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Article

On the Road Safety: Gender Differences in Risk-Taking Driving Behaviors Among Seniors Aged 65 and Older

by
Pierluigi Cordellieri
1,†,
Laura Piccardi
1,2,*,†,
Marco Giancola
3,
Anna Maria Giannini
1 and
Raffaella Nori
4,*
1
Department of Psychology, Sapienza University of Rome, 00185 Rome, Italy
2
San Raffaele Cassino Hospital, 03043 Cassino, Italy
3
Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, 67100 L’Aquila, Italy
4
Department of Psychology, University of Bologna, 40127 Bologna, Italy
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work, sharing the first authorship.
Geriatrics 2024, 9(5), 136; https://doi.org/10.3390/geriatrics9050136
Submission received: 14 August 2024 / Revised: 11 October 2024 / Accepted: 16 October 2024 / Published: 21 October 2024
(This article belongs to the Special Issue Psychological Perspectives on Driving Behavior in Aging: Implications for Assessment Tools and Training Methods)
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Abstract

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

Graphical Abstract

1. Introduction

Road accidents have been identified as one of the primary contributors to fatalities. Research has extensively reported that various factors can affect driving behavior, such as age, gender, experience, and both physiological and psychological features. However, age and gender stand out as the most measurable indicators. In 2018, the World Health Organization (WHO) reported that, worldwide, 29% of road fatalities involved car occupants, with males being three times more likely than females to suffer fatal injuries at all ages [1].
The gender gap in serious road accidents is still a problem, even when considering factors like mileage and road type [2]. Numerous studies have consistently indicated that males commit more traffic violations and take more significant road risks than females [3]. This result holds especially true for speeding, as evidenced by research conducted by Cestac et al. [4]. One of the most noticeable gender disparities between males and females regarding driving behavior is that men tend to exhibit more anger and risk-taking. In contrast, women tend to display more anxiety and distraction while driving [5].
Furthermore, males are overrepresented in crash rates due to decreased compliance with traffic rules [6,7]. Gonzalez-Iglesias et al. [8] found that men receive more fines and violate traffic rules more often than women, despite some evidence suggesting that women might commit specific violations more frequently than men [9]. Furthermore, the over-representation of males in car crashes may be attributed to females driving less than males [10].
The higher incidence of male involvement in road accidents can be attributed to biological and psychosocial factors. Men often struggle to control impulsive behavior due to lower cognitive control. They also tend to conform to gender roles and stereotypes related to capability and risk-taking. Numerous studies have revealed the presence of distinct gender stereotypes that portray males as more adept at navigating cars, even when exhibiting riskier driving behavior and more transgressions compared to females [11,12,13]. These beliefs often justify men’s tendency to engage in risky driving behavior, citing their superior driving skills.
On the other hand, risk-taking driving behaviors and traffic rule violations perpetrated by females are interpreted as a lack of capability in driving. The root cause of road accidents lies in aggressive driving behavior. In contrast, other behaviors, such as anxiety or excessive caution, disrupt the regular flow of traffic, resulting in congestion and reduced efficiency. From this perspective, both types of drivers pose serious problems that impact road safety.
However, overlooking other factors that could add context, influence, or alter the risk trend would be a mistake. Although experience directly affects driving ability, one cannot discount the impact of a driver’s age on their likelihood of taking risks. Various studies have indicated that young males, in particular, tend to display a higher propensity for risk-taking behaviors [14,15].
Several studies have repeatedly demonstrated that individuals under 25 are more inclined to partake in hazardous driving practices than their older counterparts [16]. While it is widely recognized that turning 25 marks a significant neurodevelopmental milestone for the prefrontal cortex [17], it is crucial to acknowledge that not all tendencies toward risk-taking dissipate at this age. Unfortunately, there exists a need for more research investigating the propensities of individuals in their late twenties and early thirties to engage in reckless driving behaviors, as this represents a critical age group to study. McCartt et al. [18] found that older and younger drivers have different crash rates, with younger drivers reporting more crashes. Jimenez-Mejıas et al. [19] observed 1574 students for three consecutive academic years to evaluate patterns in risky driving behaviors. Their longitudinal results confirmed that male drivers often participated in dangerous driving behaviors. Curry et al. [20] highlighted how young drivers of the same age but differing driving experiences exhibit differing crash rates. Their findings demonstrated that 21-year-old novice drivers were involved in more road crashes than experienced drivers of the same age. At the same time, both groups reported notably fewer collisions compared to novice drivers aged 17 to 20. In general, drivers under 30 are more willing to overtake cyclists and buses and go through an amber traffic light than drivers over 30. The younger group consistently exhibited a greater inclination to accelerate when confronted with risky situations than the older group. Extensive research has delved into the connection between drivers under the age of 25 and their propensity for engaging in risky driving behaviors, primarily due to the over-representation of this age cohort in road crashes [21,22].
Furthermore, research indicates that males exhibit higher levels of sensation seeking, characterized by a propensity to pursue intense, novel, or potentially risky activities. Conversely, females have been found to have a lower tolerance for delayed rewards. These findings are supported by various studies [23,24,25,26].
Few studies have shown how age and driving experience affect the likelihood of engaging in risky situations. It is well known that young male drivers tend to be more optimistic about their driving ability than older male drivers and are more willing to engage in risky behavior [27]. Moreover, they tend to underestimate the risks associated with particular traffic situations, such as speeding or using a phone while driving, compared to older drivers [28]. In light of this, there may be a case for a differential impact of age and driving experience on the inclination to partake in risky driving behavior.
Chen et al. [29] compared two driver groups: one composed of individuals aged 18–60 years and the other consisting of individuals over 60. The study revealed that older adults’ reaction time was significantly higher than younger groups. Additionally, the researchers observed that older drivers with more experience exhibited decreased lateral stability, particularly when compared to their younger counterparts. This increased variability in lateral deviation made them more prone to colliding with obstacles on the right side of the road. Interestingly, older drivers with more experience demonstrated better performance in terms of speed deviation than their younger counterparts. Despite this, the study concluded that older drivers exhibited less lateral stability than younger groups, albeit in opposite directions. Consequently, these findings illustrate that ample driving experience primarily aids in controlling driving speed, as opposed to maintaining sideways vehicle stability.
In the Italian context, older drivers are significantly more susceptible to fatalities compared to younger drivers. For instance, in 2016, drivers aged 18–24 were involved in 352 fatalities (rate of 3370 across Europe), while drivers over 65 had 1045 fatalities (rate of 7071 in Europe; European Transport Safety Council, 2019). These data indicate an increased risk propensity and potential impairments in sensory, cognitive, and physical abilities [30]. Interestingly, most studies on driving by the elderly have focused on analyzing the impact of cognitive impairment on driving safety, while few have examined changes in risk tolerance among individuals over 65. The growing population of active elderly people who rely on driving as a means of transportation demands further attention. Many in this age group remain employed, while others play a significant social role, often accompanying their grandchildren to various activities. Furthermore, the risk extends beyond just driving and encompasses pedestrian behavior. According to a study by Liu et al. [31], middle-aged pedestrians in Hong Kong faced the gravest danger of fatal or severe injuries in the initial year of the COVID-19 lockdown in 2020.
Researchers widely agree that the severity of pedestrian injuries varies by age, with elderly pedestrians being at a greater risk of sustaining severe or fatal injuries. This increased vulnerability is primarily due to age-related declines in cognitive function and physical fitness, which impair their ability to react to fast-moving vehicles and avoid potential collisions [32,33,34,35]; therefore, the accident statistics related to the elderly call for a comprehensive investigation. One crucial aspect to consider is whether the difference in risk tolerance between males and females decreases or persists.
The primary aim of this study was to analyze the gender gap in risk-taking behavior among a sample of 200 individuals aged 65 and above. Additionally, we thoroughly investigated the correlation between participants’ involvement in accidents and their risk tendencies while driving.

