Driver Speed Research Articles

The Effects of Temporary Portable Rumble Strips on Vehicle Speeds in Road Work Zones

The safety of construction and maintenance work zones has been highlighted as a crucial aspect of construction management that requires special attention due to the increasing number of fatal and non-fatal injuries in recent years. Temporary traffic control (TTC) is required by the Occupational Safety and Health Administration (OSHA) to improve overall safety performance during road construction and maintenance projects. The fact that speeding and distracted drivers may overlook TTC warning signs and directions has been reported as one of the leading causes of work zone incidents. This study aimed to examine both the impact of temporary portable rumble strips (TPRSs) on traffic speeds and the response of different vehicle types in road work zones, including trucks and cars. Accordingly, field experiments were conducted in collaboration with the Road Commission for Oakland County (RCOC) in Michigan. The findings indicate that TPRSs have a statistically significant impact on the driving speed of light vehicle drivers but not on medium and heavy vehicles, such as trucks. This study contributes to the existing literature by quantifying the safety benefits of TPRS use, providing valuable data for policymakers and construction professionals. By demonstrating the effectiveness of TPRSs in reducing the speed of light vehicles, this research supports the implementation of these systems as a practical measure for enhancing safety within road construction work zones. Additionally, this study highlights the need for tailored approaches to address the limited impact on larger vehicles, underscoring the importance of developing complementary strategies to ensure comprehensive safety improvements across all vehicle types.

Bifurcation control and analysis of traffic flow model based on driver prediction effect.

Based on the measured data, in this paper, we find that there are significant differences in the ideal speed of drivers with different attributes during their driving process. Through wavelet analysis, it is proved that the regularity of the car-following behavior of the driver is poor in a short time, and it cannot be predicted and analyzed at a small time scale. As one of the important participants in the traffic system, it is necessary to consider the driving behavior factors in the traffic flow model. Therefore, in this paper, we propose a nonuniform continuous traffic flow model. Based on the difference in the expected headway of the driver, the model considers the self-stabilization effect of the prediction time of the driver on the optimal speed difference and considers control theory to further improve the model. Through this model, the bifurcation theory can be applied to the stability analysis of the traffic system to study the stability mutation behavior of the traffic system at the bifurcation point. Through linear and nonlinear analysis methods, the stability conditions of the model can be derived, and the type of equilibrium point of the model can be judged. In addition, the random function is applied to the traffic model, and the bifurcation control is carried out for the bifurcation behavior in the traffic; that is, the bifurcation characteristics of the traffic system are changed by designing linear and nonlinear random feedback controllers. In this paper, we theoretically prove the existence conditions and types of Hopf bifurcation at the equilibrium point of the model and analyze the internal causes of the stability mutation of the traffic flow through the Hopf bifurcation point. Then a feedback controller is designed to control the amplitude of the Hopf bifurcation and the limit cycle formed by the Hopf bifurcation. Finally, the theoretical derivation results are verified by experimental numerical simulation. In this paper, we show that, by adjusting the control parameters of the feedback controller, the Hopf bifurcation of the stochastic system can be delayed or even eliminated, and the amplitude of the limit cycle formed by the Hopf bifurcation can be adjusted to achieve the purpose of controlling the stability of the traffic system and prevent or alleviate traffic congestion.

Evaluation of Selected Factors Affecting the Speed of Drivers at Signal-Controlled Intersections in Poland

