Traffic Flow Density Research Articles

MDFD: A multi-source data fusion detection framework for Sybil attack detection in VANETs

Sybil attacks in Vehicular Ad-Hoc Networks (VANETs) conduct malicious behavior by falsifying and faking messages between vehicles. It poses a significant threat to the safety of vehicle movement. Meanwhile, because Sybil attacks often hide the real identity of the attacker with the help of a legitimate pseudonym, making it very difficult to detect them. Most existing detection schemes use a single data source, which is not enough to describe the specific characteristics of the attack behavior accurately, while their detection performance is also affected by real scenario factors such as traffic flow and attacker density. Therefore, we propose a multi-source data fusion detection framework for Sybil attacks based on the study of the behavior characteristics of Sybil attacks and the impact of the attacks on the traffic flow state. We get basic safety messages data, map data and sensor data and then obtain multi-dimensional data fusion features from four aspects: spatio-temporal location relationship, traffic flow state change, vehicle behavior characteristics and sensor data verification, and finally use machine learning classification model to complete the detection of attack behavior. Experimental results show that our proposed attack detection framework is able to locate the specific road section where the attack occurred in a realistic and complex traffic scenario containing different road types without using trusted vehicles as observation nodes, and has good generalization capability. the average detection accuracy of the MDFD framework for four types of compound attacks is as high as 97.69%.

Multi-Lane Traffic Load Clustering Model for Long-Span Bridge Based on Parameter Correlation

Traffic loads are the primary external loads on bridges during their service life. However, an accurate analysis of the long-term effect of the operating traffic load is difficult because of the diversity of traffic flow in terms of vehicle type and intensity. This study established a traffic load simulation method for long-span bridges based on high authenticity traffic monitoring data, and an improved k-means clustering algorithm and Correlated variables Sampling based on Sobol sequence and Copula function (CSSC) sampling method. The monitoring traffic data collected through a weigh-in-motion (WIM) system was processed to generate a multi-lane stochastic traffic flow. The dynamic response of a prototype suspension bridge under a traffic load was analyzed. The results show that the traffic load can be divided into clusters with identical distribution characteristics using a clustering algorithm. Combined with CSSC sampling, the generated traffic flow can effectively represent daily traffic and vehicle characteristics, which improves the accuracy of the assessment of the loads long-term effect. The dynamic response of the bridge to different traffic flows varied significantly. The maximum and minimum vertical displacement of the main girder was 0.404 m and 0.27 m, respectively. The maximum and minimum bending stresses of the short suspender were 50.676 MPa and 28.206 MPa, respectively. The maximum equivalent bending stress and axial stress were 16.068 MPa and 10.542 MPa, respectively, whereas the minimum values were 9.429 MPa and 8.679 MPa, respectively. These differences directly influence the short and long-term evaluation of bridge components. For an accurate evaluation of the bridge operation performance, the traffic flow density must be considered.

Research on en route capacity evaluation model based on aircraft trajectory data

<abstract> <p>For the sake of refined assessment of airspace operation status, improvement of the en route air traffic management performance, and alleviation of the imbalance of demand-capacity and airspace congestion, an en route accessible capacity evaluation model (based on aircraft trajectory data) is proposed in this paper. Firstly, from the perspective of flux, the en route capacity is defined and expanded from a two-dimensional concept to a three-dimensional concept. Secondly, based on the indicators of spatial flow and instantaneous density, an evaluation model of en route capacity is given. Finally, a case study is performed to validate the applicability and feasibility of the model. Results show that the en route accessible capacity, instantaneous density, and spatial flow can describe the temporal and spatial distribution of air traffic flow more precisely, as compared to the conventional indicators, such as route capacity, density, and flow. The proposed model envisages three innovations: (ⅰ) the definition of airspace accessible capacity with reference to capacity of road traffic, (ⅱ) the computation model for flux-based airspace accessible capacity and en route accessible capacity, and (ⅲ) two indicators of en route characteristics named instantaneous density and spatial flow are introduced for evaluating the micro-state of the en route. Furthermore, because of the capacity depiction of the spatial and temporal distribution of air traffic congestion within an airspace unit, this model can also help air traffic controllers balance the distribution of traffic flow density, reduce the utilization rate of horizontal airspace, and resolve flight conflicts on air routes in advance.</p> </abstract>

Обгрунтування критерію стійкості транспортного потоку на дільницях дорожньої мережі

