Intersection traffic organization robustness optimization method, system, equipment and medium

Abstract

The present invention discloses a method, system, device and medium for optimizing traffic organization robustness at an intersection. The method comprises the following steps: determining the geometric structure of an intersection; determining, based on historical data of the intersection, X travel demand scenarios including car traffic and large vehicle traffic, the maximum and minimum permitted cycle durations of the intersection, the maximum and minimum permitted green light durations of the phase, the green light interval, and the maximum acceptable saturation; determining the conversion coefficients of left-turning, straight-going, and right-turning car traffic and large vehicle traffic on each lane in each direction of the intersection; determining the mutual influence coefficients of the conversion coefficients of different types of left-turning, straight-going, and right-turning traffic on each lane of the intersection due to the influence of the other two types of turning traffic; constructing a conflict set based on the running trajectories of different turning traffic flows in the intersection; and determining, based on a preset intersection traffic organization robustness optimization model, a lane organization plan for the intersection and corresponding signal timing plans for each of the X travel demand scenarios.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent transportation technology, and in particular relates to a method, system, equipment and medium for optimizing the robustness of intersection traffic organization. Background Art

[0002] With the rapid development of my country's economy and the continuous advancement of urbanization, the number of urban motor vehicles has increased rapidly, and residents' travel needs have increased significantly. Despite the continuous construction of transportation infrastructure, cities are becoming increasingly congested. Much of this congestion is mainly caused by the unreasonable configuration of intersection control measures. To improve travel efficiency at intersections, traditional intersection management mainly adjusts intersection signal schemes based on manually collected data or coil data to make them as suitable as possible for the current intersection traffic flow. Although this has achieved certain results, once the intersection traffic volume exceeds a certain threshold, simply adjusting the signal scheme is unable to alleviate the current intersection congestion.

[0003] To this end, some scholars have proposed optimizing intersection traffic organization schemes to further alleviate intersection congestion. Currently, two approaches exist in this area. One approach involves proposing unconventional organization schemes, such as tandem control and continuous flow control, to alleviate intersection congestion. The other approach involves optimizing conventional organization schemes based on collected traffic flow data. However, unconventional organization schemes can easily confuse drivers and are difficult to implement in practice. Optimizing conventional organization schemes, on the other hand, has limited effectiveness in alleviating intersection congestion.

[0004] On the other hand, the aforementioned intersection traffic organization scheme optimization is primarily based on traffic flow or average traffic flow at a specific time period. However, in actual traffic systems, traffic flow arriving at an intersection is not fixed but varies over time, exhibiting significant time-varying fluctuations. The optimal traffic organization scheme may differ for different travel demand scenarios. While a particular traffic organization scheme may be well-suited for certain travel demand scenarios, it may also severely impact travel efficiency at the intersection in other travel demand scenarios, reducing the scheme's practical application.

[0005] Therefore, there is an urgent need for a traffic organization plan optimization method that can significantly improve intersection capacity, alleviate intersection congestion, and adapt well to different travel needs, thereby overcoming the defects of unconventional organization plans that easily cause driver confusion and conventional organization plan optimization that has limited effect on alleviating intersection congestion. Summary of the Invention

[0006] In view of this, the main purpose of the present invention is to provide a method, system, device and medium for optimizing the robustness of intersection traffic organization.

[0007] To achieve the above object, the technical solution of the present invention is achieved as follows:

[0008] In a first aspect, an embodiment of the present invention provides a method for optimizing the robustness of traffic organization at an intersection, the method comprising:

[0009] Determine intersection geometry, including the number of directions and lanes in each direction;

[0010] Based on historical intersection data, determine X travel demand scenarios including car and large vehicle traffic, the maximum and minimum permitted cycle times, the maximum and minimum permitted green light durations, the green light interval, and the maximum acceptable saturation.

[0011] Determine the conversion coefficients for left-turning, straight-going, and right-turning car traffic and large vehicle traffic on each lane in each direction of the intersection;

[0012] Determine the influence coefficients of the conversion coefficients of different types of left, straight and right turning traffic flows on each lane of the intersection on the influence of the other two types of turning traffic flows;

[0013] Construct a conflict set based on the running trajectories of different turning traffic flows in the intersection;

[0014] The lane organization scheme of the intersection and the corresponding signal timing schemes under the X travel demand scenarios are determined according to a preset intersection traffic organization robustness optimization model.

[0015] In this exemplary embodiment, the conversion coefficients of the three different types of left, straight and right turning traffic flows on each lane of the intersection are determined to be influenced by the other two types of turning traffic flows. Specifically, when any turning traffic flow of any type on the lane coexists with other turning traffic flows, the rate of change of the traffic flow parameters of the turning traffic flow of this type is determined compared with the existence of the turning traffic flow of this type alone. The influence coefficient is determined based on the product of the rate of change of the traffic flow parameters of the turning traffic flow of this type and the conversion coefficient of the turning traffic flow of this type on the lane.

[0016] In this exemplary embodiment, a conflict set is constructed based on the running trajectories of different turning traffic flows in the intersection, specifically including: constructing a basic conflict set, a potential same-direction conflict set, a potential left-turn conflict set, a set of import lanes with potential left-turn conflicts in each direction, a two-traffic pair set of potential exit conflicts, and a three-traffic pair set of potential exit conflicts based on the running trajectories of different turning traffic flows in the intersection.

[0017] In this exemplary embodiment, the basic conflict set, potential same-direction conflict set, potential left-turn conflict set, entry lane set with potential left-turn conflict in each direction, two-vehicle flow pair set with potential exit conflict, and three-vehicle flow pair set with potential exit conflict are constructed according to the running trajectories of different turning traffic flows in the intersection. Specifically, the following steps are used: determining whether two different turning traffic flows conflict, whether the conflict relationship is affected by the permitted lane layout position, and whether they are the same exit according to the intersection geometry and the running trajectories of each traffic flow; then, adding all conflicts that are not affected by the permitted lane layout position to the same set to construct the basic conflict set; adding conflicts with the same entrance but different turns to the same exit to the same exit Add the conflicts between the left-turn traffic flows in the opposite directions to the same set to construct the potential same-direction conflict set; add a pair of left-turn traffic flows in the opposite directions that have a potential left-turn conflict to the same set to construct the potential left-turn conflict set; add the lanes where the potential left-turn conflict occurs to the import lane set of potential left-turn conflicts in that direction to construct the import lane set of potential left-turn conflicts in each direction; pair the left, straight, and right-turn traffic flows at the same exit in pairs and add them to the same set to construct a two-vehicle flow pair set of potential exit conflicts in that exit direction; add the left, straight, and right-turn traffic flows at the same exit to the same set to construct a three-vehicle flow pair set of potential exit conflicts in that exit direction.

