Trunk line coordination method, device, equipment and medium

Abstract

The invention relates to the technical field of intelligent transportation, in particular to a trunk line coordination method, device and equipment and a medium. The invention provides a trunk-oriented multi-stage dynamic closed-loop coordination optimization method. Firstly, a trunk line coordination optimization scheme is calculated according to a background scheme and traffic flow data of a trunk line; in the execution process after trunk line coordination optimization issuing, the electronic equipment carries out real-time sensing adjustment of green time ratio time according to the traffic flow data; and finally, the electronic equipment performs time period aggregation to form a time period table according to the execution result of the intersection all-day optimization scheme, and calculates timing scheme parameters of the time periods to form a set of new background scheme, so that the adaptive capability of trunk line coordination is improved, and the generated trunk line coordination scheme meets actual requirements.

Description

Technical Field

[0001] This application relates to the field of intelligent transportation technology, and in particular to a trunk line coordination method, apparatus, equipment and medium. Background Technology

[0002] Traditional arterial coordinated control methods are usually based on fixed timing schemes, which divide the traffic into several time periods according to historical traffic data, and preset a uniform signal cycle, green ratio and phase difference for each time period to form a "green wave" so that vehicles can pass through multiple intersections continuously.

[0003] However, traditional arterial traffic coordination control methods rely on timing schemes that are typically unchanged over long periods or require periodic manual adjustments. This results in long update cycles and delayed responses, failing to provide rapid feedback and closed-loop optimization in response to traffic pattern evolution. Consequently, over time, the originally optimized arterial traffic coordination scheme may become severely out of touch with actual needs, making it difficult to meet the complex and ever-changing traffic management requirements of modern cities.

[0004] Therefore, there is an urgent need for a trunk line coordination method that can accurately control traffic signals on trunk lines. Summary of the Invention

[0005] This application provides a trunk line coordination method, apparatus, equipment, and medium to solve the problem that existing trunk line coordination methods rely on fixed timing schemes, lack adaptive and optimization capabilities, resulting in a serious disconnect between the generated trunk line coordination schemes and actual needs, making it difficult to meet the complex and ever-changing traffic management needs of modern cities.

[0006] In a first aspect, embodiments of this application provide a trunk line coordination method, the method comprising: Based on the phase duration of multiple phases corresponding to multiple intersections on the trunk line to be coordinated in the current time period and the traffic flow data within the set time period, the cycle range corresponding to the multiple intersections is determined; the background scheme is generated based on the trunk line coordination scheme of the previous day. Based on preset vehicle operation data and the cycle range corresponding to the multiple intersections, determine the optimal cycle corresponding to the multiple intersections when the vehicle runs on the trunk line with the preset vehicle operation data. The optimal cycle is within the cycle range. Based on the optimal cycle, determine and execute the trunk line coordination optimization scheme. The system acquires real-time traffic flow data from the multiple intersections and adjusts the phase duration of multiple phases within an optimal cycle for each intersection based on the real-time traffic flow data.

[0007] Secondly, embodiments of this application also provide a trunk line coordination device, the device comprising: The processing module is used to determine the cycle range corresponding to the multiple intersections based on the phase duration of multiple phases corresponding to multiple intersections on the trunk line to be coordinated in the current time period and the traffic flow data within a set time period, as carried in the background plan; the background plan is generated based on the trunk line coordination plan of the previous day. The trunk line coordination module is used to determine the optimal cycle for vehicles to run on the trunk line at the multiple intersections based on preset vehicle operation data and the cycle range corresponding to the multiple intersections, wherein the optimal cycle is within the cycle range, and to determine and execute a trunk line coordination optimization scheme based on the optimal cycle; to acquire real-time traffic flow data of the multiple intersections, and to adjust the phase duration of multiple phases within an optimal cycle of each of the multiple intersections based on the real-time traffic flow data.

[0008] Thirdly, embodiments of this application also provide an electronic device, the electronic device including a processor, the processor being configured to execute a computer program stored in a memory to implement the steps of the trunk coordination method as described above.

[0009] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the trunk coordination method described above.

[0010] This application proposes a multi-level dynamic closed-loop coordination optimization method for trunk lines. First, based on the background plan and traffic flow data of the trunk line, a coordination optimization scheme is calculated. During the execution of the coordinated optimization scheme after its issuance, electronic equipment performs real-time sensing and adjustment of the green light ratio based on traffic flow data. Finally, the electronic equipment also aggregates time periods to form a time period table based on the execution results of the intersection's all-day optimization scheme, and calculates the timing scheme parameters for each time period, forming a new background plan. This improves the adaptive capability of trunk line coordination, ensuring that the generated trunk line coordination scheme meets actual needs. Attached Figure Description

[0011] To more clearly illustrate the technical solutions of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A schematic diagram of a trunk line coordination process provided in an embodiment of this application; Figure 2 A phase diagram of an intersection provided in an embodiment of this application; Figure 3A schematic diagram of coordination provided for an embodiment of this application; Figure 4 A schematic diagram of trunk line coordination provided for an embodiment of this application; Figure 5a A schematic diagram of a periodic sequence provided in an embodiment of this application; Figure 5b A schematic diagram of the peak segment range provided in the embodiments of this application; Figure 5c A schematic diagram of the peak segment range provided in the embodiments of this application; Figure 5d A schematic diagram of the optimized periodic sequence provided in an embodiment of this application; Figure 6a This is a schematic diagram illustrating the coordinated bandwidth of the initial optimization scheme provided in the embodiments of this application. Figure 6b This is a schematic diagram illustrating the coordinated bandwidth of the initial scheme already executed, as provided in an embodiment of this application. Figure 7 A schematic diagram of a trunk line coordination device provided in an embodiment of this application; Figure 8 This is a schematic diagram of an electronic device structure provided in an embodiment of this application. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown here.

[0014] The terms "first" and "second" in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the term "comprising" and any variations thereof are intended to cover non-exclusive protection. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices. The term "multiple" in this application can mean at least two, for example, two, three, or more, and the embodiments of this application do not impose limitations.

[0015] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of this application, including various details to aid understanding. These embodiments should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of this application. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description. It should be noted that in the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solutions of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0016] This application provides a trunk line coordination method, apparatus, device, and medium. The method includes: determining the cycle range corresponding to the multiple intersections based on the phase duration of multiple phases corresponding to multiple intersections on the trunk line to be coordinated in the current time period and traffic flow data within a set time period, as carried in a background scheme; the background scheme is generated based on the trunk line coordination scheme of the previous day; determining the optimal cycle corresponding to the multiple intersections when vehicles run on the trunk line according to the preset vehicle operation data and the cycle range corresponding to the multiple intersections, wherein the optimal cycle is within the cycle range, and determining and executing a trunk line coordination optimization scheme based on the optimal cycle; acquiring real-time traffic flow data of the multiple intersections, and adjusting the phase duration of multiple phases within an optimal cycle of each intersection based on the real-time traffic flow data.

[0017] Figure 1 A schematic diagram of a trunk line coordination process provided in this application embodiment is shown, the process including: S101: Based on the phase duration of multiple phases corresponding to multiple intersections on the trunk line to be coordinated in the current time period and the traffic flow data within a set time period carried in the background scheme, determine the cycle range corresponding to the multiple intersections; the background scheme is generated based on the trunk line coordination scheme of the previous day.

[0018] The trunk coordination method provided in this application is applied to electronic devices, such as PCs, servers, and traffic control systems.

[0019] In urban intelligent transportation systems, the core objective of arterial coordinated control is to optimize the signal timing parameters of multiple consecutive intersections, enabling vehicles to pass through green lights on main roads as continuously as possible, thereby improving traffic efficiency, reducing the number of stops, and minimizing delays. However, traditional arterial coordination methods typically rely on static or semi-static background schemes. Once configured, these schemes are likely to remain in use indefinitely and are rarely updated, making it difficult to adapt to the dynamic changes in traffic flow over time.