The work considers the justification and obtaining the criteria for assessing the stability of the traffic flow on various sections of the street and road network under the influence of external disturbances. Analysis of the criterion allows to formulate the parameters on which stability depends. As follows from the expressions by which the criterion is calculated, the stability of the traffic flow is affected by the density and intensity of the traffic flow. They must be calculated for each section of the road network or highway in the form of amplification factors. The time constants depend on the qualification and psychophysiological properties of the driver, the degree of his fatigue, the dynamic properties of the car and road conditions. It is shown that when the value of the criterion is equal to one, the transport flow functions on the verge of loss of stability. If the value of the criterion is less than one, the traffic flow has lost its stability, traffic stops - traffic jam. If the value of the criterion is greater than one, the transport flow is stable, i.e. functions without delays and traffic jams. The larger the value of the criterion, the greater the margin of stability. Based on the obtained results of theoretical studies, the robustness of the traffic flow is defined. The robustness of the traffic flow (English robust range) is a dimensionless value that characterizes the range of stable movement of vehicles on sections of the road network, taking into account its infrastructure, density and intensity of traffic without delays and traffic jams. It is shown that the developed criterion of robustness must be applied in the analysis of the road network for the occurrence of delays during traffic and traffic jams, as well as in the design of a new urban road network. The obtained result differs from the known ones given in the review of literary sources in that it allows to determine the limits of the loss of stability - the formation of traffic jams - through modeling. Determining the limit values of traffic flow density and intensity, their gradients, accounting for multi-lane traffic will allow developing measures to prevent traffic jams.

Map-enhanced generative adversarial trajectory prediction method for automated vehicles

Trajectory prediction in dynamic and highly interactive scenarios is a critical method for achieving advanced autonomous driving. Maximizing the guidance and constraints provided by high-definition (HD) maps can help improve prediction performance across the board. In this paper, we propose a map-enhanced generative adversarial network (ME-GAN) for vehicle trajectory prediction. The vehicle motion features, map constraints, traffic flow density, and vehicle interactions are comprehensively considered in the generator, and a graph query mechanism is proposed to realize the reuse of the global map. In the discriminator, in addition to considering the authenticity of a generated trajectory and whether it is consistent with the historical trajectory, additional map information is introduced to establish a matching model between the generated trajectory and the current map. Experiments based on the Argoverse and nuScenes dataset are subsequently performed. The experimental results show that our prediction method outperforms state-of-the-art prediction systems, namely, TNT, PRIME and P2T. The strong coupling of the HD map significantly improves the reasonableness of the predicted trajectory.

A Malware Attack Enabled an Online Energy Strategy for Dynamic Wireless EVs within Transportation Systems

Developing transportation systems (TSs) under the structure of a wireless sensor network (WSN) along with great preponderance can be an Achilles’ heel from the standpoint of cyber-attacks, which is worthy of attention. Hence, a crucial security concern facing WSNs embedded in electrical vehicles (EVs) is malware attacks. With this in mind, this paper addressed a cyber-detection method based on the offense–defense game model to ward off malware attacks on smart EVs developed by a wireless sensor for receiving data in order to control the traffic flow within TSs. This method is inspired by the integrated Nash equilibrium result in the game and can detect the probability of launching malware into the WSN-based EV technology. For effective realization, modeling the malware attacks in conformity with EVs was discussed. This type of attack can inflict untraceable detriments on TSs by moving EVs out of their optimal paths for which the EVs’ power consumption tends toward ascending thanks to the increasing traffic flow density. In view of this, the present paper proposed an effective traffic-flow density-based dynamic model for EVs within transportation systems. Additionally, on account of the uncertain power consumption of EVs, an uncertainty-based UT function was presented to model its effects on the traffic flow. It was inferred from the results that there is a relationship between the power consumption and traffic flow for the existence of malware attacks. Additionally, the results revealed the importance of repressing malware attacks on TSs.

Effects of electronic throttle dynamics in non-equilibrium heterogeneous traffic flow without lane discipline

Urban transportation systems are quickly developing more intelligent and networked systems. To further enhance the intelligent networked traffic environment, this study undertakes a thorough investigation and analysis of the features of heterogeneous traffic flow in a linked environment without lane discipline. A new non-equilibrium mixed traffic flow macroscopic model is proposed in connection with electronic throttle dynamics in light of the significance of electronic throttle angle information for vehicle-to-vehicle communication technologies. This paper investigates the stability of mixed traffic systems using a small disturbance method, as well as the nonlinearity of the systems is analyzed near the neutral stability curve. The effects of electronic throttle opening angle, lateral gap, and CAV penetration rate on the evolution of the traffic flow density wave are numerically investigated. The numerical experiment also compares and evaluates how the new model and three other scene models fares in terms of tiny perturbation evolution, energy consumption, and extra emission. The experiment's finding demonstrated that this macro model had a larger stable domain and less energy consumption and extra emissions when compared to other scenarios.

