Traffic Flow Parameters Research Articles

Traffic flow parameter estimation for complex environments based on improved YOLOv8

Estimation of traffic flow parameters based on computer vision is still a very popular challenge. In particular, the detection problems such as occlusion, small targets, luminance variations, and lighting jitter, etc., are caused by dense traffic scenes and complex weather conditions. Previous studies about detecting and tracking methods focused on daytime and nighttime or single weather condition, but the performance of this model will decline dramatically due to complex conditions. In this paper, a framework for estimating traffic flow parameters in complex environments is presented, in which Ghost-YOLOv8 vehicle detection model is proposed. GhostConv is used to replace part of Conv and a new C2fGhost module is designed to replace part of C2f, reducing the number of parameters and improving the detection performance of the model; The global attention mechanism module is added to the neck network, which strengthens the semantic and location information in the features and improves the model’s ability of feature fusion; In view of the loss of semantic information caused by different scales in detecting small targets, a small target detection layer is added to enhance a combination of deep and shallow semantic information; The GIoU bounding loss function is used instead of the original loss function, which improves the performance of the network’s bounding box regression. Then, combining DeepSORT tracking algorithm and virtual line counting method, the proposed framework estimates traffic flow parameters, including volume, speed, and density in complex environments. The experimental results show that compared with the original model in the UA-DETRAC dataset, the improved YOLOv8 model’s the accuracy is increased by 1.4%, and the parameter number and model size are reduced by 0.229G and 0.2MB respectively. In addition, the framework has good robustness, reaching 97.56% accuracy when estimating average traffic flow parameters. It is sufficient to overcome complex weather conditions and effectively help control traffic for intelligent transportation systems and traffic management to provide good data support. In conclusion, it shows that the model can reduce the number and size of model parameters and improve the detection precision as well as meeting the requirements of edge computing devices and having better real-time performance, so it has practical application value.

Development and Validation of Asphalt Pavement Rutting Prediction Model with Transient Temperature Field Considered

This study developed a transient rutting analysis program based on the principles of heat transfer and the viscoelastic–viscoplastic theory of asphalt mixtures. Using the finite element model (FEM) model corresponding to the multi-layer pavement structure and the subprogram interface of ABAQUS software, along with Python programming, the program was designed to estimate rutting under transient temperature field conditions with climate conditions, traffic flow, and structure layer constitutive parameters of pavement considered. Subsequently, the program was used to validate the efficiency of the solution and analyze the influencing factors of internal temperature field and rutting depth within the asphalt pavement structure. Results showed that, based on meteorological data and recommended values of material thermal properties, the FEM of transient temperature field can accurately simulate the temperature field distribution of pavement structure. Based on loading information and dynamic modulus test, the transient temperature variable rutting with FEM proposed in this paper can simulate and calculate the rutting of pavement structure layer with some errors for different pavement conditions from 5% to 40%. Through parametric analysis of the influence factors of temperature field and rutting, the modified transient temperature field rutting prediction program can effectively simulate rutting of pavement structure, and the minor errors were less than 20%.

Multi-lane cellular automata model considering bus-HOV lane

To quantify the impacts of high occupancy vehicles (HOV) entering the exclusive bus lane on road traffic efficiency, the car following and lane changing characteristics of bus, HOV and non-HOV were analyzed when setting the bus-HOV lane. Using the theory of cellular automata (CA) and combining the control rules of the bus-HOV lane, the lane occupied by a vehicle and the type of a vehicle regarded as constraints were introduced into the asymmetric lane changing rules, and then a multi-lane CA model was established for the car following and lane changing of multi-type vehicles considering the bus-HOV lane. MATLAB software was used to accomplish numerical simulation to analyze the specific effects of the proportion of HOV, bus departure frequency and bus stop spacing on the traffic flow parameters and their relationships. The results show that: when the proportion of HOV is less than 0.5 on a three-lane road segment, setting the bus-HOV lane can not only improve road traffic efficiency but also ensure the speed of bus operation under the condition of high initial space occupancy; when the proportion of HOV exceeds 0.5 , it is difficult to improve road traffic efficiency by restricting non-HOV from entering the bus-HOV lane; when the initial space occupancy is greater than 0.2 , the higher the bus departure frequency is, the greater the decline of traffic volume of the bus-HOV lane is; when there are 5 bus routes to dispatch a bus every 3 minutes, compared with the low initial space occupancy, the traffic volume of the bus-HOV lane under the condition of the high initial space occupancy reduces by about 30%; the bus stop spacing more significantly affects the traffic efficiency of the bus-HOV lane, the longer the bus stop spacing is, the higher the frequency of HOV lane changing is, the more the increase of the congestion segments on the bus-HOV lane and the adjacent normal lane is, thus the traffic efficiency of the whole road is influenced.

