Lane control strategy optimization method and system for traffic congestion

Abstract

The present invention provides a lane control strategy optimization method and system for traffic congestion conditions. First, a real-time traffic status data set of a target road section is obtained, including vehicle flow, driving speed and road space occupancy rate data. Then, traffic congestion characteristics are analyzed on the real-time traffic status data set to obtain congestion area distribution, congestion degree assessment and congestion cause correlation characteristics. Based on the above characteristics, an initial lane control strategy is generated, including variable lane direction settings, lane opening and closing status and lane traffic priority allocation parameters. The initial strategy is sent to a road control device for execution and a feedback traffic status data set is collected. The strategy parameters are adjusted according to the comparative analysis results of the feedback traffic status data set and the real-time traffic status data set to generate an optimized lane control strategy, thereby improving the adaptability of the lane control strategy and effectively improving road traffic efficiency and congestion relief capabilities.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation technology, and in particular to a lane control strategy optimization method and system for traffic congestion. Background Art

[0002] In the current field of urban traffic management, traffic congestion has become a prominent issue that seriously impacts urban operational efficiency and residents' quality of life. Traditional traffic lane control strategies are mostly based on fixed rules and preset patterns, such as pre-setting the directions of certain lanes during specific time periods or allocating lane rights according to fixed priorities. However, urban traffic conditions are highly dynamic and complex. Parameters such as traffic flow, vehicle speeds, and road space occupancy vary continuously across time periods and regions. Furthermore, the causes of traffic congestion are diverse, potentially due to traffic accidents, road construction, large-scale events, and other factors. Existing fixed lane control strategies cannot be adjusted promptly and flexibly based on real-time traffic status data, making it difficult to accurately respond to various complex traffic congestion situations. Consequently, they are limited in their effectiveness in alleviating traffic congestion, fail to fully utilize road capacity, and fail to effectively meet the needs of modern urban traffic management. Summary of the Invention

[0003] In view of the above-mentioned problems, in combination with the first aspect of the present invention, an embodiment of the present invention provides a lane control strategy optimization method for traffic congestion, the method comprising:

[0004] Acquire a real-time traffic status data set for a target road section, wherein the real-time traffic status data set includes vehicle flow data, vehicle speed data, and road space occupancy rate data;

[0005] Performing traffic congestion feature analysis on the real-time traffic status data set to obtain congestion area distribution characteristics, congestion level assessment characteristics, and congestion cause correlation characteristics of the target road section;

[0006] generating an initial lane control strategy based on the congestion area distribution characteristics, the congestion level assessment characteristics, and the congestion cause association characteristics, wherein the initial lane control strategy includes a variable lane direction setting parameter, a lane opening and closing state parameter, and a lane passage priority allocation parameter;

[0007] Sending the initial lane control strategy to a road control device to execute a lane control operation, and continuously collecting a real-time traffic status data set after the implementation as a feedback traffic status data set;

[0008] Based on the comparative analysis results of the feedback traffic status data set and the real-time traffic status data set, the variable lane direction setting parameters, lane opening and closing state parameters, and lane traffic priority allocation parameters in the initial lane control strategy are adjusted to generate an optimized lane control strategy.

[0009] On the other hand, an embodiment of the present invention also provides a lane control strategy optimization system for traffic congestion conditions, including a processor and a machine-readable storage medium, wherein the machine-readable storage medium is connected to the processor, the machine-readable storage medium is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the machine-readable storage medium to implement the above method.

[0010] Based on the above aspects, the embodiment of the present invention obtains a real-time traffic status data set of the target road section, comprehensively covering key information such as vehicle flow, driving speed and road space occupancy, and conducts in-depth analysis of the real-time traffic status data set to obtain characteristics such as congestion area distribution, congestion level assessment and congestion cause association. It can accurately grasp the actual situation and internal causes of traffic congestion, generate an initial lane control strategy based on these characteristics, and achieve a preliminary match between the lane control strategy and the real-time traffic conditions. The initial strategy is sent to the road control device for execution and feedback data is collected. The strategy parameters are then adjusted based on the comparative analysis results of the feedback data and the initial data. The lane control strategy can be optimized in real time and automatically according to the dynamic changes in the traffic status, thereby improving the adaptability and flexibility of the lane control strategy, effectively improving the road's traffic efficiency, and significantly enhancing the ability to alleviate traffic congestion. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a schematic diagram of the execution flow of the lane control strategy optimization method for traffic congestion provided by an embodiment of the present invention.

[0012] Figure 2 Schematic diagram of exemplary hardware and software components of a lane control strategy optimization system for traffic congestion provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0013] The present invention will be described in detail below with reference to the accompanying drawings. Figure 1 This is a flow chart of a lane control strategy optimization method for traffic congestion provided by an embodiment of the present invention. The lane control strategy optimization method for traffic congestion is introduced in detail below.

[0014] Step S110: Acquire a real-time traffic status data set of the target road section, wherein the real-time traffic status data set includes vehicle flow data, vehicle speed data, and road space occupancy data.

[0015] This example focuses on a specific target road segment. To effectively address potential traffic congestion on that segment, it's necessary to obtain real-time traffic status data. This data includes three key data types: vehicle flow, vehicle speed, and road space occupancy. To obtain this real-time traffic status data, a series of traffic monitoring devices are deployed along the target road segment.

[0016] Specifically, video detection units, microwave detection units, and coil detection units are installed at various key locations along the target road section. The video detection units can be sent image acquisition instructions with preset sampling intervals. Following these instructions, the video detection units periodically capture real-time traffic images of the target road section. These images are then processed using an image recognition algorithm to extract information about the number of vehicles and their travel trajectories. Further analysis and calculation of this information yields the number of vehicles passing through the detection section per unit time, which serves as vehicle flow data.

[0017] A radar detection command can be sent to the microwave detection unit. After receiving the command, the microwave detection unit uses radar technology to detect the vehicle and calculate the vehicle's speed, which is the vehicle's speed data.

[0018] The coil detection unit can be sent an induction signal acquisition instruction. When a vehicle passes through the coil, electromagnetic induction changes are generated. The coil detection unit detects and records these changes, as well as the time interval and duration of the vehicle's passage. Combined with the coil's layout length, a series of analyses and calculations are performed to determine the proportion of time that road space is occupied by vehicles. This is the road space occupancy data. Finally, the collected vehicle flow data, vehicle speed data, and road space occupancy data are integrated to form a real-time traffic status data set.

[0019] Step S120: performing traffic congestion feature analysis on the real-time traffic status data set to obtain congestion area distribution features, congestion level assessment features, and congestion cause correlation features of the target road section.

[0020] After obtaining a real-time traffic status data set, it is necessary to conduct in-depth traffic congestion feature analysis to obtain the congestion area distribution characteristics, congestion level assessment characteristics, and congestion cause correlation characteristics of the target road section. This process includes multiple sub-steps:

[0021] Step S121: performing spatial gridding processing on the vehicle flow data in the real-time traffic status data set, dividing the target road section into a plurality of traffic analysis units of equal size, and obtaining vehicle flow density data of each traffic analysis unit.

[0022] In this step, the vehicle flow data within the real-time traffic status data set is spatially gridded. Through this processing method, the target road section is divided into multiple traffic analysis units of equal size. To achieve spatial gridding, an appropriate grid size can be set based on factors such as the geographic information and length of the target road section. After the division is completed, each traffic analysis unit has its corresponding vehicle flow data. By calculating the ratio of the vehicle flow within each traffic analysis unit to the spatial size of that unit, the vehicle flow density data for each traffic analysis unit is obtained.