Modeling mixed traffic flows of human-driving vehicles and connected and autonomous vehicles considering human drivers’ cognitive characteristics and driving behavior interaction

Connected and autonomous vehicles (CAVs) are developing rapidly nowadays. In the near future, we may see human-driving vehicles (HVs) and CAVs running on the same road. The characteristics of human drivers and the interaction between HVs and CAVs, therefore, need to be further studied. All these factors will have a great impact on traffic operation. This paper aims to explore a modeling method to analyze the operation mechanism of mixed traffic flows of HVs and CAVs. Considering HV drivers’ cognitive behavioral characteristics and HV–CAV interaction effects, the forward and lateral movement rules of HVs and CAVs are proposed based on the Cellular Automata model. Then the fundamental diagram, congestion degree, lane-changing frequency, and time-spatial diagram are obtained by numerical simulation. The results show that the presence of CAVs is positively related to macroscopic traffic parameters, including velocity, flow and critical density. CAVs help to relieve traffic congestion and instabilities. The congestion degree of pure CAV traffic flow is about 1/3 that of pure HV traffic flow. When the CAV penetration rate is relatively low, the interaction between HV and CAV has a significant impact on traffic operation. Specifically, when the CAV penetration rate is below 0.5∼0.6, increasing the CAV penetration rate will increase the lane-changing frequency of the whole traffic flow.

Study on mixed traffic of autonomous vehicles and human-driven vehicles with different cyber interaction approaches

The emergence of autonomous vehicles will significantly improve traffic efficiency and safety. Before the fully autonomous driving of traffic system, the mixed traffic with autonomous and human-driven vehicles will be a typical feature for the future road traffic. To study the properties of a mixed traffic mode combining autonomous and human-driven vehicles, a cellular automata model based traffic system considering autonomous and human-driven vehicles is proposed. In the mixed traffic model, human-driven vehicles can use multiple preceding vehicles' information within drivers' visual range for vehicle motion. For autonomous vehicles, the interaction mechanism of information is iterative and the communication range for vehicle-to-vehicle is considered. The results from this study show that the average traffic flow can be enhanced with the increase of communication range for autonomous vehicles. But when the communication range is big enough, its role on traffic flow is little. Also, it is found that human-driven vehicles' behavior of obtaining multiple preceding vehicles' information can significantly impact the meta-stable state of traffic flow in the average flow density phase. Further, the relationship between the average flow and vehicle density of the mixed traffic flow with random distribution, uniform distribution and separate distribution is discussed. And it reveals that the traffic capacity of the mixed traffic flow with separate distribution is more impressive comparing to the traffic capacity with random distribution and uniform distribution. Finally, the traffic congestion rate under different values of penetration rate is verified and it uncovers that the congestion rate is reduced with the increase of penetration rate.

Road pricing and spatial distribution of traffic flow in radial-arc network

This paper discusses the effect of road pricing on the spatial distribution of traffic flow. The traffic flow density is derived for a circular city with a radial-arc network. The traffic flow density describes the amount of traffic as a function of position and allows us to identify the location of potential congestion areas. The analytical expression for the traffic flow density demonstrates how the size of the toll area and the toll level affect the spatial distribution of traffic flow. As the size of the toll area increases, the decrease in traffic flow inside the toll area becomes smaller. As the toll level increases, the increase in traffic flow at the boundary of the toll area becomes greater. The effect of the travel cost on the spatial distribution of traffic flow is also examined. These findings can be used to determine the size of the toll area and the toll level required to achieve a certain level of traffic congestion.

