CN107180541B - Dynamic adjustment method for traffic control cell - Google Patents
Dynamic adjustment method for traffic control cell Download PDFInfo
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- CN107180541B CN107180541B CN201710402885.1A CN201710402885A CN107180541B CN 107180541 B CN107180541 B CN 107180541B CN 201710402885 A CN201710402885 A CN 201710402885A CN 107180541 B CN107180541 B CN 107180541B
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- G08G—TRAFFIC CONTROL SYSTEMS
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- G08G1/08—Controlling traffic signals according to detected number or speed of vehicles
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Abstract
The invention discloses a dynamic adjustment method of a traffic control cell, which comprises the steps of analyzing the distribution of an initial cell and the traffic association degree of adjacent intersections of cell boundaries in an original state and a new state, selecting intersections needing to be judged in the new state, judging which cell the intersections belong to, then counting the number of intersections of all cells and judging each cell one by one, and if the number exceeds the lower limit, merging the cell to the cell with the maximum association degree of the adjacent cells; if the upper limit is exceeded, the cell is divided. The method further improves the effectiveness of the traffic management and control scheme, improves the vehicle operation efficiency, obtains better road network traffic control effect and has better social benefit and economic benefit.
Description
Technical Field
The invention relates to the technical field of road traffic control, in particular to a dynamic adjustment method for a traffic control cell.
Background
With the rapid increase of the urban automobile holding capacity, the urban traffic congestion problem becomes more serious, wherein the main reason for the urban traffic congestion is the imbalance and the mismatching of the supply and demand of urban road network traffic flow in space and time distribution. Traffic congestion has a tendency of normalization and regionalization, at present, city managers increasingly pay more attention to the application of a signal control strategy of road network traffic, and various cities have gradually built city traffic control systems. The control mode comprises single-point control, main road coordination control and regional control. The regional control comprises three layers of intersection control, local regional control and central control, wherein the central control divides the whole control range into a plurality of cells and is uniformly coordinated by the central control, and the local regional control is controlled by a sub-region consisting of a plurality of single points. In urban traffic management and control, regional control is widely applied. When regional control is implemented, the whole road network needs to be partitioned (such as traffic control cells), and then corresponding traffic control schemes are adopted for all the partitions, so that a good control effect is achieved in the practice of all cities at present.
Many studies and applications have been made at home and abroad for traffic control communities. The division method of the traffic control cell is mainly divided into a static division method and a dynamic division method. The static division method of the traffic control cell is based on the physical characteristics of a road network and is researched based on urban land utilization properties, administrative region function division, urban population distribution, natural topography and landform, road network structure and layout, related characteristics and the like. With the development of traffic, the static division method of the cell cannot meet the requirement of dynamic change of the traffic of the road network, and researchers begin to research from a dynamic perspective. The dynamic division method of the traffic control cell is based on the physical characteristics and traffic characteristics of a road network, and a research area is dynamically divided into a plurality of cells through a clustering model, a traffic association degree model and the like. In addition, the existing urban traffic signal control systems SCATS, SCOOT and the like all adopt the concept of zone control, and the traffic control cells of the control systems are mainly divided manually.
However, at present, no research result and application for realizing dynamic adjustment of the traffic control cell based on the combination of the initial traffic control cell and the dynamic change of road traffic exist. The invention finely adjusts, divides and combines the initial traffic control cells according to the characteristic that the road traffic state can be changed greatly, thereby establishing the traffic control cells suitable for different traffic states and obtaining better road network traffic control effect.
Disclosure of Invention
The invention aims to overcome the defects and shortcomings of the prior art, provides an effective dynamic adjustment method of a traffic control cell, establishes the traffic control cells suitable for different traffic states, so that more effective traffic management and control can be adopted, the effectiveness of a traffic management and control scheme is further improved, the vehicle operation efficiency is improved, a better road network traffic control effect is obtained, and better social benefits and economic benefits are achieved.