[0023] Step S122: performing statistical processing on the vehicle speed data by road section, calculating the mean and standard deviation of the vehicle speed in each traffic analysis unit, and obtaining speed distribution characteristic parameters.

[0024] After obtaining the traffic analysis unit, the vehicle speed data needs to be statistically processed on a segment-by-segment basis. Specifically, for each traffic analysis unit, the speeds of all vehicles within the unit are collected. These speeds are then summed and divided by the number of vehicles to obtain the mean speed for that traffic analysis unit. Furthermore, to measure the dispersion of vehicle speeds, the standard deviation of these speeds also needs to be calculated. The standard deviation is obtained by calculating the sum of the squares of the differences between each speed and the mean, dividing the sum by the number of vehicles, and performing conventional standard deviation mathematical operations on the result. The mean and standard deviation together constitute the speed distribution characteristic parameters, which reflect the distribution of vehicle speeds within each traffic analysis unit.

[0025] Step S123: Calculate the space occupancy ratio of each traffic analysis unit according to the road space occupancy data, and determine the congestion determination result of each traffic analysis unit in combination with the vehicle flow density data and the speed distribution characteristic parameters.

[0026] This step further processes the road space occupancy data.

[0027] Step S1231: normalizing the road space occupancy data to convert the space occupancy values ​​of different traffic analysis units into a space occupancy ratio of a unified dimension, where the value range of the space occupancy ratio is 0 to 1.

[0028] When performing normalization, the actual conditions of different traffic analysis units need to be considered. Because factors such as road conditions and traffic volume may vary across traffic analysis units, directly using the raw spatial occupancy values ​​will result in a lack of data comparability. Therefore, a suitable normalization method is needed to convert the spatial occupancy values ​​of different traffic analysis units into a uniform spatial occupancy ratio. This can be achieved by performing a series of linear or nonlinear transformations on the raw spatial occupancy values ​​to bring their values ​​within the range of 0 to 1. The specific transformation method should be selected based on the characteristics and distribution of the actual data.

[0029] Step S1232: extracting the number of vehicles per unit time from the vehicle flow density data, calculating the ratio of the number of vehicles per unit time to the spatial size parameter of the traffic analysis unit, and obtaining a flow parameter per unit area.

[0030] Extracting the number of vehicles per unit time from the vehicle flow density data is crucial for this step. Once the number of vehicles per unit time is obtained, it is then ratioed with the spatial size parameter of the traffic analysis unit. The spatial size parameter of the traffic analysis unit reflects the actual spatial size of the unit. This ratio calculation yields the flow rate per unit area parameter, which more accurately reflects the vehicle flow density within each traffic analysis unit.

[0031] Step S1233: normalizing the mean of the speed distribution characteristic parameters to generate a speed normalization coefficient, wherein the speed normalization coefficient is positively correlated with the mean of the vehicle's travel speed.

[0032] The mean values ​​of speed distribution characteristic parameters can vary significantly between different traffic analysis units. To make these means comparable, standardization is necessary. The purpose of standardization is to convert the speed means of different traffic analysis units to a unified standard scale. A series of mathematical operations are performed on the speed means to generate a speed normalization coefficient. This speed normalization coefficient is positively correlated with the mean vehicle speed; that is, the greater the mean vehicle speed, the greater the speed normalization coefficient.

[0033] Step S1234: The space occupancy ratio, the unit area flow parameter and the speed normalization coefficient are input into a congestion determination model. The congestion determination model calculates a comprehensive congestion index by weighted summation, wherein the weight coefficient of the space occupancy ratio is higher than the weight coefficients of the unit area flow parameter and the speed normalization coefficient.

[0034] After obtaining the spatial occupancy ratio, flow rate per unit area parameter, and speed normalization coefficient, they are input into the congestion determination model. The congestion determination model is a trained model that performs a weighted summation of these input parameters. Because the spatial occupancy ratio plays a more important role in determining traffic congestion, its weighting coefficient is higher than that of the flow rate per unit area parameter and speed normalization coefficient. This weighted summation yields a comprehensive congestion index, which comprehensively reflects the degree of congestion in the traffic analysis unit.

[0035] Step S1235: Compare the comprehensive congestion index with the preset congestion threshold range. If the comprehensive congestion index is higher than the first threshold, the corresponding traffic analysis unit is determined to be in a congested state; if the comprehensive congestion index is lower than the second threshold, it is determined to be in a smooth state; if the comprehensive congestion index is between the first threshold and the second threshold, it is determined to be in a slow-moving state, where the first threshold is higher than the second threshold.

[0036] Comparing the calculated comprehensive congestion index with the preset congestion threshold range is a key step in determining the congestion status of the traffic analysis unit. The preset congestion threshold range includes a first threshold and a second threshold, with the first threshold being higher than the second threshold. If the comprehensive congestion index is higher than the first threshold, it indicates that the congestion level of the traffic analysis unit is high and is determined to be congested; if the comprehensive congestion index is lower than the second threshold, it indicates that the traffic conditions of the traffic analysis unit are good and are determined to be unobstructed; if the comprehensive congestion index is between the first and second thresholds, it indicates that the traffic of the traffic analysis unit is in a relatively slow state and is determined to be in a slow-moving state.

[0037] Step S1236: The congestion status determination result of each traffic analysis unit is associated and stored with the corresponding traffic analysis unit location identifier, to generate a congestion determination result set including a correspondence between the location identifier and the congestion status.

[0038] To facilitate subsequent analysis and processing, the congestion status determination results of each traffic analysis unit must be associated and stored with the corresponding traffic analysis unit location identifier. This associated storage provides a clear understanding of the location of each traffic analysis unit and its corresponding congestion status. This information from all traffic analysis units is integrated to generate a set of congestion determination results that includes the correspondence between location identifiers and congestion status.

[0039] Step S124: performing regional merging processing on consecutive adjacent traffic analysis units based on the congestion determination result to generate congestion area distribution characteristics of the target road section, wherein the congestion area distribution characteristics include a starting position identifier, an ending position identifier, and an area continuous length parameter of the congestion area.

[0040] After obtaining the congestion determination results for each traffic analysis unit, it is necessary to merge consecutive and adjacent traffic analysis units. If consecutive and adjacent traffic analysis units are all congested, they are merged into a single congested area. By traversing and analyzing the set of congestion determination results, all consecutive and adjacent congested traffic analysis units are found and merged.

[0041] After the merging is complete, the starting and ending location identifiers of each congested area are determined. The starting location identifier is the location identifier of the traffic analysis unit where the congested area begins, and the ending location identifier is the location identifier of the traffic analysis unit where the congested area ends. Simultaneously, the area continuous length parameter of the congested area is calculated based on the starting and ending location identifiers. This area continuous length parameter reflects the actual length of the congested area. This information is combined to generate the congested area distribution characteristics of the target road segment.

[0042] Step S125: Extract the vehicle flow density data, speed distribution characteristic parameters and space occupancy ratio corresponding to each congested area in the congested area distribution characteristics, calculate the product of the vehicle flow density and the speed distribution characteristic parameters in the congested area as the congestion intensity index, match the congestion intensity index with the preset congestion level classification standard, and obtain the congestion degree assessment characteristics.

[0043] Based on the congestion area distribution characteristics, we extract the corresponding vehicle flow density data, speed distribution characteristic parameters, and spatial occupancy ratio for each congested area. For each congested area, we multiply the vehicle flow density data with the speed distribution characteristic parameters to calculate the congestion intensity index. This index comprehensively considers the impact of vehicle flow and speed on congestion severity.

