CN111862598B - Variable speed limit control method based on high-definition checkpoint data and accident risk - Google Patents

Variable speed limit control method based on high-definition checkpoint data and accident risk Download PDF

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CN111862598B
CN111862598B CN202010563308.2A CN202010563308A CN111862598B CN 111862598 B CN111862598 B CN 111862598B CN 202010563308 A CN202010563308 A CN 202010563308A CN 111862598 B CN111862598 B CN 111862598B
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CN111862598A (en
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王俊骅
宋昊
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Tongji University
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    • G—PHYSICS
    • G08—SIGNALLING
    • 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
    • G08G1/0125—Traffic data processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/067—Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G—PHYSICS
    • G08—SIGNALLING
    • 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
    • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
    • G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control

Abstract

The invention relates to a variable speed limit control method based on high-definition checkpoint data and accident risk, which comprises the following steps: 1) Determining the range of a control road section and the arrangement position of a high-definition bayonet, and sequentially arranging the high-definition bayonet, a variable speed limit control indication board and a microclimate station at reasonable positions in the highway section in a matched manner; 2) Constructing a binary logistic prediction model of real-time accident risk based on high-definition traffic flow data of a checkpoint and weather data of a microclimate station, and predicting the occurrence probability of traffic accidents at each position of a traffic flow; 3) Judging whether the current moment is integral multiple of the variable speed limit control period or not, and calculating the variable speed limit value of the variable speed limit control indication board; 4) And judging whether the current actual measurement traffic flow running state reaches a variable speed limit control stop condition or not, and controlling the variable speed limit. Compared with the prior art, the method has the advantages of overcoming the randomness of core control parameter values in variable speed limit control, improving the variable speed control effect and the like.

Description

Variable speed limit control method based on high-definition checkpoint data and accident risk
Technical Field
The invention relates to the technical field of traffic control, in particular to a variable speed limit control method based on high-definition checkpoint data and accident risk.
Background
The variable speed limit control is a traffic control strategy which is more and more widely used for improving the traffic safety of the highway, and the control effect of the variable speed limit control is closely related to the method adopted in the variable speed limit value determining process. The binary logistic model is used as a machine learning model, the relation between traffic flow data and accident risks can be established, and the value of the optimal variable speed limit control core parameter can be obtained by combining different variable speed limit strategy parameters in the microscopic simulation model.
The key parameter values related in the current variable speed limit control strategy are mainly determined by experience supervisors of engineers, the core control parameter values are random, the variable speed limit values at different positions and different moments have jumpiness, and the too frequent and large fluctuation of the variable speed limit values easily causes the potential safety hazard of a variable speed limit control area. The existing research does not establish a highway accident prediction model based on weather and high-definition checkpoint traffic flow data, so that accident risks cannot be considered when the variable speed limit control effect is investigated, and the variable speed limit control effect is poor.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide a variable speed-limiting control method based on high-definition bayonet data and accident risk, which overcomes the defect that the conventional variable speed-limiting control strategy lacks control on accident risk under the influence of weather, has subjective randomness for the value of key parameters of the variable speed-limiting control strategy of a highway section and improves the variable speed control effect.
The purpose of the invention can be realized by the following technical scheme:
a variable speed-limiting control method based on high-definition checkpoint data and accident risks is characterized in that a binary logistic model is adopted to establish a machine learning model among traffic flow data, weather data and accident risks, the road section traffic accident risks under different traffic flows and weather conditions are obtained, the accident risk values and traffic delays under different variable speed-limiting strategy parameter combinations are obtained by combining different variable speed-limiting strategy parameters in a microscopic simulation model, and the randomness of the values of core control parameters in the variable speed-limiting control in the prior art is overcome by selecting the core control parameters of the variable speed-limiting control strategy with the minimum accident risk and the minimum traffic delay. The method specifically comprises the following steps:
step 1, determining the range of a control road section and the setting position of a high-definition bayonet, and sequentially arranging the high-definition bayonet, a variable speed limit control indication board and a microclimate station in a matched manner by utilizing the existing high-definition bayonet in a highway section or the reasonable position in the highway section.
