CN113706899A - Traffic operation risk prevention and control method based on highway bridge deck meteorological icing prediction - Google Patents

Traffic operation risk prevention and control method based on highway bridge deck meteorological icing prediction Download PDF

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CN113706899A
CN113706899A CN202110816132.1A CN202110816132A CN113706899A CN 113706899 A CN113706899 A CN 113706899A CN 202110816132 A CN202110816132 A CN 202110816132A CN 113706899 A CN113706899 A CN 113706899A
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bridge deck
highway
highway bridge
formula
vehicle
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王福海
马士杰
王孜健
郭忠印
申全军
张昱
么新鹏
樊兆董
张瀚坤
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Innovation Research Institute Of Shandong Expressway Group Co ltd
Tongji University
Shandong Transportation Institute
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Tongji University
Shandong Transportation Institute
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    • G—PHYSICS
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    • G08G1/00—Traffic control systems for road vehicles
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    • G—PHYSICS
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    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
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    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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Abstract

The invention provides a traffic operation risk prevention and control method based on highway bridge deck meteorological icing prediction, which comprises the steps of highway bridge deck meteorological data acquisition, highway bridge deck icing prediction, highway bridge deck traffic operation risk quantification model establishment and highway bridge deck traffic operation risk prevention and control strategy formulation, wherein a meteorological data acquisition system is used for acquiring the highway bridge deck meteorological conditions and carrying out bridge deck icing prediction, a highway bridge deck traffic risk quantification model is established, the relative driving safety index RDSI of vehicles on the highway bridge deck is calculated, and the highway traffic operation risk prevention and control strategy formulation is displayed on an information board. According to the method, the influence of each risk source on the bridge deck driving safety is quantified through the real-time prediction of the highway bridge deck icing condition, real-time formulation of highway bridge deck traffic operation risk prevention and control strategies under different meteorological conditions is realized, the timeliness of traffic operation management and control is improved, and the incidence rate of traffic accidents in bad weather is effectively reduced.

Description

Traffic operation risk prevention and control method based on highway bridge deck meteorological icing prediction
Technical Field
The invention relates to the technical field of traffic transportation safety, in particular to a traffic operation risk prevention and control method based on highway bridge deck meteorological icing prediction.
Background
With the rapid development of highway construction in China, the influence of bad weather on the traffic safety problem of the highway is increasingly prominent. Reports of accidents in traffic safety production (2019) particularly indicate that the influence of extreme weather on traffic safety cannot be ignored. The highway traffic safety is closely related to meteorological conditions, strong wind, heavy fog and rainfall are main meteorological factors influencing highway driving safety, and in order to better reduce the adverse effect of adverse weather on highway traffic operation, a stable and reliable risk prevention and control method for real-time prediction of highway traffic meteorological information is urgently needed.
The safety and the smoothness of the highway bridge deck directly influence the passing efficiency of the whole network, and due to the structure of the highway bridge deck and the environmental characteristics of a road area, phenomena such as bridge deck icing, road black ice and the like easily occur, so that the traffic operation risk of the highway bridge deck is improved, and the traffic operation safety cannot be guaranteed. At present, the meteorological sensing equipment on the expressway has the problems of more qualitative monitoring, insufficient quantitative analysis, low meteorological early warning precision, unreasonable distribution of hardware technical conditions of facilities, insufficient observation data analysis capability, delayed release of prediction results and the like.
Therefore, the traffic operation risk prevention and control method based on the highway bridge deck meteorological icing prediction is provided, and has very important significance on highway traffic safety.
Disclosure of Invention
The invention aims to solve the problems and provides a traffic operation risk prevention and control method based on highway bridge deck meteorological icing prediction, which makes full use of an edge calculation technology and realizes accurate prediction and timely prevention and control of highway bridge deck traffic operation risks.