2. Materials and Methods

2.1. Participants

This survey was conducted with an opportunistic sample of 204 older individuals (age M = 70.59 years; SD = 4.36 years; education M = 11.49 years; SD = 3.98 years; 102 females). All participants were members of Italian local over-65 associations and declared that they had a driving license. Both groups reported identical weekly driving times and the same use of the car (see Table 3). They were all still actively involved in life, with no reported neurological or psychiatric disorders during an initial brief anamnestic interview. In addition, all the participants stated that they did not take drugs that impair driving for the treatment of any illnesses. The z-test, with ±4 z scores as the reference values for samples > 100 [36,37], indicated 4 outliers, which were discarded from the dataset. The final sample consisted of 200 participants (Meanage = 70.54 years; SDage = 4.37 years; Meaneducation = 11.51 years; SDeducation = 3.97 years; 101 females). Table 1 reports the main features of the research sample by gender.

2.2. Measures

The measures employed encompassed a variety of distinct variables, such as driving expertise (years of driving license, weekly car use), driving risk behaviors (Driver Road Risk Perception Scale, DRPS), traffic violation variables (driving accidents, responsibility in accidents, contributory negligence), and pedestrian risk behavior (Pedestrian Behavior Appropriateness Perception Scale, PBAPS).

2.2.1. Driving Risk Behaviors

Driver Road Risk Perception Scale (DRPS). DRPS is a 13-item scale (see supplementary materials A) developed to measure the perception of driving risk through a wide array of driving conditions, such as mobile phone use (e.g., “typing a message on your mobile phone while driving”), eating (e.g., “eating in the car: sandwich, snacks, etc.”; “driving under conditions of significant physical fatigue”), and other specific risk behaviors (e.g., “Arguing animatedly with a passenger while driving”). Responses are rated on a 5-point Likert scale ranging from “not at all risky” (1) to “extremely risky” (5). The items used in this scale were derived from previous works by Fraschetti et al. and Cordellieri et al. [38,39]. The structure of the DPRS was tested by exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). In EFA, the principal component analysis method was used to evaluate factor extraction, while the varimax method was performed for rotation. The Keiser–Meyer–Olkin (KMO) coefficient was 0.95, while the Chi-square value for Bartlett’s Sphericity Test was 3960.85 (p < 0.001). All extraction values were above the threshold of 0.30, and all diagonal values in the anti-image correlation matrix were above 0.50. A unidimensional structure was revealed, explaining 78% of the variance. As for CFA, because the data did not meet the assumption of multiple normality (thresholds for skewness and kurtosis: <2| and <7|, respectively; [40,41]), bootstrap maximum likelihood (ML) with 800 resamples was performed. The model fit indices were as follows: minimum discrepancy function by degrees of freedom divided (CMIN/DF) (122.722/54) = 2.273, goodness of fit Index (GFI) = 0.921, incremental fit index (IFI) = 0.982, Tucker–Lewis Index (TLI) = 0.974, comparative fit index (CFI) = 0.982, root mean square residual (RMSEA) = 0.078. The model fit indices ranged from acceptable to good limits. All items of the DRPS showed statistically significant path coefficients (p < 0.001). The internal consistency reliability was excellent: Cronbach’s α (0.97) and McDonald’s ω (0.97). Additionally, all items in the DRPS displayed excellent item-total correlation coefficients, exceeding the threshold of 0.30.

2.2.2. Traffic Violation Variables

Three items were used to inquire about this: (1) driving accidents “In how many accidents have you been involved?”; (2) responsibility in accidents “How many times have you been responsible for a traffic accident?”; and (3) contributory negligence “In how many accidents were you found to have contributory negligence?”.

2.2.3. Pedestrian Risk Behavior

Pedestrian Behavior Appropriateness Perception Scale (PBAPS). This uses a 7-item scale (see supplementary materials B) developed to assess the perceived appropriateness of various behaviors of elderly pedestrians in the road environment. The scale includes 7 items, each of which evaluates a specific behavior, such as being distracted on a cell phone (e.g., reading a text message on the phone while crossing the street if the street has little traffic) or engaging in risky conduct (e.g., crossing when the pedestrian light is red, if you are in a hurry). Respondents rate the appropriateness of each behavior on a 10-point Likert scale ranging from “not at all appropriate” (1) to “completely appropriate” (10).
The structure of the PBAPS was tested by EFA and CFA factor analyses. In EFA, the KMO coefficient was 0.90, while the Chi-square value for Bartlett’s Sphericity Test was 1068.52 (p < 0.001). All extraction values were above the threshold of 0.30, and all diagonal values in the anti-image correlation matrix were above 0.50. A unidimensional structure was revealed, explaining 69% of the variance. As data did not meet the assumption of multiple normality, CFA was computed using bootstrap ML with 800 resamples. The model fit indices ranged from acceptable to good limits, as follows: CMIN/DF (23.705/10) = 2.371, GFI = 0.970, IFI = 0.987, TLI = 0.972, CFI = 0.987, RMSEA = 0.080. Path coefficients for all items were statistically significant (p < 0.001), and Cronbach’s α (0.92) and McDonald’s ω (0.92) revealed excellent internal consistency reliability. In addition, all items in the PBAPS revealed excellent item–total correlation coefficients (>0.30).

3. Results

3.1. Data Screening and Preliminary Analysis

Statistical analyses were performed using SPSS Statistics version 24 for Windows (IBM Corporation, Armonk, New York, NY, USA). Shapiro–Wilk’s test indicated that all continuous variables were not normally distributed. Due to the non-normality of the data, Spearman’s correlation analysis was undertaken to ascertain the interrelationships among all the study variables. Table 2 reports all correlations among the study variables by gender.

