A virtual ad hoc network signal control method and system when the device is offline

Abstract

The present invention discloses a virtual ad hoc network signal control method and system when the equipment is offline, including: determining N adjacent online intersections of the offline intersection according to the road network structure, and clarifying the upstream and downstream relationship; calculating the expected maximum traffic volume of the constrained flow direction of the online intersection; determining the constrained flow direction of the online intersection; constructing an objective function and solving the timing plan of the online intersection. The present invention analyzes the association between upstream and downstream intersections, performs real-time calculation and deduction of virtual networking between offline intersections and online intersections according to the road network topology relationship, automatically generates an online intersection plan according to the traffic parameters around the offline intersection, constructs unified collaborative optimization of the ad hoc network area, and improves the degree of intelligent control of the area around the offline intersection.

Description

Technical Field

[0001] The invention belongs to the field of traffic signal control, and in particular relates to a virtual ad hoc network signal control method and system when equipment is offline. Background Art

[0002] As the road network structure becomes increasingly complex and the number of motor vehicles gradually increases, signal control equipment has gradually become an indispensable part of intelligent control of intersections. However, the working status of signal control equipment depends on the telecommunications operator network. When the network is unstable, the equipment is prone to offline, resulting in intersection overflow and empty release. How to achieve intelligent control of regional signals when the equipment is offline has become an urgent problem to be solved.

[0003] The existing technology mainly involves coordinated control when the signal control equipment is online, or operation and maintenance dispatch when the equipment is offline. In the offline state, the equipment cannot transmit signal control information to the system and surrounding intersections, and can only perform single intersection control, which affects the coordinated linkage effect of multiple intersections. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the purpose of the present invention is to provide a virtual ad hoc network signal control method and system when the equipment is offline, so as to solve the problem that the traffic signal control equipment in the prior art is prone to offline when the network is unstable, affecting the coordinated linkage of multiple intersections. The present invention analyzes the association between upstream and downstream intersections, performs real-time calculation and deduction of virtual networking between offline intersections and online intersections according to the road network topology relationship, automatically generates online intersection plans according to the traffic parameters around the offline intersection, constructs unified collaborative optimization of the ad hoc network area, and improves the degree of intelligent control of the area around the offline intersection.

[0005] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] A virtual ad hoc network signal control method in the case of a device being offline of the present invention comprises the following steps:

[0007] 1) According to the road network structure, determine the N adjacent online intersections of the offline intersection and clarify the upstream and downstream relationships;

[0008] 2) Calculate the predicted carrying capacity of the section downstream of the online intersection connected to the offline intersection as the expected maximum traffic volume c' of the constrained flow direction of the online intersection;

[0009] 3) Compare the online intersection (located upstream) with the offline intersection (located downstream) to determine the constrained flow direction (more than one) of the online intersection;

[0010] 4) Based on the expected maximum throughput of the constrained flow direction of the online intersection, the objective function is constructed and the timing scheme of the restricted online intersection is solved.

[0011] Furthermore, in step 1), determining N adjacent online intersections of the offline intersection and clarifying the upstream and downstream relationships includes the following steps:

[0012] 11) Determine an online intersection adjacent to the offline intersection according to the location of the offline intersection in the road network;

[0013] 12) The traffic flow from the online intersection to the offline intersection is affected, and the traffic flow from the offline intersection to the online intersection is not affected; for each online intersection, it is called the upstream online intersection; for the offline intersection, it is called the downstream offline intersection.

[0014] Furthermore, the step 2) calculates the predicted carrying capacity of the section downstream of the online intersection and connected to the offline intersection as the expected maximum throughput of each constrained flow direction of the online intersection, including the following steps:

[0015] 21) Obtain the number of vehicles leaving the stop line at the downstream offline intersection according to the detection data, including left-turning vehicles, straight-going vehicles, and right-turning vehicles leaving the stop line, which represents the release capacity of the offline intersection;

[0016] 22) Obtain the number of vehicles turning right from the online intersection to the offline intersection according to the detection data, which represents the number of vehicles that must be released at the online intersection that are not controlled by traffic lights;

[0017] 23) Calculate the expected maximum traffic volume c' of the constrained flow direction at the online intersection:

[0018] c' = the number of vehicles leaving the stop line at the downstream offline intersection - the number of vehicles turning right at the online intersection.