In order to achieve the purpose, the invention adopts the following technical scheme:
a traffic operation risk prevention and control method based on highway bridge deck meteorological icing prediction comprises the steps of highway bridge deck meteorological data acquisition, highway bridge deck icing prediction, highway bridge deck traffic operation risk quantification model establishment and highway bridge deck traffic operation risk prevention and control strategy formulation, and specifically comprises the following steps:
step 1, collecting meteorological data of a highway bridge floor, and specifically comprising the following substeps:
step 1.1, mounting meteorological detection equipment and information release equipment on a bridge floor of a highway, wherein the meteorological detection equipment comprises a visibility meter, a remote sensing type bridge floor condition detector and a rain measuring cylinder, and the information release equipment comprises a communication unit and an information board;
step 1.2, setting a highway bridge surface meteorological detection system, wherein the highway bridge surface meteorological detection system comprises a meteorological detection sub-node, a meteorological detection substation and a meteorological detection main station, the meteorological detection sub-node comprises a remote sensing type bridge surface condition detector, the meteorological detection substation comprises a visibility meter and a remote sensing type bridge surface condition detector, and the meteorological detection main station comprises the visibility meter, the remote sensing type bridge surface condition detector, a rain gauge and an edge computer;
step 1.3, measuring the visibility of the highway bridge floor by using an visibility meter, measuring the bridge floor temperature, the icing thickness, the air temperature and the humidity of the highway bridge floor by using a remote sensing type bridge floor condition detector, measuring the precipitation of the highway bridge floor by using a rain measuring cylinder, and storing meteorological data acquired by each instrument in an edge computer;
step 2, predicting the icing of the bridge deck of the highway, which specifically comprises the following substeps:
step 2.1, predicting the bridge deck temperature of the future expressway bridge deck;
forming a highway bridge deck temperature change time sequence by using bridge deck temperatures stored in an edge computer, establishing a bridge deck temperature prediction model by using historical values and current values in the highway bridge deck temperature change time sequence as input parameters based on an ARIMA autoregressive sum moving average algorithm, and predicting the bridge deck temperature of a future highway bridge deck;
the bridge deck temperature prediction model is shown as the formula (1):
Xt＝φ1Xt-1+φ2Xt-2+…+φpXt-p+∈t-θ1∈t-1-…-θq∈t-q (1)
in the formula, q is the order of the bridge deck temperature prediction model; xtThe observed value of the t-th moment in the time sequence of the temperature change of the bridge surface of the expressway is obtained; e is the same astA random error term of the bridge deck temperature prediction model at the t-th moment is obtained; phi is apIs a self-review parameter to be estimated; thetaqIs the moving average parameter to be estimated; p is the average number of motion terms;
step 2.2, predicting the future dew point temperature of the highway bridge deck;
according to the air temperature obtained by the weather detection substation, the saturated water vapor pressure E at the current temperature is calculated by utilizing the Goff-Gray formulawAs shown in formula (2)The following steps:
Figure BDA0003170170180000021
wherein T is air temperature and has a unit of K; t is1Is the triple point temperature of water in K;
calculating the actual vapor pressure e under the current temperature and humidity state according to the calculated saturated vapor pressure under the current temperature and the air humidity obtained by the weather detection substation, wherein the actual vapor pressure e is shown in formula (3):
e＝U×Ew/100 (3)
in the formula, e is the actual water vapor pressure under the current temperature and humidity state, and the unit is hPa; u is air humidity in units of%;
calculating the dew point temperature T under the current meteorological condition by using the Maglas formuladAs shown in formula (4):
Figure BDA0003170170180000031
in the formula, E0Is a saturated water vapor pressure of 0 ℃ E06.1078 hPa; a. b is a coefficient, a is 7.69, b is 243.92;
step 2.3, predicting the icing of the bridge deck of the future expressway;
judging whether the highway bridge floor reaches the formation condition of the dew point or not according to the dew point temperature under the current meteorological condition and the precipitation obtained by the meteorological detection main station, and predicting the future icing of the highway bridge floor; if the bridge deck temperature of the future expressway bridge deck is less than 0 ℃, no precipitation exists, and the bridge deck temperature of the future expressway bridge deck is lower than the dew point temperature, the edge computer generates first-stage icing early warning information of the expressway bridge deck; if the bridge deck temperature of the highway bridge deck is less than 0 ℃ and precipitation exists in the future, the edge computer generates early warning information of secondary icing of the highway bridge deck;
step 3, establishing a highway bridge deck traffic operation risk quantification model, wherein the highway bridge deck traffic operation risk quantification model comprises highway bridge deck environment influence factors and highway bridge deck vehicle influence factors, and specifically comprises the following substeps:
step 3.1, obtaining highway bridge deck parameters by carrying out on-site measurement on the highway bridge deck;
step 3.2, determining the influence factors of the bridge deck environment of the expressway;
the highway bridge surface environment influence factors comprise icing thickness, visibility, curvature change rate and longitudinal slope gradient of the highway bridge surface;
measuring the friction coefficient of the highway bridge surface under different icing thickness conditions by using a pendulum instrument, and taking the friction coefficient of the highway bridge surface in a dry state as a reference value N of the friction coefficientavg(μ*) Establishing an evaluation function psi for the icing risk of the bridge deck of the highwayμAs shown in formula (5):
Figure BDA0003170170180000032
in the formula, Navg(mu) is the friction coefficient of the highway bridge floor when the icing thickness is mu;
taking the average number of dead people of traffic accidents with visibility greater than 200m as a reference value N of visibilityavg(δ*) Establishing a visibility risk evaluation function psi of the bridge deck of the highwayδAs shown in formula (6):
Figure BDA0003170170180000033
in the formula, Navg(δ) is the average number of deaths from a traffic accident with visibility δ;
