the demographics of drivers and in particular on the age group given that younger drivers account for the highest number of road safety-related incident/ accidents and fatalities. Other factors impacting road safety include the adequacy of the national driver training system, legislative requirements and organisational solutions for ensuring road safety. Behavioural and characteristics of drivers are considered as part of the causal analysis.

Design and evaluation of a rural intersection conflict warning system and alternative designs among various driver age groups

Advanced Rural Intersection Conflict Warning Systems (RICWS) were deployed as countermeasures to reduce severe right-angle crashes at rural thru-STOP controlled intersections across the United States (U.S.). The simulator study designed and evaluated alternative RICWS designs to existing RICWS interventions, in varying rural driving scenarios, across age groups (N = 40 novice teenage, 40 middle-aged, and 40 older drivers). Each participant was randomly assigned to a RICWS design, either the original or an alternative, and drove through sequences of 17 thru-STOP controlled rural intersections (nine RICWS intervention and eight control intersections). Drivers’ gap acceptance performance, intersection driving performance, traffic violation behaviors and self-reported workload were evaluated between intervention and control intersections. Regression models, applying the Generalized Estimating Equation (GEE), enabled efficacy determination of each RICWS design and an aggregated RICWS intervention effect, averaged across all simulated RICWS designs, among different levels of moderating factors. The safety performance and possible risks associated with the use of different RICWS designs were identified. Specifically, the original RICWS design had a significantly greater risk of STOP-sign violations at clear-view intersections with low traffic volumes, compared with control intersections (Risk Ratio = 2.18, 95% CI = 1.03 to 4.64). Except for Alternative RICWS Design 1, the alternative RICWS designs did not appear to outperform the Original RICWS Design. The moderating effects of drivers’ ages and intersection types on aggregated RICWS intervention effects were also examined. This study provides important safety implications for development and evaluation of intelligent intersection warning systems, targeted to vulnerable driver populations at high-risk rural intersections.

Assessing Willingness to Engage in Risky Driving Behaviour Using Naturalistic Driving Footage: The Role of Age and Gender.

Young novice drivers are more prone than older drivers to get involved in a risky driving situation. Some young drivers underestimate risk while overestimating their driving abilities, increasing the likelihood of engaging in risky behaviour. Age and inexperience both influence risk estimation, though it is not clear which of these variables is more important. Can drivers’ willingness to engage in risky behaviour be assessed in a similar way to hazard perception skill using video-based risky situations? The aim of the current study was to assess whether a video-based tool could measure the willingness to participate in risky driving situations and whether it can distinguish between different types of risky driving scenarios across gender and driver age groups. We also explored the moderating effect of age and gender on drivers’ experience in relation to the risky manoeuvres and participants’ willingness to engage in risky situations. Participants were presented with naturalistic videos from the perspective of the driver that contained active risky situations (result of driver’s own actions) and were asked to make a decision regarding a potential action (to overtake a bus/bicycle or pass through an amber light) and whether they would accelerate at this point. Participants reported that they were more willing to accelerate and overtake cyclists and buses and less willing to pass a light in amber. Young drivers were more willing to both engage in the risky behaviours and accelerate than older drivers, with young males reporting higher scores than the other groups. Gender differences were observed, with males being more prone to overtake and pass through a light in amber than females; however, this difference was not observed for the intention to accelerate. All the above effects remained when we tested the impact of experience on decision making while controlling for age and gender, although driving experience was no longer significant. These results demonstrate that drivers’ intention to assume risk can indeed be measured in a similar video-based methodology to that used by hazard perception tests. The findings raise the possibility of assessing and training drivers on a wider range of safety-related behaviours.

Assessment of Risk Associated with the Quiet Nature of Electric Vehicle: A Perception of EV Drivers and Pedestrians at Mumbai Metropolitan Region-India