Analyzing work zone effects on vehicular flow on an annular freeway with a tunnel

This paper develops a three lane continuum model to analyze the effects of a work zone on vehicular flow on an annular freeway with a tunnel. The model expresses the mandatory lane changing rate just upstream of the work zone in an explicit algebraic form with the relevant random parameter generated from golden section analysis, and describes the discretionary lane changing rate between adjacent lanes with a lane changing time depending on local traffic flow density and relaxation time. It is assumed that the annular freeway has three lanes, a work zone with a length of 0.2 km and lane II completely blocked, an upstream tunnel of 1.5km long, and an initial jam between the tunnel and work zone. The three lane continuum model is applied in vehicular flow simulation with a 3rd order accuracy numerical scheme. Numerical results also indicate that golden section application in the analysis of work zone effects is helpful in obtaining density thresholds of traffic jam formation, the time averaged traffic speed through the tunnel, dependencies of mean travel time, and vehicle fuel consumption on the initial density normalized by jam density. Numerical results reveal that there are two density thresholds of traffic jam formation, if both density thresholds are normalized by traffic jam density, the first threshold relating to the work zone is 0.14, while the second depending on the tunnel is 0.21. In the absence of a work zone, the mean travel time through lane I is slightly longer than that estimated for cases with a work zone. As soon as initial density is above the second threshold, the time averaged speed through the tunnel is 31.91 km/h, which agrees well with published data.

Link-Based Traffic Estimation and Simulation for Road Networks Using Electronic Registration Identification Data

Traffic estimation and traffic simulation are essential parts of the intelligent transportation system. In recent years, massive traffic data has brought many data-based traffic estimation methods, but few are utilized in traffic simulation. The cause is that the data-based traffic simulation has high requirements for data quality, needing the trip information of all vehicles, not met by common traffic data. Fortunately, the electronic registration identification (ERI) data of the vehicle can satisfy. Therefore, we utilize ERI data to study the traffic estimation and simulation for the road network. The core of this research is the travel time estimation model, which is constructed by combining traffic theory models and the deep neural network. The traffic theory models are a group of linear models, which represent the relationship between traffic flow or traffic density and travel time. The deep neural network can extract the temporal and spatial characteristics of the road network traffic by Moving Average Convergence-Divergence (MACD) and Graph Convolutional Network (GCN), respectively. We named the travel time estimation model MGCN. Then, we employed MGCN for traffic simulation. In the experiment section, we employed Chongqing ERI data to verify the research content. Compared with some baseline methods, our method is superior in travel time estimation and traffic simulation.

The External Characteristics and Mechanism of Urban Road Corridors to Agglomeration: Case Study for Guangzhou, China

Existing research on the agglomeration effect of urban roads mainly focuses on land use but ignores the differences between various locations, types, and directions of roads. Few studies have been conducted on the built buildings which can represent the actual utility, and land use as a kind of government authorization may not necessarily represent actual needs. This research provides an analytical framework and an empirical analysis to study the differences in impacts of different urban roads on land use and to identify its internal dynamic mechanism. Guangzhou, being the research object, is one of the five major central cities in China. By using the techniques of GIS and SPSS, together with the methods of corridor effect, correlation analysis, and geographic detector, we analyze the external characteristics of office buildings and land gathering along both sides of the roads, explore the urban characteristics of corridor effect, then analyze the relationship with urban traffic flow and bus network density in order to find out the internal motivation of corridor effect. The fundamental conclusion can be drawn that the corridor effect on the land used for commercial offices is mostly unnoticeable, and roads of different locations, types, and directions display various scope and intensity of corridor effects. The agglomeration power is mainly caused by private transportation and has no relationship with public transport. The article concludes the model of the corridor effect and provides some policy suggestions to the government in order to strengthen the linkage development of transportation and land and to promote the improvement of land use efficiency.

Effects of road network characteristics on bicycle safety: A multivariate Poisson-lognormal model

• Multivariate Poisson-lognormal model is applied to model the bicycle crash of different types. • Multivariate correlation between counts of bicycle-bicycle and bicycle-vehicle crashes is accounted. • Population socio-economics, land use, road density, railway station, and intersection density affect the risk of bicycle-related crashes. • Network accessibility is positively associated with bicycle-vehicle and bicycle-bicycle crashes. • Network connectivity is positively associated with bicycle-bicycle crash only. Although cycling has benefits to environment and physical health, bicyclists are vulnerable road users. Prior studies have identified the environment, traffic and road user factors that affect the risk of bicycle-related crashes. However, it is rare that difference in their effects on the risk amongst different bicycle crash types is investigated. For instance, there is possible correlation between counts of different crash types. In this study, multivariate Poisson-lognormal regression method is applied to examine the relationship between bicycle crash frequencies and possible explanatory factors, with which the correlation between bicycle-vehicle and bicycle-bicycle crashes is considered, using the crash data from London in 2018 and 2019. In addition, effects of road network characteristics on bicycle crash frequencies are also considered. Results indicate that proposed multivariate Poisson-lognormal model outperforms conventional univariate Poisson-lognormal models, in term of the metrics like deviance information criterion (DIC). In addition, factors including population socio-economics, land use, and road network characteristics that affect bicycle crash risk are identified. For instance, effects of traffic flow, residential area, network connectivity, and intersection density on the crash counts are different between bicycle-vehicle and bicycle-bicycle crashes. Findings are useful for the implementation of remedial measures that can improve overall bicycle safety in the long run.