In order to achieve the purpose, the technical scheme provided by the invention is as follows: a dynamic adjustment method of a traffic control cell comprises the following steps:
1) basic data preparation
The method comprises the steps of distributing a road network distribution map, the distribution range of each initial traffic control cell, the number of intersections, and the traffic association degrees of adjacent intersections in an original state and a new state;
2) selecting the intersection needing to be judged in the new state
2.1) determining the boundary road section of the initial adjacent traffic control cell and the corresponding adjacent intersection;
2.2) calculating the traffic association degree difference value delta w of the adjacent intersections in the original state and the new state, and sorting the adjacent intersections in a descending manner by taking the difference value as a sorting basis;
2.3) setting the average value of the traffic association degree difference value as a threshold value M, determining adjacent intersections with the difference value being greater than or equal to the threshold value M as objects needing to be judged, and keeping the original traffic control cell attribution state without judging the adjacent intersections with the difference value being less than the threshold value M;
2.4) determining a specific intersection of the adjacent intersection needing to be judged according to the direction of the traffic association degree of the adjacent intersection, wherein the specific intersection comprises adjacent intersections i and j, the direction of the traffic association degree is from i to j, and then the intersection needing to be judged can be determined as i;
3) judging affiliation of specific traffic control cell at selected intersection
3.1) the intersection needing to be judged to belong to is separated from the original traffic control cell;
3.2) calculating node degrees of membership
Calculating the degree of membership of the intersection node i to a certain adjacent traffic control cell, wherein the calculation formula is as follows:
in the formula, VuIndicating a traffic control cell GuSet of internal intersection nodes, V represents traffic control cell GuOf the interior and the boundary, wijThe node membership degree is higher, and the contact degree between the node and a traffic control cell is tighter;
3.3) calculating cell association affinity gain
3.3.1) calculating traffic control cell GuThe degree of closeness between internal nodes is calculated as:
in the formula (I), the compound is shown in the specification,indicating a traffic control cell GuThe sum of the weight values of the link sides of the internal intersection nodes,
indicating a traffic control cell GuThe sum of the weight values of the edges connecting the internal intersection node and the external intersection node;
3.3.2) calculating the correlation compactness gain of the traffic control cell, wherein the calculation formula is as follows:
in the formula, Wi inMeans that all edges connected with the intersection node i are merged into a traffic control cell GuWeight value of the edge of (1), Wi outMeans that all edges connected with the intersection node i are not merged into the traffic control cell GuThe greater the associated compactness gain of the traffic control cell is, the greater the compactness degree between the nodes in the traffic control cell after the intersection node i is added into the traffic control cell is;
3.4) judgment conditions
Re-determining the affiliation of the traffic control cell for all the intersections needing to be judged, wherein the judgment conditions for a single intersection are as follows:
if so, i belongs to Gv(ii) a If notI keeps the original attribution of the traffic control cell, i belongs to Gu；
4) Merging and splitting of traffic control cells
4.1) counting the number of intersection nodes of all the cells, setting a lower limit value Min and an upper limit value Max of the number of intersections of the traffic control cell, and judging each cell one by one;
4.2) if the number of the intersections of the traffic control cell exceeds the lower limit, executing the step 4.3) to the traffic control cell; if the number of intersections of the traffic control cell exceeds the upper limit, executing the step 4.4) to the traffic control cell; if the number of the intersections of the traffic control cell meets the conditions, executing the step 5) on the traffic control cell;
4.3) traffic control cell merging
4.3.1) too small a traffic control cell GuControl of cell G with respect to trafficvThe expression is calculated as follows:
in the formula, W (G)u,Gv) Indicating a traffic control cell GuAnd GvThe sum of the weight values of all connected edges between the two;
4.3.2) merging the traffic control cell into the adjacent traffic control cell with the maximum associated closeness;
4.3.3) if the number of the intersections of the new traffic control cell after combination exceeds the upper limit, executing the step 4.4); otherwise, executing step 5);
4.4) traffic control cell segmentation
4.4.1) a traffic control cell two-path spectrum clustering partitioning method is adopted for partitioning, and the partitioning steps are as follows:
a) constructing a matrix W according to the traffic relevance of adjacent intersections in a new state, constructing a degree matrix D according to the number of edges connected by intersection nodes, and constructing a Laplace matrix
b) Calculating an eigenvalue and an eigenvector of the Laplace matrix L;
c) sorting the characteristic values, and selecting a second small characteristic value and a corresponding characteristic vector, wherein the ith row in the characteristic vector represents the ith intersection of the research area;
d) sorting the elements of the feature vector, and selecting a required partitioning point to divide the nodes into two types, namely B1 and B2;
4.4.2) determining the relationship between the number of elements in the sets B1 and B2 and the lower limit Min
a) If the number of the elements in the sets B1 and B2 is larger than or equal to the lower limit Min, executing the step 5);
b) if the number of elements in the set B1 or B2 is less than the lower limit Min, executing the step 4.4.3) on the set less than the lower limit;
4.4.3) calculating traffic control cell G corresponding to sets B1 and B2B1And GB2Each boundary of the road segment L is connected with the road segment LijAdjacent crossing in traffic control district GB2Correlation loss ratio cut in directionijWherein, in the step (A),
the intersection i belongs to a traffic control cell G with less intersectionsB1J belongs to a traffic control cell G with a large number of intersectionsB2，WjThe sum of the correlation degrees of adjacent intersections corresponding to the road sections connected with the intersection j is referred to;
4.4.4) calculating the lower limit Min and the traffic control cell GB1Difference value delta of the number of intersectionsMinB1For cutijSorting is performed, and the smallest delta is selectedMinXIndividual correlation loss ratio cutijThe corresponding intersection j is divided into a traffic control cell GB1Determining the attribution of the traffic control cells of the divided intersections, forming two traffic control cells meeting the conditions, and executing the step 5);
5) and storing the result.
