CN110491154A - Suggestion speed formulating method based on security risk and distance - Google Patents

Abstract

The invention discloses a method for formulating a suggested vehicle speed based on safety risks and distances, comprising: acquiring road traffic flow conditions, road characteristic conditions, and weather conditions; integrating and matching traffic flow data, road characteristic data, and weather data; The risk assessment model calculates the traffic risk; the traffic risk value obtained by the traffic risk assessment model is used for clustering, and the traffic risk early warning classification is implemented; the safe speed based on the traffic risk distribution is calculated, and according to the traffic risk distribution under different conditions, the traffic risk value less than The traffic flow state equal to 0.2 is used as the safe speed determination range, and the 85th percentile speed of the vehicle running speed in this state is regarded as the safe driving speed; the calculation is based on the stopping sight distance (for example, the road without strict physical separation needs to calculate the passing sight distance) The safe driving speed of the road; compare the road safe driving speed obtained by the two methods, and give the suggested speed of the road under different conditions (including weather conditions, road conditions, etc.).

Description

Method of formulating suggested vehicle speed based on safety risk and distance

technical field

The invention belongs to the field of traffic safety management, in particular to a method for formulating suggested vehicle speeds based on safety risks and distances.

Background technique

In recent years, the construction of my country's road transportation infrastructure has been accelerating, and the mileage of traffic has continued to increase. While road traffic has brought huge social and economic benefits, the problem of traffic accidents has also attracted more and more attention from the society. In road traffic safety management, vehicle speed management is an important management strategy.

Generally speaking, the driver will form a psychologically considered safe driving speed, that is, the subjective expected speed, after comprehensive consideration of factors such as road conditions, traffic conditions, and the performance of the vehicle he is driving during the driving process. However, the actual traffic flow is affected by many factors such as people, vehicles, roads, and the environment, and the driver's subjective expectation speed is not the real safe speed. Related studies have shown that the closer the average traffic flow speed is to the safe speed, the lower the risk; the closer the individual expected speed is to the traffic flow speed, the lower the risk. Existing vehicle speed management often adopts the highest speed limit strategy, which is determined through the analysis of parking sight distance, meeting vehicle sight distance and road superelevation, lacks consideration of variable factors such as the environment, and cannot be dynamically changed in real time with changes in the environment .

Therefore, there is a need for a method for formulating suggested vehicle speeds based on safety risks and distances. In a comprehensive situation composed of specific roads, environments, and traffic, the maximum vehicle speed recommended for the driver to maintain safe driving is given, that is, the objective suggested vehicle speed. To meet the requirements of road traffic safety.

Contents of the invention

The purpose of the present invention is to provide a suggested vehicle speed formulation method based on safety risks and distances, which overcomes the lack of consideration of variable factors such as the environment in existing maximum speed limit strategies, and dynamically formulates suggested vehicle speeds in real time, thereby ensuring road traffic safety.

The main feature of the present invention is that traffic risk early warning levels are divided through traffic safety risk assessment, and safe vehicle speeds under different conditions are determined according to traffic risk early warning levels according to traffic risk distribution under different conditions. At the same time, the safe vehicle speed under different conditions is determined by using the principle of parking sight distance (known technology). Comparing the safe vehicle speed obtained by the two methods, correcting the safe vehicle speed, and giving the suggested road speed.

In order to solve the above technical problems, the present invention provides a method for formulating a suggested vehicle speed based on safety risks and distances, comprising the following steps:

Step 1: Obtain information such as traffic flow status conditions, road characteristic conditions, weather conditions, and historical traffic accidents

The traffic flow state conditions mainly include flow rate, vehicle speed, occupancy rate data, the road characteristic conditions mainly include road alignment, road section type data, and the weather conditions mainly include data such as rainfall, snowfall, visibility, wind direction, and wind speed; Historical traffic accidents include accident information including accident time, place, driving direction, accident type and accident level, etc.