[0020] Based on this, this application proposes a multi-level dynamic closed-loop coordination optimization method for trunk lines, which generates a trunk line coordination scheme based on the background scheme and real-time traffic flow data. When executing the trunk line coordination scheme, adjustments are made in real time according to a dynamic sensing mechanism, so that the trunk line control process is more in line with the traffic demand at the current moment, thereby improving the effectiveness and accuracy of trunk line coordination.

[0021] Specifically, the electronic equipment performs trunk coordination control every certain period of time. The time interval between two adjacent trunk coordination controls can be 10 minutes or 15 minutes. This time interval can be set according to actual needs. The time interval between two trunk coordination controls in different time periods can be the same or different.

[0022] Before each trunk line coordination control, the electronic equipment acquires a background scheme, which is generated based on the trunk line coordination scheme of the previous day. That is, the background scheme in this embodiment is not fixed, but is updated at the end of each day according to the trunk line coordination scheme of that day, so as to ensure the real-time performance of the background scheme.

[0023] The background scheme refers to a standardized timing template formed by aggregating the execution data of the arterial coordination schemes at all intersections throughout the day after the previous day's operation. This background scheme includes, but is not limited to, the division of a day into several time periods, and key parameters such as the cycle, phase duration, and phase difference for each time period. Specifically, a time period refers to dividing a day into different time periods based on traffic flow changes, with relatively stable traffic demand within each period; a cycle refers to the total time required for a traffic light to complete one full cycle, including the green, yellow, and red light times for all phases; a phase duration refers to the specific time allocation for green, yellow, and red lights within a given phase; and a phase difference refers to the time offset between the signal cycles of adjacent intersections.

[0024] Figure 2 This is a phase diagram of an intersection provided in an embodiment of this application, as shown below. Figure 2 As shown, an intersection can include 8 phases.

[0025] In this embodiment, the electronic device extracts each time period from the acquired background technology and determines the time period to which the current moment belongs. This time period is then used as the current time period. Subsequently, the electronic device obtains information from the background technology regarding the phase durations of multiple phases corresponding to multiple intersections on the trunk line to be coordinated during the current time period. The phase duration of each phase includes the green light time, yellow light time, and red light time for that phase.

[0026] In addition, electronic devices will also obtain traffic flow data within a set time period from traffic detection equipment deployed at various intersections. These traffic detection equipment includes, but is not limited to, radar, video analysis units, or geomagnetic coils. The set time period indicates the time range between the current moment and the last time the trunk line coordination control data was generated.

[0027] After acquiring the phase duration of multiple phases at multiple intersections on the trunk line to be coordinated in the current time period and the traffic flow data within the set time period, the electronic device will independently perform periodic estimation for each intersection.

[0028] Specifically, for each intersection on the trunk line to be coordinated, the electronic equipment can update the phase duration of each phase corresponding to that intersection in the current time period based on the traffic flow data of each phase of that intersection within a set time period. Then, it determines the total duration of the updated phase durations for each phase and defines this total duration as the cycle of that intersection. The electronic equipment constructs a range for the cycle of each intersection, defining a cycle range corresponding to multiple intersections. It can also increase or decrease set values ​​based on the maximum cycle to obtain the maximum and minimum values ​​of the cycle range.

[0029] When the electronic device updates the phase duration of each phase of the intersection in the current time period based on the traffic flow data of each phase of the intersection within a set time period, it can adopt the following method: if the traffic flow data of a certain phase of an intersection is large, then the phase duration of that phase of the intersection in the current time period carried in the background plan is increased by some time; if the traffic flow data of a certain phase of an intersection is small, then the phase duration of that phase of the intersection in the current time period carried in the background plan is decreased by some time.

[0030] For example, the electronic device is pre-configured with traffic flow ranges. For each phase at each intersection, if the traffic flow data for that phase falls within the traffic flow range, the phase duration for that phase is not modified. If the traffic flow data for that phase is less than the minimum value of the traffic flow range, the phase duration for that phase is reduced. If the traffic flow data for that phase is greater than the minimum value of the traffic flow range, the phase duration for that phase is increased. The specific values ​​for the reduced and increased phase durations can be determined in a step-like manner or as a preset fixed value; no restrictions are placed here.

[0031] S102: Based on the preset vehicle operation data and the cycle range corresponding to the multiple intersections, determine the optimal cycle corresponding to the multiple intersections when the vehicle runs on the trunk line with the preset vehicle operation data, wherein the optimal cycle is within the cycle range, and determine and execute the trunk line coordination optimization scheme based on the optimal cycle.

[0032] After the electronic device determines the cycle range corresponding to each intersection, it will search for the optimal cycle within the cycle range that will achieve the best coordination effect for the entire trunk line. At the same time, it will generate and execute a complete trunk line coordination optimization scheme based on the optimal cycle.

[0033] In one possible implementation, the electronic device can initiate a coordination optimization algorithm based on preset vehicle operation data, such as a default coordination speed of 45 km / h, or the average speed of the road segment learned from historical data, and the aforementioned cycle range. This coordination optimization algorithm is used to maximize the bidirectional coordination bandwidth, that is, the time window during which vehicles continuously pass through green lights between upstream and downstream intersections.

[0034] Specifically, the electronic equipment can traverse every candidate cycle within the cycle range and perform the following operations for each candidate cycle: force all intersections on the trunk line to unify the candidate cycle; redistribute phase durations according to the ratio of critical flow rates for each phase based on the effective green light time of each intersection within that cycle; then, using the first intersection as a baseline, determine that the phase difference of that intersection is 0, and sequentially calculate the optimal phase difference for each subsequent intersection relative to the previous intersection. The phase difference calculation uses an enumeration method, constructing a time-distance map model to evaluate the coordination bandwidth of forward and reverse traffic flows under different phase differences, and using a weighted sum as the evaluation index. Finally, the candidate cycle that maximizes the total coordination bandwidth is selected as the optimal cycle, and the corresponding phase durations and phase differences for each intersection are recorded, forming a complete trunk line coordination optimization scheme.

[0035] After determining the trunk line coordination optimization scheme, the electronic equipment will also verify the scheme, such as verifying whether the green light time of each phase in the trunk line coordination optimization scheme is not less than the preset minimum green light time, and verifying whether the cycle of each intersection does not exceed the maximum cycle limit.

[0036] Once the electronic equipment confirms that the trunk line coordination optimization plan is qualified, it can distribute the plan to the traffic signals at each intersection via the signal control network. Upon receiving the new plan, the traffic signals activate a smooth transition mechanism to gradually switch the current operating plan to the new plan, ensuring that no conflicts or abnormal interruptions occur during the transition.

[0037] S103: Obtain real-time traffic flow data of the multiple intersections, and adjust the phase duration of multiple phases within an optimal cycle of each of the multiple intersections based on the real-time traffic flow data.

[0038] Once the coordination plan is issued to the intersection and implemented, and the transition is completed, the intersection implementation plan will dynamically allocate the green ratio in real time for each intersection in each cycle based on short-term fluctuations in traffic flow, while maintaining coordination.

[0039] In one possible implementation, the electronic device continuously monitors real-time traffic flow data for each phase. When the green light turns on in a certain phase, the electronic device first calculates the sensing start time for that phase. This time is determined by three parts: the minimum green light time, used to ensure basic traffic flow; the queue clearing time, calculated based on the current number of vehicles in the queue and the saturation flow; and a configurable sensing start time. The electronic device only initiates the sensing judgment logic after the green light has been on for more than the sensing start time.

