Expressway traffic state discrimination method based on vehicle-level macro-micro evaluation

Abstract

The invention belongs to the technical field of intelligent traffic, and particularly relates to an expressway traffic state judgment method based on vehicle-level macro and micro evaluation. The method comprises the following steps: firstly, acquiring millimeter-wave radar data of a target road section in a target time period, and preprocessing the millimeter-wave radar data; then, according to millimeter wave radar data, evaluation indexes are calculated, and the evaluation indexes comprise a microcosmic index and a macroscopic index; the correlation between the indexes is checked, part of indexes with high correlation are removed, and the obtained microcosmic indexes comprise the vehicle drift angle variance, the lane changing behavior frequency, the time headway variance, the acceleration variance and the aggressive driving behavior variation index variance; the macroscopic indexes comprise average flow, a speed variation coefficient, a cart mixing rate and a congestion index; and finally, based on the macro and micro evaluation indexes, calculating the traffic state entropy of each road section in the target road section in the target time period, and classifying the traffic state according to the traffic state entropy. According to the method, the aggressive driving behavior variation index is provided for measuring the vehicle danger degree caused by sudden acceleration, deceleration or too high driving speed, so that the traffic state is reflected more finely from the microcosmic level, and meanwhile, the traffic state is accurately quantified by considering the mutual influence among the road sections.

Description

Technical Field

[0001] The invention belongs to the field of intelligent transportation technology, and in particular relates to a highway traffic state discrimination method based on vehicle-level macro-micro evaluation. Background Art

[0002] Highways have high speeds and high risks. Real-time monitoring of traffic conditions can not only identify sporadic traffic incidents, but also reflect the operating status of highways, which is crucial for accurate operational risk management.

[0003] In terms of traffic state characterization, macroscopic parameters such as flow rate, vehicle speed and density are still the main parameters for obtaining and characterizing traffic states. Existing research is mainly based on traffic state analysis based on macroscopic parameters, but the macroscopic expression of traffic state cannot reveal the microscopic characteristics of traffic flow and cannot characterize the changing mechanism of traffic flow. With the continuous development of intelligent transportation, the interactive coordination between road-side sensor equipment and vehicle-side provides support for obtaining microscopic parameters of traffic state, but the indicator system based on microscopic parameters in existing research is not comprehensive enough. For example, key indicators reflecting the characteristics of microscopic traffic flow, such as vehicle deviation angle and number of lane changes, are not considered enough, and the potential impact of driver's aggressive behavior on traffic state is not considered. In addition, the existing traffic state judgment method is mainly based on weighted entropy value, which mainly quantifies the traffic state of the current section without considering the mutual influence between sections. Based on the above problems, the present invention proposes a highway traffic state judgment method based on vehicle-level macro-micro evaluation. Summary of the invention

[0004] In view of the deficiencies in the prior art, the technical problem to be solved by the present invention is to provide a method for distinguishing highway traffic status based on vehicle-level macro-micro evaluation.

[0005] The present invention solves the technical problem by adopting the following technical solution:

[0006] A method for distinguishing highway traffic status based on vehicle-level macro-micro evaluation, characterized in that the method comprises the following steps:

[0007] Step 1: Obtain the millimeter-wave radar data of the target road section within the target period and pre-process the millimeter-wave radar data;

[0008] Step 2: Calculate evaluation indicators based on millimeter-wave radar data, including micro and macro indicators; test the correlation between indicators, remove some indicators with strong correlation, and obtain micro indicators including vehicle deviation angle variance, lane change behavior frequency, headway variance, acceleration variance and aggressive driving behavior variation index variance; macro indicators include average flow, speed variation coefficient, large vehicle mixing rate and congestion index;

[0009] Among them, the aggressive driving behavior variation index is used to measure the degree of vehicle danger, and the calculation formula is as follows:

[0010]

[0011] In the formula, E k is the aggressive driving behavior variation index of vehicle k, a t 、v t are the acceleration and speed of vehicle k at time t, ω1 and ω2 are weights, M represents the time period length, and T represents the total time that vehicle k appears on the target road section;

[0012] The variance of the aggressive driving behavior variation index is used to reflect the discrete degree of vehicle danger. The calculation formula is as follows:

