Driver Age Groups Research Articles

Driver Logo Sign Detection and Hazard Responses during Partially Automated Driving

This study investigates the presentation of service logo information under partially automated driving. Drivers completed simulated drives with partial automation during which they had to detect target logo signs and react to hazards by taking over vehicle control when needed. Driver performance was measured in terms of sign detection rate, crash rate, and hazard response time. A number of factors, including sign information source, sign information load, and driver age group, were investigated. In general, our findings support the delivery of service logo information via in-vehicle display under partially automated driving, especially when the in-vehicle display occurred simultaneously with the on-road signage. Under this presentation condition, drivers were most accurate in detecting target logo signs, and showed little impairment from processing sign information as a secondary task when negotiating a hazard. Implications of the findings and future directions were discussed.

Work-related factors, fatigue, risky behaviours and traffic accidents among taxi drivers: a comparative analysis among age groups

The main purpose of this study was to investigate the effect of work-related factors, fatigue, risky behaviours on accident involvement among different age groups of taxi drivers in China. A total of 2391 taxi drivers were selected to complete a self-reported questionnaire about their demographic data and information on working conditions, fatigue, risky behaviours, as well as involvement in traffic accidents between 2014 and 2016. The drivers were divided into three categories according to their age. Then, a set of comparative analyses and three structural equation models were used to analyze the samples of specific age groups. The results indicated that taxi drivers in the younger group rest the least with the most dissatisfaction with income while those in the mid-age group worked the longest time and were charged the most management fee, but the older taxi drivers more frequently engaged in risky behaviours and traffic accidents. Furthermore, two mediating chain processes were confirmed (i.e. ‘work-related factors – fatigue – accidents’ and ‘work-related factors – risky behaviours – accidents’) across the three age groups. However, the causes of fatigue, risky behaviours and accidents in different age groups are not exactly the same. These findings suggest that the regulation of the taxi industry should be carefully improved. Incentive policy and education aimed at taxi drivers may also hold promise.

Advanced vehicle technologies and road safety: A scoping review of the evidence

The proliferation of Advanced Vehicle Technologies (AVTs) has generated both excitement and concern among researchers, policymakers, and the general public. An increasing number of driver assistance systems are already available in today’s automobiles; many of which are expected to become standard. Therefore, synthesizing the available evidence specific to the safety of AVTs is critical. The goal of this scoping review was to summarize this evidence with a focus on AVTs that require some driver oversight (i.e., Levels 0–3 as per the Society of Automotive Engineers (SAE) levels of automation taxonomy).A scoping review of research literature on AVTs was conducted for studies up to March 2018. Inclusion criteria consisted of: any study with empirical data of AVTs that included male and female drivers aged 16 years and older, healthy people (i.e., without impairments), passenger vehicles, driving simulators and/or large databases with road safety information that could be analyzed for the purpose of examining AVTs (SAE Levels 0–3), as well as measures of driving outcomes.A total of 324 peer-reviewed studies from 25 countries met the inclusion criteria for this review with over half published in the last 5 years. Data was extracted and summarized according to the following categories: measures used to evaluate the effect of AVTs on road safety (objective) and driver perceptions of the technology (subjective), testing environment, and study populations (i.e., driver age). The most commonly reported objective measures were longitudinal control (50 %), reaction time (40 %), and lateral position (23 %). The most common subjective measures were perceptions of trust (27 %), workload (20 %), and satisfaction (17 %). While most studies investigated singular AVTs (237 of 324 studies), the number of studies after 2013 that examined 2 or more AVTs concurrently increased. Studies involved drivers from different age groups (51 %) and were conducted in driving simulators (70 %). Overall, the evidence is generally in favour of AVTs having a positive effect on driving safety, although the nature and design of studies varied widely.Our examination of this evidence highlights the opportunities as well as the challenges involved with investigating AVTs. Ensuring such technologies are congruent with the needs of drivers, particularly younger and older driver age groups, who are known to have a higher crash risk, is critical. With automotive manufacturers keen to adopt the latest AVTs, this scoping review highlights how testing of this technology has been undertaken, with a focus on how new research can be conducted to improve road safety now and in the future.