[0040] In this embodiment, the core basis for the electronic device's sensing judgment is the lane space occupancy rate, which is the ratio of the length occupied by a vehicle in the lane to the total length of the lane. The system collects lane space occupancy rate data for each lane every second. If more than 2 / 3 of the lane space occupancy rates in the current permitted phase are lower than a preset threshold (e.g., 21%), the phase is determined to be "no vehicle," and the decision-making process for whether to cut off the green light begins. If the phase is not a trunk line coordination phase, the green light can be immediately ended, and the system can switch to the next phase. However, if the phase is a key phase participating in trunk line coordination, an additional judgment is required: calculating the ratio of the currently executed coordination bandwidth time to the coordination bandwidth time of that phase in the initial plan. If this ratio is less than the preset minimum bandwidth coefficient, it indicates that the coordination effect has not been fully released, and even if there are currently no vehicles, the green light needs to be extended to ensure the continuous passage of downstream vehicles; otherwise, early termination is allowed.

[0041] This application proposes a multi-level dynamic closed-loop coordination optimization method for trunk lines. First, based on the background plan and traffic flow data of the trunk line, a coordination optimization scheme is calculated. During the execution of the coordinated optimization scheme after its issuance, electronic equipment performs real-time sensing and adjustment of the green light ratio based on traffic flow data. Finally, the electronic equipment also aggregates time periods to form a time period table based on the execution results of the intersection's all-day optimization scheme, and calculates the timing scheme parameters for each time period, forming a new background plan. This improves the adaptive capability of trunk line coordination, ensuring that the generated trunk line coordination scheme meets actual needs.

[0042] To improve the adaptive capability of trunk line coordination and ensure that the generated trunk line coordination scheme meets actual needs, based on the above embodiments, in this embodiment, determining the cycle range corresponding to the multiple intersections based on the phase duration of multiple phases corresponding to multiple intersections on the trunk line to be coordinated in the current time period and the traffic flow data of the multiple intersections within a set time period, includes: For each of the multiple intersections, determine the phase loss duration carried in the phase duration of multiple phases at that intersection, and determine the total phase loss duration of that intersection; based on the sub-vehicle flow data of each phase carried in the traffic flow data of that intersection and the saturation flow corresponding to each phase, determine the lane flow ratio corresponding to each phase; based on the total phase loss duration and the lane flow ratio corresponding to each phase, determine the cycle corresponding to that intersection; The maximum cycle among the cycles corresponding to multiple intersections is determined, and the difference between the maximum cycle and a set value is taken as the minimum value of the cycle range. The sum of the maximum cycle and the set value is taken as the maximum value of the cycle range.

[0043] In arterial road coordination control, the reasonable setting of the cycle at each intersection is a prerequisite for achieving effective green wave coordination. Traditional methods often use a fixed cycle or simply take the maximum value, ignoring the impact of traffic load differences at each intersection on cycle requirements. In the embodiments of this application, the electronic equipment can scientifically calculate the theoretical cycle of each intersection based on the phase duration of multiple phases and real-time traffic flow data of each intersection in the background scheme, and construct a reasonable cycle search range based on this.

[0044] Specifically, for each intersection, the electronic equipment acquires the phase duration of all phases of that intersection from the background plan, and analyzes each phase duration separately to obtain the green light time, red light time, and yellow light time for each phase. The yellow light time and red light time are identified as phase loss durations because they do not contribute to effective traffic flow. The electronic equipment sums the phase loss durations of each phase at the intersection to obtain the total phase loss duration for that intersection. This total phase loss duration reflects the inherent time loss in the intersection's signal control and is a key input for cycle calculation.

[0045] In addition, electronic devices acquire traffic flow data for each intersection within a set time period from traffic detection equipment. This data is subdivided by phase and lane, enabling the system to identify the critical lanes with the highest flow demand in each phase. By dividing the measured flow of the critical lane by the corresponding saturation flow of that lane, the system calculates the lane flow ratio for that phase. The sum of the lane flow ratios for all phases of an intersection represents the overall traffic load intensity of that intersection. The corresponding saturation flow of a lane is determined by road design parameters and historical calibration data.

[0046] Then, for each intersection, the electronic equipment determines the corresponding cycle based on the lane flow ratio corresponding to each phase of the intersection and the total phase loss time of the intersection.

[0047] For example, electronic devices can use the following formula to determine the cycle at each intersection:

[0048] Where L is the total phase loss time at the intersection, and Y is the sum of the lane flow ratios corresponding to each phase of the intersection. This formula reveals the nonlinear relationship between cycle time and traffic load: the higher the load, the longer the required cycle time.

[0049] After determining the cycle time for each intersection, the electronic device can also determine the phase duration of each phase at that intersection based on the cycle time. Specifically, the electronic device allocates the effective time within the cycle, i.e., the difference CL between the cycle time and the total phase duration, to each phase according to the critical flow rate ratio of each phase, and adds the phase loss time for each phase; that is, the duration of phase i. .in, Let i be the yellow light duration. Let i be the red light duration. Let L be the period of the intersection where phase i is located, and L be the total phase loss duration of the intersection where phase i is located. Let Y be the lane flow ratio corresponding to phase i, and let Y be the sum of the lane flow ratios corresponding to each phase at the intersection where phase i is located.

[0050] To ensure the feasibility of arterial traffic coordination, all intersections must adopt the same coordination cycle. Therefore, the electronic equipment can select the maximum cycle from multiple intersections as a benchmark. However, directly using the maximum cycle may be too rigid and unable to adapt to minor fluctuations in traffic flow. Therefore, the system introduces a set value (e.g., 10 seconds) to establish a cycle range. The minimum value of the period range The difference between the maximum period and the set value; the maximum value within the period range. This is the sum of the maximum cycle and the set value. The minimum cycle range prevents the cycle from being too short, which could lead to insufficient traffic capacity at high-load intersections; the maximum cycle range avoids the cycle from being too long, which could cause unnecessary waiting at low-load intersections.

[0051] Based on the above-described determination of the period range, the process of determining the optimal period within this period range will be explained below with reference to a specific embodiment: Let the coordination period for the main line be C. Intersection phase difference , All intersections on the line are standardized to cycle C, and the phase duration of each intersection is allocated to each phase according to the ratio of the critical flow rate of each phase.

[0052] The optimal coordination cycle and phase difference at each intersection are found through enumeration, as detailed below: Figure 3 This is a schematic diagram of coordination provided for an embodiment of this application, as shown in the diagram. Figure 3 As shown, the intersection spacing is . The average running speed from the upstream intersection to the downstream intersection is ,Should To optimize the results, the initial value was 45 km / h, and the average running speed from the downstream intersection to the upstream intersection was... ,Should To optimize the results, the initial value is 45 km / h, and the optimal phase difference at the upstream intersection is... In this case, the phase difference at the first intersection is 0, and the phase duration of the upstream coordination direction is... ,Should The phase duration in the downstream coordinated direction is obtained by allocating the critical flow rate ratios for each phase after a unified period C. ,Should To obtain the flow rate q from the upstream intersection to the downstream intersection after a unified cycle C, through the allocation of key flow rate ratios for each phase, where q is obtained through traffic flow detection equipment, the flow rate q from the downstream intersection to the upstream intersection is calculated. ,Should It is obtained through traffic flow detection equipment.

[0053] The phase duration in the upstream coordination direction is the phase duration of the phase to be coordinated among multiple phases at the upstream intersection, and the phase duration in the downstream coordination direction is the phase duration of the phase to be coordinated among multiple phases at the downstream intersection. The phase to be coordinated can be configured according to requirements, and the number of the phases to be coordinated can be one or more.

[0054] Based on the above parameters, the phase difference at the downstream intersection t The following process can be used to optimize the results: Electronic devices can use the following formula to calculate the green light interval range from the upstream intersection t-1 to the downstream intersection t:

[0055] in, The optimal phase difference at the upstream intersection The distance between upstream intersection t-1 and downstream intersection t is the distance between the two intersections. The average running speed from upstream intersection t-1 to downstream intersection t. The phase duration is t-1 at the upstream intersection.

