A multi-intersection variable guide lane and traffic signal cooperative control optimization method

Abstract

The present application relates to a kind of multi-intersection variable guide lane and traffic signal cooperative control optimization method, obtain and analyze the traffic flow data of each intersection each import lane each steering in period, judge whether to satisfy the setting and opening condition of variable guide lane;According to intersection correlation degree model, the correlation between intersection is calculated, and the coordination area of multi-intersection is determined;Multi-intersection variable guide lane and traffic signal cooperative control double-layer optimization model is used, with the minimum regional vehicle delay as optimization goal, the variable guide lane function division scheme and signal timing scheme of multi-intersection are optimized;Whether to meet the optimization goal requirement after optimization is compared;Judge whether the result of model leads to overflow.This application constructs a kind of multi-intersection variable guide lane and traffic signal cooperative control double-layer optimization mathematical model, realizes the dynamic optimization of regional road network road space resource and time resource, improves the utilization of entire regional road network space-time resource.

Description

Technical Field

[0001] This invention relates to the fields of intelligent transportation and traffic signal control technology, and in particular to an optimization method for coordinated control of variable guidance lanes and traffic signals at multiple intersections. Background Technology

[0002] In urban transportation systems, traffic signal control plays a crucial role in reducing traffic congestion, improving road safety, and mitigating environmental pollution. Signal control has long been considered one of the most effective and economical methods for avoiding or alleviating traffic congestion. With rapid population and economic growth, there is an urgent need to improve the overall performance of urban road networks through advanced intelligent traffic control technologies. Existing research on regional traffic signal control primarily focuses on optimizing control strategies and signal control parameters, such as signal cycle, green light duration, and phase difference for arterial signal coordination. Simultaneously, various simulation platforms and commercial software have been widely used to achieve optimal regional traffic signal control strategies. However, the effectiveness of regional traffic signal control in improving road traffic congestion is limited. Even though online real-time signal control systems can obtain real-time data such as queue length and traffic flow, these studies only consider single signal control parameters and do not fully integrate actual road traffic conditions and lane space resource allocation.

[0003] To fill the current technological gap, this invention, based on the optimization model of variable lane and traffic signal coordinated control at a single intersection, takes into account the correlation between intersections and combines regional signal control parameters to analyze the interaction between the functional division of variable lanes and regional signal control parameters. It then invents a two-layer optimization control method for the coordinated control of variable lanes and traffic signals at multiple intersections. This provides a scientific theoretical basis for the functional division of variable lanes and signal coordinated control at multiple intersections, enabling dynamic optimization of regional road network spatial and temporal resources and improving the utilization rate of the entire regional road network's spatiotemporal resources. Summary of the Invention

[0004] I. Technical problems to be solved

[0005] The technical problem to be solved by the present invention is to provide an optimization method for coordinated control of variable guidance lanes and traffic signals at multiple intersections to improve the utilization rate of spatiotemporal resources in regional road networks.

[0006] II. Technical Solution

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for optimizing the coordinated control of variable guidance lanes and traffic signals at multiple intersections, comprising the following steps:

[0008] S1. Determine whether the lane meets the setting and opening conditions for variable guidance lanes;

[0009] S2. Calculate the degree of correlation between intersections;

[0010] S3, calculate the average vehicle delay in the region;

[0011] S4. Calculate the variable guidance lane function division scheme and signal timing scheme for multiple intersections;

[0012] S5. Solving the two-layer optimization model for coordinated control of variable guidance lanes and traffic signals at multiple intersections;

[0013] S6. Determine if overflow occurs in each lane.

[0014] Furthermore, the step S1, determining whether the lane meets the conditions for setting and opening a variable guidance lane, specifically includes the following steps:

[0015] S11. Obtain the channelization design and lane distribution of each intersection approach road within the area;

[0016] S12. Calculate the saturation of the intersection approach lanes;

[0017] S13. Calculate the steering imbalance coefficient.

[0018] Furthermore, the S2 calculation of the correlation between intersections specifically includes the following steps:

[0019] S21. Calculate the traffic demand correlation degree at the intersection;

[0020] S22. Calculate the geometric dimension correlation of the intersection;

[0021] S23. Calculate the signal periodic correlation degree at the intersection;

[0022] S24. Calculate the correlation degree of multiple intersections in the region and determine the intersection division region.

[0023] Furthermore, step S3, calculating the average vehicle delay in the region, specifically includes the following steps:

[0024] S31. Calculate vehicle delays at intersections;

[0025] S32. Calculate the delay of vehicles passing through the intersection;

[0026] S33, calculate the average delay per vehicle on the road segment.