In traffic engineering, vehicle speed is a critical determinant of both the risk and severity of road crashes, a fact that holds particularly important for signalized intersections. Accurately selecting vehicle speeds is crucial not only for minimizing accident risks but also for ensuring the proper calculation of intergreen times, which directly influences the efficiency and safety of traffic flow. Traditionally, the design of signal programs relies on fixed speed parameters, such as the posted speed limit or the operational speed, typically represented by the 85th percentile speed from speed distribution data. Furthermore, many design guidelines allow for the selection of these critical speed values based on the designer’s own experience. However, such practices may lead to discrepancies in intergreen time calculations, potentially compromising safety and efficiency at intersections. Our research underscores the substantial variability in the speeds of passenger vehicles traveling intersections under free-flow conditions. This study encompassed numerous intersections with the highest number of accidents, using unmanned aerial vehicles to conduct surveys in three Polish cities: Toruń, Bydgoszcz, and Warsaw. The captured video footage of vehicle movements at predetermined measurement sections was analyzed to find appropriate speeds for various travel maneuvers through these sections, encompassing straight-through, left-turn, and right-turn relations. Our analysis focused on how specific infrastructure-related factors influence driver behavior. The following were evaluated: intersection type, traffic organization, approach lane width, number of lanes, longitudinal road gradient, trams or pedestrian or bicycle crossing presence, and even roadside obstacles such as buildings, barriers or trees, and others. The results reveal that these factors significantly affect drivers’ speed choices, particularly in turning maneuvers. Furthermore, it was observed that the average speeds chosen by drivers at signalized intersections did not reach the permissible speed limit of 50 km/h as established in typical Polish urban areas. A key outcome of our analysis is the recommendation for a more precise speed model that contributes to the design of signal programs, enhancing road safety, and aligning with sustainable transport development policies. Based on our statistical analyses, we propose adopting a more sophisticated model to determine actual vehicle speeds more accurately. It was proved that, using the developed model, the results of calculating the intergreen times are statistically significantly higher. This recommendation is particularly pertinent to the design of signal programs. Furthermore, by improving speed accuracy values in intergreen calculation models with a clear impact on increasing road safety, we anticipate reductions in operational costs for the transportation system, which will contribute to both economic and environmental goals.

Endogeneity of pedestrian survival time and emergency medical service response time: Variations across disadvantaged and non-disadvantaged communities

The Vision Zero-Safe Systems Approach prioritizes fast access to Emergency Medical Services (EMS) to improve the survivability of road users in transportation crashes, especially concerning the recent increase in pedestrian-involved crashes. Pedestrian crashes resulting in immediate or early death are considerably more severe than those taking longer. The time gap between injury and fatality is known as survival time, and it heavily relies on EMS response time. The characteristics of the crash location may be associated with EMS response and survival time. A US Department of Transportation initiative identifies communities often facing challenges. Six disadvantaged community (DAC) indicators, including economy, environment, equity, health, resilience, and transportation access, enable an analysis of how survival and EMS response times vary across DACs and non-DACs. To this end, this study created a unique and comprehensive database by linking DACs data with 2017–2021 pedestrian-involved fatal crashes. This study utilizes two-stage residual inclusion models with segmentation for DACs and non-DACs accounting for the endogenous relationship between EMS response and pedestrian survival time. The results indicate that EMS response time is higher and pedestrian survival time is lower in DACs than in non-DACs. A delayed EMS response time is associated with a greater reduction in survival time in DACs compared to non-DACs. Factors, e.g., nighttime and interstate crashes, contribute to higher EMS response time, while pedestrian drugs, driver speeding, and hit-and-run behaviors are associated with a greater reduction in survival time in DACs than non-DACs. The implications of the findings are discussed in the paper.

Impact of Median Separation Facilities on Drivers’ Shy-Away Effects: Why Are Median Separation Facilities Widely Used in China?

In recent years, China has begun introducing readily installable median separation facilities into its urban road network. Although some studies have recognized the benefits of using median separation, few have investigated the effects of different types of facilities on driver behavior. This driving simulator study aims to determine whether different types of medians on urban arterial roads in China affect driving behavior, and to provide new insights for designing more effective and safer forms of median separation. A driving simulator study was conducted, with 50 participants. The scenario exactly reproduced an existing six-lane urban arterial road in China, and four different median separation types were tested: (1) double yellow line; (2) raised pavement markers; (3) reboundable traffic cylinders; (4) traffic separator railing. Their effects were statistically analyzed using data on driver eye movements, trajectories, and speeds collected from simulation tests. The results showed that the median separation facilities significantly improved drivers’ access to road information, corrected drivers’ attention, and suppressed driving speeds. Although median separation facilities can be an effective solution to the current problem of chaotic vehicular traffic on China’s urban roads, their setup still needs to be optimized. This study demonstrates the calculation method for optimal median facility setup. The resulting 1.21-m-high median facility, paired with a 1.17-m-wide median strip and a certain lane-width compression, more effectively addresses driver factors, the driving task, and the spatial right-of-way. The study’s calculations can also be used to improve other types of roads that can take a median separation facility.