[0056] The downstream phase difference green light interval range is calculated using the following formula:

[0057] in, For vehicles at speed The range of cycles traversed when traveling from upstream intersection t-1 to downstream intersection t. , Let t be the phase difference at the downstream intersection to be solved. For trunk line coordination cycle, Let t be the phase duration at the downstream intersection. The distance between upstream intersection t-1 and downstream intersection t is the distance between the two intersections. The average running speed is from upstream intersection t-1 to downstream intersection t.

[0058] The electronic device determines the coordination bandwidth from upstream intersection t-1 to downstream intersection t by the intersection's green light interval range when the upstream intersection t-1 reaches the downstream intersection t and the downstream phase difference green light interval range. This coordination bandwidth, in this embodiment, refers to the green light time of upstream intersection t-1 within one cycle.

[0059] The coordination bandwidth can be determined using the following formula:

[0060] Where D < 0, then D = 0.

[0061] Electronic devices can also use the following formula to calculate the green light interval from downstream intersection t to upstream intersection t-1:

[0062] in, The phase difference at the downstream intersection t, The distance between upstream intersection t-1 and downstream intersection t is the distance between the two intersections. The average running speed from downstream intersection t to upstream intersection t-1. Let t be the phase duration at the downstream intersection.

[0063] Calculate the upstream phase difference green light interval range:

[0064] in, For vehicles at speed The range of cycles traversed when traveling from downstream intersection t to downstream intersection t-1. , Let be the phase difference at the upstream intersection t-1 that needs to be solved. For trunk line coordination cycle, Let t-1 be the phase duration at the upstream intersection. The distance between upstream intersection t-1 and downstream intersection t is the distance between the two intersections. The average running speed is from the downstream intersection t to the upstream intersection t-1.

[0065] The electronic device determines the size of the intersection of the green light intervals from the downstream intersection t to the upstream intersection t-1 as the coordination bandwidth from the downstream intersection t to the upstream intersection t-1. This coordination bandwidth, in this embodiment, refers to the green light time of the downstream intersection t within one cycle.

[0066]

[0067] The electronic device determines the optimal phase difference at the downstream intersection t under a given period C based on an optimization objective function. The objective function for optimization can be expressed by the following formula:

[0068] Among them, when When there are multiple values, it makes Minimum.

[0069] In addition, electronic devices can also use the following optimization objective function to determine the optimal coordination cycle at the intersection. :

[0070] In determining During the process, the duration of the synchronization phase and phase difference That confirms it.

[0071] Figure 4 This is a schematic diagram of trunk line coordination provided in an embodiment of this application, as shown in the diagram. Figure 4 As shown, the phase duration of the upstream intersection t-1 is from Adjusted to The phase duration of the downstream intersection t from Adjusted to .

[0072] To improve the adaptive capability of trunk line coordination and ensure that the generated trunk line coordination scheme meets actual needs, based on the above embodiments, the method in this application embodiment further includes: If the current time is the last time of the day, then obtain the trunk line coordination execution data for the multiple intersections mentioned that day; Based on the historical cycles of the multiple intersections within each historical time period carried in the trunk line coordination execution data, time period aggregation is performed to obtain multiple target time periods; Based on the historical phase durations of multiple phases of the multiple intersections carried in the trunk line coordination execution data, the target phase durations of multiple phases of the multiple intersections in each target time period are determined. Based on the target time period and the target phase duration of multiple phases at multiple intersections in each target time period, a target background scheme is determined, and the target background scheme is used to replace the background scheme.

[0073] In the operation of a trunk line coordination control system, the background plan serves as the starting point for daily optimization, and its quality directly determines the upper limit of the day's coordination effect. However, if the background plan remains unchanged for a long time, it will be difficult to adapt to seasonal changes in traffic patterns, holiday effects, or adjustments to the urban road network structure. Therefore, this application proposes a closed-loop update mechanism that dynamically generates new background plans based on the day's execution data, ensuring the system has continuous learning and adaptive capabilities.

[0074] The closed-loop update mechanism of the aforementioned background scheme is automatically triggered at the end of each day's operation, meaning the current time is the final moment of the day. The electronic equipment first aggregates the entire day's trunk line coordination execution data from the signal controllers at each intersection. This data is stored with high temporal granularity (e.g., one record every 5 minutes), including the actual cycles used at each intersection within each historical time period, the actual green light duration, yellow light duration, and all-red light duration for each phase, as well as corresponding performance indicators such as traffic flow, queue length, and delays. This data constitutes a true reflection of the interaction between the daily traffic conditions and the control strategy.

[0075] Subsequently, the electronic equipment aggregates time periods based on the historical cycles of multiple intersections within each historical time period carried in the trunk line coordination execution data, obtaining multiple target time periods. Based on the historical phase durations of multiple phases at multiple intersections carried in the trunk line coordination execution data, the electronic equipment determines the target phase durations of multiple phases at multiple intersections within each target time period; based on the target time periods and the target phase durations of multiple phases at multiple intersections within each target time period, it determines the target background scheme and replaces the original background scheme with the target background scheme.

[0076] To improve the adaptive capability of trunk line coordination and ensure that the generated trunk line coordination scheme meets actual needs, based on the above embodiments, in this embodiment, the step of aggregating time periods according to the historical cycles of the multiple intersections carried in the trunk line coordination execution data to obtain multiple target time periods includes: The historical cycles of the multiple intersections within each historical time period carried in the trunk line coordination execution data are sorted according to time order to obtain a cycle sequence. Perform a first-order difference operation on the periodic sequence, and determine at least one peak point of the periodic sequence based on the result of the first-order difference operation. Based on the at least one peak point, determine at least one peak period with a time interval greater than a preset threshold; Based on the polynomial fitting algorithm and the root mean square error algorithm, other time periods besides the peak period are grouped to obtain multiple non-peak periods; The at least one peak period and the plurality of off-peak periods are determined as the plurality of target periods.

[0077] In the process of constructing a target background plan, how to scientifically extract representative time periods from the raw execution data is a key factor in determining the quality of the plan.

[0078] Specifically, electronic devices can traverse all historical time periods throughout the day, extract the cycle values ​​of all intersections on the main roads in each time period, and take the maximum value as the representative cycle for that time period. This forms a cycle sequence arranged in chronological order. This cycle sequence visually demonstrates the dynamic evolution of demand throughout the day, typically showing a trend of high demand during the morning peak, low demand during the off-peak period, and a further increase during the evening peak.

[0079] To identify traffic state intervals with significant differences, electronic devices can perform a first-order difference operation on the periodic sequence. The difference result reflects the drastic change in the period between adjacent time periods. When the period of a certain time period is significantly higher than that of the time periods before and after it, its difference value will show a "positive-negative" jump, forming a local peak point. The electronic device filters out at least one peak point by setting a threshold and uses it as a boundary candidate for potential peak time periods.

[0080] The electronic device marks the time points corresponding to at least one identified peak point as the start and end times of the peak and further verifies their rationality. For example, if the time interval between two peak points is less than a preset threshold, they are merged into a continuous peak; if the duration of a peak is too short, it may be noise interference and needs to be merged with adjacent off-peak periods. After cleaning, several stable and significant peak periods are obtained.

[0081] For the remaining time after peak hours, the electronic device uses a combination of polynomial fitting and root mean square error (RMSE) for intelligent grouping. First, the electronic device treats the periodic sequence of off-peak hours as a time function and attempts to fit it with polynomials of different orders (e.g., first, second, and third order). The RMSE is calculated for each fitting result, and the fit with the smallest RMSE and moderate model complexity is selected as the optimal description. Then, based on the slope of the fitted curve or the residual distribution, the off-peak hours are divided into several sub-intervals, ensuring that the periodic variation trend within each sub-interval is relatively stable. These sub-intervals constitute the multiple off-peak periods.

[0082] Ultimately, the electronic devices will identify peak and off-peak periods to form multiple target time periods. Each target time period represents a typical traffic operation pattern with relatively stable cyclical demand characteristics.

