Traffic flow state analysis method considering multi-party delay factors

Abstract

The invention belongs to the technical field of crossing of intelligent traffic systems, traffic engineering and urban planning, and discloses a traffic flow state analysis method considering multi-party delay factors, which comprises the following steps of: S1, updating a car following model by considering the response time of a driver; s2, considering cooperative adaptive cruise control (CACC), and updating an adaptive cruise control (ACC) vehicle-to-vehicle communication delay car-following model; s3, analyzing the traffic flow state of the heterogeneous traffic environment under the delayed view angle; and S4, carrying out validity check on the traffic flow state analysis method. According to the traffic flow state analysis method considering the multi-party delay factors, the intelligent level of a traffic system is improved, traffic control and planning are optimized, traffic safety is enhanced, and traffic efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the cross-technical field of intelligent transportation systems, traffic engineering and urban planning, and in particular to a traffic flow state analysis method taking multiple delay factors into consideration. Background Art

[0002] With the continuous development of artificial intelligence, information technology and wireless communication technology, connected and automated vehicles (CAVs) will be one of the important means to solve many existing traffic problems. The entry of CAVs into the traditional traffic system and the interaction between CAVs and human-driven vehicles (HDVs) and cooperative adaptive cruise control (CACC) vehicles will jointly constitute "mixed" heterogeneous traffic. This is the general trend of the development of the traffic system and is also a development process that all vehicles must go through before they have intelligent autonomous driving functions.

[0003] In terms of vehicle following models, in heterogeneous traffic environments, drivers and vehicles differ due to individual differences in age, gender, risk-taking behavior, vehicle type, etc. Drivers show different driving styles, and different driving styles lead to different reaction times. When simulating actual vehicle following behavior, it is crucial to consider different driver reaction times. At the same time, in heterogeneous traffic environments, due to external factors such as vehicle-to-vehicle communication frequency, external environment, and communication equipment, there is a time delay for the driver to receive intelligent network information, resulting in differences in actual speed, acceleration, and driver reaction time. Existing studies on traditional and intelligent network following models have not considered the following model under the conditions of comprehensive driver reaction time and vehicle communication delay.

[0004] Therefore, the present invention proposes a traffic flow state analysis method that takes multiple delay factors into consideration to make up for the above technical defects. Summary of the invention

[0005] The purpose of the present invention is to provide a traffic flow state analysis method taking into account multiple delay factors, so as to enhance the intelligence level of the traffic system, optimize traffic control and planning, enhance traffic safety and improve traffic efficiency.

[0006] To achieve the above object, the present invention provides a traffic flow state analysis method considering multiple delay factors, comprising the following steps:

[0007] Step S1, updating the car-following model taking into account the driver's reaction time;

[0008] Step S2, considering the coordinated adaptive cruise control CACC and the adaptive cruise ACC vehicle-to-vehicle communication delay following model update;

[0009] Step S3, analyzing the traffic flow state in a heterogeneous traffic environment from a delay perspective;

[0010] Step S4: verify the effectiveness of the traffic flow state analysis method.

[0011] Preferably, in step S1, the following model is updated by taking into account the driver's reaction time, and the following behavior of the human-driven vehicle HDV in a heterogeneous traffic environment is deduced based on the intelligent driver model IDM. As shown below:

[0012]

[0013] Among them, ν n (t) is the speed of vehicle n at time t; Δν n (t) is the speed difference between vehicle (n-1) and vehicle n at time t; h n (t) is the headway of vehicle n at time t; a is the maximum acceleration parameter of the vehicle; v0 is the free flow speed; s0 is the minimum safe stopping distance; T is the safe headway time; b is the comfortable deceleration; L V It's the driver.