[0027] Furthermore, step S4, which calculates the variable guidance lane function allocation scheme and signal timing scheme for multiple intersections, specifically includes the following steps:

[0028] S41. Calculation scheme for variable guidance lane division at multiple intersections with minimum regional delay;

[0029] S42. Calculate the optimal common signal period for the region;

[0030] S43. Calculate the minimum green light duration for the coordinated phase at critical intersections;

[0031] S44. Calculate the effective green light duration for coordinated phases at non-critical intersections;

[0032] S45, Phase Difference Optimization Model.

[0033] Furthermore, the solution of the two-layer optimization model for the coordinated control of variable guidance lanes and traffic signals at multiple intersections in step S5 includes the following steps:

[0034] S51. Initialize variables;

[0035] S52. Calculate the initial solution set;

[0036] S53. Generate a new solution set for the offspring population;

[0037] S54. Combine the initial solution set with the solution set of the offspring population into a new solution set;

[0038] S55. Calculate the fitness value;

[0039] S56. Determine whether to generate a new parent population;

[0040] S57. Output the optimal solution of the model.

[0041] Furthermore, step S6, determining whether overflow occurs in each lane, includes the following steps:

[0042] S61. Use lane detectors to detect the queue length of each lane;

[0043] S62. Determine if the lane is overflowing. If not, maintain the status quo. If so, continue to optimize the lane and signal timing scheme using the model.

[0044] Furthermore, in S3, S35 uses real-time queue length calculation to calculate the average vehicle delay in the calculation area. The constructed real-time queue length calculation model includes variable directional lane control parameters. The delay of vehicles in different lanes at the intersection varies under different saturation levels.

[0045] Furthermore, in step S41 of S4, regarding the calculation of the multi-intersection variable lane division scheme with the minimum average vehicle delay in the calculation area, the method is based on the constructed two-layer model of multi-intersection variable lane and traffic signal coordinated control optimization. The upper-layer model is a multi-intersection variable lane functional division model based on average vehicle delay, and the lower-layer model is a multi-intersection traffic signal coordinated control optimization model considering variable lanes. The output of the upper-layer model is used as the input of the lower-layer model, and similarly, the output of the lower-layer model is used as the input of the upper-layer model, realizing the dynamic interaction of spatial resources and temporal resources, iterating until the optimal solution of both is output.

[0046] Furthermore, in S42 of the above, in terms of calculating the optimal common cycle duration, since multiple intersections in the area are equipped with variable guidance lanes, the traffic flow in the area is redistributed, and the flow ratio of each phase and the total flow ratio of each intersection will also change. The calculation model of the intersection signal cycle duration includes the control parameters of the variable guidance lanes. Therefore, the common signal cycle duration solved by the model is the optimal solution for realizing the spatiotemporal resources of the regional road network.

[0047] In S4, S45, in terms of calculating the phase difference, combines the constructed intersection vehicle delay model with the distance between intersections and the driving speed to establish a phase difference optimization model. The phase difference optimization model can improve the traffic efficiency of vehicles choosing variable guidance lanes through the area, and can also guide the speed of vehicles exiting from variable guidance lanes to reduce vehicle delays at intersections.

[0048] Furthermore, the traffic parameters required for the setting and activation conditions of the variable guidance lanes include: traffic flow at different turns at each intersection, the number of lanes in each direction of travel, the capacity of each lane, the saturation of the approach lanes, and the turning flow imbalance coefficient. The activation judgment process is detailed below. Figure 1 .

[0049] Furthermore, the specific parameters also include signal timing scheme design and requirements, specifically including: signal control scheme setting requirements and signal phase sequence design, see details below. Figure 2 .

[0050] Furthermore, by combining the intersection correlation model, we provide judgment conditions and theoretical basis for the division of multi-intersection coordination areas. The parameters required for the intersection correlation model include: traffic demand from upstream to downstream of adjacent intersections, traffic flow density, maximum signal cycle, and minimum signal cycle. The criteria for intersection correlation division are detailed below. Figure 3 .

[0051] Furthermore, a two-layer optimization model for coordinated control of variable guidance lanes and traffic signals at multi-intersections is adopted to optimize the functional allocation scheme and signal timing scheme of variable guidance lanes at multi-intersections, and the model results are output; the model application scenarios and assumptions are detailed in [link to model description]. Figure 4 See model framework Figure 5 .

