Intersection traffic capacity assessment method and system based on multi-source data fusion

Abstract

The invention discloses an intersection traffic capacity assessment method and system based on multi-source data fusion. The method comprises the steps of obtaining intersection static model data and delay index data; counting the number of effective lanes in each flow direction based on the intersection static model data, determining the maximum number of lanes in each phase, and calculating the basic green time ratio of the corresponding phase; calculating a flow direction comprehensive delay index based on the real-time delay index data and the flow direction lane number, and taking the ratio of the flow direction comprehensive delay index to the intersection comprehensive delay index as a flow direction delay green time ratio; time period division is carried out based on historical delay index data, and fusion weights of a basic green time ratio and a flow direction delay green time ratio in each time period are distributed; calculating a final green time ratio of each flow direction; and evaluating the traffic capacity of each flow direction of the intersection according to the final green signal ratio and the theoretical traffic capacity. According to the method, the basic lane configuration data and the real-time delay data are combined, intersection traffic capacity evaluation is carried out under the condition of not depending on intersection equipment arrangement and flow data, and the accuracy and universality of traffic capacity evaluation are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent transportation, in particular to a method and system for intersection traffic capacity evaluation based on multi-source data fusion. BACKGROUND

[0002] Intersection traffic capacity is an important basic parameter relied on by traffic planning and intersection signal control, and its accuracy is directly related to the utilization efficiency of road resources and the relief effect of traffic congestion. The existing intersection traffic capacity evaluation method is highly dependent on the real-time or historical flow data collected by the physical detection equipment installed at the intersection (such as coils, video cameras, etc.). This technology path based on intersection equipment and flow data has significant limitations in practical application. First of all, it requires each intersection to have a perfect equipment layout and data collection system, and the installation coverage rate of the intersection collection equipment is extremely high, so it is difficult to obtain accurate traffic capacity calculation results in areas with insufficient equipment coverage or construction lag. Secondly, the problems of fixed green signal ratio, fixed lane function static division, and unquantized modeling of variable lane dynamic characteristics in the traditional method make the final traffic capacity evaluation result have large error, which cannot accurately reflect the real running state of the intersection. Therefore, breaking the serious dependence on intersection equipment layout and flow data, developing a widely applicable, high-precision, and self-adaptive traffic capacity evaluation method has become a key problem to be solved to improve the intelligent level of traffic management.

[0003] The "real-time lane traffic capacity calculation method and system based on radar and video fusion" disclosed in Chinese patent literature, with publication number CN115547065A and publication date of 2022-12-30, includes: acquiring video image data and radar detection data; performing data fusion on the video image data and the radar detection data to obtain relative space-time information of each vehicle relative to the intersection, and obtaining absolute space-time information of all vehicles at the intersection according to the relative space-time information; acquiring traffic timing information and map information of the intersection, and according to the traffic timing information, the map information and the absolute space-time information, obtaining the saturation flow rate of the jth lane in phase i and the stage green signal ratio of the jth lane in phase i or the passable time proportion of the jth lane in phase i; according to the saturation flow rate of the jth lane in phase i and the stage green signal ratio of the jth lane in phase i, obtaining the traffic capacity of the jth lane in phase i. This technology improves the accuracy of traffic capacity calculation through the fusion of radar data and video data, but it still relies on radar and video equipment set at the intersection and the acquisition of flow data on the road, and has a high requirement for the coverage rate of intersection equipment. SUMMARY

[0004] The application provides a method and system for intersection traffic capacity evaluation based on multi-source data fusion to overcome the problem that the evaluation of intersection traffic capacity in the prior art mainly depends on the arrangement of intersection equipment and the acquisition of traffic flow data, and accurate evaluation results of traffic capacity are difficult to obtain in the case that the coverage of intersection equipment is insufficient or traffic flow data cannot be acquired.

[0005] In order to achieve the above-mentioned purpose, the application adopts the following technical scheme: The application provides a method for intersection traffic capacity evaluation based on multi-source data fusion, which comprises the following steps: acquiring intersection static model data and delay index data; statistically determining the effective lane number of each flow direction based on the intersection static model data, determining the maximum lane number of each phase, and calculating the basic green ratio of the corresponding phase; calculating the flow direction comprehensive delay index based on the real-time delay index data and the flow direction lane number, and taking the ratio of the intersection comprehensive delay index as the flow direction delay green ratio; dividing time periods based on the historical delay index data, assigning the fusion weight of the basic green ratio and the flow direction delay green ratio in each time period, and calculating the final green ratio of each flow direction; evaluating the traffic capacity of each flow direction of the intersection based on the final green ratio and the theoretical traffic capacity.

[0006] In the application, the basic lane configuration data and real-time delay data are combined to construct a green ratio dynamic calculation model with adaptive space-time weight, which can automatically adjust the green ratio according to the traffic demand and real-time traffic condition in different time periods, realize the optimized control of traffic signals, and effectively relieve traffic congestion. The threshold key parameters of time period division are automatically generated based on the historical delay data, which can dynamically generate weight parameters suitable for different traffic scenes, fuse the basic green ratio and the delay green ratio, and eliminate the dependence on artificial experience as much as possible. The dependence on traditional hardware detectors is eliminated by using Internet crowd source data, and the intersection traffic capacity can still be evaluated without relying on the arrangement of intersection equipment and the acquisition of flow data, which significantly improves the precision and universality of traffic capacity evaluation. The calculation result can be directly served for adaptive signal control system, traffic state evaluation, navigation optimization and other applications, effectively relieves traffic congestion, and improves the utilization efficiency of road resources.

[0007] Preferably, the determination of the maximum lane number of each phase comprises: dynamically analyzing the functional flow direction combination of the variable lane based on the lane type and spatial topological relationship in the intersection static model data, and statistically determining the effective lane number of each import direction left turn and straight travel; taking the maximum value in the effective lane number of the east import left turn and the west import left turn as the maximum lane number of the east-west import left turn phase; The maximum lane number of the east-west import straight phase, the south-north import left turn phase, and the south-north import straight phase is determined in the same way.

[0008] Preferably, the calculation of the base green ratio of the corresponding phase comprises: The maximum lane number of each phase comprises the maximum lane number of the east-west import left turn phase and the straight phase, and the maximum lane number of the south-north import left turn phase and the straight phase. The maximum lane numbers of all phases are summed to obtain a total maximum lane number. The base green ratio of each phase is the ratio of the corresponding maximum lane number to the total maximum lane number.