[0044] The congestion intensity index is then matched against a pre-defined congestion classification standard. This pre-defined congestion classification standard, developed based on extensive traffic data and practical experience, categorizes the congestion intensity index into different levels. Through this matching operation, the corresponding congestion level for each congested area is determined, thereby generating a congestion assessment feature.

[0045] Step S126: Analyze the correlation between the spatial position of the congested area in the congested area distribution characteristics and the vehicle flow data and speed distribution characteristic parameters of the upstream and downstream traffic analysis units, identify the main influencing factors causing congestion, and generate congestion cause correlation characteristics including the type of influencing factors and the ranking of the degree of influence.

[0046] This step aims to provide an in-depth analysis of the causes of congested areas, for example, through the following steps.

[0047] Step S1261: Determine the upstream traffic analysis unit set and the downstream traffic analysis unit set corresponding to the spatial position of each congested area in the congested area distribution characteristics, the upstream traffic analysis unit set is the traffic analysis unit located before the starting position identifier of the congested area, and the downstream traffic analysis unit set is the traffic analysis unit located after the ending position identifier of the congested area.

[0048] To accurately analyze the causes of congestion, it is necessary to determine the upstream and downstream traffic analysis unit sets corresponding to the spatial location of each congestion area. By analyzing the starting and ending location markers in the congestion area distribution characteristics, all traffic analysis units located before the starting location marker are found, and these units constitute the upstream traffic analysis unit set; all traffic analysis units located after the ending location marker are found, and these units constitute the downstream traffic analysis unit set.

[0049] Step S1262: Extract the vehicle flow data of the upstream traffic analysis unit set, calculate the total vehicle flow entering the congested area per unit time as the inflow flow, and extract the vehicle flow data of the downstream traffic analysis unit set, calculate the total vehicle flow leaving the congested area per unit time as the outflow flow.

[0050] From the vehicle flow data of the upstream traffic analysis units, the vehicle flow per unit time of each traffic analysis unit is counted and summarized to obtain the total vehicle flow entering the congested area per unit time, i.e., the inflow flow. Similarly, from the vehicle flow data of the downstream traffic analysis units, the vehicle flow per unit time of each traffic analysis unit is counted and summarized to obtain the total vehicle flow leaving the congested area per unit time, i.e., the outflow flow.

[0051] Step S1263: Calculate the difference between the inflow flow and the outflow flow. If the inflow flow is continuously greater than the outflow flow and the difference exceeds a preset flow difference threshold, it is preliminarily determined that the congestion is caused by excess flow input.

[0052] The difference between the calculated inflow and outflow is calculated. If the inflow is consistently greater than the outflow, and the difference exceeds the preset flow difference threshold, it indicates that the number of vehicles entering the congested area is far greater than the number of vehicles leaving. In this case, the congestion is initially determined to be caused by excess inflow.

[0053] Step S1264: extracting the mean of the speed distribution characteristic parameters of the upstream traffic analysis unit set, calculating the upstream speed mean, and extracting the mean of the speed distribution characteristic parameters in the congested area, calculating the congested area speed mean.

[0054] The speed distribution characteristic parameters of the upstream traffic analysis unit set are used to extract the mean speed of each traffic analysis unit and perform a weighted average calculation to obtain the upstream speed mean. Similarly, the speed distribution characteristic parameters of the congested area are used to extract the mean speed of each traffic analysis unit and perform a weighted average calculation to obtain the congested area speed mean.

[0055] Step S1265: Calculate the ratio of the upstream average speed to the congestion area average speed. If the ratio exceeds a preset speed ratio threshold, it is preliminarily determined that the congestion is caused by a sudden speed drop.

[0056] The calculated average upstream speed is then compared to the average speed in the congested area. If this ratio exceeds a preset speed ratio threshold, it indicates that the speed of vehicles upstream is significantly higher than that of vehicles in the congested area. In this case, the congestion is initially determined to be caused by a speed drop.

[0057] Step S1266: Analyze whether the spatial location of the congested area coincides with a road intersection, a ramp entrance or exit, or a road section with a changed number of lanes. If so, preliminarily determine that the cause of the congestion is a geometric bottleneck type.

[0058] Analyze the spatial location of the congestion area in detail to see if it overlaps with intersections, ramp entrances and exits, or sections with varying lane counts. If so, the congestion area is likely caused by road geometry limitations. In this case, the initial cause of the congestion is determined to be a geometric bottleneck.

[0059] Step S1267: Collect all preliminarily determined congestion cause types, and calculate the correlation coefficient between each cause type and the distribution characteristics of the congested area through the association analysis algorithm. The higher the correlation coefficient, the greater the contribution of the cause type to the congestion.

[0060] All preliminarily identified congestion contributing factors were collected and organized. Then, using an association analysis algorithm, each contributing factor was analyzed and calculated to determine its correlation coefficient with the distribution characteristics of the congested areas. This coefficient reflects the contribution of each contributing factor to the formation of the congested area; a higher coefficient indicates a greater contribution.

[0061] Step S1268: Sort the preliminarily determined congestion cause types from high to low according to the correlation coefficient, select the top K cause types with the highest correlation coefficient as the main influencing factor types, and record their corresponding correlation coefficients as the influence degree indicators.

[0062] Based on the calculated correlation coefficients, the preliminarily determined congestion contributing factors are ranked from high to low. The top K contributing factors with the highest correlation coefficients are selected as the primary influencing factors. The corresponding correlation coefficients for these primary influencing factors are also recorded and used as indicators of impact.

[0063] Step S1269: combining the main influencing factor types and the corresponding influencing degree indicators into congestion cause correlation features, wherein the congestion cause correlation features include an influencing factor type identifier, a correlation coefficient, and a sorting sequence number.

[0064] The main influencing factor types and their corresponding impact indicators are combined to generate a congestion cause correlation feature. This feature includes an influencing factor type identifier, a correlation coefficient, and a ranking number. The influencing factor type identifier uniquely identifies each main influencing factor type, the correlation coefficient reflects the degree of impact of the factor on congestion, and the ranking number indicates the importance of the factor among all other main influencing factors.

[0065] Step S130: Generate an initial lane control strategy based on the congestion area distribution characteristics, the congestion level assessment characteristics, and the congestion cause association characteristics. The initial lane control strategy includes variable lane direction setting parameters, lane opening and closing state parameters, and lane traffic priority allocation parameters.

[0066] After obtaining the congestion area distribution characteristics, congestion level assessment characteristics, and congestion cause association characteristics, it is necessary to generate an initial lane control strategy based on these characteristics. The initial lane control strategy includes parameters for setting the direction of variable lanes, parameters for lane opening and closing status, and parameters for assigning lane priority. The specific generation process is as follows:

[0067] Step S131: parsing the congestion area distribution characteristics and the congestion area start position identifier and the end position identifier to determine the target control section range where lane control is required.

[0068] By analyzing the congestion area's starting and ending location markers in the congestion area distribution characteristics, the specific scope of the congestion area can be determined. Based on these markers, the target control section range for lane control is determined. The target control section range is a continuous section that includes all traffic analysis units in a congested state.

[0069] Step S132: According to the congestion intensity index in the congestion level assessment feature, the congested area within the target controlled section is divided into different control priority levels, and the area with a higher congestion intensity index corresponds to a higher control priority level.