In the step 4), a lower limit Min and an upper limit Max of the number of intersections of the traffic control cell need to be set, two adjacent signal control intersections can be subjected to coordination control according to the traffic signal coordination control attribute of the traffic control cell, and in order to obtain a better coordination effect, the value range of the lower limit Min of the number of adjacent intersections subjected to signal coordination control is recommended to be 2-4; in addition, the intersection coordination control is not as good as more intersections, and the urban road network density, the intersection distance and the traffic control system need to be considered, so the minimum value of the upper limit value Max is recommended to be twice as large as the maximum value of the lower limit value Min, namely the upper limit value Max is more than or equal to 8.
In step 4), the required partitioning points are required to be selected to divide the nodes into two types, namely B1 and B2, 0 is suggested to be selected as the partitioning point, elements smaller than 0 are firstly divided into a set a1, elements larger than 0 are divided into a set a2, the numbers of elements in the sets a1 and a2 are counted, if the numbers of the elements in the sets a1 and a2 are equal and an element 0 exists in a feature vector, the element 0 is divided into a set a2, the set not containing the element 0 is called B1, and the set containing the element 0 is called B2; if the numbers of elements in the sets a1 and a2 are not equal, the element 0 is divided into a small number of sets, the set including the element 0 is referred to as B1, and the set not including the element 0 is referred to as B2.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the invention provides a new dynamic adjustment method for traffic control cells, and the traffic control cells divided according to the method can adopt a more effective traffic management and control scheme, thereby improving the vehicle operation efficiency and obtaining a better road network traffic control effect.
2. Compared with the existing static division method and the ordinary dynamic division method, the method has higher matching degree with the road traffic state, and can further improve the social benefit and the economic benefit.
Drawings
FIG. 1 is a logic flow diagram of the method of the present invention.
FIG. 2 is a schematic diagram of intersection attribution fine tuning according to the method of the present invention.
Fig. 3 is a schematic diagram of the merging of traffic control cells according to the method of the present invention.
Fig. 4 is a schematic diagram of traffic control cell segmentation according to the method of the present invention.
FIG. 5 is a road network diagram of an embodiment of the method of the present invention.
Fig. 6 is a diagram of initial traffic control cell distribution and correlation degree labels according to an embodiment of the method of the present invention.
Fig. 7 is a diagram illustrating a fine tuning result of a traffic control cell according to an embodiment of the present invention.
Fig. 8 is a result diagram of the traffic control cell merging and splitting according to the embodiment of the present invention.
Detailed Description
The present invention will be further described with reference to the following specific examples.
The basic idea of the dynamic adjustment method for traffic control cells provided in this embodiment is to select an intersection to be determined in a new state by inputting basic data, determine the affiliation of a specific traffic control cell at the selected intersection, count the number of nodes of all the cells, determine each cell one by one, and merge or divide the corresponding traffic control cells according to the determination result.
As shown in fig. 1, the method for dynamically adjusting a traffic control cell includes the following steps:
step 1: preparing basic data: the method comprises a road network distribution map, the distribution range of each initial traffic control cell, the number of intersections, and the traffic association degrees of adjacent intersections in an original state and a new state.
Step 2: selecting the intersection needing to be judged in the new state
2.1) determining the boundary road section of the initial adjacent traffic control cell and the corresponding adjacent intersection.