[0009] Preferably, the function flow direction combination of the dynamic analysis of the variable lane comprises: When the variable lane is located at the leftmost side, if the right adjacent lane has no left turn function, it is a left turn and straight combination; if the right adjacent lane has a left turn function, it is a left turn. When the variable lane is located at the rightmost side, if the left adjacent lane has no right turn function, it is a straight and right turn combination; if the left adjacent lane has a right turn function, it is a right turn. If all lanes are variable lanes, the left, straight, and right function proportions are allocated according to the number of lanes.

[0010] Preferably, the calculation of the flow direction delay green ratio comprises: The flow direction comprehensive delay index is calculated, which is the product of the real-time delay index data of the corresponding flow direction and the effective lane number thereof. The sum of the comprehensive delay indexes of all left turn flow directions and straight flow directions of the intersection is calculated to obtain the intersection comprehensive delay. The flow direction delay green ratio of each flow direction is the ratio of the comprehensive delay index thereof to the intersection comprehensive delay. The calculated flow direction delay green ratio is checked and corrected in the numerical range.

[0011] Preferably, the allocation of the fusion weight of the base green ratio and the flow direction delay green ratio in each period comprises: The periods are sorted in descending order according to the number of congestion samples in each period; the congestion sample is the historical delay index data whose service level exceeds a preset level threshold. The period with the highest congestion degree is allocated the highest delay green ratio weight, and the period with the lowest congestion degree is allocated the lowest delay green ratio weight; the delay green ratio weight of the remaining periods is determined by linear interpolation between the highest value and the lowest value according to its sorting.

[0012] Preferably, the period division based on the historical delay index data comprises: The historical delay index data is divided according to a preset length of a statistical period. counting the number of congestion samples in each statistical period; marking the statistical period as a traffic congestion period or a non-congestion period according to whether the number of congestion samples exceeds a preset number threshold; integrating adjacent statistical periods with the same mark, and constraining the total number of divided time periods in a day to obtain a final time period division result.

[0013] Preferably, the traffic capacity of each flow direction of the intersection is evaluated based on the final green ratio and the theoretical traffic capacity, including: the traffic capacity of each flow direction is the product of the final green ratio of the flow direction and the theoretical traffic capacity of the flow direction; the theoretical traffic capacity of the flow direction is the product of the single-lane traffic capacity and the road grade reduction coefficient, multiplied by the lane reduction coefficient corresponding to the effective lane number of the flow direction.

[0014] A traffic capacity evaluation system for an intersection based on multi-source data fusion, comprising: a data acquisition module configured to acquire intersection static model data and delay index data; a lane analysis module configured to analyze variable lane functions based on lane types and spatial topological relationships, and to count effective lane numbers of each flow direction; a green ratio generation module configured to calculate basic green ratios and flow direction delay green ratios, and to calculate final green ratios; a weight generation module configured to automatically divide time periods based on historical delay index data, and to determine green ratio fusion weights of each time period; a traffic capacity evaluation module configured to calculate traffic capacities of each flow direction by comprehensively considering various parameters.

[0015] Preferably, the data acquisition module is configured to access a Gaode API interface to acquire intersection static model data and delay index data.

[0016] The application has the following beneficial effects: traffic analysis and control are carried out in a data-driven manner, a lane function reasoning model based on spatial topological relationship is established, the multi-flow direction combination of the variable lane can be dynamically and accurately determined according to the real-time traffic characteristics of adjacent lanes, so that the potential of the variable lane is fully tapped, and the utilization efficiency of road resources is improved; a space-time weight adaptive green signal ratio dynamic calculation model is constructed in combination with basic lane configuration data and real-time delay data, the green signal ratio can be automatically adjusted according to the traffic demand and real-time traffic condition in different time periods, the optimization control of the traffic signal is realized, and the traffic congestion is effectively relieved; the time period division threshold key parameter is automatically generated based on the historical delay data, the weight parameter suitable for different traffic scenes can be dynamically generated, the basic green signal ratio and the delay green signal ratio are fused, and the dependence on artificial experience is eliminated as much as possible; the intersection capacity can be evaluated without relying on the intersection equipment arrangement and the flow data acquisition, and the precision and universality of the capacity evaluation are significantly improved. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 is a flow chart of a method for evaluating intersection capacity based on multi-source data fusion in the application.

[0018] Figure 2 is a flow chart of a method for analyzing the function flow direction combination of a variable lane in the application.

[0019] Figure 3 is a flow chart of a method for performing time period division and fusion weight distribution according to historical delay index data in the application.

[0020] Figure 4 is a schematic diagram of a system for evaluating intersection capacity based on multi-source data fusion in the application. DETAILED DESCRIPTION

[0021] The application will be further described below in combination with the drawings and specific embodiments.

[0022] As shown in Figure 1 , a method for evaluating intersection capacity based on multi-source data fusion, comprising: acquiring intersection static model data and delay index data; based on the intersection static model data, counting the effective lane number of each flow direction, determining the maximum lane number of each phase, and calculating the basic green signal ratio of the corresponding phase; based on the real-time delay index data and the flow direction lane number, calculating the flow direction comprehensive delay index, and taking the ratio of the flow direction comprehensive delay index and the intersection comprehensive delay index as the flow direction delay green signal ratio; based on the historical delay index data, performing time period division, and distributing the fusion weight of the basic green signal ratio and the flow direction delay green signal ratio in each time period; calculating the final green signal ratio of each flow direction; The green ratio and the theoretical passing capacity are used to evaluate the passing capacity of each flow direction of the intersection.

[0023] It should be noted that, in the present application, the basic lane configuration data is combined with the real-time delay data to construct a green ratio dynamic calculation model with adaptive space-time weight, which can automatically adjust the green ratio according to the traffic demand and real-time traffic condition in different time periods, realize the optimization control of the traffic signal, and effectively relieve the traffic congestion; the threshold key parameter of the time period division is automatically generated based on the historical delay data, which can dynamically generate the weight parameter adaptive to different traffic scenes, fuse the basic green ratio and the delay green ratio, and eliminate the dependence on artificial experience as much as possible; the dependence on the traditional hardware detector is eliminated by using the Internet crowd source data, and the intersection passing capacity can still be evaluated without relying on the intersection equipment arrangement and the flow data acquisition, which significantly improves the accuracy and universality of the passing capacity evaluation. The calculation result can be directly served for the adaptive signal control system, the traffic state evaluation, the navigation optimization and the like, effectively relieves the traffic congestion, and improves the road resource utilization efficiency. It is suitable for the variable lane scene, and realizes the adaptive optimization calculation of the intersection passing capacity in different time periods.

