CN110491154A - Suggestion speed formulating method based on security risk and distance - Google Patents
Suggestion speed formulating method based on security risk and distance Download PDFInfo
- Publication number
- CN110491154A CN110491154A CN201910665948.1A CN201910665948A CN110491154A CN 110491154 A CN110491154 A CN 110491154A CN 201910665948 A CN201910665948 A CN 201910665948A CN 110491154 A CN110491154 A CN 110491154A
- Authority
- CN
- China
- Prior art keywords
- traffic
- risk
- speed
- condition
- road
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
Abstract
The invention discloses a kind of suggestion speed formulating method based on security risk and distance, comprising: obtain road traffic flow condition, roadway characteristic condition, weather condition；Traffic flow data, roadway characteristic data and weather data are merged and matched, traffic risk is calculated by traffic risk evaluation model；The traffic value-at-risk obtained using traffic risk evaluation model is clustered, and the classification of traffic Risk-warning is implemented；Calculate the safe speed based on traffic risk distribution, according to the traffic risk distribution under different condition, it chooses traffic flow modes of the traffic value-at-risk less than or equal to 0.2 and determines range as safe speed, using the 85%th speed of running velocity under the state as safety traffic speed；Calculate the safety traffic speed based on stopping sight distance (as meeting sighting distance need to be calculated without the stringent road in kind separated)；The road safety speed of operation that two methods of comparison obtain, provides the suggestion speed of road under different condition (including weather condition, road conditions etc.).
Description
Technical field
The invention belongs to traffic safety management field more particularly to a kind of suggestion speed systems based on security risk and distance
Determine method.
Background technique
In recent years, the construction of China's Transportation facilities is constantly accelerated, and mileage open to traffic is continuously increased.Road traffic band
While carrying out huge economic results in society, the also concern increasingly by society of traffic accident problem.In traffic safety
In management, driving speed management is important management strategy.
Generally, driver can in the process of moving according to road conditions, traffic state, driven vehicle performance etc. because
Element forms the safety traffic speed psychologically identified oneself after comprehensively considering, i.e., subjective desired speed.But actual traffic stream by people,
The influence of many factors such as vehicle, road, environment, the subjective desired speed of driver are not real safe speed.Correlative study table
Bright, traffic flow average speed is closer with safe speed, and risk is lower；Individual desired speed and traffic flow speed are closer, wind
Danger is lower.Existing driving speed management often uses Maximum speed limit strategy, passes through point of stopping sight distance, meeting sighting distance and road superelevation
Analysis is determined, lack to variable factors such as environment the considerations of, and can not with the variation of environment real-time dynamic change.
Therefore, it is necessary to a kind of suggestion speed formulating method based on security risk and distance, specific road, environment,
Under the synthesis situation of traffic composition, provides driver and be able to maintain the max speed taken proposed by safety traffic, i.e., it is objective to build
Speed is discussed, to meet the requirement of traffic safety.
Summary of the invention
The purpose of the present invention is to provide a kind of suggestion speed formulating method based on security risk and distance, overcomes existing
Lack the deficiency considered variable factors such as environment in Maximum speed limit strategy, dynamic, which is formulated, in real time suggests speed, so that it is guaranteed that road
Road traffic safety.
The present invention is characterized in divide traffic risk warning grade, according to not by traffic safety risk assessment
Traffic risk distribution under the conditions of, the safe speed under different condition is determined according to traffic risk warning grade.Meanwhile it utilizing
Stopping sight distance principle (known technology) determines the safe speed under different condition.The safe speed that two methods of comparison obtain, it is right
Safe speed is modified, and is provided road and is suggested speed.
In order to solve the above technical problems, the present invention provides a kind of suggestion speed formulation side based on security risk and distance
Method, comprising the following steps:
Step 1 obtains the information such as traffic flow modes condition, roadway characteristic condition, weather condition, historical traffic accident
The traffic flow modes condition mainly includes flow, speed, occupation rate data, and the roadway characteristic condition is mainly wrapped
Road alignment, road segment classification data are included, the weather condition mainly includes rainfall, snowfall, visibility, wind direction, wind speed etc.
Data；Historical traffic accident includes that accident information includes traffic injury time, scene, direction of traffic, accident pattern and thing
Therefore grade etc..
Step 2, traffic safety risk assessment
Traffic flow data, roadway characteristic data, meteorological data and traffic accident data that step 1 obtains are merged
Time point traffic risk when traffic risk evaluation model calculates each is constructed, traffic risk is obtained as input data with matching
Value；
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 carried out
Grade classification is divided into almost without a point risk, allows risk, moderate risk, 5 material risk, unacceptable risk grades,
In almost less than 0.2, two grades are considered as safer by this for devoid of risk and the corresponding value-at-risk of two grades of permissible risk
State；
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
As safety traffic speed.