[0014] Preferably, since the process of manually driven vehicles following cars is affected by multiple factors, the factors affecting the braking reaction time are decomposed in a step-by-step manner:

[0015] (1) Driver’s physiological attributes: This is the fundamental factor affecting braking reaction time and is related to the inherent characteristics of the individual;

[0016] ① Gender: Men and women have differences in physiological structure and emergency response patterns;

[0017] ② Age: There are differences between young and older drivers in vision, hearing, and reaction speed;

[0018] (2) Accumulation of driving experience: Long-term driving will give you rich experience in various driving scenarios, which will affect your reaction time;

[0019] ① Driving experience: the length of time since the driver obtained the driver's license;

[0020] ② Driving mileage: the total mileage actually driven by the driver;

[0021] ③ Driving frequency: how often you drive, such as the number of times you drive per day, week, or month;

[0022] (3) Driver expectations: related to the driver's personality, habits, and driving style, and directly or indirectly affect the driver's braking response in specific scenarios;

[0023] ① Speed ​​expectation: The speed at which the driver expects to drive is related to driving habits and safety awareness;

[0024] ② Following distance expectation: the safe distance the driver expects to maintain when following the vehicle ahead;

[0025] ③ Comfort expectation: the driver’s expectation of comfort during driving;

[0026] Through the above-mentioned step-by-step decomposition, it is easy to discover the various driver characteristic factors that affect the braking reaction time, which is helpful to update and optimize the manually driven vehicle following model considering the driver's braking reaction time, and provide theoretical support for analyzing the traffic flow state considering delays in heterogeneous traffic environments.

[0027] Preferably, the driver's braking reaction time τ is h , the selection range is 0.3~1.3s, the driver data conforms to the normal distribution, and the improved HDV following model is obtained by formula (1), as shown below:

[0028]

[0029] Among them, τ h The driver's braking reaction time.

[0030] Preferably, in step S2, adaptive cruise control CACC and ACC vehicle-to-vehicle communication delay following model update are considered, and the specific steps are as follows:

[0031] Step S21, analyzing vehicle platoon technology and vehicle-to-vehicle communication topology;

[0032] Step S22, linear feedback control in CACC vehicle controller.

[0033] Preferably, in step S21, the vehicle platooning technology includes four key elements: vehicle dynamics model, vehicle spacing strategy, network topology and controller;

[0034] The vehicle-to-vehicle communication topologies include leading vehicle following, leading vehicle-leader following vehicle transmission topology PLF and dual leading vehicle following transmission topology TPF.

[0035] Preferably, in step S22, the linear feedback control in the CACC vehicle controller is specifically performed as follows:

[0036] The CACC vehicle following model is as follows:

[0037]

[0038] Among them, Δt is the control step length; k p is the CACC vehicle spacing error control coefficient, and the calibration result is 0.45; e n (t) is the actual and expected headway error between vehicle n at time t; k d is the CACC vehicle spacing error differential control coefficient, and the calibration result is 0.25; for e n The differential of (t); t c is the expected inter-vehicle time distance of CACC vehicles, which is 0.6s;

[0039] When the communication delay reaches 0.4s, the linear controller begins to show unstable characteristics; therefore, the linear controller delay, τ c is 0~0.4S; the improved CACC following model is obtained from formula (3), as shown below:

[0040]

[0041] Similarly, for ACC vehicles with CACC degradation, use τ a The improved ACC following model is obtained by formula (4), as shown below:

[0042]

[0043] Among them, k1 is the inter-vehicle distance error control coefficient; k2 is the speed difference control coefficient;

[0044] Based on the above process, a heterogeneous traffic environment following model based on driving reaction time and communication delay is obtained as follows:

[0045]

[0046] Where ρ is the density; p is the penetration rate of intelligent connected vehicles; v e For speed.

[0047] Therefore, the present invention adopts the above-mentioned traffic flow state analysis method considering multiple delay factors, and the beneficial effects are as follows:

[0048] (1) Improving the intelligence level of the traffic system: By integrating multiple factors such as driver reaction time and vehicle-to-vehicle communication delay, the model provides a more accurate and comprehensive traffic flow analysis tool for the intelligent transportation system, thereby enhancing the intelligent processing capability of the system;

[0049] (2) Optimizing traffic control and planning: The model can help traffic managers and planners understand the complex dynamics of heterogeneous traffic environments and provide a scientific basis for traffic signal control, route planning, and traffic induction.