Density waves in an improved car-following model under intelligent transportation system environment

The intelligent transportation system (ITS) can effectively utilize the existing transportation facilities and improve traffic safety and efficiency. To study and evaluate the dynamic characteristics of traffic flow, a novel car-following model is proposed based on the two-velocity difference model (TVDM) with connected consideration of headway memory and backward looking effect under ITS environment. The linear stability theory is used to acquire the stability condition of the improved model, which shows that the proposed model can effectively strengthen the traffic flow stability. According to the nonlinear theory, the time-dependent Ginzburg–Landau (TDGL) equation and modified Korteweg–de Vries (mKdV) equation are obtained to exhibit the evolution characteristics of traffic flow density wave. At last, numerical simulation is performed to further validate that the improved model can enhance the traffic flow stability and eliminate traffic congestion.

TCSA: A Traffic Congestion Situation Assessment Scheme Based on Multi-Index Fuzzy Comprehensive Evaluation in 5G-IoV

The traffic congestion situation is an important reference indicator for the orderly control and management of traffic systems. As intelligent transport systems (ITS) become increasingly popular, the challenge of realizing real-time traffic congestion situation assessments (TCSAs) in the post-traffic era is particularly important. In this study, we propose a TCSA scheme for multi-metric fuzzy integrated evaluation based on three predicted vehicle traffic parameters for the 5G Internet of Vehicles (5G-IoV) environment, which is dedicated to accelerating the development of ITS. Firstly, the scheme uses dynamic multi-model adaptive exponential smoothing (DMMAES), which can calculate the optimal smoothing coefficients and weight of each model based on historical prediction errors to predict the average speed and traffic volume and then calculate the predicted traffic speed, traffic flow density, and road saturation of the three traffic congestion indicators. Secondly, the predicted values of the three traffic congestion indicators are used as fuzzy comprehensive evaluation, taking into account the vagueness of the traffic congestion levels, the uncertainty of the indicators, and the conflict among the indicators, using a trapezoidal affiliation function to determine the degree of affiliation of each indicator through the adaptive CRITIC method to determine the weights. Finally, the predicted traffic congestion situations are classified into five levels. The effectiveness of the scheme was verified by the measured data of Yanta North Road in Xi’an. The results showed that the traffic congestion level predicted by TCSA was basically consistent with the actual situation and had a high prediction accuracy.

Analysis of the Relationship between the Density and Lane-Changing Behavior of Circular Multilane Urban Expressway in Mixed Traffic

The behavior of changing lanes has a great impact on road traffic with heavy traffic. Traffic flow density is one of the important parameters that characterize the characteristics of traffic flow, and it will also be affected by the behavior of changing lanes, especially in the case of each lane. The penetration of autonomous vehicles can effectively reduce lane-changing behavior. Studying the relationship between traffic flow density and lane-changing behavior under different autonomous vehicle penetration rates is of great significance for describing the operation mechanism of mixed traffic flow and the control of mixed traffic. In this article, we use empirical, simulation, and data-driven methods to analyze the urban expressway of autonomous vehicles with penetration rates of 10%, 20%, 30%, 40%, 50%, 60%, 70%, and 80%, respectively. A simulation experiment was carried out on the road, and data related to density, the rate of changing into the lanes, and the rate of changing out lanes were collected. The analysis of the experimental results found the following: (1) The increase in penetration of autonomous vehicles leads to a certain degree of downward trend in density, the rate of changing into the lanes, and the rate of changing out lanes. (2) Different lanes have different effects on the penetration of autonomous vehicles. In a 4-lane road, the two lanes farther from the entrance and exit are closer in appearance, while the two lanes closer to the entrance and exit are similar. (3) The relationship between density and the rate of changing into the lanes and the rate of changing out lanes shows a linear relationship with the penetration of autonomous vehicles. Although the performance of each lane is slightly different, in general, it can be carried out by a multiple regression model. The given parameter value range is relatively close under different permeability. In summary, autonomous vehicles effectively reduce the traffic density and lane-changing behavior of each lane. There is a linear relationship between traffic flow density and lane-changing behavior with the penetration of autonomous vehicles. The density-lane-changing behavior model proposed in this paper can better describe the relationship between the density of the circular multilane urban expressway and the lane-changing behavior in the case of a large traffic flow in mixed traffic.

