Method, device and equipment for risk assessment of vehicle-to-vehicle operation safety on provincial highways

Abstract

The present invention discloses a method, device and equipment for assessing the safety risk of inter-vehicle operation on provincial highways, including: obtaining the heavy truck mixing rate and traffic saturation of each observation unit in the province, wherein the observation unit refers to the section between two adjacent toll stations on the provincial highway network topology map; inputting the heavy truck mixing rate and the traffic saturation into a safety risk assessment model to obtain the number of accidents per 100 kilometers per day for each observation unit; and obtaining the inter-vehicle operation safety risk level for each observation unit based on the number of accidents per 100 kilometers per day for each observation unit and in combination with the highway inter-vehicle operation safety risk assessment standard. The purpose of the present invention is to solve the problem of difficulty in dynamically monitoring and accurately predicting driving safety risks before an accident.

Description

Technical Field

[0001] The present invention belongs to the field of highway traffic safety, and in particular relates to a method, device and equipment for assessing the operational safety risks between vehicles on provincial highways. Background Art

[0002] Highways are managed by provincial administrative divisions. To improve traffic efficiency, provincial highways basically operate in a mixed mode of passenger and freight vehicles. Trucks have a longer wheelbase than buses, and cover a larger area of ​​the road when turning. A certain proportion of trucks mixed with high-speed passenger traffic will increase driving risks. According to statistics from 2008 to 2019, the speed difference between passenger and freight vehicles caused by different truck mixing rates is 23.56 to 34.42 km / h -1 , when the bus is moving at 4m·s- 2 When the deceleration brake is applied to the same speed as the passenger and freight vehicles, the braking time left for the driver is only 1.64s to 2.39s, which also includes the driver's identification and operation time. The driving safety risk of vehicles has different rules in free flow, stable flow, unstable flow, saturated flow and blocked flow. Previous studies have found that the safety risk is the greatest when the traffic saturation is 0.40 to 0.60. Therefore, the traffic saturation and the mixing rate of heavy trucks are two key parameters affecting the driving safety risk of the road section. With the gradual improvement of the quality of highway network toll collection data (TCD), the time and conditions for carrying out dynamic profiling of these key parameters are in place. The evaluation of the driving safety risk of the road section has an important impact on monitoring the accident rate between vehicles, the location and scheduling of emergency resources, etc.

[0003] Most existing studies use a single traffic flow parameter as the entry point and obtain accident scene data through traffic monitoring equipment. The limitations are that they have not yet comprehensively considered the two key parameters of traffic saturation and heavy truck mixing rate. Both have the characteristics of inferring the laws of pre-accident parameters based on post-accident characteristics, making it difficult to dynamically monitor and accurately predict driving safety risks before an accident. Summary of the invention

[0004] In view of the problems existing in the prior art, the present invention provides a method, device and equipment for assessing the operational safety risks between vehicles on provincial highways, which aims to solve the problem that it is difficult to dynamically monitor and accurately predict driving safety risks before an accident.

[0005] In order to solve the above technical problems, the present invention is implemented by the following technical solutions:

[0006] According to a first aspect of the present invention, a method for assessing the risk of vehicle-to-vehicle operation on a provincial highway is provided, comprising:

[0007] Obtain the heavy truck mixing rate and traffic saturation of each observation unit in the province, where the observation unit refers to the section between two adjacent toll stations on the provincial highway network topology map;

[0008] Inputting the heavy truck mixing rate and the traffic flow saturation into a safety risk assessment model to obtain the number of accidents per 100 kilometers per day for each observation unit;

[0009] According to the number of accidents per 100 kilometers per day of each observation unit, combined with the highway vehicle-to-vehicle operation safety risk assessment standard, the vehicle-to-vehicle operation safety risk level of each observation unit is obtained.

[0010] In a possible implementation of the first aspect, obtaining the heavy truck mixing rate and traffic saturation of each observation unit in the province is specifically:

[0011] Extract traffic data of each vehicle type from the provincial highway network toll collection database;

[0012] The traffic data of each vehicle type are processed respectively to obtain the natural traffic volume of each vehicle type in each observation unit;

[0013] The natural traffic volume of each type of vehicle in each observation unit is input into the heavy truck mixing rate calculation formula and the traffic flow saturation calculation formula respectively to obtain the heavy truck mixing rate and traffic flow saturation of each observation unit in the province.