And 2, constructing a binary logistic prediction model of real-time accident risk by using high-definition traffic flow data of a checkpoint and weather data of a microclimate station which correspond to 5-10 minutes before the accident occurs on the road section, and predicting the occurrence probability of the traffic accident at each position of the traffic flow. The expression of the binary logistic prediction model is:
Figure GDA0003985348980000021
in the formula, P (y) i ＝1|x i ) Is x i Probability of occurrence of traffic accident, beta 0 +∑β k x ki As a utility function, x ki As a variable of traffic flow, beta 0 Term of intercept constant, beta k Is a variable x ki And (4) the coefficient. The expression of the utility function is:
β 0 +∑β k x ki ＝1.42+0.03*x 1i -2443.29*x 2i -334.22*x 3i -5318.68*x 4i +3230.91*x 5i +18758.55*x 6i +0.15*x 7i +0.02*x 8i +0.19*x 9i -0.14*x 10i -0.13*x 11i -0.06*x 12i -0.08*x 13i
in the formula, x 1i Is the mean value of the flow time distribution of the first lane, x 2i Is the mean value of the flow time distribution, x, of the second lane 3i Is the mean value of the flow time distribution of the third lane, x 4i Is the variance of the flow time distribution, x, of the first lane 5i Flow time distribution variance, x, for the second lane 6i Flow time distribution variance, x, for the third lane 7i Is the mean value of the velocity time distribution of the first lane, x 8i Is the mean value of the velocity time distribution, x, of the second lane 9i Is the variance of the velocity time distribution, x, of the first lane 10i Is the variance of the velocity time distribution, x, of the second lane 11i Is the variance of the velocity time distribution, x, of the third lane 12i Is the speed range of the third lane, x 13i And the weather state mean value is obtained by the mean value of the day weather int in the weather data obtained by the micro weather station or the mean value of the night weather int.
And 3, judging whether the current moment is integral multiple of the variable speed limit control period, if so, calculating the variable speed limit value of the variable speed limit control indication board, and otherwise, directly entering the next traffic flow data detection period. Specifically, the method comprises the following steps:
carrying out microscopic simulation on the binary logistic prediction model in the step 2 by using AIMSUN to obtain each position x under traffic flow simulation i The probability of occurrence of a traffic accident; obtaining target speed limit value T aiming at different traffic flows VSL (x i T), speed change period delta t, and the optimal parameter combination of variable speed limit value adjustment range delta V; binding each position x i And selecting the variable speed limit control parameter with the minimum accident risk according to the occurrence probability of the traffic accident.
Current time position x i The expression of the target speed limit value of the variable speed limit sign is as follows:
Figure GDA0003985348980000031
in the formula, T VSL (x i T) is the current time position x of the road section i at the time t i The target speed limit value of the variable speed limit indication board; and flow is the hourly traffic flow of the road section.
Current time position x i Target speed limit value T of variable speed limit indicating board VSL (x i T), the position x can be determined in combination with the step of variation Δ V i The change amplitude value of the variable speed limit sign at time t.
Figure GDA0003985348980000032
In the formula, Δ V is a variable speed limit value adjustment range, and Δ V =15km/h; t is VSL For the current time position x i Target speed limit value V of variable speed limit indication board SL (x i T) is the position x in the road section i i The variable speed limit sign of (2) indicates the speed limit value at the time point t.
The speed variation period Δ t is:
Figure GDA0003985348980000033
using Δ V SL (x i T) calculating the position x i The final value of the variable speed limit sign at the time t is substituted into a speed limit value calculation formula in the road section i as shown in the following to obtain the final speed limit value in the road section i:
V SL (x i ,t+Δt)＝V SL (x i ,t)+ΔV SL (x i ,t)
in the formula, V SL (x i T + Δ t) is the position x within the section i i The speed limit value of the variable speed limit sign at the moment of t + delta t; Δ V SL (x i T) is the position x i The variable speed limit sign of (2) the variation amplitude at the time t.