[0024] Specifically, the following steps are included: Multi-source data acquisition: two aspects of data are acquired through the open API interface provided by the Internet map service provider (such as Gaode Map): a) intersection static model data, including but not limited to the number of lanes of the intersection, the lane function type (left turn, straight, right turn, variable lane, etc.), the import direction, the road grade and the like; b) delay index data, including the delay index and the corresponding service level of each flow direction of each import direction, and a fixed update frequency is set.

[0025] Variable lane dynamic analysis and effective lane number statistics: based on the acquired intersection static model data, especially the type and spatial topological relationship (such as lane position, adjacent lane function) of the lane, the specific function flow combination (such as “left turn + straight” or “straight + right turn” and the like) of the variable lane is dynamically inferred and judged through the pre-set logical rules. According to the judgment result, the lane number is evenly divided according to the flow direction combination of the variable lane, and the effective left turn, straight and right turn lane numbers of each import direction are counted.

[0026] Basic green ratio calculation: according to the effective lane numbers of each flow direction, the intersection imports are divided into east-west direction group and south-north direction group. The maximum lane numbers of left turn and straight of each group are calculated, and the basic green ratio of each phase (east-west straight, east-west left turn, south-north straight, south-north left turn) is calculated, which reflects the signal time allocation proportion determined by the basic lane configuration of the intersection.

[0027] Flow direction delay green ratio calculation: According to the obtained real-time delay index data, combined with the effective lane number of each flow direction, the flow direction delay green ratio of each flow direction is calculated. The calculation includes preprocessing of the original delay data, calculation of the flow direction comprehensive delay index, calculation of the intersection comprehensive delay index, and numerical range verification and correction of the flow direction delay green ratio, aiming to reflect the demand of the current real-time traffic congestion situation on signal timing.

[0028] Period automatic division and weight allocation: Based on historical delay index data, different traffic periods in a day (such as morning peak, evening peak, and flat peak) are automatically identified and divided; and the fusion weight of the basic green ratio and the flow direction delay green ratio is dynamically allocated for each period. Higher weight is given to the flow direction delay green ratio in peak hours to quickly respond to congestion, and higher weight is given to the basic green ratio in flat peak hours.

[0029] Final green ratio calculation and capacity assessment: According to the basic green ratio, the flow direction delay green ratio and the fusion weight, the final green ratio of each flow direction is calculated by weighted average; the final green ratio of a certain flow direction is multiplied by the lane reduction coefficient, the road grade reduction coefficient and the single lane capacity to obtain the final capacity value of the import direction of the flow direction.

[0030] As a specific example, in the calculation of the basic green ratio and the flow direction delay green ratio and the final capacity assessment, the effective lane number of the flow direction is needed to participate in the calculation. The acquisition of the effective lane number of the flow direction includes two steps of dynamically analyzing the functional flow combination of variable lanes and lane number statistics according to the functional flow results.

[0031] First, as shown in Figure 2 Dynamic analysis of functional flow combination of variable lanes includes: When the variable lane is located at the leftmost side, if the adjacent lane on the right side has no left turn function, it is a combination of left turn and straight; if the adjacent lane on the right side has a left turn function, it is a left turn; When the variable lane is located at the rightmost side, if the adjacent lane on the left side has no right turn function, it is a combination of straight and right turn; if the adjacent lane on the left side has a right turn function, it is a right turn; If all lanes are variable lanes, the proportion of left, straight and right functions is allocated according to the number of lanes.

[0032] Specifically, when the lane is marked as "G" (variable lane), if the variable lane is located at the leftmost side and the adjacent right lane has no left-turn flow (marked as 1), the flow combination of the variable lane is determined as "1, 2", i.e. left turn or straight. This is because in this case, the leftmost variable lane can flexibly allocate part of the time to straight vehicles to improve lane utilization when there is no right side left turn lane to compete for left turn resources. If the variable lane is located at the leftmost side and the adjacent right lane has left-turn flow, the flow combination of the variable lane is determined as "1", i.e. left turn. Since there is a dedicated left turn lane on the right side, the variable lane focuses on left turn function in order to avoid traffic conflicts.

[0033] When the variable lane is located at the rightmost side and the adjacent left lane has no right-turn flow (marked as 3), the flow combination of the variable lane is determined as "2, 3", i.e. straight or right turn. At this time, the rightmost variable lane can reasonably allocate time to straight and right turn vehicles to optimize traffic flow when there is no right turn lane on the left side to compete for right turn resources. If the variable lane is located at the rightmost side and the adjacent left lane has right-turn flow, the flow combination of the variable lane is determined as "3", i.e. right turn. Because there is a dedicated right turn lane on the left side, the variable lane mainly performs straight function to avoid conflicts.

[0034] When all lane types of the import lane are variable lanes, the number of lanes is determined and the corresponding left, straight and right flow types are returned according to the self-defined rules (or pre-set rules). For example, when there are two variable lanes, the function combination of ["1", "2"] or ["2", "3"] can be set; when there are three variable lanes, the function combination of ["1", "2", "3"] can be set; when there are four variable lanes, the function combination of ["1", "2", "2", "3"] can be set; when there are five variable lanes, the function combination of ["1", "1, 2", "2", "2", "3"] can be set, and so on. Each intersection can be pre-set according to actual needs.

[0035] For other variable lanes "G" whose position and adjacent lane relationship are not explicitly defined, the flow of the variable lane is uniformly determined as "2", i.e. straight. This is based on the traffic rules that in most cases, such lanes perform straight function by default without special identification and conditions.

[0036] After the analysis of the functional flow combination of the variable lane, the corresponding effective lane number of the straight, left turn and right turn flow is counted. The counting rules are as follows: if the lane corresponds to the flow type of "1", the number of left turn lanes +1; if the lane corresponds to the flow type of "2", the number of straight lanes +1; if the lane corresponds to the flow type of "3", the number of right turn lanes +1; if the lane corresponds to the flow type of "1, 2", the number of left turn lanes +0.5, and the number of straight lanes +0.5; if the lane corresponds to the flow type of "2, 3", the number of right turn lanes +0.5, and the number of straight lanes +0.5; if the lane corresponds to the flow type of "1, 2, 3", the number of left turn lanes +0.33, the number of straight lanes +0.33, and the number of right turn lanes +0.33.