[0052] The upper-level model is a multi-intersection variable guidance lane function partitioning model based on average vehicle delay. Average vehicle delay includes three parts: vehicle delay caused by signal control, delay through the intersection, and road segment delay, such as... Figure 6 As shown. The specific calculation is shown in the following formula:

[0053]

[0054] Among them, D ijk Indicates vehicle delays caused by signal control at intersection i, entrance j, lane k, s; D ijk Indicates the delay of vehicles passing through the intersection at intersection i, entrance j, lane k, s; T ijk Indicates the road segment delay at intersection i, entrance j, lane k, s; z ijk pcu represents the number of vehicles in lane k at intersection j within the analysis period.

[0055] Furthermore, vehicle delays are calculated based on vehicle queue lengths, which are typically determined by the uniform phase queue length N. u It consists of the oversaturated queue length N0. Therefore, the vehicle delay D at intersection i, entrance j, lane k. ijk The calculation formula is as follows:

[0056] D ijk =(N uijk +N oijk )·q ijk / (1-J / v ijk )

[0057] Where i represents the intersection; j represents the approach lane; k represents the lane; J represents the length of each vehicle, in meters; Nuijk represents the uniform phase queue length on lane k at intersection i, in cubic meters; N 0ijk q represents the length of the oversaturated queue at intersection i, entrance j, lane k, pcu; ijk V represents the vehicle arrival rate at intersection i, entrance j, lane k, in pcu / s; ijk Let represent the average speed of vehicles on lane k at intersection i, entrance j, in m / s.

[0058] Furthermore, delays in vehicle passage through intersections are caused by the dissipation of queues due to varying vehicle acceleration, deceleration, and turning. To more closely approximate actual operational conditions, the formula for calculating intersection delays is as follows:

[0059] D′ ijk =N ijk ·((v ijkt -v ijk0 / a)-(K ijk / v ijkt ))+l s

[0060] Among them, v ijkt V represents the speed of a vehicle in lane k of the entrance lane j at intersection i, in m / s; ijk0 Let 'a' represent the initial vehicle speed in lane k of entrance lane j at intersection i, in m / s; 'a' represent acceleration, in m. 2 / s;L ijk The distance to the intersection (from lane k in lane j of the upstream entrance i at the stop line of the intersection to the downstream exit of the intersection), in meters (m); l s This represents the time lost by vehicles accelerating and decelerating through the intersection, expressed in seconds.

[0061] Furthermore, road segment delays T ijk The calculation formula is as follows:

[0062]

[0063] Among them, t ijk Indicates the travel time (s) of lane k at intersection i, entrance j; t 0ijk q represents the free-flow time (s) of lane k at intersection j entrance. ijk Q represents the vehicle arrival rate at intersection i, entrance j, lane k, in pcu / s; ijk The capacity of lane k at intersection i is represented by pcu / s; α represents the congestion coefficient, typically taken as 0.15; β represents the congestion factor, typically taken as 4.

[0064] Furthermore, after obtaining the functional division scheme of variable guidance lanes at multiple intersections through the upper-level model, the flow ratio and saturation of each lane in the area will also change. Therefore, in order to achieve coordinated optimization of the regional road network traffic efficiency at both the spatial and temporal levels, it is necessary to coordinately optimize the regional traffic signal control scheme based on the functional division scheme of variable guidance lanes. The lower-level regional signal control optimization model includes two parts: a multi-intersection traffic signal coordinated control optimization model considering variable guidance lanes and a phase difference optimization model for coordinated control.

[0065] Among them, the multi-intersection traffic signal cooperative control optimization model considering variable guidance lanes mainly optimizes the signal control parameters, including common cycle, green ratio and phase difference.

[0066] Furthermore, the signal cycle for each intersection is optimized, as shown in the following formula:

[0067]

[0068] in,

[0069]

[0070] Among them, l n The time lost for phase n is represented in seconds; N represents the number of phases at intersection i; y nk Y represents the flow ratio of phase n to lane k; i This represents the sum of the traffic flows at intersection i.

[0071] Furthermore, to facilitate coordinated regional signal control, after optimizing the signal cycle at each intersection, it is necessary to determine the common signal cycle for regional signal control. The calculation formula is as follows:

[0072] C m =max(C1, C2…C i )

[0073] Among them, C m The period of a public signal in a road network is represented by s.

[0074] Furthermore, the minimum green time for the coordinated phase at the critical intersection is calculated using the following formula:

[0075]

[0076] Among them, t EGm L represents the minimum green light time (in seconds) for the coordinated phase at a critical intersection (m). m y represents the total loss time at critical intersection m, in seconds; m Y represents the critical traffic flow ratio of the coordinated phase at the critical intersection m; m This represents the sum of the critical traffic flow ratios for each phase at a critical intersection m.