Step 5 calculates the security row based on stopping sight distance (as that need to calculate meeting sighting distance without the stringent road in kind separated)
Sail speed
According to the safety traffic speed model based on stopping sight distance and meeting sighting distance, (prior art utilizes stopping sight distance original
Reason), by taking different numerical value to visibility and road longitudinal grade, the safety traffic speed under different condition is calculated；
Step 6, comparison Step 4: road safety speed of operation under the conditions of the different brackets that two methods of step 5 wait until,
Comprehensively consider traffic risk distribution under different condition and stopping sight distance and meeting sighting distance, provides and suggest vehicle under the conditions of different brackets
Speed.
Further, in step 3, the standard that the traffic safety risk is divided according to risk class can be divided into several
Devoid of risk allows risk, moderate risk, 5 material risk, unacceptable risk grades.
The 1 risk class criteria for classifying of table
Further, in step 4, the safety traffic speed of the calculating based on traffic risk distribution, at different conditions,
Determination range of traffic flow modes of the traffic value-at-risk less than or equal to 0.2 as safety traffic speed is chosen, is drawn under the state
Speed integral distribution curve and the 85%th speed for calculating running velocity, can be obtained security row under different condition after rounding
Sail speed.
Further, in step 5, the safety traffic speed model based on stopping sight distance and meeting sighting distance uses following
Formula calculates:
Under normal circumstances, driver find preceding vehicle when, preceding vehicle speed be less than Ben Che and be in on-position, at this time after
Safe distance needed for vehicle parking should meet,
L1+L2+Ls≤Lv+L3 (1)
In formula,
L1--- the vehicle driving distance in front vehicle time of driver's reaction, m；
L2--- the operating range in front vehicle braking time, m；
Ls--- safe distance, general value are 5~10m, to ensure the traffic safety under bad weather, LsValue is
20m；
Lv--- the visual range in section, m；
L3--- operating range of the front vehicles in time of driver's reaction and vehicle braking time, m.
In inclement weather, the effective sighting distance of driver and coefficient of road adhesion can change, for the peace of support vehicles
Full traveling, it is contemplated that worst situation, i.e., due to vehicle trouble, damaged tyres, cast anchor, cargo is unrestrained and the originals such as accident
Cause, the speed of objects in front are zero, occur serious speed difference in wagon flow, and rear car must carry out emergency braking, and rear car is stopped at this time
Safe distance needed for vehicle is,
L1+L2+Ls≤Lv (2)
Vehicle driving distance L in front vehicle time of driver's reaction1,
L1=vt1 (3)
In formula,
V --- front vehicle travel speed, m/s；
t1--- front vehicle time of driver's reaction, s.
The reaction time of driver is 0.5~1.7s under normal circumstances, in inclement weather, road travel bad environments,
Driver's reaction time may be more than 1.7s, t1Value is 2.5s.
Sufficiently to ensure bad weather down train safety, most dangerous combination situation is considered, i.e. vehicle is in descending section simultaneously
Ignore air drag, the operating range L in front vehicle braking time2,
In formula,
A --- front vehicle deceleration, m/s2；
The attachment coefficient on f --- road surface；
I --- the gradient, %；
G --- acceleration of gravity takes g=9.8m/s2。
Formula (3) and (4), which are brought into formula (2), can obtain secure visual distance,
Using the visibility under bad weather as visual range, the peace based on stopping sight distance can be calculated by formula (5)
Full speed, as shown in formula (6),
In formula,
V --- safe speed, km/h.
By the safety traffic speed the model calculation based on stopping sight distance and meeting sighting distance and it is based on traffic risk distribution
Safety traffic speed compare, to ensure traffic safety under mal-condition, select lower safety traffic speed work
For suggestion speed under this condition.
Compared with prior art, the invention mainly comprises following advantages:
1. the present invention has comprehensively considered many factors for influencing speed, including people, vehicle, road, environment, traffic is especially considered
The influence of stream condition and weather condition to speed；
2. present invention introduces traffic safety risks as the main foundation for suggesting that speed is formulated, from influence traffic safety risk
Factor set out, can fundamentally improve traffic safety level；Simultaneously using based on stopping sight distance and meeting stadimeter
The safety traffic speed of calculation is modified suggestion speed, ensure that the safety of vehicle driving；
3. the present invention, which can be realized dynamic in real time and formulate, suggests speed, guidance driver reduces speed by speed traveling is suggested
Difference is spent, traffic safety risk is reduced, improves traffic safety level.
Detailed description of the invention
The step of Fig. 1 is the suggestion speed formulating method provided in an embodiment of the present invention based on security risk and distance.
Fig. 2 is provided in an embodiment of the present invention based on the suggestion speed of security risk and distance formulation flow chart.
Fig. 3 embodiment traffic risk time-varying figure
Fig. 4 embodiment traffic risk hierarchical agglomerate result
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to embodiments, to the present invention
It is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not used to
Limit the present invention.