[0037] For example, assuming that the lane function list of a certain entrance lane (south entrance) is ["G", "2", "2", "3"]. Lane 1, the leftmost lane, is "G" (variable lane), so the right adjacent lane (lane 2) is determined to have a function of "2" (straight), which does not include left turn function. Apply the rule: if the variable lane is located at the leftmost side and the right adjacent lane has no left turn function, the flow combination of the variable lane is determined to be "1, 2", i.e. the combination of left turn and straight. Therefore, the number of left turn lanes +0.5, and the number of straight lanes +0.5. Lane 2 is an explicit straight lane, so the number of straight lanes +1. Lane 3 is an explicit straight lane, so the number of straight lanes +1. Lane 4 is an explicit right turn lane, so the number of right turn lanes +1. The result output: the effective lane number of this south entrance lane is: left turn 0.5, straight 2, right turn 1.

[0038] Optionally, for other variable lanes "G" that are not explicitly defined in position and adjacent lane relationship, further consideration can be given to the case where several lanes in the middle position are variable lanes.

[0039] If the left adjacent lane of a variable lane is a left turn function and the right adjacent lane is a straight function, it is a combination of left turn and straight; if there are several variable lanes between the left turn lane and the straight lane, the proportion of left turn and straight is allocated according to the number of lanes. If the left adjacent lane of a lane is a straight function and the right adjacent lane is a right turn function, it is a combination of straight and right turn; if there are several variable lanes between the left straight lane and the right right turn lane, the proportion of straight and right turn is allocated according to the number of lanes.

[0040] Through the above lane function analysis based on spatial topological relationship, the multi-flow combination of the variable lane can be accurately determined, and the effective lane number of each flow can be obtained, which provides accurate data support for subsequent traffic capacity calculation.

[0041] As a specific embodiment, the effective lane numbers of each flow direction are determined based on intersection static model data, the maximum lane numbers of each phase are determined, and the basic green ratio of the corresponding phase is calculated. After the effective lane numbers of each flow direction are obtained, the maximum lane number calculation of each phase and the basic green ratio calculation of each phase are included.

[0042] The process of determining the maximum lane number of each phase includes: Based on the lane type and spatial topological relationship in the intersection static model data, the functional flow direction combination of the variable lane is dynamically analyzed, and the effective lane numbers of each import direction left turn and straight are counted.

[0043] The intersection is divided into two groups according to the import direction. The first group contains east to west and west to east (import direction = 1 or 3), and the second group contains south to north and north to south (import direction = 2 or 4). If there are first group and second group, the intersection signal phase is calculated according to four, phase one is east-west import straight, phase two is east-west import left turn, phase three is south-north import straight, and phase four is south-north import left turn.

[0044] When calculating the maximum lane number of each phase, The maximum value of the effective lane numbers of east import left turn and west import left turn is taken as the maximum lane number of the east-west import left turn phase; The maximum value of the effective lane numbers of east import straight and west import straight is taken as the maximum lane number of the east-west import straight phase; the maximum value of the effective lane numbers of south import left turn and north import left turn is taken as the maximum lane number of the south-north import left turn phase; and the maximum value of the effective lane numbers of south import straight and north import straight is taken as the maximum lane number of the south-north import straight phase.

[0045] The process of calculating the basic green ratio of the corresponding phase includes: The maximum lane number of each phase includes the maximum lane number of the east-west import left turn phase and the straight phase, and the maximum lane number of the south-north import left turn phase and the straight phase; The maximum lane numbers of all phases are summed to obtain the total maximum lane number; The basic green ratio of each phase is the ratio of its corresponding maximum lane number to the total maximum lane number.

[0046] The basic green ratio of the east-west import left turn phase is the maximum lane number of the east-west import left turn phase divided by the total maximum lane number; the basic green ratio of the east-west import straight phase is the maximum lane number of the east-west import straight phase divided by the total maximum lane number; the basic green ratio of the south-north import left turn phase is the maximum lane number of the south-north import left turn phase divided by the total maximum lane number; and the basic green ratio of the south-north import straight phase is the maximum lane number of the south-north import straight phase divided by the total maximum lane number.

[0047] In addition to the left-turn and straight flow directions, there is also a right-turn flow direction in the lane; since the right-turn vehicles are less restricted by traffic than the left-turn and straight vehicles, in general, a relatively fixed green split is given to the right-turn flow direction on the basis of ensuring the basic traffic demand of other flow directions, and a fixed value of 0.15 is assigned by default.

[0048] The contents of the present embodiment will be described in detail below with a specific case. The number of lanes of each entrance of a certain intersection is as follows: 1 left-turn lane, 3 straight lanes, and 1 right-turn lane for the east entrance; 2 left-turn lanes, 2 straight lanes, and 1 right-turn lane for the south entrance; 2 left-turn lanes, 4 straight lanes, and 1 right-turn lane for the west entrance; 1 left-turn lane, 3 straight lanes, and 1 right-turn lane for the north entrance.

[0049] Based on the 1 left-turn lane of the east entrance and the 2 left-turn lanes of the west entrance, the maximum number of lanes of the east-west entrance left-turn phase is 2; based on the 3 straight lanes of the east entrance and the 4 straight lanes of the west entrance, the maximum number of lanes of the east-west entrance straight phase is 4; based on the 2 left-turn lanes of the south entrance and the 1 left-turn lane of the north entrance, the maximum number of lanes of the south-north entrance left-turn phase is 2; based on the 2 straight lanes of the south entrance and the 3 straight lanes of the north entrance, the maximum number of lanes of the south-north entrance straight phase is 3.

[0050] The maximum number of lanes of the four phases is added to obtain the total maximum number of lanes, which is 2+4+2+3=11. Therefore, the basic green split of the east-west entrance left-turn phase is 2 / 11; the basic green split of the east-west entrance straight phase is 4 / 11; the basic green split of the south-north entrance left-turn phase is 2 / 11; and the basic green split of the south-north entrance straight phase is 3 / 11. The basic green split of the right-turn flow direction is set to a fixed value of 0.15.