Real-time accident risk identification for freeway weaving segments based on video analytics

Accurately identifying the risk of traffic accidents in real time for specific road sections is crucial for implementing proactive traffic control strategies. The existing studies have focused on real-time prediction of collisions between two vehicles, identification of accident risks on road segments relies heavily on historical accident data. This study proposes a framework for identifying road section accident risks at the granular level by utilizing traffic conflicts as an intermediary. In this framework, vehicles are detected and tracked in real time and the pixel coordinates are then efficiently converted into world coordinates based on the camera projection principle. A binary logit model is constructed to identify road section accident risks using carefully selected macroscopic traffic flow parameters. The proposed framework was validated on two one-sided ramp weaves in Jinan, China, achieving an accuracy of 76.2 %. The findings will serve as a foundation for traffic safety monitoring and proactive traffic control.

Simulation Modeling of the Operation of the Toll Plaza with Reversible Lanes

The construction of toll roads depends on the available territory. The limited area often does not allow the construction of a full-fledged toll plaza (hereinafter referred to as TP) with high capacity and many lanes. In such cases, the configuration of TP is linked with the configuration of reversible lanes. Reversible lanes carry traffic and help optimize traffic flow. The challenge is to choose the optimal configuration of a TP that provides the highest capacity (traffic flow increases in both directions) and helps control operators’ errors resulting in traffic congestion problems. The present study estimates TP with reversible lanes throughput capacity in different configurations and traffic flow parameters. The study employs discrete-event simulation modeling in the AnyLogic software environment. Our results show the optimal configuration of the reversible lanes and explain what traffic flow parameters affect their capacity. The paper concludes with practical recommendations on how to effectively apply simulation modeling to a TP operation and optimize it.

Scene adaptation in adverse conditions: a multi-sensor fusion framework for roadside traffic perception

Robust roadside traffic perception requires integrating the strengths of multi-source sensors under various adverse conditions, which is challenging but indispensable for formulating effective traffic management strategies. One limitation of existing radar-camera perception systems is that they focus on integrating multi-source information without directly considering scene information, leading to difficulties in achieving scene adaptive fusion. How to establish the connection between scene information and multi-source information is the key challenge to solving this problem. In this article, we propose a Scene adaptive Sensor Fusion (SSF) framework that characterizes scene information and integrates it into radar-camera fusion schemes, aiming to achieve high-quality roadside traffic perception. Specifically, we introduce a multi-source object association method that accurately associates multi-source sensor information on the roadside. We then utilize coding techniques to characterize the scene information, including visibility characterization regarding lighting and weather conditions, and road characterization regarding sensor viewpoint. By incorporating sensor and scene information into the fusion model, the SSF framework effectively establishes the connection between them. We evaluate the SSF framework on the Roadside Radar and Video Dataset (RRVD) and the Traffic flow Parameter Estimation Dataset (TPED), both collected from real-world traffic scenarios. Experiments demonstrate that SSF significantly improves vehicle detection accuracy under various adverse conditions compared to traditional single-source sensing methods and other state-of-the-art fusion techniques. Furthermore, vehicle trajectories based on SSF detection results enable accurate traffic parameter estimation, such as volume, speed, and density, in complex and dynamic environments.