[0018] In this exemplary embodiment, the preset intersection traffic organization robustness optimization model is specifically a mixed integer linear programming model constructed with the goal of achieving the optimal overall intersection capacity and the robustness characteristics of the overall capacity with respect to the X travel demand scenarios. The model takes into account intersection lane organization, the influence of permitted lane positions on car and large vehicle traffic, traffic flow conflicts, signal timing schemes, and travel demand scenarios as constraints.

[0019] In this exemplary embodiment, the constraints specifically include: establishing a first constraint to design lane organization plans for each direction in the intersection; establishing a second constraint to allocate car traffic and large vehicle traffic to their respective permitted lanes under the X travel demand scenarios; establishing a third constraint to identify whether potential same-direction conflicts, potential left-turn conflicts, and potential exit conflicts conflict; establishing a fourth constraint to separate basic conflicts from potential conflicts in the intersection and optimize the corresponding signal timing plans for the X travel demand scenarios based on the conflict status; establishing a fifth constraint to determine the mutual impact between various traffic flows on the shared lanes under the X travel demand scenarios; and establishing a sixth constraint to determine the overall intersection capacity and the robustness of the capacity to the X travel demands under the X travel demand scenarios.

[0020] In this exemplary embodiment, the robustness of the overall traffic capacity to X travel demand scenarios is characterized by the degree to which the intersection traffic capacity deviates from the overall traffic capacity in each scenario.

[0021] In a second aspect, an embodiment of the present invention further provides a system for applying the intersection traffic organization robustness optimization method as described in any one of the above solutions, the system comprising: a parameter acquisition unit, a conflict set construction unit, and a solution generation unit;

[0022] The parameter acquisition unit is used to determine the intersection geometry, including the number of directions and the number of lanes in each direction; is also used to determine, based on historical data of the intersection, X travel demand scenarios including car traffic and large vehicle traffic, the maximum and minimum permitted cycle durations of the intersection, the maximum and minimum permitted phase green light durations, the green light interval, and the maximum acceptable saturation; is also used to determine the conversion coefficients of left-turning, straight-going, and right-turning car traffic and large vehicle traffic in each lane of each direction of the intersection; is also used to determine the influence coefficients of the conversion coefficients of different types of left-turning, straight-going, and right-turning traffic on each lane of the intersection under the influence of the other two types of turning traffic;

[0023] The conflict set construction unit is used to construct a conflict set according to the running trajectories of different turning vehicle flows in the intersection;

[0024] The scheme generation unit is used to determine the lane organization scheme of the current intersection and the corresponding signal timing schemes under the X travel demand scenarios according to a preset intersection traffic organization robustness optimization model.

[0025] In a third aspect, an embodiment of the present invention further provides an electronic device comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to implement the intersection traffic organization robustness optimization method described in the first aspect above by executing the executable instructions.

[0026] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor implements the intersection traffic organization robustness optimization method according to the first aspect above.

[0027] Compared with the existing technology, the present invention constructs a robust optimization model for intersection traffic organization that takes into account fluctuations in travel demand. In the model, the influence of the layout position of permitted lanes, the distribution of the number of import and export lanes, the simultaneous release of multiple traffic flows at the same exit, and the influence of multiple travel demands on the optimization of intersection traffic organization are considered. The number of import and export lanes and the relative layout positions of left, straight, and right turn lanes at the same entrance are no longer fixed, so that a lane organization plan and signal timing plan for the intersection with higher travel efficiency and stronger risk response capabilities can be obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The accompanying drawings described herein are used to further understand the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0029] Figure 1 A flowchart of a method for optimizing robustness of traffic organization at an intersection is provided for an embodiment of the present invention;

[0030] Figure 2 A schematic diagram of basic conflicts in an intersection traffic organization robustness optimization method is provided for an embodiment of the present invention

[0031] Figure 3 A schematic diagram of potential same-direction conflicts in a method for optimizing robustness of traffic organization at an intersection is provided for an embodiment of the present invention;

[0032] Figure 4 A schematic diagram of potential left-turn conflicts in a method for optimizing robustness of traffic organization at an intersection is provided for an embodiment of the present invention;

[0033] Figure 5 A schematic diagram of potential exit conflicts in a method for optimizing robustness of traffic organization at an intersection is provided for an embodiment of the present invention;

[0034] Figure 6 A schematic diagram of an optimal lane organization scheme in an intersection traffic organization robustness optimization method is provided for an embodiment of the present invention;

[0035] Figure 7 The present invention also provides a schematic structural diagram of a system for applying a method for optimizing robustness of intersection traffic organization;

[0036] Figure 8 A schematic structural diagram of a computer-readable storage medium is also provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0038] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, article, or device comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, article, or device comprising the element.

[0039] The embodiment of the present invention provides a method for optimizing the robustness of traffic organization at an intersection. Figure 1 As shown, the method is implemented by the following steps:

[0040] Step 101: Determine the intersection geometry, including the number of directions and the number of lanes in each direction;

[0041] Step 102: Determine, based on historical intersection data, X travel demand scenarios including car traffic and large vehicle traffic, the maximum and minimum permitted cycle durations at the intersection, the maximum and minimum permitted green light durations at each phase, the green light interval, and the maximum acceptable saturation.

[0042] Step 103: Determine the conversion coefficients of left-turning, straight-going, and right-turning car flows and large vehicle flows on each lane in each direction of the intersection;

[0043] Specifically, the conversion coefficient can be determined according to a theoretical model method and an actual measurement method.