Application principle of the invention is explained in detail with reference to the accompanying drawing.
As shown in Figure 1, the suggestion speed formulating method provided in an embodiment of the present invention based on security risk and distance.
Application principle of the invention is further described combined with specific embodiments below.
Embodiment 1:
Suggest speed formulating method under severe weather conditions provided in an embodiment of the present invention, with G15 Shen Hai high speed (Shanghai
Section) data instance calculated.
(1) information such as traffic flow modes condition, roadway characteristic condition, weather condition are obtained
G15 Shen Hai high speed (Shanghai section) is a north-south highway in Shanghai City west area, North gets Jiading District with
The intersection of Taicang connects Shen Hai high speed (Jiangsu Section) Shanghai Soviet Union provincial boundaries, the intersection in south to Jinshan District and Pinghu City, with Shen Haigao
Fast (Zhejiang Section) intersection, southwester successively by Jiading District, Qingpu District, Songjiang District and Jinshan District.G15 Shen Hai high speed (Shanghai section)
Position is in Shanghai S20 outer ring high speed and the Shanghai G1501 between the high speed of city.
G15 Shen Hai high speed Shanghai section lays 12 groups of coil checkers altogether, and the sampling interval of coil checker initial data is 5
Minute, induction coil number, acquisition time, data validity, flow, speed, occupation rate comprising each lane and divide vehicle
The information such as flow, speed, the occupation rate of type.
The acquisition time of G15 Shen Hai high speed (Shanghai section) traffic accident data is December 31 1 day to 2013 January in 2013
Day, accident 255 is acquired altogether to be risen.After deleting the incomplete casualty data of partial information, it is finally extracted 193 traffic things
Therefore.Accident information includes traffic injury time, scene, direction of traffic, accident pattern and incident classification etc..
Total along G15 Shen Hai high speed (Shanghai section) to lay 5 automatic weather stations, each automatic weather station can acquire often
The meteorological elements such as 1 minute temperature, humidity, rainfall, air pressure, wind speed and direction, visibility.
Traffic flow modes condition data and roadway characteristic condition etc. can be obtained from Shanghai City road network Operation Centre.It is meteorological
Information can be that weather bureau obtains from Shanghai.
(2) bad weather expressway traffic safety risk evaluation model is established
Bad weather mainly accounts for the influence factor of traffic risk there are two aspect:
First is the variation of visibility, and the sight of driver will receive significant impact, and then influence driver for row
Sail the judgement of vehicle spacing；
Second is the variation of coefficient of road adhesion, so that the friction on vehicle tyre and ground changes, drives artificial guarantee vehicle
Safety traffic, speed can occur significantly to change.
The method that the building of freeway traffic risk evaluation model uses Bayes Logistic regression model；Bayes
The formula that Logistic is returned is as follows:
yi~Bernoulli (pi)
In formula,
pi--- the probability that traffic accident occurs；
ηi--- utility function；
xji--- the value of variable k in sample i；
β0--- return intercept；
βj--- the regression coefficient of explanatory variable k.
The likelihood function expression formula of model is as follows:
Parameter in model is all made of no information prior probability distribution:
Usually the prior distribution with big variance can represent no information prior probability distribution, enable μj=0,
Parameter in representative model does not have prior information.
According to Bayes' theorem, the posteriority joint probability density distribution direct proportion and likelihood function and prior probability point of parameter
The product of cloth, it may be assumed that
The modeling of traffic risk evaluation model is carried out with the data of G15 Shen Hai high speed (Shanghai section), it is right using non-matching case-
Traffic flow data and weather data under normal circumstances are extracted according to research method, accident and normal data take the ratio of 1:4.Benefit
With random forests algorithm, screening model variable, the variable in final mask is as shown in table 2.
2 Variable Selection result of table
Using the rstanarm packet of R software, the foundation of Bayes Logistic model is realized, and calculated by MCMC methodology
The Posterior probability distribution of each regression coefficient.The calibration result of model parameter is as shown in table 3.
3 model parameter calibration result of table
Using G15 Shen Hai high speed (Shanghai section), certain section as application case, acquires the data conduct of the whole day on the 11st of September in 2013
Input data calculates the traffic risk of day part by freeway traffic risk evaluation model under bad weather, as a result such as Fig. 3
It is shown.
(3) Expressway Road traffic Risk-warning is classified
The classification of freeway traffic Risk-warning is constructed using Fuzzy C-Means Clustering Algorithm；Based on sample and C
Weighting similarity measure between cluster centre is iterated minimum to objective function, to determine its optimal classification.Target letter
Number is defined as follows:
I=1,2, L, c
K=1,2, L, n
And meet condition:
0≤uik≤1
K=1,2, L, n
I=1,2, L, c
In formula, X={ x1,x2,L,xnIt is cluster sample set, n is the number of samples of Cluster space；V={ v1,v2,L,
vnIt is c cluster centre, c is the classification number of cluster；||xk-vi| | indicate xkWith viBetween normalized cumulant；U=[uik] be
The matrix of c × n dimension；uikIt is that k-th of sample is subordinate to angle value to i class.