X at the releasing position of the speed-limiting indicating board set by matching i The speed limit value of (2).
And 4, judging whether the current actually-measured traffic flow running state reaches the variable speed limit control stop condition or not based on the traffic flow data obtained from the road section gate, and controlling the variable speed limit. Specifically, the method comprises the following steps:
judging whether the current actual measurement traffic flow running state reaches the variable speed limit control stop condition or not based on the traffic flow data obtained by the road section gate, namely whether the current actual measurement traffic flow running state reaches the current V SL (x i ,t)-ΔV≤T VSL (x i ,t+Δt)＜V SL (x i When t) + Δ V, Δ V SL (x i T) =0; when the traffic demand in the road section does not drop, maintaining the variable speed limit control state and entering the next traffic flow data detection period; when the traffic demand in the road section is reduced, the variable speed limit is gradually recovered to the default speed limit value.
Compared with the prior art, the invention has the following beneficial effects:
1) The method comprises the steps of establishing a relation between traffic flow data and accident risks by adopting a binary logistic model, simulating different variable speed limit strategy parameter combinations in a microscopic simulation model, namely in AIMSUN microscopic traffic flow simulation software, adopting different variable speed limit control periods and variable speed limit value adjusting amplitude parameter combinations aiming at different traffic flows, and selecting the minimum variable speed limit strategy parameter combination of the accident risks according to a real-time risk probability value, so that the randomness of core control parameter values in variable speed limit control in the prior art is overcome, and the variable speed control effect is improved;
2) Because the accident rate under the weather conditions of rain, snow and the like is greater than that under the weather conditions of fine weather, the distribution trend of the traffic flow hour is consistent with that of the accident hour, particularly, as for the traffic flow condition corresponding to the road section where the accident is located in real time, the traffic flow condition before the accident occurs has sudden change or unsafe driving behaviors such as illegal behaviors and the like, the research and judgment on the accident risk can be realized by judging the bad traffic condition, so the influence of the weather and the traffic flow on the traffic accident risk is considered, the variable speed limit control effect is effectively improved, and meanwhile, the application condition and the installation cost of the traffic flow data obtained by utilizing the high-definition bayonet are lower.
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FIG. 1 is a schematic flow diagram of a variable speed limit control strategy;
FIG. 2 is a schematic diagram of the matching arrangement of a high-definition bayonet and a variable speed limit indicating board in a road section in the embodiment of the invention;
FIG. 3 is a schematic structural diagram of an accident risk prediction model established by using a binary logistic model in the embodiment of the present invention;
fig. 4 is a schematic flow chart of a variable speed limit control method based on high definition checkpoint data and accident risk in an embodiment of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, shall fall within the protection scope of the present invention.
Examples
The invention is mainly applied to the variable speed-limiting control of all one-way three lanes of the highway section, because the accident rate is greater than the accident rate under the fine weather state under the weather state such as rain and snow, and the traffic flow hour distribution and the accident hour distribution trend are consistent, especially for the traffic flow state corresponding to the section where the accident is located in real time, the traffic flow state before the accident occurs has the existence of unsafe driving behaviors such as sudden change or illegal behaviors, and the research and judgment of the accident risk can be realized by judging the bad traffic state. Based on the variable speed limit control method, the variable speed limit control method based on high-definition checkpoint data and accident risks is provided.
The invention discloses a variable speed limit control method based on high-definition checkpoint data and accident risks, which is a method for determining core parameters in a variable speed limit control strategy based on a basic flow of a binary logistic model and the variable speed limit control strategy. Traffic flow data obtained by a high-definition gate of an expressway intersection and weather data obtained by a microclimate station are used for judging traffic flow operation risks, when the current time reaches the integral multiple of a variable speed limit control period, the speed limit value of the position of a variable speed limit sign is corrected, and a flow chart of a variable speed limit control strategy is shown in figure 1.