[0044] For example, the conversion coefficients of left-turning, straight-turning, and right-turning car traffic and large vehicle traffic in each lane in each direction are determined according to the theoretical model method; the left-turning and right-turning car traffic conversion coefficients are calculated according to the formula Calculated; the conversion coefficient of large vehicle traffic flow turning left and right is as follows: Calculated, where d i,j,k is the turning radius of the turning traffic flow (i, j) on lane k; the conversion coefficient of the through traffic flow is not affected by the lane position. The calculated conversion coefficients of the car traffic flow and the large vehicle traffic flow on each turning lane are shown in Table 1. In the table, the traffic flow is represented by (i, j), i, j∈I.

[0045] Table 1 Conversion coefficients of turning car traffic and large vehicle traffic on each lane

[0046]

[0047]

[0048] The conversion coefficient is used to indicate the operating efficiency of car traffic and large vehicle traffic with different turns on different import lanes.

[0049] Step 104: determining the influence coefficients of the conversion coefficients of the three different types of turning traffic flows (left, straight, and right) on each lane of the intersection under the influence of the other two types of turning traffic flows;

[0050] Specifically, a certain type of turning traffic flow coexists with other turning traffic flows on the lane. Compared with the existence of the turning traffic flow of the type alone, the change rate of the traffic flow parameters of the turning traffic flow of the type is determined, and the influence coefficient is determined based on the product of the change rate of the traffic flow parameters of the turning traffic flow of the type and the conversion coefficient of the turning traffic flow of the type on the lane.

[0051] The traffic flow parameters may be any parameters related to the conversion coefficient, such as headway, flow, speed, density, occupancy, delay, etc.

[0052] The influence coefficient can be determined according to actual measurement method.

[0053] The impact of the (i, j2) turning traffic flow on the import lane (i, j1) turning into type m can be calculated using the following model:

[0054]

[0055] In the formula is the traffic flow efficiency parameter of the m-type traffic flow (i, j1) when the (i, j1) and (i, j2) turning traffic flows coexist on lane k. is the traffic flow operation efficiency parameter of (i, j1) turning m type traffic flow when there is only (i, j1) turning traffic flow on lane k. The calculated influence of other turning traffic flows on the car traffic flow and large vehicle traffic flow on each turning lane is shown in Table 2-4.

[0056] Table 2 The impact of through traffic on different lanes on other turning traffic

[0057]

[0058]

[0059] Table 3 The impact of left-turn traffic on different lanes due to other turning traffic

[0060]

[0061] Table 4 The impact of right-turn traffic on different lanes on other turning traffic

[0062]

[0063] Step 105: Construct a conflict set based on the running trajectories of different turning traffic flows in the intersection;

[0064] Specifically, according to the running trajectories of different turning traffic flows in the intersection, a basic conflict set, a potential same-direction conflict set, a potential left-turn conflict set, a set of entrance lanes with potential left-turn conflicts in each direction, a set of two-vehicle flow pairs with potential exit conflicts, and a set of three-vehicle flow pairs with potential exit conflicts are constructed.

[0065] According to the intersection geometry and the running trajectory of each traffic flow, determine whether two different turning traffic flows conflict, whether the conflict relationship is affected by the permitted lane layout position, and whether they are at the same exit; then, add all conflicts that are not affected by the permitted lane layout position to the same set to construct a basic conflict set; add conflicts of different turning traffic flows at the same entrance to the same set to construct a potential same-direction conflict set; add a pair of left-turn traffic flows in opposite entrance directions that have a potential left-turn conflict to the same set to construct a potential left-turn conflict set; add the lanes where the potential left-turn conflict occurs to the set of import lanes with potential left-turn conflicts in that direction to construct a set of import lanes with potential left-turn conflicts in each direction; pair the left straight and right turning traffic flows at the same exit in pairs and add them to the same set to construct a two-traffic pair set of potential exit conflicts in that exit direction; add the left straight and right turning traffic flows at the same exit to the same set to construct a three-traffic pair set of potential exit conflicts in that exit direction.

[0066] Step 106: Determine the lane organization plan for the intersection and the corresponding signal timing plans for each of the X travel demand scenarios based on the preset intersection traffic organization robustness optimization model.

[0067] Specifically, a mixed integer linear programming model is constructed with the goal of comprehensively optimizing the overall traffic capacity of the intersection and the robustness characteristics of the overall traffic capacity for various travel demand scenarios. Considering the influence of intersection lane organization, car traffic flow and large vehicle traffic flow efficiency on the position of permitted lanes, traffic flow conflicts, signal timing schemes and multiple travel demand scenarios as constraints, the model is constructed to collaboratively optimize the lane organization scheme and signal timing scheme of the intersection.

[0068] Construct the first constraint and design a lane organization plan for each direction in the intersection. In this example implementation, the first constraint includes:

[0069]

[0070]

[0071] Where: I is the global number set of the intersection direction, numbered in clockwise order; J is the local number set of other directions of the intersection with direction i as the reference, numbered in clockwise order; K i is the lane number set in direction i, numbered clockwise; H is the set of X travel demand scenarios at the intersection; α i,kIt is a 0-1 variable, belonging to the model variable, indicating whether lane k in direction i is set as the import lane. If yes, then α i,k =1, otherwise, α i,k =0, indicating that lane k in direction i is set as the exit lane; Δ i,j,k It is a 0-1 variable, belonging to the model variable, indicating whether lane k in direction i allows traffic (i, j) to enter the intersection through this lane, that is, whether lane k is the permitted entry lane for traffic (i, j). If it is allowed, Δ i,j,k =1, otherwise Δ i,j,k =0;N i is the number of lanes in direction i, which is a model parameter; is the travel demand of m types of traffic (i, j) under h demand scenario, which belongs to the model parameters, m∈{P,HT}, where P represents car traffic and HT represents large vehicle traffic; z i,j1,j2,k is a 0-1 variable belonging to the model variable, indicating whether lane k is a shared lane for traffic flows (i, j1) and (i, j2). If yes, then z i,j1,j2,k =1, otherwise z i,j1,j2,k =0; M is the model parameter.