Steps are as follows for Fuzzy C-mean clustering calculating:
Step 1: according to sample xkDivide the number c, power exponent m > 1 and initial subordinated-degree matrix U of class(0)=(uik (0)),
The uniform random number on [0,1] is taken to determine U(0).L=1 is enabled to indicate step 1 iteration.
Step 2: the cluster centre V of l step is calculated(l):
Step 3: amendment subordinated-degree matrix U(l), calculating target function value J(l)。
I=1,2, L, c
K=1,2, L, n
In formula: dik (l)=| | xk-vi (l)||。
Step 4: to given degree of membership termination tolerance εu> 0 or objective function termination tolerance εJ> 0, or change for maximum
Ride instead of walk long Lmax, when max | uik l-uik (l-1)| < εu, or work as l > 1, | J(l)-J(l-1)| < εjThere is l > LmaxOr l > LmaxWhen, repeatedly
In generation, stops, otherwise l=l+1, then repeatedly step 2, step 3.
After above-mentioned loop iteration, when objective function reaches minimum value, according in final subordinated-degree matrix U
The value of element determines the ownership of all samples, whenWhen, it can be by sample xkIt is classified as jth class.
By Fuzzy C-Means Clustering Algorithm, with freeway traffic wind under G15 Shen Hai high speed (Shanghai section) bad weather
The value-at-risk that dangerous assessment models are calculated determines that cluster numbers are 5 as sample characteristics, according to traffic risk, by traffic value-at-risk
Clustering is carried out, as a result as shown in Figure 4.
The maximum value and minimum value that table 4 respectively clusters
| Cluster classification | The first kind | Second class | Third class | 4th class | 5th class |
| Minimum value | 0.41 | 0.29 | 0.20 | 0.13 | 0.04 |
| Maximum value | 0.93 | 0.41 | 0.28 | 0.19 | 0.12 |
The standard that maximum value, minimum value and the risk class of each cluster classification according to traffic value-at-risk divide, determines and dislikes
Traffic value-at-risk range corresponding to freeway traffic difference early warning risk class under bad weather, as shown in table 4.
When traffic risk is less than 0.13, freeway traffic operation at this time is in a safe condition.When traffic risk is greater than
0.13 and when less than 0.2, freeway traffic early warning risk class is level Four risk, with the potential wind that traffic accident occurs
Danger.When traffic risk is greater than 0.2 and when less than 0.3, freeway traffic early warning risk class is tertiary risk, has traffic thing
Therefore the risk occurred, the latent risk for thering is casualty accident to occur.When traffic risk is greater than 0.3 and when less than 0.4, highway is handed over
Logical early warning risk class is second level risk, and the risk of street accidents risks is larger, and traffic accident frequency is higher or can
Energy property is larger, it may occur however that more people's injuries will cause more people's injures and deaths.When traffic risk is greater than 0.4, freeway traffic
A possibility that early warning risk class is prime risk, and the risk of street accidents risks is very big, and traffic accident occurs is very big, once
Generation accident will will cause more people's injures and deaths risks.
5 traffic risk warning grade of table divides
| Traffic value-at-risk R | Traffic risk warning grade |
| R > 0.4 | Prime risk |
| 0.3 R≤0.4 < | Second level risk |
| 0.2 R≤0.3 < | Tertiary risk |
| 0.13 R≤0.2 < | Level Four risk |
| R≤0.13 | Pyatyi risk |
(4) the expressway safety speed based on traffic risk distribution is calculated
Referring to " freeway traffic Meteorological Grade " (QX/T111-2010), the visibility that is divided into of greasy weather grade is greater than
200m and be less than or equal to 500m, visibility be greater than 100m and be less than or equal to 200m, visibility be greater than 50m and be less than or equal to 100m,
Visibility is less than or equal to 50m, 4 grades.Under different greasy weather rating conditions, freeway traffic value-at-risk is selected to be less than or equal to
0.2 traffic flow modes calculate safe speed, draw speed integral distribution curve under the state and calculate running velocity
85%th speed can be obtained highway suggestion safe speed under the conditions of the greasy weather after being rounded amendment, drive for convenience of highway
It sails personnel to receive to issue speed-limiting messages with administrative department, provides suggestion restricted speed, as shown in table 6.