The first step is to determine the range of the control road section and the position of the high-definition bayonet, and the high-definition bayonet (or the existing high-definition bayonet), the variable speed limit control indicating board and the microclimate station are sequentially matched and arranged at reasonable positions in the highway section. The variable speed limit sign is used for issuing a speed limit value. The high-definition bayonet is used for detecting traffic flow data of a road section, and the microclimate station is used for acquiring road section meteorological data. The high-definition bayonet acquires traffic flow data at an entrance of a highway section by taking 30s as a time period. The high-definition bayonet and the variable speed limit indicator which are matched and arranged in the road section of the embodiment are shown in fig. 2, and the road section in fig. 2 comprises three lanes.
The second step is that the real-time accident risk prediction model is constructed by utilizing the corresponding high-definition traffic flow data of the checkpoint and weather data of the microclimate station 5-10 minutes before the accident occurs on the road section and is used for the traffic accidentCalculating positions x during current simulation i The probability of occurrence of traffic accidents. The high-definition traffic flow data at the gate mainly comprises the mean value of the flow time distribution of each lane, the variance of the flow time distribution of each lane, the extremely poor speed of each lane and the like. Weather data obtained by the microclimate station are shown in table 1.
TABLE 1 weather information Table Structure and field description
Figure GDA0003985348980000051
The invention adopts a binary logistic model to establish an accident risk prediction model, the structure diagram is shown in figure 3, and the binary logistic model form of the model is as follows:
Figure GDA0003985348980000061
in the formula, P (y) i ＝1|x i ) Is x i Probability of occurrence of traffic accident, beta 0 +∑β k x ki As a utility function, x ki Is a traffic flow variable; beta is a beta 0 Is an intercept constant term; beta is a k Is a variable x ki And (4) the coefficient. The weather control parameters in the model are not significant, so the weather control parameters are deleted in the final model formula.
Wherein the position x is calculated using equation (1) i When the probability of the traffic accident is obtained, the expression of the utility function is as follows:
β 0 +∑β k x ki ＝1.42+0.03*x 1i -2443.29*x 2i -334.22*x 3i -5318.68*x 4i +3230.91*x 5i +18758.55*x 6i +0.15*x 7i +0.02*x 8i +0.19*x 9i -0.14*x 10i -0.13*x 11i -0.06*x 12i -0.08*x 13i (2)
in the formula (2), x 1i Is the average value of the traffic 1 flow time distribution, x 2i Is the average value of the traffic time distribution of lane 2, x 3i Is the mean value of the 3-flow time distribution of the lane, x 4i For lane 1 flow time distributionVariance, x 5i Is the lane 2 flow time distribution variance, x 6i For lane 3 flow time distribution variance, x 7i Mean value of time distribution of speed of lane 1, x 8i Is the mean value of the speed time distribution of lane 2, x 9i Is the lane 1 speed time distribution variance, x 10i Is the lane 2 speed time distribution variance, x 11i For lane 3 speed time distribution variance, x 12i Extremely poor speed, x, of lane 3 13i The weather state mean value is obtained by the mean value of day weather int or the mean value of night weather int in the weather data obtained by the microclimate station.
And thirdly, judging whether the current time is integral multiple of the variable speed limit control period, if so, calculating the variable speed limit value of the variable speed limit control indication board, otherwise, performing no operation, and entering the next traffic flow data detection period. The specific calculation content is as follows:
performing microscopic simulation in AIMSUN by using the accident risk prediction model based on binary logistic acquired in the second step, and calculating each position x when traffic flow simulation is obtained i The probability of occurrence of traffic accidents. Target speed limit value T is obtained aiming at different traffic flows VSL (x i T), speed change period delta t, and variable speed limit value adjusting amplitude delta V. According to real-time risk probability value (i.e. each position x) i The traffic accident occurrence probability) to select the variable speed-limiting control parameter with the minimum accident risk.