[0013]

[0014] In the formula, is the variance of the aggressive driving behavior variability index, is the average value of the aggressive driving behavior variation index, and N represents the number of vehicles on the target road section during the target period;

[0015] Step 3: Based on the macro and micro evaluation indicators, calculate the traffic state entropy value of each section in the target period of the target road section, and classify the traffic state according to the traffic state entropy value;

[0016] The gray correlation analysis method is used to calculate the gray correlation coefficient of each indicator of each road section, and the weight of the indicator is calculated according to the gray correlation coefficient. The calculation formula is as follows:

[0017]

[0018] In the formula, w ij represents the weight of the jth index of the target road segment i, δ ij is the normalized grey correlation coefficient of the jth index of road section i, and m is the number of indicators;

[0019] The traffic state entropy value of the road section is calculated according to the weight of each indicator. The calculation formula is:

[0020]

[0021] Where D i is the traffic state entropy value of road section i.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] 1. Based on the interaction between people, vehicles, roads and environment, the present invention proposes a more sophisticated and comprehensive multi-dimensional macro-micro traffic status evaluation index system, which describes the traffic flow characteristics from both macro and micro levels. For the first time, the aggressive driving behavior variation index is proposed to measure the degree of vehicle danger caused by sudden acceleration, deceleration or driving too fast, and the variance of the aggressive driving behavior variation index is used as a micro-indicator to reflect the discrete degree of vehicle danger in a certain time period or road section, thereby reflecting the traffic status in a more detailed manner.

[0024] 2. The entropy value of traffic status is calculated by combining the grey correlation analysis method with the entropy method. The traffic status is accurately quantified by considering the mutual influence between each road section. The traffic status is classified into different levels according to the entropy value, which provides an important basis for traffic management departments to make reasonable traffic planning decisions, alleviate traffic congestion, solve traffic safety problems, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is the overall flow chart of the present invention;

[0026] Figure 2 It is a correlation diagram among the various indicators of the present invention;

[0027] Figure 3 It is a schematic diagram of the multi-dimensional macro and micro traffic status evaluation index system of the present invention. DETAILED DESCRIPTION

[0028] Specific embodiments are given below in conjunction with the accompanying drawings. The specific embodiments are only used to introduce the technical solutions of the present invention in detail and are not intended to limit the protection scope of the present application.

[0029] The present invention provides a highway traffic state discrimination method based on vehicle-level macro-micro evaluation (hereinafter referred to as method, see Figures 1 to 3 ), including the following steps:

[0030] Step 1: Obtain the millimeter-wave radar data of the target road section within the target period and pre-process the millimeter-wave radar data;

[0031] Step 2: Calculate evaluation indicators based on millimeter-wave radar data, including micro and macro categories; micro indicators include vehicle deviation angle variance, lane change behavior frequency, headway variance, acceleration variance, and aggressive driving behavior variation index variance, a total of 5 types; macro indicators include average flow, average density, speed variation coefficient, large vehicle mixing rate, congestion index, road saturation, and average travel delay, a total of 7 types.

[0032] The vehicle deflection angle variance is used to measure the discreteness of the vehicle deflection angle measurement value, that is, the stability of the vehicle deflection angle measurement value. If the vehicle deflection angle variance is large, it means that the measurement value fluctuates greatly, which may affect the stability and safety of vehicle driving. The calculation formula of vehicle deflection angle variance is as follows:

[0033]

[0034] In the formula, represents the vehicle deflection angle variance, AS k represents the deflection angle of vehicle k, represents the average value of vehicle deviation angles, and N represents the number of vehicles on the target road section during the target period.

[0035] Lane changing frequency is an important indicator for measuring driving behavior. It reflects how frequently a driver changes lanes while driving. Lane changing frequency is usually defined as the number of lane changes that occur within a period of time or a distance. The calculation formula for lane changing frequency is as follows:

[0036]

[0037] In the formula, F adb Indicates the frequency of lane changing behavior, sum(event adb ) represents the sum of the number of lane changing behaviors, and Q represents the real-time traffic volume.