Age and road safety performance: Focusing on elderly and young drivers

The existing literature on young and elderly drivers indicates that they have the highest crash risks compared to other age groups of drivers. This study improves our understanding of the risk factors contributing to young and elderly drivers' elevated crash risk by examining self-report data from the E-Survey of Road User's Safety Attitudes (ESRA). The primary objective of this study is to compare the attitudes and behaviours of young, elderly, and middle-age drivers in Canada, the United States, and Europe. The main focus is on the practice of driving while distracted by mobile phones and driving while fatigued, as these are two dangerous behaviours that demonstrate the impact age may have. The analyses consistently showed that there are differences in the responses attributable to age. In all regions, drivers aged 18–21 years consistently reported higher rates of distracted and fatigued driving and higher rates of perceived social and personal acceptability of these behaviours than drivers aged 35–54 years. Elderly drivers aged 65+ years reported even lower rates of these behaviours and acceptability. Young drivers were also the least likely to believe that distraction and fatigue are frequent causes of road crashes, while elderly drivers were the most likely to believe this. This pattern with respect to age repeats in the support for policy measures as well; young drivers are least likely to support zero tolerance policies for mobile phone use when driving, while elderly drivers are the most likely to support this measure. Multivariate logistic regression modeling confirmed that elderly drivers were the least likely to engage in the use of mobile phones while driving or driving while fatigued. Statistically significant results showed that the middle-age group was less likely than young drivers to read a text message/email or check social media while driving and driving while fatigued.

Vehicle safety characteristics in vulnerable driver populations

Objective National data suggest drivers who are younger, older, and have lower socioeconomic status (SES) have heightened crash-related injury rates. Ensuring vulnerable drivers are in the safest vehicles they can afford is a promising approach to reducing crash injuries in these groups. However, we do not know the extent to which these drivers are disproportionately driving less safe vehicles. Our objective was to obtain population-based estimates of the prevalence of important vehicle safety criteria among a statewide population of drivers. Methods We analyzed data from the NJ Safety and Health Outcomes warehouse, which includes all licensing and crash data from 2010–2017. We borrowed the quasi-induced exposure method’s fundamental assumption—that non-responsible drivers in clean (i.e., only one responsible driver) multi-vehicle crashes are reasonably representative of drivers on the road—to estimate statewide prevalence of drivers’ vehicle characteristics across four driver age groups (17–20; 21–24; 25–64, and ≥65) and quintiles of census tract median household income (n = 983,372). We used NHTSA’s Product Information Catalog and Vehicle Listing platform (vPIC) to decode the VIN of each crash-involved vehicle to obtain model year, presence of electronic stability control (ESC), vehicle type, engine horsepower, and presence of front, side, and curtain air bags. Results The youngest and oldest drivers were more likely than middle-aged drivers to drive vehicles that were older, did not have ESC, and were not equipped with side airbags. Additionally, across all age groups drivers of higher SES were in newer and safer vehicles compared with those of lower SES. For example, young drivers living in lowest-income census tracts drove vehicles that were on average almost twice as old as young drivers living in highest-income tracts (median [IQR]: 11 years [6–14] vs. 6 years [3–11]). Conclusions Vehicle safety is an important component of seminal road safety philosophies that aim to reduce crash fatalities. However, driver groups that are overrepresented in fatal crashes—young drivers, older drivers, and those of lower SES—are also driving the less safe vehicles. Ensuring drivers are in the safest car they can afford should be further explored as an approach to reduce crash-related injuries among vulnerable populations.

Crash rates over time among younger and older drivers in the SHRP 2 naturalistic driving study

Objective: To examine crash rates over time among 16–17-year-old drivers compared to older drivers. Methods: Data were from a random sample of 854 of the 3,500 study participants in SHRP 2, a U.S. national, naturalistic driving (instrumented vehicle) study. Crashes/10,000 miles by driver age group, 3-month period, and sex were examined within generalized linear mixed models. Results: Analyses of individual differences between age cohorts indicated higher incidence rates in the 16–17-year old cohort relative to older age groups each of the first four quarters (except the first quarter compared to 18–20 year old drivers) with incident rate ratios (IRR) ranging from 1.98 to 18.90, and for the full study period compared with drivers 18–20 (IRR = 1.69, CI = 1.00, 2.86), 21 to 25 (IRR = 2.27, CI = 1.31, 3.91), and 35 to 55 (IRR = 4.00, CI = 2.28, 7.03). Within the 16–17-year old cohort no differences were found in rates among males and females and the decline in rates over the 24-month study period was not significant. Conclusions: The prolonged period of elevated crash rates suggests the need to enhance novice young driver prevention approaches such as Graduated Driver’s Licensing limits, parent restrictions, and post-licensure supervision and monitoring. Practical Applications: Increases are needed in Graduated Driver’s Licensing limits, parent restrictions, and postlicensure supervision and monitoring.