Step 2, Traffic Safety Risk Assessment

The traffic flow data, road characteristic data, meteorological data and traffic accident data obtained in step 1 are fused and matched as input data, and a traffic risk assessment model is constructed to calculate the traffic risk at each time point and obtain the traffic risk value;

Step 3, traffic risk early warning classification

Use the traffic risk value obtained by the traffic risk assessment model in step 2 to cluster, and classify the road traffic risk into five levels: almost no risk, allowable risk, moderate risk, major risk, and unacceptable risk. The risk values corresponding to the two grades of almost no risk and permissible risk are less than 0.2, and the two grades should be regarded as a relatively safe state;

Step 4. Calculate the safe driving speed on the road based on the traffic risk distribution

Count the traffic flow state under the safe state in step 3, sort all the vehicle speeds, and take 85% of the vehicle speed as the safe driving speed.

Step 5. Calculate the safe driving speed based on the parking sight distance (for roads without strict physical separation, the passing sight distance needs to be calculated)

According to the safe driving speed model based on parking sight distance and meeting traffic sight distance (the prior art, utilizing the parking sight distance principle), by getting different values to visibility and road longitudinal slope, the safe driving speed under different conditions is calculated;

Step 6: Comparing the road safety driving speeds obtained by the two methods of step 4 and step 5 under different grade conditions, comprehensively considering the traffic risk distribution under different conditions and the stopping sight distance and the sight distance of meeting vehicles, the recommended speed under different grade conditions is given.

Further, in Step 3, the road traffic safety risks can be divided into five levels according to risk levels: almost no risk, allowable risk, moderate risk, major risk, and unacceptable risk.

Table 1 Risk Classification Criteria

Further, in step 4, the calculation is based on the safe driving speed of the traffic risk distribution. Under different conditions, the traffic flow state with a traffic risk value less than or equal to 0.2 is selected as the determination range of the safe driving speed, and the cumulative distribution curve of the vehicle speed in this state is drawn And calculate the 85th percentile speed of the vehicle running speed, and after rounding, the safe driving speed under different conditions can be obtained.

Further, in step five, the safe driving speed model based on the parking sight distance and the meeting vehicle sight distance is calculated by the following formula:

In general, when the driver finds the vehicle in front, the speed of the vehicle in front is lower than that of the vehicle and it is in the braking state. At this time, the safety distance required for the vehicle behind to stop should be satisfied.

L ₁ +L ₂ +L _s ≤L _v +L ₃ (1)

In the formula,

L ₁ ——the driving distance of the vehicle within the reaction time of the driver of the rear vehicle, m;

L ₂ ——The driving distance of the rear vehicle within the braking time, m;

L _s ——safety distance, the general value is 5-10m, in order to ensure driving safety in bad weather, the value of L _s is 20m;

L _v ——Visible distance of road section, m;

L ₃ ——The driving distance of the vehicle in front during the driver's reaction time and vehicle braking time, m.

In bad weather, the driver's effective sight distance and road adhesion coefficient will change. In order to ensure the safe driving of the vehicle, the most unfavorable situation should be considered, that is, due to vehicle failure, tire damage, breakdown, cargo spillage and accidents, etc. The speed of the object is zero, and there is a serious speed difference in the traffic flow, the vehicle behind must perform emergency braking. At this time, the safety distance required for the vehicle behind to stop is,

L ₁ +L ₂ +L _s ≤L _v (2)

The driving distance L ₁ of the vehicle within the reaction time of the driver of the vehicle behind,

L ₁ = vt ₁ (3)

In the formula,

v——travel speed of the vehicle behind, m/s;

t ₁ ——reaction time of the driver of the vehicle behind, in s.

In general, the driver's reaction time is 0.5-1.7s. In bad weather and the road driving environment is harsh, the driver's reaction time may exceed 1.7s, and the value of t ₁ is 2.5s.