taking the traffic accident rate when the gradient of the longitudinal slope is less than 2 percent as the reference value N of the gradientavg(τ*) Establishing an evaluation function psi for the slope risk of the bridge deck of the highwayτAs shown in formula (7):
Figure BDA0003170170180000041
in the formula, Navg(tau) is the corresponding traffic accident rate when the slope gradient tau of the longitudinal slope;
determining curvature change rate according to the curvature of the highway bridge floor, and taking the traffic accident rate when the curvature change rate is equal to 1 as a reference value N of the curvature change rateavg(ρ*) Establishing a risk evaluation function psi of the curvature of the bridge deck of the highwayρAs shown in formula (8):
Figure BDA0003170170180000042
in the formula, Navg(rho) is the corresponding traffic accident rate when the curvature change rate is rho;
calculating the influence factor R of the highway bridge floor environment according to the icing thickness, visibility, curvature and longitudinal slope gradient of the highway bridge flooriAs shown in formula (9):
Ri＝Ri(μ,ρ,τ,δ)＝Ψμ(μ)·Ψρ(ρ)·Ψτ(τ)·Ψδ(δ) (9)
in the formula, mu is the icing thickness of the highway bridge floor, rho is the curvature change rate of the highway bridge floor, tau is the longitudinal slope gradient of the highway bridge floor, and delta is the visibility of the highway bridge floor;
step 3.3, determining influence factors of vehicles on the bridge surface of the expressway;
based on the driving kinetic energy field theory, selecting a target vehicle, wherein the driving vehicles exist in front of, behind, on the left of and on the right of the target vehicle, and calculating the risk field intensity E of the target vehiclei1As shown in formula (10):
Figure BDA0003170170180000043
in the formula, i denotes a vehicle number, i denotes a target vehicle when i is 1, denotes a left vehicle of the target vehicle when i is 2, denotes a rear vehicle of the target vehicle when i is 3, and denotes a rear vehicle of the target vehicle when i is 4A vehicle on the right of the target vehicle, and when i is 5, the vehicle indicates a vehicle ahead of the target vehicle; grad Ei1Gradient vectors of the field intensity of the kinetic energy field formed for the vehicle i at the position of the center of mass of the target vehicle; miIs the virtual mass, v, of vehicle iiIs the speed, θ, of the vehicle iiIs the angle between the direction of motion and the direction of speed of the vehicle i,
Figure BDA0003170170180000051
is the angle between the speed direction of vehicle i and the x-axis, ri1Is the distance between vehicle i and the target vehicle center of mass; ei1Forming a field intensity vector of a kinetic energy field for the vehicle i at the position of the center of mass of the target vehicle;
vehicle driving comprehensive safety potential energy SPE for calculating target vehicle1And rate of change
Figure BDA0003170170180000052
As shown in formula (11):
Figure BDA0003170170180000053
in the formula, SPEV,i1Forming a kinetic energy field for the target vehicle at the vehicle i position; SPE1A comprehensive safety potential for the target vehicle;
Figure BDA0003170170180000054
the change rate of the safety potential energy of the target vehicle in a driving safety field formed at the position of the vehicle i along with the time is obtained;
Figure BDA0003170170180000055
the change rate of the comprehensive safety potential energy of the target vehicle along with the time is obtained;
comprehensive safety potential energy SPE according to target vehicle1And rate of change
Figure BDA0003170170180000056
Calculating a driving safety index DSI of the target vehicle, as shown in formula (12):
Figure BDA0003170170180000057
in the formula, eta is a weight factor and has a value range of 0-1;
calculating a vehicle relative driving safety index RDSI (vehicle safety index), as shown in formula (13):
Figure BDA0003170170180000058
in the formula, DSI*The standard driving safety index of the vehicle in a specific dangerous scene is obtained;
step 4, making a highway bridge floor traffic operation risk prevention and control strategy;
determining a highway traffic operation risk prevention and control strategy by using an edge computer according to a relative driving safety index RDSI of the vehicles on the highway bridge floor and a threshold value of a following distance, and determining the safe driving speed of the vehicles on the highway bridge floor;
and 5, transmitting the safe driving speed, the bridge deck temperature of the future expressway bridge deck and the icing prediction information stored in the edge computer to an information board by using the communication unit, displaying the meteorological condition of the future expressway bridge deck through the information board, and reminding bridge deck vehicles of adjusting the driving state in advance.
Preferably, in step 1.3, a meteorological data acquisition program is arranged inside the edge computer, and is used for receiving meteorological data acquired by the meteorological detection equipment.
Preferably, in the step 2.3, the early warning of the first-stage icing on the bridge deck of the expressway is that the part of the bridge deck of the expressway is iced, and the average icing thickness is less than 2 mm; the early warning of the secondary icing of the highway bridge floor is that the average icing thickness of the highway bridge floor is more than 2 mm.
Preferably, in step 3.1, the highway deck parameters include curvature of the deck and longitudinal slope gradient.
The invention has the following beneficial technical effects:
the invention provides a traffic operation risk prevention and control method based on highway bridge deck meteorological icing prediction, which realizes prediction of highway bridge deck icing conditions, controls the highway bridge deck in advance based on prediction results and improves timeliness of traffic operation control.
According to the method, the influence of each risk source on the driving safety of the highway bridge floor is quantified by constructing the traffic risk field model, and the timely formulation of the highway bridge floor vehicle management and control strategies under different meteorological conditions is realized.
According to the invention, a structure of the Internet of things from end to edge is adopted, and the acquired data is directly processed by using an edge computer close to a front-end sensor, so that the timeliness of the predicted information is improved, the loss of the data in the transmission process is avoided, and the stability of the system is greatly improved.
Drawings
FIG. 1 is a flow chart of a traffic operation risk prevention and control method based on highway bridge deck meteorological icing prediction.
FIG. 2 is a data acquisition flow chart of the visibility meter of the present invention.
FIG. 3 is a flow chart of data acquisition of the remote sensing type bridge deck condition detector of the invention.
Fig. 4 is a flow chart of data acquisition for a rain gauge according to the present invention.
FIG. 5 is a flow chart of the edge computer data reception according to the present invention.