[0014] In a possible implementation of the first aspect, the heavy truck mixing rate calculation formula is:

[0015]

[0016] Where M j is the mixing rate of heavy trucks in the j observation unit; Q ij is the natural traffic volume of observation unit j of type i vehicle; k i is the reduced traffic volume coefficient of category i vehicles.

[0017] In a possible implementation manner of the first aspect, the traffic flow saturation calculation formula is:

[0018]

[0019] In the formula, S j is the traffic saturation of the j observation unit; Q ij is the natural traffic volume of observation unit j of type i vehicle; k i is the reduced traffic volume coefficient of type i vehicles; m j1 For j observation unit 120 km·h -1 Number of lanes with design speed; m j2For j observation unit 100km·h- 1 Number of lanes with designed speed; C jd1 For j observation unit 120 km·h -1 Design speed capacity; C jd2 For j observation unit, 100 km·h -1 Design speed capacity; C1 = 2200 pcu·h -1 ln; C2 = 2100pcu·h -1 ln;f cj is the traffic composition coefficient of the j observation unit; P ij is the traffic volume ratio of type i vehicle j in observation unit.

[0020] In a possible implementation manner of the first aspect, the traffic data of each vehicle type is processed separately to obtain the natural traffic volume of each vehicle type on each observation unit, specifically:

[0021] The traffic data of each type of vehicle are processed separately using the TransCAD traffic assignment method to obtain the natural traffic volume of each type of vehicle in each observation unit.

[0022] In a possible implementation manner of the first aspect, the security risk assessment model is:

[0023]

[0024] In the formula, Z j is the number of accidents per 100 kilometers per day in the j observation unit; M j is the mixing rate of heavy trucks in the j observation unit; S j is the traffic saturation of the j observation unit.

[0025] In a possible implementation manner of the first aspect, define F j is the number of accidents per 100 kilometers per day in the j observation unit Z j Quantile in [0.01,1.03];

[0026] The highway vehicle-to-vehicle operation safety risk assessment standards include:

[0027] When F j ≤25.00%, and 0.01<Z j When ≤0.26, the inter-vehicle operation safety risk level of the j observation unit is the first level;

[0028] When 25.00%<F j ≤54.69%, and 0.26<Z j When ≤0.57, the inter-vehicle operation safety risk level of observation unit j is the second level;

[0029] When 54.69%<F j ≤80.39%, and 0.57<Z j When ≤0.83, the inter-vehicle operation safety risk level of observation unit j is the third level;

[0030] When 80.39%<F j ≤90.78%, and 0.83<Z j When ≤0.94, the inter-vehicle operation safety risk level of observation unit j is level 4;

[0031] When 90.78%<F j ≤100%, and 0.94<Z j When ≤1.03, the inter-vehicle operation safety risk level of observation unit j is level 5;

[0032] Among them, the first level is the lowest risk level, the fifth level is the highest risk level, and the risk levels of the first level, the second level, the third level, the fourth level and the fifth level increase successively.

[0033] According to a second aspect of the present invention, there is provided a provincial highway vehicle operation safety risk assessment device, comprising:

[0034] An acquisition module is used to obtain the heavy truck mixing rate and traffic saturation of each observation unit in the province, where the observation unit refers to the section between two adjacent toll stations on the provincial highway network topology map;

[0035] An accident number calculation module, used for inputting the heavy truck mixing rate and the traffic flow saturation into a safety risk assessment model to obtain the number of accidents per 100 kilometers per day for each observation unit;

[0036] The safety risk level assessment module is used to obtain the vehicle-to-vehicle operation safety risk level of each observation unit based on the number of accidents per 100 kilometers per day of each observation unit and the highway vehicle-to-vehicle operation safety risk assessment standard.

[0037] According to a third aspect of the present invention, there is provided a device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of a method for assessing the operational safety risks between vehicles on provincial highways when executing the computer program.

[0038] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for assessing the operational safety risks between vehicles on provincial highways are implemented.

[0039] Compared with the prior art, the present invention has at least the following beneficial effects:

[0040] The present invention provides a method for assessing the safety risk of inter-vehicle operation on provincial highways. First, the heavy truck mixing rate and traffic saturation of each observation unit in the province are obtained; then the heavy truck mixing rate and traffic saturation are input into the safety risk assessment model to obtain the number of accidents per 100 kilometers per day for each observation unit; finally, according to the number of accidents per 100 kilometers per day for each observation unit, combined with the highway inter-vehicle operation safety risk assessment standard, the vehicle inter-vehicle operation safety risk level of each observation unit is obtained. It can be seen that the present invention comprehensively considers the two key parameters of traffic saturation and heavy truck mixing rate, proposes a method for assessing the safety risk of inter-vehicle operation on provincial highways, realizes dynamic monitoring and accurate prediction of driving safety risks before accidents, determines the safety risk status of each section of provincial highways, has good economic efficiency, and is of great significance for guiding the orderly implementation of engineering applications such as traffic accident prevention, traffic evacuation management, and emergency resource scheduling.

