Driver Reaction Time Research Articles

Red Light Reaction – A Statistics Project with Real Life Application

We describe a hands-on project in which students collect data on the impact of distracted driving on driver reaction time. Initially they do this in class via a virtual driving applet, using themselves and fellow students as test subjects. Different applet versions simulate driving with and without distraction and measure the time it takes to apply brakes after the red brake lights of the car ahead appear. Students use the collected data to practice a range of statistical techniques, such as assessing data for outliers, creating a hypothesis to be tested, performing an appropriate test, and then translating their results to determine a safe driving distance. In the second part of the project, students work in groups outside of class. Each group recruits a category of test subjects (e.g., athletes, video gamers, STEM majors) of their choosing, collects data, and performs statistical analysis. Finally, students develop hypotheses as to whether different categories of drivers have better or worse reaction times, collect additional relevant data, and perform the appropriate statistical test. The applets, the project guide, the dataset, and supporting videos can be downloaded from https://osf.io/h8yxw/files/osfstorage. For users of the Canvas LMS, we also provide an export cartridge that contains the various project components. Supplementary materials for this article are available online.

Method for Determining the Conditions for Ensuring Stable Rearward Movement of a Semi-trailer Combination with Non-turning Semi-trailer Wheels

The efficiency of organising freight transport and using motor transport in a Smart City depends on a set of its properties, which in the process of operation determine its suitability for use in given operating conditions. Vehicles have different overall dimensions and designs, which determine their maneuverability and directional stability, their ability to make a curve-line movement in the city, as well as to move safely in reverse. Nowadays, tractor-trailer combinations with non-turning semi-trailer wheels are widely used for freight transport. Such a scheme of vehicle construction does not ensure its directional stability when reversing, which can lead to the folding of the tractor and semi-trailer and loss of mobility of the combination. In the absence of additional special systems, the driver controls the rearward movement of the road train using the steering wheel and rear-view mirrors, and the accuracy of delivery to the object depends on the level of the driver's training. The research of driver's reaction time spent on situation assessment, decision making, and realisation has been carried out. It has been revealed that with manual control, there are difficulties connected with training of drivers to estimate the parameters of movement and control of the road train, in particular, with the process of parking and its accurate delivery to the object. With the help of the developed mathematical model of the rearward movement of the road train and the proposed new turn control law, the research of maneuvering of the road train during its delivery to the object has been carried out and the results confirming the possibilities of building an automated system allowing to reduce the influence of the human factor on the parking process, as well as to reduce the time required to perform the maneuver have been obtained. The conducted analysis of system stability with the help of Routh–Hurwitz criterion has determined the conditions of providing stability of the system moving in reverse. The method of controlling the backward movement of the road train and the design of the device realising it has been developed. The results of simulation modeling allowed us to find the necessary values for the control law for the movement of road trains of different lengths, as well as the preferred variants of the initial positions of the road train for the realisation of its precise delivery to the object.

Attention-based cross-frequency graph convolutional network for driver fatigue estimation.

Fatigue driving significantly contributes to global vehicle accidents and fatalities, making driver fatigue level estimation crucial. Electroencephalography (EEG) is a proven reliable predictor of brain states. With Deep Learning (DL) advancements, brain state estimation algorithms have improved significantly. Nonetheless, EEG's multi-domain nature and the intricate spatial-temporal-frequency correlations among EEG channels present challenges in developing precise DL models. In this work, we introduce an innovative Attention-based Cross-Frequency Graph Convolutional Network (ACF-GCN) for estimating drivers' reaction times using EEG signals from theta, alpha, and beta bands. This method utilizes a multi-head attention mechanism to detect long-range dependencies between EEG channels across frequencies. Concurrently, the transformer's encoder module learns node-level feature maps from the attention-score matrix. Subsequently, the Graph Convolutional Network (GCN) integrates this matrix with feature maps to estimate driver reaction time. Our validation on a publicly available dataset shows that ACF-GCN outperforms several state-of-the-art methods. We also explore the brain dynamics within the cross-frequency attention-score matrix, identifying theta and alpha bands as key influencers in fatigue estimating performance. The ACF-GCN method advances brain state estimation and provides insights into the brain dynamics underlying multi-channel EEG signals.