[0072] Among them, formula (1) means that if the k+1 lane in the i direction is set as the entrance lane, the adjacent k lane also needs to be set as the entrance lane; formula (2) means that if the k lane in the i direction allows the traffic flow (i, j) to enter the intersection through this lane, then the k lane is set as the entrance lane; formula (3) means that if the k lane in the i direction is set as the entrance lane, then at least one traffic flow is allowed to enter the intersection from this lane; formula (4) means that if the current intersection does not allow the traffic flow (i, j) to pass through this intersection, then no permitted lane is allocated for this traffic flow; formula (5) ensures that the number of entrance lanes allowed for each turning traffic flow should be less than the number of its exit lanes, thereby avoiding congestion at the exit; formula (6) is used to determine whether there is a shared lane in each import direction, so as to characterize the mutual influence of different turning traffic flows on the shared lane; when Δ i,j1,k =Δ i,j2,k = 1, lane k allows traffic (i, j1) and (i, j2) to pass at the same time. Formula (6) determines that lane k is a shared lane for traffic (i, j1) and (i, j2). i,j1,j2,k =1; otherwise, lane k is not a shared lane for traffic flows (i, j1) and (i, j2), and equation (6) determines the shared lane variable z i,j1,j2,k =0.

[0073] A second constraint condition is constructed to allocate the car traffic and large vehicle traffic under the X travel demand scenarios to their respective permitted lanes. In this example implementation, the second constraint condition includes:

[0074]

[0075] Where: represents the flow of type m vehicles (i, j) leaving the intersection through lane k under the h demand scenario, which is a model variable; is the conversion coefficient of type m traffic (i, j) in lane k, which is a model parameter; The influence of the (i, j2) turning traffic flow on the shared lane k on the (i, j1) turning traffic flow m is affected by the (i, j2) turning traffic flow, which is a model parameter; f i,j1,j2,k,h is the amount of through traffic that is added when traffic (i, j2) affects traffic (i, j1) in lane k under the h-demand scenario. It is an auxiliary variable of the model. When lane k is a shared lane for traffic (i, j1) and (i, j2), that is, z i,j1,j2,k =1, the increased through traffic flow affected is When lane k is not a shared lane between traffic flows (i, j1) and (i, j2), f i,j1,j2,k,h =0;C i,k represents the traffic capacity of lane k in the direction of import i, which is a model parameter; μ h It is a common multiplier, equivalent to the intersection capacity under the h demand scenario, and is a model variable; is the travel demand of m types of traffic (i, j) that can pass through the intersection under h demand scenario.

[0076] Among them, formula (7) ensures that the traffic flow is only distributed on the import lanes that allow it to pass. i,j,k = 0, lane k does not allow traffic (i, j) to pass through, and at this time, formula (7) ensures Formula (8) ensures that if there are multiple entrance lanes allowing a certain traffic flow to pass, the traffic flow is evenly distributed on its permitted lanes, satisfying the queuing theory; when lanes k1 and k2 allow traffic flow (i, j) to pass at the same time, Formula (8) ensures The queuing theory is satisfied. Otherwise, the above relationship does not hold and Equation (8) is relaxed. Equation (9) ensures that the sum of the traffic flows distributed on each import lane under the h demand scenario is equal to the travel demand that it can pass.

[0077] A third constraint is constructed to identify potential same-direction conflicts, potential left-turn conflicts, and potential exit conflicts. This is used to subsequently implement signal plans to ensure the safe operation of each turning traffic flow within the intersection under each organization plan. In this example implementation, the third constraint includes:

[0078]

[0079] Where: S is the set of traffic pairs with potential same-direction conflicts;L is the set of traffic pairs with potential left-turn conflicts; PE is the set of two-vehicle flow pairs with potential exit conflicts; TE K is a set of three vehicle flow pairs including left, straight and right turns with potential exit conflicts; i,LC represents the set of lanes with potential left-turn conflicts in direction i. When the left-turn lane in direction i is arranged in the set K i,LC On the lane outside, the left-turning traffic in direction i does not conflict with the left-turning traffic in the opposite direction; otherwise, a conflict may occur; i,j1,j2,k1 is a 0-1 variable, belonging to the model variable, indicating whether the traffic flow (i, j1) conflicts with the traffic flow (i, j2) with the same entrance but different turning direction in lane k1. If so, γ i,j1,j2,k1 =1, otherwise γ i,j1,j2,k1 =0;χ i,j,t,v is a 0-1 variable, indicating whether a pair of traffic flows (i, j) and (t, v) with potential conflict occurs. If yes, then χ i,j,t,v =1, otherwise χ i,j,t,v =0;λ i,1,j,1,k1 is a 0-1 variable, belonging to the model variable, indicating whether the left-turning traffic (i, 1) conflicts with the opposite left-turning traffic (j, 1) in lane k1. If so, λ i,1,j,1,k1 =1, otherwise λ i,1,j,1,k1 =0;η j is a 0-1 variable belonging to the model variable, indicating whether the sum of the number of entrance lanes of the three traffic flows (i1, j), (i2, j) and (i3, j) turning left, straight and right at the same exit is greater than the number of exit lanes. If yes, then η j =1, otherwise η j =0.