3.2. Gender Differences in Expertise and Risk Behaviors

Furthermore, we employed odds ratios (ORs) to examine gender differences across various variables, including years of holding a driving license, weekly car use, risky driving behavior, driving accidents, responsibility in accidents, contributory negligence, and risky pedestrian behavior. The odds ratio is a statistical measure that quantifies the strength of the association between two events and is frequently utilized in survey research, epidemiology, and clinical studies, particularly in case–control designs.
In this study, our primary objective in using odds ratios was to evaluate the differences in risk propensity between males and females. To this end, we selected a threshold of one z-score above the mean (1 z-score = 84.1% of the sample). We posited that a higher score at this threshold, within variables related to road risk, signifies a high-risk condition, whereas a lower score indicates a low-risk condition. Accordingly, we categorized responses as high-risk or low-risk within both the female and male populations.
The odds ratio revealed a significant difference between male and female participants for the years of holding a driving license (OR = 2.83, 95% CI [0.971–8.27]), while no significant differences were found for weekly car use (OR = 0.660, 95% CI [0.108–4.04]). Regarding the risk-taking variables, the odds ratios indicated a statistically significant difference in the likelihood of engaging in risky behavior between females and males for driving risk behaviors (OR = 5.25, 95% CI [1.71–16.1]), driving accidents (OR = 5.03, 95% CI [ 1.82–13.9]), responsibility in accidents (OR = 2.71, 95% CI [1.07–6.86]), contributory negligence (OR = 2.26, 95% CI [1.11–4.63]), and pedestrian risk behaviors (OR = 3.34, 95% CI [1.17–9.58]). Table 3 shows that males exhibited higher years of holding a driving license, driving risk behaviors, driving accidents, responsibility in accidents, contributory negligence, and pedestrian risk behaviors than females. Figure 1 summarizes graphically the results of the odds ratio analysis.

4. Discussion

Taking into consideration the substantial increase in human life expectancy and the evidence that the European Union (EU) is renowned as the region with the world’s oldest population [42], with a staggering aging rate of 94.1% in 2001, which further surged to 125.8% in 2017 [43,44], it appears increasingly important to pay attention to the elderly population. European countries are experiencing population aging due to increased life expectancy and declining birth rates. Recent data from Eurostat (https://ec.europa.eu/eurostat/web/products-eurostat-news/w/DDN-20240503-2, accessed on 11 July 2024) indicates that Italy’s life expectancy exceeds the EU average following the pandemic.
Undoubtedly, the growing number of elderly drivers and their extended life expectancy have raised considerable concerns about road safety. Research has shown that older individuals are more likely to be involved in severe accidents compared to younger drivers, mainly because of their increased physical vulnerability and declining cognitive and sensory abilities associated with aging.
Our study contributes to the overall effort of accident prevention and safeguarding vulnerable groups. In particular, we focused on studying the risk-taking behavior of older drivers, while considering gender differences. We aimed to determine if the patterns observed in young male drivers persist in elderly individuals. In the Italian context, it is evident that older drivers face a significantly higher risk of fatalities compared to their younger counterparts.
Our data demonstrate that the gender disparity in driving risk-taking remains consistent even among individuals over 65. Men are more inclined to take risks than women, as indicated by their involvement in more accidents. This evidence highlights their perception of risk and reveals a distinct pattern of risk-taking driving behavior. These findings indicate that males exhibit a higher propensity for risk-taking as both drivers and pedestrians, emphasizing irresponsible behavior on the roads.
Although gender differences can be regarded as a protective factor supporting cognitive reserve concerning certain processes, such as visual–spatial abilities, as is the case for patients with Alzheimer’s disease [45] or patients with right brain injury [46], in the case of risk-taking driving it seems to persist in a negative sense, making men more vulnerable to accidents, even in old age. Indeed, men commit more errors and violations on the road than women [47,48]. Nori et al. [49] found that drivers, despite gender, with superior spatial orientation skills exhibited a higher frequency of aggressive driving violations than drivers with lower spatial skills. Women tend to exhibit lower visuo-spatial skills than men when assessing their spatial abilities. In contrast, men are more confident about their sense of direction and considered better than women in spatial navigation. Literature reports that men outperform women in distance estimation, following directions, reading maps, and mental rotations skills (e.g., [50]).
It is reasonable to assume that the same may be true for our particular sample. This higher competence leads older adults to display overconfidence regarding their driving skills and vehicle handling capabilities, creating an overconfidence bias [51]. This bias involves overestimating one’s actual performance, leading men to engage in riskier behaviors because they are confident that their abilities exceed reality. These feelings may also endure among older drivers. This framework also takes into account gender variations. Studies have shown that both novice and experienced male drivers tend to exhibit higher scores in risky and aggressive driving styles compared to women. Conversely, women display higher patient and cautious driving scores [52,53].
In addition, recent data suggest a change in the driving behaviors of young people, indicating that women are exhibiting more risky driving behaviors traditionally associated with men [54]. Moreover, the differences between male and female driving behaviors have diminished over time. This shift in trend can be attributed to increased traffic exposure among young women and a rise in risk-taking behaviors. Several studies have highlighted these changes in driving habits among young individuals [14,55,56,57,58]. It will be interesting to see if and how these shifts in women’s driving behaviors will impact the elderly population in the future.
However, until now, this change has not been found to affect individuals over 65, who maintain distinct driving styles and risk tendencies based on gender. It is important to note that, in our sample, the difference in risk tendency persists, even with equal car use. Men tend to take more risks and report more accidents compared to women, and experience in driving does not improve this tendency; instead, it increases their confidence in their ability to handle a vehicle in risky situations. Additionally, this risk tendency is evident in pedestrian behavior, with men showing lower awareness of their psychophysical state than women.
Silva et al. [59] found differences in strength between males and females in a study involving European countries; nevertheless, men and women have been found to report different perceptions of health and, consequently, quality of life. For instance, research has discovered that older women report a substantial 25% more complaints regarding their quality of life, ranging from poor to very poor, in comparison to older men [60]. These differences are also evident in the observation that older men are more vulnerable to environmental factors than women and that higher levels of physical fitness have a more significant impact on men’s quality of life and health assessment compared to women. This is in line with recent data reported by Piccardi et al. [61] showing that being active is essential, regardless of the type of motor activity or intensity of activity, although some forms of motor activity involving recreational and social conditions (i.e., dancing) are better than others [62].
Finally, the high estimation of one’s abilities, always present in males, could also result in an overestimation of the ability to perceive risky situations. However, various studies have demonstrated that hazard perception abilities, crucial for anticipating dangerous situations, decline with age, resulting in slower reaction times and an increased risk of severe accidents [63,64]. Based on these considerations, in fact, older men aged 70 and over have higher mortality rates than women of the same age [65].
It is crucial to provide specialized training for elderly drivers in addressing these issues. Research indicates that, despite the naturally declining cognitive and sensory abilities that come with age, the performance of elderly drivers can be improved through targeted training interventions. For example, studies have demonstrated that training focused on enhancing hazard perception can substantially decrease reaction times, reducing the risk of accidents. These training programs can assist elderly drivers in preserving their driving independence while minimizing associated risks (e.g., [66,67,68,69,70,71,72,73,74,75,76,77,78,79]).
It is important to note that, while training programs have shown promise, their implementation should be carefully tailored to meet the diverse needs of the elderly population. Tailored training that considers individual differences in cognitive and sensory decline could enhance the effectiveness of these programs. Furthermore, integrating regular assessments and updates into these training programs may help address the progressive nature of age-related decline.
In summary, data indicate that training for elderly drivers could be an effective strategy for improving road safety, reducing the severity of accidents, and supporting the safe mobility of older individuals. However, policies and training programs must consider these aspects to adequately address the increasing population of elderly drivers and the associated risks. Future research should focus on refining these training interventions and exploring additional strategies to ensure they are both accessible and practical for elderly drivers.

