Traffic Behavior Research Articles

Impact of driver prediction with density deviation and anticipation in lattice hydrodynamic model with passing

This paper presents an integral lattice hydrodynamic model to examine the impact of driver’s anticipation and driving prediction with density deviation of leading vehicle under passing behavior. Both linear and nonlinear investigations have been used to obtain the stability condition and ‘modified Korteweg–de Vries (mKdV)’ equation is derived to further classify the nonlinear behavior of vehicular flow in terms of density waves, respectively. The linear stability condition shows that the stable region can be increased by decreasing the coefficient of predicted density deviation. Additionally, the stable region expands with a positive value of driver anticipation but contracts with a negative value. In comparison of the Nagatani and Redhu models, it is observed that for fixed value of density deviation coefficient, the new model conveys greater stability zone. To verify the theoretical findings, ‘numerical simulation’ has been conducted to examine the evolution of traffic flow in the presence of a small disturbances. The analytical results have been discussed for different passing rate with fixed value of driver’s anticipation and different values of density deviation coefficient. Furthermore, it has been noted that the stable region decreases for all passing rates when driver become more aware of the average speed of any neighbouring vehicles. The obtained results in this paper show that the traffic behavior with the existing model is more realistic. Additionally, this model will help in boosting vehicle movement efficiency, reducing congestion and enhancing road safety effectively .

An LDoS attack detection method based on FSWT time–frequency distribution

The Low-rate Denial-of-Service (LDoS) attack is a stealthy and periodic attack, which belongs to the category of DoS attacks. The LDoS attack maliciously preempts and consumes target resources, causing the targeted network performance to decline and affecting the service quality. The LDoS attack is highly destructive and difficult to detect and defend against due to its abnormal attack behavior. In this paper, we discuss the network traffic behavior in the time domain, frequency domain, and time–frequency domain, and we find that the time–frequency domain contains more detailed information than the time domain and frequency domain alone. Considering the limitations of the existing time–frequency domain transformation methods, an LDoS attack detection method based on Frequency Slice Wavelet Transformation (FSWT) is presented in this paper. The entropy, ratio of energy, contrast, and correlation are extracted from the time–frequency distribution to depict the network traffic, and a decision tree is trained to detect the LDoS attack in this paper. According to the experimental results on NS2, testbed, and the comparative experiment, we conclude that the method presented in this paper performs well.

The struggle to overcome traffic congestion: A study of social interaction and effects on deviant behavior of motorcycle riders

Background: Motorcycle riding as a means of daily transportation in Southeast Asia, especially Jakarta, Indonesia, manifests the struggle to use the road. This struggle - associated with congestion, irregularity, and lack of control - can lead to physical harm or material loss. Studies on deviant behavior related to motorcycle use mainly discuss motorcycle gangs and traffic violations. This paper intends to explicate how the context of this struggle informs the different meanings of the deviant behavior of motorcyclists in daily traffic life. It employs concepts of innovation and ritualism, learning, harm, visuality, and secret deviance to shatter the prevailing understanding of motorcyclist traffic behavior. Methods: This study chooses T.B. Simatupang Street, one of the busiest roads in Jakarta, as a research location. A mixed method is used to examine the context of the struggle, first qualitatively by utilizing visual data collected through (a) direct video recordings on the road and (b) aerial drone video recordings. Both recordings captured images of motorcyclist behavior considered deviant and can cause harm, such as stopping illegally, clamoring while cutting lanes and other vehicles, and slipping between two automobiles. Then quantitatively, it conducts a survey to collect data on motorcyclists' experiences. Included in the survey were questions with images captured from the direct video recording footage to collect responses toward motorcyclist riding behavior. Findings: The survey findings show that responses toward images of motorcyclist behavior - concerning harm - show a lack of understanding of driving safety and traffic rules, and some consider it as just the daily routine of motorcyclists. Conclusion: Therefore, the visuality of motorcyclist traffic violation is learned through innovation (driving recklessly) or ritualism (daily habit) as a struggle that is ironically visibly secretly upheld by fellow motorcyclists. Novelty/Originality of this Study: This study offers a novel perspective by framing motorcyclists' behavior in Jakarta's traffic as a form of adaptive struggle rather than mere deviance or rule-breaking. It uniquely utilizes visual criminology to argue that what is often seen as traffic violations may be better understood as contextual adaptations to urban congestion, thereby challenging prevailing interpretations of motorcyclist behavior.