FIG. 6 is a diagram illustrating a format of a weather data message according to the present invention.
FIG. 7 is a flow chart of the highway bridge deck icing prediction process of the present invention.
FIG. 8 is a schematic view of a driving scene according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
In this embodiment, taking a yellow river grand bridge deck as an example, by using the traffic operation risk prevention and control method based on the prediction of the meteorological icing on the highway deck provided by the invention, as shown in fig. 1, a yellow river grand bridge traffic operation risk quantification model is established and a yellow river grand bridge traffic operation risk prevention and control strategy is formulated through the acquisition of the yellow river grand bridge meteorological data, the prediction of the icing on the yellow river grand bridge deck, so as to provide guarantee for the traffic operation safety of the yellow river grand bridge, specifically comprising the following steps:
step 1, acquiring meteorological data of a yellow river grand bridge, and specifically comprising the following substeps:
step 1.1, mounting meteorological detection equipment and information release equipment on a bridge floor of a yellow river grand bridge, wherein the meteorological detection equipment comprises a visibility meter, a remote sensing type bridge floor condition detector and a rain graduated cylinder, and the information release equipment comprises a communication unit and an information board.
The visibility meter is used for measuring the visibility of the bridge floor of the yellow river grand bridge, as shown in fig. 2, the visibility meter is provided with a transmitting component and a receiving component, an infrared light emitting diode of the transmitting component can emit infrared light with a central wavelength of 0.87 micrometers, the infrared light is scattered in the air and then converged on a silicon photoelectric sensor of the receiving component, the silicon photoelectric sensor converts light intensity into an electric signal, a signal conditioning circuit generates a digital signal representing the visibility, and finally the digital signal is converted into the visibility value through conversion to obtain the visibility value.
The remote sensing type bridge deck condition detector is used for measuring bridge deck temperature, icing thickness, air temperature and air temperature of a yellow river super bridge, as shown in figure 3, an optical detection system is arranged in the remote sensing type bridge deck condition detector, infrared radiation is sensed by an optical detector, the infrared detector converts the infrared radiation into an electric signal to measure the bridge deck temperature and the icing thickness of the yellow river super bridge, a temperature sensor and a humidity sensor are further arranged in the remote sensing type bridge deck condition detector, and the temperature sensor and the humidity sensor are used for measuring to obtain the air humidity and the air temperature.
The rain gauge is used for measuring the rainfall of the grand bridge of the yellow river, as shown in fig. 4, a spring leaf switch and a drainage switch are arranged in the rain gauge, the spring leaf switch is triggered through rainfall to measure the rainfall, when the accumulation depth of rainwater in the rain gauge reaches 0.2mm, the drainage switch is triggered, and the rainfall is obtained through the change of output high and low levels.
The communication unit is used for transmitting the collected data, and the information board is used for displaying the yellow river grand bridge traffic operation risk prevention and control strategy.
Step 1.2, the yellow river grand bridge deck meteorological detection system is arranged and comprises a meteorological detection sub-node, a meteorological detection substation and a meteorological detection main station, wherein the meteorological detection sub-node comprises a remote sensing type bridge deck condition detector, the meteorological detection substation comprises a visibility meter and a remote sensing type bridge deck condition detector, and the meteorological detection main station comprises a visibility meter, a remote sensing type bridge deck condition detector, a rain gauge and an edge computer.
The edge computer is used for receiving meteorological data acquired by meteorological detection equipment, the message format of the meteorological data is shown in figure 6, a meteorological data acquisition program and a bridge deck icing prediction program are arranged in the edge computer, as shown in figure 5, the meteorological data acquisition program in the edge computer receives data acquired by each measuring instrument in real time, when a target port has data to send, communication connection is established, and the edge computer receives and stores meteorological data messages sent by each measuring instrument.
Step 1.3, measuring the visibility of the yellow river grand bridge deck by using an visibility meter, measuring the deck temperature, the icing thickness, the air temperature and the humidity of the yellow river grand bridge deck by using a remote sensing type deck condition detector, measuring the precipitation of the yellow river grand bridge deck by using a rain measuring cylinder, and storing meteorological data acquired by each instrument in an edge computer.
Step 2, performing bridge deck icing prediction on the yellow river grand bridge, as shown in fig. 7, specifically comprising the following substeps:
step 2.1, predicting bridge deck temperature of the future yellow river grand bridge;
forming a bridge deck temperature change time sequence by utilizing the bridge deck temperature of the yellow river grand bridge stored in the edge computer, establishing a bridge deck temperature prediction model by utilizing historical values and current values in the bridge deck temperature change time sequence as input parameters based on an ARIMA autoregressive sum moving average algorithm, and predicting the bridge deck temperature of the future yellow river grand bridge deck;
the bridge deck temperature prediction model is shown as the formula (1):
Xt＝φ1Xt-1+φ2Xt-2+…+φpXt-p+∈t-θ1∈t-1-…-θq∈t-q (1)
wherein q is the order of the bridge deck temperature prediction model, and in this embodiment q is 5; xtThe observed value of the t-th moment in the time sequence of the temperature change of the bridge surface of the expressway is obtained; e is the same astA random error term of the bridge deck temperature prediction model at the t-th moment is obtained; phi is apIs a self-review parameter to be estimated; thetaqIs the moving average parameter to be estimated; p is the average number of motion terms, and in this embodiment p is 5.