Suggest restricted speed (km/h) under the different greasy weather rating conditions of table 6
| Greasy weather grade | Visibility 200-500 | Visibility 100-200 | Visibility 50-100 | Visibility is less than 50 |
| 85%th speed | 87.58 | 83.79 | 53.63 | 47.82 |
| It is recommended that safe speed | 87 | 83 | 53 | 47 |
| It is recommended that restricted speed | 85 | 80 | 50 | 45 |
Referring to " freeway traffic Meteorological Grade " (QX/T111-2010), rainy day grade to be divided into rainfall in one hour strong
Spend 10.0mm/h~14.9mm/h, one hour rainfall intensity 15.0mm/h~29.9mm/h, one hour rainfall intensity 30.0mm/h
~49.9mm/h, rainfall intensity is greater than 50.0mm/h, 4 grades within one hour.Under different rainy day rating conditions, it is public to choose high speed
Traffic flow modes of the traffic value-at-risk in road less than or equal to 0.2 calculate safe speed, draw speed integral distribution curve under the state
And the 85%th speed of running velocity is calculated, highway suggests accident-free vehicle under the conditions of the different rainy days can be obtained after rounding
Speed, receives for convenience of turnpike driving personnel and administrative department issues speed-limiting messages, suggestion restricted speed is provided, such as 7 institute of table
Show.
Suggest safe speed (km/h) under the different rainy day rating conditions of table 7
| Rainy day grade | Rainfall intensity 10-15 | Rainfall intensity 15-30 | Rainfall intensity 30-50 | Rainfall intensity is greater than 50 |
| 85%th speed | 77.62 | 75.27 | 73.56 | 65.32 |
| It is recommended that safe speed | 77 | 75 | 73 | 65 |
| It is recommended that restricted speed | 75 | 75 | 70 | 65 |
(5) the expressway safety speed based on stopping sight distance is calculated
Stopping sight distance refers on same lane, when vehicle driving encounters front obstacle and must take Brake stop
Required most short running distance.Stopping sight distance can be analyzed to reaction distance, braking distance and safe distance three parts.Due to high speed
Highway section wagon flow is in fleet's form, therefore obtains phase from following state come safe stopping distance needed for calculating vehicle driving
The regulation speed answered.
Expressway safety speed based on stopping sight distance can be calculated by following equation:
Under normal circumstances, driver find preceding vehicle when, preceding vehicle speed be less than Ben Che and be in on-position, at this time after
Safe distance needed for vehicle parking should meet,
L1+L2+Ls≤Lv+L3 (1)
In formula,
L1--- the vehicle driving distance in front vehicle time of driver's reaction, m；
L2--- the operating range in front vehicle braking time, m；
Ls--- safe distance, general value are 5~10m, to ensure the traffic safety under bad weather, LsValue is
20m；
Lv--- the visual range in section, m；
L3--- operating range of the front vehicles in time of driver's reaction and vehicle braking time, m.
In inclement weather, the effective sighting distance of driver and coefficient of road adhesion can change, for the peace of support vehicles
Full traveling, it is contemplated that worst situation, i.e., due to vehicle trouble, damaged tyres, cast anchor, cargo is unrestrained and the originals such as accident
Cause, the speed of objects in front are zero, occur serious speed difference in wagon flow, and rear car must carry out emergency braking, and rear car is stopped at this time
Safe distance needed for vehicle is,
L1+L2+Ls≤Lv (2)
Vehicle driving distance L in front vehicle time of driver's reaction1,
L1=vt1 (3)
In formula,
V --- front vehicle travel speed, m/s；
t1--- front vehicle time of driver's reaction, s.
The reaction time of driver is 0.5~1.7s under normal circumstances, in inclement weather, road travel bad environments,
Driver's reaction time may be more than 1.7s, t1Value is 2.5s.
Sufficiently to ensure express way driving safety under bad weather, most dangerous combination situation is considered, i.e. vehicle is in down
Air drag is simultaneously ignored in slope section, the operating range L in front vehicle braking time2,
In formula,
A --- front vehicle deceleration, m/s2；
The attachment coefficient on f --- road surface；
I --- the gradient, %；
G --- acceleration of gravity takes g=9.8m/s2。
Formula (3) and (4), which are brought into formula (2), can obtain secure visual distance,
Using the visibility under bad weather as visual range, the height based on stopping sight distance can be calculated by formula (5)
Fast highway safety speed, as shown in formula (6),
In formula,
V --- safe speed, km/h.
When greasy weather weather, since fog falls within road surface, so that wet road surface, attachment coefficient is reduced, and takes the attached of wet road surface
Coefficient f=0.6.According to the regulation in " highway technical standard " (B01-2014 JTG) about highway longitudinal slope, if
Meter speed degree is 120km/h, maximum longitudinal grade 3%, desin speed 100km/h, maximum longitudinal grade 4%.G15 Shen Hai high speed is set
Meter speed degree is 100km/h, maximum longitudinal grade 4%.According to the expressway safety speed model based on stopping sight distance, by energy
Degree of opinion and road longitudinal grade take different numerical value, and it is as shown in table 8 that safe speed under different greasy weather grades is calculated.