[0050] (3) Enhance traffic safety: Taking into account multiple delay factors, the model can predict and identify potential traffic accident risk points and take measures in advance to reduce the possibility of accidents;

[0051] (4) Improve traffic efficiency: By analyzing traffic flow stability, the model helps reduce traffic congestion, improve road capacity, and thus improve overall traffic efficiency.

[0052] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 The vehicle platoon and communication topology of the present invention are as follows: (a) is a leading vehicle-leader following type; (b) is a double leading vehicle following type;

[0054] Figure 2 It is the basic diagram of the present invention considering the minimum reaction time of the driver to brake and without vehicle-to-vehicle communication delay; wherein, (a) is the flow-speed diagram; (b) is the density-speed diagram; (c) is the flow-density diagram;

[0055] Figure 3 It is a flow-speed diagram of the present invention taking into account the driver's braking reaction time and the communication delay of the vehicle-to-vehicle communication linear controller; wherein, (a) is a flow-speed diagram of p=80% braking reaction change; (b) is a flow-speed diagram of p=80% CACC communication delay change; (c) is a flow-speed diagram of p=60% braking reaction change; (d) is a flow-speed diagram of p=60% CACC communication delay change; (e) is a flow-speed diagram of p=40% braking reaction change; (f) is a flow-speed diagram of p=40% CACC communication delay change; (g) is a flow-speed diagram of p=20% braking reaction change; (h) is a flow-speed diagram of p=20% CACC communication delay change;

[0056] Figure 4 It is a density-speed diagram of the present invention taking into account the driver's braking reaction time and the communication delay of the vehicle-to-vehicle communication linear controller; wherein, (a) is a p=80% braking reaction change density-speed diagram; (b) is a p=80% CACC communication delay change density-speed diagram; (c) is a p=60% braking reaction change density-speed diagram; (d) is a p=60% CACC communication delay change density-speed diagram; (e) is a p=40% braking reaction change density-speed diagram; (f) is a p=40% CACC communication delay change density-speed diagram; (g) is a p=20% braking reaction change density-speed diagram; (h) is a p=20% CACC communication delay change density-speed diagram;

[0057] Figure 5is the simulation result of the present invention when setting p=60% and considering the change of driver's reaction time; where (a) τ h =0.3s; (b) T h =0.5s; (c)T h =0.7s; (d)T h =0.9s; (e)T h =1.1s; (f)T h =1.3s;

[0058] Figure 6 The simulation results of the present invention when p=60% is considered when the communication delay of the vehicle-to-vehicle communication linear controller changes; where (a) τ c = 0s; (b) T c =0.1s; (c)T c =0.2s; (d)T c =0.3s; (e)T c =0.4s;

[0059] Figure 7 It is a flow chart of a traffic flow state analysis method considering multiple delay factors of the present invention. DETAILED DESCRIPTION

[0060] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0061] like Figure 7 As shown, a traffic flow state analysis method considering multiple delay factors includes the following steps:

[0062] Step S1, updating the car-following model taking into account the driver's reaction time;

[0063] Step S2, considering the coordinated adaptive cruise control CACC and the adaptive cruise ACC vehicle-to-vehicle communication delay following model update;

[0064] Step S3, analyzing the traffic flow state in a heterogeneous traffic environment from a delay perspective;

[0065] Step S4: verify the effectiveness of the traffic flow state analysis method.

[0066] Example

[0067] Step S1: updating the car-following model by taking the driver's reaction time into consideration.

[0068] In the study of car-following models of human-driven vehicles, the Intelligent Driver Model (IDM) has important physical significance. IDM is a mathematical model used to describe the car-following behavior of vehicles and simulates the state between vehicles. Under this theoretical framework, the car-following behavior of human-driven vehicles (HDVs) in heterogeneous traffic environments is simulated, and the HDV vehicle acceleration is As shown below:

[0069]

[0070] Among them, ν n (t) is the speed of vehicle n at time t; Δν n (t) is the speed difference between vehicle (n-1) and vehicle n at time t; h n (t) is the headway of vehicle n at time t; a is the maximum acceleration parameter of the vehicle, which is 1 m / s 2 ; v0 is the free flow speed, which is 33.3m / s; s0 is the minimum safe parking distance, which is 2m; T is the safe headway, which is 1.5s; b is the comfortable deceleration, which is 2m / s 2 ; L V is the vehicle length, take 5m.