Enhancing Safety in Autonomous Vehicles: The Impact of Auditory and Visual Warning Signals on Driver Behavior and Situational Awareness

Semi-autonomous vehicles (AVs) enable drivers to engage in non-driving tasks but require them to be ready to take control during critical situations. This “out-of-the-loop” problem demands a quick transition to active information processing, raising safety concerns and anxiety. Multimodal signals in AVs aim to deliver take-over requests and facilitate driver–vehicle cooperation. However, the effectiveness of auditory, visual, or combined signals in improving situational awareness and reaction time for safe maneuvering remains unclear. This study investigates how signal modalities affect drivers’ behavior using virtual reality (VR). We measured drivers’ reaction times from signal onset to take-over response and gaze dwell time for situational awareness across twelve critical events. Furthermore, we assessed self-reported anxiety and trust levels using the Autonomous Vehicle Acceptance Model questionnaire. The results showed that visual signals significantly reduced reaction times, whereas auditory signals did not. Additionally, any warning signal, together with seeing driving hazards, increased successful maneuvering. The analysis of gaze dwell time on driving hazards revealed that audio and visual signals improved situational awareness. Lastly, warning signals reduced anxiety and increased trust. These results highlight the distinct effectiveness of signal modalities in improving driver reaction times, situational awareness, and perceived safety, mitigating the “out-of-the-loop” problem and fostering human–vehicle cooperation.

Analysis of Mixed Traffic Flow Characteristics Based on Fleet Composition

In urban road networks, the integration of connected and autonomous vehicles (CAV) significantly influences traffic flow patterns, with the Fleet Composition—representing the positioning of these vehicles within convoys—being crucial in dictating the symmetry of information exchange amongst them. First, the vehicle composition of the mixed traffic flow is analyzed, and the mathematical analytical expressions of the random distribution characteristics of different types of vehicles are constructed. Second, we analyze the vehicle according to human characteristics in different situations. Then, consider the following characteristics and reaction times of manual drivers, establish a mixed traffic flow following model, and validate the established following. Finally, the basic graph model considering Fleet Composition is derived, and the effects of reaction time, Fleet Composition, driver following characteristics, and other parameters on road capacity under different penetration rates of CAV are analyzed by a Python and SUMO joint simulation. Finally, the characteristics of mixed traffic flow at intersections were analyzed.

ENHANCING ROAD SAFETY FOR VULNERABLE ROAD USERS – THE ROLE OF AUTONOMOUS EMERGENCY BRAKING SYSTEMS

Vulnerable road users are the largest group of road accident victims in the world. At the same time, it should be noted that most road accidents are caused by the motor vehicle driver. An opportunity to increase the level of road safety is emergency braking systems installed in vehicles. Their task is to detect the risk of a collision with another object, issue a warning to the driver and, in the absence of reaction, perform emergency braking. The publication presents the results of research conducted by the Motor Transport Institute as part of the PEDICRASH project. As part of the work, a series of tests of the autonomous emergency braking system were carried out to check unusual but probable cases of events in which it should work. The tests were carried out in a closed area using a few selected models of popular passenger car brands. An additional data acquisition system and dummies imitating vulnerable road users: pedestrians and cyclists were used. The constructions represent the silhouette of an adult person and meet the requirements of the Euro NCAP AEB Protocol. The time of the collision at the time of issuing a warning to the driver was analyzed. Assuming that the vehicle was moving uniformly, the distance from the obstacle was calculated at the time of issuing the warning and after the driver's reaction time (0.7 s and 1 s were assumed) from this signal. The deceleration necessary to brake the vehicle was calculated and it was determined whether the driver would have a chance to brake the vehicle before a pedestrian or a cyclist in a given situation. For cases in which the driver would not be able to brake in time, the speed of the vehicle with which it would hit the vulnerable road users was calculated. Possible injuries and accident costs were then estimated.

A new car-following model with incorporation of Markkula's framework of sensorimotor control in sustained motion tasks

This study develops a car-following model called the “Markkula Intelligent Driver Model (MIDM)” based on Markkula's Framework of Sensorimotor Control. The MIDM predicts the start time of driver's reaction based on the evidence accumulation within Markkula's framework unlike the existing car-following models that use a constant reaction time parameter. The MIDM also accurately represents the actual shape and duration of acceleration maneuvers. Fifty drivers’ car-following behavior was observed in 2 different scenarios using a driving simulator – reaction to a decelerating lead vehicle and reaction to a stopped lead vehicle. Trajectory data from the NGSIM project were also used for the evaluation. Compared to the Gipps Model, the Wiedemann Model and the IDM, the MIDM realistically reproduced trajectories of speed, acceleration, jerk and spacing for both simulator and NGSIM data. The MIDM can also incorporate the effects of lead vehicle brake lights for more accurate estimation of the driver reaction time.