[0077]

[0078] Among them, y n This represents the traffic flow ratio of each phase at the critical intersection m; M represents the number of phases at the critical intersection m.

[0079] Where, if d = 1, the variable guide lane is a left-turn lane, then y m=max{y ij-l y ij-v If d = 0, the variable lane can be converted into a straight lane, then y m =max{y ij-sr y ij-v}

[0080] The flow ratio for each phase and lane is calculated using the following formula:

[0081] y ij-l =q ij-l / (S ij-l (1+d ij )

[0082] y ij-v =(q ij-l ·d ij +q ij-s ·(1-d ij ))2S ij-v

[0083] y ij-sr =q ij-sr / (S ij-sr (2-d ij ))

[0084] Where, d ij Indicates the direction of travel for the variable lane at intersection i's entrance j; y ij-l This represents the flow rate ratio of the left-turn lane at intersection i's entrance j; y ij-v Indicates the flow ratio of the variable-direction lane at intersection i's entrance j; y ij-sr This represents the ratio of the flow rate of the straight-through lane to the right-turn lane at intersection i's entrance j.

[0085] Furthermore, the minimum green light time for non-critical intersection non-coordinated phases is calculated using the following formula:

[0086]

[0087] Among them, t EGn q represents the minimum green light time (s) for n non-critical intersections with uncoordinated phases. n S represents the critical traffic flow (pcu / h) at a non-critical intersection n-phase non-coordinated traffic flow. n The lane saturation flow rate (pcu / h) represents the uncoordinated phase of a non-critical intersection n; y n This represents the traffic flow ratio of the critical lanes in the non-critical phase at a non-critical intersection; x p This indicates the saturation of the non-coordinated phase at a non-critical intersection.

[0088] Where, if d = 1, the variable guide lane is a left-turn lane, then y n=max{y ij-sr y ij-v If d = 0, the variable lane can be converted into a straight lane, then y n ={y ij-l y ij-v}

[0089] Furthermore, the effective green time for coordinated phases at non-critical intersections is calculated using the following formula:

[0090]

[0091] Among them, t EG The minimum green light time for the coordinated phase, in seconds; L n The total lost time for non-critical intersections is expressed in seconds; K represents the total number of non-coordinated phases at non-critical intersections.

[0092] Furthermore, to achieve coordinated regional signal control, the phase difference optimization model is classified according to the functional division scheme of the downstream variable guidance lanes. The phase difference optimization model is mainly divided into: critical path phase difference optimization model and non-critical path phase difference optimization model.

[0093] The critical path phase difference optimization model is shown in the following formula:

[0094]

[0095] in, L represents the phase difference between intersection i and intersection i+1, expressed as s; i，i+1 The distance, in meters (m), is the distance from the stop line at intersection i to the stop line at intersection i+1. i，i+1 The speed of vehicles traveling from intersection i to intersection i+1 is expressed in m / s; s D represents the time lost due to acceleration and deceleration of a vehicle passing through an intersection, in seconds. i，i+1 The vehicle delay, s, represents the vehicle delay caused by the coordination of phases between intersection i and intersection i+1.

[0096] Furthermore, in actual traffic operation, considering that traffic flow dissipation takes a certain amount of time, the vehicle dissipation time at the downstream intersection i+1 is calculated using the following formula:

[0097]

[0098] Among them, t i，i+1 N represents the dissipation time of a vehicle exiting intersection i, in seconds; ijk S represents the queue length of lane k at intersection i, entrance j; pcu. ijk The saturation flow rate of lane k at intersection i (entrance j) is expressed in pcu / h.

[0099] Finally, the phase difference of the coordinated phase between intersection i and intersection i+1 is obtained. As shown in the formula below:

[0100]

[0101] Similarly, the phase difference between the coordinated phases of opposite intersection i+1 and intersection i. The calculation formula is as follows:

[0102]

[0103] Among them, D i+1,i N represents the vehicle delay s, which is the time required for phase coordination between intersection i and intersection i+1. i+1,jk S represents the queue length of lane k at entrance j of intersection i+1, pcu; i+1,jk L represents the saturation flow rate of lane k at entrance j of intersection i+1, in pcu / h; i+1,i The distance, in meters (m), is the distance from the stop line at intersection i+1 to the stop line at intersection i. i+1,i This represents the speed of vehicles traveling from intersection i+1 to intersection i, in m / s.