Optimizing energy efficiency in intelligent vehicle-oriented road network design: A novel traffic assignment method for sustainable transportation

In light of the development of intelligent vehicles and the persistent energy crisis, improving the predictive accuracy of macroscopic traffic flow and formulating energy-efficient road network design solutions are essential to promote sustainable development in road transportation. This study introduces a dual-objective impedance function that incorporates considerations of both travel time and energy consumption. The function is developed utilizing the principles of intelligent vehicle energy flow and traffic flow parameters. Then, an enhanced user equilibrium traffic assignment (E-UE) model is formulated. The effectiveness of the E-UE function was validated through road experimental data. The performance of E-UE was analyzed using a planned road network consisting of 28 sections. In comparison with the classical UE model, the E-UE model promotes uniform traffic distribution and alleviates congestion, leading to a 1.1 % reduction in average road energy consumption. This highlights the E-UE model’s effectiveness in accurately capturing the impacts on energy consumption with enhanced predictive precision. Strategies to mitigate potential energy consumption include implementing the E-UE model with realistic weights, adjusting weights for specific roads, employing a design speed of 50 km/h, and utilizing 6-lane roads. This study contributes to the advancement of energy-saving technologies in sustainable transportation.

Impacts of connected autonomous vehicle platoon breakdown on highway

Connected Autonomous Vehicles (CAVs) are seen as an opportunity to improve traffic efficiency and safety. However, further studies are needed to prove such positive effects. Particularly, there is a lack of quantitative research on the impacts of CAV breakdown in mixed traffic flow conditions (i.e., CAVs and Human Driven Vehicles, HDVs). The aim of this research is to explore the impacts generated by the breakdown of the leading vehicle of a CAV platoon in mixed traffic conditions, which in turn generates CAV platoon breakdown (i.e., platoon dispersion), by considering several CAV market penetration rates (MPRs) and platoon size. In this perspective, a control algorithm (“avoidance algorithm”) for modelling CAVs and HDVs behaviour to avoid obstacles (i.e., breakdown CAV platoon) has been developed and tested on some simulation scenarios in order to derive key traffic flow parameters. The dynamic characteristics of traffic flow with CAV platoon breakdown have been explored for both low and high traffic flow volumes. Finally, potential conflicts, congestions, as well as energy consumption and CO2 emissions resulting from the breakdown of CAV platoons in mixed traffic streams have been assessed and discussed. Results suggest that (1) CAV platoon breakdown can reduce the traffic capacity by about 20 %; (2) higher CAV MPRs are more suitable for enhancing highway safety even in breakdown conditions, decreasing energy consumption, and reducing CO2 emissions; (3) platoon size should be limited to 4, since larger sizes affect traffic safety, increases vehicle average delay time as well as energy consumption. The obtained results provide useful insights for transportation planners and transport infrastructure management companies to design and apply policies aimed at improving driving conditions, traffic quality, and safety on highways.

A Short-Term Vessel Traffic Flow Prediction Based on a DBO-LSTM Model

To facilitate the efficient prediction and intelligent analysis of ship traffic information, a short-term ship traffic flow prediction method based on the dung beetle optimizer (DBO)-optimized long short-term memory networks (LSTM) is proposed. Firstly, according to the characteristics of vessel traffic flow, speed, and density, the traffic flow parameters are extracted from the AIS data; secondly, the DBO-LSTM model is established, and the optimal hyperparameter combinations of the LSTM are found using the DBO algorithm to improve the model prediction accuracy; then, taking the AIS data of a part of the coastal port area in Xiangshan as an example, we compare and analyze the results of the recurrent neural network, temporal convolutional network, LSTM, and DBO-LSTM prediction models; finally, the results are displayed and analyzed by visualization. The experimental results show that each error is reduced in predicting the flow parameter, speed parameter, and density parameter, and the accuracy reaches 95%, 92%, and 95%, respectively. After predicting the three parameters in the next 24 h, the accuracy rate reaches 93%, 91%, and 94%, respectively, compared with the real data, which surpasses the comparison model and achieves better prediction accuracy, verifying the feasibility and reasonableness of the proposed prediction model.

