Off-ramp control method and system based on space optimization and electronic equipment

Abstract

The invention provides an off-ramp control method and system based on space optimization and electronic equipment, and relates to the technical field of traffic management, and the method comprises the steps: solving a historical mean value of queuing lengths under a time sequence based on historical vehicle queuing data, and processing a missing value of the historical vehicle queuing data; fitting the processed vehicle queuing data, and identifying and processing an abnormal value; acquiring traffic data in real time, and dynamically adjusting lane line changes according to the real-time traffic data and historical data subjected to missing value processing and abnormal value processing; dynamically adjusting the phase duration of the intersection signal lamp according to the lane line change, the lane function change of the ground entrance lane and the queuing length; a control strategy of lane line change and signal lamp timing adjustment is transmitted to an actuator through an intersection local area network to realize dynamic adjustment; and evaluating an execution result and optimizing a control strategy. According to the invention, the passing efficiency and safety are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic management, and in particular to a ramp control method, system and electronic equipment based on space optimization. Background Art

[0002] With the acceleration of urbanization and the improvement of residents' living standards, urban transportation systems are facing unprecedented challenges. Traffic congestion, as a byproduct of urban development, has become one of the key factors restricting the efficient operation of cities and the improvement of residents' quality of life. Especially during peak hours and under complex road conditions, the sharp increase in traffic flow has made the traffic bottleneck problem more prominent. Among them, the off-ramp is an important node connecting the highway and the urban road, and its traffic efficiency is directly related to the smoothness of the entire transportation network.

[0003] Traditional off-ramp control technologies mainly include fixed signal control, timing control and induction control. Fixed signal control controls vehicle traffic through a preset signal light timing scheme, but lacks flexibility and is difficult to cope with real-time changing traffic conditions; although timing control improves control accuracy to a certain extent, it is also limited by the preset scheme and cannot be dynamically adjusted according to real-time fluctuations in traffic flow; induction control relies on vehicle detectors to sense traffic flow and then adjust signal light timing, but its response speed and adjustment range are limited, making it difficult to meet complex and changing traffic needs.

[0004] These traditional technologies have exposed significant inefficiencies in efficiency and safety in actual applications. On the one hand, faced with the sharp increase in traffic flow during peak hours, traditional control methods often find it difficult to quickly and accurately adjust traffic signals, causing off-ramps to become traffic bottlenecks and exacerbating congestion; on the other hand, under complex road conditions, such as rainy days, foggy days, or traffic accidents, traditional control methods lack sufficient intelligence and adaptability, making it difficult to ensure the safety and stability of vehicle driving.

[0005] Therefore, a more efficient, intelligent and safe off-ramp control method is urgently needed to cope with the complexity and variability of modern urban traffic. Summary of the invention

[0006] To this end, an embodiment of the present invention provides a ramp control method, system and electronic device based on space optimization, which are used to solve the problem that the existing ramp control technology in the prior art has significant efficiency and safety deficiencies when facing complex and changeable traffic environments, and is difficult to dynamically adjust in real time to adapt to rapid changes in traffic flow.

[0007] In order to solve the above problems, an embodiment of the present invention provides a ramp control method based on space optimization, the method comprising:

[0008] Step S1: based on the historical vehicle queue data, solve the historical mean of the queue length in the time series, and process the missing values ​​of the historical vehicle queue data;

[0009] Step S2: fitting the processed vehicle queue data, and identifying and processing outliers;

[0010] Step S3: acquiring traffic data in real time, and dynamically adjusting lane line changes based on the real-time traffic data and historical data that has been processed for missing values ​​and outliers;

[0011] Step S4: dynamically adjusting the phase duration of the intersection signal light according to changes in lane lines, changes in the function of the ground entrance lanes and the queue length;

[0012] Step S5: passing the control strategy of lane line change and signal light timing adjustment to the actuator through the intersection local area network to achieve dynamic adjustment;

[0013] Step S6: Evaluate the execution results and optimize the control strategy.