Mobile User Traffic Generation Via Multi-Scale Hierarchical GAN

Mobile user traffic facilitates diverse applications, including network planning and optimization, whereas large-scale mobile user traffic is hardly available due to privacy concerns. One alternative solution is to generate mobile user traffic data for downstream applications. However, existing generation models cannot simulate the multi-scale temporal dynamics in mobile user traffic on individual and aggregate levels. In this work, we propose a multi-scale hierarchical generative adversarial network (MSH-GAN) containing multiple generators and a multi-class discriminator. Specifically, the mobile traffic usage behavior exhibits a mixture of multiple behavior patterns, which are called micro-scale behavior patterns and are modeled by different pattern generators in our model. Moreover, the traffic usage behavior of different users exhibits strong clustering characteristics, with the co-existence of users with similar and different traffic usage behaviors. Thus, we model each cluster of users as a class in the discriminator’s output, referred to as macro-scale user clusters. Then, the gap between micro-scale behavior patterns and macro-scale user clusters is bridged by introducing the switch mode generators, which describe the traffic usage behavior in switching between different patterns. All users share the pattern generators. In contrast, the switch mode generators are only shared by a specific cluster of users, which models the multi-scale hierarchical structure of the traffic usage behavior of massive users. Finally, we urge MSH-GAN to learn the multi-scale temporal dynamics via a combined loss function, including adversarial loss, clustering loss, aggregated loss, and regularity terms. Extensive experiment results demonstrate that MSH-GAN outperforms state-of-art baselines by at least 118.17% in critical data fidelity and usability metrics. Moreover, observations show that MSH-GAN can simulate traffic patterns and pattern switch behaviors.

Inferring mobile applications usage from DNS traffic

In the digital era, our lives are intrinsically linked to the daily use of mobile applications. As a consequence, we generate and transmit a large amount of personal data that puts our privacy in danger. Despite having encrypted communications, the DNS traffic is usually not encrypted, and it is possible to extract valuable information from the traffic generated by mobile applications. This study focuses on the analysis of the DNS traffic behavior found in mobile application traces, developing a methodology capable of identifying mobile applications based on the domains they query. With this methodology, we were able to identify apps with 98% accuracy. Furthermore, we have validated the effectiveness of the characterization obtained with one dataset by identifying traces from other independent datasets. The evaluation showed that the methodology provides successful results in identifying mobile applications.

Response of Low Carbon Level to Transportation Efficiency in Megacities: A Case Study of Beijing, China

Global warming caused by massive carbon dioxide emissions can lead to a chain of ecological disasters. As one of the main sources of carbon emissions, transportation is of great significance, and the evaluation of its connections with carbon emissions is necessary to achieve “carbon neutrality”. Taking Beijing as an example, this study evaluated traffic efficiency (TE) by utilizing principal component analysis and fuzzy comprehensive evaluation. Using the Tapio decoupling model and coupling coordination degree model, the corresponding relationship between urban low carbon level (LCL) and TE was explored. The results showed the following: (1) The total carbon emission (CE) level exhibited fluctuating variation from increasing to decreasing. The carbon emission intensity (CEI) continued to slow down, and the rapid growth of population density played a key role in low-carbon development. (2) The traffic operations continually showed a positive trend in development. TE increased from a step-like to a slow shape, until it declined in 2020 due to the pandemic. (3) TE and LCL both developed from low coordination to an extreme level of coordination. Per capita carbon emission (CEP) and TE presented an inverted U-shaped curve; meanwhile, with increases in TE, the decline in CEI slowed. In addition, the weak decoupling of TE changed to become strong, due to CE and CEP, and maintained a strong decoupling state from CEI. (4) There is a necessity for the rational planning of land use for transportation infrastructure, the encouragement of a combination of public and private transportation, and the strengthening of the maintenance of the relative infrastructure and the management of traffic behaviors to attain a win–win situation. The results provide a reference for optimizing the traffic structure to achieve “carbon neutrality”.