[0083] Specifically, the electronic device first acquires periodic data from various historical time periods throughout the day, and arranges it into a periodic sequence in chronological order. To capture periodic abrupt changes, the electronic device performs a first-order difference on P, obtaining a difference sequence. ,in The difference sequence visually reflects the rate of change of the period over time. When traffic conditions shift from off-peak to peak, the period typically increases rapidly, leading to... A significant positive value appears; conversely, at the end of the peak... This results in a significantly negative value. Therefore, the electronic device searches for a value in D that satisfies... (e.g., τ_diff = 8 seconds) points, and further determine whether the point is a local maximum point where the previous difference is positive and the next difference is negative. If so, the point is marked as a candidate peak point.

[0084] Each candidate peak point corresponds to a potential peak center. The electronic device extends forward and backward around this center until it encounters the next peak point or the cycle drops to a flat level (e.g., less than 45 seconds), thus initially defining the peak period interval.

[0085] For non-peak regions, the electronic device introduces a polynomial fitting and root mean square error evaluation mechanism. Specifically, the electronic device takes a continuous non-peak periodic subsequence as input and attempts to use a k-th order polynomial... Perform a fitting operation, where t is the time index. Fit the data for k=1, 2, and 3 respectively, and calculate the results for each fitting operation. For electronic devices, the model with the smallest RMSE and a relatively small k value is selected as the best fit for this segment.

[0086] However, a single fit may not cover the entire off-peak period. Therefore, electronic devices employ a sliding window or recursive segmentation strategy: if the RMSE of a certain segment is too high (e.g., >5 seconds), it indicates an internal trend reversal, requiring segmentation at the location of the largest residual, and refitting the left and right segments separately. This process is repeated until the RMSE of all segments is below the threshold. Each final segment represents an off-peak period, and its periodic variation can be described by a simple polynomial, facilitating the standardization of subsequent timing parameters.

[0087] To improve the adaptability of trunk line coordination and ensure that the generated trunk line coordination scheme meets actual needs, based on the above embodiments, in this embodiment, after grouping the time periods other than the peak period according to the polynomial fitting algorithm and the root mean square error algorithm to obtain multiple off-peak time periods, the method further includes: If there are two candidate peak periods that are adjacent to each other during the peak period, the two first candidate peak periods will be merged. If there is a first candidate time period with a duration less than a preset duration threshold, then the candidate time period is merged with the adjacent time period with a shorter duration; the first candidate time period is either a peak time period or an off-peak time period. If there are two adjacent second candidate time periods with a period difference less than a preset period difference threshold, then the two second candidate time periods will be merged.

[0088] After initial time segmentation, some structurally flawed segments often emerge, such as excessively short peaks or unmerged adjacent similar time segments. Directly applying these to the background scheme would result in overly fragmented schemes, increasing the burden on signal handover and reducing stability. Therefore, this application introduces multi-level post-processing merging rules to finely optimize candidate time segments.

[0089] Specifically, optimization methods include, but are not limited to, the following: The first type of merging targets peak hours. In the initial identification process, if the time interval between the center points of two peaks is less than a preset time difference threshold, and the period of the intermediate trough does not significantly decrease, the electronic equipment determines that the two peaks are a continuation of the same traffic event and should be merged into a single continuous peak period. The merged new peak period covers the entire time from the start of the first peak to the end of the second peak.

[0090] The second type of merging process involves candidate time segments with excessively short durations. The electronic device iterates through all candidate time segments, and if the duration of a segment is less than a preset duration threshold, it is considered an invalid segment. In this case, the electronic device compares the difference in period mean between the segment and its preceding and following time segments. If the difference in period is smaller than that of the preceding time segment, it is merged into the preceding time segment; otherwise, it is merged into the following time segment. This operation effectively filters out short-lived periodic fluctuations caused by detection noise or temporary events.

[0091] The third type of merging focuses on the periodic similarity between adjacent time periods. Even if two time periods are consecutive in time, if the difference between their average periods is less than a preset period difference threshold, and both belong to the off-peak type, the electronic equipment considers them to represent the same traffic operation state and should merge them into a larger time period. For example, the periods of 9:30–10:30 AM and 10:30–11:30 AM are 52 seconds and 50 seconds respectively, with minimal difference. Merging them can simplify the scheme structure and reduce unnecessary period switching.

[0092] The following describes the process of updating the background solution in this application embodiment, using a specific example: Obtain the execution results data for the entire day, including the execution end time and corresponding execution cycle, and assemble the execution cycle sequence by time series. Where t represents the execution end time of the plan. Corresponding execution cycle. Figure 5a This is a schematic diagram of a periodic sequence provided in an embodiment of this application, wherein the horizontal axis represents time and the vertical axis represents the period.

[0093] 1. Determine peak hours

[0094] 1.1 Calculation First-order difference operation ; , where i = 1, 2, 3, ... 1440.

[0095] 1.2 For all difference vectors Perform sign operations to obtain vectors Sets. That is, traversal. ,like If, then take 1; if If the result is positive, then take -1; otherwise, take 0.

[0096] 1.3 Traversing the vector from the tail For elements, perform the following operations: like and ,but ; like and ,but .

[0097] 1.4, Regarding Perform a first-order difference operation to obtain a set of vectors R.

[0098] Traverse the difference vector R, if Then i+1 is a peak position of the data, and the corresponding peak value is ;if Then i+1 is a valley value position of the data, and the corresponding valley value is .

[0099] 1.5. Based on the set of peak point values ​​found in the previous step, let it be... Perform the following processing: A time series set composed of small to large values ,calculate First-order difference operation .

[0100] Traversal To address situations where the time span of peak points is too short: If <30min indicates and The time between the two peaks is too short, comparison and Corresponding data value and Keep the larger data values ​​and their corresponding times, and delete the smaller data values ​​and their corresponding times.

[0101] The new dataset has a time difference greater than 30 minutes between all adjacent peaks; let this be defined as... .

[0102] 1.6 From Find the first peak period in the data: Calculate the maximum value Let the corresponding time be ; Peak hour lower limit: From Start searching sequentially backwards; if the corresponding value is less than... If the time reaches a low point, the process will stop, and the final stopping time will be determined as the lower limit of the time period. .in, This is the modulo operation.

[0103] Peak hour upper limit: From Start by searching sequentially, if the corresponding value is less than If the time reaches a low value, the process will stop, and the final stopping time will be determined as the upper limit of the time period. .in, This is the modulo operation.

[0104] Electronic devices determined This is the first peak period. Figure 5b This is a schematic diagram of the peak segment interval provided in the embodiments of this application, as shown in the figure. Figure 5b As shown in the figure, the area selected by the box represents the defined peak period.

[0105] 1.7. Using the same method as above, from Find the second peak period from the remaining data after removing the data from the first peak period. Figure 5c This is a schematic diagram of the peak segment interval provided in the embodiments of this application, as shown in the figure. Figure 5c As shown in the figure, the area selected by the box represents another peak period.

[0106] 2. Determine other time periods

[0107] In the original data In the remaining dataset obtained after removing the peak period data from the previous step, the following calculations are performed: 2.1 Perform a first-order polynomial fitting on the data as follows, and calculate the root mean square error between the actual and predicted values ​​for each group of data: The x-axis of the first group of data is... The x-axis of the second set of data is [ The x-axis of the third set of data is []. ], and so on. Calculate the root mean square error between each set of fitted values ​​and the true values. Each set of data consists of three adjacent time points in the remaining dataset.

[0108] 2.2 After completing the fitting and root mean square error calculation of all data, select the time period with the smallest root mean square error and merge it with the group with the smallest difference in mean between the left and right sides. If there are no other groups to the left or right of the selected group with the smallest root mean square error, no merging operation is performed, and its root mean square error is set to the largest integer to avoid being selected again in subsequent iterations.