[0038] The headway variance is used to quantify the fluctuation of the headway. The larger the headway variance, the greater the fluctuation of the headway. Specifically, it refers to the average of the squared differences between the headway of all vehicles and their average value. The calculation formula is as follows:

[0039]

[0040] In the formula, represents the headway variance, TH k represents the headway of vehicle k, is the average headway time.

[0041] Acceleration variance reflects the degree of dispersion of vehicle acceleration within a certain time period or road section, that is, the amplitude and stability of acceleration change; the acceleration equation calculation formula is as follows:

[0042]

[0043] In the formula, is the acceleration variance, a k is the acceleration of vehicle k, is the average acceleration.

[0044] The aggressive driving behavior variation index is used to measure the degree of danger caused by sudden acceleration, deceleration or driving too fast. It is calculated by the acceleration and speed of the vehicle at each moment in a certain period of time. The calculation formula is as follows:

[0045]

[0046] In the formula, E k is the aggressive driving behavior variation index of vehicle k, a t 、v t are the acceleration and speed of vehicle k at time t, ω1 and ω2 are weights, M represents the time period length, and T represents the total time that vehicle k appears on the target road section.

[0047] The variance of the aggressive driving behavior variation index reflects the discrete degree of vehicle danger within a certain time period or road section. The calculation formula is as follows:

[0048]

[0049] In the formula, is the average value of the aggressive driving behavior variation index.

[0050] Average flow refers to the average traffic volume passing through a road section within a certain period of time. The calculation formula is as follows:

[0051]

[0052] In the formula, is the average flow rate of the target section, q i , l i is the flow and length of segment i in the target segment.

[0053] Average density refers to the density of vehicles on a certain road section, also known as vehicle density. The calculation formula is as follows:

[0054]

[0055] In the formula, is the average density of the target road segment, ρ i is the vehicle density of road segment i.

[0056] The speed variation coefficient refers to the ratio of the standard deviation of vehicle speed to the average value, which is used to quantify the fluctuation of vehicle speed. It reflects the discrete degree of vehicle speed in traffic flow, that is, the difference between vehicle speeds. The calculation formula of the speed variation coefficient is as follows:

[0057]

[0058] In the formula, C v is the speed variation coefficient, S vis the standard deviation of vehicle speed, v k is the speed of vehicle k, is the average vehicle speed;

[0059] The large vehicle mixing rate is usually defined as the proportion of large vehicles in the running traffic, and the calculation formula is as follows:

[0060]

[0061] In the formula, R lm is the large vehicle mixing rate, Q l is the number of large vehicles, and Q is the real-time traffic volume.

[0062] The congestion index, also known as the traffic congestion index or road traffic operation index, is an indicator that comprehensively reflects the traffic operation status of the road network. The calculation formula is as follows:

[0063]

[0064] In the formula, CI i represents the congestion index of road section i, v i , are the free flow speed and the average vehicle speed of section i, respectively.

[0065] Road saturation refers to the ratio of actual traffic flow to road capacity. It reflects the traffic load of the road in a certain period of time, that is, the degree of road congestion. The calculation formula is as follows:

[0066]

[0067] Where S is the road saturation, f is the number of vehicles on a unit road section, and c is the maximum capacity of the road.

[0068] Average travel delay is an important indicator reflecting road operation efficiency and service level. It refers to the travel time loss caused by factors beyond the driver's control, such as road and environmental conditions, traffic interference, and traffic management and control facilities. The calculation formula is as follows:

[0069]

[0070] Where d is the average travel delay and v0 is the free flow speed of the target section.

[0071] Considering the correlation between the indicators, the Pearson test method is used to test the correlation between the indicators. The test results are as follows: Figure 2 As shown in the figure, the average flow (e8), average density (e9), road saturation (e 11 ) are strongly correlated with each other, and the congestion index (e 10 ) and average trip delay (e12 ) have a strong correlation, and one of the indicators with strong correlation is retained. This embodiment retains the two indicators of average flow and congestion index, and eliminates the three indicators of average density, road saturation and average travel delay. Therefore, the nine indicators including vehicle deviation angle variance, lane change behavior frequency, headway variance, acceleration variance, aggressive driving behavior variation index variance, average flow, speed variation coefficient, large vehicle mixing rate and congestion index constitute a multi-dimensional macro-micro traffic status evaluation indicator system, such as Figure 3 shown.