Target speed limit value T VSL (x i T), a speed change period delta t and a variable speed limit value adjustment amplitude delta V are mainly set by the traffic hour flow of the current road section.
The target speed limit value is set as follows:
Figure GDA0003985348980000062
in the formula (3), the flow is the traffic hour flow of the current road section; t is VSL (x i T) is the position x of the road section i at the current moment i Variable speed-limiting signLimiting the speed value; the position x can be determined in conjunction with the step size Δ V of the change i The variable speed limit sign has a change amplitude value at the time t, and the specific formula is as follows:
Figure GDA0003985348980000071
in the formula (4), Δ V is a variable speed limit value adjustment range, and Δ V =15km/h; t is a unit of VSL Is a target speed limit value; v SL (x i T) is the position x in the road section i i The speed limit value of the variable speed limit sign at the time t;
the speed variation period Δ t is:
Figure GDA0003985348980000072
the position x is calculated by the formula (4) i The final value of the variable speed limit sign at the time t is substituted into a speed limit value calculation formula in the road section i as shown in the following to obtain the final speed limit value in the road section i:
V SL (x i ,t+Δt)＝V SL (x i ,t)+ΔV SL (x i ,t) (6)
in the formula (6), V SL (x i T + Δ t) is the position x within the section i i The speed limit value of the variable speed limit sign at the moment of t + delta t; Δ V SL (x i T) is the position x i The variable speed limit sign of (1) changes the amplitude at the moment t; variable speed-limiting sign issuing position x set by matching i The speed limit value of (c).
The fourth step is that whether the current actual measurement traffic flow running state reaches the variable speed limit control stop condition is judged based on the traffic flow data obtained by the road section gate, namely whether the current actual measurement traffic flow running state reaches the if V in the formula (4) SL (x i ,t)-ΔV≤T VSL (x i ,t+Δt)＜V SL (x i ,t)+ΔV，ΔV SL (x i T) =0; when the traffic demand in the road section does not drop, the variable speed limit control state is maintained to enter the next traffic flow numberAccording to the detection period; and when the traffic demand in the road section is reduced, the variable speed limit is gradually recovered to the default speed limit value.
The default speed limit value is determined according to the speed limit value of the local road section, and the speed limit value is generally 120km/h or 100km/h for a passing lane and is generally 90km/h for a slow lane.
3) The invention considers the influence of weather and traffic flow on the risk of traffic accidents, effectively improves the variable speed limit control effect, and simultaneously obtains the traffic flow data by utilizing the high-definition checkpoint, and has lower application condition and installation cost. The relation between traffic flow data and accident risks is established by adopting a binary logistic model, different variable speed limit control periods and parameter combinations of variable speed limit value adjusting amplitudes are adopted for different traffic flows, and the combination of variable speed limit strategy parameters with the minimum accident risk is selected according to a real-time risk P value, so that the randomness of core control parameter values in variable speed limit control in the prior art is overcome, and the variable speed control effect is improved.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and those skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (4)

1. A variable speed limit control method based on high-definition checkpoint data and accident risks is characterized by comprising the following steps:
1) Determining the range of a control road section and the arrangement position of a high-definition bayonet, and sequentially arranging the high-definition bayonet, a variable speed-limit control indication board and a microclimate station in a matched manner by utilizing the existing high-definition bayonet in the highway section or the reasonable position in the highway section;
2) Constructing a binary logistic prediction model of real-time accident risk by utilizing high-definition checkpoint traffic flow data and microclimate station weather data which correspond to 5-10 minutes before an accident occurs on a road section, and predicting the occurrence probability of the traffic accident at each position of a traffic flow;
3) Judging whether the current time is integral multiple of the variable speed limit control period, if so, calculating the variable speed limit value of the variable speed limit control indication board, otherwise, directly entering the next traffic flow data detection period;