[0071] However, the process of following a manually driven vehicle is affected by many factors, among which the generated factors are reflected in the braking reaction time. The braking reaction time is affected by many factors. It is easier to understand each influencing factor by decomposing it step by step from the perspective of driver characteristics. The following is a deconstruction of the factors affecting the braking reaction time in a step-by-step manner:

[0072] (1) Driver’s physiological attributes: This is the fundamental factor affecting braking reaction time and is related to the inherent characteristics of the individual.

[0073] ① Gender: Men and women have differences in physiological structure and emergency response patterns;

[0074] ② Age: There are differences between young and older drivers in vision, hearing, reaction speed, etc.

[0075] (2) Accumulation of driving experience: Long-term driving will give you rich experience in various driving scenarios, which will affect your reaction time.

[0076] ① Driving experience: the length of time since the driver obtained the driver's license;

[0077] ② Driving mileage: the total mileage actually driven by the driver;

[0078] ③ Driving frequency: the frequency of driving, such as the number of times you drive per day, week or month.

[0079] (3) Driver expectations: This is related to the driver's personality, habits and driving style, and directly or indirectly affects the driver's braking response in specific scenarios.

[0080] ① Speed ​​expectation: The speed at which the driver expects to drive is related to driving habits and safety awareness;

[0081] ② Following distance expectation: the safe distance the driver expects to maintain when following the vehicle ahead;

[0082] ③ Comfort expectations: The driver’s expectations for comfort during driving, such as smoothness, noise, etc.

[0083] Through the above-mentioned step-by-step decomposition, various driver characteristic factors that affect the braking reaction time can be found. This helps to update and optimize the artificial driving vehicle following model considering the driver's braking reaction time, and provides theoretical support for analyzing the traffic flow state considering delays in heterogeneous traffic environments.

[0084] Driver braking reaction time τ h The value is selected in the range of 0.3 to 1.3s. The data of the actual vehicle test personnel conforms to the normal distribution, and the mean is 0.75. The improved HDV following model is obtained from formula (1), as shown below:

[0085]

[0086] Step S2: updating the delayed following model of the coordinated adaptive cruise control CACC and the adaptive cruise ACC vehicle-to-vehicle communication.

[0087] Step S21, analyzing vehicle platoon technology and vehicle-to-vehicle communication topology.

[0088] ACC relies on the on-board millimeter-wave radar sensor to monitor the operating status of the vehicle in front, including speed and the distance between the two vehicles. This allows the ACC system to adjust the vehicle speed in real time and coordinate with the following vehicle to maintain a safe distance from the vehicle in front. It can achieve autonomous driving in specific scenarios, such as on highways and smooth urban road environments.

[0089] CACC not only inherits the characteristics of ACC, but also adds inter-vehicle communication technology, which means that CACC vehicles can share more dynamic information between vehicles in the queue.

[0090] Therefore, compared with ACC, CACC can respond to the dynamic changes of surrounding vehicles more quickly, and use V2V technology to achieve a tighter vehicle following distance and higher road utilization efficiency, which can not only improve the stability of heterogeneous traffic flows, but also help reduce vehicle fuel consumption.

[0091] Currently, ACC vehicles and CACC vehicles are gradually supporting vehicle platooning technology to improve the efficiency of urban road operation. Vehicle platooning technology is a method to optimize traffic flow by comprehensively considering four key elements: vehicle dynamics model, vehicle spacing strategy, network topology and controller. Each element plays a key role in building a more efficient and safer transportation system.

[0092] Among them, the vehicle dynamics model analyzes and predicts the movement and response of the vehicle, providing basic data for queue control; the vehicle spacing strategy defines and optimizes the safe distance between vehicles to reduce the risk of traffic congestion and collision; the network topology reveals the communication structure between vehicles and supports the fast and accurate transmission of information; the controller is responsible for executing and adjusting various strategies to ensure the stable and safe operation of the vehicle queue.