Design of Multimodal In-Vehicle Notifications at Highway-Rail Grade Crossings: A Perception Study

Up to 67% of accidents at Highway-Rail Grade Crossings (HRGCs) are due to motorists not stopping in time. To reduce the likelihood of vehicle-train accidents, the current study investigated the design of five novel visual and auditory in-vehicle notifications. An “Inform” notification was tested for “display time.” Notifications, “Slow,” “Intersection,” and “Stop” were tested for “display time” and “speech length”; a notification, “Over Tracks” was tested for “speech length” and “presentation order.” Twenty-six participants viewed driving simulator recordings, and rated the notifications at active rail crossing scenarios using Likert scales. The results showed that shorter speech length was preferred with delayed notification displays, and longer speech length was preferred with early notification displays. However, complex scenarios might require longer speech even with delayed displays, depending on additional variables such as driver speed or visibility. Additionally, the tradeoff between notifications being too urgent and startling should also be considered in safety-critical scenarios.

Understanding the influence of environmental factors on driver speed: A structural equation modeling analysis

This study examined the influential roadway and contextual factors affecting drivers' speed selection through a comprehensive investigation employing structural equation modeling. Roadway and contextual variables including road curvature, presence of roundabouts, adverse weather conditions, and access were investigated using infrared speed sensors. The analysis revealed that specific roadway and contextual factors such as field variables, uphill and downhill road inclines, particular road curvature, and rainy weather significantly influence drivers' chosen speeds, while factors such as road access, nighttime conditions, larger road curvature, and signage exhibit a lesser impact. Notably, the study also found a positive correlation between road curvature radius and driver-chosen speed. The study's implications for transportation infrastructure planning and road safety interventions are underscored, with potential applications in road design, signage improvement, and weather-responsive measures to regulate driver speed choices in specific roadway and contextual conditions.

Model of The Distance Between The Readability of Word Messages on Conventional Signs and Electronic Signs

Traffic signs are divided into conventional signs and electronic signs. The message conveyed is in the form of a pigtogram and/or electronic message. (Budiati, 2014) compared the legibility distance of letter dimensions on conventional signs, it was found that H=1.032+0.273D with H being the height of the letter and D being the reading distance. (Budiati, 2023), examined the relationship between the driver's reading speed and the number of words and lines in the Variable Message Sigh. This research produces a reading speed of 1.37 words/second. The research was continued to obtain a modeling of the readability distance for conventional and electronic word message signs with the dependent variable (Y) reading distance. Data on reading distance and reading time in the field were analyzed using the SPSS v.23 approach, and the obtained was Y= 2.990 (X1) + 0.009(X2) + 0.001(X3) for conventional signs Y = 3.557 (X1) + 0.440 (X2) + 0.004 (X3), while Based on collineatity the modeling value of reading distance (2) versus reading distance 1 is as follows: Y = 5.463 + 0.513 (X1) + 0.10 (X2) + 0.009 (X3) + 0.004.

Deep learning approach for unified recognition of driver speed and lateral intentions using naturalistic driving data

Driver intention recognition is a critical component of advanced driver assistance systems, with significant implications for improving vehicle safety, intelligence, and fuel economy. However, previous research on driver intention recognition has not fully considered the influence of the driving environment on speed intentions and has not exploited the temporal dependency inherent in the lateral intentions to prevent erroneous changes in recognition. Furthermore, the coupling of speed and lateral intentions was overlooked; they were generally considered separately. To address these limitations, a unified recognition approach for speed and lateral intentions based on deep learning is presented in this study. First, extensive naturalistic driving data are collected, and information related to road slope and driving trajectories is extracted. A comprehensive classification of driver intentions is then performed. Toeplitz inverse covariance-based clustering and trajectory clustering methods are applied separately to label speed and lateral intentions, so that the influence of driving environments and the coupling of speed and lateral intentions are integrated into intention recognition. Finally, a deep-learning-based unified recognition model for driver intention is developed. This model uses a hierarchical recognition approach for speed intentions and includes a double-layer networks architecture with long short-term memory for the recognition of lateral intention. The validation results show that the created driver intention recognition model can accurately and stably recognize both speed and lateral intentions in complex driving environments.