Step 2.2, predicting the dew point temperature of the bridge deck of the yellow river grand bridge;
according to the air temperature obtained by the weather detection substation, the saturated water vapor pressure E at the current temperature is calculated by utilizing the Goff-Gray formulaw(ii) a Calculating the actual vapor pressure e under the current temperature and humidity state according to the saturated vapor pressure and the air humidity U obtained by the weather detection substation; finally, calculating the dew point temperature T under the current meteorological condition by using the Maglas formulad。
Step 2.3, predicting future bridge deck icing of the yellow river super bridge;
judging whether the bridge floor of the yellow river super bridge reaches the formation condition of the dew point or not according to the dew point temperature under the current meteorological condition and the precipitation obtained by the meteorological detection master station, and predicting the icing of the bridge floor of the yellow river super bridge in the future; if the bridge deck temperature of the future yellow river super bridge is less than 0 ℃, no precipitation exists, and the bridge deck temperature in the future is lower than the dew point temperature, the edge computer generates bridge deck primary icing early warning information; and if the bridge deck temperature of the yellow river super bridge is less than 0 ℃ and precipitation exists in the future, generating bridge deck secondary icing early warning information by the edge computer, wherein the bridge deck primary icing early warning is that partial areas of the bridge deck of the yellow river super bridge are iced, the average icing thickness is less than 2mm, and the bridge deck secondary icing early warning is that the average icing thickness of the bridge deck of the yellow river super bridge is more than 2 mm.
Step 3, establishing a traffic operation risk quantification model of the bridge deck of the yellow river grand bridge, wherein the traffic operation risk quantification model of the bridge deck of the yellow river grand bridge comprises bridge deck environment influence factors and bridge deck vehicle influence factors, and the traffic operation risk quantification model specifically comprises the following substeps:
and 3.1, obtaining bridge deck parameters of the yellow river super large bridge by carrying out field measurement on the yellow river super large bridge, wherein the bridge deck parameters comprise the curvature and the longitudinal slope gradient of the bridge deck of the yellow river super large bridge.
Step 3.2, calculating bridge deck environment influence factors of the yellow river grand bridge, and respectively establishing an expressway bridge deck icing risk evaluation function psiμVisibility risk evaluation function psi of highway bridge floorδExpressway bridge floor gradient risk evaluation function psiτAnd highway bridge deck curvature risk evaluation function psiρ。
The pendulum instrument is used for measuring the friction coefficient of the bridge deck under different icing thickness conditions, and the corresponding friction coefficient of the bridge deck under different icing thickness conditions is obtained, as shown in table 1.
TABLE 1 bridge floor friction coefficient table corresponding to different icing thicknesses
Figure BDA0003170170180000091
In the dry state (. mu.)*1) coefficient of friction of the deck as a reference value Navg(μ*) I.e. Ψμ(1)＝1、Navg(μ*) Determining a bridge deck icing risk evaluation function psi of the yellow river super bridge according to the icing thickness mu of the yellow river super bridge and the ratio of the friction coefficient of the bridge deck to the standard value of the friction coefficient under the icing thickness conditionμAs shown in formula (5):
Figure BDA0003170170180000092
in the formula, NavgAnd (mu) is the friction coefficient of the highway bridge floor when the icing thickness is mu.
According to the Chinese traffic accident data statistical table, statistical data about traffic accidents under different visibility conditions are obtained, as shown in Table 2.
Table 2 traffic accident statistics data table about visibility
Figure BDA0003170170180000093
Taking the average number of death people of traffic accidents when the visibility delta is more than 200m as the reference value N of the visibilityavg(δ*) I.e. Navg(δ*) Establishing a visibility risk evaluation function psi of the highway bridge floor as 0.282δAs shown in formula (6):
Figure BDA0003170170180000094
in the formula, Navg(δ) is the visibility δiAverage number of deaths in occasional traffic accidents.
According to the Chinese traffic accident data statistical table, the statistical data of the traffic accidents under different longitudinal slope conditions is obtained, as shown in table 3.
TABLE 3 traffic accident rate statistics table about longitudinal slope gradient
Figure BDA0003170170180000095
Taking the traffic accident rate when the gradient of the longitudinal slope is less than 2 percent as the reference value N of the gradientavg(τ*) I.e. Navg(τ*) And (5) establishing a highway bridge floor gradient risk evaluation function psi of 0.75τAs shown in formula (7):
Figure BDA0003170170180000101
in the formula, NavgAnd (tau) is the corresponding traffic accident rate when the longitudinal slope gradient tau.
According to the Chinese traffic accident data statistical table, the traffic accident statistical data under the conditions of all curvature change rates are obtained, as shown in table 4.
TABLE 4 traffic accident rate statistics table on curvature change rate
Figure BDA0003170170180000102
Determining the rate of change of curvature from the measured curvature of the deck, at a rate of change of curvature ρ*The traffic accident rate when 1 is taken as a reference value N of the curvature change rateavg(ρ*) I.e. Navg(ρ*) Establishing a risk evaluation function psi of the curvature of the bridge deck of the expressway at 1.11ρAs shown in formula (8):
Figure BDA0003170170180000103
in the formula, NavgAnd (rho) is the corresponding traffic accident rate when the curvature change rate is rho.