[0051] The calculation of the basic green split takes into account the actual lane configuration, and the more lanes there are, the greater the basic green split obtained. The sum of the green splits of the left-turn and straight flow directions is 1, which ensures the rationality of the allocation and adapts to the characteristics of different intersections. The calculation process is simple and clear, and the result is interpretable. This basic green split will serve as the basis for subsequent calculation of the final green split, together with the flow direction delay green split, to determine the final signal timing scheme.

[0052] As a specific embodiment, the calculation of the flow direction delay green split includes: calculating a flow direction comprehensive delay index, which is represented as the product of the real-time delay index data of the corresponding flow direction and the number of effective lanes thereof; calculating the sum of the comprehensive delay indexes of all left-turn flow directions and straight flow directions of the intersection to obtain the intersection comprehensive delay; the flow direction delay green split of each flow direction is the ratio of its comprehensive delay index to the intersection comprehensive delay. The calculated flow direction delay green ratio is verified and corrected for its numerical range.

[0053] It should be noted that the flow direction delay green ratio in this invention is a green ratio calculated based on real-time delay index data, reflecting the current congestion demand in each flow direction. The aim is to dynamically optimize signal timing response to real-time traffic congestion conditions. The required parameters include real-time delay index data for each flow direction at each approach and the number of effective lanes in each flow direction.

[0054] Specifically, this includes: obtaining real-time flow-level delay index data from Gaode Maps, with the delay index data ranging from 0 to 100.

[0055] In data preprocessing, to prevent extremely small delay values ​​(such as close to 0, which represents extremely smooth operation) from causing calculation instability or excessively small numerators and denominators, delay indicators less than 10 are uniformly set to 10 to ensure the rationality and stability of the data.

[0056] The comprehensive flow delay index is calculated by combining congestion level (real-time delay index data) and lane size (number of effective lanes). Directions with higher congestion levels and more lanes have greater traffic demand. The comprehensive flow delay index is calculated by multiplying the real-time delay index data for that flow direction by its number of effective lanes.

[0057] The overall intersection delay is calculated by summing the overall delay indices for all left-turn and straight-ahead traffic flows. It's important to note that right-turn traffic is typically processed separately and is not included in the summation of left-turn and straight-ahead traffic flows, as it has higher traffic efficiency and is less dependent on signal timing.

[0058] To calculate the delay green ratio, for left-turn and straight-ahead traffic flows: the flow delay green ratio is the overall flow delay index of that flow flow divided by the overall intersection delay; for right-turn traffic flows, a fixed value is assigned, for example, the flow delay green ratio for right-turn traffic flows is 0.1.

[0059] Numerical range verification and correction are necessary to prevent abnormal congestion or other abnormal delay indicators from causing the calculated flow delay green ratio to be too high (closing out time for other flows) or too low (failing to receive basic services), thus affecting the overall stability of the system. This requires correction. A secondary numerical range verification is performed on the calculated flow delay green ratio. For example, if the flow delay green ratio is <0.1, it is corrected to 0.1; if the flow delay green ratio is >0.3, it is corrected to 0.3. 0.1 is the lower threshold, and 0.3 is the upper threshold. The specific threshold range can be adjusted according to actual needs.

[0060] The flow delay green ratio is obtained based on the number of import lanes in the previous embodiment. East import left turn delay index is 25 (lane number 1), east import straight delay index is 40 (lane number 3), west import left turn delay index is 30 (lane number 2), and west import straight delay index is 50 (lane number 4). South import left turn delay index is 15 (lane number 2), south import straight delay index is 20 (lane number 2), north import left turn delay index is 5, which is repaired to 10 (lane number 1), and north import straight delay index is 35 (lane number 3).

[0061] For each flow direction comprehensive delay index, the east import left turn flow direction is 25, the east import straight flow direction is 120, the west import left turn flow direction is 60, the west import straight flow direction is 200, the south import left turn flow direction is 30, the south import straight flow direction is 40, the north import left turn flow direction is 10, and the north import straight flow direction is 105. The intersection comprehensive delay is the sum of all flow direction comprehensive delay indexes, which is 590.

[0062] The flow direction delay green ratio of each flow direction is calculated. The east import left turn flow direction is 25 / 590, which is repaired to 0.1 because the value is less than 0.1. The east import straight flow direction is 120 / 590, which is about 0.2034. The west import left turn flow direction is 60 / 590, which is about 0.1017. The west import straight flow direction is 200 / 590, which is repaired to 0.3 because the value is greater than 0.3. The south import left turn flow direction is 30 / 590, which is repaired to 0.1 because the value is less than 0.1. The south import straight flow direction is 40 / 590, which is repaired to 0.1 because the value is less than 0.1. The north import left turn flow direction is 10 / 590, which is repaired to 0.1 because the value is less than 0.1. The north import straight flow direction is 105 / 590, which is about 0.1780. If the sum of all flow direction delay green ratios is greater than 1, the total sum is 1 by proportionally reducing the calculation results.

[0063] As a specific embodiment, the period division based on historical delay index data includes: Divide the historical delay index data into statistical periods of a preset length; Calculate the number of congestion samples in each statistical period; according to whether the number of congestion samples exceeds a preset number threshold, mark the statistical period as a traffic congestion period or a non-congestion period; the congestion sample is historical delay index data with a service level exceeding a preset level threshold; Integrate adjacent statistical periods with the same mark, and constrain the total number of periods divided in a day to obtain the final period division result.

[0064] Specifically, data processing is included, historical data is processed according to workdays and weekends respectively, and a 30-minute period is taken as a statistical period (for example, 00:00-00:30, 00:30-01:00, …, 23:30-00:00). Each statistical period contains 6 historical delay index data with a granularity of 5 minutes as a sample.

[0065] Congestion judgment: for each statistical period, the sum n of the number of samples with service levels of D, E and F is calculated. That is, the number of congestion samples with historical delay index data greater than or equal to 35 is calculated.

[0066] Labeling peak hours: a preset number threshold is set, for example, the preset number threshold is one-tenth of the total number of samples (for example, the total number of samples in a statistical period of five workdays is 5 multiplied by 6, which is 30, and the preset number threshold is 3; if the calculation result is a decimal number, rounding up or rounding down can be selected). If n obtained in the congestion judgment process is greater than or equal to 3, the period is marked as a peak hour (flag=1), otherwise it is a flat peak (flag=0). It should be noted that the preset number threshold can be adjusted according to the size of the city and the distribution of data.