Factors affecting crash severity in Roundabouts: A comprehensive analysis in the Jordanian context

Roundabouts are widely embraced for their perceived safety advantages over other types of unsignalized intersections. However, there has been an observed increase in crash rates at roundabouts over time in Jordan. This paper delves into modeling traffic crash severity at roundabouts, considering various factors such as weather, lighting, vehicle characteristics, geometric features, and driver age and gender. To comprehensively analyze roundabout crashes in Jordan, we constructed rule-based classifiers and Random Forest models after balancing the dataset. Rule-based models offer interpretability, albeit with some simplicity trade-off, while Random Forest models provide deeper analysis but require additional explanation. Presenting both outputs to subject matter experts and policymakers facilitates a holistic understanding of factors contributing to roundabout crashes in Jordan. Subsequently, CN2 results revealed that injury severity crashes are influenced by the time of the day, driver age, day of the week, speed, and number of vehicles involved. On the other hand, property damage-only crashes are affected by the number of lanes, time of the day, type of driver fault, lighting conditions, speed, and day of the week. The RF model analysis unveiled crucial factors influencing crash severity in roundabouts, notably the varying impact of driver age, time of day, the number of vehicles involved, seasonality, and vehicle speed. This proposed approach is promising, comprehensive, and not only enhances the understanding of roundabout crashes but also informs the development of effective, localized safety interventions.

Design and implementation of an innovative single-phase direct AC-AC bipolar voltage buck converter with enhanced control topology

Direct AC–AC converters are strong candidates in the power converting system to regulate grid voltage against the perturbation in the line voltage and to acquire frequency regulation at discrete step levels in variable speed drivers for industrial systems. All such applications require the inverted and non-inverted form of the input voltage across the output with voltage-regulating capabilities. The required value of the output frequency is gained with the proper arrangement of the number of positive and negative pulses of the input voltage across the output terminals. The period of each such pulse for low-frequency operation is almost the same as the half period of the input grid or utility voltage. These output pulses are generated by converting the positive and negative input half cycles in noninverting and inverting forms as per requirement. There is no control complication to generate control signals used to adjust the load frequency as the operating period of the switching devices is normally greater than the period of the source voltage. However, high-frequency pulse width modulated (PWM) control signals are used to regulate the output voltage. The size of the inductor and capacitor is inversely related to the value of the switching frequency. Similarly, the ripple contents of voltage and currents in these filtering components are also inversely linked with PWM frequency. These constraints motivate the circuit designer to select high PWM frequency. However, the alignment of the high-frequency control input with the variation in the input source voltage is a big challenge for a design engineer as the switching period of a high-frequency signal normally lies in the microsecond. It is also required to operate some high-frequency devices for various half cycles of the source voltage, creating control complications as the polarities of the half cycles are continuously changing. This requires at least the generation of two high-frequency signals for different intervals. The interruption of the filtering inductor current is a big source of high voltage surges in circuits where the high-frequency transistors operate in a complementary way. This may be due to internal defects in the switching transistors or some unnecessary inherent delay in their control signals. In this research work, a simplified AC–AC converter is developed that does not need alignment of high-frequency control with the polarity of the source voltage. With this approach, high-frequency signals can be generated with the help of any analog or digital control system. By applying this technique, only one high-frequency control signal is generated and applied in AC circuits, as in a DC converter, without applying a highly sensitive polarity sensing circuit. So, controlling complications is drastically simplified. The circuit and configuration always avoid the current interruption problem of filtering the inductor. The proposed control and circuit topology are tested both in computer-based simulation and practically developed circuits. The results obtained from these platforms endorse the effectiveness and validation of the proposed work.

Impact of freeway slope photovoltaic panels on drivers: A study based on driving simulator

Aiming at the impact of freeway slope photovoltaic construction on driving safety, a driving simulator experiment was carried out. By designing eight slope photovoltaic schemes under varying sunlight conditions and road alignment parameters, we recruited 35 drivers to collect detailed driving behavior data. Seven driving indicators were selected to develop an evaluation index system. Statistical analysis was conducted to compare driver behavioral performance under eight slope photovoltaic schemes. The findings reveal that the installation of photovoltaic panels on freeway slopes significantly affects driving behavior. Specifically, the driver's speed and degree of steering wheel angle decreased significantly, while acceleration increased significantly on straight alignment. The standard deviation of driver speed, acceleration and standard deviation of acceleration significantly reduced, while the degree of steering wheel angle and the depth of the gas pedal significantly increased when photovoltaic panels were laid on the side slopes on curved road sections. In addition, different curve radius, the impact of photovoltaic panels on acceleration and degree of steering wheel turning angle were different. There is a significant effect of installing side slope photovoltaic panels on driver acceleration, standard deviation of acceleration and degree of steering wheel turning angle in curved sections with different curve radius. Under different sunlight conditions, the impact of photovoltaic panels on the degree of steering wheel angle were also different. The actual setup needs to take into account the impact of different road alignments on drivers. The is study provided theoretical support for the installation of freeway slope photovoltaic panels.