Assessing the impact of reaction time on the crossing and merging conflicts and identifying suitable reaction time to detect the critical conflict

ABSTRACT Due to the increased vehicular traffic in developing countries, safety in mixed traffic conditions has become a concern. When critical speed was used with surrogate safety measures to identify critical conflicts, studies assumed some driver reaction time without any analysis. This study aims to assess the impact of different reaction times on the Percentage of Critical Conflicts (PCC) and identify the suitable reaction time for critical conflict identification. Post Encroachment Time was used in this study to assess the safety of crossing and merging traffic at unsignalized T-intersections. It was observed that an increase of 1 s reaction time increases 26.92% of PCC. PET extremes of different reaction times were modelled using Generalized Extreme Value distribution. The 1-second reaction time was found suitable as the estimated crash frequencies were similar to the actual field crashes. This approach could also be employed in the homogenous traffic conditions for critical conflict identification.

Drivers' use of different types of multimedia devices while driving - a survey study

Accidents and collisions continue to be an ongoing problem in road safety. While driving, drivers are influenced by stimuli that can affect their behaviour in various ways. Nowadays, various types of multimedia devices have become popular, which, when used by drivers while driving, can influence driver attention. The literature undertakes studies of the impact of such devices on the driver's degree of distraction as measured, for example, by the driver's reaction time. A survey was conducted to identify sources of driver distraction. A total of 237 drivers aged between 18 and 78 years participated in the sample. Of the respondents, 64.6% use a mobile phone while driving, 20.3% use a smartwatch, 33.3% use a multimedia control system for vehicle functions. Respondents indicated that they had either witnessed (18.1%) or been involved (6.3%) in a collision/accident when the driver used various types of multimedia devices while driving. Of those surveyed, 2.1% had caused a collision or accident by using a multimedia device while driving. Respondents are aware of the risks of using different types of media devices while driving, but they still use them. The analyses of the survey data will be used in future research using a car simulator.

A microscopic traffic flow model for explaining nonlinear traffic phenomena: Modeling, stability analysis and validation

As the core of reproducing the real nonlinear phenomena of traffic congestion, the investigation of car-following models, which take into account human factors, has consistently assumed a central role within the domain of transportation science. Nevertheless, it is noteworthy that prior studies have invariably employed a fixed value for driver reaction time to stimuli, whereas it is imperative to recognize the dynamic nature of this parameter, closely associated with the instantaneous vehicle speed. To address this issue, this paper constructs a dynamic sensitivity coefficient (DSC), and further develops a nonlinear human factoring car-following model to reveal the relation between the reaction time and the current speed. Firstly, the linear critical stability condition and the mKdV equation of the model are derived by the linear stability analysis and kink–antikink solution analytic method, respectively. Then, the numerical experiments are conducted to demonstrate that the proposed model is more practical and can reproduce the real driving behavior and phenomena. Finally, calibration and validation results exhibit the actual vehicle trajectory can be simulated well.

Research on collision warning strategy considering characteristics of drivers and road conditions

Collision warning strategy considering different characteristics of drivers and complex road conditions is of great importance for active safety control of autonomous vehicles. However,existing estimation methods for Tire-road friction coefficient (TRFC) cannot deal with the problem of estimation accuracy degradation caused by state mutation. Therefore, an adaptive cascaded strong tracking unscented Kalman filter (ACSTUKF) is proposed to estimate TRFC. The proposed algorithm consists of two strong tracking filters, which are connected in series with each other. Fuzzy control is introduced to estimate the reaction time of different drivers. Simulation experiments and real vehicle tests are carried out to demonstrate the effectiveness of the proposed strategy. Results show that the proposed ACSTUKF has a more excellent performance for TRFC than EKF and UKF, which greatly reduces the impact of state mutation. Moreover, ACSTUKF shows a strong robustness to driving conditions and road conditions. The established collision warning strategy is consistent with the actual situation, which realizes the adaptation of human and road.