[0070] Based on the congestion intensity index in the congestion assessment feature, congested areas within the target control section are divided. Areas with higher congestion intensity indicators are assigned higher control priority levels, while areas with lower congestion intensity indicators are assigned lower control priority levels. This division helps prioritize areas with higher congestion levels when formulating lane control strategies.

[0071] Step S133: Extract the main influencing factor type from the congestion cause association characteristics. If the main influencing factor type is tidal flow characteristics, determine that the variable lane direction setting parameters need to be adjusted; if the main influencing factor type is lane occupation construction characteristics, determine that the lane opening and closing state parameters need to be adjusted; if the main influencing factor type is vehicle interweaving conflict characteristics, determine that the lane traffic priority allocation parameters need to be adjusted.

[0072] Extract the main influencing factor type from the congestion cause correlation characteristics. Determine the lane control parameters that need to be adjusted based on the different main influencing factor types. If the main influencing factor type is tidal flow characteristics, it means that there are obvious directional differences in traffic flow at different time periods. In order to better adapt to these flow changes, it is necessary to adjust the variable lane direction setting parameters. If the main influencing factor type is lane occupation construction characteristics, it means that some lanes may not be able to pass normally due to construction and other reasons. The lane opening and closing state parameters need to be adjusted. If the main influencing factor type is vehicle interweaving conflict characteristics, it indicates that the interweaving and conflict between vehicles in the lanes have caused congestion. The lane traffic priority allocation parameters need to be adjusted.

[0073] Step S134: When it is necessary to adjust the variable lane direction setting parameters, analyze the directional distribution characteristics of the vehicle flow data of the upstream and downstream traffic analysis units within the target control section, determine the target driving direction of the variable lane, and generate the variable lane direction setting parameters including the direction identification and the effective period parameters.

[0074] When it is determined that the variable lane direction setting parameters need to be adjusted, it is necessary to analyze the directional distribution characteristics of the vehicle flow data of the upstream and downstream traffic analysis units within the target control section. The specific process is as follows:

[0075] Step S1341: extracting vehicle flow data of the upstream traffic analysis unit within the target controlled road section, and counting the number of vehicles leaving the upstream traffic analysis unit within a unit time as the upstream outgoing flow.

[0076] From the vehicle flow data of the upstream traffic analysis unit within the target control section, the vehicle flow per unit time of each traffic analysis unit is counted and summarized to obtain the number of vehicles leaving the upstream traffic analysis unit per unit time, that is, the upstream outgoing flow.

[0077] Step S1342: extracting vehicle flow data of the downstream traffic analysis unit within the target controlled road section, and counting the number of vehicles entering the downstream traffic analysis unit within a unit time as the downstream incoming flow.

[0078] Similarly, from the vehicle flow data of the downstream traffic analysis unit within the target control section, the vehicle flow per unit time of each traffic analysis unit is counted and summarized to obtain the number of vehicles entering the downstream traffic analysis unit per unit time, that is, the downstream entry flow.

[0079] Step S1343: Calculate the ratio of the upstream outgoing flow to the downstream incoming flow to obtain a directional flow ratio parameter. If the directional flow ratio parameter is greater than a preset directional threshold, it is determined that the main traffic flow direction is from upstream to downstream. If the directional flow ratio parameter is less than the inverse of the preset directional threshold, it is determined that the main traffic flow direction is from downstream to upstream.

[0080] The ratio of upstream outgoing traffic to downstream incoming traffic is calculated to obtain a directional traffic ratio parameter. This parameter is then compared with a preset directional threshold. If the directional traffic ratio parameter is greater than the preset directional threshold, it indicates that the vehicle flow from upstream to downstream is relatively large, and the primary traffic flow direction is determined to be upstream to downstream. If the directional traffic ratio parameter is less than the inverse of the preset directional threshold, it indicates that the vehicle flow from downstream to upstream is relatively large, and the primary traffic flow direction is determined to be downstream to upstream.

[0081] Step S1344: Determine the target driving direction of the variable lane according to the main traffic flow direction. When the main traffic flow direction is from upstream to downstream, the target driving direction is set to be consistent with the mainstream direction. When the main traffic flow direction is from downstream to upstream, the target driving direction is set to be opposite to the mainstream direction.

[0082] The target driving direction of the variable lane is determined based on the determined main traffic flow direction. If the main traffic flow direction is from upstream to downstream, the target driving direction of the variable lane is set to be consistent with the main flow direction, that is, from upstream to downstream. If the main traffic flow direction is from downstream to upstream, the target driving direction of the variable lane is set to be opposite to the main flow direction, that is, from downstream to upstream.

[0083] Step S1345: Analyze the time series change characteristics of the vehicle flow data in the real-time traffic status data set, identify the starting time point and the ending time point of the peak traffic period, and use the starting time point and the ending time point as the effective period start parameter and the effective period end parameter of the variable lane direction setting parameter.

[0084] Analyze the time series variation characteristics of vehicle flow data in a real-time traffic status dataset. By observing and collecting statistics on vehicle flow data over different time periods, identify the start and end times of peak traffic periods. These time points are used as the effective start and end time parameters for the variable lane direction setting parameters.

[0085] Step S1346: Combine the target driving direction, the effective period start parameter and the effective period end parameter to generate variable lane direction setting parameters including direction identification, effective period start parameter and effective period end parameter, wherein the direction identification adopts a text description from the start point to the end point or from the end point to the starting point of the road section.

[0086] The target driving direction, the effective period start parameter, and the effective period end parameter are combined to generate the variable lane direction setting parameters. Direction signs use text descriptions from the start point to the end point or the end point to the start point of the road section to clearly convey the driving direction of the variable lane.

[0087] Step S135: When the lane opening and closing status parameters need to be adjusted, the number and locations of lanes that need to be temporarily closed are determined based on the area continuous length parameter in the congested area distribution characteristics, and the width compensation parameters of the remaining open lanes are set at the same time to generate the lane opening and closing status parameters including the lane identification, status identification and width compensation parameters.

[0088] The actual congestion situation is assessed based on the continuous length parameter in the congestion area distribution characteristics. If the congestion area is long, some lanes may need to be temporarily closed to ease traffic. The number and location of lanes requiring temporary closures are determined by comprehensively considering factors such as the continuous length parameter and lane capacity.

[0089] To ensure efficient traffic flow in the remaining lanes, width compensation parameters must be set for the remaining open lanes. Width compensation parameters are calculated and determined based on factors such as the number of closed lanes and their original widths. Lane identification, status identification, and width compensation parameters are combined to generate lane opening and closing status parameters.

[0090] Step S136: When it is necessary to adjust the lane priority allocation parameters, based on the congestion level classification results in the congestion level assessment characteristics, corresponding lane access rights are allocated to areas with different congestion levels, and the associated lanes in areas with high congestion levels are set to priority access status, and lane priority allocation parameters are generated that include lane identification, priority level and permission effectiveness conditions.

[0091] Based on the congestion level classification results in the congestion assessment feature, corresponding lane access rights are assigned to areas with different congestion levels. For areas with high congestion levels, the associated lanes are set to priority access to improve traffic efficiency in these areas.

[0092] When setting lane access permissions, it's necessary to clearly define the lane identifier, priority level, and access conditions. Lane identifiers uniquely identify each lane, priority levels indicate the lane's priority order, and access conditions define the circumstances under which a lane receives right-of-way. This information is combined to generate lane priority allocation parameters.

[0093] Step S137: The variable lane direction setting parameters, the lane opening and closing state parameters, and the lane traffic priority allocation parameters are integrated to generate an initial lane control strategy including control parameter type, target road section identification, and execution timing information.