[0072] Step 3: Based on the multi-dimensional macro-micro traffic state evaluation index system, calculate the traffic state entropy value and classify the traffic state according to the traffic state entropy value;

[0073] Firstly, the grey correlation coefficient of each indicator of each road section is calculated by using the grey correlation analysis method; the target road section is divided into multiple sections, and all the indicators of each section at each time period form an indicator sequence. At the same time, each section corresponds to an optimal indicator sequence when the traffic state is optimal; according to the grey system theory, assume:

[0074]

[0075] In the formula, x i * is the indicator sequence of road section i, is the jth index value of road section i, y i * is the optimal indicator sequence for section i, is the optimal value of the jth indicator of road segment i, and m is the number of indicators;

[0076] The index sequence and the best index sequence of each section are normalized to obtain the normalized index sequence and the best index sequence; the normalization formula is:

[0077]

[0078] In the formula, is the normalized index value of the jth segment i, is the optimal value of the jth indicator of the normalized road segment i;

[0079] According to the normalized index sequence and the optimal index sequence, the maximum and minimum absolute differences of each index of the road section are calculated, and the grey correlation coefficient of each index of the road section is calculated accordingly;

[0080]

[0081] In the formula, Δ maxij , Δ minij are the maximum and minimum absolute differences of the jth index of section i, ξij is the grey correlation coefficient of the jth index of road section i, is the deviation between the current period and the optimal traffic state of the jth indicator of road section i, μ is the resolution coefficient, which is generally taken as 0.5;

[0082] The grey correlation coefficient is normalized, and the normalization formula is as follows:

[0083]

[0084] In the formula, δ ij is the normalized grey correlation coefficient of the jth index of road section i;

[0085] The weight of each indicator is calculated according to formula (20):

[0086]

[0087] In the formula, w ij represents the weight of the jth indicator of road section i;

[0088] The traffic state entropy value of the road section is calculated according to the weight of each indicator. The calculation formula is:

[0089]

[0090] Where D i is the traffic state entropy value of road section i.

[0091] Similarly, the traffic state entropy value of each section in the target section is calculated, and the traffic state is determined according to the traffic state entropy value.

[0092] Example

[0093] This embodiment takes a certain highway as an example. The length of the target section is 400 meters. The millimeter-wave radar data of the target section in the target time period (from 0:00 a.m. to 11:00 p.m., with an interval of 15 minutes) is obtained, and the traffic state entropy value of each time period is calculated using the millimeter-wave radar data, as shown in Table 1.

[0094] Table 1 Traffic state entropy values ​​of target road sections at different time periods

[0095]

[0096]

[0097] In this embodiment, the traffic status is divided into four categories, and the range of entropy values ​​of the traffic status in each category is shown in Table 2.

[0098] Table 2 Traffic state entropy value range

[0099]

[0100] Therefore, the traffic state can be determined according to the traffic state entropy value. The road network system can issue warning information to drivers, traffic police departments, etc. through warning information release facilities according to the traffic state. Warning information release facilities include variable information boards, variable speed limit signs, traffic broadcasts, signal light systems, public information query systems, vehicle-mounted information terminals, etc. Variable message boards are used to provide dynamic traffic information, and the layout information can be adjusted in real time according to the road traffic safety conditions; variable speed limit signs are used to remind drivers of their driving speed at any time, and the speed limit value can be displayed through the monitoring center according to the actual road conditions; traffic broadcasting is used to broadcast authoritative information in a timely manner according to emergency needs, deploy emergency measures, and play a role in early warning, communication and information notification when major emergencies occur; the signal light system is used for the visualization of early warnings. For example, extreme chaos corresponds to a red first-level warning signal light, indicating that the possibility of a traffic accident is very high and safety control measures must be taken immediately; chaos corresponds to an orange second-level warning signal light, indicating that a traffic accident may occur and safety control measures must be taken in time; general chaos corresponds to a yellow third-level warning signal light, indicating that a traffic accident is unlikely to occur and no safety control measures are required; normal corresponds to a green fourth-level warning signal light, indicating that traffic accidents are basically unlikely to occur and no measures are required; the public information inquiry system is used to inquire about congestion conditions, the shortest path, route information, weather conditions, etc.; the on-board information terminal is used to receive early warning information and remind drivers to drive carefully.