[0067] Period integration: adjacent statistical periods with the same flag are combined into a large period (for example, 7:00-7:30, 7:30-8:00, 8:00-8:30 are combined into early peak 7:00-8:30). Constraint: the total number of periods divided each day should be between 4 and 6. This is an empirical constraint to avoid overly complex solutions, and it can also be modified as needed. If there are less than 4 after merging, a default division scheme is used (for example: early peak 7:00-9:30, daytime flat peak 9:30-16:30, evening peak 16:30-19:00, night flat peak 19:00-7:00). If there are more than 6, adjacent periods with the shortest duration are combined until the requirement is met.

[0068] After completing the period division, the fusion weight of the basic green ratio and the flow delay green ratio in each period is allocated, including: According to the congestion degree of each period, the periods are sorted in descending order of the number of congestion samples; the congestion sample is the historical delay index data with a service level exceeding a preset level threshold; The period with the highest congestion degree is allocated the highest delay green ratio weight, and the period with the lowest congestion degree is allocated the lowest delay green ratio weight; the delay green ratio weight of the remaining periods is determined by linear interpolation between the highest value and the lowest value according to its order.

[0069] It should be noted that, in the present application, the basic green ratio and the flow delay green ratio are combined, and the final green ratio is determined through a space-time weight adaptive mechanism. In different time periods, different weights w1 and w2 (the sum of which is 1) are set to fuse the basic green ratio and the flow delay green ratio. For example, during the morning peak period, due to the large and complex traffic flow, a higher weight w2 can be given to the flow delay green ratio to respond to the traffic congestion situation more timely; and during the night, the traffic flow is relatively stable, and a higher weight w1 can be given to the basic green ratio.

[0070] Specifically, for the divided N time periods, the congestion sample number n in each time period is sorted in descending order, and the largest n is the most congested time period (order = 1), and the smallest n is the most unobstructed time period (order = N). The flow delay green ratio is assigned the highest weight in the most congested time period (order = 1), which represents strong responsiveness, such as w2 = 0.7 (corresponding to the basic green ratio w1 = 0.3). The flow delay green ratio is assigned the lowest weight in the least congested time period (order = N), which represents stability, such as w2 = 0.3 (corresponding to the basic green ratio w1 = 0.7). The intermediate time periods are linearly interpolated in order, and w2 = 0.7-[(0.7-0.3) / (N-1)]*(order-1).

[0071] As a specific example, the passing capacity of each flow direction of the intersection is evaluated based on the final green ratio and the theoretical passing capacity, including: The passing capacity of each flow direction is the product of the final green ratio of the flow direction and the theoretical passing capacity of the flow direction; The theoretical passing capacity of the flow direction is the product of the single-lane passing capacity and the road grade reduction coefficient, multiplied by the lane reduction coefficient corresponding to the effective lane number of the flow direction.

[0072] It should be noted that, in the calculation process of the final green ratio, through this dual-source green ratio fusion mechanism, the basic lane configuration data and real-time delay data can be fully utilized, and the green ratio can be dynamically adjusted according to the traffic demand and real-time traffic conditions in different time periods, so as to realize the optimal control of traffic signals and effectively alleviate traffic congestion.

[0073] In the process of evaluating the passing capacity of the flow direction, in addition to the final green ratio of the flow direction, parameters such as single-lane passing capacity, road grade reduction coefficient, and lane reduction coefficient are also needed.

[0074] Generation of single-lane passing capacity under different road grades: different road grades correspond to different design speeds, and the larger the speed, the smaller the headway of consecutive vehicles, i.e. the more vehicles that can pass continuously. According to the measured small car headway at different speeds mentioned in the “Urban Road Cross Section Planning and Design”, the passing capacity can be calculated as shown in the following table:

[0075] According to the speed and road grade mapping table as follows:

[0076] Further, the single-lane capacity of each road grade is calculated:

[0077] Through this parameter generation algorithm, the key parameters can be automatically generated according to the historical traffic peak mode to adapt to different traffic scenarios, avoiding the limitations of manually setting fixed experience parameters, and significantly improving the accuracy and reliability of the capacity calculation.

[0078] Considering the actual use efficiency of the lane, the capacities of different lanes are not completely the same. For example, the lane close to the center line of the intersection may be affected by more traffic conflicts, and the capacity will be reduced. According to a large amount of actual observation data and traffic engineering experience, the lane number reduction coefficient is determined. The lane number reduction coefficient table of the motor vehicle lane is as follows:

[0079] Road grade reduction coefficient: the better the road grade, the higher the required service level, that is, the larger the allowed capacity reduction space, so the grade reduction coefficient is small, on the contrary, the low-grade branch, the reduction coefficient is larger, the road reduction coefficient of the motor vehicle lane is as follows:

[0080] The capacity of a certain flow is the product of the final green ratio of the flow, the single-lane capacity, the road grade reduction coefficient, and the lane reduction coefficient corresponding to the effective lane number of the flow. In the case where the effective lane number is not an integer, the corresponding lane reduction coefficient can be obtained by function fitting (such as using a quadratic polynomial for fitting) according to the corresponding data group of the one-way lane number and the reduction coefficient in the lane number reduction coefficient table, and substituting the effective lane number into the fitted function.

[0081] Specifically, for an intersection model where the road classification is arterial road, the number of through lanes at the west entrance is 4, the flow delay green light ratio for through traffic at the west entrance is 0.3, and the base green light ratio for the through traffic phase at the east and west entrances is 0.36 (i.e., the base green light ratio for through traffic at the west entrance is 0.36). The time periods are divided into four segments, ordered by congestion level: morning peak, evening peak, daytime off-peak, and nighttime off-peak. During the morning peak, w1 is 0.3, w2 is 0.7, the lane conversion factor corresponding to the number of lanes is 3.25, the single-lane capacity corresponding to the arterial road is 1730, and the road classification reduction factor is 0.8. Therefore, by (0.3*0.36+0.7*0.3)*3.25*1730*0.8, the capacity of the through traffic at the west entrance can be calculated as 1430.