5. Limitations and Future Perspectives

This study shows some limitations worth mentioning. First, it involved only self-report measures of risk perception, which can suffer from the effect of social desirability. Second, no psychological variables, such as personality traits, cognitive styles, and cognitive, emotional, and motivational factors, have been included in the study. Future research should embrace a more holistic approach by incorporating objective measures, such as driving simulations, while also examining the combined effects of gender and psychological factors to better understand the individual differences in risky driving behavior. Third, we should have considered the impact of familiarity with the environment on risk perception. Research has shown that individuals are more likely to take risks in familiar environments [80]. Indeed, according to the spatial cognition perspective [81], being familiar with the environment enhances people’s sense of competence and safety [82,83], leading to a greater willingness to take risks [49].
From a practical standpoint, road safety campaigns should specifically focus on male drivers, as they are more prone to engaging in risky driving behaviors. By emphasizing responsible practices, such as adhering to speed limits, we can position these actions as socially desirable behaviors within this demographic (e.g., [84,85]). However, it is important to test the effectiveness of such messages directly through experimental studies. In general, repeated exposure to various messages, influencing the connection between driver perceptions and social desirability during and after initial driver training, could encourage less risky driving behavior.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/geriatrics9050136/s1, Supplementary Materials A: Driver Road Risk Perception Scale (DRPS); Supplementary Materials B: Pedestrian Behavior Appropriateness Perception Scale (PBAPS).

Author Contributions

Conceptualization, P.C., L.P., M.G., R.N. and A.M.G.; methodology, P.C. and A.M.G.; formal analysis, M.G.; investigation, P.C.; resources, A.M.G.; data curation, P.C. and M.G.; writing—original draft preparation, L.P., R.N., P.C. and M.G.; writing—review and editing, all authors.; funding acquisition, A.M.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Review Board of the Department of Psychology, “La Sapienza” University of Rome (IRB 2414/2019).

Informed Consent Statement

Written informed consent was obtained from all participants involved in the study.

Data Availability Statement

Data are available on request by writing to the corresponding authors.