[0094] The variable lane direction setting parameters, lane opening / closing status parameters, and lane priority allocation parameters are integrated. During the integration process, the control parameter types must be clearly defined, distinguishing between the variable lane direction setting parameters, lane opening / closing status parameters, and lane priority allocation parameters. Furthermore, the target road section identifiers must be clearly defined, determining the target road sections to which these control parameters apply. Furthermore, execution timing information must be determined, specifying the execution order and timing of each control parameter. This information is combined to generate the initial lane control strategy.

[0095] Step S140: sending the initial lane control strategy to the road control device to execute the lane control operation, and continuously collecting the real-time traffic status data set after the implementation as the feedback traffic status data set.

[0096] After generating the initial lane control strategy, it needs to be sent to the road control equipment to execute the lane control operation. At the same time, in order to evaluate the effectiveness of the lane control strategy, it is necessary to continuously collect real-time traffic status data sets after implementation and use them as feedback traffic status data sets. The specific process is as follows:

[0097] Step S141: sending the initial lane control strategy to a road control device corresponding to a target road segment identifier, wherein the road control device includes a lane indicator light control unit, a lane barrier control unit, and a traffic sign display unit.

[0098] Based on the target road segment identification in the initial lane control strategy, the strategy is sent to the corresponding road control device. The road control device consists of a lane indicator control unit, a lane barrier control unit, and a traffic sign display unit. These units will perform lane control operations according to the requirements of the initial lane control strategy.

[0099] Step S142: After receiving the initial lane control strategy, the road control device controls the road control device to sequentially start the lane indicator light control unit to adjust the lane indication direction, start the lane isolation barrier control unit to switch the lane isolation state, and start the traffic sign display unit to update the lane traffic information according to the execution timing information to complete the execution of the lane control operation.

[0100] After receiving the initial lane control strategy, the road control equipment activates each control unit sequentially according to the execution sequence information. First, the lane indicator control unit adjusts the lane indication direction based on the variable lane direction setting parameters, guiding the vehicle in the new direction. Next, the lane barrier control unit switches the lane separation state based on the lane opening and closing status parameters, opening or closing the corresponding lane. Finally, the traffic sign display unit updates the lane traffic information based on the information in the initial lane control strategy, providing the driver with a clear understanding of the lane traffic rules and status. These operations complete the lane control operation.

[0101] Step S143: During the execution of the lane control operation, the vehicle flow data, vehicle speed data and road space occupancy data after the operation are continuously collected by the traffic detection equipment set corresponding to the target road segment mark.

[0102] During the lane control operation, it is necessary to continuously collect real-time traffic status data after implementation. Traffic detection equipment corresponding to the target road section signs is used to collect vehicle flow data, vehicle speed data, and road space occupancy data. The specific process is as follows:

[0103] Step S1431: Determine the layout location and device type of the traffic detection equipment corresponding to the target road segment identifier, wherein the traffic detection equipment includes a video detection unit, a microwave detection unit, and a coil detection unit.

[0104] Based on the target road section identification, the location and type of traffic monitoring equipment are determined. At key locations on the target road section, video detection units, microwave detection units, and coil detection units are installed. These devices will be responsible for collecting different types of traffic data.

[0105] Step S1432: Send an image acquisition instruction to the video detection unit, control the video detection unit to capture the real-time traffic image of the target road section according to the preset sampling interval, extract the number of vehicles and vehicle driving trajectory information from the real-time traffic image through the image recognition algorithm, and calculate the number of vehicles passing through the detection section per unit time as the vehicle flow data after implementation.

[0106] An image acquisition command, specifying a preset sampling interval, is sent to the video detection unit. Following this command, the video detection unit periodically captures real-time traffic images of the target road section. These images are processed using an image recognition algorithm to extract the number of vehicles and their travel trajectories. Further analysis and calculation of this information yields the number of vehicles passing through the detection section per unit time, representing the vehicle flow data after implementation.

[0107] Step S1433: Sending a radar detection instruction to the microwave detection unit to control the microwave detection unit to calculate the vehicle's driving speed as the vehicle's driving speed data after implementation.

[0108] Send radar detection instructions to the microwave detection unit. After receiving the instructions, the microwave detection unit uses radar technology to detect the vehicle and calculate the vehicle's speed. This is the vehicle's speed data after implementation.

[0109] Step S1434: Send an induction signal acquisition instruction to the coil detection unit, control the coil detection unit to detect the electromagnetic induction changes generated when the vehicle passes, record the time interval and duration of the vehicle passing, and calculate the proportion of time the road space is occupied by the vehicle in combination with the layout length of the coil as the road space occupancy rate data after implementation.

[0110] Send an induction signal acquisition command to the coil detection unit. When a vehicle passes over the coil, electromagnetic induction changes are generated. The coil detection unit detects and records these changes, along with the time interval and duration of the vehicle's passage. Combined with the coil's placement length, a series of analyses and calculations are performed to determine the proportion of time the road space is occupied by vehicles. This represents the road space occupancy rate data after implementation.

[0111] Step S1435: Cross-validate the data collected by the video detection unit, microwave detection unit, and coil detection unit. When the deviation value of the same type of data collected by different devices exceeds the preset deviation threshold, a weighted average method is used to fuse the multi-device data. The weight coefficient is determined based on the historical detection accuracy of the device.

[0112] Cross-validation of data collected by different testing devices ensures data accuracy and reliability. If the deviation between the same data type collected by different devices exceeds a preset deviation threshold, this indicates potential data errors. In this case, a weighted average is used to integrate data from multiple devices. The weighting factor is determined based on the device's historical testing accuracy; devices with higher historical testing accuracy receive a larger weighting factor.

[0113] Step S1436: storing the vehicle flow data, vehicle speed data and road space occupancy data after the cross-validation process in a temporary data buffer area.

[0114] The cross-validated vehicle flow data, vehicle speed data, and road space occupancy data are stored in a temporary data cache. The temporary data cache is used to temporarily store this data for subsequent processing and analysis.

[0115] Step S144: performing timestamp alignment processing on the collected vehicle flow data, vehicle speed data and road space occupancy data after implementation, and combining the vehicle flow data, vehicle speed data and road space occupancy data after timestamp alignment into a feedback traffic status data set.

[0116] Because different detection devices may collect data at different times, to ensure data consistency and comparability, the collected post-implementation vehicle flow data, vehicle speed data, and road space occupancy data require timestamp alignment. By adjusting and matching the data timestamps, different types of data are kept consistent in time. The post-implementation vehicle flow data, vehicle speed data, and road space occupancy data with aligned timestamps are combined to generate a feedback traffic status data set.

[0117] Step S150: Based on the comparative analysis results of the feedback traffic status data set and the real-time traffic status data set, the variable lane direction setting parameters, lane opening and closing state parameters and lane traffic priority allocation parameters in the initial lane control strategy are adjusted to generate an optimized lane control strategy.

[0118] After obtaining the feedback traffic status data set, it is necessary to compare and analyze it with the real-time traffic status data set. Based on the comparative analysis results, the variable lane direction setting parameters, lane opening and closing state parameters, and lane priority allocation parameters in the initial lane control strategy are adjusted to generate an optimized lane control strategy. The specific process is as follows:

[0119] First, the feedback traffic status data set and the real-time traffic status data set are divided into time windows. Both data sets are divided into the same number of time window units, each time window unit corresponds to the same time period. This is done to compare and analyze the data within the same time range.