The key features of the electric vehicle (EV) from urban area emphasizes on clean air as well as quiet running nature to preserve atmospheric pollution conditions in the city, however, the new public health risk is arising due to calm running environment of electric vehicles. Since the electric vehicles run at low noise levels, pedestrians being troubled in road traffic accidents which tend to focus on the safety of the pedestrian. The aim of the present study is to analyze the perception of pedestrian’s and driver’s with the quiet nature of electric vehicles by evaluating the data obtained through a questionnaire survey and interviews conducted at the Mumbai Metropolitan Region (MMR), India. A total of 398 drivers of various driving experiences and 401 pedestrians from various locations of MMR had been responded to acquire technical data. A hypothesis is evaluated for public health risk associated with the quiet nature of electric vehicles from the driver’s gender and age perspective. In addition, the ANOVA study was carried out to test the statistical significance of risk with respect to age, gender, vehicle usage, driving experience. The results illustrate that moderate risk is associated with the quiet nature of EV and more attention is required by road users as well as drivers, nevertheless gender (P=0.3321), profession (P=0.6537), driving experience (P=0.8888), vehicle use (P=0.3819) are not significant based on driver’s perception (P value greater than 0.05), whereas driver age group (P=0.0000) is accountable for perceived risk. Similarly, considering pedestrian’s perception, gender (P=0.7954), profession (P=0.8188), distance walk (P=0.2313), location (P=0.3896) are not significant. The outcome of this study recommends the foremost need for the advanced control system in electric vehicles.

Road Traffic Accident Data Analysis and Its Visualization

Vehicle accidents take human life all over the world particularly in developing countries like Pakistan. It is estimated that 1.2 million people lose their lives in road accidents every year. Apart from this, 20 to 50 million are injured on a yearly basis. This annual increase in the traffic accidents trend is alarming. To bring improvement in the current road network system, the specialists need to analyze the historical data of road crashes of an area. This research aims to use the visualization technique to have a better understanding of the accident data. This study uses the data of Peshawar, Pakistan, where the raw data were first organized, filtered, pre-processed and finally, visualization was performed to construct a systemic and homogenous data model. Various infographics were produced with the help of different software interface and visualization options. It was revealed that most of the accidents occur in the daytime and with those people who do not have enough traffic education. The 30 to 45 years age group was more active in causing the accidents. Therefore, the behaviour of this age group of drivers needs further investigation. This study will be useful for concerned authorities in devising an efficient mechanism to alleviate road accident cases.

Understanding the contributing factors to young driver crashes: A comparison of crash profiles of three age groups

Despite the adoption of the Graduated Driver Licensing (GDL) in 1998, the disproportionately high rate of young driver (15–24 years) crashes is still prevalent on Louisiana roadways. This paper utilizes the five years (2014–2018) crash data in Louisiana aiming to compare the crash profiles of three age groups (15–16 years, 17–19 years, and 20–24 years) to determine the contributing factors, crash characteristics, and severity outcomes. An extensive review of previous literature along with engineering judgments facilitated the understanding of the crash variables that were eventually used in the multinomial logit model with additional consideration of the current framework of the statewide GDL program. Results revealed that factors such as driving violation, distraction, passenger presence, time of crash, and driver protection system have been highlighted as the top ten predominant factors in multiple young driver groups. This study shows there is a ground for data-driven improvement for young drivers in Louisiana by presenting a broader view of a variety of issues based on the findings. Nighttime driving outside passenger restricted hours in GDL program, violations especially ‘failure to yield’, driving behaviors such as cellphone distraction especially in drivers aged 17–19 years and non-use of driver protection system across all young driver age groups are among the addressable key issues that may be influential in reducing young driver crashes. The study concludes that implementation of the GDL program would be more effective through continuous improvement and reevaluation of its components and strategic application of complementary young driver safety programs.

Using SHRP2 NDS data to examine infrastructure and other factors contributing to older driver crashes during left turns at signalized intersections

Drivers age 65 and over have higher rates of crashes and crash-related fatalities than other adult drivers and are especially over-represented in crashes during left turns at intersections. This research investigated the use of SHRP2 Naturalistic Driving Study (NDS) data to assess infrastructure and other factors contributing to left turn crashes at signalized intersections, and how to improve older driver safety during such turns. NDS data for trips involving signalized intersections and crash or near-crash events were obtained for two driver age groups: drivers age 65 and over (older drivers) and a sample of drivers age 30−49, along with NDS pre-screening and questionnaire data. Video scoring of all trips was performed to collect additional information on intersection and trip conditions. To identify the most influential factors of crash risk during left turns at signalized intersections, machine learning and regression models were used. The results found that in the obtained NDS dataset, there was a relatively small volume of crashes during left turns at signalized intersections. Further, model results found the statistically significant variables of crash risk for older drivers were associated more with health and cognitive factors rather than the infrastructure or design of the intersections. The results suggest that a study using only SHRP2 NDS data will not lead to definitive findings or recommendations for infrastructure changes to increase safety for older drivers at signalized intersections and during left turns. Moreover, the findings of this study indicates the need to consider other data sources and data collection methods to address this critical literature gap in older driver safety.