К ВОПРОСУ ОБ ИЗМЕНЕНИИ ВРЕМЕНИ РЕАКЦИИ ВОДИТЕЛЯ В ТРАНСПОРТНОМ ПОТОКЕ ВЫСОКОЙ ПЛОТНОСТИ

This article considers the problem of determining the reaction time of sanguine drivers of different age categories in the city's transport system in conditions of high-density traffic flow, close to congestion. Prolonged exposure to high-density traffic leads to a deterioration in the functional state of the driver and to an increase in his psycho-emotional tension. A change in the functional state causes fatigue in the driver, which in turn leads to an increase in his reaction time, which affects road safety. The assessment of the driver's fatigue level was carried out by measuring an electrocardiogram and analyzing the unevenness of cardiac intervals, which are a universal response to all types of loads. A model for estimating changes in the reaction time of a sanguine driver based on a regression model of changes in his fatigue level is proposed. The graphs show the changes in the reaction time of drivers of different ages during their stay in traffic congestion. It was revealed that the reaction time of the driver in the first minutes of being in a traffic jam changes slightly, due to the presence of reserve compensatory capabilities of the body, however, after the fifth to sixth minute, an objective process of deterioration of the functional state begins. It was found that the change in the reaction time of a sanguine driver in a traffic jam primarily depends on the initial level of fatigue. When the driver stays in a high-density traffic flow with the initial state of optimal voltage of the regulatory systems, the change in his reaction time by the end of the jam reaches the greatest value. It was also revealed that the greatest changes in the reaction time of a sanguine driver occur in older drivers.

Effects of vehicle vibration on driver alertness: a pilot study

Road accidents resulting from the loss of driving alertness cause significant social and economic damage. In recent years, studies have revealed that whole-body vibration affects driver drowsiness directly. However, the impact of different frequency ranges of vibration on driver alertness has not been extensively explored. This driving simulator study aimed to investigate the impact of low frequency (1-4 Hz) and high frequency (16-32 Hz) whole-body vibration on a driver's reaction time during a monotonous highway driving task. Nine participants completed the task for one hour, with reaction tasks presented every 8-12 seconds. After 25 minutes of exposure to whole-body vibration, participants exhibited significantly longer reaction times for both low and high frequency ranges when compared to the control condition. The low frequency range had a stronger effect on driver vigilance, with reaction times increasing by as much as 135 ms within 35 minutes. Subjective evaluations using the Karolinska Sleepiness Scale (KSS) were consistent with the reaction time results. Overall, this study confirms that both low and high frequency vibration can reduce driver alertness, with the effect of low frequency vibration being more pronounced. Further research is suggested with larger sample sizes and wider frequency ranges.

Are Warnings Suitable for Presentation in Head-Up Display? A Meta-Analysis for the Effect of Head-Up Display Warning on Driving Performance

Head-up display (HUD) warnings are gaining increasing attention with the growing importance of advanced warning systems in vehicles for improving driving safety. However, the effectiveness of HUD warnings is still controversial considering the various driving performance indexes, warning modalities, and driving scenarios used in previous studies. This study conducts meta-analyses of the relevant literature to examine whether HUD warnings (both unimodal HUD warnings and multimodal warnings with HUD) have advantages compared with other warnings (head-down display warnings, auditory warnings, tactile warnings, and multimodal warnings without HUD) for driver reaction times and driving quality. Overall, 33 articles were included in four meta-analyses. Additionally, the effects of the related moderator variables of scenario, mapping of warnings, and non-driving-related tasks (NDRTs) on the relationship between using HUD warnings and using non-HUD warnings was analyzed. The results show that using unimodal HUD warnings leads to faster reaction times compared with head-down display warnings but has no significant advantage on driving performance over auditory warnings in general and results in slower reaction times than using tactile warnings. However, results of the moderating effects suggest that using HUD warnings in non-critical scenarios and using high mapping HUD icons (i.e., HUD warnings with specific hazard information) are conducive to improving driving performance in comparison with other warning types. These results highlight the potential advantages of HUD in transmitting warning information. The future application and design of HUD warnings may need to focus on specific situations considering iconic mapping to an event.