[0104] Furthermore, in the non-critical path phase difference optimization model, the phase difference is determined by introducing a weighting coefficient based on traffic flow, and the calculation formula is shown below:

[0105]

[0106] in,

[0107]

[0108] in, s represents the phase difference of the non-critical path from intersection i to intersection i+1; q represents the phase difference of the non-critical path. ijk Indicates the traffic flow at intersection i, entrance j, lane k, in pcu / h; q i+1,jk ω represents the traffic flow of lane k at intersection j entrance of intersection i+1, pcu / h; ω represents the weighting coefficient.

[0109] Furthermore, for solving the two-layer optimization model of coordinated control of variable guidance lanes and traffic signals at multiple intersections, based on considerations of model complexity, different control parameters (variable guidance lane design scheme, signal cycle, green light time and phase difference) are optimized. The Non-Dominated Sorting Genetic Algorithm II (NSGA-II algorithm) is selected, as this algorithm can effectively find the optimal solution for multi-objective optimization problems. The detailed solution process of the model is as follows... Figure 7 As shown.

[0110] Furthermore, compare the average vehicle delay in the region after model optimization. If it is smaller than the result of the previous iteration, continue optimization; otherwise, stop optimization and adopt the lane scheme and signal timing scheme obtained from the previous iteration.

[0111] Furthermore, in order to prevent excessively long queues from affecting the traffic efficiency of surrounding intersections, lane detectors are needed to monitor lane queue lengths. This will determine whether the model results lead to intersection overflow. If so, the optimized sub-region will be expanded; otherwise, the lane scheme and signal timing scheme will be sent to the signal controller.

[0112] III. Beneficial Effects

[0113] The advantages of this invention compared to the prior art are:

[0114] First, based on real-time traffic flow data for each turn at each intersection's approach lanes, the setting and activation conditions for variable direction lanes at multiple intersections in the region are determined. Then, considering the correlation between intersections, the research scope is expanded to multiple intersections. Based on the intersection correlation model, the correlation between intersections is calculated, and the coordination area of ​​multiple intersections is determined. Furthermore, combining vehicle delay models at intersections, road segment impedance models, and regional signal control parameters, this invention constructs a two-layer optimization model for coordinated control of variable direction lanes and traffic signals at multiple intersections. The model optimizes the functional allocation scheme of variable direction lanes at multiple intersections with the minimum average vehicle delay in the region as the optimization objective. Based on the functional allocation scheme of variable direction lanes at multiple intersections in the region, traffic flow for each turn in the region is redistributed. Based on the changed flow ratio, the optimal common cycle length, effective green light duration for each phase at each intersection, and phase difference are recalculated. Finally, it is determined whether the model results lead to overflow. If so, the optimized sub-region is expanded; otherwise, the lane scheme and signal timing scheme are sent to the signal controller.

[0115] In summary, this invention takes into account the correlation between intersections and combines regional signal control parameters to analyze the interaction between the functional division of variable guidance lanes and regional signal control parameters. It constructs a two-layer optimization mathematical model for the coordinated control of variable guidance lanes and traffic signals at multiple intersections, providing a scientific theoretical basis for the functional division of variable guidance lanes and coordinated signal control at multiple intersections. This enables the dynamic optimization of spatial and temporal resources of the regional road network and improves the utilization rate of the spatiotemporal resources of the entire regional road network. Attached Figure Description

[0116] Figure 1 The variable guidance lane opening determination process provided by this invention;

[0117] Figure 2 The phase sequence diagram for the variable guidance lane signal control provided by this invention;

[0118] Figure 3 The basis for dividing the intersection correlation degree provided by this invention;

[0119] Figure 4 The model provided by this invention includes the research object and assumptions;

[0120] Figure 5 This invention provides a two-layer optimization model framework for the coordinated control of variable guidance lanes and traffic signals at multiple intersections.

[0121] Figure 6 This is a schematic diagram of the regional average vehicle delay composition provided by the present invention;

[0122] Figure 7 The model solution process provided by this invention;

[0123] Figure 8 This is a schematic diagram of an embodiment of the control device for the interaction between variable guidance lanes and intersection signal control provided by the present invention. Detailed Implementation

[0124] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0125] It should be noted that all directional indications in the embodiments of the present invention, such as up, down, left, right, front, back, etc., are only used to explain the relative positional relationship and movement of the components in a specific posture as shown in the attached figure. If the specific posture changes, the directional indication will also change accordingly.