[0041] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the specific implementation modes of the present invention, the drawings required for use in the description of the specific implementation modes will be briefly introduced below. Obviously, the drawings described below are some implementation modes of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 It is a flow chart of a method for assessing the risk of vehicle-to-vehicle operation on a provincial highway in an embodiment of the present invention;

[0044] Figure 2 A block diagram of a provincial highway vehicle operation safety risk assessment device in an embodiment of the present invention;

[0045] Figure 3 A diagram showing fluctuations in driving safety risk parameters for a certain provincial example road section in an embodiment of the present invention;

[0046] Figure 4 The d value and Z value of the key parameters of a province from January to December 2019 in the embodiment of the present invention j The change diagram of S j With Z j The discreteness comparison diagram of M j With Z j The dispersion comparison chart of

[0047] Figure 5 In the embodiment of the present invention, S j With Z j Theoretical relationship and actual fitting curve diagram, where (a) is S j With Z j The theoretical relationship diagram of S j With Z j The data fitting curve diagram of

[0048] Figure 6 M in the embodiment of the present invention j With Z j The actual data fitting curve diagram;

[0049] Figure 7 It is a graph showing the convergence rule of R as h increases in an embodiment of the present invention;

[0050] Figure 8 is the Z value in the range [0.01, 1.03] with an increment of 10% in the embodiment of the present invention. j Distribution map;

[0051] Fig. 9 is the M of 57 road sections (684 observation units) in a province in an example of the present invention. j With S j Traffic assignment result diagram of M j The distribution result diagram of S j The distribution result diagram of . DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] First of all, it should be noted that heavy trucks are a general term for trucks with a length of more than 6 meters and a total mass of more than 12 tons. It should be understood that highways charge fees according to vehicle types. Passenger cars are divided into 4 types of models, namely 1, 2, 3, and 4, and trucks are divided into 6 types of models, namely 1, 2, 3, 4, 5, and 6. For statistical convenience, this application treats passenger cars and trucks uniformly when establishing a safety risk assessment model, and divides them into 10 types of models, of which 1-4 represent passenger cars, 5-10 represent trucks, and heavy trucks refer to trucks of category 7 and above.

[0054] Combination Figure 1As shown, a method for assessing the risk of vehicle-to-vehicle operation on a provincial highway provided by an embodiment of the present invention specifically includes the following steps:

[0055] Step 1: Obtain the heavy truck mixing rate and traffic saturation of each observation unit in the province, wherein the observation unit refers to the section between two adjacent toll stations on the provincial highway network topology map.

[0056] In one embodiment, the specific implementation steps of "obtaining the heavy truck mixing rate and traffic saturation of each observation unit in the province" are as follows:

[0057] Step 1.1: Extract traffic data of each vehicle type from the provincial highway network toll collection database.

[0058] Exemplarily, the traffic data include vehicle type, driving time, driving speed, driving distance, etc.

[0059] Step 1.2: Process the traffic data of each vehicle type separately to obtain the natural traffic volume of each vehicle type in each observation unit.

[0060] Preferably, the traffic data of each vehicle type is processed separately using the TransCAD traffic assignment method to obtain the natural traffic volume of each vehicle type in each observation unit.

[0061] Step 1.3, input the natural traffic volume of each type of vehicle in each observation unit into the heavy truck mixing rate calculation formula and the traffic flow saturation calculation formula respectively, and obtain the heavy truck mixing rate and traffic flow saturation of each observation unit in the province.

[0062] Specifically, the heavy truck mixing rate calculation formula is:

[0063]

[0064] Where M j is the mixing rate of heavy trucks in the j observation unit; Q ij is the natural traffic volume of observation unit j of type i vehicle; k i is the reduced traffic volume coefficient of category i vehicles, taken from JTG B01—2014.