Impact of driver's advanced reaction time in car-following model with optimal velocity deviation under V2X environment

The vehicle-to-everything (V2X) technology has made a significant advancement in the field of intelligent transportation in recent years. When operating a vehicle in a V2X environment, the driver can get real-time updates on the motion of nearby vehicles. In a real-world traffic situation, there is always some deviation between the actual and anticipated traffic information, and this deviation will undoubtedly have a significant effect on traffic flow. Drivers always maintain the flow of traffic by taking some time to assess and decide how the drivers in front of them are behaving behind the car in the traffic system. As a result, by taking into account the driver's advanced reaction time and optimal deviation in a V2X environment, a novel car-following model is developed and the effects of these parameters on the traffic flow are examined. In order to determine the stability requirements for the new model, the linear and nonlinear stability of the proposed model is examined using the perturbation methods. Studies have found that when the driver's reaction time and the velocity deviation are optimized, it can reduce the amplitude of the stability curve, thus enlarging the area of more stable motion. Also, the numerical simulation supports the theoretical research by showing that the new model may effectively reduce traffic congestion and improve the stability of traffic flow as the influence of these factors on the traffic flow increases.

Do driver monitoring technologies improve the driving behaviour of distracted drivers? A simulation study to assess the impact of an auditory driver distraction warning device on driving performance

Being distracted while driving is a major cause of road crashes. To help prevent this particular type of crash, new driver monitoring technologies track ocular and head movement and alert drivers when periods of prolonged distraction are detected, thus preventing hazardous situations on the road. In this mixed-factorial multi-level experiment, the behaviour of drivers was measured as they performed a secondary task while being monitored via an auditory Driver Distraction Warning (A-DDW) device which reminded them to look at the road ahead and cease the activity which was the source of distraction. Here, we evaluated the effectiveness of this type of DDW device by measuring longitudinal and lateral behaviour in situations where the driver is intentionally distracted for a significant period of time, and when the device repeatedly reminds the driver of his/her primary task.Forty-two participants were randomly stratified into three groups and subjected to the following distraction levels: (i) not distracted (baseline), (ii) distracted, and (iii) distracted but interacting with the A-DDW device. All participants drove in (i) free-flowing and (ii) stable traffic conditions along straight motorway segments. Speed and speed deviation values for distracted drivers were lower than for undistracted ones but they also experienced a significant deterioration in vehicle lateral control. Conversely, drivers assisted by the A-DDW device experienced a considerable improvement in their lateral control even though their speed and speed deviation values were higher. The reaction times of distracted drivers interacting with the A-DDW device deteriorated as revealed in cognitive tests conducted before and after the driving task.

The effect of a startle-based warning, age, sex, and secondary task on takeover actions in critical autonomous driving scenarios.

Introduction: In highly autonomous driving scenarios, it is critical to identify strategies to accelerate reaction times since drivers may take too long to take over control of the vehicle. Previous studies reported that an Acoustic Startling Pre-Stimulus (ASPS, i.e., a loud warning preceding an action) accelerated reaction times in simple ankle flexion exercises. Methods: In this study, we examined if an ASPS warning leads to shorter takeover reaction times in a sled-simulated evasive swerving maneuver. Twenty-eight participants (seven male adults, seven male teenagers, seven female adults, and seven female teenagers) were instructed to align a marker on the steering wheel with a marker on a lateral post as fast as they could as soon as the lateral sled perturbation (0.75g) started. Four conditions were examined: with and without an ASPS (105dB, played 250ms before sled perturbation for 40ms), and with and without a secondary task (i.e., texting). A catch trial (ASPS only) was used to minimize anticipation. Human kinematics were captured with an 8-camera 3D motion capture system. Results: Results showed that the drivers' hands lifted towards the steering wheel more quickly with the ASPS (169 ± 55ms) than without (194 ± 46ms; p = 0.01), and that adult drivers touched the steering wheel quicker with the ASPS (435 ± 54ms) than without (470 ± 33ms; p = 0.01). Similar findings were not observed for the teen drivers. Additionally, female drivers were found to lift their hands towards the steering wheel faster than male drivers (166 ± 58ms vs. 199 ± 36ms; p = 0.009). Discussion: Our findings suggest that the ASPS may be beneficial to accelerate driver reaction times during the initiation of a correction maneuver, and that autonomous vehicle warnings may need to be tailored to the age and sex of the driver.