[0126] Furthermore, in this invention, descriptions involving "first," "second," etc., are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0127] To make the objectives, technical solutions, and advantages of this invention clearer, the 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 merely illustrative of the invention and are not intended to limit the invention.

[0128] The invention will now be further described with reference to the accompanying drawings.

[0129] This invention provides an optimization method for the coordinated control of variable guiding lanes and traffic signals at multiple intersections. Applied to the timing of traffic signals at regional multiple intersections, it analyzes the interaction between the functional division of variable guiding lanes and regional signal control parameters based on the correlation between the lanes and the intersections. A two-layer optimization mathematical model for the coordinated control of variable guiding lanes and traffic signals at multiple intersections is constructed, providing a scientific theoretical basis for the functional division of variable guiding lanes and coordinated signal control at multiple intersections. This enables dynamic optimization of the spatial and temporal resources of the regional road network, improving the utilization rate of the entire regional road network's spatiotemporal resources. Detailed descriptions follow:

[0130] Figure 8 This is a schematic diagram illustrating an embodiment of a multi-intersection variable guidance lane and traffic signal coordinated control system provided by the present invention. The system may include a server 100, which integrates devices for multi-intersection lane and area signal timing control schemes, such as... Figure 8 The server in the middle.

[0131] In this embodiment of the invention, server 100 is mainly used for:

[0132] Obtain traffic flow data for each approach lane and each turn at each intersection during the analysis period to determine whether the conditions for setting and opening variable direction lanes are met.

[0133] Based on the intersection correlation model, the correlation between intersections is calculated to determine the coordination area of ​​multiple intersections;

[0134] With the goal of minimizing the average vehicle delay in the region, the functional division scheme of variable guidance lanes and the signal timing scheme of multiple intersections are determined and optimized.

[0135] Compare whether the optimized result meets the optimization objective requirements. If it does, the process ends; otherwise, continue optimizing.

[0136] If the model results lead to overflow, expand the scope of the optimized sub-region; otherwise, send the lane scheme and signal timing scheme to the signal controller.

[0137] In this embodiment of the invention, the server 100 can be a standalone server, a server network, or a server cluster. For example, the server 100 described in this embodiment includes, but is not limited to, a computer, a network host, a single network server, a set of multiple network servers, or a cloud server composed of multiple servers. The cloud server is composed of a large number of computers or network servers based on cloud computing.

[0138] It is understood that the terminal 200 used in this embodiment of the invention can be a device that includes both receiving and transmitting hardware, that is, a device with receiving and transmitting hardware capable of performing bidirectional communication on a bidirectional communication link. Such a device may include: cellular or other communication devices having a single-line display, a multi-line display, or a cellular or other communication device without a multi-line display. Specifically, the terminal 200 may be a desktop computer, a portable computer, a network server, a PDA (Personal Digital Assistant), a mobile phone, a tablet computer, a wireless terminal device, a communication device, an embedded device, etc. This embodiment does not limit the type of terminal 200.

[0139] Those skilled in the art will understand that Figure 8 The application environment shown is merely one application scenario of the present invention and does not constitute a limitation on the application scenarios of the present invention. Other application environments may include those that are more specific to the present invention. Figure 1 The number of more or fewer terminals shown, for example Figure 1 Only two terminals are shown in the image. It is understood that the order allocation system may also include one or more other terminals, which are not specified here.

[0140] In addition, such as Figure 8 As shown, the control system of this multi-intersection variable guidance lane and regional signal timing optimization method may also include a memory 200 for storing data such as traffic parameters, real-time queue length, and optimal signal timing scheme.

[0141] It should be noted that, Figure 1 The schematic diagram of the control system of the multi-intersection variable guidance lane and area signal timing optimization method shown is merely an example. The control system and scenario described in the embodiments of the present invention are for the purpose of more clearly illustrating the technical solutions of the embodiments of the present invention, and do not constitute a limitation on the technical solutions provided by the embodiments of the present invention. As those skilled in the art will know, with the evolution of the control system of the multi-intersection variable guidance lane and area signal timing optimization method and the emergence of new business scenarios, the technical solutions provided by the embodiments of the present invention are also applicable to similar technical problems.

[0142] This invention provides a control method for a multi-intersection variable guidance lane and intersection signal control optimization model. Based on the computer-readable storage medium and computing device provided in the above embodiments of this invention, the invention content specifically described above can be implemented with reference to the implementation of the control method, which will not be repeated here.