[0065] Specifically, the traffic saturation calculation formula is:

[0066]

[0067] In the formula, S j is the traffic saturation of observation unit j; m j1 For j observation unit 120 km·h -1 Number of lanes with design speed; mj2 For j observation unit 100km·h- 1 Number of lanes with designed speed; C jd1 For j observation unit 120 km·h -1 Design speed capacity; C jd2 For j observation unit, 100 km·h -1 Design speed capacity; C1 = 2200 pcu·h -1 ln; C2 = 2100pcu·h -1 ln;f cj is the traffic composition coefficient of the j observation unit; P ij is the traffic volume ratio of type i vehicle j in observation unit.

[0068] It should be noted that the parameter algorithm is implemented through big data operations under constraints. j , S j The definition of should satisfy the vehicle speed and travel time within a reasonable range. Considering that the safety risk of the observation unit caused by speeding (long straight sections, etc.) and low-speed (climbing lanes, etc.) vehicles cannot be ignored, the sampling speed of passenger cars is [60,140] km·h -1 , the sampling speed of the truck is [40,100] km·h -1 Trucks will stop for a long time in service areas, so the sampling time of vehicles on the way should be controlled within 24 hours to ensure that reliable samples are obtained.

[0069] According to the Highway Engineering Technical Standard (JTG B01-2014), the roadside interference coefficient and driver characteristic coefficient are both 1.00, the lane number coefficient is 0.99, and the design speed is 120 km / h. -1 , 100km·h -1 The single lane benchmark capacity is C1 and C2 respectively. Number of static lanes m j1 、m j2 Provided by the competent department, substitute into S j The formula completes the calculation.

[0070] For example, in order to verify the operability of the heavy truck mixing rate calculation formula and the traffic saturation calculation formula and analyze the parameter dispersion, the parameter fluctuation of a provincial example section was obtained based on the 2019 TCD. The section contains 6 sub-sections. j and S j The calculation results are used to classify the parameters and draw the parameter effect diagram of each sub-section. Figure 3 .

[0071] Depend on Figure 3 Visible: M j and S jIt can effectively realize the refined calculation of parameter changes; the M of each sub-section j and S j There is no necessary connection between j The fluctuation range is larger than S j Significantly smaller.

[0072] Step 2: Input the heavy truck mixing rate and the traffic saturation into the safety risk assessment model to obtain the number of accidents per 100 kilometers per day for each observation unit.

[0073] Specifically, the security risk assessment model is:

[0074]

[0075] In the formula, Z j is the number of accidents per 100 kilometers per day in the j observation unit.

[0076] The following is a detailed description of the process of establishing the security risk assessment model in conjunction with the attached drawings, as follows:

[0077] (1) Correlation analysis between parameters and accident numbers

[0078] 1) Parameter correlation and dispersion analysis

[0079] M j and S j The independence between M and M is the basis for road safety risk modeling, and the Pearson correlation coefficient method is used to evaluate the correlation of parameters. j and S j Using linear normalization, when M with N samples j With S j Correlation coefficient R MS When the absolute value of is less than 0.20, it is considered that M j With S j Not relevant. MS The algorithm is:

[0080]

[0081] Calculate the M of each OD (origin-destination traffic) pair in TCD j and S j , the traffic distribution results of 57 road sections (a total of 684 observation units) in the example province were obtained through TransCAD allocation, and the R MS are 0.020 and 0.172 respectively, indicating that M j With S j Independent of each other.

[0082] Traffic blockages are usually divided into natural disasters, traffic accidents, non-disaster blockages, planned maintenance and large-scale activities, etc. The number of traffic accidents is extracted, and the ratio of the number of traffic accidents to mileage in the j observation unit is converted into the number of accidents per 100 km section of the j observation unit per day, recorded as Z j To evaluate the M in the j observation unit j and S j The degree of dispersion is introduced, and the coefficient of variation d is introduced to obtain the provincial road network j observation unit M j and S j The d values ​​of the province from January to December 2019 were obtained. j , S j A cubic polynomial is fitted with the d value of the number of accidents per kilometer in each month, see Figure 4 .

[0083] Depend on Figure 4 Visible: S j 、M j The overall trend is similar to the value of accidents per kilometer d; S j The low value of d in April leads to a large deviation rate; the d value of the number of accidents per kilometer in March is large, but M j The March data does not reflect this clearly.