Calculating an environmental influence factor R of the bridge floor of the grand bridge of the yellow river according to the icing thickness, visibility, curvature and longitudinal slope gradient of the bridge floor of the grand bridge of the yellow riveriAs shown in formula (9):
Ri＝Ri(μ,ρ,τ,δ)＝Ψμ(μ)·Ψρ(ρ)·Ψτ(τ)·Ψδ(δ) (9)
in the formula, mu is the icing thickness of the bridge deck of the yellow river super bridge, rho is the curvature change rate of the bridge deck of the yellow river super bridge, tau is the longitudinal slope gradient of the bridge deck of the yellow river super bridge, and delta is the visibility of the bridge deck of the yellow river super bridge.
And 3.3, determining influence factors of the vehicles on the bridge surface of the expressway.
Based on the driving kinetic energy field theory, the driving condition of the vehicles on the bridge floor of the yellow river grand bridge is shown in figure 8; selecting a target vehicle, wherein the vehicles running in front, behind, left and right of the target vehicle exist, and calculating the risk field intensity E of the target vehiclei1As shown in formula (10):
Figure BDA0003170170180000111
wherein i represents a vehicle numberWhen i is 1, the vehicle is a left side vehicle of the target vehicle, when i is 2, the vehicle is a rear side vehicle of the target vehicle, when i is 4, the vehicle is a right side vehicle of the target vehicle, and when i is 5, the vehicle is a front side vehicle of the target vehicle; grad Ei1Gradient vectors of the field intensity of the kinetic energy field formed for the vehicle i at the position of the center of mass of the target vehicle; miIs the virtual mass, v, of vehicle iiIs the speed, θ, of the vehicle iiIs the angle between the direction of motion and the direction of speed of the vehicle i,
Figure BDA0003170170180000112
is the angle between the speed direction of vehicle i and the x-axis, ri1Is the distance between vehicle i and the target vehicle center of mass; ei1A field strength vector of the kinetic energy field is formed for vehicle i at the target vehicle centroid location.
Vehicle driving comprehensive safety potential energy SPE for calculating target vehicle1And rate of change
Figure BDA0003170170180000113
As shown in formula (11):
Figure BDA0003170170180000114
in the formula, SPEV,i1Forming a kinetic energy field for the target vehicle at the vehicle i position; SPE1A comprehensive safety potential for the target vehicle;
Figure BDA0003170170180000115
the change rate of the safety potential energy of the target vehicle in a driving safety field formed at the position of the vehicle i along with the time is obtained;
Figure BDA0003170170180000116
the change rate of the comprehensive safety potential energy of the target vehicle along with the time is shown.
Comprehensive safety potential energy SPE according to target vehicle1And rate of change
Figure BDA0003170170180000117
Calculating a driving safety index DSI of the target vehicle, as shown in formula (12):
Figure BDA0003170170180000118
in the formula, eta is a weight factor and has a value range of 0-1; the weighting factor η is used to redistribute the spatial and temporal weighting of the driving risks.
Calculating a vehicle relative driving safety index RDSI (vehicle safety index), as shown in formula (13):
Figure BDA0003170170180000121
in the formula, DSI*The standard driving safety index of the vehicle in a specific dangerous scene is shown.
And 4, taking the limit that the normal weather risk grade does not exceed the third grade and the bad weather risk grade does not exceed the second grade as a limit, combining the prediction results of the visibility and the icing thickness of the bridge deck, carrying out grade division on the weather condition of the bridge deck of the yellow river grand bridge, then determining the threshold value of the vehicle following distance according to the calculated vehicle relative driving safety index RDSI and the bridge deck temperature and the icing thickness of the yellow river grand bridge in the next hour obtained by prediction, and utilizing an edge computer to make a traffic operation risk prevention and control strategy, wherein the traffic operation risk prevention and control strategy of the yellow river grand bridge deck in the embodiment is shown in a table 5.
TABLE 5 traffic risk management and control strategy table for bridge deck of yellow river grand bridge
Figure BDA0003170170180000122
And 5, according to the bridge deck temperature and the icing prediction information of the future expressway bridge deck obtained by the meteorological data acquisition program and the expressway road icing prediction program run by the edge computer, transmitting the safe driving speed, the bridge deck temperature and the icing prediction information of the future expressway bridge deck stored in the edge computer within one hour to the information board by using the communication unit, displaying the meteorological conditions of the future expressway bridge deck through the information board, and reminding bridge deck vehicles of adjusting the driving state in advance.
It is to be understood that the above description is not intended to limit the present invention, and the present invention is not limited to the above examples, and those skilled in the art may make modifications, alterations, additions or substitutions within the spirit and scope of the present invention.