[0143] More specific implementations of the various units of the control device for the interaction between variable guide lanes and intersection signal timing can be found in the description of the control method for the interaction between variable guide lanes and intersection signal timing of the present invention, which has similar beneficial effects and will not be repeated here.

[0144] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the control method for the interaction between the variable guidance lane and the intersection signal timing as described above.

[0145] Generally, computer instructions for implementing the methods of the present invention can be carried on any combination of one or more computer-readable storage media. Non-transitory computer-readable storage media can include any computer-readable medium except for signals themselves that are temporarily propagating.

[0146] Computer-readable storage media can be, for example—but not limited to—electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0147] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—as well as conventional procedural programming languages—such as the "C" language or similar programming languages. In particular, Python, suitable for neural network computation, and platform frameworks based on TensorFlow, PyTorch, etc., can be used. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0148] This invention also provides a computing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the control method described above for the interaction between the variable guidance lanes at multiple intersections and the intersection signal timing.

[0149] The computer-readable storage medium and computing device provided in the above embodiments of the present invention can be implemented with reference to the content specifically described in the present invention for implementing the control method for the interaction relationship between the variable guide lane and the intersection signal timing as described above, and have similar beneficial effects as the control method for the interaction relationship between the variable guide lane and the intersection signal timing as described above, which will not be repeated here.

[0150] This invention discloses an optimization method for coordinated control of variable guidance lanes and traffic signals at multiple intersections. First, traffic flow data for each turn at each approach lane of each intersection is acquired during the analysis period to determine if the conditions for setting and opening variable guidance lanes are met. Second, based on an intersection correlation model, the correlation between intersections is calculated to determine the coordination area of ​​the multiple intersections. Then, a two-layer optimization model for coordinated control of variable guidance lanes and traffic signals at multiple intersections is adopted, with the goal of minimizing the average vehicle delay in the area, to optimize the functional division scheme of variable guidance lanes and the signal timing scheme at the multiple intersections. Next, the optimization results are compared to see if they meet the optimization objective requirements; if they do, the optimization ends; otherwise, the optimization continues. Finally, it is determined whether the model results lead to overflow; if so, the optimized sub-area is expanded; otherwise, the lane scheme and signal timing scheme are sent to the traffic signal controller.

[0151] The technical solution of this invention takes into account the correlation between intersections, combines regional signal control parameters, analyzes the interaction between the functional division of variable guiding lanes and regional signal control parameters, and constructs a two-layer optimization mathematical model for the coordinated control of variable guiding lanes and traffic signals at multiple intersections. The proposed method provides a scientific theoretical basis for the functional division of variable guiding lanes and coordinated signal control at multiple intersections, realizes the dynamic optimization of regional road network spatial and temporal resources, and effectively improves the utilization rate of the temporal and spatial resources of the entire regional road network.

[0152] The invention and its embodiments have been described, but this description is not restrictive. The accompanying drawings show only one embodiment of the invention, and the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the scope of protection of this invention.

Claims

1. A method for optimizing the coordinated control of variable guidance lanes and traffic signals at multiple intersections, characterized in that, Includes the following steps: S1. Determine whether the lane meets the setting and activation conditions for variable guidance lanes. If the conditions are met, continue with the following steps. S2. Calculate the correlation between intersections and determine the coordination area of ​​multiple intersections; S3, calculate the average vehicle delay in the region; S4. Calculate the variable guidance lane function division scheme and signal timing scheme for multiple intersections; S5. Solving the two-layer optimization model for coordinated control of variable guidance lanes and traffic signals at multiple intersections; S6. Determine whether the model's results cause overflow; if so, expand the optimization area. Step S4, which calculates the variable lane function allocation scheme and signal timing scheme for multiple intersections, specifically includes the following steps: S41. Calculation scheme for variable guidance lane division at multiple intersections with minimum regional delay; S42. Calculate the optimal public signal cycle for this area under this scheme; S43. Based on the optimal common signal cycle in step S42, calculate the minimum green light duration for the coordinated phase at the critical intersection. S44. Based on the optimal common signal cycle in step S42, calculate the effective green light duration of the coordinated phase at non-critical intersections. S45. Calculate the phase difference optimization model under scheme S41; Step S41 of S4, in calculating the multi-intersection variable lane division scheme with the minimum average vehicle delay in the calculation area, is based on the constructed two-layer model of multi-intersection variable lane and traffic signal coordinated control optimization. The upper-layer model is a multi-intersection variable lane functional division model based on average vehicle delay, and the lower-layer model is a multi-intersection traffic signal coordinated control optimization model considering variable lanes. The output of the upper-layer model is used as the input of the lower-layer model, and similarly, the output of the lower-layer model is used as the input of the upper-layer model, realizing the dynamic interaction of spatial resources and temporal resources, iterating until the optimal solution of both is output.