And 2.2) calculating the traffic association degree difference value delta w of the adjacent intersections in the original state and the new state, and sorting the adjacent intersections in a descending manner by taking the difference value as a sorting basis.
And 2.3) setting the average value of the traffic association degree difference value as a threshold value M, determining adjacent intersections with the difference values larger than or equal to the threshold value M as objects needing to be judged, and keeping the original attribution state of the traffic control cell without judging the adjacent intersections with the difference values smaller than the threshold value M.
2.4) determining a specific intersection of the adjacent intersection needing to be determined according to the direction of the traffic association degree of the adjacent intersection, wherein if the direction of the traffic association degree of the adjacent intersections is from i to j, the intersection needing to be determined can be determined as i.
And step 3: judging affiliation of specific traffic control cell at selected intersection
3.1) the intersection needing to be judged to belong to is separated from the original traffic control cell, as shown in figure 2.
3.2) calculating node degrees of membership
Calculating the degree of membership of the intersection node i to a certain adjacent traffic control cell, wherein the calculation formula is as follows:
wherein, VuIndicating a traffic control cell GuSet of internal intersection nodes, V represents traffic control cell GuOf the interior and the boundary, wijAnd the weight values of the road sections between the adjacent intersections i and j are represented, and the higher the node membership degree is, the closer the contact degree between the node and the traffic control cell is.
3.3) calculating cell association affinity gain
3.3.1) calculating traffic control cell GuThe degree of closeness between internal nodes is calculated as:
wherein the content of the first and second substances,
indicating a traffic control cell GuThe sum of the weight values of the link sides of the internal intersection nodes,
indicating traffic control is smallRegion GuThe sum of the weight values of the edges connecting the internal intersection node and the external intersection node.
3.3.2) calculating the correlation compactness gain of the traffic control cell, wherein the calculation formula is as follows:
wherein, Wi inMeans that all edges connected with the intersection node i are merged into a traffic control cell GuWeight value of the edge of (1), Wi outMeans that all edges connected with the intersection node i are not merged into the traffic control cell GuThe weight value of the edge of (2). The larger the gain of the associated tightness of the traffic control cell is, the larger the tightness degree between the nodes inside the traffic control cell after the intersection node i is added into the traffic control cell is.
3.4) judgment conditions
And re-determining the attribution of the traffic control cell for all the intersections needing to be judged. The judgment conditions for a single intersection are as follows:
if so, i belongs to Gv(ii) a If not, i keeps the original traffic control cell attribution, i belongs to Gu。
And 4, step 4: merging and splitting of traffic control cells
4.1) counting the number of the intersection nodes of all the cells, setting a lower limit Min and an upper limit Max of the number of the intersections of the traffic control cell, and judging each cell one by one. According to the coordination control attribute of the traffic signals of the traffic control cell, two adjacent signal control intersections can be subjected to coordination control, and in order to obtain a better coordination effect, the value range of a lower limit value Min of the number of the adjacent intersections subjected to signal coordination control is recommended to be 2-4; in addition, intersection coordination control is not as good as more intersections, urban road network density, intersection intervals, traffic control systems and the like need to be considered, so that the minimum value of the upper limit value Max is two times of the maximum value of the lower limit value Min, namely the upper limit value Max is more than or equal to 8.
4.2) if the number of the intersections of the traffic control cell exceeds the lower limit, as shown in fig. 3, executing the step 4.3) on the traffic control cell; if the number of intersections of the traffic control cell exceeds the upper limit, as shown in fig. 4, executing step 4.4) on the traffic control cell; and if the number of the intersections of the traffic control cell meets the conditions, executing the step 5) on the traffic control cell.
4.3) traffic control cell merging
4.3.1) calculating the traffic control cell G with the number of intersections exceeding the lower limituControl of cell G with respect to trafficvThe expression is calculated as follows:
wherein, W (G)u,Gv) Indicating a traffic control cell GuAnd GvThe sum of the weight values of all connected edges in between.
4.3.2) merging the traffic control cell to the neighboring traffic control cell with the highest associated closeness.
4.3.3) if the number of the intersections of the new traffic control cell after combination exceeds the upper limit, executing the step 4.4); otherwise step 5) is performed.