[0019] Furthermore, the step 3) of determining the constrained flow direction of the online intersection includes the following steps:

[0020] 31) Eliminate the flow direction from the online intersection to other non-offline intersections, and only keep the flow direction from the online intersection to the offline intersection;

[0021] 32) Eliminate the right-turn flows that are not controlled by traffic lights, and the remaining flows are the constrained flows at the online intersection.

[0022] Furthermore, in step 4), based on the expected maximum throughput of the constrained flow direction of the online intersection, constructing an objective function and solving the restricted timing plan of the online intersection includes the following steps:

[0023] 41) Construct the objective function f(x) = f(x1, x2) = h1x1 + h2x2, and minimize the function. The constraint condition is h(x) = x1 + x2 - c' = 0; that is:

[0024] minf(x1,x2)

[0025] sth(x)=x1+x2-c'=0

[0026] Among them, h1 is the headway of the first flow direction going straight; h2 is the headway of the second flow direction turning left; x1 is the number of vehicles expected to be released in the first flow direction; x2 is the number of vehicles expected to be released in the second flow direction;

[0027] 42) Determine the range of values ​​for the queue:

[0028] 0≤x1≤min(c1,q1)

[0029] 0≤x2≤min(c2,q2)

[0030] Among them, c1 is the number of vehicles queuing to 80% position in the first flow direction, and c2 is the number of vehicles queuing to 80% position in the second flow direction; q1 is the actual number of vehicles queuing in the first flow direction, and q2 is the actual number of vehicles queuing in the second flow direction; in order to prevent overflow, c1 and c2 are selected from the number of vehicles queuing to 80% position, rather than the number of vehicles queuing to the upstream exit;

[0031] 43) Substitute the equation h(x) in step 41) into the objective function f(x), and the elimination method can obtain the following problem: minf(x1,x2)=(h1-h2)x1+h2c'; Under normal circumstances, the headway of the straight flow direction is smaller than the headway of the left turn flow direction, h1-h2<0, and x1 takes the maximum value to maximize the number of vehicles arriving at the downstream offline intersection in the restricted flow direction; If h1-h2>0 exists at some intersections, then x1 takes the minimum value to maximize the number of vehicles arriving at the downstream offline intersection in the restricted flow direction; The determined x1 and x2 are the expected released flow in the first flow direction and the second flow direction;

[0032] 44) According to the traffic volume expected to be released in the constrained flow direction of the online intersection and the traffic volume of other flows detected, the green light time for each flow direction is allocated as the timing plan for the online intersection.

[0033] The present invention also provides a virtual ad hoc network signal control system when the device is offline, comprising:

[0034] The online intersection determination module is used to determine N adjacent online intersections of the offline intersection according to the road network structure and clarify the upstream and downstream relationships;

[0035] A calculation module, used for calculating the expected maximum traffic volume of the constrained flow direction at the online intersection;

[0036] A flow direction determination module, used to determine the constrained flow direction of the online intersection;

[0037] The scheme timing module is used to construct the objective function and solve the timing scheme with limited online intersections.

[0038] Beneficial effects of the present invention:

[0039] The method of the present invention analyzes the association between upstream and downstream intersections, performs real-time calculation and deduction of virtual networking between offline intersections and online intersections according to the road network topology relationship, automatically generates online intersection plans according to the traffic parameters around the offline intersections, constructs unified collaborative optimization of self-organizing network areas, and improves the degree of intelligent control of areas around offline intersections. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 Flow chart of the method of the present invention.

[0041] Figure 2 Schematic diagram of constrained flow direction.

[0042] Figure 3 This is an implementation case diagram. DETAILED DESCRIPTION

[0043] In order to facilitate the understanding of those skilled in the art, the present invention is further described below in conjunction with embodiments and drawings. The contents mentioned in the implementation modes are not intended to limit the present invention.

[0044] The present invention analyzes the association between upstream and downstream intersections, performs real-time calculation and deduction of virtual networking between offline intersections and online intersections according to the road network topology relationship, adjusts the online intersection plan in time according to the offline intersection plan, and constructs unified collaborative optimization of the self-organizing network area. The virtual self-organizing network signal control process under the condition of offline equipment is detailed in Figure 1 .