[0014] Preferably, in step S1, based on the historical vehicle queue data, the historical mean of the queue length in the time series is solved, and the missing values ​​of the historical vehicle queue data are processed, which specifically includes:

[0015] Step S11: collecting historical vehicle queue data, wherein the historical vehicle queue data includes a timestamp and a corresponding queue length;

[0016] Step S12: classify the data by time period, wherein the time period includes morning and evening peaks, morning and evening off-peaks, nighttime, and working days and non-working days;

[0017] Step S13: For each classified time period, calculate the historical mean of the queue length;

[0018] Step S14: Check whether there are missing values ​​in the historical vehicle queue data. If there are missing values, fill them according to the historical mean of the time period;

[0019] Step S15: If all data have been processed, that is, there are no missing values ​​or they have been filled with historical averages, execute step S2; otherwise, return to execute step S14 until all missing values ​​are processed.

[0020] Preferably, in step S2, the processed vehicle queue data is fitted, and outliers are identified and processed, specifically including:

[0021] Step S21: Select a suitable time series model to fit the processed vehicle queue data;

[0022] Step S22: using the fitted model to predict the vehicle queue data and generate a prediction value sequence;

[0023] Step S23: Calculate the residual between the actual value and the predicted value to evaluate the model fitting effect and identify outliers;

[0024] Step S24: setting a residual threshold to determine which points are outliers;

[0025] Step S25: For the identified abnormal values, replace them with historical averages or predicted values.

[0026] Preferably, in step S3, the lane line change is dynamically adjusted according to the real-time traffic data and the historical data after missing value processing and outlier processing, specifically including:

[0027] Step S31: read the number of lanes of the off-ramp as n, and the location of the landing point of the off-ramp is located at the ground entrance road, and the location is divided into left, middle, and right;

[0028] Step S32: adopting different lane line change strategies according to the number of lanes of the off-ramp and the location of the off-ramp landing point on the ground entrance road;

[0029] Step S33: Implement the lane change strategy and change the lane function of the ground lane to meet the needs of ground vehicles.

[0030] Preferably, the lane change strategy includes:

[0031] When the landing point of the ramp is on the left or right of the ground entrance road, the "more expansion" principle is adopted; when the landing point of the ramp is in the middle of the ground entrance road, the "less competition" principle is adopted. The "more expansion" principle is to turn n+1 lanes into exclusive lanes for the ramp, and the "less competition" principle is to prohibit other ground lanes from participating in spatial competition. From the perspective of lane lines, it refers to the "empty inside and solid outside" of the ramp lanes.

[0032] Preferably, the method for changing the lane function of the ground lane is:

[0033] Based on historical data that has been processed for missing values ​​and outliers, the minimum switching duration is set, and the solid line mode or dashed line mode of the lane line is determined based on the number of data points and the set threshold; after the lane line is solidified, the lane function of the ground lane is adjusted.

[0034] Preferably, in step S4, according to the change of lane line, the change of lane function of the ground entrance road and the queue length, the phase duration of the intersection signal light is dynamically adjusted, which specifically includes:

[0035] Step S41: when a lane line change is triggered, the duration of the signal light phase corresponding to the off-ramp is immediately adjusted, that is, the duration of the corresponding signal light phase is increased, and the duration of other signal light phases is appropriately reduced;

[0036] Step S42: Perform periodic evaluation and adjust the signal light phase duration according to changes in queue length.

[0037] Preferably, in step S6, the execution result is evaluated and the control strategy is optimized, specifically including:

[0038] Step S61: Regularly evaluate the execution results, calculate the optimized queue length and saturation, and use the lane-level average within a set time as the evaluation basis;

[0039] Step S62: Use the t-test method to determine whether the queue length and saturation after optimization have changed significantly. If not, take further optimization measures to form a closed-loop optimization.

