CN112885088A - Multi-turn road coordination control method based on dynamic traffic flow - Google Patents
Multi-turn road coordination control method based on dynamic traffic flow Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
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- G08G1/00—Traffic control systems for road vehicles
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- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
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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
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- G—PHYSICS
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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
- 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
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- G—PHYSICS
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- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/07—Controlling traffic signals
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Abstract
The invention relates to a multi-ramp coordination control method based on dynamic traffic flow, which comprises the steps of judging whether a road section is a bottleneck road section or not according to the real-time occupancy rate or the density of the road section at the downstream of a ramp, and adopting local regulation rate for each entrance ramp when detecting that no bottleneck road section exists in a multi-ramp control area. When a bottleneck road section is generated in the road section, historical flow data of the ramps are selected, and the trend similarity between the upstream ramps and the main line flow is determined by utilizing a cross-correlation method. And performing grey correlation analysis on the similarity, the ramp flow, the spatial distance and the downstream speed, determining the ratio of the attributes, and obtaining a correlation matrix between the ramp and the main line section. And distributing the excess flow of each bottleneck according to the incidence matrix to obtain the coordination regulation rate of each ramp. And taking the smaller value of the coordination regulation rate and the local regulation rate, and considering the limitation of the queuing length of the ramp to obtain the final ramp regulation rate. And the green duration of the signal lamp is calculated through the ramp coordination rate, so that the control purpose is achieved.
Description
Technical Field
The invention relates to the technical field of intelligent traffic, in particular to a multi-turn road coordination control method based on dynamic traffic flow.
Background
The traffic jam phenomenon of the urban road network is a necessary result generated by the rapid development of social economy and the imbalance of effective traffic management. Urban traffic congestion causes great impact on urban operation efficiency and stability, and therefore, social, economic and environmental problems such as traffic safety, difficulty in traveling of urban residents, tail gas and noise pollution and the like are caused. In road traffic congestion, in addition to the congestion of the common urban central roads, the congestion problem of urban expressways as the "main artery" of the urban road network is increasing, and the problem becomes a social hotspot.
The single-turn road control is the most direct and convenient method for solving the problem of overhead traffic jam. However, when the express way is in a severe congestion situation such as a rush hour in the morning and at night, the adjacent ramps on the upstream and downstream are simultaneously subjected to a great traffic pressure. The single-ramp control only considers the traffic state of one section of ramp confluence area in the main line of the express way, lacks the consideration of the whole situation, and can aggravate the traffic jam condition of each bottleneck road section. And the traffic facilities upstream and downstream cannot be effectively utilized. When a main line has a plurality of bottlenecks or the physical capacity of a ramp is limited, a coordination control strategy taking smooth main line traffic and queuing without back overflow of a plurality of ramps as control targets may be more effective than a local control strategy.
The methods for multi-ramp coordination control can be mainly divided into model-based methods and non-model-based methods. Model-based methods include the Payne continuous traffic flow model, the cellular transmitter traffic flow model (CTM), and the METANET traffic flow model, among others. Zhang adopts a preemptive hierarchical control scheme with a three-priority structure to control a plurality of ramps, expands the control method of a single target, and Meshkat firstly applies a quantitative hierarchical model to the ramp coordination control to allocate priorities to different ramps and expressways, Chen adds real-time OD information on the basis of the priority, and determines the priority of an entrance ramp according to the total travel distance of vehicles. In the field of non-model control, most research is concentrated on various heuristic algorithms, such as HELPER, SWARM and the like. Papamichail proposes an HERO algorithm, aims at critical occupancy rate, and solves the problem of uncertainty of the traffic capacity of the expressway. Jacobson provides a bottleneck algorithm, and considering from two aspects of single point and coordination, Zhang extracts OD information through complete track data instead of a traditional sensor based on the bottleneck algorithm, and performs coordination control on multiple turns of tracks. The researches provide good insight and thinking for solving the multi-ramp coordination control task.
Disclosure of Invention
The invention aims to overcome the defects and provides a multi-ramp coordination control method based on dynamic traffic flow. When a bottleneck road section is generated in the road section, historical flow data of the ramps are selected, and the trend similarity between the upstream ramps and the main line flow is determined by utilizing a cross-correlation method. And performing grey correlation analysis on the similarity, the ramp flow, the spatial distance and the downstream speed, determining the ratio of the attributes, and obtaining a correlation matrix between the ramp and the main line section. And distributing the generated excess flow of each bottleneck according to the incidence matrix to obtain the coordination regulation rate of each ramp. And taking the smaller value of the coordination regulation rate and the local regulation rate, and considering the limitation of the queuing length of the ramp to obtain the final ramp regulation rate. The green duration of the signal lamp can be calculated through the final ramp regulation rate, and the control purpose is achieved.
The invention achieves the aim through the following technical scheme: a multi-turn road coordination control method based on dynamic traffic flow comprises the following steps:
(1) screening and counting original traffic microwave data, and screening to obtain flow data of an express way main line and flow data of a ramp entrance with a period of several minutes;
(2) preprocessing the flow data obtained in the step (1);
(3) determining trend similarity of each ramp on the upstream and the main line flow by using a cross-correlation method, performing grey correlation analysis on the similarity, the ramp flow, the spatial distance and the downstream speed, and determining the ratio of each attribute to obtain a correlation matrix between the ramp and the main line section;
(4) and establishing a multi-ramp coordination control method by utilizing a heuristic control strategy, and obtaining the optimal control effect by applying the optimized incidence matrix.
Preferably, the preprocessing in the step (2) comprises filling missing data, processing error data and data normalization; in order to improve the utilization rate of data, missing data is processed by utilizing the time correlation of traffic flow, an average value method is adopted to repair the missing data, and a repair formula is as follows:
where x (t) is missing data to be completed, and k is the total number of adjacent data.
Preferably, in the step (3), a cross-correlation algorithm is adopted to measure the correlation between the ramp flow and the main line downstream flow, and the connection between the main line section and each upstream ramp is searched; for time series X ═ X1,x2,…,xn) And Y ═ Y (Y)1,y2,…,yn) The cross-correlation method holds Y stationary, slides X along Y, and calculates their inner product for each slide s of X as shown in the following equation:
X＝(x1,x2,…,xn)
wherein X and Y represent two different time series, X and Y represent sequence values, s is all possible sliding values, and n is the sequence number of the time series;
the inner product CC (X, Y) is calculated as the similarity between the two time series X and Y, as shown in the following equation:
wherein CC (X, Y) is a calculated inner product value, i is a time series index value, s is a sliding value, n is a sequence number of a time series, and X and Y represent sequence values;
wherein the normalized cross-correlation coefficient NCC (X, Y) is used, the range is controlled to [ -1,1], wherein 1 represents that the two have strong correlation, and-1 represents that the two are completely opposite, and positive NCC (X, Y) represents that the two are in the same direction, and negative NCC (X, Y) represents that when one of the sequences tends to increase, the other tends to decrease, and vice versa; NCC (X, Y) is defined by the following formula:
wherein X and Y represent two different time series, CC (X, Y) is the calculated inner product value, and s is the sliding value;
taking the cross-correlation coefficient NCC (X, Y), the normalized physical distance d between the ramp and the bottleneck and the flow predicted value q of the entrance ramp as the characteristic parameters of the incidence matrix; if the cross-correlation coefficient NCC (X, Y) is less than 0, the fact that the flow time sequence of the ramp and the congested road section is in negative correlation is shown, so that the influence of dynamic traffic flow is not considered, and the normalized physical distance d between the ramp and a bottleneck and the flow predicted value q of the entrance ramp are used as characteristic parameters of the correlation matrix; the specific formula is as follows:
w (i, j) represents the weight of the jth ramp to the ith bottleneck road section; d (i, j) represents the normalized distance between the jth ramp and the ith bottleneck section; q (i) represents the normalized flow for the ith on-ramp; mu represents a correlation limit, a value smaller than the correlation limit represents weak correlation between two time sequences, a value larger than the correlation limit represents strong correlation, and the value is generally an intermediate value, which is 0.5 in the formula; alpha is alpha1、α2、α3、β1、β2、β3All represent the proportion of time sequence correlation coefficient, and under the condition of weak correlation, the cross correlationThe coefficient has less influence on the speed, and has larger influence under the strongly relevant condition;
and comparing the relationship between the downstream bottleneck speed curve and the cross-correlation coefficient, the flow value of the entrance ramp and the distance to obtain a correlation GRC (i, j) based on grey correlation analysis:
wherein GRC (i, j) represents a set of correlations between the ith attribute and the ramp j, and GRC (k, j) represents a correlation between the kth attribute and the ramp j; the final correlation matrix W (i, j) is as follows:
wherein m is the number of bottlenecks, n is the number of ramps, W (i, j) is the weight between the ith bottleneck and the jth ramp, and W (i, j) represents the incidence matrix of the whole ramp section.
Preferably, the step (4) is specifically as follows:
(i) judging whether the road section is a bottleneck road section according to the real-time density rho (i, k) of the downstream road section of the ramp:
ρ(i,k)＞ρthreshold(i)
where ρ (i, k) is the real-time density at time k of the ith downstream segment, ρthreshold(i) A density threshold value of the ith road section;
(ii) if the bottleneck-free section in the multi-ramp control area is detected to exist, the local regulation rate r is adopted for each entrance rampL(j, k +1) regulating:
wherein r isL(j, K +1) is the local regulation rate at the moment K +1, K is a regulation parameter,
the critical density value of the downstream road section of the ramp is rho (k), and the average density of the downstream road section at the moment k is rho (k);
if a bottleneck road section is generated in the road section, the generated excess flow of each bottleneck needs to be distributed to obtain the coordination regulation rate r of each rampC(j,k+1)；
Wherein r isC(j, k +1) is the ramp coordination regulation rate at the moment of k +1, rC(j, k) is the coordinated regulation rate at time k, qin(i, k) denotes the mainline upstream inflow, qon(i, k) represents the incoming flow on the on-ramp, qout(i, k) represents the main line downstream outflow at time k, qoff(i, k) represents the flow rate of the exit ramp at the moment k; w (i, j) represents a correlation matrix;
(iii) local regulation rate r through rampL(j, k +1) and the coordinated regulation rate rC(j, k +1) obtaining a system regulation rate rS(j,k+1)：
rS(j,k+1)＝min[rL(j,k+1),rC(j,k+1)]
Wherein r isS(j, k +1) represents the system regulation rate of the jth ramp at time k +1, rL(j, k +1) represents the local regulation rate of the jth ramp at time k +1, rC(j, k +1) represents the coordinated regulation rate of the jth ramp at the moment k + 1;
(iv) according to the queuing length of the ramp, the regulation rate is restrained, and the ramp regulation rate r of the queuing restraint isQThe formula for the calculation (j, k +1) is as follows:
wherein r isQ(j, k +1) represents the ramp dispatching rate of the queuing constraint at the time k +1, a (j, k) represents the arrival rate of vehicles entering the entrance ramp at the time k,
representing the capacity of the jth ramp, wherein omega (j, k) is the queuing length of the jth entrance ramp at the moment k, and T refers to a time step;
(v) get the system regulation rate rSRamp regulation rate r of (j, k +1) and queuing constraintQThe larger value of (j, k +1) results in the final ramp regulation rate r (j, k +1), as follows:
r(j,k+1)＝max[rS(j,k+1),rQ(j,k+1)]
wherein r (j, k +1) represents the ramp regulation rate of the jth ramp at the moment k +1, rQ(j, k +1) represents the ramp regulation rate of the queuing constraint for the jth ramp at time k +1, rS(j, k +1) represents the system regulation rate of the jth ramp at the time k + 1;
(vi) calculating the green light time length g (j, k +1) according to the regulation rate, and sending the green light time length g (j, k +1) to each signaler;
wherein g (j, k +1) represents the green light duration of the jth ramp at the moment k + 1; r (j, k +1) represents the ramp regulation rate of the jth ramp at the time of k +1, C (j) represents the signal period duration, rs(j) Represents the saturation flow rate;
and finally, adjusting the signal green light time of the multi-ramp by combining the dynamic traffic flow.
The invention has the beneficial effects that: the invention adjusts the green time of the signal of the multi-ramp by combining the dynamic traffic flow, controls the green time of the signal lamp, achieves the aim of coordinative control of the multi-ramp and ensures the smoothness of the main line and the ramps of the express way.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a flow chart of the multi-turn lane coordination control of the present invention;
FIG. 3 is a coordinated adjustment rate solving diagram of the present invention.
Detailed Description
The invention will be further described with reference to specific examples, but the scope of the invention is not limited thereto:
example (b): as shown in fig. 1, a multi-turn road coordination control method based on dynamic traffic flow specifically includes:
1. and screening and counting the original traffic microwave data to obtain flow data of the expressway main line with 5 minutes as a period, and counting the vehicle passing data of the upper ramp into the flow data according to the 5 minutes as the period. In order to improve the utilization rate of data, missing data is processed by utilizing the time correlation of traffic flow, an average value method is adopted to repair the missing data, and a repair formula is as follows:
where x (t) is missing data to be completed, and k is the total number of adjacent data.
2. In the multi-turn road coordination control method based on the dynamic traffic flow, a section of traffic flow time sequence data is a continuous observation value collected by a loop detector according to time stamps at equal intervals. The cross-correlation algorithm is used to measure the correlation between the ramp flow and the main line downstream flow, and the connection between the main line segment and each upstream ramp is found, as shown in fig. 2. For time series X ═ X1,x2,…,xn) And Y ═ Y (Y)1,y2,…,yn) The cross-correlation method holds Y stationary, slides X along Y, and calculates their inner product for each slide s of X as shown in the following equation:
X＝(x1,x2,…,xn)
where X and Y represent two different time series, X and Y represent sequence values, s is all possible sliding values, and n is the number of sequences in the time series.
The inner product CC (X, Y) is calculated as the similarity between the two time series X and Y, as shown in the following equation:
where CC (X, Y) is the calculated inner product value, i is the time series index value, s is the sliding value, n is the sequence number of the time series, and X and Y represent the sequence values.
In practice, a regularized cross-correlation coefficient NCC (X, Y) is typically employed, thus controlling the range between [ -1,1], where 1 indicates that the two have a strong correlation, and-1 indicates that the two are diametrically opposed, and further a positive NCC (X, Y) indicates that the two are in the same direction, and a negative NCC (X, Y) indicates that one of the sequences tends to increase, the other tends to decrease, and vice versa. NCC (X, Y) is defined by the following formula:
where X and Y represent two different time series, CC (X, Y) is the calculated inner product value and s is the sliding value.
And taking the cross-correlation coefficient NCC (X, Y), the normalized physical distance d between the ramp and the bottleneck and the flow predicted value q of the entrance ramp as characteristic parameters of the incidence matrix. If the cross-correlation coefficient NCC (X, Y) is less than 0, the fact that the flow time sequence of the ramp and the congested road section is in negative correlation is shown, therefore, the influence of dynamic traffic flow is not considered, and the normalized physical distance d between the ramp and the bottleneck and the flow predicted value q of the entrance ramp are used as characteristic parameters of the correlation matrix. The specific formula is as follows:
w (i, j) represents the weight of the jth ramp for the ith bottleneck section. d (i, j) represents the normalized distance between the jth ramp and the ith bottleneck segment. q (i) represents the normalized flow for the ith on-ramp. Mu represents the correlation limit, less thanThe value represents weak correlation between two time series, and if the value is larger than the weak correlation, the strong correlation is represented, and the value is generally a middle value, which is 0.5 in the formula. Alpha is alpha1、α2、α3、β1、β2、β3Both represent the specific gravity of the time sequence correlation coefficient, and under the weak correlation condition, the cross correlation coefficient has small influence on the speed, and under the strong correlation condition, the influence is large.
By comparing the relationship between the downstream bottleneck velocity curve and the cross-correlation coefficient, the flow value of the entrance ramp and the distance, the correlation GRC (i, j) based on the grey correlation analysis can be obtained.
Wherein GRC (i, j) represents a set of correlations between the ith attribute and the ramp j, and GRC (k, j) represents a correlation between the kth attribute and the ramp j.
The final correlation matrix W (i, j) is as follows. The incidence matrix is used for subsequent coordination control, and the distribution of excess flow is weighted, so that the reasonability of reducing the flow of each ramp is ensured.
Wherein m is the number of bottlenecks, n is the number of ramps, W (i, j) is the weight between the ith bottleneck and the jth ramp, and W (i, j) represents the incidence matrix of the whole ramp section.
3. The application of the correlation matrix is mainly embodied in the calculation of the coordination rate, as shown in fig. 3. The algorithm firstly judges whether the road section is a bottleneck road section according to the real-time density rho (i, k) of the downstream road section of the ramp.
ρ(i,k)＞ρthreshold(i)
Where ρ (i, k) is the real-time density at time k of the ith downstream segment, ρthreshold(i) Is the density threshold of the ith road segment.
When no bottleneck is detected in the multi-ramp control areaIf a road section exists, then the local regulation rate r is adopted for each entrance rampL(j, k +1) is regulated.
Wherein r isL(j, K +1) is the local regulation rate at the moment K +1, K is a regulation parameter,
and p (k) is the average density of the downstream road section at the moment k.
When a bottleneck road section is generated in the road section, the generated excess flow of each bottleneck needs to be distributed, and the coordination regulation rate r of each ramp is obtainedC(j,k+1)。
Wherein r isC(j, k +1) is the ramp coordination regulation rate at the moment of k +1, rC(j, k) is the coordinated regulation rate at the last moment in time, qin(i, k) denotes the mainline upstream inflow, qon(i, k) represents the incoming flow on the on-ramp, qout(i, k) represents the dominant downstream outflow, qoff(i, k) represents the flow rate of the exit ramp exit. W (i, j) represents a correlation matrix.
Local regulation rate r through rampL(j, k +1) and the coordinated regulation rate rC(j, k +1) the system regulation rate r can be obtainedS(j,k+1)。
rS(j,k+1)＝min[rL(j,k+1),rC(j,k+1)]
Wherein r isS(j, k +1) represents the system regulation rate of the jth ramp at time k + 1, rL(j, k +1) represents the local regulation rate of the jth ramp at time k + 1, rCAnd (j, k +1) represents the coordinated regulation rate of the jth ramp at the time k + 1.
If the adjustment rate of the ramp is too low, the queuing length of the ramp exceeds the ramp capacity, and turns are causedThe road overflow affects the traffic on the ground, so that the adjustment rate needs to be restricted according to the queuing length of the ramp and the ramp adjustment rate r of the queuing restriction in addition to the consideration of the downstream of the ramp and the bottleneck road sectionQThe formula for the calculation (j, k +1) is as follows:
wherein r isQ(j, k +1) represents the ramp dispatching rate of the queuing constraint at the time k + 1, a (j, k) represents the arrival rate of vehicles entering the entrance ramp at the time k,
represents the capacity of the jth ramp, ω (j, k) is the queue length of the jth ingress ramp at time k, and T refers to a time step.
Get the system regulation rate rSRamp regulation rate r of (j, k +1) and queuing constraintQThe larger value of (j, k +1) results in the final ramp regulation rate r (j, k +1), as follows.
r(j,k+1)＝max[rS(j,k+1),rQ(j,k+1)]
Wherein r (j, k +1) represents the ramp regulation rate of the jth ramp at the moment k +1, rQ(j, k +1) represents the ramp regulation rate of the queuing constraint for the jth ramp at time k + 1, rS(j, k +1) represents the system turn rate for the j-th ramp at time k + 1.
And calculating the green light time length g (j, k +1) according to the regulation rate, and sending the green light time length g (j, k +1) to each signaler.
Wherein g (j, k +1) represents the green time length of the jth ramp at the time k + 1. r (j, k +1) represents the ramp regulation rate of the jth ramp at the time of k +1, C (j) represents the signal period duration, rs(j) Indicating the saturation flow rate.
Through the design of the method, a multi-turn road coordination control method is finally established, and the signal green light time length of the multi-turn road is adjusted by combining with the dynamic traffic flow.
While the invention has been described in connection with specific embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (4)
1. A multi-turn road coordination control method based on dynamic traffic flow is characterized by comprising the following steps:
(1) screening and counting original traffic microwave data, and screening to obtain flow data of an express way main line and flow data of a ramp entrance with a period of several minutes;
(2) preprocessing the flow data obtained in the step (1);
(3) determining trend similarity of each ramp on the upstream and the main line flow by using a cross-correlation method, performing grey correlation analysis on the similarity, the ramp flow, the spatial distance and the downstream speed, and determining the ratio of each attribute to obtain a correlation matrix between the ramp and the main line section;
(4) and establishing a multi-ramp coordination control method by utilizing a heuristic control strategy, and obtaining the optimal control effect by applying the optimized incidence matrix.
2. The multi-turn road coordination control method based on the dynamic traffic flow according to claim 1, characterized in that: the preprocessing in the step (2) comprises the steps of filling up missing data, processing error data and normalizing data; in order to improve the utilization rate of data, missing data is processed by utilizing the time correlation of traffic flow, an average value method is adopted to repair the missing data, and a repair formula is as follows:
where x (t) is missing data to be completed, and k is the total number of adjacent data.
3. The multi-turn road coordination control method based on the dynamic traffic flow according to claim 1, characterized in that: in the step (3), a cross-correlation algorithm is adopted to measure the correlation between the ramp flow and the main line downstream flow, and the connection between the main line section and each upstream ramp is searched; for time series X ═ X1,x2,…,xn) And Y ═ Y (Y)1,y2,…,yn) The cross-correlation method holds Y stationary, slides X along Y, and calculates their inner product for each slide s of X as shown in the following equation:
X＝(x1,x2,…,xn)
wherein X and Y represent two different time series, X and Y represent sequence values, s is all possible sliding values, and n is the sequence number of the time series;
the inner product CC (X, Y) is calculated as the similarity between the two time series X and Y, as shown in the following equation:
wherein CC (X, Y) is a calculated inner product value, i is a time series index value, s is a sliding value, n is a sequence number of a time series, and X and Y represent sequence values;
wherein the normalized cross-correlation coefficient NCC (X, Y) is used, the range is controlled to [ -1,1], wherein 1 represents that the two have strong correlation, and-1 represents that the two are completely opposite, and positive NCC (X, Y) represents that the two are in the same direction, and negative NCC (X, Y) represents that when one of the sequences tends to increase, the other tends to decrease, and vice versa; NCC (X, Y) is defined by the following formula:
wherein X and Y represent two different time series, CC (X, Y) is the calculated inner product value, and s is the sliding value;
taking the cross-correlation coefficient NCC (X, Y), the normalized physical distance d between the ramp and the bottleneck and the flow predicted value q of the entrance ramp as the characteristic parameters of the incidence matrix; if the cross-correlation coefficient NCC (X, Y) is less than 0, the fact that the flow time sequence of the ramp and the congested road section is in negative correlation is shown, so that the influence of dynamic traffic flow is not considered, and the normalized physical distance d between the ramp and a bottleneck and the flow predicted value q of the entrance ramp are used as characteristic parameters of the correlation matrix; the specific formula is as follows:
w (i, j) represents the weight of the jth ramp to the ith bottleneck road section; d (i, j) represents the normalized distance between the jth ramp and the ith bottleneck section; q (i) represents the normalized flow for the ith on-ramp; mu represents a correlation limit, a value smaller than the correlation limit represents weak correlation between two time sequences, a value larger than the correlation limit represents strong correlation, and the value is generally an intermediate value, which is 0.5 in the formula; alpha is alpha1、α2、α3、β1、β2、β3The time sequence correlation coefficient proportion is represented, under the weak correlation condition, the cross correlation coefficient has small influence on the speed, and under the strong correlation condition, the influence is large;
and comparing the relationship between the downstream bottleneck speed curve and the cross-correlation coefficient, the flow value of the entrance ramp and the distance to obtain a correlation GRC (i, j) based on grey correlation analysis:
wherein GRC (i, j) represents a set of correlations between the ith attribute and the ramp j, and GRC (k, j) represents a correlation between the kth attribute and the ramp j; the final correlation matrix W (i, j) is as follows:
wherein m is the number of bottlenecks, n is the number of ramps, W (i, j) is the weight between the ith bottleneck and the jth ramp, and W (i, j) represents the incidence matrix of the whole ramp section.
4. The multi-turn road coordination control method based on the dynamic traffic flow according to claim 1, characterized in that: the step (4) is specifically as follows:
(i) judging whether the road section is a bottleneck road section according to the real-time density rho (i, k) of the downstream road section of the ramp:
ρ(i,k)＞ρthreshold(i)
where ρ (i, k) is the real-time density at time k of the ith downstream segment, ρthreshold(i) A density threshold value of the ith road section;
(ii) if the bottleneck-free section in the multi-ramp control area is detected to exist, the local regulation rate r is adopted for each entrance rampL(j, k +1) regulating:
wherein r isL(j, K +1) is the local regulation rate at the moment K +1, K is a regulation parameter,
the critical density value of the downstream road section of the ramp is rho (k), and the average density of the downstream road section at the moment k is rho (k);
if a bottleneck road section is generated in the road section, the generated excess flow of each bottleneck needs to be distributed to obtain the coordination regulation rate r of each rampC(j,k+1)；
Wherein r isC(j, k +1) is the ramp coordination regulation rate at the moment of k +1, rC(j, k) is the coordinated regulation rate at time k, qin(i, k) denotes the mainline upstream inflow, qon(i, k) represents the incoming flow on the on-ramp, qout(i, k) represents the main line downstream outflow at time k, qoff(i, k) represents the flow rate of the exit ramp at the moment k; w (i, j) represents a correlation matrix;
(iii) local regulation rate r through rampL(j, k +1) and the coordinated regulation rate rC(j, k +1) obtaining a system regulation rate rS(j,k+1)：
rS(j,k+1)＝min[rL(j,k+1),rC(j,k+1)]
Wherein r isS(j, k +1) represents the system regulation rate of the jth ramp at time k +1, rL(j, k +1) represents the local regulation rate of the jth ramp at time k +1, rC(j, k +1) represents the coordinated regulation rate of the jth ramp at the moment k + 1;
(iv) according to the queuing length of the ramp, the regulation rate is restrained, and the ramp regulation rate r of the queuing restraint isQThe formula for the calculation (j, k +1) is as follows:
wherein r isQ(j, k +1) represents the ramp dispatching rate of the queuing constraint at the time k +1, a (j, k) represents the arrival rate of vehicles entering the entrance ramp at the time k,
representing the capacity of the jth ramp, wherein omega (j, k) is the queuing length of the jth entrance ramp at the moment k, and T refers to a time step;
(v) get the system regulation rate rSRamp regulation rate r of (j, k +1) and queuing constraintQThe larger value of (j, k +1) results in the final ramp regulation rate r (j, k +1), as follows:
r(j,k+1)＝max[rS(j,k+1),rQ(j,k+1)]
wherein r (j, k +1) represents the ramp regulation rate of the jth ramp at the moment k +1, rQ(j, k +1) represents the ramp regulation rate of the queuing constraint for the jth ramp at time k +1, rS(j, k +1) represents the system regulation rate of the jth ramp at the time k + 1;
(vi) calculating the green light time length g (j, k +1) according to the regulation rate, and sending the green light time length g (j, k +1) to each signaler;
wherein g (j, k +1) represents the green light duration of the jth ramp at the moment k + 1; r (j, k +1) represents the ramp regulation rate of the jth ramp at the time of k +1, C (j) represents the signal period duration, rs(j) Represents the saturation flow rate;
and finally, adjusting the signal green light time of the multi-ramp by combining the dynamic traffic flow.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113205676A (en) * | 2021-03-30 | 2021-08-03 | 浙江高速信息工程技术有限公司 | Modeling method and parameter calibration method based on cellular transmission model |
| CN114037587A (en) * | 2021-10-19 | 2022-02-11 | 东南大学 | City commuting main channel multi-ramp network connection control method based on heterogeneous cause and effect inference |
| CN114299714A (en) * | 2021-12-07 | 2022-04-08 | 东南大学 | Multi-turn-channel coordination control method based on different strategy reinforcement learning |
| CN114842642A (en) * | 2022-04-01 | 2022-08-02 | 山东高速股份有限公司 | Combined type intercommunication main line advanced lane changing and inlet multi-turn lane linkage control system and method |
| CN115376341A (en) * | 2022-08-10 | 2022-11-22 | 重庆市城投金卡信息产业(集团)股份有限公司 | Ramp traffic signal lamp self-adaptive control method based on RFID |
| CN115497300A (en) * | 2022-11-16 | 2022-12-20 | 四川省公路规划勘察设计研究院有限公司 | Vehicle safety shunting scheduling method and device based on vehicle-road cooperative system |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101299298A (en) * | 2008-06-30 | 2008-11-05 | 上海电科智能系统股份有限公司 | Road self-adapting entrance ramp afflux control equipment and method |
| US20170191834A1 (en) * | 2016-01-05 | 2017-07-06 | Here Global B.V. | Road Segments with Multi-Modal Traffic Patterns |
| CN107507421A (en) * | 2017-08-22 | 2017-12-22 | 重庆交通大学 | Method for rapidly judging traffic state and device |
| CN108109398A (en) * | 2017-12-28 | 2018-06-01 | 山东易构软件技术股份有限公司 | A kind of overhead expressway Coordinated Ramp Control System and control method |
| CN108922174A (en) * | 2018-06-20 | 2018-11-30 | 同济大学 | A kind of path dynamic classification method in intersection group around through street Entrance ramp |
| CN108922183A (en) * | 2018-07-17 | 2018-11-30 | 重庆大学 | A kind of highway short term traffic forecasting method of space time correlation degree in conjunction with SVR |
| US20190310648A1 (en) * | 2018-04-09 | 2019-10-10 | Toyota Jidosha Kabushiki Kaisha | Machine Learning Enhanced Vehicle Merging |
| CN111540198A (en) * | 2020-04-17 | 2020-08-14 | 浙江工业大学 | Urban traffic situation recognition method based on directed graph convolution neural network |
| CN111640297A (en) * | 2020-05-09 | 2020-09-08 | 天津市市政工程设计研究院 | Multi-turn-lane cooperative control and driving assisting method under cooperative vehicle and road environment |
-
2021
- 2021-01-25 CN CN202110095102.6A patent/CN112885088B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101299298A (en) * | 2008-06-30 | 2008-11-05 | 上海电科智能系统股份有限公司 | Road self-adapting entrance ramp afflux control equipment and method |
| US20170191834A1 (en) * | 2016-01-05 | 2017-07-06 | Here Global B.V. | Road Segments with Multi-Modal Traffic Patterns |
| CN107507421A (en) * | 2017-08-22 | 2017-12-22 | 重庆交通大学 | Method for rapidly judging traffic state and device |
| CN108109398A (en) * | 2017-12-28 | 2018-06-01 | 山东易构软件技术股份有限公司 | A kind of overhead expressway Coordinated Ramp Control System and control method |
| US20190310648A1 (en) * | 2018-04-09 | 2019-10-10 | Toyota Jidosha Kabushiki Kaisha | Machine Learning Enhanced Vehicle Merging |
| CN108922174A (en) * | 2018-06-20 | 2018-11-30 | 同济大学 | A kind of path dynamic classification method in intersection group around through street Entrance ramp |
| CN108922183A (en) * | 2018-07-17 | 2018-11-30 | 重庆大学 | A kind of highway short term traffic forecasting method of space time correlation degree in conjunction with SVR |
| CN111540198A (en) * | 2020-04-17 | 2020-08-14 | 浙江工业大学 | Urban traffic situation recognition method based on directed graph convolution neural network |
| CN111640297A (en) * | 2020-05-09 | 2020-09-08 | 天津市市政工程设计研究院 | Multi-turn-lane cooperative control and driving assisting method under cooperative vehicle and road environment |
Non-Patent Citations (2)
| Title |
|---|
| 朱炯: "基于蚁群算法的城市快速路优化控制", 《计算机工程》 * |
| 汪正勇: "高速公路合流区上下游交通流量特性分析及预测研究", 《万方学位论文》 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113205676A (en) * | 2021-03-30 | 2021-08-03 | 浙江高速信息工程技术有限公司 | Modeling method and parameter calibration method based on cellular transmission model |
| CN113205676B (en) * | 2021-03-30 | 2022-03-11 | 浙江高速信息工程技术有限公司 | Modeling method and parameter calibration method based on cellular transmission model |
| CN114037587A (en) * | 2021-10-19 | 2022-02-11 | 东南大学 | City commuting main channel multi-ramp network connection control method based on heterogeneous cause and effect inference |
| CN114037587B (en) * | 2021-10-19 | 2024-04-16 | 东南大学 | Urban commute main channel multi-ramp network connection control method based on heterogeneous causal inference |
| CN114299714A (en) * | 2021-12-07 | 2022-04-08 | 东南大学 | Multi-turn-channel coordination control method based on different strategy reinforcement learning |
| CN114299714B (en) * | 2021-12-07 | 2022-12-27 | 东南大学 | Multi-turn-channel coordination control method based on different strategy reinforcement learning |
| CN114842642A (en) * | 2022-04-01 | 2022-08-02 | 山东高速股份有限公司 | Combined type intercommunication main line advanced lane changing and inlet multi-turn lane linkage control system and method |
| CN115376341A (en) * | 2022-08-10 | 2022-11-22 | 重庆市城投金卡信息产业(集团)股份有限公司 | Ramp traffic signal lamp self-adaptive control method based on RFID |
| CN115497300A (en) * | 2022-11-16 | 2022-12-20 | 四川省公路规划勘察设计研究院有限公司 | Vehicle safety shunting scheduling method and device based on vehicle-road cooperative system |
| CN115497300B (en) * | 2022-11-16 | 2023-01-31 | 四川省公路规划勘察设计研究院有限公司 | Vehicle safety shunting scheduling method and device based on vehicle-road cooperative system |
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