In order to fully guarantee the driving safety in bad weather, the most unfavorable combination is considered, that is, the vehicle is on a downhill road section and the air resistance is ignored, the driving distance L ₂ of the rear vehicle braking time,

In the formula,

a——deceleration of the rear vehicle, m/s ² ;

f—adhesion coefficient of the road surface;

i——slope, %;

g—gravitational acceleration, take g=9.8m/s ² .

Bring formulas (3) and (4) into formula (2) to get the safe viewing distance,

Taking the visibility in bad weather as the visible distance, the safe vehicle speed based on the parking sight distance can be calculated by the formula (5), as shown in the formula (6),

In the formula,

V—safe vehicle speed, km/h.

Comparing the calculation results of the safe driving speed model based on the parking sight distance and meeting traffic sight distance with the safe driving speed based on the traffic risk distribution, in order to ensure road traffic safety under severe conditions, a lower safe driving speed is selected as a suggestion under this condition speed.

Compared with prior art, the present invention mainly comprises following advantage:

1. The present invention comprehensively considers various factors affecting the speed of a vehicle, including people, vehicles, roads, and the environment, especially considering the influence of traffic flow conditions and weather conditions on speed;

2. The present invention introduces the traffic safety risk as the main basis for the formulation of the suggested vehicle speed. Starting from the factors that affect the traffic safety risk, the road traffic safety level can be fundamentally improved; meanwhile, the safe driving speed calculated based on the parking sight distance and the meeting traffic sight distance is utilized. Correct the suggested speed to ensure the safety of the vehicle;

3. The present invention can realize real-time dynamic formulating of suggested vehicle speeds, guide drivers to drive at suggested speeds, reduce speed differences, reduce traffic safety risks, and improve road traffic safety levels.

Description of drawings

Fig. 1 shows the steps of a method for formulating a suggested vehicle speed based on safety risks and distances provided by an embodiment of the present invention.

Fig. 2 is a flow chart for formulating suggested vehicle speeds based on safety risks and distances provided by an embodiment of the present invention.

Fig. 3 time-varying diagram of traffic risk in the embodiment

Figure 4 Example Traffic Risk Level Clustering Results

Detailed ways

In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

The application principle of the present invention will be described in detail below in conjunction with the accompanying drawings.

As shown in FIG. 1 , the embodiment of the present invention provides a method for formulating a suggested vehicle speed based on safety risks and distances.

The application principle of the present invention will be further described below in combination with specific embodiments.

Example 1:

The method for formulating the recommended vehicle speed under severe weather conditions provided by the embodiment of the present invention is calculated by taking the data of the G15 Shenhai Expressway (Shanghai section) as an example.

(1) Obtain information such as traffic flow state conditions, road characteristic conditions, weather conditions, etc.

G15 Shenhai Expressway (Shanghai section) is a north-south expressway in the western part of Shanghai, starting from the junction of Jiading District and Taicang City in the north, connecting Shenhai Expressway (Jiangsu Section) to the Shanghai-Suzhou border, and south to Jinshan District and Pinghu At the junction of the city, it intersects with Shenhai Expressway (Zhejiang section), and passes through Jiading District, Qingpu District, Songjiang District and Jinshan District in turn to the southwest. G15 Shenhai Expressway (Shanghai Section) is located between Shanghai S20 Outer Ring Expressway and G1501 Shanghai Ring Expressway.

A total of 12 sets of coil detectors are deployed in the Shanghai section of G15 Shenhai Expressway. The sampling interval of the original data of the coil detectors is 5 minutes, including the induction coil number, collection time, data validity, flow rate, speed, occupancy rate and analysis of each lane. Vehicle traffic, speed, occupancy rate and other information.

The traffic accident data of G15 Shenhai Expressway (Shanghai Section) was collected from January 1, 2013 to December 31, 2013, and a total of 255 accidents were collected. After deleting some accident data with incomplete information, 193 traffic accidents were finally extracted. Accident information includes accident time, place, driving direction, accident type and accident level, etc.