Do NHTSA vehicle safety ratings affect side impact crash outcomes?

Introduction: Side impact crash injuries tend to be severe, mainly due to the effects of the mechanism of such crashes. This study addresses the relationship between side impact crash injury severities and side impact safety ratings of the passenger cars involved in such crashes. It is motivated by the lack of research on side impact safety ratings in relation to the real-world crash outcomes. Method: Analysis of Crashworthiness Data System’s (CDS) data show the head and thorax are the most common regions of impact of severe injuries, while the neck is the least. Irrespective of body regions, higher-rated vehicles were found to provide better occupant protection to both younger and older driver age groups. Assessment based on injury severity score (ISS) indicates that higher-rated vehicles have an overall lower average ISS compared to lower-rated vehicles. Results: Ultimately, this study shows that vehicles rated with National Highway Traffic Safety Administration’s (NHTSA) new criteria had lower average ISS compared to vehicles rated under the old criteria. The 2011 NHTSA side impact rating criteria being relatively new, it has very few crashes to draw meaningful statistically significant conclusions. However, this paper establishes the fact that vehicles with higher star ratings (under experimental conditions) indeed offer increased occupant protection in the field conditions. Practical applications: Previous studies have found that safety was given priority while buying new vehicles. However, people associated vehicle safety with technologies and specific safety features rather than the vehicle’s crash test results or ratings (Koppel, Charlton, Fildes, & Fitzharris, 2008). The results from this study provide a point of reference for safety advocates to educate the drivers about the importance of considering vehicle safety ratings during a vehicle purchase.

A comparison between Artificial Neural Network and Hybrid Intelligent Genetic Algorithm in predicting the severity of fixed object crashes among elderly drivers

Run-off-road (ROR) crashes have always been a major concern as this type of crash is usually associated with a considerable number of serious injury and fatal crashes. A substantial portion of ROR fatalities occur in collisions with fixed objects at the roadside. Thus, this study seeks to investigate the severity of ROR crashes where elderly drivers, aged 65 years or more, hit a fixed object. The reason why the present study investigates this issue among older drivers is that, comparing to younger drivers, this age group of drivers have different psychological and physical features. Because of these differences, they are more likely to get injured in ROR types of crashes. This paper applies two types of Artificial Intelligence (AI) techniques, including hybrid Intelligent Genetic Algorithm and Artificial Neural Network (ANN) using the crashe information of California in 2012 obtained from Highway Safety Information System (HSIS) database. Although the results showed that the developed ANN outperformed the hybrid Intelligent Genetic Algorithm, the hybrid approach was more capable of predicting high-severity crashes. This is rooted in the way the hybrid model was trained by taking advantage of the Genetic Algorithm (GA). The results also indicated that the light condition has been the most significant parameter in evaluating the level of severity associated with fixed object crashes among elderly drivers, which is followed by the existence of the right and left shoulders. Following these three contributing factors, cause of collision, Average Annual Daily Traffic (AADT), number of involved vehicles, age, road surface condition, and gender have been identified as the most important variables in the developed ANN, respectively. This helps to identify gaps and improve public safety towards improving the overall highway safety situation of older drivers.

Use of Technology-Based Strategies for Older Driver Risk Mitigation

Older drivers present a unique challenge to transportation system throughout the world. A need for balancing mobility needs and safety issues for elderly drivers has been well documented. Past research has shown that older drivers are more involved in left turn crashes and crashes involving complex maneuvers, in comparison to other driver age groups. Recent integration of mobile technology solutions and increased familiarity of older driver population with technology, especially in younger cohorts, presents a unique opportunity to develop potential solutions. The objective of this research is to identify automated route finding that may be used by older drivers to identify “safest” routes through increased risk assessment and guidance on route choice, time of travel and required maneuvers at intersections, to reduce the overall crash representation of this driver population. This research is based on specific case study and it used the ArcGIS Network Analyst extension to develop a scoring for routes and optimize them based on minimizing exposure to turns while balancing route length. The results showed that it is feasible to identify safer routing choices for older drivers. These “safe” routes are also significantly differentiated from routes optimized by travel time alone. Moreover, safe routing may be quantified easily from readily available information sources, such as by limiting or eliminating the need to make a left turn.