Automatic Definition of Traffic Analysis Zones Based on Big Data

The planning process for any transport system can be considered complete if it is accompanied by a modeling system to evaluate the intervention. The study area should always be divided into traffic zones. Correct zoning is the key to any transport system study. The basic principles of zone creation require a thorough understanding of the area and local traffic conditions. However, this is not always a given, especially if a universally applicable assessment system is to be developed. This has led to the need to develop an algorithm that is able to provide an estimate for the definition of traffic zones based on some automatically observable or measurable phenomena or sequence of events. The aim of this research is to identify the observable events that are suitable for characterizing the area, so that an automatic zone definition procedure can be developed based on these. In this paper, automatic WAZE-generated congestion data were processed in a selected district of Budapest. During the processing, the area was divided into a grid network and time series were developed that show the traffic flow on the grid network as a function of the congestion level. The area subdivisions were then clustered using spectral clustering to create spatially distinct districts with identical traffic behavior.

Heart Zone as a measure to influence traffic safety culture: which factors influence culture change?

Measures to influence traffic safety culture in designated areas (e.g. schools) exist in several different countries across the world. The Norwegian traffic safety scheme Heart Zone is a measure that aims to influence the traffic culture within a specific geographical area. The study examines whether four Norwegian schools have managed to establish a Heart Zone traffic safety culture, factors facilitating and impeding the implementation of Heart Zone traffic safety culture and subsequent changes in parents' traffic safety behavior. The study is based on survey data from four Norwegian schools (n = 220) and qualitative interviews (n = 19). Although the level of implementation varies, results from the interviews and the survey suggest that the schools have established Heart Zone cultures, where large proportions of the parents drop the children off at the recommended drop-off zone, when driving, are attentive to students who come walking/cycling within the Heart Zone and walk or cycle when transporting children within the Heart Zone. The Heart Zone culture is related to changes in parents' traffic safety behaviors and reductions in dangerous traffic situations. The study identifies specific factors facilitating and impeding traffic culture change, related to enclosed geographical areas like the Heart Zone, developing an analytical model that researchers and future traffic culture measures like this can learn from.

From parents to children: associations of traffic risks with impulsivity, family relationships and serotonin transporter genotype.

Road traffic injuries are the leading cause of death for young adults, and parents play a major role in shaping their traffic behaviour. Higher impulsivity (predictor of higher traffic risk) has been shown to be dependent on family relations and the serotonin transporter gene promoter polymorphism (5-HTTLPR). The specific mechanisms for the inheritance of risky traffic behaviour from parents to children are not clear, and the genetic aspect has not been studied before. We used data of Estonian Children Personality Behaviour and Health Study subjects (n = 596, mean age = 25.2 ± 0.6) and their parents (mothers, n = 460, mean age = 52.1 ± 5.8; fathers, n = 339, mean age = 54.1 ± 6.5). Family relationships scale, traffic risk questionnaires and Adaptive and Maladaptive Impulsivity Scale were filled out. The increased risk-taking behaviour of parents and worse quality of family relationship were significant predictors of higher traffic risk among subjects. Family support and impulsivity of fathers significantly predicted the subjects' traffic risk score in interaction with 5-HTTLPR genotype: l'/l' homozygous subjects with adaptively impulsive fathers had higher traffic risk, whereas for s'-allele carrying subjects family support was more significant. Parental role modelling and family relationships are significant predictors of future traffic behaviour of the child. Whether the behavioural example of the father or the influence of family relationships is more important in predicting future risky traffic behaviour, depends on the 5-HTTLPR genotype of the child.

IMTIBOT: An Intelligent Mitigation Technique for IoT Botnets

The tremendous growth of the Internet of Things (IoT) has gained a lot of attention in the global market. The massive deployment of IoT is also inherent in various security vulnerabilities, which become easy targets for hackers. IoT botnets are one type of critical malware that degrades the performance of the IoT network and is difficult to detect by end-users. Although there are several traditional IoT botnet mitigation techniques such as access control, data encryption, and secured device configuration, these traditional mitigation techniques are difficult to apply due to normal traffic behavior, similar packet transmission, and the repetitive nature of IoT network traffic. Motivated by botnet obfuscation, this article proposes an intelligent mitigation technique for IoT botnets, named IMTIBoT. Using this technique, we harnessed the stacking of ensemble classifiers to build an intelligent system. This stacking classifier technique was tested using an experimental testbed of IoT nodes and sensors. This system achieved an accuracy of 0.984, with low latency.