[0109] 2.3 After merging, refit using the above method, calculate the root mean square error, merge the groups, and repeat the iterative calculation until the number of groups is K (K≥2, configuration value).

[0110] 2.4 Record the grouping results for groups of number K, K-1, ..., 2, and determine the optimal grouping.

[0111] For each grouping result: Calculate the cohesion within the group. Calculate the sum of squared distances from each element within the group to the centroid. The centroid of a class is the mean within the class.

[0112] Suppose that a certain group Group elements within group mean Sum of squared distances The intra-group clustering was determined to be the mean of the sum of squared intra-group distances. .

[0113] Calculate the sum of squared distances from the centroid of each group to the centroids of all other groups. This is called the sum of squared distances between groups.

[0114] Let, group The intra-class centroid is The sum of squared distances to other classes is The group separation was determined to be the mean of the sum of squared distances between all groups. .

[0115] Based on the above group cohesion and group separation, the profile coefficient S = 1 - a / b is determined.

[0116] The optimal number of categories is determined based on the silhouette coefficients. The silhouette coefficients of all groups in K~2 are calculated, and the group with the highest silhouette coefficient is the optimal grouping method. Each group constitutes a time period.

[0117] 3. Final time slot determined: 3.1 Merging: If the time difference between the highest peak and the second highest peak is less than 30 minutes, the two peaks will be merged into one peak, which will be merged into the highest peak. 3.2 Merging: Merge time periods shorter than 15 minutes. The merging rules are as follows: if there is no time period on the left, merge it with the right; if there is no time period on the right, merge it with the left; select the time period with the smallest difference between the maximum value of the adjacent left and right time periods and the maximum value of the current time period. 3.3 Merging: Merge values ​​where the difference between maximum values ​​in adjacent time periods is too small. Obtain the maximum value within each time period. If the absolute value of the difference between the maximum values ​​of two adjacent time periods is too small, merge them. If so, then these two time periods will be merged; 3.4. Sorting: Determine whether the divided morning and evening peak periods overlap. If the two peak periods overlap, cut off the peak period by taking the lowest point of the valley value in the two periods.

[0118] Figure 5d A schematic diagram of the optimized periodic sequence provided in the embodiments of this application, as shown below. Figure 5d As shown, compared to Figure 5a , Figure 5d The optimized periodic sequence shown has a simpler structure and reduces unnecessary period switching.

[0119] Based on the above example, after the electronic device determines the optimized periodic sequence, it can calculate the period, phase duration and phase difference in each time period based on the optimized periodic sequence to form a new background scheme for updating.

[0120] Specifically, the process by which electronic devices form new background schemes is as follows: The timing scheme period and phase duration in the new background scheme are: the average of the period and phase duration of the schemes already executed within the time range, rounded down.

[0121] The key phase difference update method is as follows: The phase difference is calculated and updated by calculating the coordination speed and using the phase difference optimization method.

[0122] Let the coordination speed of the current route segment be... The update coordination speed is .

[0123] Among them, update coordination speed The following formula can be used for calculation:

[0124] in, The adjustment value is calculated as follows: 1. Calculate the theoretical coordination results under the current scheme: Obtain the phase difference and period C of the currently executed scheme at the intersection, such as the phase difference at intersection t. Phase difference with intersection t-1 .

[0125] like This indicates that the green light at intersection t was turned on too late, and the late turn-on time is... ,in, ;like This indicates that the green light at intersection t was activated too early, and the early activation time is... ,in, . Here, % represents the modulo operation.

[0126] 2. Calculate the actual coordination results under the current plan: The arrival times of coordinated convoys at the intersection are obtained using traffic detection equipment (radar or video) for each cycle. and the number of vehicles queuing in the phase-related lanes when the convoy arrives. and saturation flow When the green light for the coordinated phase of the periodic scheme is turned on. The electronic device can determine the coordinated phase queue clearing time using the following formula. .

[0127]

[0128] Among them, if This indicates the actual green light time for the coordinated phase. Late lighting, late lighting time ;like This indicates the actual green light time for the coordinated phase. Premature lighting, early lighting time .

[0129] 3. Determine the feedback adjustment value by comparing theory with practice. : If the theoretical activation is too late, and the actual activation is too late, then adjustments should be made. ; If the theoretical activation is too late and the actual activation is too early, then adjustments should be made. ; If the theory is activated too early, or the practice is activated too early, adjustments should be made. ; If the theory is activated too early and the actual activation is too late, then adjustments should be made. .

[0130] Based on the obtained Calculate the update coordination speed And further calculate to obtain the updated phase difference.

[0131] To improve the adaptive capability of arterial coordination and ensure that the generated arterial coordination scheme meets actual needs, based on the above embodiments, in this embodiment, adjusting the phase duration of multiple phases within an optimal cycle for each of the multiple intersections according to the real-time traffic flow data includes: For each phase at each intersection, if the green light for that phase is on and the current sensing start condition for that phase is met, then based on the real-time traffic flow data and the preset saturation flow, the lane space occupancy rate of each lane in that phase is determined, and the number of lanes whose lane space occupancy rate exceeds a set threshold is determined; if the number of lanes is not less than a set threshold, then the green light for that phase remains on until the number of lanes is less than the set threshold; if the number of lanes is less than the set threshold, then the green light for that phase is turned off.

[0132] In this embodiment, while the arterial road coordination scheme can improve the traffic efficiency of main roads, completely ignoring real-time traffic flow changes at intersections may lead to problems such as empty lanes or queue overflow. Therefore, this embodiment embeds intersection-level sensing control logic within the coordination framework to achieve integrated control of macro-coordination and micro-sensing.

[0133] Specifically, when the green light turns on in a certain phase at an intersection, the electronic equipment does not mechanically execute the preset duration, but instead initiates a dynamic cutoff judgment. First, the electronic equipment continuously collects real-time traffic flow data for each lane in that phase. This traffic flow data can be obtained through video or radar detectors, showing vehicle arrival times and speeds. Simultaneously, the electronic equipment pre-stores the saturation flow value for that lane, which is typically determined by factors such as road width and vehicle type ratio.

[0134] The electronic system calculates the lane occupancy rate for each lane every second, defined as the proportion of the lane length occupied by vehicles at the current moment. This metric reflects queuing status better than simple traffic flow. If the lane occupancy rate exceeds a set threshold, it indicates that there are still vehicles waiting to pass in that lane.

[0135] The electronic system counts the number of lanes with occupancy rates exceeding a threshold in the current phase. If the number of lanes exceeds a set threshold, such as 2 / 3 of the total number of lanes, it is determined that there is still significant demand in the current phase, and the green light remains on. Conversely, if the number of lanes is less than the threshold, the green light is cut off.

[0136] Furthermore, the above judgment only takes effect after the sensing start condition is met. The sensing start condition is usually set as: green light duration ≥ minimum green light duration + queue clearing time × sensing coefficient. This ensures that vehicles in the basic queue have sufficient time to pass through, avoiding safety hazards caused by frequent switching.

[0137] Through the embodiments of this application, electronic devices can flexibly extend or shorten the phase green light time within the coordination cycle framework, which not only ensures the basic structure of the trunk green wave band, but also avoids resource waste and significantly improves the intersection operation efficiency and energy saving level.

[0138] To improve the adaptive capability of trunk coordination and ensure that the generated trunk coordination scheme meets actual needs, based on the above embodiments, in this embodiment, before turning off the green light for that phase, the method further includes: Determine whether the phase is a pre-configured phase that participates in trunk coordination; If the phase is not a pre-configured phase participating in trunk coordination, then proceed with the subsequent step of turning off the green light for that phase; If the phase is a pre-configured phase participating in trunk coordination, then determine the ratio between the duration the green light of the phase has been lit and the set duration; if the ratio is not less than the preset ratio, then execute the subsequent step of turning off the green light of the phase; if the ratio is less than the preset ratio, then keep the green light of the phase lit.