[0120] In each time window unit, the difference between the vehicle flow data in the feedback traffic status data set and the vehicle flow data in the real-time traffic status data set is calculated to obtain the flow change; the difference between the vehicle driving speed data in the feedback traffic status data set and the vehicle driving speed data in the real-time traffic status data set is calculated to obtain the speed change; the difference between the road space occupancy data in the feedback traffic status data set and the road space occupancy data in the real-time traffic status data set is calculated to obtain the occupancy change.

[0121] Strategy effectiveness evaluation indicators are constructed based on changes in flow, speed, and occupancy. Sign judgment is performed on these changes, and based on different sign combinations and preset rules, the effectiveness of the initial lane control strategy for each time window unit is determined. The specific rules are as follows: When the speed change is positive and the occupancy change is negative, it indicates that the lane control strategy has increased vehicle speed and decreased road space occupancy after implementation. Regardless of the sign of the flow rate change, the initial lane control strategy is considered effective for the corresponding time window unit. When the speed change is negative, it indicates that the lane control strategy has decreased vehicle speed after implementation. Regardless of the signs of the occupancy change and flow rate change, the initial lane control strategy is considered ineffective for the corresponding time window unit. When the speed change is positive but the occupancy change is positive, the sign of the flow rate change is further determined. If the flow rate change is positive and the increase exceeds the preset flow rate threshold, it indicates that the vehicle flow rate has increased excessively and the strategy is considered invalid. If the flow rate change is negative or the increase does not exceed the preset flow rate threshold, it indicates that although the road space occupancy rate has increased, the increase in vehicle flow rate is within an acceptable range and the strategy is considered effective. The judgment result for each time window unit is marked as effective or invalid and stored in association with the corresponding time window unit identifier to form a set of strategy effectiveness evaluation results.

[0122] Count the proportion of valid judgment results in all time window units. If the proportion of valid judgment results is higher than the preset effectiveness threshold, it indicates that the initial lane control strategy is effective. Keep the variable lane direction setting parameters, lane opening and closing state parameters, and lane priority allocation parameters in the initial lane control strategy unchanged. If the proportion of valid judgment results is lower than the preset effectiveness threshold, it indicates that the initial lane control strategy is ineffective and the corresponding control parameters need to be adjusted.

[0123] When the variable lane direction setting parameters need to be adjusted, the directional flow ratio parameters are recalculated based on the directional distribution characteristics of the vehicle flow data in the feedback traffic status data set. Based on the new directional flow ratio parameters, the target driving direction and effective time period parameters are adjusted.

[0124] When lane opening and closing parameters need to be adjusted, the area continuous length parameter is re-determined based on the distribution characteristics of the congested areas in the feedback traffic status data set. Based on the new area continuous length parameter, the number and location of closed lanes are adjusted, and the width compensation parameter is updated.

[0125] When lane priority allocation parameters need to be adjusted, the congestion level is reclassified based on the congestion assessment characteristics in the feedback traffic status data set. Based on the new congestion level, the lane priority level and permission activation conditions are adjusted.

[0126] Finally, the adjusted variable lane direction setting parameters, lane opening and closing status parameters, and lane traffic priority allocation parameters are integrated to generate an optimized lane control strategy.

[0127] Step S151: performing time window division on the feedback traffic status data set and the real-time traffic status data set, dividing the two data sets into the same number of time window units, each time window unit corresponding to the same time period.

[0128] To compare and analyze the feedback traffic status data set and the real-time traffic status data set within the same time range, the feedback traffic status data set and the real-time traffic status data set need to be divided into time windows. Based on the preset time window size, the feedback traffic status data set and the real-time traffic status data set are divided into the same number of time window units. Each time window unit corresponds to the same time period, thus ensuring accurate comparison of data within each time window unit.

[0129] Step S152: Within each time window unit, the difference between the vehicle flow data in the feedback traffic status data set and the vehicle flow data in the real-time traffic status data set is calculated as the flow change, the difference between the vehicle driving speed data in the feedback traffic status data set and the vehicle driving speed data in the real-time traffic status data set is calculated as the speed change, and the difference between the road space occupancy data in the feedback traffic status data set and the road space occupancy data in the real-time traffic status data set is calculated as the occupancy change.

[0130] Within each time window, the difference between the vehicle flow data, vehicle speed data, and road space occupancy data in the feedback and real-time traffic status data sets is calculated. Specifically, the vehicle flow data in the real-time traffic status data set is subtracted from the vehicle flow data in the feedback traffic status data set to obtain the flow change; the vehicle speed data in the real-time traffic status data set is subtracted from the vehicle speed data in the feedback traffic status data set to obtain the speed change; and the road space occupancy data in the real-time traffic status data set is subtracted from the road space occupancy data in the feedback traffic status data set to obtain the occupancy change. These changes reflect the changes in traffic conditions after the lane control strategy is implemented.

[0131] Step S153: Construct a strategy effectiveness evaluation index based on the flow change, the speed change, and the occupancy change. When the speed change is positive and the occupancy change is negative, determine that the initial lane control strategy is valid for the corresponding time window unit; when the speed change is negative or the occupancy change is positive, determine that the initial lane control strategy is invalid for the corresponding time window unit.

[0132] The strategy effect evaluation index is constructed based on the change in flow rate, speed, and occupancy rate to evaluate the effect of the initial lane control strategy in each time window unit. The specific judgment rules are as follows:

[0133] Step S1531: Determine the sign of the flow change. If the flow change is a positive value, it indicates that the vehicle flow increases after the lane control strategy is implemented; if the flow change is a negative value, it indicates that the vehicle flow decreases after the lane control strategy is implemented.

[0134] Determining the sign of the traffic flow change is the first step in evaluating the effectiveness of a strategy. A positive traffic flow change indicates that the implementation of the lane control strategy has increased vehicle flow within that time window; a negative traffic flow change indicates that the implementation of the lane control strategy has decreased vehicle flow.

[0135] Step S1532: Determine the sign of the speed change. If the speed change is a positive value, it indicates that the vehicle speed increases after the lane control strategy is implemented; if the speed change is a negative value, it indicates that the vehicle speed decreases after the lane control strategy is implemented.

[0136] Similarly, the sign of the speed change is determined. If the speed change is positive, it means that the vehicle's speed has increased after the lane control strategy is implemented; if the speed change is negative, it means that the vehicle's speed has decreased after the lane control strategy is implemented.

[0137] Step S1533: Determine the sign of the occupancy change. If the occupancy change is a positive value, it indicates that the road space occupancy ratio increases after the lane control strategy is implemented; if the occupancy change is a negative value, it indicates that the road space occupancy ratio decreases after the lane control strategy is implemented.

[0138] The sign of the occupancy change is used to determine how the road space occupancy ratio has changed after the lane control strategy was implemented. A positive occupancy change indicates an increase in the road space occupancy ratio; a negative occupancy change indicates a decrease in the road space occupancy ratio.

[0139] Step S1534: Construct a judgment rule for the strategy effect evaluation index: when the speed change is positive and the occupancy change is negative, regardless of the sign of the flow change, the initial lane control strategy is determined to be valid for the corresponding time window unit; when the speed change is negative, regardless of the signs of the occupancy change and the flow change, the initial lane control strategy is determined to be invalid for the corresponding time window unit; when the speed change is positive but the occupancy change is positive, further judge the sign of the flow change. If the flow change is positive and the increase exceeds the preset flow threshold, it is determined to be invalid; if the flow change is negative or the increase does not exceed the preset flow threshold, it is determined to be valid.