A total of 5 automatic weather stations are deployed along the G15 Shenhai Expressway (Shanghai Section), and each automatic weather station can collect meteorological elements such as temperature, humidity, rainfall, air pressure, wind speed and direction, and visibility every 1 minute.

Traffic flow state condition data and road characteristic conditions can be obtained from Shanghai Road Network Operation Center. Meteorological information can be obtained from the Shanghai Meteorological Bureau.

(2) Establishing a severe weather highway traffic safety risk assessment model

There are two main considerations for the influence factors of severe weather on traffic risk:

The first is the change of visibility, the driver's line of sight will be significantly affected, which in turn will affect the driver's judgment on the distance between vehicles;

The second is the change of the adhesion coefficient of the road surface, which makes the friction between the vehicle tire and the ground change, and the driver will obviously change the speed of the vehicle in order to ensure the safe driving of the vehicle.

The construction of the expressway traffic risk assessment model adopts the method of Bayesian Logistic regression model; the formula of Bayesian Logistic regression is as follows:

y _i ～Bernoulli(p _i )

In the formula,

p _i —the probability of traffic accidents;

η _i ——utility function;

x _ji - the value of variable k in sample i;

β ₀ ——regression intercept;

β _j ——regression coefficient of explanatory variable k.

The expression of the likelihood function of the model is as follows:

The parameters in the model all adopt the uninformative prior probability distribution:

Usually a prior distribution with a large variance can represent an uninformative prior probability distribution, let μ _j =0, Represents no prior information about the parameters in the model.

According to Bayes' theorem, the posterior joint probability density distribution of the parameter is proportional to the product of the likelihood function and the prior probability distribution, namely:

The data of G15 Shenhai Expressway (Shanghai Section) was used to model the traffic risk assessment model, and the unmatched case-control research method was used to extract traffic flow data and weather data under normal conditions. The ratio of accident and normal data was 1:4. Using the random forest algorithm to screen the model variables, the variables in the final model are shown in Table 2.

Table 2 Variable screening results

Using the rstanarm package of R software, the Bayesian Logistic model was established, and the posterior probability distribution of each regression coefficient was calculated by the MCMC method. The calibration results of the model parameters are shown in Table 3.

Table 3 Calibration results of model parameters

Taking a certain road section of G15 Shenhai Expressway (Shanghai section) as an application case, the data of the whole day on September 11, 2013 was collected as input data, and the traffic risk of each time period was calculated through the expressway traffic risk assessment model under bad weather. The results are shown in the figure 3.

(3) Highway road traffic risk early warning classification

The expressway traffic risk early warning classification is constructed by fuzzy C-means clustering algorithm; based on the weighted similarity measure between samples and C cluster centers, the objective function is iteratively minimized to determine the best category. The objective function is defined as follows:

i=1,2,L,c

k=1,2,L,n

And meet the conditions:

0 ≤ u _ik ≤ 1

k=1,2,L,n

i=1,2,L,c

In the formula, X={x ₁ ,x ₂ ,L,x _n } is the clustering sample set, n is the number of samples in the clustering space; V={v ₁ ,v ₂ ,L,v _n } is c Clustering center, c is the number of clustering categories; ||x _k -v _i || represents the normalized distance between x _k and v _i ; U=[u _ik ] is a c×n-dimensional matrix; u _ik is the membership degree value of the kth sample to class i.

The calculation steps of the fuzzy C-means clustering method are as follows:

Step 1: Divide the number c of classes according to the sample x _k , the power exponent m>1 and the initial membership degree matrix U ⁽⁰⁾ = (u _ik ⁽⁰⁾ ), take a uniformly distributed random number on [0, 1] to determine U ⁽⁰⁾ . Let l=1 to represent the 1st iteration.