[0082] In addition to a method for assessing intersection capacity based on multi-source data fusion, this invention also provides, for example, a method for assessing intersection capacity based on multi-source data fusion. Figure 4 The aforementioned intersection capacity assessment system based on multi-source data fusion includes: The data acquisition module acquires static model data and delay index data for intersections; the data acquisition module is configured to access the Gaode API interface to acquire static model data and delay index data for intersections. The lane analysis module analyzes the variable lane function based on lane type and spatial topology, and counts the number of effective lanes in each direction of flow. The green ratio generation module calculates the basic green ratio and the flow delay green ratio, and then calculates the final green ratio. The weight generation module automatically divides time periods based on historical delay index data and determines the green credit ratio fusion weight for each time period; The traffic capacity assessment module calculates the traffic capacity for each flow direction by integrating various parameters.

[0083] It should be noted that traditional traffic signal control systems often employ fixed green light ratio schemes, which cannot adapt to the drastic fluctuations in traffic flow at different times of the day (such as morning rush hour, evening rush hour, and off-peak hours). This leads to exacerbated congestion during peak hours and wasted green light time during off-peak hours, resulting in low efficiency in road resource utilization. They lack mechanisms for coordinating and optimizing real-time traffic conditions (such as delays) with historical data and basic road configurations; they often rely on historical traffic flow data or fixed empirical parameters, resulting in slow response to real-time traffic events and insufficient timeliness and accuracy in calculation results. Furthermore, traditional traffic signal control systems heavily rely on hardware detector technologies (such as loop detectors and video vehicle detectors), which have significant bottlenecks; their coverage is limited, construction and maintenance costs are high, and they are prone to damage, resulting in a lack of necessary traffic flow data at many intersections, making it difficult to achieve refined and large-scale capacity calculation and application.

[0084] In addition, with the wide application of variable lanes, the dynamic change characteristics of the lane function are not effectively quantified and modeled by traditional models. The prior art usually statically divides the lane function, and when calculating the traffic capacity, either ignores the variable lane or simply classifies it as a certain fixed flow direction, which undoubtedly introduces a large calculation error and cannot truly reflect the dynamic traffic capacity of the intersection over time. The system of the present application can effectively fuse the intersection static model data and real-time delay data, significantly improve the calculation accuracy through dynamic analysis of the lane function and adaptive distribution of multi-period weights, and calculate the traffic capacity of all intersections without relying on intersection equipment and traffic; especially suitable for variable lane scenarios, realizing adaptive optimization calculation of traffic intersection capacity at different time periods.

[0085] Specifically, the data acquisition module includes an intersection model acquisition unit and a delay data acquisition unit.

[0086] The intersection model acquisition unit acquires the intersection model data of the acquisition area through the Gaode API interface in a timed or triggered manner, and the data content includes: municipal number, regional number included under the municipal level, intersection id, import direction (east, south, west, north), road grade, main lane number, main lane corresponding flow type details (including left turn, straight, right turn, left straight, left U-turn, U-turn, right straight, left straight right, variable lane, etc.), auxiliary lane number, auxiliary lane corresponding flow type details, etc.; left turn can be marked with "1", straight with "2", right turn with "3", variable lane with "G", etc.

[0087] The delay data acquisition unit acquires the real-time delay index data and the corresponding service level data of the regional intersection import flow direction through the Gaode API interface in a timed or triggered manner, and the data content includes: municipal number, regional number included under the municipal level, intersection id, import direction (east, south, west, north), flow type (left, straight, right, U-turn), delay index (range 0-100), service level (ABCDEF, corresponding delay index range [0, 10), [10, 20), [20, 35), [35, 55), [55, 80), [80, 100]), update time (for example, update frequency is 5 minutes).

[0088] The lane analysis module includes a variable lane analysis unit and a flow lane number calculation unit.

[0089] The variable lane analysis unit dynamically infers and determines the specific function flow combination (such as "left turn + straight" or "straight + right turn") of the variable lane based on the acquired intersection static model data, especially the type and spatial topological relationship of the lane (such as lane position, adjacent lane function), through the pre-set logical rules.

[0090] The flow direction lane number calculation unit calculates the number of effective left-turn, straight and right-turn lanes for each import direction according to the flow direction combination of the variable lane and the number of lanes divided equally according to the number of flow direction combinations of the variable lane.

[0091] The green ratio generation unit includes a basic green ratio unit, a flow direction delay green ratio unit and a green ratio fusion unit.

[0092] The basic green ratio unit divides the import of the intersection into east-west and north-south groups according to the number of effective lanes of each flow direction. The maximum number of lanes for left-turn and straight in each group is calculated, and the total maximum number of lanes of the intersection is solved. The basic green ratio of each phase (east-west straight, east-west left-turn, north-south straight, north-south left-turn) is calculated by dividing the maximum number of lanes of each phase by the total maximum number of lanes. The basic green ratio reflects the proportion of signal time allocation determined by the basic lane configuration of the intersection.

[0093] The flow direction delay green ratio unit calculates the flow direction delay green ratio (flow direction comprehensive delay index divided by intersection comprehensive delay index) of each flow direction according to the real-time delay index data obtained and the number of effective lanes of each flow direction. The calculation includes preprocessing of the original delay data, calculation of the flow direction comprehensive delay index (product of the number of effective lanes of the flow direction and the implementation delay index data), calculation of the intersection comprehensive delay index (sum of all flow direction comprehensive delay indexes), and range checking and correction of the flow direction delay green ratio, aiming to reflect the demand of current real-time traffic congestion on signal timing.

[0094] The green ratio fusion unit calculates the final green ratio of each flow direction in a period by weighted average according to the basic green ratio, the flow direction delay green ratio and the fusion weight of the period. The flow direction delay green ratio has a higher weight in peak hours, and the basic green ratio has a higher weight in off-peak hours.

[0095] The weight generation module includes a period division unit and a fusion weight distribution unit.

[0096] The period division unit automatically identifies and divides different traffic periods (such as morning peak, evening peak and off-peak period) in a day based on historical delay index data; divides the historical delay index data according to a preset length of statistical period; and according to the comparison result of the number of congestion samples and the preset number threshold, marks the statistical period as a traffic congestion period or a non-congestion period; merge adjacent statistical periods with the same mark, and constrain the lower and upper limits of the number of periods divided in a day, to obtain the final period division result.

[0097] The fusion weight distribution unit sorts the time periods according to the congestion degree of the congestion samples in the time periods in descending order; the time period with the highest congestion degree is assigned the highest delay green ratio weight, the time period with the lowest congestion degree is assigned the lowest delay green ratio weight, and the delay green ratio weights of the remaining time periods are determined according to linear interpolation between the highest value and the lowest value.