[0140] Based on the sign combination of speed change, occupancy change, and flow rate change, a judgment rule for evaluating the strategy's effectiveness is constructed. When the speed change is positive and the occupancy change is negative, it indicates an increase in vehicle speed and a decrease in road space occupancy, representing an ideal traffic state. Regardless of the sign of the flow rate change, the initial lane control strategy is deemed effective for that time window. When the speed change is negative, it indicates a decrease in vehicle speed and worsening traffic conditions. Regardless of the signs of the occupancy change and flow rate change, the initial lane control strategy is deemed ineffective for that time window. When both the speed change and occupancy change are positive, further consideration is required regarding the flow rate change. If the flow rate change is positive and exceeds a preset flow rate threshold, it indicates an excessive increase in vehicle flow, potentially leading to increased traffic congestion, and the strategy is deemed ineffective. If the flow rate change is negative or does not exceed the preset flow rate threshold, it indicates that, despite an increase in road space occupancy, the increase in vehicle flow is within an acceptable range, and the strategy is deemed effective.

[0141] Step S1535: Mark the determination result of each time window unit as valid or invalid, and store it in association with the corresponding time window unit identifier to form a strategy effect evaluation result set.

[0142] The results of each time window unit are marked as valid or invalid, and these results are associated and stored with the corresponding time window unit identifier. This associated storage allows for a clear understanding of the policy effectiveness judgment for each time window unit. This information is then integrated across all time window units to form a set of policy effectiveness evaluation results.

[0143] Step S154: Count the proportion of valid judgment results in all time window units. If the proportion of valid judgment results is higher than the preset effect threshold, the variable lane direction setting parameters, lane opening and closing state parameters, and lane traffic priority allocation parameters in the initial lane control strategy are kept unchanged; if the proportion of valid judgment results is lower than the preset effect threshold, it is determined that the corresponding control parameters need to be adjusted.

[0144] Count the proportion of valid determination results for all time window units in the strategy effectiveness evaluation result set. Compare this proportion with the preset effectiveness threshold. If the proportion of valid determination results is higher than the preset effectiveness threshold, it indicates that the initial lane control strategy is effective. Adjustments to the variable lane direction setting parameters, lane opening and closing status parameters, and lane priority allocation parameters do not need to be made; these parameters should remain unchanged. If the proportion of valid determination results is lower than the preset effectiveness threshold, it indicates that the initial lane control strategy is ineffective and the corresponding control parameters need to be adjusted.

[0145] Step S155: When the variable lane direction setting parameters need to be adjusted, the direction flow ratio parameters are recalculated according to the direction distribution characteristics of the vehicle flow data in the feedback traffic status data set, and the target driving direction and effective period parameters are adjusted based on the new direction flow ratio parameters.

[0146] If it is determined that the variable lane directional setting parameters need to be adjusted, the directional flow ratio parameters are recalculated based on the directional distribution characteristics of the vehicle flow data in the feedback traffic status dataset. The specific method for calculating the directional flow ratio parameters is the same as when generating the initial lane control strategy. Based on the new directional flow ratio parameters, the main traffic flow direction is re-determined, and the target driving direction of the variable lane is adjusted. Simultaneously, the time series variation characteristics of the vehicle flow data in the feedback traffic status dataset are analyzed to identify the start and end times of the new peak traffic period and adjust the effective period parameters.

[0147] Step S156: When the lane opening and closing state parameters need to be adjusted, the area continuous length parameters are re-determined according to the congested area distribution characteristics in the feedback traffic status data set, the number and position of closed lanes are adjusted based on the new area continuous length parameters, and the width compensation parameters are updated.

[0148] If lane opening and closing parameters need to be adjusted, the area continuous length parameters are re-determined based on the distribution characteristics of the congested areas in the feedback traffic status data set. Based on the new area continuous length parameters, the actual situation in the congested area is re-evaluated, and the number and location of lanes requiring temporary closures are adjusted. Simultaneously, the width compensation parameters for the remaining open lanes are recalculated and updated based on the new lane closure conditions.

[0149] Step S157: When it is necessary to adjust the lane priority allocation parameters, the congestion level is re-divided according to the congestion level assessment characteristics in the feedback traffic status data set, and the lane priority level and permission validation conditions are adjusted based on the new congestion level.

[0150] If lane priority parameters need to be adjusted, congestion levels are reclassified based on the congestion assessment characteristics in the feedback traffic status data set. The specific method used to classify congestion levels when generating the initial lane control strategy is the same. Based on the new congestion levels, lane access rights are reassigned to areas of different congestion levels, and lane priority levels and access authorization conditions are adjusted.

[0151] Step S158: The adjusted variable lane direction setting parameters, lane opening and closing state parameters, and lane traffic priority allocation parameters are integrated to generate an optimized lane control strategy.

[0152] The adjusted lane direction setting parameters, lane opening and closing status parameters, and lane priority allocation parameters are integrated. During the integration process, the control parameter type, target road segment identifier, and execution timing information are clarified. This information is combined to generate an optimized lane control strategy.

[0153] Figure 2 A schematic diagram illustrates exemplary hardware and software components of a lane control strategy optimization system 100 for traffic congestion, provided in some embodiments of the present application, that can implement the concepts of the present application. For example, a processor 120 can be used in the lane control strategy optimization system 100 for traffic congestion and to perform the functions described in the present application.

[0154] The lane control strategy optimization system 100 for traffic congestion can be a general-purpose server or a special-purpose server, both of which can be used to implement the lane control strategy optimization method for traffic congestion in this application. Although only one server is shown in this application, for convenience, the functions described in this application can be implemented in a distributed manner on multiple similar platforms to balance the processing load.

[0155] For example, the lane control strategy optimization system 100 for traffic congestion may include a network port 110 connected to a network, one or more processors 120 for executing program instructions, a communication bus 130, and storage media 140 in different forms, such as a disk, ROM, or RAM, or any combination thereof. Exemplarily, the lane control strategy optimization system 100 for traffic congestion may also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The method of the present application may be implemented according to these program instructions. The lane control strategy optimization system 100 for traffic congestion also includes an I / O interface 150 between the computer and other input and output devices.

[0156] For ease of explanation, only one processor is described in the lane control strategy optimization system 100 for traffic congestion. However, it should be noted that the lane control strategy optimization system 100 for traffic congestion in the present application may also include multiple processors, so the steps performed by one processor described in the present application may also be performed jointly or individually by multiple processors. For example, if the processor of the lane control strategy optimization system 100 for traffic congestion executes step A and step B, it should be understood that step A and step B may also be performed jointly by two different processors or individually in one processor. For example, the first processor executes step A, the second processor executes step B, or the first processor and the second processor execute steps A and B together.

[0157] In addition, an embodiment of the present invention further provides a readable storage medium, in which computer-executable instructions are preset. When a processor executes the computer-executable instructions, the lane control strategy optimization method for traffic congestion conditions as described above is implemented.

[0158] It should be noted that in order to simplify the description of the present invention and thus help understand one or more embodiments of the invention, in the foregoing description of the embodiments of the present invention, multiple features are sometimes combined into one embodiment, figure or description thereof.