Acknowledgments

Authors thank the Italian Ministry of Infrastructures and Transport.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Global Status Report on Road Safety 2023; World Health Organization: Geneva, Switzerland, 2023; ISBN 978-92-4-008651-7.
  2. European Conference of Ministers of Transport. Young Drivers. The Road to Safety; OECD Publishing: Paris, France, 2006. [Google Scholar]
  3. Varet, F.; Apostolidis, T.; Granié, M.-A. Social Value, Normative Features and Gender Differences Associated with Speeding and Compliance with Speed Limits. J. Saf. Res. 2023, 84, 182–191. [Google Scholar] [CrossRef] [PubMed]
  4. Cestac, J.; Paran, F.; Delhomme, P. Young Drivers’ Sensation Seeking, Subjective Norms, and Perceived Behavioral Control and Their Roles in Predicting Speeding Intention: How Risk-Taking Motivations Evolve with Gender and Driving Experience. Saf. Sci. 2011, 49, 424–432. [Google Scholar] [CrossRef]
  5. Sun, L.; Chang, R.; Dong, Y. Effect of Driving Experience on Drivers’ Driving Styles and Traffic Violations. China J. Health Psychol. 2014, 222–224. [Google Scholar] [CrossRef]
  6. Williams, A.F. Views of U.S. Drivers about Driving Safety. J. Saf. Res. 2003, 34, 491–494. [Google Scholar] [CrossRef] [PubMed]
  7. Moghaddam, V.; Yazdani, A.; Wang, H.; Parlevliet, D.; Shahnia, F. An Online Reinforcement Learning Approach for Dynamic Pricing of Electric Vehicle Charging Stations. IEEE Access 2020, 8, 130305–130313. [Google Scholar] [CrossRef]
  8. González-Iglesias, B.; Gómez-Fraguela, J.A.; Luengo-Martín, M.Á. Driving Anger and Traffic Violations: Gender Differences. Transp. Res. Part F Traffic Psychol. Behav. 2012, 15, 404–412. [Google Scholar] [CrossRef]
  9. Mercer, G.W. Traffic Accidents and Convictions: Group Totals Versus Rate per Kilometer Driven. Risk Anal. 1989, 9, 71–77. [Google Scholar] [CrossRef]
  10. Laapotti, S.; Keskinen, E.; Rajalin, S. Comparison of Young Male and Female Drivers’ Attitude and Self-Reported Traffic Behaviour in Finland in 1978 and 2001. J. Saf. Res. 2003, 34, 579–587. [Google Scholar] [CrossRef]
  11. Degraeve, B.; Granié, M.-A.; Pravossoudovitch, K.; Lo Monaco, G. Social Representations Associated with Men and Women Drivers among French Adolescents and Adults. Effects of the Perceiver’s Age, Sex, and Socioeconomic Status. Transp. Res. Part F Traffic Psychol. Behav. 2015, 34, 1–17. [Google Scholar] [CrossRef]
  12. Granié, M.-A.; Papafava, E. Gender Stereotypes Associated with Vehicle Driving among French Preadolescents and Adolescents. Transp. Res. Part F Traffic Psychol. Behav. 2011, 14, 341–353. [Google Scholar] [CrossRef]
  13. Pravossoudovitch, K.; Martha, C.; Cury, F.; Granié, M.-A. Sex and Age Differences in the Endorsement of Sex Stereotypes Associated with Driving. Span. J. Psychol. 2015, 18, E100. [Google Scholar] [CrossRef] [PubMed]
  14. Cordellieri, P.; Baralla, F.; Ferlazzo, F.; Sgalla, R.; Piccardi, L.; Giannini, A.M. Gender Effects in Young Road Users on Road Safety Attitudes, Behaviors and Risk Perception. Front. Psychol. 2016, 7, 1412. [Google Scholar] [CrossRef] [PubMed]
  15. Machin, S.; Pekkarinen, T. Global Sex Differences in Test Score Variability. Science 2008, 322, 1331–1332. [Google Scholar] [CrossRef] [PubMed]
  16. Rhodes, N.; Pivik, K. Age and Gender Differences in Risky Driving: The Roles of Positive Affect and Risk Perception. Accid. Anal. Prev. 2011, 43, 923–931. [Google Scholar] [CrossRef] [PubMed]
  17. Arain, M.; Haque, M.; Johal, L.; Mathur, P.; Nel, W.; Rais, A.; Sandhu, R.; Sharma, S. Maturation of the Adolescent Brain. Neuropsychiatr. Dis. Treat. 2013, 9, 449–461. [Google Scholar] [CrossRef]
  18. McCartt, A.T.; Mayhew, D.R.; Braitman, K.A.; Ferguson, S.A.; Simpson, H.M. Effects of Age and Experience on Young Driver Crashes: Review of Recent Literature. Traffic Inj. Prev. 2009, 10, 209–219. [Google Scholar] [CrossRef]
  19. Jiménez-Mejías, E.; Prieto, C.A.; Martínez-Ruiz, V.; Castillo, J.d.D.L.d.; Lardelli-Claret, P.; Jiménez-Moleón, J.J. Gender-Related Differences in Distances Travelled, Driving Behaviour and Traffic Accidents among University Students. Transp. Res. Part F Traffic Psychol. Behav. 2014, 27, 81–89. [Google Scholar] [CrossRef]
  20. Curry, A.E.; Metzger, K.B.; Williams, A.F.; Tefft, B.C. Comparison of Older and Younger Novice Driver Crash Rates: Informing the Need for Extended Graduated Driver Licensing Restrictions. Accid. Anal. Prev. 2017, 108, 66–73. [Google Scholar] [CrossRef]
  21. Adanu, E.K.; Smith, R.; Powell, L.; Jones, S. Multilevel Analysis of the Role of Human Factors in Regional Disparities in Crash Outcomes. Accid. Anal. Prev. 2017, 109, 10–17. [Google Scholar] [CrossRef]
  22. Ventsislavova, P.; Crundall, D.; Garcia-Fernandez, P.; Castro, C. Assessing Willingness to Engage in Risky Driving Behaviour Using Naturalistic Driving Footage: The Role of Age and Gender. Int. J. Environ. Res. Public Health 2021, 18, 10227. [Google Scholar] [CrossRef]
  23. Cross, C.P.; Copping, L.T.; Campbell, A. Sex Differences in Impulsivity: A Meta-Analysis. Psychol. Bull. 2011, 137, 97–130. [Google Scholar] [CrossRef] [PubMed]
  24. Lazarus, S.A.; Choukas-Bradley, S.; Beeney, J.E.; Byrd, A.L.; Vine, V.; Stepp, S.D. Too Much Too Soon?: Borderline Personality Disorder Symptoms and Romantic Relationships in Adolescent Girls. J. Abnorm. Child Psychol. 2019, 47, 1995–2005. [Google Scholar] [CrossRef] [PubMed]
  25. Lucidi, F.; Mallia, L.; Giannini, A.M.; Sgalla, R.; Lazuras, L.; Chirico, A.; Alivernini, F.; Girelli, L.; Violani, C. Riding the Adolescence: Personality Subtypes in Young Moped Riders and Their Association With Risky Driving Attitudes and Behaviors. Front. Psychol. 2019, 10, 300. [Google Scholar] [CrossRef] [PubMed]
  26. Navas, J.F.; Martín-Pérez, C.; Petrova, D.; Verdejo-García, A.; Cano, M.; Sagripanti-Mazuquín, O.; Perandrés-Gómez, A.; López-Martín, Á.; Cordovilla-Guardia, S.; Megías, A.; et al. Sex Differences in the Association between Impulsivity and Driving under the Influence of Alcohol in Young Adults: The Specific Role of Sensation Seeking. Accid. Anal. Prev. 2019, 124, 174–179. [Google Scholar] [CrossRef] [PubMed]