[0101] Any matters not described in the present invention are applicable to the prior art.

Claims

1. A highway traffic status identification method based on vehicle-level macro-micro evaluation, characterized in that: The method comprises the following steps: Step 1: Obtain the millimeter-wave radar data of the target road section within the target period and pre-process the millimeter-wave radar data; Step 2: Calculate evaluation indicators based on millimeter-wave radar data, including micro and macro indicators; test the correlation between indicators, remove some indicators with strong correlation, and obtain micro indicators including vehicle deviation angle variance, lane change behavior frequency, headway variance, acceleration variance and aggressive driving behavior variation index variance; macro indicators include average flow, speed variation coefficient, large vehicle mixing rate and congestion index; Among them, the aggressive driving behavior variation index is used to measure the degree of vehicle danger, and the calculation formula is as follows: In the formula, E k is the aggressive driving behavior variation index of vehicle k, a t 、v t are the acceleration and speed of vehicle k at time t, ω1 and ω2 are weights, M represents the time period length, and T represents the total time that vehicle k appears on the target road section; The variance of the aggressive driving behavior variation index is used to reflect the discrete degree of vehicle danger. The calculation formula is as follows: In the formula, is the variance of the aggressive driving behavior variation index, E is the average value of the aggressive driving behavior variation index, and N represents the number of vehicles on the target road section during the target period; Step 3: Based on the macro and micro evaluation indicators, calculate the traffic state entropy value of each section in the target period of the target road section, and classify the traffic state according to the traffic state entropy value; The gray correlation analysis method is used to calculate the gray correlation coefficient of each indicator of each road section, and the weight of the indicator is calculated according to the gray correlation coefficient. The calculation formula is as follows: In the formula, w ij represents the weight of the jth index of the target road segment i, δ ij is the normalized grey correlation coefficient of the jth index of road section i, and m is the number of indicators; The traffic state entropy value of the road section is calculated according to the weight of each indicator. The calculation formula is: Where D i is the traffic state entropy value of road section i.

2. The highway traffic status identification method based on vehicle-level macro-micro evaluation according to claim 1 is characterized in that: The calculation formula of vehicle deflection angle variance is as follows: In the formula, represents the vehicle deflection angle variance, AS k represents the deflection angle of vehicle k, Indicates the average value of vehicle deviation angle; The calculation formula of lane changing behavior frequency is as follows: In the formula, F adb Indicates the frequency of lane changing behavior, sum(event adb ) represents the sum of the number of lane-changing behaviors, and Q represents the real-time traffic volume; The calculation formula of headway variance is as follows: In the formula, represents the headway variance, TH k represents the headway of vehicle k, is the average headway time; The average flow rate is calculated as follows: In the formula, is the average flow rate of the target section, q i , l i is the flow and length of road section i; The calculation formula of speed variation coefficient is as follows: In the formula, C v is the speed variation coefficient, S v is the standard deviation of vehicle speed, v k is the speed of vehicle k, v is the average speed of the vehicles; The formula for calculating the large vehicle mixing rate is as follows: In the formula, R lm is the large vehicle mixing rate, Q l is the number of large vehicles; The calculation formula of congestion index is as follows: In the formula, CI i represents the congestion index of road section i, v i , are the free flow speed and the average vehicle speed of section i, respectively.

3. The highway traffic status identification method based on vehicle-level macro-micro evaluation according to claim 1 or 2 is characterized in that: According to the traffic state entropy value, the traffic state is divided into four categories: normal, general chaos, chaos and extremely chaos; extremely chaos means that the possibility of a traffic accident is very high and safety control measures must be taken immediately; chaos means that a traffic accident may occur and safety control measures must be taken in time; general chaos means that a traffic accident is unlikely to occur and no safety control measures are necessary; normal means that traffic accidents basically will not occur and no measures need to be taken.

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