Context aware ontology‐based hybrid intelligent framework for vehicle driver categorization

AbstractIn public vehicles, one of the major concerns is driver's level of expertise for its direct proportionality to safety of passengers. Hence, before a driver is subjected to certain type of vehicle, he should be thoroughly evaluated and categorized with respect to certain parameters instead of only one‐time metric of having driving license. These aspects may be driver's expertise, vigilance, aptitude, experience years, cognition, driving style, formal education, terrain, region, minor violations, major accidents, and age group. The purpose of this categorization is to ascertain suitability of a driver for certain vehicle type(s) to ensure passengers' safety. Currently, no driver categorization technique fully comprehends the implicit as well as explicit characteristics of drivers dynamically. In this paper, machine learning–based dynamic and adaptive technique named D‐CHAITs (driver categorization through hybrid of artificial intelligence techniques) is proposed for driver categorization with an objective focus on driver's attributes modeled in DriverOntology. A supervised mode of learning has been employed on a labeled dataset, having diverse profiles of drivers with attributes pertinent to drivers' perspectives of demographics, behaviors, expertise, and inclinations. A comparative analysis of D‐CHAIT with three other machine learning techniques (fuzzy logic, case‐based reasoning, and artificial neural networks) is also presented. The efficacy of all techniques was empirically measured while categorizing the drivers based on their profiles through metrics of accuracy, precision, recall, F‐measure performance, and associated costs. These empirical quantifications assert D‐CHAIT as a better technique than contemporary ones. The novelty of proposed technique is signified through preprocessing of feature attributes, quality of data, training of machine learning model on more relevant data, and adaptivity.

Driver Parking Behaviour: An Observational and Experimental Intervention Study

Illegal parking has become a major problem in Malaysia especially in urban area due to the increased traffic volumes and limited parking space. When the parking facilities are limited, driver factor thus has a significant effect on the formation of traffic congestion. Extensive work has been conducted to address this issue, however most studies did not incorporate the impact of driver behaviour at limited parking condition whereas it is understood that “cruising for parking” at limited parking facilities has a very strong influence on the road traffic jams. Thus, objectives of this study are to analyse drivers’ personality traits which focus on consideration and selfishness traits; and to investigate drivers’ personality traits and their impact on driver parking behaviour in Kuching, Sarawak. Analyzation of driver parking behaviour was based on observational study of driver, vehicle, and situational variables. Experimental intervention then conducted to different age groups of drivers. They were asked to respond to questions that related to the driver parking behaviour characteristic. Analyses of the data reported in this study revealed that most of the drivers are considerate enough but they become a selfish person when confront with difficult situation at the parking space and their action sometimes are susceptible to illegal parking. The findings significantly beneficial to the traffic engineer, policy maker and researcher in enhancing parking control and management system.

Identifying Fatality Risk Factors for the Commercial Vehicle Driver Population

Commercial/large-truck fatal crash involvement by drivers of different age groups is a critical issue for the trucking industry. Escalating safety concerns related to these heavy vehicles serving the freight economy in the U.S. have an impact national freight reliability and economic growth. This study identifies major contributing factors leading to large-truck fatal crashes for four age groups of driver: <30, 30–49, 50–65, and 65+. The analysis in this study is based on five years (2012–2016) of Fatality Analysis Reporting System data and provides an overall picture of risk factors in large-truck fatal crashes. In total, 30 variables were found to be significant in the logit models, indicating varying risks associated with large-truck drivers of these four age groups. Model results indicate different risk factors associated with driver characteristics, spatial and temporal characteristics, vehicle and vehicle maneuvering characteristics, and environmental conditions at the time of the crashes. Identifying the risk factors for different age groups of drivers is important so proper countermeasures can be implemented from the perspective of human factors (e.g., safe speed choice, fatigue), roadway engineering (e.g., design of roadside barriers, radius of ramps), enforcement (e.g., presence of law enforcement personnel at critical locations), and emergency medical attention in remote areas. Considering the aging of the truck driver population in the U.S. and around the world, the findings of this study are vital to understand better the importance of safety in relation to large-truck fatal crashes.

Development of Model for Estimation of Age Group of Drivers by using Soft Computing Tools

Development of Model for Estimation of Age Group of Drivers by using Soft Computing Tools

Prevalence of drug use among drivers based on mandatory, random tests in a roadside survey.