  27. Farrand, P.; McKenna, F.P. Risk Perception in Novice Drivers: The Relationship between Questionnaire Measures and Response Latency. Transp. Res. Part F Traffic Psychol. Behav. 2001, 4, 201–212. [Google Scholar] [CrossRef]
  28. Ivers, R.; Senserrick, T.; Boufous, S.; Stevenson, M.; Chen, H.-Y.; Woodward, M.; Norton, R. Novice Drivers’ Risky Driving Behavior, Risk Perception, and Crash Risk: Findings From the DRIVE Study. Am. J. Public Health 2009, 99, 1638–1644. [Google Scholar] [CrossRef]
  29. Chen, B.; Zhao, X.; Li, Y.; Liu, X. Exploring the Associations of Demographics and Scale Measures with Cognitive Driving Behavior among Older Drivers in China. Accid. Anal. Prev. 2024, 200, 107542. [Google Scholar] [CrossRef]
  30. Zahabi, M.; Abdul Razak, A.M.; Shortz, A.E.; Mehta, R.K.; Manser, M. Evaluating Advanced Driver-Assistance System Trainings Using Driver Performance, Attention Allocation, and Neural Efficiency Measures. Appl. Ergon. 2020, 84, 103036. [Google Scholar] [CrossRef]
  31. Liu, R.; Hong, F.; Sun, Z. Pedestrian Perception Tracking in Complex Environment of Unmanned Vehicles Based on Deep Neural Networks. EAI Endorsed Trans. Energy Web 2024, 11. [Google Scholar] [CrossRef]
  32. Haleem, K.; Alluri, P.; Gan, A. Analyzing Pedestrian Crash Injury Severity at Signalized and Non-Signalized Locations. Accid. Anal. Prev. 2015, 81, 14–23. [Google Scholar] [CrossRef]
  33. Zhai, X.; Huang, H.; Sze, N.N.; Song, Z.; Hon, K.K. Diagnostic Analysis of the Effects of Weather Condition on Pedestrian Crash Severity. Accid. Anal. Prev. 2019, 122, 318–324. [Google Scholar] [CrossRef]
  34. Doulabi, S.; Hassan, H.M.; Ferguson, M.R.; Razavi, S.; Paez, A. Exploring the Determinants of Older Adults’ Susceptibility to Pedestrians’ Incidents. Accid. Anal. Prev. 2021, 155, 106100. [Google Scholar] [CrossRef]
  35. Rampinelli, A.; Calderón, J.F.; Blazquez, C.A.; Sauer-Brand, K.; Hamann, N.; Nazif-Munoz, J.I. Investigating the Risk Factors Associated with Injury Severity in Pedestrian Crashes in Santiago, Chile. Int. J. Environ. Res. Public. Health 2022, 19, 11126. [Google Scholar] [CrossRef]
  36. Giancola, M.; Palmiero, M.; D’Amico, S. The Green Adolescent: The Joint Contribution of Personality and Divergent Thinking in Shaping pro-Environmental Behaviours. J. Clean. Prod. 2023, 417, 138083. [Google Scholar] [CrossRef]
  37. Mertler, C.A.; Vannatta, R.A.; LaVenia, K.N. Advanced and Multivariate Statistical Methods: Practical Application and Interpretation, 7th ed.; Routledge, Taylor & Francis Group: New York, NY, USA; London, UK, 2022; ISBN 978-0-367-49747-7. [Google Scholar]
  38. Fraschetti, A.; Cordellieri, P.; Lausi, G.; Mari, E.; Paoli, E.; Burrai, J.; Quaglieri, A.; Baldi, M.; Pizzo, A.; Giannini, A.M. Mobile Phone Use “on the Road”: A Self-Report Study on Young Drivers. Front. Psychol. 2021, 12, 620653. [Google Scholar] [CrossRef]
  39. Cordellieri, P.; Lausi, G.; Fraschetti, A.; Mari, E.; Burrai, J.; Quaglieri, A.; Paoli, E.; Giannini, A.M. Young Drivers ‘on the Phone: Personality Traits, Driving Attitudes, Beliefs, and Risk Perception on Using Mobile Phone While Driving. Psychol. Stud. 2022, 67, 459–467. [Google Scholar] [CrossRef]
  40. Schreiber, J.B. Core Reporting Practices in Structural Equation Modeling. Res. Soc. Adm. Pharm. 2008, 4, 83–97. [Google Scholar] [CrossRef]
  41. Curran, P.J.; West, S.G.; Finch, J.F. The robustness of test statistics to non-normality and specification error in confirmatory factor analysis. Psychol. Methods 1996, 1, 16–29. [Google Scholar] [CrossRef]
  42. Walker, A. A European Perspective on Quality of Life in Old Age. Eur. J. Ageing 2005, 2, 2–12. [Google Scholar] [CrossRef]
  43. United Nations, Department of Economic and Social Affairs. World Economic and Social Survey 2002; United Nations, Department of Economic and Social Affairs: New York, NY, USA, 2002. [Google Scholar]
  44. Blackman, S.; Matlo, C.; Bobrovitskiy, C.; Waldoch, A.; Fang, M.L.; Jackson, P.; Mihailidis, A.; Nygård, L.; Astell, A.; Sixsmith, A. Ambient Assisted Living Technologies for Aging Well: A Scoping Review. J. Intell. Syst. 2016, 25, 55–69. [Google Scholar] [CrossRef]
  45. Stein, M.B.; McAllister, T.W. Exploring the Convergence of Posttraumatic Stress Disorder and Mild Traumatic Brain Injury. Am. J. Psychiatry 2009, 166, 768–776. [Google Scholar] [CrossRef]
  46. Piccardi, L.; Matano, A.; D’Antuono, G.; Marin, D.; Ciurli, P.; Incoccia, C.; Verde, P.; Guariglia, P. Persistence of Gender Related-Effects on Visuo-Spatial and Verbal Working Memory in Right Brain-Damaged Patients. Front. Behav. Neurosci. 2016, 10, 139. [Google Scholar] [CrossRef]
  47. Reason, J.; Manstead, A.; Stradling, S.; Baxter, J.; Campbell, K. Errors and Violations on the Roads: A Real Distinction? Ergonomics 1990, 33, 1315–1332. [Google Scholar] [CrossRef]
  48. Byrnes, J.P.; Miller, D.C.; Schafer, W.D. Gender Differences in Risk Taking: A Meta-Analysis. Psychol. Bull. 1999, 125, 367–383. [Google Scholar] [CrossRef]
  49. Nori, R.; Palmiero, M.; Bocchi, A.; Giannini, A.M.; Piccardi, L. The Specific Role of Spatial Orientation Skills in Predicting Driving Behaviour. Transp. Res. Part F Traffic Psychol. Behav. 2020, 71, 259–271. [Google Scholar] [CrossRef]
  50. Lawton, C.A. Gender, Spatial Abilities, and Wayfinding. In Handbook of Gender Research in Psychology; Chrisler, J.C., McCreary, D.R., Eds.; Springer: New York, NY, USA, 2010; pp. 317–341. ISBN 978-1-4419-1464-4. [Google Scholar]
  51. Pallier, G.; Wilkinson, R.; Danthiir, V.; Kleitman, S.; Knezevic, G.; Stankov, L.; Roberts, R.D. The Role of Individual Differences in the Accuracy of Confidence Judgments. J. Gen. Psychol. 2002, 129, 257–299. [Google Scholar] [CrossRef]
  52. Taubman-Ben-Ari, O.; Skvirsky, V. The Multidimensional Driving Style Inventory a Decade Later: Review of the Literature and Re-Evaluation of the Scale. Accid. Anal. Prev. 2016, 93, 179–188. [Google Scholar] [CrossRef]