[0084] 2) Z j Follow S j Changes

[0085] Previous studies have shown that when S j Transition from free flow to stable flow and unstable flow, Z j It shows a trend of first decreasing and then increasing. j Transition from saturated flow to blocked flow, Z j Continue to decrease. j With Z j The theoretical relationship curve is shown in Figure 5 Figure (a) in Figure 1. Based on the S of 684 observation units j Calculate and distribute the results, and get S according to the fifth-order polynomial fitting j With Z j The relationship curve is shown in Figure 5 Figure (b) in .

[0086] Depend on Figure 5 As shown in Figure (b): S j With Z j The theoretical curve and the fitting curve have good trend consistency; the R 2 The goodness of fit is poor. j ∈[0.07,0.17] free flow interval and S jThe fitting value and Z in the stable flow interval ∈[0.27,0.37] j The actual value has a large deviation. 2 , we should find another way to model it.

[0087] 3) Z j Follow M j Changes

[0088] The gradual mixing of heavy trucks into passenger car traffic will reduce the speed of passenger cars and cause traffic conflicts. There should be at least one peak value to make Z j It increases first and then decreases. After the peak, it tends to effectively merge passenger and freight traffic. j With Z j The theoretical relationship should be a cubic polynomial curve. j With Z j Corresponding data, Lagrange interpolation method is used to process M j Z within [80%, 100%] j Data, for 684M j With Z j The data were fitted with a cubic polynomial and the results are shown in Figure 6 .

[0089] Depend on Figure 6 Visible: Z j The overall trend is to increase first and then decrease. j ∈[30%~80%] when Z j There is a distribution concentration with a peak at 33%; M j -Z j The left end of the fitting curve does not touch the origin of the coordinate system, indicating that when heavy trucks began to mix in, the number of accidents did not increase immediately. j When the passenger and freight traffic reaches 33%, it is completely integrated. j Highest. R of the cubic polynomial fitting result 2 The value is 0.840, which indicates a good fit.

[0090] (2) Security risk assessment model

[0091] 1) Model building

[0092] Given that S j With M j Independent of each other, building a security risk assessment model

[0093] Z(S j ,M j )=Z(S j )+Z(M j )+ε (1)

[0094] ε=εsj +ε Mj (2)

[0095] Where: Z(S j ) is the traffic saturation model; Z(M j ) is the heavy truck mixing rate model; ε is the error term.

[0096] According to the actual accident data fitting results of 684 observation unit data, S j -Z j The curve shows a non-steady-state fluctuation law, Z j There are two peaks. The peak under unstable flow is higher than that under free flow. After the second peak, Z j Gradually tends to 0. This is consistent with the feeding characteristics of insects when they are hunting. It is difficult for insects to catch food at a small food density. As the food density increases, the amount of food consumed increases. When the food is saturated, the amount of food consumed by insects gradually decreases. The Peal-Reed model often describes the feeding characteristics of insects. Let Z sj is the food intake, then Z(S j ) can be expressed as

[0097] Z sj =Z(S j )+ε sj ≈Z(ε j ) (3)

[0098]

[0099] Where: Z sj For S j The number of accidents per 100 kilometers per day; sj is the saturation random error; C1, C2, C3, C4 and C5 are parameters to be calibrated, and C1 is the saturation value of the number of accidents.

[0100] According to the fitting results of the actual accident data of 684 observation units, M j -Z j The curve is Z j A cubic polynomial single-peak curve that increases first and then decreases. Z(M j ) can be expressed as

[0101] Z mj =Z(M j )+ε mj ≈Z(M j ) (5)

[0102]

[0103] Where: Z mj For M j The number of accidents per 100 kilometers per day;mj is the random error of the mixing rate; C6, C7, C8 and C9 are parameters to be calibrated.

[0104] is the calibration parameter C i (i=1,2,…,9), take any set of observations without loss of generality (S j ,Z j ), treat the fitting function as Z sj =Z(S j ,C1,C2,C3,C4,C5) use the Levenberg-Marquardt algorithm, the goal is

[0105]

[0106] 2) Model solution

[0107] For Z = Z(S j ,M j ,C1,C2,…,C9) using Taylor series in Expand at , ignoring the quadratic and higher fractional terms, let get

[0108]

[0109] Then formula (7) can be decomposed into

[0110]

[0111] In formula (9), γ (γ ≥ 0) is the impedance factor. By setting γ to a larger value, μ i , which improves the iterative convergence performance of formula (9). In order to minimize Z, μ in formula (9) i Find the partial derivative and set it to 0. is a real number with fixed parameters, for μ i Finding the partial derivative is equivalent to C i Taking partial derivatives, we get

[0112]