Influence of Curb Return and Crosswalk Setback on Turning Driver Decision-Making and Speed Selection

Influence of Curb Return and Crosswalk Setback on Turning Driver Decision-Making and Speed Selection

An Alternative to the Basic Causal Requirement for Liability Under the Negligence Rule

Abstract The primary causal requirement that must be met for a negligent party to be held liable for a harm is a demonstration that the harm would not have occurred if the party had not been negligent. Thus, for a speeding driver to bear liability for harm done in a car accident, it must be shown that the accident would not have happened if the driver had driven at a reasonable speed. The main point made here is that this basic causal requirement may be difficult or impossible to satisfy and hence may interfere with the discouragement of negligence. Therefore, an alternative and usually easier-to-meet causal requirement is proposed – that the harm would not have occurred if the negligent party had not been engaged in his activity (if the driver had not been engaged in driving).

Impact of Speed Limit Enforcement Cameras on Speed Behavior: Naturalistic Evidence from Brazil

Speeding is widely recognized as a key contributor to the occurrence and severity of road crashes, making studies on speed reduction devices particularly relevant given poor road safety outcomes worldwide. This study investigates the impact of fixed speed cameras on driver behavior and speed reduction in urban arterials using a naturalistic driving study methodology. Data from 13 drivers and 116 trips in Curitiba, Brazil, were analyzed, with a focus on speed cameras placed on arterial roads. Speed data were grouped and analyzed by various categories, including topographic profile, day and week periods, and rain conditions. Mean comparisons were used to compare data sets, revealing an overall speed reduction effect of 0.69 km/h (−1.33%) around the speed camera. The study identified a pattern of punctual speed reduction, known as a “kangaroo jump,” a speed reduction followed by an increase in speed, referred to as the “compensation effect,” and a new pattern characterized by a non-significant speed reduction at the speed camera site followed by an increase in speed, referred to as the “cobra strike effect” because of its curve pattern. The largest speed reductions were observed for flat topographic profiles (−2.98%), daytime travel (−1.58%), and travel on working days (−1.75%) with rain (−1.80%). Conversely, the speed camera had little impact on vehicle speed for uphill topographic profiles, no rain conditions, and travels during weekend.

Behavioral Patterns of Drivers under Signalized and Unsignalized Urban Intersections

Under the general trend of mixed traffic flow, an in-depth understanding of the driving behaviors of traditional vehicles is of great significance for the design of autonomous vehicles and the improvement in the safety and acceptance of autonomous vehicles. This study first obtained microdata on the behaviors of drivers through driving simulation experiments and conducted research in stages. Then, generalized linear mixed-effects models were constructed to study the main effects and interaction effects of driver attributes and traffic conditions on driving behaviors. The data analysis shows that the overall speed of drivers passing through intersections follows a “deceleration acceleration” mode, but the fluctuations are more pronounced at signalized intersections, and the signal control significantly changes the position of the lowest speed when turning left. According to the different signal control and driving tasks, there are significant differences in a driver’s acceleration patterns between the entry and exit stages. A driver’s heart rate fluctuates greatly during the exit phase, especially during straight tasks. Compared with other indicators, the change in the gaze duration is not significant. In addition, interaction effects were observed between driver attributes and traffic conditions, with participants exhibiting different behavioral patterns based on their different attributes. The research results can provide a basis for the design of driving assistance systems and further improve the interactions between autonomous vehicles and traditional vehicles at intersections.

Deep Learning Approach for Driver Speed Intention Recognition Based on Naturalistic Driving Data

Deep Learning Approach for Driver Speed Intention Recognition Based on Naturalistic Driving Data