[0139] Based on the above embodiments, this application further introduces a coordinated phase protection mechanism to prevent the overall coordination effect of the trunk line from being damaged by local induction logic.

[0140] When the electronic equipment determines that a phase meets the green light cutoff condition, it does not immediately turn off the green light. Instead, it first checks whether the phase belongs to the pre-configured phases participating in trunk line coordination. These phases are usually the straight-ahead direction of the main road and are marked as coordination-critical phases in the background scheme. If the phase is not a coordination phase, such as a left-turn phase on a side road or a pedestrian phase, immediate cutoff is allowed, and the system switches to the next phase. However, if the phase is a coordination phase, the contribution of the current green light duration to the trunk line coordination bandwidth needs to be additionally evaluated.

[0141] Specifically, the electronic device calculates the ratio of the current lit duration to the initially set lit duration. If the ratio is not less than the preset ratio, the subsequent step of turning off the green light of that phase is executed; if the ratio is less than the preset ratio, the green light of that phase remains lit.

[0142] For example, the preset ratio can be 0.8, and the ratio of the current illuminated duration to the initially set illuminated duration is R. If R ≥ 0.8, it means that the coordination bandwidth has been basically released, and downstream vehicles have enough opportunity to pass. At this time, the green light can be safely cut off. If R < 0.8, even if there are no vehicles at present, the electronic equipment will still force the green light to remain on until R ≥ 0.8 or an emergency occurs. This process ensures the integrity of the green wave band on the main line and avoids downstream vehicles encountering red lights due to the upstream prematurely ending the green light, thus disrupting the coordination effect.

[0143] The sensing adjustment process of this application embodiment will be described below with reference to a specific example: 1. Calculate the phase sensing start time: When the green light of a phase is turned on, calculate the queue clearing time for the current phase. To ensure the time for clearing the queue of vehicles, among which, The number of vehicles queuing in lane l included in the phase. The saturation flow of lane l within the phase is obtained through traffic flow detection equipment.

[0144] The phase sensing start time is calculated using the following formula:

[0145] in, The sensing coefficient, defaults to 0.8, but is configurable; The minimum green light time is manually configured. The phase duration is determined based on the trunk line coordination optimization scheme.

[0146] 2. Adjustment method based on the sensor's sensitivity per second: Once the current clearance phase meets the sensing start time, the sensing extension or stop judgment begins: if the lane space occupancy rate of more than 2 / 3 of the current phase is less than the threshold. If the default value is 21%, it is determined that there is no vehicle in that phase, and the system enters the logic to determine whether to stop sensing; otherwise, the current phase continues to extend.

[0147] The logic for determining whether to trigger a stop includes the following: (1) When there is no vehicle in the non-coordinated phase, the phase induction extension stops; (2) When there are no cars in the coordinated phase, the ratio of the coordinated bandwidth time already executed during the scheme adjustment process to the initial scheme coordinated bandwidth time is calculated as follows: The calculation formula is as follows:

[0148] in, This indicates that the initial bandwidth coordination time has been executed in the current operating plan. This indicates the initial scheme coordination phase coordination bandwidth time.

[0149] like If the current phase cannot be truncated, the induction continues to extend; otherwise, the phase induction stops. To ensure the minimum bandwidth factor, the default value is 0.8, but it can be configured.

[0150] Based on the above example, Figure 6a This is a schematic diagram of the coordinated bandwidth of the initial optimization scheme provided in the embodiments of this application, as shown in the figure. Figure 6a As shown, the initial scheme coordinates the phase coordination bandwidth time as follows: ; Figure 6b This is a schematic diagram of the coordinated bandwidth of the initial scheme already executed, provided in an embodiment of this application. Figure 6b As shown, the initial scheme coordination bandwidth time is [time]. .

[0151] It should be noted that the executed initial scheme coordination bandwidth in the above example is the duration for which the green light has been lit in this application embodiment, and the initial scheme coordination phase coordination bandwidth time in the above example is the set lighting duration of the green light in this application embodiment.

[0152] Based on the above embodiments, this application also provides a trunk line coordination device. Figure 7 A schematic diagram of a trunk line coordination device provided in this application embodiment is shown. The device includes: The processing module 701 is used to determine the cycle range corresponding to the multiple intersections based on the phase duration of multiple phases corresponding to multiple intersections on the trunk line to be coordinated in the current time period and the traffic flow data within a set time period carried in the background plan; the background plan is generated based on the trunk line coordination plan of the previous day. The trunk line coordination module 702 is used to determine the optimal cycle for vehicles to run on the trunk line at the multiple intersections based on preset vehicle operation data and the cycle range corresponding to the multiple intersections, wherein the optimal cycle is within the cycle range, and to determine and execute a trunk line coordination optimization scheme based on the optimal cycle; to acquire real-time traffic flow data of the multiple intersections, and to adjust the phase duration of multiple phases within an optimal cycle of each of the multiple intersections based on the real-time traffic flow data.

[0153] In one possible implementation, the processing module 701 is specifically configured to, for each of the multiple intersections, determine the phase loss duration carried in the phase duration of multiple phases of the intersection, and determine the total phase loss duration of the intersection; determine the lane flow ratio corresponding to each phase based on the sub-vehicle flow data of each phase carried in the traffic flow data of the intersection and the saturation flow corresponding to each phase; determine the cycle corresponding to the intersection based on the total phase loss duration and the lane flow ratio corresponding to each phase; determine the maximum cycle among the cycles corresponding to the multiple intersections, and take the difference between the maximum cycle and a set value as the minimum value of the cycle range, and determine the sum of the maximum cycle and the set value as the maximum value of the cycle range.

[0154] In one possible implementation, the processing module 701 is further configured to: if the current time is the last time of the day, acquire the trunk line coordination execution data of the multiple intersections for the day; perform time period aggregation based on the historical cycles of the multiple intersections within each historical time period carried in the trunk line coordination execution data to obtain multiple target time periods; determine the target phase duration of multiple phases of the multiple intersections in each target time period based on the historical phase duration of multiple phases of the multiple intersections carried in the trunk line coordination execution data; determine a target background scheme based on the target time period and the target phase duration of multiple phases of the multiple intersections in each target time period, and replace the background scheme with the target background scheme.

[0155] In one possible implementation, the processing module 701 is specifically configured to: sort the historical cycles of the multiple intersections within each historical time period carried in the trunk line coordination execution data according to time sequence to obtain a cycle sequence; perform a first-order difference operation on the cycle sequence and determine at least one peak point of the cycle sequence based on the result of the first-order difference operation; determine at least one peak period with a time interval greater than a preset threshold based on the at least one peak point; group the other time periods besides the peak periods according to a polynomial fitting algorithm and a root mean square error algorithm to obtain multiple off-peak periods; and determine the at least one peak period and the multiple off-peak periods as the multiple target time periods.

[0156] In one possible implementation, the processing module 701 is further configured to: merge the two first candidate peak periods if there are two adjacent candidate peak periods with a time difference less than a preset time difference threshold; merge the candidate period with the period with the shorter duration among adjacent periods if there are two adjacent candidate peak periods with a duration less than a preset duration threshold; the first candidate period is a peak period or a non-peak period; and merge the two second candidate periods if there are two adjacent second candidate periods with a period difference less than a preset period difference threshold.

[0157] In one possible implementation, the trunk line coordination module 702 is specifically used for each phase at each intersection. If the green light for that phase is on and the current sensing start condition corresponding to that phase is met, then based on the real-time traffic flow data and a preset saturation flow, it determines the lane space occupancy rate of each lane in that phase and determines the number of lanes whose lane space occupancy rate exceeds a set threshold. If the number of lanes is not less than a set threshold, then the green light for that phase remains on until the number of lanes is less than the set threshold. If the number of lanes is less than the set threshold, then the green light for that phase is turned off.