[0113] Let l=1,2…,9, then

[0114]

[0115] Substituting formula (10) into formula (9), we can get

[0116]

[0117] Using Levenberg-Marquardt algorithm to solve μ in the system of nine linear equations i , and then through Get a C i When μ is solved i When it is larger, it can be Recalculate ρ il and ρ iZ , and then substitute it into equation (12) to solve the new μ i .μ i The h-th iteration result is recorded as The convergence error is preset to 5%, and the iteration μ i , until the discrete statistics Satisfy the iteration accuracy requirements. The end condition of the iteration process is

[0118]

[0119] C that satisfies the iteration condition of formula (13) i This is the optimal solution after calibration.

[0120] (3) Parameter calibration and model verification

[0121] According to the model solution method, Python is used to implement the iterative algorithm and solve the equations using the Levenberg-Marquardt algorithm. The algorithm reaches the first result when the number of iterations is h = 337. The accuracy requirement is 1.33s; when h=523, It took 1.42s to get C i Substituting (i=1,2,…,9) into equation (1), we get

[0122]

[0123] Let Z j To M j and S j The partial derivative of is 0, so we can get Z j S when reaching minimum and maximum values j are 100% and 71% respectively; j M when reaching minimum and maximum values j 0, 33%, respectively, and Z min (100%, 0) = 0.01 times / d·100km, Z max (71%, 33%) = 1.03 times / d·100km. The efficiency of model iteration using Python is basically not affected by the number of iterations. The relationship between R and h (h≤600) is shown in Figure 7 .

[0124] Depend on Figure 7It can be seen that R converges with the increase of h, and the safety risk assessment model is feasible; the convergence speed is fast before h = 300 times, and when h = 500 times, R ≈ 0, which reflects the fast convergence characteristic of the safety risk assessment model. In the safety risk measurement and control project of large-scale highway networks, the safety risk assessment model can significantly improve the efficiency.

[0125] Step 3: According to the number of accidents per 100 kilometers per day of each observation unit, combined with the highway vehicle-to-vehicle operation safety risk assessment standard, the vehicle-to-vehicle operation safety risk level of each observation unit is obtained.

[0126] In one embodiment, define F j Z j The quantiles are within [0.01,1.03], and the highway vehicle operation safety risk assessment standards are shown in Table 1.

[0127] Table 1 Levels and color schemes of highway vehicle-to-vehicle operation safety risk assessment standards

[0128]

[0129] It should be noted that the green, yellow, orange, bright red and dark red in Table 1 correspond to the safety risk levels of level 1, level 2, level 3, level 4 and level 5 respectively; among them, level 1 is the lowest risk level, level 5 is the highest risk level, and the risk levels of level 1, level 2, level 3, level 4 and level 5 increase successively.

[0130] The following is a detailed description of the process of establishing the highway vehicle-to-vehicle operation safety risk assessment standard with reference to the attached drawings, as follows:

[0131] The evaluation criteria are the prerequisite for assessing the safety risk of mixed traffic flow on a road section. min (100%, 0) = 0.01 times / d·100km, Z max (71%, 33%) = 1.03 times / d·100km, M j , S j In the range of [0,100%], the values ​​are separated by 0.01 and substituted into equation (14) as known parameters to form Z of 101×101 j Matrix, according to Z j Increment by 10% in [0.01,1.03] for Z j The statistical distribution using the normalization algorithm is shown in Figure 8 .

[0132] Depend on Figure 8 It can be seen that the distribution within each group is inconsistent. The Z jDifferent colors are used to mark the risk level of observation units in the risk assessment system. The color matching should not be too much or too little. A 5-level Z j Safety evaluation standards: Groups 1 to 4, 5 to 6, 7 to 8 are combined, and Groups 9 and 10 are set up as independent groups. j Z j In the quantile range of [0.01,,1.03], in the newly formed 5-level grouping, Z j The distribution ratios from small to large are 25.00%, 29.69%, 25.70%, 10.39% and 9.22% respectively. j The corresponding groups and color schemes are shown in Table 1.

[0133] Introducing the idea of ​​85% and 15% quantile segmentation, and Z j Correspondingly, M j , S j The same five levels are divided into M j With S j Substitute the two end thresholds of each level into formula (14) to calculate Z j The range of change forms a 5×5 Z j Safety evaluation standards are shown in Table 2.