HARNESSING ARTIFICIAL INTELLIGENCE FOR ADVANCED THREAT DETECTION IN NETWORK INFRASTRUCTURES

As cyber threats grow increasingly sophisticated, traditional security measures are falling behind in the face of these evolving attacks. This article highlights the role of Smart Sentinel, a cutting-edge threat detection system that utilizes Artificial Intelligence (AI) to strengthen network defenses. Unlike conventional security solutions, Smart Sentinel employs machine learning algorithms to continuously learn and adapt, enabling it to detect anomalies and potential threats in real-time. By analyzing a variety of data sources, including network traffic and user behavior, the system establishes a baseline of normal activity and continually enhances its threat detection capabilities. Key features such as anomaly detection, behavioral analysis, real-time response, and adaptive learning contribute to an improved security posture, reduced false positives, enhanced operational efficiency, and cost-effectiveness. Smart Sentinel represents a significant breakthrough in cybersecurity, providing organizations with a proactive and resilient defense against the ever-evolving cyber threat landscape. Key features such as anomaly detection, behavioral analysis, real-time response, and adaptive learning contribute to Smart Sentinel’s effectiveness in protecting organizations against cyber threats. Anomaly detection: Smart Sentinel identifies unusual patterns and activities that deviate from established norms, alerting security teams to potential threats. Behavioral analysis: Smart Sentinel goes beyond simply detecting anomalies by analyzing user and system behavior to identify suspicious patterns that may indicate malicious intent. Real-time response: Smart Sentinel’s real-time response capabilities enable it to take immediate action upon detecting a threat, such as isolating compromised systems, blocking malicious IP addresses, or generating alerts for human intervention. Adaptive learning: Through continuous monitoring and analysis, Smart Sentinel continuously learns and improves its ability to discern between normal and malicious activities, reducing false positives and improving overall security posture. Smart Sentinel’s impact on organizations is multifaceted: Enhanced security posture: Smart Sentinel proactively identifies and mitigates emerging threats, strengthening an organization’s overall security posture. Reduced false positives: Smart Sentinel’s adaptive learning capabilities minimize the number of false positives, preventing alert fatigue and allowing security teams to focus on genuine threats. Operational efficiency: Smart Sentinel automates routine tasks, freeing up security teams to focus on strategic initiatives and improve operational efficiency. Cost-effectiveness: Smart Sentinel optimizes cybersecurity investments by automating threat detection and response, reducing the need for costly manual intervention and minimizing the potential for financial losses from successful cyberattacks.

Spatio-temporal communication network traffic prediction method based on graph neural network

The function of network traffic prediction plays an important role in many network operations such as security, path planning and congestion control etc. Most traditional traffic prediction methods only consider temporal correlation but ignore spatial correlation, which may result in limited accuracy. In this paper, we propose an effective traffic prediction method based on the graph multi-head attention convolution neural network model, termed as FlowDiviner, which combines graph convolutional network (GCN) and multi-head attention mechanism in its encoder-decoder architecture. Specifically, GCN is used to extract spatial correlation from complex network topologies and multi-head attention mechanism is used to capture dynamic temporal correlations based on monitored traffic behaviors. Meanwhile, a middle attention module is introduced between encoder and decoder to model the relationship between historical and future timesteps of traffic, thus it can alleviate the error accumulation and improve accuracy. The experiments based on both real-life dataset as well as synthetically generated traffic traces show that FlowDiviner can effectively obtain temporal and spatial correlation from the network historical traffic data, and the test results of all metrics are significantly improved from the baseline schemes.

Bridging multi-level vehicle behavioural patterns with long-term pavement condition: a bidirectional modelling approach for progressively mixed autonomous traffic flow

The increasing of AVs’ market penetration rate (MPR) changes the traffic behavioural pattern and ultimately influences pavement. Pavement condition, in turn, influences microscopic behaviours. The above bidirection influencing mechanism has not been investigated fully yet. This research fill this gap by modelling the linkage between multi-level microscopic behaviour and pavement sustainability. Three levels of behaviour are considered: travel behaviour, microscopic traffic behaviour, and driving behaviour. The latter two are modelled using stochastic lateral movement model, while the former one is captured by a mixed autonomous traffic assignment model. Microscopic behaviours’ influence on pavement is described by a data-driven model, while the reverse counterpart is described by connecting pavement condition with link wandering and disutility. Our analysis results reveal that at link level, AVs can cause extra 50% drop in free flow speed due to pavement deterioration. Neglecting pavement evolution in strategic assignments leads to a 0.2% increase in network cost.