Study of the correspondences distribution of vehicle traffic on the road network of cities

The problems of traffic management in large cities are complicated as the increase in traffic volume far exceeds the road network capacity. It leads to saturation of the road network, which negatively affects its functioning. This article analyzes the state of the art of improving the quality of road traffic by predicting the size of traffic after the implementation of traffic management measures. While the issue of modeling traffic flow parameters based on technological factors has been sufficiently studied, the problems of considering the human factor need to be clarified. The object of study is traffic flow on the city's road network. The criteria used by drivers to compare the characteristics of alternative traffic routes were identified based on the field study results. Based on the data obtained, the parameters of alternative routes that drivers choose when driving on the road network are formed. According to the survey results, the most significant factor is the minimum mileage along the route. Deviations from the shortest route were determined to determine the patterns of distribution of correspondence of non-route vehicles along alternative routes. It made it possible to define the distribution of transport correspondence along alternative routes. Since the process under consideration is probabilistic, the law of distribution of a random variable was determined. After having determined the law of distribution of a random variable for the data obtained, the calculations showed that the change in the random variable is well described by the gamma distribution. It was determined that the share of transport correspondence that will be carried on them decreases as the length of the route deviates from the shortest one. The obtained results make it possible to predict road network congestion by modeling the distribution of traffic flows. In further research, it would be advisable to analyze the distribution of transport correspondence by other criteria.

A multiple directions turning vehicle counting method at intersections based on arbitrary-oriented detection and stack Long Short-Term Memory

The traffic flow parameters of turning vehicles at urban road intersections play an important role in the intelligent traffic system. Traffic road monitoring is difficult to cover large intersection scenes. With the application of unmanned aerial vehicles in traffic, drone-based intersection monitoring has great potential due to the large perspective. But there is rarely research on multiple turning vehicle counting at intersections based on drones. A few existing multiple turning vehicle counting methods are based on vehicle detection and tracking methods. However, the vehicles at the intersection have various orientations, the accurate information of the vehicle cannot be obtained by detection, and the tracking process is complicated and redundant for the counting task. Based on the traffic video at intersections taken by unmanned aerial vehicles, we propose a eight turnings vehicle counting model without complex tracking. Firstly, a spatial attention and channel adaptive attention model net is proposed for arbitrary-oriented vehicle detection to get the orientation and position of vehicles. Secondly, a turning spatio-temporal counting feature for different turning vehicles and its extraction method are proposed. Finally, the stack Long Short-Term Memory net based counting model is designed to process the turning spatio-temporal counting feature and counting eight different turnings vehicles at intersection. The experiments show that the proposed method can realize counting eight different turnings vehicles simultaneously in drone-based traffic video at the intersection without relying on complex multi-target tracking. The average counting accuracy has reached 98.18%.

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.

A trajectory data-driven approach for traffic risk prediction: incorporating variable interactions and pre-screening

ABSTRACT Although historical crash data and trajectory data have been widely applied to crash and risk predictions, both types of data have their own limitations. As a solution, this study investigates the impact of the traffic flow parameters and their interaction terms on risk prediction performance, employing a variable pre-screening approach (i.e. Smoothly Clipped Absolute Deviation (SCAD)). A research framework is proposed for more efficient risk prediction, and a detailed case study is further conducted using the proposed approach. In the case study, real vehicle trajectory data from HighD are processed and used, which can be aggregated to extract both traffic flow parameters and corresponding risk data during a specific time interval. As for the risk detection, Time-to-Collision (TTC) index is utilized to identify risky conditions. For different lanes (i.e. inner, middle and outer lanes), the impact of variables, including interaction terms, on risk is explored using the SCAD-logistic models. Furthermore, machine learning methods are employed to compare the risk prediction performance before and after considering interaction terms, as well as before and after variable pre-screening. Finally, the superiority of the machine learning models after SCAD-based variable pre-screening is demonstrated. Results indicate that the interaction terms between traffic flow parameters have significant impacts on the traffic risk. Besides, considering interaction terms and variable pre-screening can improve risk prediction accuracy. Furthermore, the proposed models outperform Random Forest (RF) in terms of predicting traffic risk, achieving a maximum 21.24% accuracy improvement and reducing computational time by up to 31.51%. Findings of this study are expected to contribute to the high-precision prediction of real-time risk in the future.