[0158] In one possible implementation, the trunk coordination module 702 is specifically used to determine whether the phase is a pre-configured phase participating in trunk coordination; if the phase is not a pre-configured phase participating in trunk coordination, then the subsequent step of turning off the green light of the phase is executed; if the phase is a pre-configured phase participating in trunk coordination, then the ratio between the duration the green light of the phase has been lit and the set duration of lighting is determined; if the ratio is not less than a preset ratio, then the subsequent step of turning off the green light of the phase is executed; if the ratio is less than the preset ratio, then the green light of the phase is kept lit.

[0159] Based on the above embodiments, this application also provides an electronic device. Figure 8 This application provides a schematic diagram of an electronic device structure, such as... Figure 8 As shown, it includes: processor 801, communication interface 802, memory 803 and communication bus 804, wherein processor 801, communication interface 802 and memory 803 communicate with each other through communication bus 804. The memory 803 stores a computer program that, when executed by the processor 801, causes the processor 801 to perform the steps of any of the trunk coordination methods provided in the above embodiments.

[0160] Since the principle of the above-mentioned electronic equipment in solving the problem is similar to that of the trunk coordination method, the implementation of the above-mentioned electronic equipment can be found in the embodiments of the method, and repeated details will not be repeated.

[0161] The communication bus mentioned in the aforementioned electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used in the figure, but this does not indicate that there is only one bus or one type of bus. The communication interface 802 is used for communication between the aforementioned electronic device and other devices. The memory can include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory can also be at least one storage device located remotely from the aforementioned processor.

[0162] The processors mentioned above can be general-purpose processors, including central processing units, network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits, field-programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0163] Based on the above embodiments, this invention also provides a computer-readable storage medium storing a computer program executable by a processor. When the program is run on the processor, it causes the processor to execute the steps of any of the trunk coordination methods provided in the above embodiments.

[0164] Since the principle of the computer-readable storage medium in solving the problem is similar to that of the trunk coordination method, the implementation of the computer-readable storage medium can be found in the embodiments of the method, and repeated details will not be repeated.

[0165] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0166] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0167] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0168] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0169] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A trunk line coordination method, characterized in that, The method includes: Based on the phase duration of multiple phases corresponding to multiple intersections on the trunk line to be coordinated in the current time period and the traffic flow data within the set time period, the cycle range corresponding to the multiple intersections is determined; the background scheme is generated based on the trunk line coordination scheme of the previous day. Based on preset vehicle operation data and the cycle range corresponding to the multiple intersections, determine the optimal cycle corresponding to the multiple intersections when the vehicle runs on the trunk line with the preset vehicle operation data. The optimal cycle is within the cycle range. Based on the optimal cycle, determine and execute the trunk line coordination optimization scheme. The system acquires real-time traffic flow data from the multiple intersections and adjusts the phase duration of multiple phases within an optimal cycle for each intersection based on the real-time traffic flow data.

2. The method according to claim 1, characterized in that, The step of determining the cycle range corresponding to the multiple intersections based on the phase duration of multiple phases corresponding to multiple intersections on the trunk line to be coordinated in the current time period, and the traffic flow data of the multiple intersections within a set time period, includes: For each of the multiple intersections, determine the phase loss duration carried in the phase duration of multiple phases at that intersection, and determine the total phase loss duration of that intersection; based on the sub-vehicle flow data of each phase carried in the traffic flow data of that intersection and the saturation flow corresponding to each phase, determine the lane flow ratio corresponding to each phase; based on the total phase loss duration and the lane flow ratio corresponding to each phase, determine the cycle corresponding to that intersection; The maximum cycle among the cycles corresponding to multiple intersections is determined, and the difference between the maximum cycle and a set value is taken as the minimum value of the cycle range. The sum of the maximum cycle and the set value is taken as the maximum value of the cycle range.

3. The method according to claim 1, characterized in that, The method further includes: If the current time is the last time of the day, then obtain the trunk line coordination execution data for the multiple intersections mentioned that day; Based on the historical cycles of the multiple intersections within each historical time period carried in the trunk line coordination execution data, time period aggregation is performed to obtain multiple target time periods; Based on the historical phase durations of multiple phases of the multiple intersections carried in the trunk line coordination execution data, the target phase durations of multiple phases of the multiple intersections in each target time period are determined. Based on the target time period and the target phase duration of multiple phases at multiple intersections in each target time period, a target background scheme is determined, and the target background scheme is used to replace the background scheme.

4. The method according to claim 3, characterized in that, The step of aggregating time periods based on the historical cycles of the multiple intersections carried in the trunk line coordination execution data to obtain multiple target time periods includes: According to the time sequence, the historical cycles of the multiple intersections within each historical time period carried in the trunk line coordination execution data are sorted to obtain a cycle sequence; Perform a first-order difference operation on the periodic sequence, and determine at least one peak point of the periodic sequence based on the result of the first-order difference operation. Based on the at least one peak point, determine at least one peak period with a time interval greater than a preset threshold; Based on the polynomial fitting algorithm and the root mean square error algorithm, other time periods besides the peak period are grouped to obtain multiple non-peak periods; The at least one peak period and the plurality of off-peak periods are determined as the plurality of target periods.

5. The method according to claim 4, characterized in that, After grouping the time periods other than the peak period according to the polynomial fitting algorithm and the root mean square error algorithm to obtain multiple off-peak time periods, the method further includes: If there are two candidate peak periods that are adjacent to each other during the peak period, the two first candidate peak periods will be merged. If there is a first candidate time period with a duration less than a preset duration threshold, then the candidate time period is merged with the adjacent time period with a shorter duration; the first candidate time period is either a peak time period or an off-peak time period. If there are two adjacent second candidate time periods with a period difference less than a preset period difference threshold, then the two second candidate time periods will be merged.

6. The method according to claim 1, characterized in that, The step of adjusting the phase duration of multiple phases within an optimal cycle for each of the multiple intersections based on the real-time traffic flow data includes: For each phase at each intersection, if the green light for that phase is on and the current sensing start condition for that phase is met, then based on the real-time traffic flow data and the preset saturation flow, the lane space occupancy rate of each lane in that phase is determined, and the number of lanes whose lane space occupancy rate exceeds a set threshold is determined; if the number of lanes is not less than a set threshold, then the green light for that phase remains on until the number of lanes is less than the set threshold; if the number of lanes is less than the set threshold, then the green light for that phase is turned off.

7. The method according to claim 6, characterized in that, Before turning off the green light for that phase, the method further includes: Determine whether the phase is a pre-configured phase that participates in trunk coordination; If the phase is not a pre-configured phase participating in trunk coordination, then proceed with the subsequent step of turning off the green light for that phase; If the phase is a pre-configured phase participating in trunk coordination, then determine the ratio between the duration the green light of the phase has been lit and the set duration; if the ratio is not less than the preset ratio, then execute the subsequent step of turning off the green light of the phase; if the ratio is less than the preset ratio, then keep the green light of the phase lit.

8. A trunk line coordination device, characterized in that, The device includes: The processing module is used to determine the cycle range corresponding to the multiple intersections based on the phase duration of multiple phases corresponding to multiple intersections on the trunk line to be coordinated in the current time period and the traffic flow data within a set time period, as carried in the background plan; the background plan is generated based on the trunk line coordination plan of the previous day. The trunk line coordination module is used to determine the optimal cycle for vehicles to run on the trunk line at the multiple intersections based on preset vehicle operation data and the cycle range corresponding to the multiple intersections, wherein the optimal cycle is within the cycle range, and to determine and execute a trunk line coordination optimization scheme based on the optimal cycle; to acquire real-time traffic flow data of the multiple intersections, and to adjust the phase duration of multiple phases within an optimal cycle of each of the multiple intersections based on the real-time traffic flow data.

9. An electronic device, characterized in that, The electronic device includes a processor that executes a computer program stored in a memory to implement the steps of the trunk coordination method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the steps of the trunk coordination method as described in any one of claims 1-7.

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