Suggest speed (km/h) under the different greasy weather rating conditions of table 8
When rainy weather, since fog falls within road surface, so that wet road surface, attachment coefficient is reduced, and takes the attached of wet road surface
Coefficient f=0.35.According to the expressway safety speed model based on stopping sight distance, by being taken to visibility and road longitudinal grade
It is as shown in table 9 that safe speed under different rainy day grades is calculated in different numerical value.
Suggest speed (km/h) under the different rainy day rating conditions of table 9
(6) highway suggests speed value under bad weather
Comparison is based on high under two methods of security risk and the different bad weather rating conditions obtained based on stopping sight distance
Fast highway safety speed selects speed safer in two methods to calculate as a result, finally providing different bad weather grades
Lower highway suggests speed.
Suggest that speed value is as shown in table 10 under different greasy weather rating conditions.
Suggest speed comparison (km/h) under the different greasy weather rating conditions of table 10
Suggest that restricted speed value is as shown in table 11 under different rainy day rating conditions.
Suggest speed comparison (km/h) under the different rainy day rating conditions of table 11
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.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910665948.1A CN110491154B (en) | 2019-07-23 | 2019-07-23 | Suggested vehicle speed making method based on safety risk and distance |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910665948.1A CN110491154B (en) | 2019-07-23 | 2019-07-23 | Suggested vehicle speed making method based on safety risk and distance |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110491154A true CN110491154A (en) | 2019-11-22 |
| CN110491154B CN110491154B (en) | 2021-04-30 |
Family
ID=68547936
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910665948.1A Active CN110491154B (en) | 2019-07-23 | 2019-07-23 | Suggested vehicle speed making method based on safety risk and distance |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110491154B (en) |
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111081020A (en) * | 2019-12-26 | 2020-04-28 | 安徽揣菲克科技有限公司 | Vehicle-mounted traffic accident early warning device based on cloud edge combination |
| CN111506087A (en) * | 2020-05-21 | 2020-08-07 | 安徽江淮汽车集团股份有限公司 | Self-adaptive car following method, device, equipment and storage medium for automatic driving car |
| CN111680897A (en) * | 2020-05-27 | 2020-09-18 | 公安部道路交通安全研究中心 | Service management and control method, device and system and computer readable storage medium |
| CN112562337A (en) * | 2020-12-10 | 2021-03-26 | 之江实验室 | Expressway real-time traffic accident risk assessment method based on deep learning |
| CN112550284A (en) * | 2020-11-16 | 2021-03-26 | 东南大学 | Self-adaptive cruise driving risk assessment method under vehicle-road coupling effect |
| CN112950811A (en) * | 2021-02-02 | 2021-06-11 | 武汉理工大学 | New energy automobile region operation risk assessment and early warning system integrating whole automobile safety |
| CN113077646A (en) * | 2021-03-22 | 2021-07-06 | 北京工业大学 | Bridge operation safety multi-level differentiation prevention and control method |
| CN113257024A (en) * | 2021-04-29 | 2021-08-13 | 中汽研汽车检验中心(广州)有限公司 | Expressway rear-end collision prevention early warning method and system based on V2I |
| CN113299059A (en) * | 2021-04-08 | 2021-08-24 | 四川国蓝中天环境科技集团有限公司 | Data-driven road traffic control decision support method |
| CN113436434A (en) * | 2021-06-25 | 2021-09-24 | 中科路恒工程设计有限公司 | Mountain trunk highway high-risk road section early warning system and method |
| CN113470389A (en) * | 2021-08-06 | 2021-10-01 | 湖南省交通科学研究院有限公司 | Intelligent traffic control system and method |
| CN113724513A (en) * | 2021-11-02 | 2021-11-30 | 西南交通大学 | Vehicle driving guiding method, device and equipment in fog region and readable storage medium |
| CN113723699A (en) * | 2021-09-07 | 2021-11-30 | 南京安通气象数据有限公司 | Safety vehicle speed correction control early warning method and system for severe weather highway |
| CN113781779A (en) * | 2021-09-09 | 2021-12-10 | 济南金宇公路产业发展有限公司 | 5G communication-based highway weather early warning method, equipment and medium |
| CN113990088A (en) * | 2021-09-26 | 2022-01-28 | 河北京石高速公路开发有限公司 | Safe passing informing software system for expressway in severe weather |
| CN114234995A (en) * | 2021-12-21 | 2022-03-25 | 浙江数智交院科技股份有限公司 | Navigation method, navigation device, electronic equipment and storage medium |