The effect of formal de‐policing on police traffic stop behavior and crime: Early evidence from LAPD's policy to restrict discretionary traffic stops

AbstractResearch SummaryOn March 1, 2022, correspondence from the Los Angeles Police Department (LAPD) headquarters stated that officers can no longer use minor infractions (e.g., equipment violations) as a pretextual reason to further investigate drivers for criminal behavior. If LAPD officers are to execute a discretionary stop, they must activate their body‐worn cameras and reasonably articulate to the civilian why they are being investigated. The intent is to reduce racial/ethnic disparities in stops and build trust of the police within the community. Critics of the policy argue that elevated crime rates will result due to the crime suppression effect of such stops. This study examines racial differences in stops before and after the policy change, as well as whether Part 1 violent and property crimes increased. Descriptive findings show that while the counts of stops, arrests, and contraband seizures during stops decreased, the percentage of non‐White civilians stopped decreased only minimally following the intervention. Results from using Bayesian synthetic control methods indicate an increase in both violent and property crimes post‐intervention compared with the synthetic counterfactual. The increase in violent crimes has a low probability of being different from the counterfactual, whereas the increase in property crimes has a high probability, suggesting that the intervention led to a real change in property crimes compared with what would have been expected under the counterfactual.Policy ImplicationsThis study provides preliminary evidence that emerging policies intended to restrict discretionary stops may not have the intended effect on racial disparities in police traffic stops. Instead, agencies passing similar policies may expect to see the potential unintended consequence of a spike in jurisdictional property crimes. We argue that such types of stops across a jurisdiction may have a general deterrent effect on more common and calculative crimes such as property crimes. To combat violent crimes, agencies should consider focusing discretionary stops in known hot spots. Further, our findings offer implications for research on the effects of de‐policing on crime. Police agencies should monitor potential unintended impacts of these policies if enacted and be prepared to deal with such consequences.

Dynamic traffic signal control for heterogeneous traffic conditions using Max Pressure and Reinforcement Learning

Optimization of green signal timing for each phase at a signalized intersection in an urban area is critical for efficacious traffic management and congestion mitigation. Many algorithms are developed, yet very few are for cities in developing nations where traffic is characterized by its heterogeneous nature. While some of the recent studies have explored different variants of Max Pressure (MP) and Reinforcement Learning (RL) for optimizing phase timing, the focus is limited to homogeneous traffic conditions. In developing nations, such as India, control systems, like fixed and actuated, are still predominantly used in practice. Composite Signal Control Strategy (CoSiCoSt) is also employed at a few intersections. However, there is a notable absence of advanced models addressing heterogeneous traffic behavior, which have a great potential to reduce delays and queue lengths. The present study proposes a hybrid algorithm for an adaptive traffic control system for real-world heterogeneous traffic conditions. The proposed algorithm integrates Max Pressure with Reinforcement Learning. The former dynamically determines phase order by performing pressure calculations for each phase. The latter optimizes the phase timings for each phase to minimize delays and queue lengths using the proximal policy optimization algorithm. In contrast to the past RL models, in which the phase timing is determined for all phases at once, in the proposed algorithm, the phase timings are determined after the execution of every phase. To assess the impact, classified traffic volume is extracted from surveillance videos of an intersection in Ludhiana, Punjab and simulated using Simulation of Urban Mobility (SUMO). Traffic volume data is collected over three distinct time periods of the day. The results of the proposed algorithm are compared with benchmark algorithms, such as Actuated, CoSiCoSt, and acyclic & cyclic Max Pressure, Reinforcement Learning-based algorithms. To assess the performance, queue length, delay, and queue dissipation time are considered as key performance indicators. Of actuated and CoSiCoSt, the latter performs better, and thus, the performance of the proposed hybrid algorithm is compared with CoSiCoSt. The proposed algorithm effectively reduces total delay and queue dissipation time in the range of 77.07%–87.66% and 53.95%–62.07%, respectively. Similarly, with respect to the best-performing RL model, the drop in delay and queue dissipation time range from 55.63 to 77.12% and 22.13 to 43.7%, respectively, which is significant at 99% confidence level. The proposed algorithm is deployed on a wireless hardware architecture to confirm the feasibility of real-world implementation. The findings highlight the algorithm’s potential as an efficient solution for queues and delays at signalized intersections, where mixed traffic conditions prevail.