Step 2: Calculate the cluster center V ^(l) of step l:

Step 3: Correct the membership degree matrix U ^(l) and calculate the objective function value J ^(l) .

i=1,2,L,c

k=1,2,L,n

In the formula: d _ik ^(l) =||x _k -v _i ^(l) ||.

Step 4: For a given membership degree termination tolerance ε _u >0, or objective function termination tolerance ε _J >0, or for the maximum iteration step size L _max , when max{|u _ik ^l -u _ik ^{(l -1)} |}<ε _u , or when l>1, |J ^(l) -J ^(l-1) |<ε _j has l>L _max or l>L _max , the iteration stops, otherwise l=l +1, then repeat step 2, step 3.

After the above loop iterations, when the objective function reaches the minimum value, determine the membership of all samples according to the values of the elements in the final membership matrix U, when , the sample x _k can be classified as the jth class.

Through the fuzzy C-means clustering algorithm, the risk value calculated by the highway traffic risk assessment model of the G15 Shenhai Expressway (Shanghai section) under severe weather is used as the sample feature, and the number of clusters is determined to be 5 according to the traffic risk, and the traffic risk value The cluster analysis was carried out, and the results are shown in Figure 4.

Table 4 The maximum and minimum values of each cluster

clustering category the first sort second category third category fourth category fifth category minimum value 0.41 0.29 0.20 0.13 0.04 maximum value 0.93 0.41 0.28 0.19 0.12

According to the maximum value, minimum value and risk level division standard of each cluster category of traffic risk value, the range of traffic risk value corresponding to different early warning risk levels of expressway traffic under severe weather is determined, as shown in Table 4.

When the traffic risk is less than 0.13, the expressway traffic operation is in a safe state. When the traffic risk is greater than 0.13 and less than 0.2, the expressway traffic warning risk level is level 4 risk, which has the potential risk of traffic accidents. When the traffic risk is greater than 0.2 and less than 0.3, the expressway traffic early warning risk level is a third-level risk, with the risk of traffic accidents and the potential risk of casualty accidents. When the traffic risk is greater than 0.3 and less than 0.4, the expressway traffic early warning risk level is the second-level risk, the risk of traffic accidents is relatively high, the frequency of traffic accidents is relatively high or the possibility is relatively high, and there may be multiple injuries or accidents. Caused many casualties. When the traffic risk is greater than 0.4, the highway traffic early warning risk level is a first-level risk, the risk of traffic accidents is extremely dangerous, and the possibility of traffic accidents is extremely high. Once an accident occurs, it will cause the risk of many casualties.

Table 5 Classification of traffic risk early warning levels

Traffic risk value R Traffic risk warning level R＞0.4 level one risk 0.3<R≤0.4 secondary risk 0.2<R≤0.3 Level 3 risk 0.13<R≤0.2 Level 4 risk R≤0.13 Level 5 risk

(4) Calculate the safe speed of expressway based on traffic risk distribution

Referring to the "Meteorological Grades of Expressway Traffic" (QX/T111-2010), the classification of foggy weather is as follows: the visibility is greater than 200m and less than or equal to 500m, the visibility is greater than 100m and less than or equal to 200m, the visibility is greater than 50m and less than or equal to 100m, and the visibility is less than or equal to 50m. , 4 levels. Under the conditions of different fog levels, select the traffic flow state whose traffic risk value of expressway is less than or equal to 0.2 to calculate the safe vehicle speed, draw the cumulative distribution curve of the vehicle speed in this state and calculate the 85th percentile speed of the vehicle running speed, and round it up and correct it. The recommended safe speed of the expressway under foggy conditions is obtained. In order to facilitate the expressway drivers to accept and the management department to release the speed limit information, a suggested speed limit is given, as shown in Table 6.