[0040] The embodiment of the present invention further provides a ramp-down control system based on space optimization, which is used to implement the ramp-down control method based on space optimization described above, and specifically includes:

[0041] The data preprocessing module is used to solve the historical mean of the queue length in the time series based on the historical vehicle queue data, and to process the missing values ​​of the historical vehicle queue data;

[0042] The data fitting and anomaly detection module is used to fit the processed vehicle queue data and identify and process outliers;

[0043] Real-time traffic data monitoring and lane line dynamic adjustment module, which is used to obtain traffic data in real time and dynamically adjust lane line changes based on real-time traffic data and historical data that has been processed for missing values ​​and outliers;

[0044] The signal light timing optimization module is used to dynamically adjust the phase duration of the intersection signal lights according to the changes in lane lines, the function changes of the ground entrance lanes and the queue length;

[0045] The control strategy execution module is used to transmit the control strategy of lane line change and signal light timing adjustment to the actuator through the intersection local area network to achieve dynamic adjustment;

[0046] The effect evaluation and optimization module is used to evaluate the execution results and optimize the control strategy.

[0047] An embodiment of the present invention also provides an electronic device, which includes a processor, a memory and a bus system, wherein the processor and the memory are connected through the bus system, the memory is used to store instructions, and the processor is used to execute the instructions stored in the memory to implement the above-mentioned down-ramp control method based on space optimization.

[0048] It can be seen from the above technical solutions that the present invention has the following beneficial effects:

[0049] (1) Real-time monitoring and dynamic adjustment capabilities: The present invention can achieve real-time monitoring of vehicle queues. This real-time monitoring mechanism enables the system to quickly respond to changes in traffic conditions and dynamically adjust the configuration of lane guide lines. By adjusting the position and shape of lane lines and the phase duration of signal lights in real time, the present invention effectively guides vehicle diversion and passage, significantly improving the efficiency and safety of off-ramp traffic. At the same time, it reduces the situation of vehicles waiting in line for a long time and improves the driving experience.

[0050] (2) Flexible control strategy: The control strategy of the present invention is highly flexible and can be dynamically adjusted based on real-time traffic data and pre-processed historical data. This strategy not only takes into account the current traffic flow and vehicle queues, but also combines the patterns and trends in historical data to make the adjustment more accurate and effective. Whether it is a minor or severe congestion, the system can quickly make corresponding adjustments to ensure smooth traffic. In addition, this flexibility also enables the system to adapt to different traffic environments and scenarios and maintain an efficient operating state.

[0051] (3) Stability and continuous optimization of the integrated control system: The present invention ensures the stability and robustness of the integrated control system by continuously monitoring the implementation effect and evaluating the control effect using key performance indicators. The system can promptly detect and solve potential problems, and through continuous optimization and adjustment, the control strategy is made more complete and effective. This continuous optimization mechanism enables the system to maintain a stable operating state under various complex road conditions, providing reliable technical support for modern traffic management. At the same time, the stability and efficiency of the system also lay a solid foundation for its widespread promotion in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the implementation cases of the present invention or the technical solutions in the prior art, the following is a brief description of the drawings required for use in the embodiments. By referring to the drawings, the features and advantages of the present invention will be more clearly understood. The drawings are schematic and should not be understood as limiting the present invention in any way. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0053] Figure 1 A flowchart of a down-ramp control method based on space optimization provided in an embodiment;

[0054] Figure 2 Schematic diagram of different landing points in the embodiment (left, middle, right from top to bottom);

[0055] Figure 3 Schematic diagram of lane line changes in the embodiment

[0056] Figure 4 A block diagram of a down-ramp control system based on space optimization provided in an embodiment. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] Embodiment 1

[0059] The existing ramp control technology in the prior art has significant inefficiencies and safety deficiencies when facing complex and changing traffic environments, and is difficult to dynamically adjust in real time to adapt to the rapid changes in traffic flow. Figure 1 As shown, an embodiment of the present invention proposes a ramp control method based on space optimization, the method comprising:

[0060] Step S1: based on the historical vehicle queue data, solve the historical mean of the queue length in the time series, and process the missing values ​​of the historical vehicle queue data;

[0061] Step S2: fitting the processed vehicle queue data, and identifying and processing outliers;

[0062] Step S3: acquiring traffic data in real time, and dynamically adjusting lane line changes based on the real-time traffic data and historical data that has been processed for missing values ​​and outliers;

[0063] Step S4: dynamically adjusting the phase duration of the intersection signal light according to changes in lane lines, changes in the function of the ground entrance lanes and the queue length;

[0064] Step S5: passing the control strategy of lane line change and signal light timing adjustment to the actuator through the intersection local area network to achieve dynamic adjustment;

[0065] Step S6: Evaluate the execution results and optimize the control strategy.