4.4) traffic control cell segmentation
4.4.1) a traffic control cell two-path spectrum clustering partitioning method is adopted for partitioning, and the partitioning steps are as follows:
a) constructing a matrix W according to the traffic relevance of adjacent intersections in a new state, constructing a degree matrix D according to the number of edges connected by intersection nodes, and constructing a Laplace matrix
b) Calculating an eigenvalue and an eigenvector of the Laplace matrix L;
c) sorting the characteristic values, and selecting a second small characteristic value and a corresponding characteristic vector, wherein the ith row in the characteristic vector represents the ith intersection of the research area;
d) the elements of the feature vector are sorted and appropriate segmentations are selected to divide the nodes into two classes, B1 and B2. The method is characterized in that 0 is selected as a dividing point, elements smaller than 0 are firstly divided into a set A1, elements larger than 0 are divided into a set A2, the number of the elements in the sets A1 and A2 is counted, if the number of the elements in the sets A1 and A2 is equal and the element 0 exists in a feature vector, the element 0 can be divided into any set, in the method, the element is divided into a set A2, the set not containing the element 0 is called B1, and the set containing the element 0 is called B2. (ii) a If the numbers of elements in the sets a1 and a2 are not equal, the element 0 is divided into a smaller number of sets, the set including the element 0 is referred to as B1, and the set not including the element 0 is referred to as B2.
4.4.2) determining the relationship between the number of elements in the sets B1 and B2 and the lower limit Min
a) If the number of the elements in the sets B1 and B2 is larger than or equal to the lower limit Min, executing the step 5);
b) if the number of elements in the set B1 or B2 is less than the lower limit Min, then step 4.4.3) is performed on the set less than the lower limit.
4.4.3) calculating traffic control cell G corresponding to sets B1 and B2B1And GB2Each boundary of the road segment L is connected with the road segment LijAdjacent crossing in larger traffic control district GB2Correlation loss ratio cut in directionijWherein, in the step (A),the intersection i belongs to the traffic control cell G with less intersection numberB1J belongs to a traffic control cell G with a large number of intersectionsB2，WjAnd the sum of the relevance degrees of adjacent intersections corresponding to the road sections connected with the intersection j is referred to.
4.4.4) calculating the lower limit Min and the smaller traffic control cell GB1Difference value delta of the number of intersectionsMinB1For cutijSorting is performed, and the smallest delta is selectedMinXA correlation loss ratio cutijThe corresponding intersection j is divided into smaller traffic control cells GB1And determining the attribution of the traffic control cells of the divided intersections, forming two traffic control cells meeting the conditions, and executing the step 5).
And 5: and storing the result.
The following describes the above dynamic adjustment method for a traffic control cell in this embodiment with reference to specific parameters, which is as follows:
taking local road networks in main urban areas of the Yi Wu city as an example, as shown in fig. 5, roads from east to north of Zongze, roads from south to West city, roads from West to longitude, and roads from north to champion are in the range of the road networks. The area of the area range is about 18 square kilometers, and 28 roads are totally covered. The initial traffic control cell distribution and the traffic relevance degree situation of the new state in the area are shown in fig. 6, and the upper limit and the lower limit of the number of signalized intersections in a single traffic control cell are set to be 9 and 3 respectively.
Selecting an intersection needing to be judged in a new state:
the order of the degree of association of adjacent intersections of the initial traffic control cell border area is obtained according to the prepared basic data and is shown in table 1 below.
TABLE 1
When the road network transits from the peak time to the flat time, the degree of association of the traffic states between adjacent intersections also changes. As can be seen from the above table, the average value of the change of the traffic state association degrees of the adjacent intersections in the boundary area is 0.40, and the traffic association degree change difference values of 15 road segment units are above the average value of the change, so that intersections to be determined are further defined as shown in table 2 below.
TABLE 2
Judging the affiliation of the specific traffic control cell of the selected intersection:
calculating the membership degree of the corresponding intersection and the associated compactness gain of the affiliated traffic control cell, taking the intersection 15 as an example:
intersection 15, originally belonging to traffic control district i:
thus, it is possible to obtain
The intersection 15 still belongs to the original traffic control cell i.
The fine adjustment results of the entire road network are shown in table 3 and fig. 7.
TABLE 3
The number of intersection nodes of all traffic control cells is counted, and each traffic control cell is judged one by one, and the result is shown in the following table 4.