Table 6 Suggested speed limit under different fog conditions (km/h)

fog level Visibility 200-500 Visibility 100-200 Visibility 50-100 Visibility less than 50 85th percentile speed 87.58 83.79 53.63 47.82 recommended safe speed 87 83 53 47 Recommended speed limit 85 80 50 45

Referring to "Expressway Traffic Meteorological Grades" (QX/T111-2010), the rainy weather grades are divided into one-hour rainfall intensity of 10.0mm/h to 14.9mm/h, one-hour rainfall intensity of 15.0mm/h to 29.9mm/h, and one hour of rainfall intensity. The hourly rainfall intensity is 30.0mm/h～49.9mm/h, and the hourly rainfall intensity is greater than 50.0mm/h, with 4 grades. Under different rainy weather conditions, select the traffic flow state whose traffic risk value of expressway is less than or equal to 0.2 to calculate the safe vehicle speed, draw the cumulative distribution curve of the vehicle speed in this state and calculate the 85th percentile speed of the vehicle running speed, and get different values after rounding. The recommended safe speed of the expressway under rainy weather conditions, in order to facilitate the expressway drivers to accept and the management department to release the speed limit information, a suggested speed limit is given, as shown in Table 7.

Table 7 Suggested safe vehicle speed under different rainy weather conditions (km/h)

rain level Rain intensity 10-15 Rain intensity 15-30 Rain intensity 30-50 Rain intensity greater than 50 85th percentile speed 77.62 75.27 73.56 65.32 recommended safe speed 77 75 73 65 Recommended speed limit 75 75 70 65

(5) Calculate the safe speed of the highway based on the parking sight distance

The parking sight distance refers to the shortest driving distance required when the vehicle encounters an obstacle ahead and must brake to stop in the same lane. The parking sight distance can be decomposed into three parts: reaction distance, braking distance and safety distance. Since the traffic flow on the expressway section is in the form of a fleet, the safe stopping distance required by the vehicle is calculated from the following state to obtain the corresponding control speed.

The safe speed of the expressway based on the parking sight distance can be calculated by the following formula:

In general, when the driver finds the vehicle in front, the speed of the vehicle in front is lower than that of the vehicle and it is in the braking state. At this time, the safety distance required for the vehicle behind to stop should be satisfied.

L ₁ +L ₂ +L _s ≤L _v +L ₃ (1)

In the formula,

L ₁ ——the driving distance of the vehicle within the reaction time of the driver of the rear vehicle, m;

L ₂ ——travel distance of the rear vehicle within braking time, m;

L _s ——safety distance, the general value is 5-10m, in order to ensure driving safety in bad weather, the value of L _s is 20m;

L _v ——Visible distance of road section, m;

L ₃ ——The driving distance of the vehicle in front during the driver's reaction time and vehicle braking time, m.

In bad weather, the driver's effective sight distance and road adhesion coefficient will change. In order to ensure the safe driving of the vehicle, the most unfavorable situation should be considered, that is, due to vehicle failure, tire damage, breakdown, cargo spillage and accidents, etc. The speed of the object is zero, and there is a serious speed difference in the traffic flow, the vehicle behind must perform emergency braking. At this time, the safety distance required for the vehicle behind to stop is,

L ₁ +L ₂ +L _s ≤L _v (2)

The vehicle travel distance L ₁ within the reaction time of the driver of the rear vehicle,

L ₁ = vt ₁ (3)

In the formula,

v——travel speed of the vehicle behind, m/s;

t ₁ ——reaction time of the driver of the vehicle behind, in s.

In general, the driver's reaction time is 0.5-1.7s. In bad weather and the road driving environment is harsh, the driver's reaction time may exceed 1.7s, and the value of t ₁ is 2.5s.

In order to fully guarantee the safety of highway driving in bad weather, the most unfavorable combination is considered, that is, the vehicle is on a downhill section and the air resistance is ignored, the driving distance L ₂ of the rear vehicle braking time,

In the formula,

a——deceleration of the rear vehicle, m/s ² ;

f—adhesion coefficient of the road surface;

i——slope, %;

g—gravitational acceleration, take g=9.8m/s ² .