[0093] Typical network topologies play a key role in intelligent transportation systems, among which the predecessor-leader following type (PLF), the predecessor-leader following type (PLF), and the two-predecessor following type (TPF) are three common types. In the predecessor-following type, the following vehicle directly follows the action and speed of the preceding vehicle to achieve basic platoon operation. In a heterogeneous traffic environment, the PLF model makes full use of V2V communication technology to achieve real-time information exchange between the following vehicle and the preceding vehicle and the leader vehicle. Each vehicle can obtain the dynamic status information of other related vehicles in a timely manner, greatly enhancing the overall coordination and responsiveness of the fleet. TPF allows vehicles to obtain information about the two vehicles in front of them, further improving the dynamic response and safety performance of the fleet. By finely dividing and optimizing basic elements and network topologies, vehicle platooning technology can better cope with complex and changing traffic environments and achieve a dual improvement in traffic safety and efficiency. Vehicle platooning and communication topologies, such as Figure 1 shown.

[0094] Step S22, linear feedback control in CACC vehicle controller.

[0095] To evaluate the performance of the CACC system, the PATH lab used four real cars equipped with a CACC control system, which allows real-time intelligent control based on the position and speed of the vehicle in front. To ensure the accuracy and reliability of the CACC system, the experiment performed parameter calibration of trajectory data, including calibration of the vehicle's sensors, control algorithms, and communication protocols to ensure that the system can correctly perform tasks in complex traffic environments.

[0096] Among them, the CACC vehicle following model is as follows:

[0097]

[0098] Among them, Δt is the control step length; k p is the CACC vehicle spacing error control coefficient, and the calibration result is 0.45; e n (t) is the actual and expected headway error between vehicle n at time t; k d is the CACC vehicle spacing error differential control coefficient, and the calibration result is 0.25; for e n The differential of (t); t c is the expected inter-vehicle time distance of CACC vehicles, which is generally 0.6s.

[0099] Although CAVs are in a heterogeneous traffic environment, vehicle communication delays in sharing information through wireless communication channels are inevitable, and this delay is called communication delay. Communication delay is affected by factors such as the controller, transmitter, transmitter packet assembly, and receiver unpacking. Different scheduling strategies and air interface frame structures have an important impact on air interface transmission delay.

[0100] When the communication delay reaches 0.4s, the linear controller begins to show unstable characteristics. Therefore, the linear controller delay, τ c The improved CACC following model is obtained from formula (3), as shown below:

[0101]

[0102] Similarly, for ACC vehicles with CACC degradation, use τ a The improved ACC following model is obtained by formula (4), as shown below:

[0103]

[0104] Among them, k1 is the vehicle spacing error control coefficient; k2 is the speed difference control coefficient.

[0105] Based on the above process, a heterogeneous traffic environment following model based on driving reaction time and communication delay is obtained as follows:

[0106]

[0107] Where ρ is the density; p is the penetration rate of intelligent connected vehicles; v e For speed.

[0108] Step S3: analyzing the traffic flow status in a heterogeneous traffic environment from a delay perspective.

[0109] Using the updated car-following model, the basic traffic flow diagram considering driver reaction delay and vehicle-to-vehicle communication delay in heterogeneous traffic environment is obtained, as shown in Figure 2As shown in the figure, the fastest reaction time of the driver's instinct (τ h =0.3s), there is no basic diagram in the case of CAVs communication delay, which is convenient for comparing the changes in maximum flow and optimal density.

[0110] Depend on Figure 3 and Figure 4 It is found that with the increase of driver braking reaction time or CACC linear controller communication delay, the maximum capacity under different CAVs penetration rates is constantly decreasing. It can be found that when the penetration rate drops to 60%, the maximum flow of CACC vehicles changes little with the increase of communication delay. This shows that with the development of heterogeneous traffic environment and the continuous increase of CAVs vehicle penetration rate, the impact of vehicle-to-vehicle communication technology on heterogeneous traffic flow is more significant.