TABLE 4
Traffic control cell division:
after fine adjustment, the number of intersections of the traffic control cell I is 10 and exceeds the upper limit, so the traffic control cell is divided by adopting a traffic control cell two-way spectral clustering division method to obtain the eigenvectors corresponding to the second small eigenvalue (0.4213,0.4016,0.3295,0.3373, -0.0790, -0.3816, -0.4612, -0.2120,0.1083,0.1327)TElements greater than 0 are classified into one class, elements less than or equal to 0 are classified into another class, and the segmentation result is shown in fig. 8. The original traffic control cell I is divided into 2 sub-zones, the number of intersections in the sub-zone 1 is 6, the number of intersections in the sub-zone 2 is 4, so far, the number of intersections in all the traffic control cells meets the requirement, and the dynamic adjustment of the traffic control cells in the state is finished.
The above-mentioned embodiments are merely preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, so that the changes in the shape and principle of the present invention should be covered within the protection scope of the present invention.
Claims (3)
1. A dynamic adjustment method for a traffic control cell is characterized by comprising the following steps:
1) basic data preparation
The method comprises the steps of distributing a road network distribution map, the distribution range of each initial traffic control cell, the number of intersections, and the traffic association degrees of adjacent intersections in an original state and a new state;
2) selecting the intersection needing to be judged in the new state
2.1) determining the boundary road section of the initial adjacent traffic control cell and the corresponding adjacent intersection;
2.2) calculating the traffic association degree difference value delta w of the adjacent intersections in the original state and the new state, and sorting the adjacent intersections in a descending manner by taking the difference value as a sorting basis;
2.3) setting the average value of the traffic association degree difference value as a threshold value M, determining adjacent intersections with the difference value being greater than or equal to the threshold value M as objects needing to be judged, and keeping the original traffic control cell attribution state without judging the adjacent intersections with the difference value being less than the threshold value M;
2.4) determining a specific intersection of the adjacent intersection needing to be judged according to the direction of the traffic association degree of the adjacent intersection, wherein the specific intersection comprises adjacent intersections i and j, the direction of the traffic association degree is from i to j, and then the intersection needing to be judged can be determined as i;
3) judging affiliation of specific traffic control cell at selected intersection
3.1) the intersection needing to be judged to belong to is separated from the original traffic control cell;
3.2) calculating node degrees of membership
Calculating the degree of membership of the intersection node i to a certain adjacent traffic control cell, wherein the calculation formula is as follows:
in the formula, VuIndicating a traffic control cell GuSet of internal intersection nodes, V represents traffic control cell GuOf the interior and the boundary, wijThe node membership degree is higher, and the contact degree between the node and a traffic control cell is tighter;
3.3) calculating cell association affinity gain
3.3.1) calculating traffic control cell GuThe degree of closeness between internal nodes is calculated as:
in the formula (I), the compound is shown in the specification,indicating a traffic control cell GuThe sum of the weight values of the link sides of the internal intersection nodes,
indicating a traffic control cell GuInternal intersection sectionThe sum of the weight values of the edges connected with the external intersection nodes;
3.3.2) calculating the correlation compactness gain of the traffic control cell, wherein the calculation formula is as follows:
in the formula, Wi inMeans that all edges connected with the intersection node i are merged into a traffic control cell GuWeight value of the edge of (1), Wi outMeans that all edges connected with the intersection node i are not merged into the traffic control cell GuThe greater the associated compactness gain of the traffic control cell is, the greater the compactness degree between the nodes in the traffic control cell after the intersection node i is added into the traffic control cell is;
3.4) judgment conditions
Re-determining the affiliation of the traffic control cell for all the intersections needing to be judged, wherein the judgment conditions for a single intersection are as follows:
if so, i belongs to Gv(ii) a If not, i keeps the original traffic control cell attribution, i belongs to Gu；
4) Merging and splitting of traffic control cells
4.1) counting the number of intersection nodes of all the cells, setting a lower limit value Min and an upper limit value Max of the number of intersections of the traffic control cell, and judging each cell one by one;
4.2) if the number of the intersections of the traffic control cell exceeds the lower limit, executing the step 4.3) to the traffic control cell; if the number of intersections of the traffic control cell exceeds the upper limit, executing the step 4.4) to the traffic control cell; if the number of the intersections of the traffic control cell meets the conditions, executing the step 5) on the traffic control cell;