2. The method for optimizing the coordinated control of variable guidance lanes and traffic signals at multiple intersections according to claim 1, characterized in that: The step S1, determining whether the lane meets the conditions for setting and opening a variable guidance lane, specifically includes the following steps: S11. Obtain the channelization design and lane distribution of each intersection entrance in the area and perform the next calculation. If the information cannot be obtained, the conditions for opening the variable guidance lane are not met. S12. Calculate the saturation of the intersection approach lanes. If the saturation is greater than 0.8, proceed to the next step of calculation; otherwise, the conditions for opening the variable guide lanes are not met. S13. Calculate the steering imbalance coefficient. If the steering imbalance coefficient is greater than 0.67, the variable steering lane opening condition is met; otherwise, the variable steering lane opening condition is not met.

3. The method for optimizing the coordinated control of variable guidance lanes and traffic signals at multiple intersections according to claim 1, characterized in that: The parameters required for the intersection connectivity model include: traffic demand from upstream to downstream of adjacent intersections, traffic density, maximum signal cycle, and minimum signal cycle; When the traffic flow is 1200-1600 pcu / h, the spacing is 200-400m, and the cycle ratio is 1-1.25, the overall correlation is >0.9; When the traffic flow is 900-1200 pcu / h, the spacing is 400-600m, and the cycle ratio is 1.25-1.5, the comprehensive correlation is 0.8-0.

9. When the traffic flow is 600-900 pcu / h, the spacing is 600-800m, and the cycle ratio is 1.5-1.75, the overall correlation is 0.5-0.

8. When traffic flow is <600 pcu / h, spacing is >800m, and cycle ratio is >1.75, the overall correlation is 0.3-0.

5.

4. The method for optimizing the coordinated control of variable guidance lanes and traffic signals at multiple intersections according to claim 1, characterized in that: Step S3, which calculates the average vehicle delay in the region, specifically includes the following steps: S31. Calculate vehicle delays at intersections; S32. Calculate the delay of vehicles passing through the intersection; S33, calculate the average delay per vehicle on the road segment.

5. The method for optimizing the coordinated control of variable guidance lanes and traffic signals at multiple intersections according to claim 1, characterized in that: The solution of the two-layer optimization model for the coordinated control of variable guidance lanes and traffic signals at multiple intersections in step S5 includes the following steps: S51. Initialize variables; S52. Calculate the initial solution set; S53. Generate a new solution set for the offspring population; S54. Combine the initial solution set with the solution set of the offspring population into a new solution set; S55. Calculate the fitness value; S56. Determine whether to generate a new parent population; S57. Output the optimal solution of the model.

6. The method for optimizing the coordinated control of variable guidance lanes and traffic signals at multiple intersections according to claim 1, characterized in that: Step S6, determining whether overflow has occurred in each lane, includes the following steps: S61. Use lane detectors to detect the queue length of each lane; S62. Determine if the lane is overflowing. If not, maintain the status quo. If so, continue to optimize the lane and signal timing scheme using the model.

7. The method for optimizing the coordinated control of variable guidance lanes and traffic signals at multiple intersections according to claim 4, characterized in that: In S3, S33 calculates the average vehicle delay in the calculation area by using real-time queue length calculation. The constructed real-time queue length calculation model includes variable guidance lane control parameters. Under different saturation levels, the delay of vehicles in different lanes at the intersection is different.

8. The method for optimizing the coordinated control of variable guidance lanes and traffic signals at multiple intersections according to claim 1, characterized in that: In S4, S42 calculates the optimal common cycle duration because multiple intersections in the area have variable guidance lanes, which leads to a redistribution of traffic flow in the area. The flow ratio of each phase and the total flow ratio of each intersection will also change. The calculation model for the intersection signal cycle duration includes the control parameters of the variable guidance lanes. Therefore, the common signal cycle duration solved by the model is the optimal solution for realizing the spatiotemporal resources of the regional road network. In S4, S45, in terms of calculating the phase difference, combines the constructed intersection vehicle delay model with the distance between intersections and the driving speed to establish a phase difference optimization model. The phase difference optimization model can improve the traffic efficiency of vehicles choosing variable guidance lanes through the area, and can also guide the speed of vehicles exiting from variable guidance lanes to reduce vehicle delays at intersections.

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