[0111] Step S4: verify the effectiveness of the traffic flow state analysis method.

[0112] The present invention designs a SUMO simulation experiment to verify the basic graph model under the proposed following model considering the driver's reaction time and the vehicle-to-vehicle communication delay. A heterogeneous traffic simulation environment is created using SUMO software, and the traffic flow, density and speed data are collected using the software. The experiment constructs a regular decagonal loop consisting of ten single lanes with a length of 1 km as a simulation section. The maximum speed of each section is set to 33.3 m / s. At the same time, in order to obtain the flow-density scatter data at a larger density, when the simulation runs to a specific time, the speed reduction interference is implemented in the designated section to reduce the vehicle speed to 1 m / s. The total duration of the entire simulation experiment is 18000s, and the simulation step size is set to 0.1s. Data detection is performed on ten sections, and the detection frequency is once every 2 minutes.

[0113] When the penetration rate of intelligent connected vehicles is greater than 60%, the maximum traffic capacity of the road increases with the increase of penetration rate. Therefore, through the data collected in the simulation experiment, the relationship between traffic flow and density under the condition of CAVs penetration rate P = 60% and considering the change of driver reaction time (τ a =0.4s, τ C = 0s), such as Figure 5 As shown. Then, when the penetration rate P = 60%, the relationship between traffic flow and density considering the communication delay of CACC vehicle linear controller (τ a =0.4s, τ h =0.3s), such as Figure 6 shown.

[0114] Figure 5 and Figure 6The simulation results of SUMO software are shown when the penetration rate is 60%, considering the driver's reaction time and the delay of the linear controller of vehicle-to-vehicle communication. The results depict the distribution of the simulation scatter points and the theoretical curve. It is found that the simulation scatter points are generally distributed on both sides of the theoretical curve, showing a highly consistent feature with the theoretical curve. At the same time, due to the 60% penetration rate in the heterogeneous environment as the critical value of the maximum traffic capacity and the rapid growth of density, in this scenario, there are still some vehicles that cannot communicate with each other. The reason for the abnormal points is due to the unstable traffic flow state and the vehicle speed limit. The consistency of the simulation scatter points and the theoretical curve confirms the accuracy and reliability of the updated basic graph model of the following model, laying a foundation for studying the stable state of heterogeneous traffic flow.

[0115] Therefore, the present invention adopts the above-mentioned traffic flow state analysis method considering multiple delay factors, and provides a more accurate and comprehensive traffic flow analysis tool for the intelligent transportation system by integrating multiple factors such as driver reaction time and vehicle-to-vehicle communication delay, thereby enhancing the intelligent processing capability of the system and improving the intelligence level of the transportation system; the present invention can help traffic managers and planners understand the complex dynamics under heterogeneous traffic environments, optimize traffic control and planning, and provide a scientific basis for traffic signal control, route planning and traffic induction; taking into account multiple delay factors, the present invention can predict and identify potential traffic accident risk points, take measures in advance to reduce the possibility of accidents, and enhance traffic safety; by analyzing traffic flow stability, it helps to reduce traffic congestion, improve road capacity, and thus improve overall traffic efficiency.

[0116] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A traffic flow state analysis method considering multiple delay factors, characterized in that: The following steps are involved: Step S1, updating the car-following model taking into account the driver's reaction time; Step S2, considering the coordinated adaptive cruise control CACC and the adaptive cruise ACC vehicle-to-vehicle communication delay following model update; Step S3, analyzing the traffic flow state in a heterogeneous traffic environment from a delay perspective; Step S4: verify the effectiveness of the traffic flow state analysis method.

2. A traffic flow state analysis method considering multiple delay factors according to claim 1, characterized in that: In step S1, the following model is updated considering the driver's reaction time. Based on the intelligent driver model IDM, the following behavior of the HDV in a heterogeneous traffic environment is simulated. The HDV vehicle acceleration As shown below: Among them, ν n (t) is the speed of vehicle n at time t; Δν n (t) is the speed difference between vehicle (n-1) and vehicle n at time t; h n (t) is the headway of vehicle n at time t; a is the maximum acceleration parameter of the vehicle; v0 is the free flow speed; s0 is the minimum safe stopping distance; T is the safe headway time; b is the comfortable deceleration; L V It's the driver.