  53. Freuli, F.; De Cet, G.; Gastaldi, M.; Orsini, F.; Tagliabue, M.; Rossi, R.; Vidotto, G. Cross-Cultural Perspective of Driving Style in Young Adults: Psychometric Evaluation through the Analysis of the Multidimensional Driving Style Inventory. Transp. Res. Part F Traffic Psychol. Behav. 2020, 73, 425–432. [Google Scholar] [CrossRef]
  54. Piccardi, L.; Palmiero, M.; Guariglia, P.; Dacquino, C.; Cordellieri, P.; Giannini, A.M. Is the Risk Behaviour Related to the Ordinary Driving Violations? Psychol. Stud. 2021, 66, 26–35. [Google Scholar] [CrossRef]
  55. Romano, E.; Kelley-Baker, T.; Voas, R.B. Female Involvement in Fatal Crashes: Increasingly Riskier or Increasingly Exposed? Accid. Anal. Prev. 2008, 40, 1781–1788. [Google Scholar] [CrossRef]
  56. Scott-Parker, B.; Proffitt, C. Validation of the Behaviour of Young Novice Drivers Scale (BYNDS) in a New Zealand Young Driver Population. Accid. Anal. Prev. 2015, 77, 62–71. [Google Scholar] [CrossRef]
  57. Wohleber, R.W.; Matthews, G. Multiple Facets of Overconfidence: Implications for Driving Safety. Transp. Res. Part F Traffic Psychol. Behav. 2016, 43, 265–278. [Google Scholar] [CrossRef]
  58. Harbeck, E.L.; Glendon, A.I.; Hine, T.J. Young driver perceived risk and risky driving: A theoretical approach to the “fatal five”. Transp. Res. Part F Traffic Psychol. Behav. 2018, 58, 392–404. [Google Scholar] [CrossRef]
  59. Silva, A.F.; Cancela, J.M.; Mollinedo, I.; Camões, M.; Bezerra, P. The Relationship between Health Perception and Health Predictors among the Elderly across European Countries. Int. J. Environ. Res. Public Health 2021, 18, 4053. [Google Scholar] [CrossRef]
  60. Rodrigues, R.A.S.; Teodózio, M.M.; Espinosa, M.M.; Fett, W.C.R.; Melo, C.D.; Fett, C.A. Timed up and Go Test and Self-Perceived Health in Elderly: Population-Based Study. Braz. J. Kinanthropometry Hum. Perform. 2018, 20, 247–257. [Google Scholar] [CrossRef]
  61. Piccardi, L.; Pecchinenda, A.; Palmiero, M.; Giancola, M.; Boccia, M.; Giannini, A.M.; Guariglia, C. The Contribution of Being Physically Active to Successful Aging. Front. Hum. Neurosci. 2023, 17, 1274151. [Google Scholar] [CrossRef]
  62. Pecchinenda, A.; Yankouskaya, A.; Boccia, M.; Piccardi, L.; Guariglia, C.; Giannini, A.M. Exploring the Relationship between Perceived Loneliness and Subjective Cognitive Decline in Older Individuals. Aging Ment. Health 2024, 28, 73–82. [Google Scholar] [CrossRef]
  63. Horswill, M.S.; Marrington, S.A.; McCullough, C.M.; Wood, J.; Pachana, N.A.; McWilliam, J.; Raikos, M.K. The Hazard Perception Ability of Older Drivers. J. Gerontol. B Psychol. Sci. Soc. Sci. 2008, 63, P212–P218. [Google Scholar] [CrossRef]
  64. McKenna, F.P.; Horswill, M.S.; Alexander, J.L. Does Anticipation Training Affect Drivers’ Risk Taking? J. Exp. Psychol. Appl. 2006, 12, 1–10. [Google Scholar] [CrossRef] [PubMed]
  65. Ostan, R.; Monti, D.; Gueresi, P.; Bussolotto, M.; Franceschi, C.; Baggio, G. Gender, aging and longevity in humans: An update of an intriguing/neglected scenario paving the way to a gender-specific medicine. Clin. Sci. 2016, 130, 1711–1725. [Google Scholar] [CrossRef] [PubMed]
  66. Chapman, P.; Underwood, G.; Roberts, K. Visual Search Patterns in Trained and Untrained Novice Drivers. Transp. Res. Part F Traffic Psychol. Behav. 2002, 5, 157–167. [Google Scholar] [CrossRef]
  67. Nouchi, R.; Kobayashi, A.; Nouchi, H.; Kawashima, R. Newly Developed TV-Based Cognitive Training Games Improve Car Driving Skills, Cognitive Functions, and Mood in Healthy Older Adults: Evidence From a Randomized Controlled Trial. Front. Aging Neurosci. 2019, 11, 99. [Google Scholar] [CrossRef]
  68. Ball, K.K.; Berch, D.B.; Helmers, K.F.; Jobe, J.B.; Leveck, M.D.; Marsiske, M.; Morris, J.N.; Rebok, G.W.; Smith, D.M.; Tennstedt, S.L.; et al. Effects of cognitive training interventions with older adults: A randomized controlled trial. JAMA 2002, 288, 2271–2281. [Google Scholar] [CrossRef]
  69. Ball, K.K.; Ross, L.A.; Roth, D.L.; Edwards, J.D. Speed of processing training in the ACTIVE study: How much is needed and who benefits? J. Aging Health 2013, 25, 65S–84S. [Google Scholar] [CrossRef]
  70. Boot, W.R.; Stothart, C.; Charness, N. Improving the safety of aging road users: A mini-review. Gerontology 2014, 60, 90–96. [Google Scholar] [CrossRef]
  71. Casutt, G.; Martin, M.; Jäncke, L. Driving simulator training is associated with reduced inhibitory workload in older drivers. Geriatrics 2016, 1, 16. [Google Scholar] [CrossRef]
  72. Casutt, G.; Martin, M.; Keller, M.; Jäncke, L. The relation between performance in on-road driving, cognitive screening and driving simulator in older healthy drivers. Transp. Res. Part F Traffic Psychol. Behav. 2014, 22, 232–244. [Google Scholar] [CrossRef]
  73. Casutt, G.; Theill, N.; Martin, M.; Keller, M.; Jäncke, L. The drive-wise project: Driving simulator training increases real driving performance in healthy older drivers. Front. Aging Neurosci. 2014, 6, 85. [Google Scholar] [CrossRef]
  74. Cheng, Y.; Wu, W.; Feng, W.; Wang, J.; Chen, Y.; Shen, Y.; Li, Q.; Zhang, X.; Li, C. The effects of multi-domain versus single-domain cognitive training in non-demented older people: A randomized controlled trial. BMC Med. 2012, 10, 30. [Google Scholar] [CrossRef]
  75. Haeger, M.; Bock, O.; Memmert, D.; Hüttermann, S. Can driving-simulator training enhance visual attention, cognition and physical functioning in older adults? J. Aging Res. 2018, 2018, 7547631. [Google Scholar] [CrossRef]
  76. Hay, M.; Adam, N.; Bocca, M.-L.; Gabaude, C. Effectiveness of two cognitive training programs on the performance of older drivers with a cognitive self-assessment bias. Eur. Transp. Res. Rev. 2016, 8, 20. [Google Scholar] [CrossRef]
  77. Marottoli, R.A.; Allore, H.; Araujo, K.L.B.; Iannone, L.P.; Acampora, D.; Gottschalk, M.; Charpentier, P.; Kasl, S.; Peduzzi, P. A randomized trial of a physical conditioning program to enhance the driving performance of older persons. J. Gen. Intern. Med. 2007, 22, 590–597. [Google Scholar] [CrossRef]