4) Judging whether the current actual measurement traffic flow running state reaches the variable speed limit control stop condition or not based on the traffic flow data obtained by the road section gate, and controlling the variable speed limit;
the expression of the binary logistic prediction model is as follows:
Figure FDA0003985348970000011
in the formula, P (y) i ＝1|x i ) Is x i Probability of occurrence of traffic accident, beta 0 +∑β k x ki As a utility function, x ki As a variable of the traffic flow, beta 0 Is an intercept constant term, beta k Is a variable x ki A coefficient;
the expression of the utility function is:
β 0 +∑β k x ki ＝1.42+0.03*x 1i -2443.29*x 2i -334.22*x 3i -5318.68*
x 4i +3230.91*x 5i +18758.55*x 6i +0.15*x 7i +0.02*x 8i +0.19*x 9i -0.14*x 10i -0.13*x 11i -0.06*x 12i -0.08*x 13i
in the formula, x 1i Is the mean value of the flow time distribution of the first lane, x 2i Is the mean value of the flow time distribution, x, of the second lane 3i Is the mean value of the flow time distribution of the third lane, x 4i Is the variance of the flow time distribution, x, of the first lane 5i Flow time distribution variance, x, for the second lane 6i Is the variance of the flow time distribution of the third lane, x 7i Is the mean value of the velocity time distribution of the first lane, x 8i Is the mean value of the velocity time distribution, x, of the second lane 9i Velocity time profile for first laneVariance, x 10i Is the variance of the velocity time distribution, x, of the second lane 11i Is the variance of the velocity time distribution, x, of the third lane 12i Is the speed range of the third lane, x 13i The weather state mean value is obtained by the mean value of day weather int or the mean value of night weather int in the weather data obtained by the microclimate station.
2. The variable speed-limiting control method based on high-definition bayonet data and accident risks according to claim 1, characterized in that the specific content of step 3) is as follows:
carrying out microscopic simulation on the binary logistic prediction model in the step 2) by using AIMSUN to obtain each position x under traffic flow simulation i The probability of occurrence of traffic accidents; target speed limit value T is obtained aiming at different traffic flows VSL (x i T), speed change period delta t, and the optimal parameter combination of variable speed limit value adjustment range delta V; binding to each position x i And selecting the variable speed limit control parameter with the minimum accident risk according to the occurrence probability of the traffic accident.
3. The variable speed-limiting control method based on high-definition checkpoint data and accident risk according to claim 1, characterized in that the position x i The variable speed limit sign has a change amplitude delta V at the time t SL (x i And t) is calculated as:
Figure FDA0003985348970000021
in the formula, Δ V is the adjustment range of the variable speed limit value, T VSL For the current time position x i The target speed limit value of the variable speed limit indication board; the speed variation period Δ t is:
Figure FDA0003985348970000022
in the formula, the flow is the traffic hour flow of the road section,
the adjusting amplitude delta V of the variable speed limit value is 15km/h.
4. The variable speed-limiting control method based on high-definition checkpoint data and accident risk according to claim 3, characterized in that the current time position x i The expression of the target speed limit value of the variable speed limit sign is as follows:
Figure FDA0003985348970000023
in the formula, T VSL (x i T) is the current time position x of the road section i at the time t i And the target speed limit value of the variable speed limit sign.
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CN102542831B (en) * 2011-12-16 2014-04-09 东南大学 Variable speed-limit control method of expressway based on real-time traffic flow and weather information
CN103198709B (en) * 2013-03-21 2015-07-15 东南大学 Vehicle regulating and controlling method for reducing traffic accidents under raining conditions
JP6334687B2 (en) * 2013-10-01 2018-05-30 エヌイーシー ラボラトリーズ ヨーロッパ ゲーエムベーハー Method and computer program product for accurate motorway speed control
CN105931460A (en) * 2016-05-13 2016-09-07 东南大学 Variable speed limit control strategy optimization method for continuous bottleneck section of expressway
CN105931459A (en) * 2016-05-13 2016-09-07 东南大学 Variable speed limit control strategy optimization method for isolated bottleneck section of expressway

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