Continued trends in older driver crash involvement rates in the United States: Data through 2017–2018

Introduction: With the growing older adult population due to the aging baby-boom cohort, there was concern that increases in fatal motor-vehicle crashes would follow. Yet, previous analyses showed this to be untrue. The purpose of this study was to examine current trends to determine if previous declines have persisted or risen with the recent increase in fatalities nationwide. Methods: Trends among drivers ages 70 and older were compared with drivers 35–54 for U.S. passenger vehicle fatal crash involvements per 100,000 licensed drivers from 1997 to 2018, fatal and all police-reported crash involvements per vehicle miles traveled using the 1995, 2001, 2009, and 2017 National Household Travel Surveys, and driver deaths per 1,000 crashes. Results: Since the mid-1990s, fatal crashes per licensed driver trended downward, with greater declines for drivers ages 70 and older than for middle-aged drivers (43% vs. 21%). Fatal crash rates per 100,000 licensed drivers and police-reported crash rates per mile traveled for drivers ages 70–79 are now less than those for drivers ages 35–54, but their fatal crash rates per mile traveled and risk of dying in a crash remain higher as they drive fewer miles. As the economy improved over the past decade, fatal crash rates increased substantially for middle-aged drivers but decreased or remained stable among older driver age groups. Conclusions: Fatal crash involvements for adults ages 70 and older has recently increased, but they remain down from their 1997 peak, even as the number of licensed older drivers and the miles they drive have increased. Health improvements likely contributed to long-term reductions in fatal crash rates. As older drivers adopt vehicles with improved crashworthiness and safety features, crash survivability will improve. Practical Application: Older adults should feel confident that their independent mobility needs pose less risk than previously expected.

Detecting Risk Factors of Road Work Zone Crashes from the Information Provided in Police Crash Reports: The Case Study of Portugal

Several studies have shown that European police crash reports provide different detail degrees of work zone crash-related data. In this sense, the present study aims to verify the possibility of identifying significant risk factors involved in the occurrence of road work zone crashes with casualties, based on the official data usually available, through a descriptive, binary logistic, and probit regression statistical analysis. To accomplish the analysis, a total of 2597 police-reports related to 1767 Portuguese work zone crashes that occurred during the 2013–2015 period were considered and binary logistic and probit regression models were estimated by the main type of crash, contributing factor, and driver age group. Fifteen explanatory variables, selected based on the literature review and crash data provided in police crash reports, were considered in the analysis. The results obtained for the estimated coefficients and goodness-of-fit test values were found very similar for both link functions (logit and probit) and it was possible to identify risk factors. The modeling results pointed to excessive speed, disregard for vertical signs, luminosity, intersections, and motorcycle and heavy vehicle involvement as the most significant risk factors. Given the results, it is possible to conclude that binary logistic regression can be used in the statistical analysis of the available police official work zone crash data to identify and get some insight into the risk factors involved in work zone crashes. Data analysis also revealed the need to promote adequate and complete crash report filling by police officers. While police crash reports are not revised and standardized to incorporate more detailed work zone crash information, this approach can be used to support a more efficient road operation decision making and the review of some aspects related to work zone layout design.

Driver's age and rear-end crashes associated with distraction

Background and Objectives: Distraction is one of the affective factors in the occurrence of traffic accidents, which, in its turn, is the second leading cause of death and the main cause of years of life lost due to premature death and disability in Iran. The aim of the present study was to investigate the distracting behaviors of male drivers in rear-end crashes. Materials and Methods: This descriptive cross-sectional study was conducted on male drivers who had experienced a rear-end crash in Tabriz in 2018. In total, 395 samples (whose main reason for the crashes was inattention to the forward field of view) were selected through the information and files available at the traffic police, and after contacting the subjects, the required information was entered in the checklist. Nine distraction factors were classified and ranked into three age groups of young, middle-aged, and older drivers. Results: Findings showed that distractions caused by mobile phone use and passengers were ranked first to third in all three age groups and distracting factors such as eating or drinking while driving, alcohol consumption, and sleepiness were in the last ranks. In addition, older drivers were ranked first in not identifying the distraction factors, and in the middle-aged group, the distraction caused by billboards was the leading cause of crashes. Conclusions: Adopting appropriate policies, such as law enforcement and cultural interventions to prevent driver distractions, and to control the in-car conversation, and cell phone use while driving can be considered as effective interventions to prevent rear-end crashes.