[0066] It can be seen from the above technical solutions that the present invention provides a method for off-ramp control based on spatial optimization, which realizes efficient management of off-ramp traffic by real-time monitoring of vehicle queue data, combining historical data preprocessing and anomaly detection, and dynamically adjusting lane guide lines and signal light phase durations. Its technical steps cover data preprocessing, fitting optimization, real-time dynamic adjustment and strategy execution, and continuous optimization of control strategies. The present invention improves traffic efficiency and safety through real-time monitoring and dynamic adjustment mechanisms, and the flexible control strategy adapts to changing traffic environments. The integrated control system is stable and reliable, providing an efficient solution for modern traffic management, and has significant application prospects and practical value.

[0067] In step S1, based on the historical vehicle queue data, the historical mean of the queue length in the time series is solved. Considering the differences in the operating stability of the sensing equipment and the uneven quality of the output data, the missing values ​​of the historical vehicle queue data are processed, including:

[0068] Step S11: Collect historical vehicle queue data, wherein the historical vehicle queue data includes a timestamp and a corresponding queue length.

[0069] Step S12: Classify the data by time period, where the time period includes morning and evening peaks, morning and evening off-peaks, and nighttime, as well as working days and non-working days.

[0070] As an optional specific implementation, the default queue length is set according to the morning and evening peak, morning and evening off-peak and night, with the morning and evening peak being 100 meters, the morning and evening off-peak being 50 meters, and the night being 15 meters. The data set is divided into working days and non-working days, and the average is calculated by lane every 5 minutes, and the default value is replaced. The results are also stored according to working days and non-working days.

[0071] Step S13: For each classified time period, calculate the historical mean of the queue length.

[0072] Specifically, the historical mean of lane level every 5 minutes is obtained based on the historical data of the past month. i,t It represents the observation value of the i-th lane in the time interval, and the calculation formula is as follows:

[0073]

[0074] In the formula, μ i,t is the historical mean, and N is the number of observations at the same time interval in the past month.

[0075] Step S14: Check whether there are missing values ​​in the historical vehicle queue data. If there are missing values, fill them according to the historical mean of the time period.

[0076] Furthermore, if the historical average of the lane-level queue length is 0 for 6 consecutive 5-minute periods (0 for 30 minutes) during the time period from 5:00 to 23:00, it is judged to be missing.

[0077] Step S15: If all data have been processed, that is, there are no missing values ​​or they have been filled with historical averages, execute step S2; otherwise, return to execute step S14 until all missing values ​​are processed.

[0078] In step S2, the processed vehicle queue data is fitted, and outliers are identified and processed, specifically including:

[0079] Step S21: Select a suitable time series model to fit the processed vehicle queue data.

[0080] As an optional specific implementation, the time series model of the present invention adopts an autoregressive integrated moving average (ARIMA) model, and the optimal model is obtained by adjusting parameters p, d, and q, where the order of the autoregressive term is p (generally 0-5), the difference order is d (generally 0-2), and the order of the moving average term is q (generally 0-5).

[0081] The mathematical expression of the autoregressive integrated moving average model is:

[0082]

[0083] In the formula, y t is the observed value at time t, c is a constant term, φ 1 ,φ 2 ,…,φ p is the sliding mean coefficient, p is the order of the autoregressive term, θ 1 ,θ 2 ,…,θ q is the autoregressive coefficient, q is the order of the sliding average term, ∈ t is white noise at time t.

[0084] Step S22: Use the fitted model to predict the vehicle queue data and generate a prediction value sequence. For example, predict the data of the past 5 minutes, and compare the predicted value with the actual value to find potential anomalies.

[0085] Step S23: Calculate the actual value y t With the predicted value The residual between t , to evaluate the model's fit and identify outliers. t The calculation formula is:

[0086]

[0087] Step S24: Set a residual threshold to determine which points are outliers.

[0088] As an optional specific implementation, in this embodiment, the residual threshold is set to 25.