[0080] Wherein, formula (10) identifies the conflict state of potential same-direction conflicts lane by lane; when lane k1 allows traffic flow (i, j1) to pass, if a lane k2 on its left allows traffic flow (i, j2) with potential same-direction conflicts to pass, that is, Δ i,j1,k1 =1, Then the traffic flows (i, j1) and (i, j2) conflict on lane k1, and the variable γ is determined by equation (10): i,j1,j2,k1 =1; otherwise, the traffic flows (i, j1) and (i, j2) do not conflict on lane k1, and the variable γ is determined by equation (10) i,j1,j2,k1 =0; Formula (11) is the final identification of the conflict state of potential same-direction conflict. When a pair of traffic flows with potential same-direction conflict do not conflict on all import lanes, that is, Then the pair of traffic flows does not conflict, and the variable χ is determined by formula (11) i,j1,i,j2 =0; otherwise, the traffic flow conflicts, and the variable χ is determined by formula (11) i,j1,i,j2=1; Formula (12) identifies the conflict state of potential left-turn conflicts lane by lane; when the left-turn lane in direction i is arranged on the lane k1 with potential left-turn conflicts, and at the same time, the left-turn lane in direction j is arranged on the lane with potential left-turn conflicts in direction j, that is, Δ i,1,k1 =1, Then the left-turning traffic (i,1) and (j,1) conflict in lane k1. Equation (12) determines the variable λ i,1,j,1,k1 =1; otherwise, the left-turn traffic (i, 1) and (j, 1) do not conflict on lane k1, and Equation (12) determines the variable λ i,1,j,1,k1 =0; Formula (13) is the final identification of the conflict state of potential left-turn conflict. When a pair of opposite left-turn lanes are both arranged in the set of lanes with potential left-turn conflict, that is, Then for this left-turn traffic conflict, formula (13) determines the variable χ i,1,j,1 =1; otherwise, the pair of left-turning vehicles does not conflict, and the variable χ i,1,j,1 = 0. Formula (14) is used to identify the conflict state of a pair of traffic flows with potential exit conflict. When the sum of the number of entrance lanes of traffic flow pairs (i1, j) and (i2, j) is Greater than the number of its exit lanes Then the traffic pairs (i1, j) and (i2, j) conflict and cannot be released at the same time. Formula (15) is used to determine the sum of the number of entrance lanes of the left, straight and right traffic flows (i1, j), (i2, j) and (i3, j) at the same exit. Is it greater than the number of its exit lanes? If so, η j =1, otherwise η j = 0. Formula (16) is used to determine whether the left, straight, and right traffic flows at the same exit can be released at the same time. When the sum of the number of entrance lanes of the left, straight, and right traffic flows (i1, j), (i2, j), and (i3, j) is greater than the number of their exit lanes, that is, η j =1, then the left, straight and right traffic flows (i1,j), (i2,j) and (i3,j) cannot be released at the same time. There is at most one pair of traffic flows at the same exit that can be released at the same time, that is, i1,j,i2,j +χ i1,j,i3,j +χ i2,j,i3,j ≥2, otherwise, there are enough lanes at the exit to release these three traffic flows at the same time, and these three traffic flows do not conflict with each other, that is, when η j =1, χ i1,j,i2,j +χ i1,j,i3,j +χ i2,j,i3,j =0.

[0081] Construct a fourth constraint to separate basic conflicts from potential conflicts at the intersection, ensure safe operation at the intersection, and optimize the green light signal scheme for each traffic flow under X travel demand scenarios;

[0082] In this example implementation, the fourth constraint condition includes:

[0083]

[0084]

[0085] Where: h It represents the inverse of the cycle length under the h demand scenario and is a model variable; c h,max and c h,min They represent the maximum and minimum cycle durations allowed under the h demand scenario, which are model parameters; g h,max and g h,min They represent the maximum and minimum green light durations allowed under the h demand scenario, which are model parameters; θ i,j,h and φ i,j,h They represent the green light starting point and duration of traffic flow (i, j) under the h demand scenario, in units of cycle duration, and are model variables; Θ i,k,h and Φ i,k,h They represent the starting point and duration of the green light in lane k in direction i under demand scenario h, in units of cycle duration, and are model variables; Ω i,j,t,v,h is a 0-1 variable, indicating the order of green lights displayed for conflicting traffic flows (i, j) and (t, v) under the h demand scenario, Ω i,j,t,v,h =0 means that the green light of the traffic flow (i, j) is turned on before the green light of the conflicting traffic flow (t, v), otherwise Ω i,j,t,v,h =1, belongs to the model variable; B is the basic conflict set; w i,j,t,v is the green light interval, which is a model parameter; p i,k is the maximum acceptable saturation of lane k in direction i, which is a model parameter; e is the effective green light compensation time, which is a model parameter.

[0086] Among them, formula (17) constrains the range of cycle duration under the h demand scenario; formulas (18)-(20) respectively constrain the range of green light starting point, duration, and end point of m type traffic flow (i, j) under the h demand scenario; formulas (21) and (22) ensure that multiple traffic flows with shared lanes in the h demand scenario are assigned the same green light starting point and duration; when the k lane in the i direction allows traffic flows (i, j1) and (i, j2) to pass at the same time, that is, Δ i,j1,k =Δ i,j2,k =1, Equations (21) and (22) make the green light starting point and duration assigned to traffic flows (i, j1) and (i, j2) θ respectively. i,j1,h =Θ i,k,h =θ i,j2,h ,φ i,j1,h =Φ i,k,h =φi,j2,h , thereby ensuring that multiple traffic flows with shared lanes are assigned the same green light starting point and duration. Equations (23) and (24) are used to separate traffic flow pairs with basic conflicts under the traffic demand scenario h; Equation (23) ensures that the green light phase sequence of traffic flow pairs with basic conflicts is consistent, that is, the variable Ω i,j,t,v,h and Ω t,v,i,j,h There is only one 0 and one 1; when Ω i,j,t,v,h =0,Ω t,v,i,j,h = 1, the green light of traffic flow (i, j) turns on before the green light of conflicting traffic flow (t, v). Formula (24) ensures that the starting point of the green light of traffic flow (t, v) turns on later than the sum of the end point of the green light of traffic flow (i, j) and the required green light interval, thereby ensuring the operation safety of traffic flow pairs (i, j) and (t, v) with basic conflicts. On the contrary, Formula (24) ensures that the starting point of the green light of traffic flow (i, j) turns on later than the sum of the end point of the green light of traffic flow (t, v) and the required green light interval. Formulas (25) and (26) are used to separate traffic flow pairs with potential conflicts under the traffic demand scenario. When the traffic flow pairs (i, j) and (t, v) with potential conflicts conflict, that is, χ i,j,t,v =1, Equations (25) and (26) ensure that the phase green lights of traffic flows (i, j) and (t, v) are turned on separately; when χ i,j,t,v = 0, the traffic pairs (i, j) and (t, v) do not conflict and can be released at the same time, and Equations (25) and (26) are relaxed; Equation (27) ensures that the saturation of import lane k is less than the acceptable saturation, ensuring that the traffic intensity of each import lane is balanced.

[0087] Determine the corresponding signal timing schemes for each of the X travel demand scenarios according to the conflict status;

[0088] Constructing a fifth constraint to determine the mutual impact between the various traffic flows on the respective shared lanes under the X travel demand scenarios;

[0089] In this example implementation, the fifth constraint condition includes:

[0090]

[0091] Equations (28)-(31) are used to linearly determine the variable f i,j1,j2,k,h , so that the variable f i,j1,j2,k,h With nonlinear terms equivalence.