4.3) traffic control cell merging
4.3.1) too small a traffic control cell GuControl of cell G with respect to trafficvThe expression is calculated as follows:
in the formula, W (G)u,Gv) Indicating a traffic control cell GuAnd GvThe sum of the weight values of all connected edges between the two;
4.3.2) merging the traffic control cell into the adjacent traffic control cell with the maximum associated closeness;
4.3.3) if the number of the intersections of the new traffic control cell after combination exceeds the upper limit, executing the step 4.4); otherwise, executing step 5);
4.4) traffic control cell segmentation
4.4.1) a traffic control cell two-path spectrum clustering partitioning method is adopted for partitioning, and the partitioning steps are as follows:
a) constructing a matrix W according to the traffic relevance of adjacent intersections in a new state, constructing a degree matrix D according to the number of edges connected by intersection nodes, and constructing a Laplace matrix
b) Calculating an eigenvalue and an eigenvector of the Laplace matrix L;
c) sorting the characteristic values, and selecting a second small characteristic value and a corresponding characteristic vector, wherein the ith row in the characteristic vector represents the ith intersection of the research area;
d) sorting the elements of the feature vector, and selecting a required partitioning point to divide the nodes into two types, namely B1 and B2;
4.4.2) determining the relationship between the number of elements in the sets B1 and B2 and the lower limit Min
a) If the number of the elements in the sets B1 and B2 is larger than or equal to the lower limit Min, executing the step 5);
b) if the number of elements in the set B1 or B2 is less than the lower limit Min, executing the step 4.4.3) on the set less than the lower limit;
4.4.3) calculating traffic control cell G corresponding to sets B1 and B2B1And GB2Each boundary of the road segment L is connected with the road segment LijAdjacent crossing in traffic control district GB2Correlation loss ratio cut in directionijWherein, in the step (A),the intersection i belongs to a traffic control cell G with less intersectionsB1J belongs to a traffic control cell G with a large number of intersectionsB2，WjThe sum of the correlation degrees of adjacent intersections corresponding to the road sections connected with the intersection j is referred to;
4.4.4) calculating the lower limit Min and the traffic control cell GB1Difference value delta of the number of intersectionsMinB1For cutijSorting is performed, and the smallest delta is selectedMinXIndividual correlation loss ratio cutijThe corresponding intersection j is divided into a traffic control cell GB1Determining the attribution of the traffic control cells of the divided intersections, forming two traffic control cells meeting the conditions, and executing the step 5);
5) and storing the result.
2. The method of claim 1, wherein the method comprises: in the step 4), a lower limit Min and an upper limit Max of the number of intersections of the traffic control cell need to be set, two adjacent signal control intersections can be subjected to coordination control according to the traffic signal coordination control attribute of the traffic control cell, and in order to obtain a better coordination effect, the value range of the lower limit Min of the number of adjacent intersections subjected to signal coordination control is recommended to be 2-4; in addition, the intersection coordination control is not as good as more intersections, and the urban road network density, the intersection distance and the traffic control system need to be considered, so the minimum value of the upper limit value Max is recommended to be twice as large as the maximum value of the lower limit value Min, namely the upper limit value Max is more than or equal to 8.
3. The method of claim 1, wherein the method comprises: in step 4), the required partitioning points are required to be selected to divide the nodes into two types, namely B1 and B2, 0 is suggested to be selected as the partitioning point, elements smaller than 0 are firstly divided into a set a1, elements larger than 0 are divided into a set a2, the numbers of elements in the sets a1 and a2 are counted, if the numbers of the elements in the sets a1 and a2 are equal and an element 0 exists in a feature vector, the element 0 is divided into a set a2, the set not containing the element 0 is called B1, and the set containing the element 0 is called B2; if the numbers of elements in the sets a1 and a2 are not equal, the element 0 is divided into a small number of sets, the set including the element 0 is referred to as B1, and the set not including the element 0 is referred to as B2.
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| CN109887297B (en) * | 2019-04-23 | 2021-03-26 | 太原理工大学 | Method for dividing urban traffic control subareas based on rapid global K-means spectral clustering |
| CN110335478B (en) * | 2019-07-10 | 2021-01-12 | 江苏航天大为科技股份有限公司 | Cross-subarea linkage control method based on deep learning |
| CN110322694A (en) * | 2019-07-16 | 2019-10-11 | 青岛海信网络科技股份有限公司 | A kind of method and device of urban traffic control piece Division |
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