3. A traffic flow state analysis method considering multiple delay factors according to claim 2, characterized in that: Since the process of manually driven vehicles following cars is affected by many factors, the factors affecting the braking reaction time are decomposed step by step: (1) Driver’s physiological attributes: This is the fundamental factor affecting braking reaction time and is related to the inherent characteristics of the individual; ① Gender: Men and women have differences in physiological structure and emergency response patterns; ② Age: There are differences between young and older drivers in vision, hearing, and reaction speed; (2) Accumulation of driving experience: Long-term driving will give you rich experience in various driving scenarios, which will affect your reaction time; ① Driving experience: the length of time since the driver obtained the driver's license; ② Driving mileage: the total mileage actually driven by the driver; ③ Driving frequency: how often you drive, such as the number of times you drive per day, week, or month; (3) Driver expectations: related to the driver's personality, habits, and driving style, and directly or indirectly affect the driver's braking response in specific scenarios; ① Speed ​​expectation: The speed at which the driver expects to drive is related to driving habits and safety awareness; ② Following distance expectation: the safe distance the driver expects to maintain when following the vehicle ahead; ③ Comfort expectation: the driver’s expectation of comfort during driving; Through the above-mentioned step-by-step decomposition, it is easy to discover the various driver characteristic factors that affect the braking reaction time, which is helpful to update and optimize the manually driven vehicle following model considering the driver's braking reaction time, and provide theoretical support for analyzing the traffic flow state considering delays in heterogeneous traffic environments.

4. A traffic flow state analysis method considering multiple delay factors according to claim 3, characterized in that: Assume the driver's braking reaction time τ h , the selection range is 0.3~1.3s, the driver data conforms to the normal distribution, and the improved HDV following model is obtained by formula (1), as shown below: Among them, τ h The driver's braking reaction time.

5. The method for analyzing traffic flow status considering multiple delay factors according to claim 1, characterized in that: In step S2, the adaptive cruise control CACC and ACC vehicle-to-vehicle communication delay following model are updated, and the specific steps are as follows: Step S21, analyzing vehicle platoon technology and vehicle-to-vehicle communication topology; Step S22, linear feedback control in CACC vehicle controller.

6. A traffic flow state analysis method considering multiple delay factors according to claim 5, characterized in that: In step S21, the vehicle platooning technology includes four key elements: vehicle dynamics model, vehicle spacing strategy, network topology and controller; The vehicle-to-vehicle communication topologies include leading vehicle following, leading vehicle-leader following vehicle transmission topology PLF and dual leading vehicle following transmission topology TPF.

7. A traffic flow state analysis method considering multiple delay factors according to claim 5, characterized in that: In step S22, the linear feedback control in the CACC vehicle controller is performed as follows: The CACC vehicle following model is as follows: Among them, Δt is the control step length; k p is the CACC vehicle spacing error control coefficient, and the calibration result is 0.45; e n (t) is the actual and expected headway error of vehicle n at time t; is the CACC vehicle spacing error differential control coefficient, and the calibration result is 0.25; for e n The differential of (t); t c is the expected inter-vehicle time distance of CACC vehicles, which is 0.6s; When the communication delay reaches 0.4s, the linear controller begins to show unstable characteristics; therefore, the linear controller delay, τ c is 0~0.4S; the improved CACC following model is obtained from formula (3), as shown below: Similarly, for ACC vehicles with CACC degradation, use τ a The improved ACC following model is obtained by formula (4), as shown below: Among them, k1 is the inter-vehicle distance error control coefficient; k2 is the speed difference control coefficient; Based on the above process, a heterogeneous traffic environment following model based on driving reaction time and communication delay is obtained as follows: Where ρ is the density; p is the penetration rate of intelligent connected vehicles; v e For speed.

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