[0089] Step S25: For the identified abnormal values, use the historical mean or predicted value to replace them. The replacement formula is:

[0090]

[0091] In step S3, the lane line changes are dynamically adjusted based on the real-time traffic data and the historical data after missing value processing and outlier processing, specifically including:

[0092] Step S31: read the number of lanes of the off-ramp as n, and the landing point of the off-ramp is located at the direction of the ground entrance road, which is divided into left, middle and right. Figure 2 shown.

[0093] Step S32: Different lane line change strategies are adopted according to the number of lanes of the off-ramp and the position of the off-ramp landing point on the ground entrance road. The lane line change strategy includes adopting the "more extension" principle when the off-ramp landing point is located on the left or right of the ground entrance road, and adopting the "less competition" principle when the off-ramp landing point is located in the middle of the ground entrance road. The "more extension" principle is to turn n+1 lanes into off-ramp exclusive lanes, and the "less competition" principle is to prohibit other ground lanes from participating in space competition. From the perspective of lane lines, it refers to the "empty inside and solid outside" of the off-ramp lanes. The lane line change strategy is shown in Table 1 below.

[0094] Table 1 Lane change strategy

[0095]

[0096] Step S33: Implement the lane change strategy and change the lane function of the ground lane to meet the needs of ground vehicles.

[0097] Furthermore, the method for changing the lane function of the ground lane is: based on the historical data after missing value processing and outlier processing, the minimum switching time is set (three cycles are the minimum switching time, and the time interval of each cycle is T), and the solid line mode or dashed line mode of the lane line is determined according to the number of data points and the set threshold. The judgment formula is as follows:

[0098]

[0099] Where 1(·) is the indicator function, which is 1 when the condition is met and 0 otherwise; β is the threshold of the data point; γ is the threshold of the control strategy. Figure 3 .

[0100] After the lane lines are solidified, adjust the lane function of the ground lane.

[0101] In step S4, according to the changes in lane lines, the changes in the functions of the ground entrance lanes and the queue length, the phase duration of the intersection signal lights is dynamically adjusted, specifically including:

[0102] Step S41: When the lane line change is triggered, the duration of the signal light phase corresponding to the off-ramp is immediately adjusted, that is, the duration of the corresponding signal light phase is increased (10 seconds), while the duration of other signal light phases is appropriately reduced (2 seconds), and the yellow light full red time remains unchanged.

[0103] Step S42: Perform periodic evaluation and adjust the signal light phase duration according to the change in queue length. In this embodiment, it is determined whether the signal light phase duration needs to be changed every three cycles. The judgment is based on the change in queue length after three cycles. If the change is 0.7-1.05 times the original data, the change in signal light phase duration is maintained; if the change is more than 1.05 times the original data, the phase of the down-ramp signal light is increased by 10 seconds, and the phase of other signal lights is reduced by 2 seconds; if the change is 0.7 times or less of the original data, it is gradually reduced to the default solution.

[0104] In step S5, the control strategy of lane line change and traffic light timing adjustment is transmitted to the actuator through the intersection local area network to achieve dynamic adjustment.

[0105] In step S6, the execution results are evaluated and the control strategy is optimized, which specifically includes:

[0106] Step S61: Regularly evaluate the execution results, calculate the optimized queue length and saturation, and use the lane-level average value within a set time (5 minutes) as the evaluation basis;

[0107] Step S62: Use the t-test method to determine whether the queue length and saturation after optimization have changed significantly. If not, take further optimization measures to form a closed-loop optimization. 1 If the value is less than 0.05, it can be considered that there is a significant change before and after the optimization; if it is greater than 0.05, the next signal light phase change will be extended from 10 seconds to 13 seconds, and the other signal light phases will be reduced by 2 seconds to 4 seconds, and so on (called the second-level optimization plan). If there is a significant change after the extension time modification, it will be restored to the first-level optimization plan. The two duration change logics before and after the evaluation are shown in Table 2.