[0092] Constructing the sixth constraint to determine the overall capacity of the intersection and the robustness of the overall capacity to the X types of travel demands;

[0093] In this example implementation, the sixth constraint condition includes:

[0094]

[0095]

[0096] Where: is the expected value of the intersection capacity under the X travel demand scenarios, which is a model variable used to represent the overall intersection capacity and ensure the basic operating efficiency of the intersection; s represents the robustness of the intersection capacity to the X travel demands, which is a model variable. The smaller s is, the greater the robustness is, and the smaller the impact of travel demand fluctuations on the intersection capacity is. Conversely, the intersection capacity is more significantly affected by travel demand, and the current intersection traffic organization plan cannot well match all travel demands; d h It represents the probability of occurrence of travel demand h in actual intersection operation and is a model parameter; h,1 and κ h,2 are two indicator variables, both 0-1 variables, belonging to the model variables, representing the intersection capacity μ under the travel demand h scenario h and the overall traffic capacity of the intersection The difference, when κ h,1 =1,κ h,2 =0, otherwise, κ h,1 =0,κ h,2 =1;ν h,1 and ν h,2 are auxiliary variables, indicating the overall traffic capacity of the intersection under the travel demand h scenario μ h The sensitivity of and

[0097] Among them, formula (32) is used to determine the overall traffic capacity of the intersection under the current intersection traffic organization scheme under the X travel demand scenarios; formula (33) is used to determine the robustness characteristics of the overall traffic capacity of the intersection under the X travel demand scenarios to the X travel demands; formulas (34) and (35) are used to determine the indicator variable κ h,1 and κ h,2 The value of When , equations (34) and (35) respectively determine the indicator variable κ h,1 =1 and κ h,2 =0, otherwise, κ h,1 =0 and κ h,2 =1; Equations (36)-(39) and (40)-(43) are used to linearly determine the overall capacity of the intersection for the travel demand h scenario μ h Sensitivity ν h,1 and ν h,2The value of κ is obtained by equations (36)-(39) and (40)-(43). The guarantee model can be applied to large-scale travel demand scenarios to find the optimal solution. h,1 =1, Equations (36)-(39) ensure that the auxiliary variable Otherwise, ν h,1 =0; when κ h,2 =1, Equations (40)-(43) ensure that the auxiliary variable Otherwise, ν h,2 =0.

[0098] It should be noted that model parameters are variables that need to be determined in advance and introduced into the model; model variables are variables that need to be obtained by solving the model and have specific physical meanings; auxiliary variables are similar to model variables, which are variables that need to be obtained by solving the model, but do not have specific physical meanings. They are variables generated during the model construction process to facilitate model construction.

[0099] The objective function is used to achieve the optimal comprehensive performance of the overall intersection capacity and the robustness of the overall capacity to X types of travel demands.

[0100] In this embodiment, the objective function is constructed: Where β is a model parameter that describes the balance between the overall intersection capacity and its robustness to the X types of travel demands. It serves as the weight of the intersection's overall capacity in the objective function. A larger β value places greater emphasis on improving the intersection's overall capacity when optimizing intersection traffic organization robustness. Conversely, a smaller value emphasizes improving the robustness of overall capacity to the X types of travel demands, minimizing the impact of travel demand fluctuations on the effectiveness of the intersection's spatiotemporal resource allocation plan.

[0101] In this embodiment, β=0.1 is determined based on manual experience.

[0102] Example

[0103] Take a typical cross signal intersection as an example, Figure 2 The east-west direction is the main road direction, each containing 8 lanes, and the north-south direction is the branch road direction, each containing 4 lanes. The import directions are globally numbered in the order of northwest, southeast, as shown in the figure below. Figure 3 Shown are examples of potential same-direction conflicts, such as Figure 4 Shown are examples of potential left-turn conflicts, such as Figure 5 Shown are examples of potential export conflicts, such as Figure 6 The optimal organization scheme is shown.

[0104] Conduct field surveys or use intersection detectors to identify X travel demand scenarios involving both car and large vehicle traffic.

[0105] The X travel demand scenarios can be calculated by directly using the historical data obtained from the survey to calculate the frequency of each scenario. When the difference in the flow rate of each turn between the two demands is small, such as the relative difference is less than 5%, they can be treated as the same scenario and the average value of the flow rate of each turn can be taken. Otherwise, they can be treated as different scenarios.

[0106] This embodiment investigates and obtains three traffic demand scenarios, as shown in Table 5. The probability of occurrence of each scenario is 1 / 3.

[0107] Table 5 Travel demand (veh / h)

[0108]

[0109]

[0110] Determine the maximum and minimum permitted cycle time C of the intersection based on manual experience max =120s, C min =60s, maximum green light duration g max = 80s, based on the pedestrian crossing time requirement, the minimum permitted phase green light duration g is determined min =6s, based on the principle of avoiding the “dilemma zone” at the intersection, the green light interval w = 6s. The maximum acceptable saturation of the lane p i,k The recommended value in traffic engineering is 0.9. Traffic engineering recommends that the effective green light compensation time be between [0s, 4s]. Based on manual experience, the effective green light compensation time e in this embodiment is 3s.

[0111] Lane capacity can be determined based on theoretical models, actual measurements, and national standards. In this example implementation, the national standard CJJ37-2012, "Urban Road Engineering Design Code," is used to determine that the capacity of each entrance lane at the intersection is 1800 pcu / h.

[0112] The model parameter M can be any value greater than the travel demand A positive integer, in this embodiment, M=10000.

[0113] Based on whether two different turning traffic flows conflict and whether the conflict relationship is affected by the layout of permitted lanes, we construct a basic conflict set, a potential same-direction conflict set, a potential left-turn conflict set, a set of import lanes with potential left-turn conflicts at each import, a set of two-traffic flow pairs with potential exit conflicts, and a set of three-traffic flow pairs with potential exit conflicts.