An improved stochastic car-following model considering the complete state information of multiple preceding vehicles under connected vehicles environment

Studying the car-following model contributes to improving vehicle driving efficiency and traffic flow stability within the context of connected automated vehicles (CAVs). Therefore, an improved stochastic car-following model (SMLVM) is proposed in this paper, building upon the traditional FVD model and BLVD model. This new model comprehensively considers four deterministic factors: acceleration, velocity, headway, and optimal speed memory of multiple preceding vehicles, as well as the comprehensive influence of stochastic factors on car-following behavior during the traveling process. Meanwhile, weight functions are utilized to represent the varying strengths of influence from vehicles ahead with different positions and speeds on the following vehicle. The SMLVM model is categorized and discussed based on practical application scenarios in this paper. When the focus is primarily on fundamental traffic flow behavior, relative motion between vehicles, and other basic principles, the SMLVM model degenerates into the MLVM model. Through stability analysis and simulation experiments on MLVM model, critical stability conditions are derived. Comparisons with other classical models demonstrate that the MLVM model exhibits stronger stability and resistance to disturbances. When the focus is primarily on various realistic driving scenarios, through simulation experiments on SMLVM models in different scenarios, it is found that the influence of stochastic factors on SMLVM models mainly depends on the magnitude of the intensity of stochastic fluctuations, which is rarely affected by the specific functional expressions of the stochastic terms. Finally, empirical analysis is conducted by selecting five sets of processed data from the next-generation simulation (NGSIM) dataset. Based on parameter estimation using the SMLVM model, and a comparison is made with the FVD model and BLVD model. The results confirm that the model proposed in this paper fits vehicle speeds better and absorbs disturbances more effectively, and exhibiting greater stability in traffic flow.

Familial aggregation of traffic risky behaviours among pedestrians: a cross-sectional study in northwestern Iran

ObjectiveThis study aims to assess the familial aggregation of traffic risky behaviours among pedestrians and describe the sociodemographic profile of pedestrians in northwestern Iran.MethodsA cross-sectional study was conducted among 933...

Botnet detection based on Markov chain and Fuzzy rough set

Botnets now make up a wide range of cyber-attacks, which are a network of infected computers connected to the Internet, with remote control. So far, a lot of research has been done in this field, the proposed methods are based on the signatures of discovered botnets, anomalies, traffic behavior, and addresses. Each method has both advantages and disadvantages. This research proposes a structure for performing identification operations, which is presented in this research based on the Markov chain and is based on behavioral analysis. A disadvantage of the past methods is the inability to receive network information at a very high speed. In this research, it has tried using a solution to receive traffic at a very high speed of about 40 Gbps and analyze it. To be able to perform the analysis with a lower overhead. The proposed method can investigate the behavior of botnets by examining the area of behavior better than the previous solutions, and as a result, during the solutions used by botnets to hide their behavior, it can counter and identify suspicious flows. The accuracy of the proposed method was found to be 96.170%. DOI: https://doi.org/10.52783/pst.390

USING MACHINE LEARNING ALGORITHMS TO DETECT ANOMALOUS TRAFFIC BEHAVIOR

The article describes a method of using machine learning for detecting anomalous traffic behavior. For this purpose, a data set containing a significant amount of traffic collected at the time of the attack on the Web application is used. The set contains three attack options: Brute Force, XSS, SQL injection. A traffic dump containing an Infiltration attack is considered separately. A comparative analysis of machine learning models was carried out with the selection of the most optimal one. The article also provides a description of the data preprocessing procedure, which is carried out in order to eliminate anomalies and voids in array records, which can lead to incorrect operation of the trained model. Models were trained on selected data in order to identify anomalous traffic behavior indicating a specific type of attack. In addition, a study was conducted on a data set that does not contain information about attacks.

Features of the Application of Traffic Flow Management Methods and Tools

This article examines the causes and consequences of traffic jams, describes typical traffic flow behavior and analyzes traffic control methods and means. The paper demonstrates the proposed classification of traffic lights by type of regulation. In summary, the article represents a detailed overview of existing cyber-physical traffic control systems, such as SEA TCS, InSync and MASSTR. The article analyzes the existing methods of traffic regulation, examines the causes and consequences of congestion, the division of intersections into regulated and unregulated, and the classification of traffic lights by type of traffic control. Among the main parameters of traffic flow used by cyberphysical traffic control systems, the primary and most used are speed, density, and volume of vehicles. The article also reviews the existing cyber-physical traffic control systems and the primary technologies.