Integrating variable speed limit and ramp metering to enhance vehicle group safety and efficiency in a mixed traffic environment

The integrated variable speed limits and ramp metering (VSL-RM) strategy is a useful method to avoid crashes and alleviate congestion on expressways. Previous studies have mostly focused on specific roadway segments and optimized them with the single goal of efficiency or safety, which does not allow for a prompt response to high-risk moving vehicle groups to improve safety and efficiency. To reduce the crash and congestion risk of vehicle groups in real time, this study developed three VSL-RM strategies with different optimization objectives based on predicted risks in a mixed traffic flow environment including connected vehicles (CVs) and regular vehicles (RVs). Due to the different behaviors of CVs and RVs under the VSL-RM control strategy, a mixed traffic METANET model was introduced to predict traffic flow parameters, e.g., volume and speed. Furthermore, two risk prediction models were utilized to predict crash risk and congestion risk based on the traffic flow parameters predicted by the mixed-traffic METANET model. The objectives of the three VSL-RM strategies were to minimize the crash risk, congestion risk, and both crash and congestion risks of the vehicle groups, respectively. These strategies were evaluated using a well-calibrated micro-simulation network. The mixed flow METANET model and the risk prediction model were validated to be consistent with the simulated traffic flow. The results demonstrated that the three strategies could simultaneously improve safety and efficiency benefits in most scenarios. However, the safety-targeted strategy provided the highest safety benefits, while the efficiency-targeted strategy provided the highest efficiency benefits. The bi-objective strategy outperformed the other two strategies in balancing the benefits of safety and efficiency. Moreover, increasing the CV penetration rate resulted in higher benefits for all three strategies.

ASSESSMENT OF THE INFLUENCE OF THE INTENSITY OF TRAFFIC FLOWS ON THE STATE OF THE STREET AND ROAD NETWORK OF THE CITY

The article is devoted to the assessment and forecasting of the level of atmospheric air pollution in street canyons in Kyiv by controlling the parameters of traffic flow. Today, road transport is the main anthropogenic source of environmental pollution. The specificity of air pollution by traffic flows lies in their ground location, close proximity to people's homes, which leads to the accumulation of pollutants near the ground surface - in the breathing zone of people. The article analyzes the main approaches to modeling the dispersion of pollutants in the atmosphere from motor vehicles. It is advisable to assess the level of urban air pollution in homogeneous elements of the urban area - street canyons. Based on the urban street canyons model (OSPM), the level of pollution in the street canyons of the Pechersk district of Kyiv by the main harmful substances contained in car exhaust gases is determined. The critical intensity of traffic flows for each of the street canyons at which the level of pollution by the corresponding harmful substance reaches the maximum permissible value was determined. Thus, the obtained results of the study allow us to quickly predict the level of air pollution in the roadside space of cities and prevent environmentally hazardous situations by controlling the parameters of the traffic flow.

Bicycle as a traffic mode: From microscopic cycling behavior to macroscopic bicycle flow

Cities allocate dedicated road space to bicycles in favor of active-mode road users. For urban environments with a mass bicycle volume, bicycle traffic congestion is likely to occur. Hence, a thorough understanding of bicycle traffic flow is necessary for the assessment of cycling infrastructure and the development of traffic management strategies considering cycling efficiency. This study aims to investigate bicycle flow characteristics using microscopic traffic simulation. As bicycle flow performance is subject to the non-lane-based movement strategy and the behavioral heterogeneity among cyclists, various scenarios with different microsimulation settings are evaluated. Ultimately, we derive the functional form fundamental diagrams and macroscopic fundamental diagrams using a curve-fitting approach and an analytical method, respectively. Important macroscopic traffic flow parameters, such as capacity, critical speed, critical density, backward wave speed, etc., are estimated. The results show that lane width, overtaking incentive, and desired speed distribution are factors that affect bicycle flow performance. The distinct features of bicycle flow under different traffic states are identified by discussing the simulation outcome and comparing the estimated flow parameters. The findings can be utilized by future research regarding large-scale bicycle traffic flow modeling and control.