Bring formulas (3) and (4) into formula (2) to get the safe viewing distance,

Taking the visibility in bad weather as the visible distance, the safe speed of the expressway based on the parking sight distance can be calculated by the formula (5), as shown in the formula (6),

In the formula,

V—safe vehicle speed, km/h.

In foggy weather, as the fog falls on the road surface, the road surface is wet and the adhesion coefficient is reduced. The adhesion coefficient of the wet road surface is f=0.6. According to the "Technical Standards for Highway Engineering" (JTG B01-2014) on the longitudinal slope of expressways, the design speed is 120km/h, the maximum longitudinal slope is 3%, the design speed is 100km/h, and the maximum longitudinal slope is 4%. The design speed of G15 Shenhai Expressway is 100km/h, and the maximum longitudinal slope is 4%. According to the expressway safe speed model based on parking sight distance, by taking different values for visibility and road longitudinal slope, the safe speed under different fog levels is calculated as shown in Table 8.

Table 8 Suggested vehicle speed under different fog conditions (km/h)

In rainy weather, due to the fog falling on the road surface, the road surface is wet and the adhesion coefficient is reduced. The adhesion coefficient of the wet road surface is f = 0.35. According to the expressway safe speed model based on parking sight distance, by taking different values for visibility and road longitudinal slope, the safe speed under different rainy weather levels is calculated as shown in Table 9.

Table 9 Suggested vehicle speed under different rainy weather conditions (km/h)

(6) Suggested speed of expressway in bad weather

Comparing the safety risk-based and parking-sight-distance-based expressway safe speeds under different severe weather conditions, the safer speed calculation results of the two methods are selected, and finally the suggested expressway speeds under different severe weather levels are given.

Table 10 shows the suggested vehicle speed values under different fog levels.

Table 10 Comparison of suggested vehicle speeds under different fog conditions (km/h)

Table 11 shows the recommended speed limit values under different rainy weather conditions.

Table 11 Comparison of suggested vehicle speeds under different rainy weather conditions (km/h)

Claims (1)

1. a kind of suggestion speed formulating method based on security risk and distance, which comprises the following steps:

Step 1 obtains the information such as traffic flow modes condition, roadway characteristic condition, weather condition

The traffic flow modes condition mainly includes flow, speed, occupation rate data, and the roadway characteristic condition mainly includes Route shape, road segment classification data, the weather condition mainly include the data such as rainfall, snowfall, visibility, wind direction, wind speed；

Step 2, traffic safety risk assessment

Traffic flow data, roadway characteristic data and meteorological data that step 1 obtains are merged and matched, as input number According to, using traffic risk evaluation model calculate real-time traffic risk, obtain traffic value-at-risk；

Step 3, the classification of traffic Risk-warning

It is clustered using the traffic value-at-risk that step 2 traffic risk evaluation model obtains, road traffic risk is subjected to grade It divides, is divided into almost without a point risk, allows risk, moderate risk, 5 material risk, unacceptable risk grades, wherein several Less than 0.2, two grades are considered as safer state by this for devoid of risk and the corresponding value-at-risk of two grades of permissible risk；

Step 4 calculates the road safety speed of operation based on traffic risk distribution

The traffic flow modes under the step 3 safe condition are counted, all speeds are ranked up, take 85% speed conduct Drive safely speed.

Step 5 calculates the safety traffic speed based on stopping sight distance

According to the safety traffic speed model based on stopping sight distance and meeting sighting distance, by taking difference to visibility and road longitudinal grade The safety traffic speed under different condition is calculated in numerical value；

Step 6, comparison are comprehensive Step 4: road safety speed of operation under the conditions of the different brackets that two methods of step 5 wait until Consider the traffic risk distribution and stopping sight distance and meeting sighting distance under different condition, provides and suggest speed under the conditions of different brackets.