| CN114360270A (en) * | 2021-07-26 | 2022-04-15 | 云南省交通规划设计研究院有限公司 | Method and system for studying and judging maximum allowable speed of highway under adverse weather influence |
| CN114387794A (en) * | 2022-01-17 | 2022-04-22 | 南京理工大学 | Urban emergency traffic first-aid repair system and method based on snowstorm condition |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103337184A (en) * | 2013-07-11 | 2013-10-02 | 上海济安交通工程咨询有限公司 | Urban road speed limiting indication system based on time, traffic flow and weather and control method |
| DE102016101472A1 (en) * | 2016-01-28 | 2017-08-03 | Dr. Ing. H.C. F. Porsche Aktiengesellschaft | Method for determining at least one safety-relevant traffic and / or weather information |
| CN107993453A (en) * | 2017-12-28 | 2018-05-04 | 武汉理工大学 | A kind of curved road safety speed computational methods based on bus or train route collaboration |
| CN108573601A (en) * | 2018-03-26 | 2018-09-25 | 同济大学 | A kind of traffic safety risk field construction method based on WIM data |
| CN109243178A (en) * | 2018-11-14 | 2019-01-18 | 上海应用技术大学 | Town way Traffic Safety Analysis and evaluation method under the conditions of a kind of bad climate |
| CN109448369A (en) * | 2018-10-26 | 2019-03-08 | 中交第公路勘察设计研究院有限公司 | Highway real time execution Risk Calculation method |
| KR20190044901A (en) * | 2017-10-23 | 2019-05-02 | 주식회사 래도 | Active Speed Management System |
-
2019
- 2019-07-23 CN CN201910665948.1A patent/CN110491154B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103337184A (en) * | 2013-07-11 | 2013-10-02 | 上海济安交通工程咨询有限公司 | Urban road speed limiting indication system based on time, traffic flow and weather and control method |
| DE102016101472A1 (en) * | 2016-01-28 | 2017-08-03 | Dr. Ing. H.C. F. Porsche Aktiengesellschaft | Method for determining at least one safety-relevant traffic and / or weather information |
| KR20190044901A (en) * | 2017-10-23 | 2019-05-02 | 주식회사 래도 | Active Speed Management System |
| CN107993453A (en) * | 2017-12-28 | 2018-05-04 | 武汉理工大学 | A kind of curved road safety speed computational methods based on bus or train route collaboration |
| CN108573601A (en) * | 2018-03-26 | 2018-09-25 | 同济大学 | A kind of traffic safety risk field construction method based on WIM data |
| CN109448369A (en) * | 2018-10-26 | 2019-03-08 | 中交第公路勘察设计研究院有限公司 | Highway real time execution Risk Calculation method |
| CN109243178A (en) * | 2018-11-14 | 2019-01-18 | 上海应用技术大学 | Town way Traffic Safety Analysis and evaluation method under the conditions of a kind of bad climate |
Non-Patent Citations (3)
| Title |
|---|
| CHENGCHENG XU 等: "Using the Bayesian updating approach to improve the spatial and temporal transferability of real-time crash risk prediction models", 《TRANSPORTATION RESEARCH PART C 38》 * |
| 吴兵 等: "基于安全风险的恶劣天气下高速公路建议车速确定方法", 《同济大学学报（自然科学版）》 * |
| 崔航 等: "多因素动态瞬变下高速公路交通安全风险预测", 《交通与运输》 * |
Cited By (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111081020A (en) * | 2019-12-26 | 2020-04-28 | 安徽揣菲克科技有限公司 | Vehicle-mounted traffic accident early warning device based on cloud edge combination |
| CN111506087A (en) * | 2020-05-21 | 2020-08-07 | 安徽江淮汽车集团股份有限公司 | Self-adaptive car following method, device, equipment and storage medium for automatic driving car |
| CN111680897A (en) * | 2020-05-27 | 2020-09-18 | 公安部道路交通安全研究中心 | Service management and control method, device and system and computer readable storage medium |
| CN112550284B (en) * | 2020-11-16 | 2022-03-22 | 东南大学 | Self-adaptive cruise driving risk assessment method under vehicle-road coupling effect |
| CN112550284A (en) * | 2020-11-16 | 2021-03-26 | 东南大学 | Self-adaptive cruise driving risk assessment method under vehicle-road coupling effect |
| CN112562337A (en) * | 2020-12-10 | 2021-03-26 | 之江实验室 | Expressway real-time traffic accident risk assessment method based on deep learning |
| CN112950811A (en) * | 2021-02-02 | 2021-06-11 | 武汉理工大学 | New energy automobile region operation risk assessment and early warning system integrating whole automobile safety |
| CN112950811B (en) * | 2021-02-02 | 2022-12-13 | 武汉理工大学 | New energy automobile region operation risk assessment and early warning system integrating whole automobile safety |
| CN113077646A (en) * | 2021-03-22 | 2021-07-06 | 北京工业大学 | Bridge operation safety multi-level differentiation prevention and control method |
| CN113299059A (en) * | 2021-04-08 | 2021-08-24 | 四川国蓝中天环境科技集团有限公司 | Data-driven road traffic control decision support method |
| CN113257024A (en) * | 2021-04-29 | 2021-08-13 | 中汽研汽车检验中心(广州)有限公司 | Expressway rear-end collision prevention early warning method and system based on V2I |