[0134] Table 2M j With S j Z between partitions j Security Risk Assessment Standards

[0135]

[0136] From Table 2, we can see that: j Complex surface distribution, M j With S j There are many places in the safety risk assessment standards involving Z j The phenomenon of crossing levels; when M j ≤60% and S j When ≤60%, Z j Basically covers 2 levels; M j , S j When the values ​​of M and J are within 40%, the j observation unit appears green G, yellow Y and orange O, and as M j As the j observation unit increases, the complexity of the color change begins to emerge, which is consistent with the law that the Peal-Reed model curve changes from strong to weak in the early stage, and from weak to strong and then to weak in the middle and late stages. j or S j When it exceeds 60%, Z j The number of intervals covering three levels has increased significantly, especially M j ∈(15%,60%] and S j∈(85%,100%], the color changes most frequently; during non-major holidays, the road section S j There are not many cases where the percentage exceeds 60%, and the number of intervals of red R and dark red C in Table 1 is also small, which is consistent with the facts; j With S j All of these are covered in the safety risk assessment standards, proving that the grading and color scheme are reasonable.

[0137] In a specific embodiment, a TransCAD topology map of a provincial road network is drawn, and the TCD of the provincial highway network in 2021 is extracted, in which the passenger and freight samples are 755 million, and the passenger and freight ratio is 5.16:1. The M of each OD is obtained according to the calculation formula of the heavy truck mixing rate and the traffic saturation. j and S j Assigned to 57 road sections (684 observation units), the results are shown in Fig. 9 .

[0138] The present embodiment provides a method for assessing the operational safety risk of vehicles on provincial highways. First, the traffic data of each vehicle type is extracted from the provincial highway network toll database, and the traffic data of each vehicle type is processed by the TransCAD traffic distribution method to obtain the natural traffic volume of each vehicle type on each observation unit; the natural traffic volume of each vehicle type on each observation unit is input into the heavy truck mixing rate calculation formula and the traffic flow saturation calculation formula to obtain the heavy truck mixing rate and traffic flow saturation of each observation unit; then, according to the obtained heavy truck mixing rate and traffic flow saturation, the safety risk assessment model is used to obtain the number of accidents per 100 kilometers per day for each observation unit in the province; finally, according to the number of accidents per 100 kilometers per day for each observation unit, the safety risk assessment standard is used to judge the operational safety risk level of each observation unit. Based on the full coverage and high precision characteristics of the highway TCD, the safety risk status of each section of the provincial highway is determined, which has good economic efficiency and is of great significance for guiding the orderly implementation of engineering applications such as traffic accident prevention, traffic evacuation management, and emergency resource scheduling.

[0139] like Figure 2 As shown, the embodiment of the present application provides a provincial highway vehicle operation safety risk assessment device, including an acquisition module, an accident number calculation module and a safety risk level assessment module, and each module is configured as follows:

[0140] The acquisition module is used to obtain the heavy truck mixing rate and traffic saturation of each observation unit in the province. The observation unit refers to the section between two adjacent toll stations on the provincial highway network topology map.

[0141] The accident number calculation module is used to input the heavy truck mixing rate and the traffic saturation into the safety risk assessment model to obtain the number of accidents per 100 kilometers per day for each observation unit.

[0142] The safety risk level assessment module is used to obtain the vehicle-to-vehicle operation safety risk level of each observation unit based on the number of accidents per 100 kilometers per day of each observation unit and the highway vehicle-to-vehicle operation safety risk assessment standard.

[0143] Among them, regarding the specific implementation methods of the above-mentioned modules, reference can be made to the corresponding contents disclosed in the aforementioned embodiments, which will not be repeated here.

[0144] In one embodiment of the present invention, a computer device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used to implement the operation of a provincial highway vehicle-to-vehicle operation safety risk assessment method.

[0145] In one embodiment of the present invention, a method for assessing the operational safety risks of vehicles between provincial highways can be stored in a computer-readable storage medium if it is implemented in the form of a software functional unit and sold or used as an independent product. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable storage media include permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules or other data.

[0146] The computer storage medium can be any available medium or data storage device that can be accessed by a computer, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc.), optical storage (such as CD, DVD, BD, HVD, etc.), and semiconductor storage (such as ROM, EPROM, EEPROM, non-volatile memory (NANDFLASH), solid-state drive (SSD)), etc.