[0045] Reference Figure 1 As shown, in order to achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0046] A virtual ad hoc network signal control method in the case of a device being offline of the present invention comprises the following steps:

[0047] 1) According to the road network structure, determine the N adjacent online intersections of the offline intersection and clarify the upstream and downstream relationships;

[0048] Wherein, in the step 1), determining N adjacent online intersections of the offline intersection and clarifying the upstream and downstream relationships includes the following steps:

[0049] 11) Determine an online intersection adjacent to the offline intersection according to the location of the offline intersection in the road network;

[0050] 12) The traffic flow from the online intersection to the offline intersection is affected, and the traffic flow from the offline intersection to the online intersection is not affected; for each online intersection, it is called the upstream online intersection; for the offline intersection, it is called the downstream offline intersection.

[0051] 2) Calculate the predicted carrying capacity of the section downstream of the online intersection connected to the offline intersection as the expected maximum traffic volume c' of the constrained flow direction of the online intersection;

[0052] The step 2) calculates the predicted carrying capacity of the section downstream of the online intersection and connected to the offline intersection as the expected maximum throughput of each constrained flow direction of the online intersection, including the following steps:

[0053] 21) Obtain the number of vehicles leaving the stop line at the downstream offline intersection according to the detection data, including left-turning vehicles, straight-going vehicles, and right-turning vehicles leaving the stop line, which represents the release capacity of the offline intersection;

[0054] 22) Obtain the number of vehicles turning right from the online intersection to the offline intersection according to the detection data, which represents the number of vehicles that must be released at the online intersection that are not controlled by traffic lights;

[0055] 23) Calculate the expected maximum traffic volume c' of the constrained flow direction at the online intersection:

[0056] c' = the number of vehicles leaving the stop line at the downstream offline intersection - the number of vehicles turning right at the online intersection.

[0057] 3) Compare the online intersection (located upstream) with the offline intersection (located downstream) to determine the constrained flow direction of the online intersection (more than one); refer to Figure 2 As shown,

[0058] Wherein, determining the constrained flow direction of the online intersection in step 3) comprises the following steps:

[0059] 31) Eliminate the flow direction from the online intersection to other non-offline intersections, and only keep the flow direction from the online intersection to the offline intersection;

[0060] 32) Eliminate the right-turn flows that are not controlled by traffic lights, and the remaining flows are the constrained flows at the online intersection.

[0061] 4) Based on the expected maximum throughput of the constrained flow direction of the online intersection, construct the objective function and solve the constrained timing plan of the online intersection;

[0062] Wherein, in said step 4), based on the expected maximum throughput of the constrained flow direction of the online intersection, constructing an objective function and solving the timing scheme of the online intersection restriction includes the following steps:

[0063] 41) Construct the objective function f(x) = f(x1, x2) = h1x1 + h2x2, and minimize the function. The constraint condition is h(x) = x1 + x2 - c' = 0; that is:

[0064] minf(x1,x2)

[0065] sth(x)=x1+x2-c'=0

[0066] Among them, h1 is the headway of the first flow direction going straight; h2 is the headway of the second flow direction turning left; x1 is the number of vehicles expected to be released in the first flow direction; x2 is the number of vehicles expected to be released in the second flow direction;

[0067] 42) Determine the range of values ​​for the queue:

[0068] 0≤x1≤min(c1,q1)

[0069] 0≤x2≤min(c2,q2)

[0070] Among them, c1 is the number of vehicles queuing to 80% position in the first flow direction, and c2 is the number of vehicles queuing to 80% position in the second flow direction; q1 is the actual number of vehicles queuing in the first flow direction, and q2 is the actual number of vehicles queuing in the second flow direction; in order to prevent overflow, c1 and c2 are selected from the number of vehicles queuing to 80% position, rather than the number of vehicles queuing to the upstream exit;

[0071] 43) Substitute the equation h(x) in step 41) into the objective function f(x), and the elimination method can obtain the following problem: minf(x1,x2)=(h1-h2)x1+h2c'; Under normal circumstances, the headway of the straight flow direction is smaller than the headway of the left turn flow direction, h1-h2<0, and x1 takes the maximum value to maximize the number of vehicles arriving at the downstream offline intersection in the restricted flow direction; If h1-h2>0 exists at some intersections, then x1 takes the minimum value to maximize the number of vehicles arriving at the downstream offline intersection in the restricted flow direction; The determined x1 and x2 are the expected released flow in the first flow direction and the second flow direction;

[0072] 44) According to the traffic volume expected to be released in the constrained flow direction of the online intersection and the traffic volume of other flows detected, the green light time for each flow direction is allocated as the timing plan for the online intersection.