[0108] Table 2 Evaluation of the unit duration of the front and rear signal light phases

[0109]

[0110] Embodiment 2

[0111] like Figure 4 As shown, the present invention provides a ramp control system based on space optimization, which is used to implement the ramp control method based on space optimization of the above embodiment 1, and specifically includes:

[0112] The data preprocessing module 100 is used to solve the historical mean of the queue length in the time series based on the historical vehicle queue data, and process the missing values ​​of the historical vehicle queue data;

[0113] The data fitting and anomaly detection module 200 is used to fit the processed vehicle queue data and identify and process abnormal values;

[0114] The real-time traffic data monitoring and lane line dynamic adjustment module 300 is used to obtain traffic data in real time and dynamically adjust lane line changes based on the real-time traffic data and historical data that has been processed for missing values ​​and outliers;

[0115] The signal light timing optimization module 400 is used to dynamically adjust the phase duration of the intersection signal light according to the changes in lane lines, the changes in the function of the ground entrance lane and the queue length;

[0116] The control strategy execution module 500 is used to transmit the control strategy of lane line change and signal light timing adjustment to the actuator through the intersection local area network to achieve dynamic adjustment;

[0117] The effect evaluation and optimization module 600 is used to evaluate the execution results and optimize the control strategy.

[0118] A down-ramp control system based on space optimization in this embodiment is used to implement the aforementioned down-ramp control method based on space optimization. Therefore, the specific implementation method of the down-ramp control system based on space optimization can be seen in the embodiment part of the down-ramp control method based on space optimization in the previous text, for example, the data preprocessing module 100, the data fitting and anomaly detection module 200, the real-time traffic data monitoring and lane line dynamic adjustment module 300, the signal light timing optimization module 400, the control strategy execution module 500, the effect evaluation and optimization module 600, are respectively used to implement steps S1, S2, S3, S4, S5, and S6 in the above-mentioned down-ramp control method based on space optimization. Therefore, its specific implementation method can refer to the description of the corresponding embodiments of each part. In order to avoid redundancy, it will not be repeated here.

[0119] Embodiment 3

[0120] An embodiment of the present invention provides an electronic device, which includes a processor, a memory and a bus system. The processor and the memory are connected through the bus system. The memory is used to store instructions, and the processor is used to execute the instructions stored in the memory to implement the above-mentioned down-ramp control method based on space optimization.

[0121] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0122] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0123] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0124] Obviously, the above embodiments are merely examples for clear explanation and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived from them are still within the protection scope of the invention.

Claims

1. A ramp control method based on space optimization, characterized in that: include: Step S1: based on the historical vehicle queue data, solve the historical mean of the queue length in the time series, and process the missing values ​​of the historical vehicle queue data; Step S2: fitting the processed vehicle queue data, and identifying and processing outliers; Step S3: acquiring traffic data in real time, and dynamically adjusting lane line changes based on the real-time traffic data and historical data that has been processed for missing values ​​and outliers; Step S4: dynamically adjusting the phase duration of the intersection signal light according to changes in lane lines, changes in the function of the ground entrance lanes and the queue length; Step S5: The control strategy of lane line change and signal light timing adjustment is transmitted to the actuator through the intersection local area network to achieve dynamic adjustment; Step S6: Evaluate the execution results and optimize the control strategy.

2. The off-ramp control method based on space optimization according to claim 1, characterized in that: In step S1, based on the historical vehicle queue data, the historical mean of the queue length in the time series is solved, and the missing values ​​of the historical vehicle queue data are processed, specifically including: Step S11: collecting historical vehicle queue data, wherein the historical vehicle queue data includes a timestamp and a corresponding queue length; Step S12: classify the data by time period, wherein the time period includes morning and evening peaks, morning and evening off-peaks, nighttime, and working days and non-working days; Step S13: For each classified time period, calculate the historical mean of the queue length; Step S14: Check whether there are missing values ​​in the historical vehicle queue data. If there are missing values, fill them according to the historical mean of the time period; Step S15: If all data have been processed, that is, there are no missing values ​​or they have been filled with historical averages, execute step S2; otherwise, return to execute step S14 until all missing values ​​are processed.