| CN113436434B (en) * | 2021-06-25 | 2022-05-27 | 中科路恒工程设计有限公司 | Mountain trunk highway high-risk road section early warning system and method |
| CN113436434A (en) * | 2021-06-25 | 2021-09-24 | 中科路恒工程设计有限公司 | Mountain trunk highway high-risk road section early warning system and method |
| CN114360270A (en) * | 2021-07-26 | 2022-04-15 | 云南省交通规划设计研究院有限公司 | Method and system for studying and judging maximum allowable speed of highway under adverse weather influence |
| CN114360270B (en) * | 2021-07-26 | 2023-02-14 | 云南省交通规划设计研究院有限公司 | Method and system for studying and judging maximum allowable speed of highway under adverse weather influence |
| CN113470389A (en) * | 2021-08-06 | 2021-10-01 | 湖南省交通科学研究院有限公司 | Intelligent traffic control system and method |
| CN113723699A (en) * | 2021-09-07 | 2021-11-30 | 南京安通气象数据有限公司 | Safety vehicle speed correction control early warning method and system for severe weather highway |
| CN113723699B (en) * | 2021-09-07 | 2023-11-07 | 南京安通气象数据有限公司 | Method and system for warning correction handle control of expressway safety vehicle speed in severe weather |
| CN113781779A (en) * | 2021-09-09 | 2021-12-10 | 济南金宇公路产业发展有限公司 | 5G communication-based highway weather early warning method, equipment and medium |
| CN113990088A (en) * | 2021-09-26 | 2022-01-28 | 河北京石高速公路开发有限公司 | Safe passing informing software system for expressway in severe weather |
| CN113724513A (en) * | 2021-11-02 | 2021-11-30 | 西南交通大学 | Vehicle driving guiding method, device and equipment in fog region and readable storage medium |
| CN114234995A (en) * | 2021-12-21 | 2022-03-25 | 浙江数智交院科技股份有限公司 | Navigation method, navigation device, electronic equipment and storage medium |
| CN114387794A (en) * | 2022-01-17 | 2022-04-22 | 南京理工大学 | Urban emergency traffic first-aid repair system and method based on snowstorm condition |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110491154B (en) | 2021-04-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110491154A (en) | Suggestion speed formulating method based on security risk and distance | |
| CN104157156B (en) | A kind of highway Dangerous Area speed of a motor vehicle dynamic management method for early warning | |
| CN106530717B (en) | The dangerous state evaluation method of construction section driving based on cloud model | |
| CN103895649B (en) | A kind of driver safety driving warning method | |
| CN104575050B (en) | A kind of fast road ramp intellectual inducing method and device based on Floating Car | |
| CN105946860B (en) | A kind of bend speed prediction method for considering driving style | |
| CN107766945B (en) | Method for calculating bearing capacity of urban road network | |
| CN113723699B (en) | Method and system for warning correction handle control of expressway safety vehicle speed in severe weather | |
| CN109389845B (en) | Multi-factor integrated dynamic speed control system for expressway | |
| Yeo et al. | Effects of speed humps on vehicle speed and pedestrian crashes in South Korea | |
| Zhou et al. | Comparing factors affecting injury severity of passenger car and truck drivers | |
| Luo et al. | Research of vehicle rear-end collision model considering multiple factors | |
| CN109993988B (en) | Variable speed limit control system and method for expressway in ice and snow weather | |
| Wang et al. | ARIMA model and few-shot learning for vehicle speed time series analysis and prediction | |
| CN113335293A (en) | Highway road surface detection system of drive-by-wire chassis | |
| CN116740940A (en) | Severe weather high-impact road section risk prediction and safety management method, device and equipment | |
| Tribbett et al. | An evaluation of dynamic curve warning systems in the Sacramento River Canyon | |
| CN116092037A (en) | Vehicle type identification method integrating track space-semantic features | |
| CN105608315A (en) | Quantitative evaluation method and system of safety service level of bus stop | |
| Bhuyan et al. | Affinity propagation clustering in defining level of service criteria of urban streets | |
| Ambros et al. | Identification of road horizontal alignment inconsistencies–A pilot study from the Czech Republic | |
| CN110610611B (en) | Driving safety evaluation method for intelligent network-connected vehicle in mixed-driving traffic flow | |
| Abbondati et al. | Crashes comparison before and after speed control cameras installation: case studies on rural roads in Lithuania and Italy | |
| Zhang et al. | Expressway Lane Change in Fog Environment by Dynamic Strategic Game | |
| Garnaik | Effects of Highway geometric elements on Accident Modelling |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |