CN114049759A - Intersection left-forbidden evaluation method based on macroscopic basic graph - Google Patents
Intersection left-forbidden evaluation method based on macroscopic basic graph Download PDFInfo
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Abstract
The invention relates to a macroscopic basic diagram-based intersection left-forbidden evaluation method, and belongs to the technical field of traffic management control. The invention is based on the theory of macroscopic basic graph, establishes an evaluation method for the organization optimization of the left-turn forbidden traffic of a plurality of intersections and even regional intersections, makes up the defect that the traditional method only evaluates the left-turn forbidden measure of a single intersection, firstly, the method analyzes the flow direction transition of the left-turn forbidden traffic, and constructs the macroscopic basic graph model under the left-turn forbidden traffic organization based on the classical macroscopic basic graph model; and then, obtaining macroscopic basic graph model parameters under the traffic organization of the left forbidden party by carrying out simulation, and carrying out scientific evaluation on different left forbidden schemes. The method can effectively evaluate the left forbidden cases in different areas and different traffic flows, and provides effective theoretical support and scientific basis for scientifically formulating left forbidden control measures of the intersection.
Description
Technical Field
The invention belongs to the technical field of traffic management control, and particularly relates to a left-forbidden intersection evaluation method based on a macroscopic basic diagram.
Background
As vehicle occupancy continues to increase, intersection oversaturated traffic conditions have gradually become normalized, and for large cities, oversaturated traffic conditions have evolved from intersection oversaturation to regional oversaturation. In the face of such a huge traffic pressure, many cities usually relieve the traffic pressure by perfecting traffic measures such as road canalization and optimizing signal timing. The method for forbidding the left at the intersection is a traffic organization mode which can effectively relieve traffic pressure, at present, most researches only explain the advantages and disadvantages of a single intersection left forbidding measure, and a detailed index is not systematically given to quantitatively determine whether to forbid the left at the intersection; in addition, in daily traffic management, a manager cannot make effective and reasonable left-prohibiting measures due to lack of the index, so that traffic confusion is caused, and a detailed index is not provided in past researches to guide linkage of a plurality of intersections or regional left prohibition. In recent years, a macroscopic basic diagram becomes an important means for overall evaluation of traffic running states from a macroscopic level, and the universal left-forbidden evaluation method is established based on the characteristic of the macroscopic basic diagram, so that left-forbidden schemes in different regions and different traffic flows are effectively evaluated, and effective theoretical support and scientific basis are provided for scientific formulation of traffic control measures.
Disclosure of Invention
The invention aims to provide a cross entrance guard left evaluation method based on a macroscopic basic diagram, which aims to solve the existing problems that: the existing research only explains the advantages and disadvantages of a single intersection left forbidding measure, and does not systematically provide a detailed index to quantitatively determine whether to forbid the left of the intersection; in normal traffic management, a manager cannot make effective and reasonable left-hand prohibition measures due to lack of the index, so that traffic confusion is caused.
In order to solve the technical problems, the invention is realized by the following technical scheme: a cross gate inhibition left evaluation method based on a macroscopic basic graph comprises the following steps:
collecting traffic flow of each intersection entrance road, analyzing transfer modes of the traffic flow after the left prohibition and transfer proportions of the modes to obtain the traffic flow of each intersection entrance road after the left prohibition, and establishing an expression of road network weighted flow and total traffic based on a macroscopic basic graph;
performing mode transformation on the expression of the road network weighted traffic and the total traffic based on the macroscopic basic graph to obtain a basic model based on the macroscopic basic graph;
based on the basic model based on the macro basic diagram, combining with the analysis of the flow direction of the left-forbidden traffic, establishing a macro basic diagram model under the left-forbidden traffic organization;
simulating the intersection forbidden left, and obtaining macroscopic basic graph parameters under the intersection forbidden left through data analysis;
and (4) constructing a macroscopic basic diagram left-forbidden model under different left-turning traffic flow proportions, and evaluating left-forbidden schemes in different areas and different traffic flows.
Further: the expression of the road network weighted traffic and the total traffic based on the macroscopic basic graph is as follows:
wherein the content of the first and second substances,qwrepresenting the weighted flow of the road network; q represents the total traffic volume of the road network; k is a radical ofiRepresenting the density of the road section i; q. q.siRepresenting the flow of the section i; liIndicating the length of the link i.
Further: the method comprises the following steps of performing mode transformation on an expression of the road network weighted traffic and the total traffic based on the macroscopic basic graph to obtain a basic model based on the macroscopic basic graph, and mainly comprises the following steps:
according to the reason for forbidding left, the weighting mode in the expression of the road network weighted flow and the total traffic based on the macroscopic basic graph is adjusted, the original road length weighting is changed into the weighting by multiplying the road length by the number of the lanes, and the adjusted expression is as follows:
wherein q iswRepresenting the weighted flow of the road network; q represents the total traffic volume of the road network; k is a radical ofiRepresenting the density of the road section i; q. q.siRepresenting the flow of the section i; liRepresents the length of the section i; n isiRepresenting the number of lanes;
obtaining different influences of different road sections of different road networks on the whole weighted flow through simulation, drawing a macroscopic basic graph of a single road and a macroscopic basic graph of the whole road network according to the adjusted expressions, obtaining a correction coefficient through data comparison, wherein the expressions after the correction coefficient is added are as follows:
wherein q iswRepresentation road networkWeighting the flow; q represents the total traffic volume of the road network; k is a radical ofiRepresenting the density of the road section i; q. q.siRepresenting the flow of the section i; liRepresents the length of the section i; n isiRepresenting the number of lanes; alpha is alphaiIs a correction factor.
Further: based on the basic model based on the macro basic diagram, combining with the analysis of the flow direction of the left-forbidden traffic, establishing a macro basic diagram model under the left-forbidden traffic organization, which mainly comprises the following steps:
adopting flow weighting to establish a macroscopic basic graph model under a single intersection forbidden left, wherein the calculation model is as follows:
wherein q iswRepresenting the weighted flow of the road network; q represents the total traffic volume of the road network; k is a radical ofiRepresenting the density of the road section i; n isiThe number of lanes of the road section i; q. q.sijRepresenting the flow of i road section j lane;
analyzing traffic organization modes about left forbidding in a road network according to different intersection grades;
establishing a regional left-forbidden macro basic graph model:
wherein q iswCalculating the weighted flow of the road network for the whole road network; a and b are respectively
And
the weighted value of (1);
representing the calculated values of the parameters of the classical macroscopic fundamental diagram;
and (3) representing the calculated macroscopic basic diagram parameters of the single intersection.
Further: the intersection grade comprises:
grade 2, connecting the main road with the secondary road;
grade 3, connecting the secondary main road of the bidirectional four lanes with the intersection of the secondary main road;
grade 4, connecting the secondary main road and the branch road;
grade 5, connecting the branches of the two-way lanes with the intersections of the branches;
grade 6, the intersection connecting the main road and the branch road.
Further: wherein, carry out the simulation to the intersection forbidden left side, obtain the macroscopic basic diagram parameter under the intersection forbidden left side through data analysis, mainly include:
simulating a single intersection forbidden left to obtain traffic flow, obtaining macroscopic basic graph parameters under the intersection forbidden left through data analysis, constructing a macroscopic basic graph to obtain a maximum weighted flow point, judging the weighted flow after the left is forbidden to be increased compared with the weighted flow before the left is forbidden through an expression of the road network weighted flow and the total traffic flow based on the macroscopic basic graph, and obtaining a unitary quadratic regression equation after the data is fitted by a least square method:
y＝-0.0076x2+8.7113x
wherein y is a weighted flow; x is the vehicle cumulative amount;
simulating the whole trunk line forbidden left to obtain trunk line traffic flow, obtaining macro basic graph parameters under the whole trunk line forbidden left through data analysis, constructing a macro basic graph to obtain a maximum weighted flow point, judging the weighted flow after the forbidden left to be compared with the weighted flow before the forbidden left to be increased through an expression of the weighted flow and the total traffic flow of the road network based on the macro basic graph, and obtaining a unitary quadratic regression equation after the data are fitted by a least square method:
y＝-0.007x2+7.9738x
wherein y is a weighted flow; x is the vehicle cumulative amount;
carrying out simulation on the left forbidden of the whole region to obtain the traffic flow of the region, obtaining parameters of a macroscopic basic diagram of the left forbidden of the whole region through data analysis, constructing the macroscopic basic diagram to obtain a maximum weighted flow point, judging a weighted flow reduction value after the left forbidden compared with the weighted flow before the left forbidden, and obtaining a unitary quadratic regression equation after the data is fitted by a least square method:
y＝-0.0092x2+8.4786x
wherein y is a weighted flow; and x is the vehicle cumulative amount.
Further: the method comprises the following steps of constructing a macroscopic basic diagram left-forbidden model under different left-turn traffic flow proportions, and evaluating left-forbidden schemes under different regions and different traffic flows, wherein the macroscopic basic diagram left-forbidden model mainly comprises the following steps:
when the proportion of the left-turn traffic flow to the total traffic flow is 10%, the relationship between the weighted traffic flow and the vehicle cumulative amount for a single main road is as follows: y is-0.0076 x2+8.7113 x; the relationship between the weighted flow and the cumulative vehicle amount for the primary and secondary thoroughfares is: y is-0.007 x2+7.9738x, evaluated as: the single main road, the main road and the secondary main road can be left prohibited, and the branch road is left prohibited to influence traffic;
when the proportion of the left-turn traffic to the total traffic is 15%, the relationship between the weighted traffic and the vehicle cumulative amount for a single main road is as follows: y is-0.0079 x2+8.9727 x; the relationship between the weighted flow and the cumulative vehicle amount for the primary and secondary thoroughfares is: y is-0.0073 x2+8.2927x, evaluated as: the single main road, the main road and the secondary main road can be left prohibited, and the branch road is left prohibited to influence traffic;
when the proportion of the left-turn traffic to the total traffic is 20%, the relationship between the weighted traffic and the vehicle cumulative amount for a single main road is as follows: y is-0.0075 x2+8.524 x; for main and secondary main ductsThe relationship between the weighted flow and the cumulative amount of the vehicle is: y-0.0065 x2+7.4634x, evaluated as: the single main road is forbidden to left, so that the traffic pressure is relieved, the main road and the secondary main road can be forbidden to left, but the influence is smaller compared with the single main road, and the traffic is influenced by the branch forbidden to left;
when the proportion of the left-turn traffic to the total traffic is 25%, the relationship between the weighted traffic and the vehicle cumulative amount for a single main road is: y-0.0067 x2+7.6716 x; for the main road and the secondary road, a traffic deadlock state occurs, a relational expression cannot be fitted through data, and the evaluation is as follows: the capacity of relieving traffic pressure by forbidding left of a single main road is small, and the traffic is blocked by forbidding left of the main road and the secondary main road and forbidding left of a branch road.
The invention has the following beneficial effects:
(1) the invention establishes an evaluation method for the organization optimization of the traffic prohibiting left-turn at a plurality of intersections and even regional intersections based on the macroscopic basic diagram theory, makes up the defect that the traditional method only evaluates the left prohibiting measures at a single intersection, and provides effective theoretical support and scientific basis for scientifically formulating the left prohibiting traffic control measures.
(2) The invention utilizes the macroscopic basic diagram to evaluate traffic measures for forbidding left traffic in traffic environments in different areas and under different traffic flow conditions, and has stronger universality and operability.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a diagram of a left-hand-disabled bypass mode of the present invention;
FIG. 3 is a regional simulation modeling diagram of the present invention;
FIG. 4 is an original macroscopic basic diagram of the present invention;
FIG. 5 is a macro basic diagram of the invention after single-point left-out;
FIG. 6 is a macro basic diagram of the invention after trunk left-hand deprivation;
fig. 7 is a macro basic diagram after the invention is fully left disabled.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention discloses a method for evaluating left forbidden intersection based on a macro basic diagram, which specifically includes the following steps:
step 1: collecting traffic flow of each intersection entrance road, analyzing transfer modes of the traffic flow after the left prohibition and transfer proportions of the modes to obtain the traffic flow of each intersection entrance road after the left prohibition, and establishing an expression of road network weighted flow and total traffic based on a macroscopic basic graph;
in this step, the traffic flow diversion mode after left forbidding in this step 1 may be set, and as shown in fig. 2, an expression of the road network weighted flow and the total traffic flow based on the macro basic graph is established as follows:
establishing weighted flow (q) of road network according to traffic flow of each intersection entrance road after left-forbiddenw) And the relation with the total traffic (Q) of the road network. According to the theory of the macroscopic basic graph model, the calculation formula of the road network related parameters is as follows:
wherein k isiRepresenting the density of the road section i; q. q.siRepresenting the flow of the section i; liIndicating the length of the link i.
Step 2: performing mode transformation on the expression of the road network weighted traffic and the total traffic based on the macroscopic basic graph to obtain a basic model based on the macroscopic basic graph;
specifically, the method comprises the following two steps:
and 2.1, forbidding left to cause the original left-turning vehicle to become a straight-going vehicle or a right-turning detour vehicle, changing the weighting mode in the original calculation formula from the original road length weighting to the weighting by multiplying the road section length by the number of lanes, and thus, considering that the left-turning vehicle is changed into the straight-going or right-turning vehicle flow. The correlation calculation formula becomes as follows:
wherein k isiRepresenting the density of the road section i; q. q.siRepresenting the flow of the section i; liRepresents the length of the section i; n isiIndicating the number of lanes.
And 2.2, correcting the correction coefficients of different road sections in the same road network by the road network grade, and obtaining that different road sections of different road networks have different influences on the overall weighted flow through simulation. The Beijing road block area in the text has three road grades, a macroscopic basic graph of a single road and a macroscopic basic graph of the whole road network are drawn through the models, a correction coefficient is obtained through data comparison, a main road is 1.15, a secondary road is 1.05, a branch road is 0.8, and the concrete models are as follows:
wherein k isiRepresenting the density of the road section i; q. q.siRepresenting the flow of the i road section; liRepresents the length of the section i; n isiIndicating the number of lanes.
And step 3: establishing a macroscopic basic graph model under a left-forbidden traffic organization based on the macroscopic basic graph basic model and in combination with left-forbidden traffic flow direction analysis;
specifically, the method comprises two steps as follows:
step 3.1: the method comprises the following steps of establishing a macroscopic basic graph model under the condition that a single intersection is forbidden to be left, wherein the optimal left forbidding condition of the single intersection needs to be obtained, but the lengths of the road sections coming from four directions of the single intersection are negligible for the whole road network, so that the above-mentioned macroscopic basic graph model of the road network is not suitable for calculating parameters of the macroscopic basic graph of the single intersection, and the flow weighting is adopted at the moment, and the calculation model is as follows:
wherein: n isiThe number of lanes of the road section i; q. q.sijIndicating the flow of i road j lanes.
Traffic organization modes related to left forbidding in a road network can be specifically analyzed according to different intersection grades, wherein the specific intersection grades are as follows:
(1) an intersection connecting the main road (six or more bidirectional lanes) with the main road;
(2) an intersection connecting the main road and the secondary road;
(3) an intersection connecting the secondary main road (bidirectional four-lane) and the secondary main road;
(4) an intersection connecting the secondary trunk road and the branch road;
(5) an intersection connecting a branch (two-way two-lane) and a branch;
(6) and the intersection connecting the main road and the branch road.
The optimal number of left-forbidden vehicles at different levels of single intersections is different, the linkage traffic volume of linkage left-forbidden vehicles at different intersections is also different, and a macroscopic basic diagram is required to perform specific analysis on specific road sections under specific conditions.
The optimal number of left-forbidden vehicles at different levels of single intersections is different, the linkage traffic volume of linkage left-forbidden vehicles at different intersections is also different, and a macroscopic basic diagram is required to perform specific analysis on specific road sections under specific conditions.
Step 3.2: establishing a regional left-forbidden macro basic graph model:
wherein: q. q.swCalculating the weighted flow of the road network for the whole road network; a and b are respectively
And
the weighted value of (1);
representing the calculated values of the parameters of the classical macroscopic fundamental diagram;
and (3) representing the calculated macroscopic basic diagram parameters of the single intersection. In the formula, the classical macroscopic basic graph parameter calculation model and the intersection macroscopic basic graph parameter calculation model are weighted, the traffic flow in the whole road network is divided into the road traffic flow and the intersection traffic flow, the macroscopic basic graph parameter calculation model of the whole road network is obtained comprehensively, and weighted values a and b respectively correspond to the road weighted flow and the intersection weighted flow. Due to different weighting modes of macroscopic basic graph models of intersections and road sections, the obtained basic graph model is obtainedThe macroscopic basic diagram parameters are also different, and the macroscopic basic diagram parameters are averaged
Will be provided with
And
and comparing to obtain respective weighted values a and b. The calculation formula is as follows:
and 4, step 4: simulating the intersection forbidden left, and obtaining macroscopic basic graph parameters under the intersection forbidden left through data analysis;
here, the present step includes three steps, as follows:
step 4.1: firstly, simulating a single intersection forbidden left to obtain traffic flow, obtaining macroscopic basic graph parameters under the intersection forbidden left through data analysis, constructing a macroscopic basic graph, and obtaining a maximum weighted flow point (714, 2827), namely when the vehicle cumulative volume reaches 714 vehicles, the road network is most smooth at the moment, and the weighted flow is 2827 veh/h. The expression of the weighted traffic flow and the total traffic flow of the road network is obtained through the step 1, the weighted traffic flow after the Beijing road is forbidden to be left is judged to be increased by 12.6 percent compared with the weighted traffic flow before the road is not forbidden to be left, and the road traffic service capability is improved. And (3) obtaining a unitary quadratic regression equation after least square fitting of the data:
y＝-0.0076x2+8.7113x
step 4.2: firstly, simulating the left forbidding of the whole trunk line to obtain the trunk traffic flow, obtaining the parameters of a macroscopic basic graph under the left forbidding of the whole trunk line through data analysis, constructing the macroscopic basic graph, and obtaining the maximum weighted flow point (660, 2610), namely when the vehicle accumulation reaches 660 vehicles, the road network is the most unobstructed at the moment, and the weighted flow is 2610 veh/h. The expression of the weighted traffic flow of the road network and the total traffic flow is obtained through the step 1, the weighted traffic flow after the Beijing road is forbidden to be left is judged to be increased by 3.7 percent compared with the weighted traffic flow before the road is not forbidden to be left, and the regional traffic service capability is improved. And (3) obtaining a unitary quadratic regression equation after least square fitting of the data:
y＝-0.007x2+7.9738x
step 4.3: firstly, simulating to obtain the regional traffic flow for the left-forbidden whole region, obtaining the macroscopic basic graph parameters of the left-forbidden whole region through data analysis, constructing a macroscopic basic graph, and obtaining the maximum weighted flow point (455, 2288), namely when the vehicle cumulative volume reaches 455, the road network is most unobstructed at the moment, and the weighted flow is 2288 veh/h. Compared with the weighted flow before the left forbidding, the weighted flow after the left forbidding of the Beijing road is reduced by 9.1 percent, and the traffic service capability of the whole area is improved. And (3) obtaining a unitary quadratic regression equation after least square fitting of the data:
y＝-0.0092x2+8.4786x
and 5: and (4) constructing a macroscopic basic diagram left-forbidden model under different left-turning traffic flow proportions, and evaluating left-forbidden schemes in different areas and different traffic flows.
In one embodiment, the present invention selects the area surrounded by Beijing road, the northern road of city, and the east road of people in Kunming to perform simulation experiments, as shown in FIG. 3.
The analysis of the left-forbidden measure of the evaluation area through the macroscopic basic diagram shows that: when the left-turn traffic flow accounts for less than 15% of the total traffic flow, the traffic capacity of the road network can be effectively improved by forbidding the left of the single intersection, forbidding the left of the main road and forbidding the left of the secondary road; when the left-turn traffic flow accounts for 15% -20% of the total traffic flow, the traffic capacity of the road network can be effectively improved by forbidding the left of the single intersection and forbidding the left of the main road, and the effect of forbidding the left of the main road and the secondary road and improving the traffic capacity of the road network is reduced; when the left-turn traffic flow accounts for 20% -25% of the total traffic flow, the traffic capacity of a road network can be effectively improved by forbidding the left of the single intersection, the effect of relieving traffic pressure is reduced by forbidding the left of the main road, and the traffic can be blocked by forbidding the left of the main road and the secondary main road; when the left-turn traffic flow accounts for 25% -30% of the total traffic flow, the effect of the single intersection for forbidding the left and improving the traffic capacity of the road network is reduced, and traffic can be blocked when the main road is forbidden, and the main road and the secondary road are forbidden; when the left-turn traffic accounts for more than 30% of the total traffic, the left-turn prohibition of the intersection can block the traffic. The macroscopic basic diagrams under different left-forbidden traffic flows are respectively shown in fig. 4, fig. 5, fig. 6 and fig. 7.
In summary, the following can be found:
the invention establishes an evaluation method for the organization optimization of the left-turn forbidden traffic of a plurality of intersections and even regional intersections based on the macroscopic basic diagram theory, and makes up the defect that the traditional method only evaluates the left-turn forbidden measures of a single intersection. Firstly, analyzing the flow direction transition of traffic forbidden to left, and constructing a macroscopic basic graph model under traffic forbidden to left on the basis of a classical macroscopic basic graph model; and then, simulating a Beijing street region in Kunming city to obtain macroscopic basic graph model parameters under the traffic organization of the left forbidden party, and scientifically evaluating different left forbidden schemes. The method can effectively evaluate the left forbidden case in different areas and different traffic flows, and provides effective theoretical support and scientific basis for scientifically formulating traffic control measures.
In the description herein, references to the description of "one embodiment," "an example," "a specific example," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The above disclosure of the preferred embodiments of the invention is intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (7)
1. A cross entrance guard left evaluation method based on a macroscopic basic diagram is characterized in that: the method comprises the following steps:
collecting traffic flow of each intersection entrance road, analyzing transfer modes of the traffic flow after the left prohibition and transfer proportions of the modes to obtain the traffic flow of each intersection entrance road after the left prohibition, and establishing an expression of road network weighted flow and total traffic based on a macroscopic basic graph;
performing mode transformation on the expression of the road network weighted traffic and the total traffic based on the macroscopic basic graph to obtain a basic model based on the macroscopic basic graph;
based on the basic model based on the macro basic diagram, combining with the analysis of the flow direction of the left-forbidden traffic, establishing a macro basic diagram model under the left-forbidden traffic organization;
simulating the intersection forbidden left, and obtaining macroscopic basic graph parameters under the intersection forbidden left through data analysis;
and (4) constructing a macroscopic basic diagram left-forbidden model under different left-turning traffic flow proportions, and evaluating left-forbidden schemes in different areas and different traffic flows.
2. The intersection left-forbidden evaluation method based on the macroscopic basic graph as claimed in claim 1, wherein: the expression of the road network weighted traffic and the total traffic based on the macroscopic basic graph is as follows:
wherein q iswRepresenting the weighted flow of the road network; q represents the total traffic volume of the road network; k is a radical ofiRepresenting the density of the road section i; q. q.siRepresenting the flow of the section i; liIndicating the length of the link i.
3. The intersection left-forbidden evaluation method based on the macroscopic basic graph as claimed in claim 1, wherein: the method comprises the following steps of performing mode transformation on an expression of the road network weighted traffic and the total traffic based on the macroscopic basic graph to obtain a basic model based on the macroscopic basic graph, and mainly comprises the following steps:
according to the reason for forbidding left, the weighting mode in the expression of the road network weighted flow and the total traffic based on the macroscopic basic graph is adjusted, the original road length weighting is changed into the weighting by multiplying the road length by the number of the lanes, and the adjusted expression is as follows:
wherein q iswRepresenting the weighted flow of the road network; q represents the total traffic volume of the road network; k is a radical ofiRepresenting the density of the road section i; q. q.siRepresenting the flow of the section i; liRepresents the length of the section i; n isiRepresenting the number of lanes;
obtaining different influences of different road sections of different road networks on the whole weighted flow through simulation, drawing a macroscopic basic graph of a single road and a macroscopic basic graph of the whole road network according to the adjusted expressions, obtaining a correction coefficient through data comparison, wherein the expressions after the correction coefficient is added are as follows:
wherein q iswRepresenting the weighted flow of the road network; q represents the total traffic volume of the road network; k is a radical ofiRepresenting the density of the road section i; q. q.siRepresenting the flow of the section i; liRepresents the length of the section i; n isiRepresenting the number of lanes; alpha is alphaiIs a correction factor.
4. The intersection left-forbidden evaluation method based on the macroscopic basic graph as claimed in claim 1, wherein: based on the basic model based on the macro basic diagram, combining with the analysis of the flow direction of the left-forbidden traffic, establishing a macro basic diagram model under the left-forbidden traffic organization, which mainly comprises the following steps:
adopting flow weighting to establish a macroscopic basic graph model under a single intersection forbidden left, wherein the calculation model is as follows:
wherein q iswRepresenting the weighted flow of the road network; q represents the total traffic volume of the road network; k is a radical ofiRepresenting the density of the road section i; n isiThe number of lanes of the road section i; q. q.sijRepresenting the flow of i road section j lane;
analyzing traffic organization modes about left forbidding in a road network according to different intersection grades;
establishing a regional left-forbidden macro basic graph model:
wherein q iswCalculating the weighted flow of the road network for the whole road network; a and b are respectively
And
the weighted value of (1);
representing the calculated values of the parameters of the classical macroscopic fundamental diagram;
and (3) representing the calculated macroscopic basic diagram parameters of the single intersection.
5. The intersection left-forbidden evaluation method based on the macroscopic basic graph as claimed in claim 4, wherein: the intersection grade comprises:
grade 1, connecting the main road with six or more bidirectional lanes and the intersection of the main road;
grade 2, connecting the main road with the secondary road;
grade 3, connecting the secondary main road of the bidirectional four lanes with the intersection of the secondary main road;
grade 4, connecting the secondary main road and the branch road;
grade 5, connecting the branches of the two-way lanes with the intersections of the branches;
grade 6, the intersection connecting the main road and the branch road.
6. The intersection left-forbidden evaluation method based on the macroscopic basic graph as claimed in claim 1, wherein: wherein, carry out the simulation to the intersection forbidden left side, obtain the macroscopic basic diagram parameter under the intersection forbidden left side through data analysis, mainly include:
simulating a single intersection forbidden left to obtain traffic flow, obtaining macroscopic basic graph parameters under the intersection forbidden left through data analysis, constructing a macroscopic basic graph to obtain a maximum weighted flow point, judging the weighted flow after the left is forbidden to be increased compared with the weighted flow before the left is forbidden through an expression of the road network weighted flow and the total traffic flow based on the macroscopic basic graph, and obtaining a unitary quadratic regression equation after the data is fitted by a least square method:
y＝-0.0076x2+8.7113x
wherein y is a weighted flow; x is the vehicle cumulative amount;
simulating the whole trunk line forbidden left to obtain trunk line traffic flow, obtaining macro basic graph parameters under the whole trunk line forbidden left through data analysis, constructing a macro basic graph to obtain a maximum weighted flow point, judging the weighted flow after the forbidden left to be compared with the weighted flow before the forbidden left to be increased through an expression of the weighted flow and the total traffic flow of the road network based on the macro basic graph, and obtaining a unitary quadratic regression equation after the data are fitted by a least square method:
y＝-0.007x2+7.9738x
wherein y is a weighted flow; x is the vehicle cumulative amount;
carrying out simulation on the left forbidden of the whole region to obtain the traffic flow of the region, obtaining parameters of a macroscopic basic diagram of the left forbidden of the whole region through data analysis, constructing the macroscopic basic diagram to obtain a maximum weighted flow point, judging a weighted flow reduction value after the left forbidden compared with the weighted flow before the left forbidden, and obtaining a unitary quadratic regression equation after the data is fitted by a least square method:
y＝-0.0092x2+8.4786x
wherein y is a weighted flow; and x is the vehicle cumulative amount.
7. The intersection left-forbidden evaluation method based on the macroscopic basic graph as claimed in claim 1, wherein: the method comprises the following steps of constructing a macroscopic basic diagram left-forbidden model under different left-turn traffic flow proportions, and evaluating left-forbidden schemes under different regions and different traffic flows, wherein the macroscopic basic diagram left-forbidden model mainly comprises the following steps:
when the proportion of the left-turn traffic flow to the total traffic flow is 10%, the relationship between the weighted traffic flow and the vehicle cumulative amount for a single main road is as follows: y is-0.0076 x2+8.7113 x; the relationship between the weighted flow and the cumulative vehicle amount for the primary and secondary thoroughfares is: y is-0.007 x2+7.9738x, evaluated as: the single main road, the main road and the secondary main road can be left prohibited, and the branch road is left prohibited to influence traffic;
when the proportion of the left-turn traffic to the total traffic is 15%, the relationship between the weighted traffic and the vehicle cumulative amount for a single main road is as follows: y is-0.0079 x2+8.9727 x; the relationship between the weighted flow and the cumulative vehicle amount for the primary and secondary thoroughfares is: y is-0.0073 x2+8.2927x, evaluated as: the single main road, the main road and the secondary main road can be left prohibited, and the branch road is left prohibited to influence traffic;
when the proportion of the left-turn traffic to the total traffic is 20%, the relationship between the weighted traffic and the vehicle cumulative amount for a single main road is as follows: y is-0.0075 x2+8.524 x; the relationship between the weighted flow and the cumulative vehicle amount for the primary and secondary thoroughfares is: y-0.0065 x2+7.4634x, evaluated as: the single main road is forbidden to left, so that the traffic pressure is relieved, the main road and the secondary main road can be forbidden to left, but the influence is smaller compared with the single main road, and the traffic is influenced by the branch forbidden to left;
when the proportion of the left-turn traffic to the total traffic is 25%, the relationship between the weighted traffic and the vehicle cumulative amount for a single main road is: y-0.0067 x2+7.6716 x; for the main road and the secondary road, a traffic deadlock state occurs, a relational expression cannot be fitted through data, and the evaluation is as follows: the capacity of relieving traffic pressure by forbidding left of a single main road is small, and the traffic is blocked by forbidding left of the main road and the secondary main road and forbidding left of a branch road.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105702031A (en) * | 2016-03-08 | 2016-06-22 | 北京航空航天大学 | Macroscopic fundamental diagram-based road network key section identification method |
| CN106960582A (en) * | 2017-03-12 | 2017-07-18 | 浙江大学 | A kind of method of the region bottleneck control based on macroscopical parent map |
| CN108877219A (en) * | 2018-07-04 | 2018-11-23 | 长沙理工大学 | A kind of left back traffic flow branch prediction method of urban intersection taboo |
-
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- 2021-10-19 CN CN202111214411.7A patent/CN114049759B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105702031A (en) * | 2016-03-08 | 2016-06-22 | 北京航空航天大学 | Macroscopic fundamental diagram-based road network key section identification method |
| CN106960582A (en) * | 2017-03-12 | 2017-07-18 | 浙江大学 | A kind of method of the region bottleneck control based on macroscopical parent map |
| CN108877219A (en) * | 2018-07-04 | 2018-11-23 | 长沙理工大学 | A kind of left back traffic flow branch prediction method of urban intersection taboo |
Non-Patent Citations (3)
| Title |
|---|
| 梁鸣璋;孙凤英;: "基于VISSIM的交叉口调头点设置仿真研究", 森林工程, no. 01 * |
| 沈莉潇: "基于宏观基本图的区域边界交通信号控制方法", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》, vol. 2018, no. 12, pages 1 * |
| 苏小军 等: "基于VISSIM仿真的禁左区域车流组织研究", 《公路交通技术》, vol. 2015, no. 1, pages 2 - 5 * |
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