[0114] The conflict types of each pair of traffic flows in the embodiment are shown in Table 6, where the letter B indicates a basic conflict, the letter S indicates a potential same-direction conflict, the letter L indicates a potential left-turn conflict, the letter E indicates a potential exit conflict, and the symbol "-" indicates no conflict; the import lane with potential left-turn conflicts in directions 1 and 3 is lane 1. When the left-turn lane is arranged on other import lanes, the left-turn traffic flows (1, 2) and (3, 4) in the opposite directions do not conflict; there are no import lanes with potential left-turn conflicts in branch directions 2 and 4, that is, there is no potential left-turn conflict in the left-turn traffic flows (2, 3) and (4, 1) in the embodiment.

[0115] Table 6 Classification of conflicts between manual traffic flows at signalized intersections

[0116]

[0117] The GAMS solver was used to solve the above intersection traffic organization robustness optimization model, and the intersection lane organization scheme and signal timing scheme were obtained with the goal of optimizing the overall intersection capacity and robustness comprehensive performance, taking into account the influence of traffic flow efficiency on the layout position of permitted lanes, the number of entrance and exit lanes, the simultaneous release of multiple traffic flows at the same exit, and the influence of multiple travel demands on intersection traffic organization optimization. The optimal objective function was 0.96171, the intersection capacity under each scenario was μ1=1.0878, μ2=1.0812, μ3=1.0284, and the overall intersection capacity was The robustness of traffic capacity to the three travel demands is s = 0.0249, and the optimal lane organization scheme is as follows: Figure 6 The optimal signal timing scheme is shown in Table 7, with a cycle length of C = 120s.

[0118] Table 7 Optimal signal timing scheme (s)

[0119]

[0120]

[0121] The present invention constructs a robust optimization model for intersection traffic organization that takes into account fluctuations in travel demand. In the model, the influence of the layout position of permitted lanes, the allocation of the number of import and export lanes, the simultaneous release of multiple traffic flows at the same export, and the influence of multiple travel demands on the optimization of intersection traffic organization are taken into account. The number of import and export lanes and the relative layout positions of left, straight, and right turn lanes at the same import are no longer fixed, so that a lane organization plan and signal timing plan for the intersection with higher travel efficiency and stronger risk response capabilities can be obtained.

[0122] The embodiment of the present invention also provides a system for applying the intersection traffic organization robustness optimization method of the above embodiment, such as Figure 7As shown, the system includes: a parameter acquisition unit, a conflict set construction unit, and a solution generation unit;

[0123] The parameter acquisition unit is used to determine the intersection geometry, including the number of directions and the number of lanes in each direction; is used to determine, based on historical data of the intersection, X travel demand scenarios including car traffic and large vehicle traffic, the maximum and minimum permitted cycle durations of the intersection, the maximum and minimum permitted phase green light durations, the green light interval time, and the maximum acceptable saturation; is used to determine the conversion coefficients of left-turning, straight-ahead, and right-turning car traffic and large vehicle traffic in each lane of each direction of the intersection; and is also used to determine the mutual influence coefficients of the conversion coefficients of different types of left-turning, straight-ahead, and right-turning traffic flows in each lane of the intersection under the influence of the other two types of turning traffic flows;

[0124] The conflict set construction unit is used to construct a conflict set according to the running trajectories of different turning vehicle flows in the intersection;

[0125] The scheme generation unit is used to determine the lane organization scheme of the current intersection and the corresponding signal timing schemes under the X travel demand scenarios according to a preset intersection traffic organization robustness optimization model.

[0126] The conflict set construction unit is specifically used to construct a basic conflict set, a potential same-direction conflict set, a potential left-turn conflict set, a set of import lanes with potential left-turn conflicts in each direction, a two-vehicle flow pair set with potential exit conflicts, and a three-vehicle flow pair set with potential exit conflicts based on the running trajectories of different turning traffic flows in the intersection.

[0127] The conflict set construction unit is specifically used to determine whether two different turning traffic flows conflict, whether the conflict relationship is affected by the permitted lane layout position, and whether they are at the same exit based on the intersection geometry and the running trajectory of each traffic flow; then, all conflicts that are not affected by the permitted lane layout position are added to the same set to construct a basic conflict set; conflicts between different turning traffic flows at the same entrance are added to the same set to construct a potential same-direction conflict set; a pair of left-turn traffic flows in opposite entrance directions that have a potential left-turn conflict are added to the same set to construct a potential left-turn conflict set; lanes where potential left-turn conflicts occur are added to the import lane set of potential left-turn conflicts in that direction to construct an import lane set of potential left-turn conflicts in each direction; left-straight and right-turn traffic flows at the same exit are paired in pairs and added to the same set to construct a two-traffic pair set of potential exit conflicts in that exit direction; left-straight and right-turn traffic flows at the same exit are added to the same set to construct a three-traffic pair set of potential exit conflicts in that exit direction.

[0128] In some embodiments, as Figure 7FIG. 7 is a schematic diagram of the structure of a computer-readable storage medium provided in an embodiment of the present invention. The storage medium stores a readable computer program 701. The computer program 701 may be stored in the storage medium in the form of a software product and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor to execute the following steps:

[0129] Determine intersection geometry, including the number of directions and lanes in each direction;

[0130] Based on historical intersection data, determine X travel demand scenarios including car and large vehicle traffic, the maximum and minimum permitted cycle times, the maximum and minimum permitted green light durations, the green light interval, and the maximum acceptable saturation.

[0131] Determine the conversion coefficients for left-turning, straight-going, and right-turning car traffic and large vehicle traffic on each lane in each direction of the intersection;

[0132] Determine the influence coefficients of the conversion coefficients of different types of left, straight and right turning traffic flows on each lane of the intersection on the influence of the other two types of turning traffic flows;

[0133] Construct a conflict set based on the running trajectories of different turning traffic flows in the intersection;

[0134] The lane organization scheme of the intersection and the corresponding signal timing schemes under the X travel demand scenarios are determined according to a preset intersection traffic organization robustness optimization model.

[0135] The aforementioned storage media include: USB flash drives, mobile hard drives, magnetic disks or optical disks, ROM (Read-Only Memory), RAM (Random Access Memory), and other media that can store program codes, or terminal devices such as computers, service machines, mobile phones, and tablets.