Claims (4)

1. A traffic operation risk prevention and control method based on highway bridge deck meteorological icing prediction is characterized by comprising the steps of highway bridge deck meteorological data acquisition, highway bridge deck icing prediction, highway bridge deck traffic operation risk quantification model establishment and highway bridge deck traffic operation risk prevention and control strategy formulation, and specifically comprises the following steps:
step 1, collecting meteorological data of a highway bridge floor, and specifically comprising the following substeps:
step 1.1, mounting meteorological detection equipment and information release equipment on a bridge floor of a highway, wherein the meteorological detection equipment comprises a visibility meter, a remote sensing type bridge floor condition detector and a rain measuring cylinder, and the information release equipment comprises a communication unit and an information board;
step 1.2, setting a highway bridge surface meteorological detection system, wherein the highway bridge surface meteorological detection system comprises a meteorological detection sub-node, a meteorological detection substation and a meteorological detection main station, the meteorological detection sub-node comprises a remote sensing type bridge surface condition detector, the meteorological detection substation comprises a visibility meter and a remote sensing type bridge surface condition detector, and the meteorological detection main station comprises the visibility meter, the remote sensing type bridge surface condition detector, a rain gauge and an edge computer;
step 1.3, measuring the visibility of the highway bridge floor by using an visibility meter, measuring the bridge floor temperature, the icing thickness, the air temperature and the humidity of the highway bridge floor by using a remote sensing type bridge floor condition detector, measuring the precipitation of the highway bridge floor by using a rain measuring cylinder, and storing meteorological data acquired by each instrument in an edge computer;
step 2, predicting the icing of the bridge deck of the highway, which specifically comprises the following substeps:
step 2.1, predicting the bridge deck temperature of the future expressway bridge deck;
forming a highway bridge deck temperature change time sequence by using bridge deck temperatures stored in an edge computer, establishing a bridge deck temperature prediction model by using historical values and current values in the highway bridge deck temperature change time sequence as input parameters based on an ARIMA autoregressive sum moving average algorithm, and predicting the bridge deck temperature of a future highway bridge deck;
the bridge deck temperature prediction model is shown as the formula (1):
Xt＝φ1Xt-1+φ2Xt-2+...+φpXt-p+∈t-θ1∈t-1-...-θq∈t-q (1)
in the formula, q is the order of the bridge deck temperature prediction model; xtThe observed value of the t-th moment in the time sequence of the temperature change of the bridge surface of the expressway is obtained; e is the same astA random error term of the bridge deck temperature prediction model at the t-th moment is obtained; phi is apIs a self-review parameter to be estimated; thetaqIs the moving average parameter to be estimated; p is the average number of motion terms;
step 2.2, predicting the future dew point temperature of the highway bridge deck;
according to the air temperature obtained by the weather detection substation, the saturated water vapor pressure E at the current temperature is calculated by utilizing the Goff-Gray formulawAs shown in formula (2):
Figure FDA0003170170170000021
wherein T is air temperature and has a unit of K; t is1Is the triple point temperature of water in K;
calculating the actual vapor pressure e under the current temperature and humidity state according to the calculated saturated vapor pressure under the current temperature and the air humidity obtained by the weather detection substation, wherein the actual vapor pressure e is shown in formula (3):
e＝U×Ew/100 (3)
in the formula, e is the actual water vapor pressure under the current temperature and humidity state, and the unit is hPa; u is air humidity in units of%;
calculating the dew point temperature T under the current meteorological condition by using the Maglas formuladAs shown in formula (4):
Figure FDA0003170170170000022
in the formula, E0Is a saturated water vapor pressure of 0 ℃ E06.1078 hPa; a. b is a coefficient, a is 7.69, b is 243.92;
step 2.3, predicting the icing of the bridge deck of the future expressway;
judging whether the highway bridge floor reaches the formation condition of the dew point or not according to the dew point temperature under the current meteorological condition and the precipitation obtained by the meteorological detection main station, and predicting the future icing of the highway bridge floor; if the bridge deck temperature of the future expressway bridge deck is less than 0 ℃, no precipitation exists, and the bridge deck temperature of the future expressway bridge deck is lower than the dew point temperature, the edge computer generates first-stage icing early warning information of the expressway bridge deck; if the bridge deck temperature of the highway bridge deck is less than 0 ℃ and precipitation exists in the future, the edge computer generates early warning information of secondary icing of the highway bridge deck;
step 3, establishing a highway bridge deck traffic operation risk quantification model, wherein the highway bridge deck traffic operation risk quantification model comprises highway bridge deck environment influence factors and highway bridge deck vehicle influence factors, and specifically comprises the following substeps:
step 3.1, obtaining highway bridge deck parameters by carrying out on-site measurement on the highway bridge deck;
step 3.2, determining the influence factors of the bridge deck environment of the expressway;
the highway bridge surface environment influence factors comprise icing thickness, visibility, curvature change rate and longitudinal slope gradient of the highway bridge surface;
measuring highway bridge surface under different icing thickness conditions by using pendulum instrumentThe friction coefficient takes the friction coefficient of the highway bridge floor in a dry state as a reference value N of the friction coefficientavg(μ*) Establishing an evaluation function psi for the icing risk of the bridge deck of the highwayμAs shown in formula (5):
Figure FDA0003170170170000031