  78. Swain, T.A.; Snyder, S.W.; McGwin, J.G.; Owsley, C. Associations of visual functions with attitudes about motor vehicle dashboards among older drivers. Front. Neuroergon. 2022, 3, 918781. [Google Scholar] [CrossRef]
  79. Nouchi, R.; Taki, Y.; Takeuchi, H.; Hashizume, H.; Akitsuki, Y.; Shigemune, Y.; Sekiguchi, A.; Kotozaki, Y.; Tsukiura, T.; Yomogida, Y.; et al. Brain training game improves executive functions and processing speed in the elderly: A randomized controlled trial. PLoS ONE 2012, 7, e29676. [Google Scholar] [CrossRef]
  80. Intini, P.; Colonna, P.; Olaussen Ryeng, E. Route Familiarity in Road Safety: A Literature Review and an Identification Proposal. Transp. Res. Part F Traffic Psychol. Behav. 2019, 62, 651–671. [Google Scholar] [CrossRef]
  81. Giancola, M.; Verde, P.; Cacciapuoti, L.; Angelino, G.; Piccardi, L.; Bocchi, A.; Palmiero, M.; Nori, R. Do Advanced Spatial Strategies Depend on the Number of Flight Hours? The Case of Military Pilots. Brain Sci. 2021, 11, 851. [Google Scholar] [CrossRef]
  82. Nori, R.; Piccardi, L. Familiarity and Spatial Cognitive Style: How Important Are They for Spatial Representation? In Spatial Memory: Visuospatial Processes, Cognitive Performance and Developmental Effects; Nova Science Pub Inc.: Hauppauge, NY, USA, 2010; ISBN 978-1-61668-139-5. [Google Scholar]
  83. Nori, R.; Zucchelli, M.M.; Palmiero, M.; Piccardi, L. Environmental Cognitive Load and Spatial Anxiety: What Matters in Navigation? J. Environ. Psychol. 2023, 88, 102032. [Google Scholar] [CrossRef]
  84. Clay, O.J.; Wadley, V.G.; Edwards, J.D.; Roth, D.L.; Roenker, D.L.; Ball, K.K. Cumulative meta-analysis of the relationship between useful field of view and driving performance in older adults: Current and future implications. Optom. Vis. Sci. 2005, 82, 724–731. [Google Scholar] [CrossRef]
  85. Dickerson, A.E.; Molnar, L.J.; Eby, D.W.; Adler, G.; Bédard, M.; Berg-Weger, M.; Classen, S.; Foley, D.; Horowitz, A.; Kerschner, H.; et al. Transportation and aging: A research agenda for advancing safe mobility. Gerontologist 2007, 47, 578–590. [Google Scholar] [CrossRef]
Geriatrics 09 00136 g001
Figure 1. Summary of the odds ratio analysis.
Figure 1. Summary of the odds ratio analysis.
Geriatrics 09 00136 g001
Table 1. Descriptive statistics of the research sample by gender.
Table 1. Descriptive statistics of the research sample by gender.
Females (N = 99)Males (N = 101)
Frequency (%)M (SD)Min-
Max		z-MeanFrequency (%)M (SD)Min-
Max		z-Mean
Age 70.454.14−0.03 70.644.610.01
Education (years) 11.974.050.12 11.043.81−0.11
Employment
Employed1 (1%) 3 (3%)
Retired99 (99%) 97 (97%)
Marital status
Married58 (57.40%) 80 (80.8%)
Divorced11 (10.9%) 8 (8.1%)
Widowed23 (22.8%) 7 (7.1%)
Single9 (8.9%) 4 (4.0%)
Years of holding driving license 46.775.29−0.08 48.617.110.17
Weekly car use 3.981.33−0.09 4.211.190.10
Driving risk behaviors 1.751.60−0.27 2.972.770.25
Driving accidents 0.961.23−0.29 1.861.770.24
Responsibility in accidents 0.310.60−0.21 0.610.880.17
Contributory negligence 0.160.42−0.15 0.300.540.15
Pedestrian risk behavior 2.101.48−0.24 3.002.250.22
Table 2. Inter-correlations among study variables.
Table 2. Inter-correlations among study variables.
1.2.3.4.5.6.7.8.9.
1. Age1−0.070.41 ** −0.20 * −0.11 −0.2 0.02 0.03 0.02
2. Education (years)−0.031 0.18−0.18 0.18 0.13 0.00−0.4 0.29 **
3. Years of holding driving license0.58 **0.23 *1−0.17−0.10 −0.08 −0.07 0.070.05
4. Weekly car use−0.01−0.07−0.0510.090.12 −0.04 0.22 * −0.05
5. Driving risk behaviors −0.020.150.17−0.0510.110.04 0.130.46 **
6. Driving accidents−0.040.24 *−0.020.040.0310.63 **0.46 ** 0.11
7. Responsibility in accidents0.110.0110.100.080.130.67 **10.25 *0.14
8. Contributory negligence0.04−0.06−0.030.030.120.38 **0.33 **10.06
9. Pedestrian risk behaviors0.25 *0.070.17−0.150.50 **0.21 **0.15−0.011
Note. Correlations for females (N = 101) are displayed above the diagonal; correlations for males (N = 99) are displayed below the diagonal. * p < 0.05 (two-tailed); ** p < 0.01 (two-tailed).
Table 3. Summary of the results of the odds ratio.
Table 3. Summary of the results of the odds ratio.
VariablesGroupsN z-Score < 1 *N z-Score > 1 **Odds Ratio95% CIp
Years of holding driving license Female9752.830.97–8.270.048
Male8913
Weekly car useFemale 9930.660.11–4.040.651
Male1002
Driving risk behaviorsFemale 9845.251.71–16.10.002
Male8418
Driving accidentsFemale 9755.031.82–13.9<0.001
Male8121
Responsibility in accidentsFemale 9872.711.07–6.860.030
Male8517
Contributory negligenceFemale 88142.261.11–4.630.023
Male7527
Pedestrian risk behaviorsFemale 9753.341.17–9.580.019
Male8715
Note. N =200. * Number of participants with a score below 1 z-score positive (84.1% of the sample; Low Risk); ** Number of participants with a score above 1 z-score positive (15.9% of the sample; High Risk).
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Cordellieri, P.; Piccardi, L.; Giancola, M.; Giannini, A.M.; Nori, R. On the Road Safety: Gender Differences in Risk-Taking Driving Behaviors Among Seniors Aged 65 and Older. Geriatrics 2024, 9, 136. https://doi.org/10.3390/geriatrics9050136

AMA Style

Cordellieri P, Piccardi L, Giancola M, Giannini AM, Nori R. On the Road Safety: Gender Differences in Risk-Taking Driving Behaviors Among Seniors Aged 65 and Older. Geriatrics. 2024; 9(5):136. https://doi.org/10.3390/geriatrics9050136

Chicago/Turabian Style

Cordellieri, Pierluigi, Laura Piccardi, Marco Giancola, Anna Maria Giannini, and Raffaella Nori. 2024. "On the Road Safety: Gender Differences in Risk-Taking Driving Behaviors Among Seniors Aged 65 and Older" Geriatrics 9, no. 5: 136. https://doi.org/10.3390/geriatrics9050136

APA Style

Cordellieri, P., Piccardi, L., Giancola, M., Giannini, A. M., & Nori, R. (2024). On the Road Safety: Gender Differences in Risk-Taking Driving Behaviors Among Seniors Aged 65 and Older. Geriatrics, 9(5), 136. https://doi.org/10.3390/geriatrics9050136

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