[0136] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0137] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention.

Claims

1. A robustness optimization method for intersection traffic organization, characterized in that: The method comprises: Determine intersection geometry, including the number of directions and lanes in each direction; Based on historical intersection data, determine X travel demand scenarios including car and large vehicle traffic, the maximum and minimum permitted cycle times, the maximum and minimum permitted green light durations, the green light interval, and the maximum acceptable saturation. Determine the conversion coefficients for left-turning, straight-going, and right-turning car traffic and large vehicle traffic on each lane in each direction of the intersection; Determine the influence coefficients of the conversion coefficients of different types of left, straight and right turning traffic flows on each lane of the intersection on the influence of the other two types of turning traffic flows; Construct a conflict set based on the running trajectories of different turning traffic flows in the intersection; Determining a lane organization plan for the intersection and corresponding signal timing plans for each of the X travel demand scenarios based on a preset intersection traffic organization robustness optimization model; The preset intersection traffic organization robustness optimization model is specifically a mixed integer linear programming model constructed with the goal of achieving the optimal overall intersection capacity and the robustness characteristics of the overall capacity with respect to the X travel demand scenarios, taking into account the intersection lane organization, the influence of the permitted lane positions on the flow of cars and large vehicles, traffic flow conflicts, signal timing schemes, and travel demand scenarios as constraints; The constraints specifically include: establishing a first constraint to design lane organization plans for each direction of the intersection; establishing a second constraint to allocate car traffic and large vehicle traffic to their respective permitted lanes under the X travel demand scenarios; establishing a third constraint to identify whether potential same-direction conflicts, potential left-turn conflicts, and potential exit conflicts conflict; establishing a fourth constraint to separate basic conflicts from potential conflicts at the intersection and optimize the corresponding signal timing plans for the X travel demand scenarios based on the conflict status; establishing a fifth constraint to determine the mutual impact between various traffic flows on the shared lanes under the X travel demand scenarios; and establishing a sixth constraint to determine the overall traffic capacity of the intersection and the robustness characteristics of the overall traffic capacity to the X travel demand scenarios.

2. The intersection traffic organization robustness optimization method according to claim 1, characterized in that: The conversion coefficients of the three different types of left, straight and right turning traffic flows on each lane of the intersection are determined to be influenced by the other two types of turning traffic flows. Specifically, the rate of change of the traffic flow parameters of any turning traffic flow of any type on the lane coexists with the other turning traffic flows, compared with the existence of the turning traffic flow of this type alone, and the influence coefficient is determined according to the product of the rate of change of the traffic flow parameters of the turning traffic flow of this type and the conversion coefficient of the turning traffic flow of this type on the lane.

3. The intersection traffic organization robustness optimization method according to claim 1, characterized in that: The conflict set constructed according to the running trajectories of different turning traffic flows in the intersection specifically includes: constructing a basic conflict set, a potential same-direction conflict set, a potential left-turn conflict set, a set of import lanes with potential left-turn conflicts in each direction, a set of two-traffic pairs with potential exit conflicts, and a set of three-traffic pairs with potential exit conflicts according to the running trajectories of different turning traffic flows in the intersection.

4. The intersection traffic organization robustness optimization method according to claim 3 is characterized in that: The method of constructing a basic conflict set, a potential same-direction conflict set, a potential left-turn conflict set, an import lane set with potential left-turn conflicts in each direction, a two-vehicle flow pair set with potential exit conflicts, and a three-vehicle flow pair set with potential exit conflicts according to the running trajectories of different turning traffic flows in the intersection specifically includes: determining whether two different turning traffic flows conflict, whether the conflict relationship is affected by the permitted lane layout position, and whether they are the same exit according to the intersection geometry and the running trajectories of each traffic flow; then, adding all conflicts that are not affected by the permitted lane layout position to the same set to construct a basic conflict set; adding conflicts of different turning traffic flows with the same entrance to the same set to construct a basic conflict set; Add them to the same set to construct a potential same-direction conflict set; add a pair of left-turn traffic flows with potential left-turn conflicts in opposite import directions to the same set to construct a potential left-turn conflict set; add the lanes with potential left-turn conflicts to the import lane set of potential left-turn conflicts in that direction to construct a set of import lanes with potential left-turn conflicts in each direction; pair the left, straight, and right-turn traffic flows at the same exit in pairs and add them to the same set to construct a two-vehicle flow pair set for potential exit conflicts in that exit direction; add the left, straight, and right-turn traffic flows at the same exit to the same set to construct a three-vehicle flow pair set for potential exit conflicts in that exit direction.

5. The method for optimizing the robustness of intersection traffic organization according to any one of claims 1 to 4, characterized in that: The robustness of the overall traffic capacity to X travel demand scenarios is characterized by the degree to which the intersection traffic capacity deviates from the overall traffic capacity in each scenario.

6. A system using the intersection traffic organization robustness optimization method according to any one of claims 1 to 4, characterized in that: The system includes: a parameter acquisition unit, a conflict set construction unit, and a solution generation unit; The parameter acquisition unit is used to determine the intersection geometry, including the number of directions and the number of lanes in each direction; is also used to determine, based on historical data of the intersection, X travel demand scenarios including car traffic and large vehicle traffic, the maximum and minimum permitted cycle durations of the intersection, the maximum and minimum permitted phase green light durations, the green light interval, and the maximum acceptable saturation; is also used to determine the conversion coefficients of left-turning, straight-going, and right-turning car traffic and large vehicle traffic in each lane of each direction of the intersection; is also used to determine the influence coefficients of the conversion coefficients of different types of left-turning, straight-going, and right-turning traffic on each lane of the intersection under the influence of the other two types of turning traffic; The conflict set construction unit is used to construct a conflict set according to the running trajectories of different turning vehicle flows in the intersection; The scheme generating unit is used to determine the lane organization scheme of the intersection and the corresponding signal timing schemes under the X travel demand scenarios according to a preset intersection traffic organization robustness optimization model.

7. An electronic device, characterized in that: include: processor; as well as a memory for storing executable instructions of the processor; The processor is configured to implement the intersection traffic organization robustness optimization method according to any one of claims 1 to 4 when executing the executable instructions.

8. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 4.

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