3. The off-ramp control method based on space optimization according to claim 1, characterized in that: In step S2, the processed vehicle queue data is fitted, and outliers are identified and processed, specifically including: Step S21: Select a suitable time series model to fit the processed vehicle queue data; Step S22: using the fitted model to predict the vehicle queue data and generate a prediction value sequence; Step S23: Calculate the residual between the actual value and the predicted value to evaluate the model fitting effect and identify outliers; Step S24: setting a residual threshold to determine which points are outliers; Step S25: For the identified outliers, replace them with historical averages or predicted values.

4. The off-ramp control method based on space optimization according to claim 1, characterized in that: In step S3, the lane line changes are dynamically adjusted according to the real-time traffic data and the historical data after missing value processing and outlier processing, specifically including: Step S31: read the number of lanes of the off-ramp as n, and the location of the landing point of the off-ramp is located at the ground entrance road, and the location is divided into left, middle, and right; Step S32: adopting different lane line change strategies according to the number of lanes of the off-ramp and the location of the off-ramp landing point on the ground entrance road; Step S33: Implement the lane change strategy and change the lane function of the ground lane to meet the needs of ground vehicles.

5. The off-ramp control method based on space optimization according to claim 4 is characterized in that: The lane change strategy includes: When the landing point of the ramp is on the left or right of the ground entrance road, the "more expansion" principle is adopted; when the landing point of the ramp is in the middle of the ground entrance road, the "less competition" principle is adopted. The "more expansion" principle is to turn n+1 lanes into exclusive lanes for the ramp, and the "less competition" principle is to prohibit other ground lanes from participating in spatial competition. From the perspective of lane lines, it refers to the "empty inside and solid outside" of the ramp lanes.

6. The off-ramp control method based on space optimization according to claim 4, characterized in that: The method for changing the lane function of the ground lane is: Based on historical data that has been processed for missing values ​​and outliers, the minimum switching duration is set, and the solid line mode or dashed line mode of the lane line is determined based on the number of data points and the set threshold; after the lane line is solidified, the lane function of the ground lane is adjusted.

7. The off-ramp control method based on space optimization according to claim 1, characterized in that: In step S4, according to the changes in lane lines, the changes in the functions of the ground entrance lanes and the queue length, the phase duration of the intersection signal lights is dynamically adjusted, specifically including: Step S41: when a lane line change is triggered, the duration of the signal light phase corresponding to the off-ramp is immediately adjusted, that is, the duration of the corresponding signal light phase is increased, and the duration of other signal light phases is appropriately reduced; Step S42: Perform periodic evaluation and adjust the signal light phase duration according to changes in queue length.

8. The off-ramp control method based on space optimization according to claim 1, characterized in that: In step S6, the execution results are evaluated and the control strategy is optimized, which specifically includes: Step S61: Regularly evaluate the execution results, calculate the optimized queue length and saturation, and use the lane-level average value within a set time as the evaluation basis; Step S62: Use the t-test method to determine whether the queue length and saturation after optimization have changed significantly. If not, take further optimization measures to form a closed-loop optimization.

9. A ramp control system based on space optimization, characterized in that: The system is used to implement the off-ramp control method based on space optimization according to any one of claims 1 to 8, and specifically includes: The data preprocessing module is used to solve the historical mean of the queue length in the time series based on the historical vehicle queue data, and to process the missing values ​​of the historical vehicle queue data; The data fitting and anomaly detection module is used to fit the processed vehicle queue data and identify and process outliers; Real-time traffic data monitoring and lane line dynamic adjustment module, which is used to obtain traffic data in real time and dynamically adjust lane line changes based on real-time traffic data and historical data that has been processed for missing values ​​and outliers; The signal light timing optimization module is used to dynamically adjust the phase duration of the intersection signal lights according to the changes in lane lines, changes in the function of the ground entrance lanes and the length of the queue; The control strategy execution module is used to transmit the control strategy of lane line change and signal light timing adjustment to the actuator through the intersection local area network to achieve dynamic adjustment; The effect evaluation and optimization module is used to evaluate the execution results and optimize the control strategy.

10. An electronic device, characterized in that: The electronic device includes a processor, a memory and a bus system, the processor and the memory are connected through the bus system, the memory is used to store instructions, and the processor is used to execute the instructions stored in the memory to implement the down-ramp control method based on space optimization as described in any one of claims 1 to 8.

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