[0098] The capacity calculation module multiplies the final green ratio of a flow direction with a lane reduction coefficient, a road grade reduction coefficient and a single-lane passable capacity to obtain a passable capacity evaluation result of the flow direction in the import direction. The calculated dynamic passable capacity data can be output to various application systems, such as an adaptive signal control system as a key input parameter for optimizing signal timing, a traffic situation analysis system for evaluating road network operation state and congestion bottlenecks, navigation software for more accurate path planning and travel time estimation, or a visual form (such as a Web interface, a large screen) for displaying to traffic management personnel to assist decision-making.

[0099] The above embodiments are further elaboration and illustration of the present application, so as to facilitate understanding, and are not any limitation on the present application, and any modification, equivalent replacement and improvement within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for assessing intersection capacity based on multi-source data fusion, characterized in that, include: Acquire static model data and delay index data for the intersection; Based on the static model data of the intersection, the effective number of lanes in each direction is counted, the maximum number of lanes in each phase is determined, and the basic green ratio of the corresponding phase is calculated. The comprehensive flow delay index is calculated based on real-time delay index data and the number of flow lanes, and the ratio of the comprehensive delay index at the intersection is used as the flow delay green signal ratio. Based on historical delay index data, time periods are divided, and the fusion weights of the basic green credit ratio and the flow delay green credit ratio are assigned to each time period; Calculate the final green ratio for each flow direction; The traffic capacity of each direction at the intersection is assessed based on the final green light ratio and theoretical traffic capacity.

2. The method for assessing intersection capacity based on multi-source data fusion according to claim 1, characterized in that, Determining the maximum number of lanes for each phase includes: Based on the lane types and spatial topology relationships in the static model data of the intersection, the functional flow combination of the variable lanes is dynamically analyzed, and the number of effective lanes for left turns and straight-through traffic in each approach direction is counted. The maximum number of lanes for left turns at the east and west entrances is taken as the maximum number of lanes for the left-turn phase at the east and west entrances. The same method is used to determine the maximum number of lanes for the straight-ahead phase at the east and west entrances, the left-turn phase at the north and south entrances, and the straight-ahead phase at the north and south entrances, respectively.

3. A method for assessing intersection capacity based on multi-source data fusion according to claim 1 or 2, characterized in that, The calculation of the basic green light ratio for the corresponding phase includes: The maximum number of lanes in each phase includes the maximum number of lanes in the left-turn phase and straight-ahead phase at the east and west entrances, and the maximum number of lanes in the left-turn phase and straight-ahead phase at the north and south entrances; The total maximum number of lanes is obtained by summing the maximum number of lanes for all phases. The base green light ratio for each phase is the ratio of the maximum number of lanes in that phase to the total maximum number of lanes.

4. The method for assessing intersection capacity based on multi-source data fusion according to claim 2, characterized in that, The functional flow combination of the dynamically analyzed variable lane includes: When the reversible lane is located on the far left, if the adjacent lane on the right does not have a left-turn function, it is a combination of left turn and straight-ahead; if the adjacent lane on the right has a left-turn function, it is a left turn. When the reversible lane is located on the far right, if the adjacent lane on the left does not have a right turn function, it is a combination of going straight and turning right; if the adjacent lane on the left has a right turn function, it is a right turn. If all lanes are reversible lanes, then the left, straight, and right lane functions are allocated according to the number of lanes.

5. A method for assessing intersection capacity based on multi-source data fusion according to claim 1, 2, or 4, characterized in that, The calculation of the green light ratio for the flow delay includes: The comprehensive delay index for each flow direction is calculated and expressed as the product of the real-time delay index data for the corresponding flow direction and the number of effective lanes. The overall intersection delay is obtained by summing the combined delay indices of all left-turn and straight-through traffic flows at the intersection. The green light ratio for flow delay in each direction is the ratio of its overall delay index to the overall delay at the intersection; The calculated flow direction delay green ratio is verified and corrected for its numerical range.

6. A method for assessing intersection capacity based on multi-source data fusion according to claim 1, 2, or 4, characterized in that, The fusion weights for allocating the base green ratio and the flow delay green ratio for each time period include: The congestion levels of each time period are sorted in descending order based on the number of congestion samples within that time period; the congestion samples are historical delay index data where the service level exceeds a preset threshold. The time period with the highest congestion level is assigned the highest delay green ratio weight, and the time period with the lowest congestion level is assigned the lowest delay green ratio weight; the delay green ratio weight for other time periods is determined by linear interpolation between the highest and lowest values ​​according to their ranking.

7. The method for assessing intersection capacity based on multi-source data fusion according to claim 6, characterized in that, The time period division based on historical delay index data includes: The historical delay index data is divided into statistical periods of preset length; Calculate the number of congestion samples in each statistical period; based on whether the number of congestion samples exceeds a preset threshold, mark the statistical period as a traffic congestion period or a non-congestion period; Adjacent statistical periods with the same label are integrated, and the total number of time periods within a day is constrained to obtain the final time period division result.

8. A method for assessing intersection capacity based on multi-source data fusion according to claim 1, 2, 4, or 7, characterized in that, The assessment of the traffic capacity of each direction at the intersection based on the final green ratio and theoretical capacity includes: The capacity of each flow direction is the product of the final green ratio of the flow direction and the theoretical capacity of that flow direction; The theoretical traffic capacity of a flow direction is: the product of the single-lane traffic capacity and the road grade reduction factor, multiplied by the lane reduction factor corresponding to the effective number of lanes in that flow direction.

9. A crossroads capacity assessment system based on multi-source data fusion, applicable to the crossroads capacity assessment method as described in any one of claims 1-8, characterized in that, include: The data acquisition module acquires static model data and delay index data for the intersection; The lane analysis module analyzes the variable lane function based on lane type and spatial topology, and counts the number of effective lanes in each direction of flow. The green ratio generation module calculates the basic green ratio and the flow delay green ratio, and then calculates the final green ratio. The weight generation module automatically divides time periods based on historical delay index data and determines the green credit ratio fusion weight for each time period; The traffic capacity assessment module calculates the traffic capacity for each flow direction by integrating various parameters.

10. The intersection capacity assessment system based on multi-source data fusion according to claim 9, characterized in that, The data acquisition module is configured to access the Gaode API interface to obtain static model data and delay index data of the intersection.

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