[0073] Example 1:

[0074] Reference Figure 3 As shown in the figure, when intersection B is offline, it is called an offline intersection. Through the road network structure, it can be determined that intersections A and C are both adjacent online intersections and are upstream of intersection B. Specifically, a vehicle passes through section 1 from intersection A to downstream intersection B; a vehicle passes through section 2 from intersection C to downstream intersection B.

[0075] Calculate the timing plan for intersection A: Calculate the predicted carrying capacity of section 1 according to the method of the present invention as the expected maximum throughput of the constrained flow direction of intersection A, so as to ensure that there is no overflow in the west direction of intersection B; determine the west-direction and north-left flow directions as the constrained flow directions of intersection A; construct the objective function and solve the timing plan for intersection A.

[0076] Calculate the timing plan for intersection C: calculate the predicted carrying capacity of section 2 according to the method of the present invention as the expected maximum throughput of the constrained flow direction of intersection C, so as to ensure that there is no overflow in the east direction of intersection B; determine the east-direction and south-left flow directions as the constrained flow directions of intersection C; construct the objective function and solve the timing plan.

[0077] For the online intersections adjacent to the south and north of the offline intersection B, the timing plan can be solved using the same calculation method.

[0078] The present invention also provides a virtual ad hoc network signal control system when the device is offline, comprising:

[0079] The online intersection determination module is used to determine N adjacent online intersections of the offline intersection according to the road network structure and clarify the upstream and downstream relationships;

[0080] A calculation module, used for calculating the expected maximum traffic volume of the constrained flow direction at the online intersection;

[0081] A flow direction determination module, used to determine the constrained flow direction of the online intersection;

[0082] The scheme timing module is used to construct the objective function and solve the timing scheme with limited online intersections.

[0083] The present invention has many specific application paths. The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements can be made without departing from the principle of the present invention. These improvements should also be regarded as the protection scope of the present invention.

Claims

1. A virtual ad hoc network signal control method when a device is offline, characterized in that: Here are the steps: 1) According to the road network structure, determine the N adjacent online intersections of the offline intersection and clarify the upstream and downstream relationships; 2) Calculate the predicted carrying capacity of the road section downstream of the online intersection and connected to the offline intersection as the expected maximum traffic volume in the constrained flow direction of the online intersection; 3) Compare the online intersection with the offline intersection to determine the constrained flow direction of the online intersection; 4) Construct the objective function and solve the timing scheme for the restricted online intersection; In step 4), based on the expected maximum throughput of the constrained flow direction of the online intersection, an objective function is constructed and a timing scheme of the online intersection is solved, which includes the following steps: 41) Construct the objective function f(x) = f(x1, x2) = h1x1 + h2x2, and minimize the function. The constraint condition is h(x) = x1 + x2 - c' = 0; that is: minf(x1,x2) sth(x)=x1+x2-c'=0 Among them, h1 is the headway of the first flow direction going straight; h2 is the headway of the second flow direction turning left; x1 is the number of vehicles expected to be released in the first flow direction; x2 is the number of vehicles expected to be released in the second flow direction; 42) Determine the range of values ​​for the queue: 0≤x1≤min(c1,q1) 0≤x2≤min(c2,q2) Among them, c1 is the number of vehicles queuing to 80% position in the first flow direction, and c2 is the number of vehicles queuing to 80% position in the second flow direction; q1 is the actual number of vehicles queuing in the first flow direction, and q2 is the actual number of vehicles queuing in the second flow direction; c1 and c2 are the number of vehicles queuing to 80% position, not the number of vehicles queuing to the upstream exit; 43) Substitute the equation h(x) in step 41) into the objective function f(x), and the elimination method can obtain the following problem: minf(x1,x2)=(h1-h2)x1+h2c'; Under normal circumstances, the headway of the straight flow direction is smaller than the headway of the left turn flow direction, h1-h2<0, and x1 takes the maximum value to maximize the number of vehicles arriving at the downstream offline intersection in the restricted flow direction; If h1-h2>0 exists at some intersections, then x1 takes the minimum value to maximize the number of vehicles arriving at the downstream offline intersection in the restricted flow direction; The determined x1 and x2 are the expected released flow in the first flow direction and the second flow direction; 44) According to the traffic volume expected to be released in the constrained flow direction of the online intersection and the traffic volume of other flows detected, the green light time for each flow direction is allocated as the timing plan for the online intersection.