BackgroundIn the context of road safety, this study aims to examine the prevalence of drug use in a random sample of drivers.MethodsA stratified probabilistic sample was designed to represent vehicles circulating on non-urban roads. Random drug tests were performed during autumn 2014 on 521 drivers in Catalonia (Spain). Participation was mandatory. The prevalence of drug driving for cannabis, methamphetamines, amphetamines, cocaine, opiates and benzodiazepines was assessed.ResultsThe overall prevalence of drug use is 16.4% (95% CI: 13.9; 18.9) and affects primarily younger male drivers. Drug use is similarly prevalent during weekdays and on weekends, but increases with the number of occupants. The likelihood of being positive for methamphetamines is significantly higher for drivers of vans and lorries.ConclusionsDifferent patterns of use are detected depending on the drug considered. Preventive drug tests should not only be conducted on weekends and at night-time, and need to be reinforced for drivers of commercial vehicles. Active educational campaigns should focus on the youngest age-group of male drivers.

Generalized Estimating Equation Model Based Recursive Partitioning: Application to Distracted Driving

Traditional statistical methods have used a coarse aggregation of data across subjects that may not be representative of any single individual. Even though Generalized Estimating Equations procedure extends generalized linear model to allow for analysis of repeated measurements or other correlated observations, the nonlinear relationship between independent variables and dependent variable could significantly hinder the model’s performance. In this study, we propose Generalized Estimating Equation based tree model that combines the advantages of both models by separating the data set recursively into subsets with significantly different parameter estimates. For the best application of the proposed model, distracted driving on intersection is analyzed in this study. Previous studies have focused on evaluating the singular effect of individual geometry and human characteristic variables on driving behaviors. Interactions between variables associated with red-light running (e.g., cell phone usage, cell phone interface, and driver age groups) present different levels of distraction on red-light running. As an indicator of the sensitivity to distractions (referring to the distance being an impairment due to a secondary task), the drivers’ distance to the intersection onset of yellow interval is partitioned into two groups (i.e., close and far distance) that maximally differentiate the distraction behavior. Proposed cell phone impact zones produce more significant impacts of distraction on red-light running, compared against dilemma zone. We identify interactions that are sensitive to red-light running and different as a function of the level of the speed and yellow interval duration. Drivers are more vulnerable to cell phone distractions when their location is near the stop line of the intersection at onset of yellow interval.

Design characteristics of a workload manager to aid drivers in safety–critical situations

The objective of this study was to evaluate a workload manager designed to supervise the presentation of in-vehicle information for two age groups of drivers during safety–critical situations. The benefits of a workload manager were compared in various dual-task conditions involving a preceding or a concurrent in-vehicle alert during critical traffic situations. Objective measures such as drivers’ brake response times and secondary task response times as well as subjective measures of driver workload were used. Although older drivers performed worse in the dual-task scenario with longer response times and poorer performance on the secondary task in comparison to the younger drivers, results indicated that both age groups benefited from the implementation of a workload manager. There was a consistent trend of improved driving and secondary task performance when the workload manager delayed non-critical information during safety–critical situations, indicating benefits for some otherwise distracted drivers. Implications for the design of a workload manager are discussed.

Cost benefit study of a safety campaign’s impact on road safety

The aim of this paper is to identify a break in the development trend of the time series of the number of fatal, light and heavy injuries in traffic accidents and compare the progress caused by the media campaign named “Think or you’ll pay! “in the Czech Republic over the period 2000–2015. The campaign focuses on the age group of drivers under the age of 25 and the most common cause of their traffic accidents as the drivers in this age category are the most vulnerable group in road traffic. The campaign uses a method in which it tries to influence behaviour by negative action, or by causing negative emotions.The authors concentrate on the effects of mass media campaigns in the long-term development of accidents in the Czech Republic and a financial evaluation of the road safety campaign “Think or you’ll pay! “by comparing the campaign costs, the cost of road fatalities, and the cost savings from the perspective of government expenditures.The secondary source data for the chart analysis and interpolation according to the criteria of analytical and mechanical balancing time series, the Chow test and Quandt Likelihood Ratio test, choosing the appropriate model trend of accidents and consequences of traffic accidents were obtained from the Czech Ministry of Transport, the database of The Losses due to Traffic Accident Rates (CZRSO) and the Czech Association of Victims of Traffic Accidents (CSODN, 2015) from period of 1990 till 2016.The impact of the media campaign “Think or you’ll pay!“, measured by enumerating the costs was compared with the number of fatalities in the years immediately after the campaign and the impact of the media campaign was evaluated and recognised. The conclusion and the highlights summarize the findings of research and the limits of media campaign evaluation approach.