The Effect of Multifactor Interaction on the Quality of Human–Machine Co-Driving Vehicle Take-Over

This paper investigates the effects of non-driving related tasks, take-over request time, and take-over mode interactions on take-over performance in human–machine cooperative driving in a highway environment. Based on the driving simulation platform, a human–machine collaborative driving simulation experiment was designed with various take-over quality influencing factors. The non-driving related tasks included no task, listening to the radio, watching videos, playing games, and listening to the radio and playing games; the take-over request time was set to 6, 5, 4, and 3 s, and the take-over methods include passive and active take-over. Take-over test data were collected from 65 drivers. The results showed that different take-over request times had significant effects on driver take-over performance and vehicle take-over steady state (p < 0.05). Driver reaction time and minimum TTC decreased with decreasing take-over request time, maximum synthetic acceleration increased with decreasing take-over request time, accident rate increased significantly at 3 s take-over request time, and take-over safety was basically ensured at 4 s request time. Different non-driving related tasks have a significant effect on driver take-over performance (p < 0.05). Compared with no task, non-driving related tasks significantly increase driver reaction time, but they only have a small effect on vehicle take-over steady state. Vehicle take-over mode has a significant effect on human–machine cooperative driving take-over quality; compared with passive take-over mode, the take-over quality under active take-over mode is significantly lower.

A Study on the Entire Take-Over Process-Based Emergency Obstacle Avoidance Behavior.

Nowadays, conditional automated driving vehicles still need drivers to take-over in the scenarios such as emergency hazard events or driving environments beyond the system's control. This study aimed to explore the changing trend of the drivers' takeover behavior under the influence of traffic density and take-over budget time for the entire take-over process in emergency obstacle avoidance scenarios. In the driving simulator, a 2 × 2 factorial design was adopted, including two traffic densities (high density and low density) and two kinds of take-over budget time (3 s and 5 s). A total of 40 drivers were recruited, and each driver was required to complete four simulation experiments. The driver's take-over process was divided into three phases, including the reaction phase, control phase, and recovery phase. Time parameters, dynamics parameters, and operation parameters were collected for each take-over phase in different obstacle avoidance scenarios. This study analyzed the variability of traffic density and take-over budget time with take-over time, lateral behavior, and longitudinal behavior. The results showed that in the reaction phase, the driver's reaction time became shorter as the scenario urgency increased. In the control phase, the steering wheel reversal rate, lateral deviation rate, braking rate, average speed, and takeover time were significantly different at different urgency levels. In the recovery phase, the average speed, accelerating rate, and take-over time differed significantly at different urgency levels. For the entire take-over process, the entire take-over time increased with the increase in urgency. The lateral take-over behavior tended to be aggressive first and then became defensive, and the longitudinal take-over behavior was defensive with the increase in urgency. The findings will provide theoretical and methodological support for the improvement of take-over behavior assistance in emergency take-over scenarios. It will also be helpful to optimize the human-machine interaction system.

Дослідження математичної моделі стійкості транспортного потоку на ділянках дорожньої мережі міста

The paper presents the results of the research of the mathematical model of the stability of the traffic flow on the sections of the city's street and road network when external factors change. Based on the modeling results, it is proposed to divide all factors affecting the stability of the traffic flow into three groups. The first group of factors characterizes the construction of the vehicle: the length of the vehicle, the weight of the vehicle, the power of the engine. The second group of factors is called time factors, which take into account: the driver's reaction time to a change in the road situation; time for a maneuver that can be used by the car in case of a change in the road situation; the total time of delays while driving along the route. The third group of factors takes into account the peculiarities of the construction of the infrastructure of the road environment. Such factors include: the number of traffic lanes on the roadway; the number of pedestrian crossings and traffic lights. Based on the results of modeling, a rating of factors affecting the amount of traffic flow stability margin is presented. The simulation results proved that, in the first place, the impact on the loss of stability of the traffic flow is the time for the maneuver that the car can use in the event of a change in the road situation and the number of pedestrian crossings and traffic lights on the controlled section of the road. When the listed factors change, the robustness criterion has minimal values. In second place in terms of influence on is a group of factors that take into account the driver's reaction time to a change in the road situation and the number of traffic lanes on the roadway. In third place is a group of factors that take into account the length of the car and the weight of the car. It is proved that the mathematical model of traffic flow stability has limitations regarding its application. The limitations are related to the definition of the initial data for the simulation. It is necessary to determine the flow density on the controlled section of the road network. In addition, statistically determined values are the reaction time of drivers to a change in the road situation and the presence of pedestrian crossings and traffic lights on the controlled section of the road.