in the formula, Navg(mu) is the friction coefficient of the highway bridge floor when the icing thickness is mu;
taking the average number of dead people of traffic accidents with visibility greater than 200m as a reference value N of visibilityavg(δ*) Establishing a visibility risk evaluation function psi of the bridge deck of the highwayδAs shown in formula (6):
Figure FDA0003170170170000032
in the formula, Navg(δ) is the average number of deaths from a traffic accident with visibility δ;
taking the traffic accident rate when the gradient of the longitudinal slope is less than 2 percent as the reference value N of the gradientavg(τ*) Establishing an evaluation function psi for the slope risk of the bridge deck of the highwayτAs shown in formula (7):
Figure FDA0003170170170000033
in the formula, Navg(tau) is the corresponding traffic accident rate when the slope gradient tau of the longitudinal slope;
determining curvature change rate according to the curvature of the highway bridge floor, and taking the traffic accident rate when the curvature change rate is equal to 1 as a reference value N of the curvature change rateavg(ρ*) Establishing a risk evaluation function psi of the curvature of the bridge deck of the highwayρAs shown in formula (8):
Figure FDA0003170170170000034
in the formula, Navg(rho) is the corresponding traffic accident rate when the curvature change rate is rho;
calculating the influence factor R of the highway bridge floor environment according to the icing thickness, visibility, curvature and longitudinal slope gradient of the highway bridge flooriAs shown in formula (9):
Ri＝Ri(μ,ρ,τ,δ)＝Ψμ(μ)·Ψρ(ρ)·Ψτ(τ)·Ψδ(δ) (9)
in the formula, mu is the icing thickness of the highway bridge floor, rho is the curvature change rate of the highway bridge floor, tau is the longitudinal slope gradient of the highway bridge floor, and delta is the visibility of the highway bridge floor;
step 3.3, determining influence factors of vehicles on the bridge surface of the expressway;
based on the driving kinetic energy field theory, selecting a target vehicle, wherein the driving vehicles exist in front of, behind, on the left of and on the right of the target vehicle, and calculating the risk field intensity E of the target vehiclei1As shown in formula (10):
Figure FDA0003170170170000041
wherein i represents a vehicle number, i represents the target vehicle when i is 1, i represents the left vehicle of the target vehicle when i is 2, i represents the rear vehicle of the target vehicle when i is 3, i represents the right vehicle of the target vehicle when i is 4, and i represents the front vehicle of the target vehicle when i is 5; grad Ei1Gradient vectors of the field intensity of the kinetic energy field formed for the vehicle i at the position of the center of mass of the target vehicle; miIs the virtual mass, v, of vehicle iiIs the speed, θ, of the vehicle iiIs the angle between the direction of motion and the direction of speed of the vehicle i,
Figure FDA0003170170170000042
is the angle between the speed direction of vehicle i and the x-axis, ri1For vehicle iDistance from the target vehicle center of mass; ei1Forming a field intensity vector of a kinetic energy field for the vehicle i at the position of the center of mass of the target vehicle;
vehicle driving comprehensive safety potential energy SPE for calculating target vehicle1And rate of change
Figure FDA0003170170170000043
As shown in formula (11):
Figure FDA0003170170170000044
in the formula, SPEV,i1Forming a kinetic energy field for the target vehicle at the vehicle i position; SPE1A comprehensive safety potential for the target vehicle;
Figure FDA0003170170170000045
the change rate of the safety potential energy of the target vehicle in a driving safety field formed at the position of the vehicle i along with the time is obtained;
Figure FDA0003170170170000046
the change rate of the comprehensive safety potential energy of the target vehicle along with the time is obtained;
comprehensive safety potential energy SPE according to target vehicle1And rate of change
Figure FDA0003170170170000047
Calculating a driving safety index DSI of the target vehicle, as shown in formula (12):
Figure FDA0003170170170000048
in the formula, eta is a weight factor and has a value range of 0-1;
calculating a vehicle relative driving safety index RDSI (vehicle safety index), as shown in formula (13):
Figure FDA0003170170170000051
in the formula, DSI*The standard driving safety index of the vehicle in a specific dangerous scene is obtained;
step 4, making a highway bridge floor traffic operation risk prevention and control strategy;
determining a highway traffic operation risk prevention and control strategy by using an edge computer according to a relative driving safety index RDSI of the vehicles on the highway bridge floor and a threshold value of a following distance, and determining the safe driving speed of the vehicles on the highway bridge floor;
and 5, transmitting the safe driving speed, the bridge deck temperature of the future expressway bridge deck and the icing prediction information stored in the edge computer to an information board by using the communication unit, displaying the meteorological condition of the future expressway bridge deck through the information board, and reminding bridge deck vehicles of adjusting the driving state in advance.
2. The method for preventing and controlling the traffic operation risk based on the meteorological icing prediction on the bridge deck of the expressway as recited in claim 1, wherein in the step 1.3, a meteorological data acquisition program is arranged inside the edge computer and used for receiving meteorological data acquired by meteorological detection equipment.
3. The method for preventing and controlling the traffic operation risk based on the meteorological icing prediction of the bridge deck of the expressway as claimed in claim 1, wherein in the step 2.3, the early warning of the primary icing of the bridge deck of the expressway is that a part of the bridge deck of the expressway is iced, and the average icing thickness is less than 2 mm; the early warning of the secondary icing of the highway bridge floor is that the average icing thickness of the highway bridge floor is more than 2 mm.
4. The method for preventing and controlling the traffic operation risk based on the meteorological icing prediction of the highway bridge floor according to claim 1, wherein in the step 3.1, the highway bridge floor parameters comprise the curvature of the bridge floor and the gradient of a longitudinal slope.
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