A traffic dynamic operation risk assessment method using driving behaviors and traffic flow Data: An empirical analysis

Presently, traffic safety analysis is mainly based on accident-based indicators, but they are incidental and small probability events, making safety analysis based on accident-related data difficult to achieve a comprehensive application in network size, and to realize risk assessment before accidents occur. To this end, this study innovatively develops a metric named Traffic Dynamic Operation Risk (TDOR) based on aggressive driving behaviors (ADBs) and traffic flow data for traffic safety evaluation. And Non-Negative Matrix Factorization (NMF) is adopted to explore the quantitative risk under the coupling of multiple risk factors. The results show that: 1) segments with high TDOR are always concentrated in a certain area and in a spatial distribution of Gaussian, and the peaks of these distributions are almost the assesses of transportation hubs or complex intersections; 2) traffic volume and coefficient of speed variation are the significant risk factors at ordinary sections and branches, while traffic volume and speed difference between upstream are the largest contributions at intersections; 3) roads with more accidents are associated with a higher TDOR, but the relationship between the two is not completely linearly correlated. The experimental results show that it is reasonable and feasible to use ADBs and traffic flow parameters to quantify traffic safety risks. Compared with other matrix factorization methods, the importance of variables based on NMF is more interpretable and more consistent with the change pattern of risk variables. The method proposed in this paper can change the previous traffic risk assessment pattern based on accident-related indicators and provide a basis for pre-accident risk prevention.

Novel Speed-Flow Model Based on Queuing Theory and Its Potential Application in Highway Capacity Guidelines

Speed-flow relationships are incorporated in many highway capacity guidelines such as the U.S. Highway Capacity Manual (HCM) and the German Highway Capacity Manual (HBS). The speed-flow relationship can be used to predict the operating speed depending on the traffic volume. Based on traffic-flow theory, many models for the speed-flow relationship under fluid and congested traffic conditions have been developed. However, most of the speed-flow relationships used in guidelines are either based on empirical regression analysis and thus lack a theoretical background or include model parameters that do not have a realistic meaning. In this paper, a speed-flow relationship is derived based on the queuing theory in a form that the main model parameters can be expressed as real traffic-flow parameters, which can be obtained by field measurements. These parameters are the free-flow speed, the potential capacity of the imbedded queuing system, and a parameter for the stochastics of the queuing system. By varying the model parameters, different geometric, traffic, and control conditions can be considered. Even the impact of new technologies such as connected and automated vehicles can be accounted for by adjusting those parameters.

Evaluation indicators for road traffic energy consumption: a review and prospect.

Indicators for evaluating road traffic energy consumption are critical parameters in the research field of road traffic energy consumption. Improving the applicability of energy consumption indicators can promote the development of green transportation in cities. However, there is currently a lack of systematic analysis of energy consumption indicators in research. Therefore, based on a comprehensive analysis of relevant literature, this study divides the indicators for evaluating road traffic energy consumption into two categories: macro (aimed at traffic systems or traffic flow) and micro (aimed at vehicles). These indicators are subdivided into four categories according to their application characteristics, including general, specific, predictive, and comprehensive. This paper provides a complete summary of various evaluation indicators, including their scope of application, advantages, and limitations, and highlights the relationships between them. Additionally, recommendations are made for the future development of evaluation indicators. Research has found that micro-level general indicators serve as the fundamental components of the mathematical structure for almost all other indicators. Specialized indicators primarily evaluate energy consumption in different driving states of vehicles. Predictive indicators are mainly used for assessing transportation energy consumption in simulation conditions. Comprehensive indicators are mainly applied to evaluate the life cycle energy consumption of vehicles or transportation systems. In future research, the performance of indicators can be improved through the design of standardized indicators, enhancement of energy consumption prediction accuracy, and integration of traffic flow parameters. The research contributes to upgrading energy-saving technologies in road transportation and developing sustainable urban transportation systems.

Research on Vehicle Congestion Group Identification for Evaluation of Traffic Flow Parameters

The traffic flow parameters of the road network are most often evaluated through volumes, which are compared with its maximum volume (capacity) or speed and density. Capacity assessment was performed, considering horizontal and vertical orientation and characteristics of the traffic stream. This article presents the results of research on the identification of different states of creating congestion groups and their relationship to road capacity or decrease in speed. The following hypothesis was verified: when the capacity of the road is exceeded or almost reached, there is “always” a significant drop in the flow of traffic compared to when the capacity is not exceeded. The analysis showed that the average travel speed drops by 30% for the condition where groups of 25 or more vehicles are formed with a time interval of up to 4 s. The results make it possible to set traffic models in short time intervals according to real spatial conditions and to use them in the analysis of the environmental and safety impacts of road transport.