ROAD DIETS IMPACT ON DRIVING BEHAVIOR AMONG OLDER ADULTS WITH AND WITHOUT PRECLINICAL ALZHEIMER’S PATHOLOGY

Abstract Road diets (that is, the reallocation of one or more lanes of car traffic to other uses) have been proposed as a modification to increase pedestrian safety, particularly for older adults. We considered the impacts of road diets on aging drivers, and on those with early pathological accumulation of AD. We observed naturalistic driving data for 60 cognitively normal older drivers (Age Range = 62 – 87 years, Median = 75 years) driving across three road segments located in St. Louis, Missouri. We used neuroimaging and lumbar puncture derived biomarker data to determine which of the drivers had preclinical AD. Since previous AD studies identified a variety of changes in driving behavior among older drivers with preclinical AD, we examined driving speed before and after lane repurposing. We found that drivers with preclinical AD drove at lower speeds compared to those without preclinical AD prior to road diet implementation. After lanes were repurposed, there was no statistical difference in the speed between older drivers with and without preclinical AD. We evaluated cognitive performance and found that attentional control had a mediating effect on driver speed, suggesting that an individual’s ability to focus on a specific task and filter out distractions was associated with faster driving. Driving speed after lane repurposing is not mediated by attentional control, suggesting that road diets are impervious to individual driver capacity. We conclude that lane repurposing has potential as an important mobility infrastructure solution that could enhance older driver safety and facilitate aging in place.

Drivers of success, speed and performance in fisheries moving towards Marine Stewardship Council certification

AbstractWith growing concerns about global overfishing, market‐based eco‐certification programs like the Marine Stewardship Council (MSC) can incentivise adoption of sustainable practices. Several studies investigated drivers of improvement in market‐driven Fisheries Improvement Projects, but failed to use detailed, standardised measures of progress, or considered small samples. We considered the relative influence market, governance and fishery‐specific drivers have on MSC certification speed and success in 208 fisheries. To evaluate improvement we compared pre‐certification scores from MSC pre‐assessments—rapid high‐level audits against the MSC Standard—to scores from full MSC audits. Drivers considered included measures of pre‐assessment quality, as this initial advice might contribute to later certification success. National fishery management capacity, percentage of MSC catch and landed value were the strongest drivers of successful and rapid certification. Environmental improvements occurred for stock management, ecosystem health and governance aspects. While only 48% of fisheries with favourable pre‐assessment outcomes went on to be certified, improvements in many fisheries with lower pre‐assessment scores were observed in their lead‐up to full assessment. Random forest analyses allowed for considering multiple interacting variables simultaneously and revealed influential drivers under specific fishery contexts. For example, higher certification probability was associated with greater percentage of MSC catch under the full dataset, but not under subsets representing fisheries facing more challenging contexts for certification. Fisheries from lower/middle‐income countries had lower overall probability of certification, but this increased with higher management capacity. This suggests multiple drivers interact in instigating fisheries improvements, and MSC pre‐assessments provide valuable resources to understand this journey.

Roadway Weather Challenges Illuminate Real‐World Driving Biomarkers of Dementia Risk

AbstractBackgroundCognitive decline is a leading early predictor of dementia risk and may be evident in real‐world (RW) digital data even before patients ever present to clinic. This study examined the feasibility of using video and digital sensor profiles of real world (RW) driving in adverse weather as a marker of dementia risk.Method70 older drivers (mean age = 75.6 years) without dementia participated in a RW driving study for 2, 3‐month periods, one year apart (Wang, et al. 2021) . Self‐reported demographics and neuropsychological tests relevant to dementia and driving were collected at study start. Drivers were classed as cognitively neurotypical (N = 33) or impaired (N = 44) based on normed neuropsychological scores. Sensors installed in each driver’s vehicle collected video, speed, and GPS data across 405,104 miles driven. GIS data categorized roadway type (residential, commercial, interstate). A computer vision model (99% accuracy) we developed classed roads as clear or poor (rain, snow). Bayesian mixed‐effect logistic regressions assessed driver speed limit compliance (vehicle speed‐posted speed limit) across driver class, roadway, and weather.ResultDrivers were 8.01 times more likely to speed on interstates (95% CI: 7.61‐8.61) than residential roads (15‐25 mph). Impaired drivers were 15% more likely to drive slower than neurotypical drivers on clear roadways (95% C: 1.0413‐1.2829). This pattern reversed in poor weather with impaired drivers driving faster than neurotypical (b = 0.74, 95% CI: 0.57‐0.94)ConclusionDigital biomarkers from RW data show promise to screen for early dementia risk, advancing research and innovative clinical trials to prevent dementia. Driving challenges, like poor weather, discriminate cognitive decline before dementia. Impaired drivers showed self‐restriction by driving slower on clear roads and reduced self‐restriction by driving faster in poor weather despite risk. Big data strategies, like computer vision, complement driver performance data to identify RW environmental risk, paving the road for the car as a medical diagnostic device.