[0147] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0148] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products of the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0149] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0150] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0151] In the description of the present invention, the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" etc. mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0152] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for assessing the operational safety risk of vehicles on provincial highways, characterized in that: include: Obtain the heavy truck mixing rate and traffic saturation of each observation unit in the province, where the observation unit refers to the section between two adjacent toll stations on the provincial highway network topology map; Inputting the heavy truck mixing rate and the traffic flow saturation into a safety risk assessment model to obtain the number of accidents per 100 kilometers per day for each observation unit; According to the number of accidents per 100 kilometers per day of each observation unit, combined with the highway vehicle-to-vehicle operation safety risk assessment standard, the vehicle-to-vehicle operation safety risk level of each observation unit is obtained; The method of obtaining the heavy truck mixing rate and traffic saturation of each observation unit in the province is as follows: Extract traffic data of each vehicle type from the provincial highway network toll collection database; The traffic data of each vehicle type are processed respectively to obtain the natural traffic volume of each vehicle type in each observation unit; The natural traffic volume of each type of vehicle on each observation unit is input into the heavy truck mixing rate calculation formula and the traffic flow saturation calculation formula respectively, so as to obtain the heavy truck mixing rate and traffic flow saturation of each observation unit in the province; The calculation formula for the heavy truck mixing rate is: In the formula, for The heavy truck mixing rate of the observation unit; for Car natural traffic volume in the observation unit; for The reduced traffic volume coefficient of the vehicle type; The traffic saturation calculation formula is: In the formula, for Traffic saturation of the observation unit; for Car natural traffic volume in the observation unit; for The reduced traffic volume coefficient of the vehicle type; for Observation unit 120 km·h -1 Number of lanes with design speed; for Observation unit 100 km·h- 1 Number of lanes with design speed; for Observation unit 120 km·h -1 Design speed capacity; for Observation unit 100 km·h -1 Design speed capacity; pcu·h -1 ·ln; pcu·h -1 ·ln; for Traffic composition coefficient of the observation unit; for Car Traffic volume ratio of the observation unit; The security risk assessment model is: In the formula, for The number of accidents per 100 kilometers per day in the observation unit.

2. A provincial highway vehicle operation safety risk assessment method according to claim 1, characterized in that: The traffic data of each vehicle type are processed separately to obtain the natural traffic volume of each vehicle type in each observation unit, specifically: The traffic data of each type of vehicle are processed separately using the TransCAD traffic assignment method to obtain the natural traffic volume of each type of vehicle in each observation unit.

3. A provincial highway vehicle operation safety risk assessment method according to claim 1, characterized in that: definition for Number of accidents per 100 kilometers per day in the observation unit exist quantile within ; The highway vehicle-to-vehicle operation safety risk assessment standards include: when ≤25.00%, and 0.01< When ≤0.26, The safety risk level of inter-vehicle operation of the observation unit is level 1; When 25.00%< ≤54.69%, and 0.26< When ≤0.57, The vehicle-to-vehicle operation safety risk level of the observation unit is level 2; When 54.69%< ≤80.39%, and 0.57< When ≤0.83, The safety risk level of inter-vehicle operation of the observation unit is level 3; When 80.39%< ≤90.78%, and 0.83< When ≤0.94, The vehicle-to-vehicle operation safety risk level of the observation unit is level 4; When 90.78%< ≤100%, and 0.94< When ≤1.03, The safety risk level of inter-vehicle operation of the observation unit is level 5; Among them, the first level is the lowest risk level, the fifth level is the highest risk level, and the risk levels of the first level, the second level, the third level, the fourth level and the fifth level increase successively.

4. A device for evaluating the risk of vehicle-to-vehicle operation safety on a provincial highway based on any one of claims 1 to 3, characterized in that: include: An acquisition module is used to obtain the heavy truck mixing rate and traffic saturation of each observation unit in the province, where the observation unit refers to the section between two adjacent toll stations on the provincial highway network topology map; An accident number calculation module, used for inputting the heavy truck mixing rate and the traffic flow saturation into a safety risk assessment model to obtain the number of accidents per 100 kilometers per day for each observation unit; The safety risk level assessment module is used to obtain the vehicle-to-vehicle operation safety risk level of each observation unit based on the number of accidents per 100 kilometers per day of each observation unit and the highway vehicle-to-vehicle operation safety risk assessment standard.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of a method for assessing the risk of vehicle-to-vehicle operation safety on a provincial highway as described in any one of claims 1 to 3 are implemented.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of a method for assessing the risk of vehicle-to-vehicle operation safety on a provincial highway as described in any one of claims 1 to 3 are implemented.

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