2. The method for controlling virtual ad hoc network signals when the device is offline according to claim 1, characterized in that: In the step 1), the N adjacent online intersections of the offline intersection are determined, and the upstream and downstream relationships are clarified, including the following steps: 11) Determine an online intersection adjacent to the offline intersection according to the location of the offline intersection in the road network; 12) The traffic flow from the online intersection to the offline intersection is affected, and the traffic flow from the offline intersection to the online intersection is not affected; for each online intersection, it is called the upstream online intersection; for the offline intersection, it is called the downstream offline intersection.

3. The method for controlling signals in a virtual ad hoc network when the device is offline according to claim 1, characterized in that: The step 2) calculates the predicted carrying capacity of the road section downstream of the online intersection and connected to the offline intersection as the expected maximum throughput of each constrained flow direction of the online intersection, including the following steps: 21) Obtain the number of vehicles leaving the stop line at the downstream offline intersection according to the detection data, including left-turning vehicles, straight-going vehicles, and right-turning vehicles leaving the stop line, which represents the release capacity of the offline intersection; 22) Obtain the number of vehicles turning right from the online intersection to the offline intersection according to the detection data, which represents the number of vehicles that must be released at the online intersection that are not controlled by traffic lights; 23) Calculate the expected maximum traffic volume c' of the constrained flow direction at the online intersection: c' = the number of vehicles leaving the stop line at the downstream offline intersection - the number of vehicles turning right at the online intersection.

4. The method for controlling virtual ad hoc network signals when the device is offline according to claim 1, characterized in that: Determining the constrained flow direction of the online intersection in step 3) includes the following steps: 31) Eliminate the flow direction from the online intersection to other non-offline intersections, and only keep the flow direction from the online intersection to the offline intersection; 32) Eliminate the right-turn flows that are not controlled by traffic lights, and the remaining flows are the constrained flows at the online intersection.

5. A virtual ad hoc network signal control system when the device is offline, characterized in that: include: The online intersection determination module is used to determine N adjacent online intersections of the offline intersection according to the road network structure and clarify the upstream and downstream relationships; A calculation module, used for calculating the expected maximum traffic volume of the constrained flow direction at the online intersection; A flow direction determination module, used to determine the constrained flow direction of the online intersection; The scheme timing module is used to construct the objective function and solve the timing scheme for the restricted online intersection; Constructing the objective function and solving the timing scheme for the restricted online intersection includes the following steps: 41) Construct the objective function f(x) = f(x1, x2) = h1x1 + h2x2, and minimize the function. The constraint condition is h(x) = x1 + x2 - c' = 0; that is: minf(x1,x2) sth(x)=x1+x2-c'=0 Among them, h1 is the headway of the first flow direction going straight; h2 is the headway of the second flow direction turning left; x1 is the number of vehicles expected to be released in the first flow direction; x2 is the number of vehicles expected to be released in the second flow direction; 42) Determine the range of values ​​for the queue: 0≤x1≤min(c1,q1) 0≤x2≤min(c2,q2) Among them, c1 is the number of vehicles queuing to 80% position in the first flow direction, and c2 is the number of vehicles queuing to 80% position in the second flow direction; q1 is the actual number of vehicles queuing in the first flow direction, and q2 is the actual number of vehicles queuing in the second flow direction; c1 and c2 are the number of vehicles queuing to 80% position, not the number of vehicles queuing to the upstream exit; 43) Substitute the equation h(x) in step 41) into the objective function f(x), and the elimination method can obtain the following problem: minf(x1,x2)=(h1-h2)x1+h2c'; Under normal circumstances, the headway of the straight flow direction is smaller than the headway of the left turn flow direction, h1-h2<0, and x1 takes the maximum value to maximize the number of vehicles arriving at the downstream offline intersection in the restricted flow direction; If h1-h2>0 exists at some intersections, then x1 takes the minimum value to maximize the number of vehicles arriving at the downstream offline intersection in the restricted flow direction; The determined x1 and x2 are the expected released flow in the first flow direction and the second flow direction; 44) According to the traffic volume expected to be released in the constrained flow direction of the online intersection and the traffic volume of other flows detected, the green light time for each flow direction is allocated as the timing plan for the online intersection.

Images