A Comprehensive Review of Vehicle Detection Techniques Under Varying Moving Cast Shadow Conditions Using Computer Vision and Deep Learning

Design of a vision-based traffic analytic system for urban traffic video scenes has a great potential in context of Intelligent Transportation System (ITS). It offers useful traffic-related insights at much lower costs compared to their conventional sensor based counterparts. However, it remains a challenging problem till today due to the complexity factors such as camera hardware constraints, camera movement, object occlusion, object speed, object resolution, traffic flow density, and lighting conditions etc. ITS has many applications including and not just limited to queue estimation, speed detection and different anomalies detection etc. All of these applications are primarily dependent on sensing vehicle presence to form some basis for analysis. Moving cast shadows of vehicles is one of the major problems that affects the vehicle detection as it can cause detection and tracking inaccuracies. Therefore, it is exceedingly important to distinguish dynamic objects from their moving cast shadows for accurate vehicle detection and recognition. This paper provides an in-depth comparative analysis of different traffic paradigm-focused conventional and state-of-the-art shadow detection and removal algorithms. Till date, there has been only one brief survey which highlights the shadow removal methodologies particularly for traffic paradigm. In this paper, a number of research papers containing results of urban traffic scenes have been shortlisted from the last three decades to give a comprehensive overview of the work done in this area. The study reveals that the only way to make a comparative evaluation is to use the existing Highway I, II, and III datasets which are frequently used for qualitative or quantitative analysis of shadow detection or removal algorithms. Furthermore, the paper not only provides cues to solve moving cast shadow problems, but also suggests that even after the advent of Convolutional Neural Network (CNN)-based vehicle detection methods, the problems caused by moving cast shadows persists. Therefore, this paper proposes a hybrid approach which uses a combination of conventional and state-of-the-art techniques as a pre-processing step for shadow detection and removal before using CNN for vehicles detection. The results indicate a significant improvement in vehicle detection accuracies after using the proposed approach.

ИССЛЕДОВАНИЕ ВЛИЯНИЯ УПРАВЛЕНИЯ ТРАНСПОРТНЫМИ ПОТОКАМИ НА НАГРУЗКИ ОСНОВНЫХ НЕСУЩИХ ЭЛЕМЕНТОВ И ПРОПУСКНУЮ СПОСОБНОСТЬ МОСТОВ

The article shows the influence of such methods of traffic flow management as limiting the speed of vehicles on the loads of the main load-bearing elements and the capacity of bridge structures on the example of the Oktyabrsky Bridge in Lipetsk. To determine the density of traffic flow and the load on the bridge structure, to collect information about the parameters of traffic flows, road traf-fic simulation is used. The vertical moving load on the bridge is calculated.

An en route capacity optimization model based on air traffic control process.

In order to resolve the imbalance of demand-capacity and airspace congestion, improve the performance of the en route air traffic management, promote the development of air traffic control automation system in the future, this paper proposes an En route air traffic control process model from the perspective of operation requirements. Taking the minimization of operation time, instantaneous density, maximum lateral displacement and air traffic controllers' workload as the optimization objectives, the commonly used air traffic control instructions such as climb and descent and speed restriction are set as constraints, the algorithm is designed based on the air traffic control scheme, and a complete air traffic control process are modeled which outputs instructions for each aircraft. Finally, the model is applied to a case study in the northwest region of China. The simulation results show that compared with the actual operation process, the total operation time is reduced by 18.6%, the variance of the lateral displacement and the vertical separation are efficiently reduced, and the en route air traffic capacity is improved. The proposed model envisages the following two innovations: (ⅰ) the whole process of air traffic controllers' command is considered in the model, especially the control scheme and different types of instructions, and (ⅱ) the en route historical trajectory data of aircraft is used to as the key parameters of the input data to efficiently yield the acceptable results of the model. By quantifying the operation requirements of air traffic control, this model can also balance the distribution of traffic flow density, reduce the utilization rate of horizontal airspace, alleviate flight conflicts on air routes, and lessen the workload of controllers.