Driver speed selection and crash risk: Insights from the naturalistic driving study

IntroductionThis study investigates how speed limits affect driver speed selection, as well as the related crash risk, while controlling for various confounding factors such as traffic volumes and roadway geometry. Data from a naturalistic driving study are used to examine how driver speed selection varies among freeways with different posted speed limits, as well as how the likelihood of crash/near-crash events change with respect to mean speed and standard deviation. MethodRegression models are estimated to assess three measures of interest: the average speed of vehicles during the time preceding crash/near-crash and baseline (i.e., normal) driving events; the variation in travel speeds leading up to each event as quantified by the standard deviation in speeds over this period; and the probability of a specific event resulting in a crash/near-crash based on speed selection and other factors. ResultsSpeeds were relatively stable across levels-of-service A and B, within a range of 1.5mph on average. Speeds were marginally lower (3.3mph) on freeways posted at 65mph versus 70mph. In comparison, speeds were approximately 10.2 to 13.4mph lower on facilities posted at 55mph or 60mph. Speeds were shown to be 2.5mph lower in rainy weather and 11mph lower under snow or sleet. ConclusionsSignificant correlation was observed with respect to speed selection behavior among the same individuals. Mean speeds are shown to increase with speed limits. However, these increases are less pronounced at higher speed limits. Drivers tend to reduce their travel speeds in presence of junctions and work zones, under adverse weather conditions, and particularly under heavy congestion. Crash risk increased with the standard deviation in speed, as well as on vertical curves and ramp junctions, and among the youngest and oldest age groups of drivers.

Driver behavior analysis for right-turn drivers at signalized intersections using SHRP 2 naturalistic driving study data

IntroductionUnderstanding driver behavior is important for traffic safety and operation, especially at intersections where different traffic movements conflict. While most driver-behavior studies are based on simulation, this paper documents the analysis of driver-behavior at signalized intersections with the SHRP 2 Naturalistic Driving Study (NDS) data. This study analyzes the different influencing factors on the operation (speed control) and observation of right-turn drivers. MethodA total of 300 NDS trips at six signalized intersections were used, including the NDS time-series sensor data, the forward videos and driver face videos. Different factors of drivers, vehicles, roads and environments were studied for their influence on driver behavior. An influencing index function was developed and the index was calculated for each influencing factor to quantitatively describe its influencing level. The influencing index was applied to prioritize the factors, which facilitates development and selection of safety countermeasures to improve intersection safety. Drivers' speed control was analyzed under different conditions with consideration of the prioritized influencing factors. ResultsVehicle type, traffic signal status, conflicting traffic, conflicting pedestrian and driver age group were identified as the five major influencing factors on driver observation. ConclusionsThis research revealed that drivers have high acceleration and low observation frequency under Right-Turn-On-Red (RTOR), which constituted potential danger for other roadway users, especially for pedestrians. Practical applicationsAs speed has a direct influence on crash rates and severities, the revealed speed patterns of the different situations also benefit selection of safety countermeasures at signalized intersections.

Road Safety for an Aged Society: Compliance with Traffic Regulations, Knowledge About Traffic Regulations, and Risk Factors of Older Drivers

This study investigated compliance of traffic regulations, knowledge about traffic regulations, and risk factors, including road geometrics and conditions and other drivers and road users, of older drivers (65+) compared with younger drivers (middle-aged drivers ages 35 to 64 and young drivers ages 19 to 34) on the basis of a self-reporting survey of active drivers in Seoul, South Korea. The analysis included 978 survey samples. The results showed that older drivers in general had patterns of compliance and knowledge of traffic regulations and driving risk factors similar to those of younger drivers. Stopping at stop signs was reported as the least complied traffic regulation for older (71.9%), middle-aged (78.2%), and young (74.6%) drivers. Furthermore, less than 15% of drivers in all age groups failed to demonstrate correct understanding on a flashing red light. However, this study found some statistically significant differences between driver age groups in compliance to traffic regulations, knowledge about traffic regulations, and risk factors. Older drivers were less likely than younger drivers to use cell phones and digital multimedia broadcasting devices while driving and to know the right-of-way associated with the order of access at uncontrolled intersections. In addition, older drivers were more likely to report small radii of intersection curbs as a driving risk factor. These findings have substantive implications in promoting older drivers’ safety in the areas of intersection design, road management, and safety education in an aged society.