Claims

1. A lane control strategy optimization method for traffic congestion, characterized in that: The method comprises: Acquire a real-time traffic status data set for a target road section, wherein the real-time traffic status data set includes vehicle flow data, vehicle speed data, and road space occupancy rate data; Performing traffic congestion feature analysis on the real-time traffic status data set to obtain congestion area distribution characteristics, congestion level assessment characteristics, and congestion cause correlation characteristics of the target road section; generating an initial lane control strategy based on the congestion area distribution characteristics, the congestion level assessment characteristics, and the congestion cause association characteristics, wherein the initial lane control strategy includes a variable lane direction setting parameter, a lane opening and closing state parameter, and a lane passage priority allocation parameter; Sending the initial lane control strategy to a road control device to execute a lane control operation, and continuously collecting a real-time traffic status data set after the implementation as a feedback traffic status data set; Adjusting, based on the comparative analysis results of the feedback traffic status data set and the real-time traffic status data set, the variable lane direction setting parameters, lane opening and closing state parameters, and lane traffic priority allocation parameters in the initial lane control strategy to generate an optimized lane control strategy; The method of adjusting the variable lane direction setting parameters, lane opening and closing state parameters, and lane traffic priority allocation parameters in the initial lane control strategy based on the comparative analysis results of the feedback traffic status data set and the real-time traffic status data set to generate an optimized lane control strategy includes: Performing time window division on the feedback traffic status data set and the real-time traffic status data set, dividing the two data sets into the same number of time window units, each time window unit corresponding to the same time period; In each time window unit, the difference between the vehicle flow data in the feedback traffic status data set and the vehicle flow data in the real-time traffic status data set is calculated as the flow change, the difference between the vehicle speed data in the feedback traffic status data set and the vehicle speed data in the real-time traffic status data set is calculated as the speed change, and the difference between the road space occupancy data in the feedback traffic status data set and the road space occupancy data in the real-time traffic status data set is calculated as the occupancy change; constructing a strategy effect evaluation index based on the flow rate change, the speed change, and the occupancy rate change, and determining that the initial lane control strategy is effective for the corresponding time window unit when the speed change is positive and the occupancy rate change is negative; When the speed change is negative or the occupancy change is positive, the initial lane control strategy is determined to be invalid for the corresponding time window unit; The proportion of valid judgment results in all time window units is counted. If the proportion of valid judgment results is higher than the preset effect threshold, the variable lane direction setting parameters, lane opening and closing state parameters, and lane priority allocation parameters in the initial lane control strategy are kept unchanged. If the proportion of valid judgment results is lower than the preset effect threshold, it is determined that the corresponding control parameters need to be adjusted. When the variable lane direction setting parameters need to be adjusted, the direction flow ratio parameters are recalculated according to the direction distribution characteristics of the vehicle flow data in the feedback traffic status data set, and the target driving direction and effective period parameters are adjusted based on the new direction flow ratio parameters; When lane opening and closing state parameters need to be adjusted, the regional continuous length parameters are re-determined based on the congested area distribution characteristics in the feedback traffic status data set, the number and location of closed lanes are adjusted based on the new regional continuous length parameters, and the width compensation parameters are updated; When lane priority allocation parameters need to be adjusted, the congestion level is reclassified based on the congestion assessment characteristics in the feedback traffic status data set, and the lane priority level and permission validity conditions are adjusted based on the new congestion level; The adjusted variable lane direction setting parameters, lane opening and closing status parameters, and lane traffic priority allocation parameters are integrated to generate an optimized lane control strategy.

2. The lane control strategy optimization method for traffic congestion according to claim 1, characterized in that: The traffic congestion feature analysis and processing of the real-time traffic status data set to obtain the congestion area distribution characteristics, congestion degree assessment characteristics, and congestion cause correlation characteristics of the target road section includes: Performing spatial gridding processing on the vehicle flow data in the real-time traffic status data set, dividing the target road section into a plurality of traffic analysis units of equal size, and obtaining vehicle flow density data for each traffic analysis unit; Performing statistical processing on the vehicle speed data by road section, calculating the mean and standard deviation of the vehicle speed in each traffic analysis unit, and obtaining speed distribution characteristic parameters; Calculating the space occupancy ratio of each traffic analysis unit based on the road space occupancy rate data, and determining the congestion determination result of each traffic analysis unit in combination with the vehicle flow density data and the speed distribution characteristic parameters; Based on the congestion determination result, a region merging process is performed on consecutive adjacent traffic analysis units to generate a congestion region distribution feature of the target road section, wherein the congestion region distribution feature includes a start position identifier, an end position identifier, and a region continuous length parameter of the congestion region; Extracting vehicle flow density data, speed distribution characteristic parameters, and space occupancy ratio corresponding to each congested area in the congested area distribution characteristics, calculating the product of vehicle flow density and speed distribution characteristic parameters within the congested area as a congestion intensity index, matching the congestion intensity index with a preset congestion level classification standard, and obtaining a congestion degree assessment feature; Analyze the correlation between the spatial location of the congested area in the congested area distribution characteristics and the vehicle flow data and speed distribution characteristic parameters of the upstream and downstream traffic analysis units, identify the main influencing factors causing congestion, and generate congestion cause correlation characteristics including the type of influencing factors and the ranking of the degree of influence.

3. The lane control strategy optimization method for traffic congestion according to claim 2, characterized in that: The calculating of the space occupancy ratio of each traffic analysis unit according to the road space occupancy rate data and determining the congestion determination result of each traffic analysis unit in combination with the vehicle flow density data and the speed distribution characteristic parameter includes: Normalizing the road space occupancy data to convert the space occupancy values ​​of different traffic analysis units into a space occupancy ratio of a unified dimension, where the value range of the space occupancy ratio is 0 to 1; Extracting the number of vehicles per unit time from the vehicle flow density data, calculating the ratio of the number of vehicles per unit time to the spatial size parameter of the traffic analysis unit, and obtaining a flow parameter per unit area; Normalizing the mean of the speed distribution characteristic parameters to generate a speed normalization coefficient, wherein the speed normalization coefficient is positively correlated with the mean of the vehicle speed; The space occupancy ratio, the flow rate per unit area parameter, and the speed normalization coefficient are input into a congestion determination model, and the congestion determination model calculates a comprehensive congestion index by weighted summation, wherein the weight coefficient of the space occupancy ratio is higher than the weight coefficients of the flow rate per unit area parameter and the speed normalization coefficient; Comparing the comprehensive congestion index with a preset congestion threshold range, if the comprehensive congestion index is higher than a first threshold, determining that the corresponding traffic analysis unit is in a congested state; if the comprehensive congestion index is lower than a second threshold, determining that the corresponding traffic analysis unit is in a free-flowing state; if the comprehensive congestion index is between the first threshold and the second threshold, determining that the corresponding traffic analysis unit is in a slow-moving state, wherein the first threshold is higher than the second threshold; The congestion state determination result of each traffic analysis unit is associated with the corresponding traffic analysis unit location identifier and stored to generate a congestion determination result set including a correspondence between the location identifier and the congestion state.

4. The lane control strategy optimization method for traffic congestion according to claim 1, characterized in that: The generating of the initial lane control strategy based on the congestion area distribution characteristics, the congestion degree assessment characteristics, and the congestion cause association characteristics includes: Analyzing the congestion area distribution characteristics and the congestion area start position identifier and the end position identifier to determine the target control section range requiring lane control; According to the congestion intensity index in the congestion degree assessment feature, the congested areas within the target controlled section are divided into different control priority levels, where the areas with higher congestion intensity indexes correspond to higher control priority levels; Extract the main influencing factor type from the congestion cause correlation characteristics. If the main influencing factor type is tidal flow characteristics, it is determined that the variable lane direction setting parameters need to be adjusted; if the main influencing factor type is lane occupation construction characteristics, it is determined that the lane opening and closing state parameters need to be adjusted; if the main influencing factor type is vehicle interweaving conflict characteristics, it is determined that the lane traffic priority allocation parameters need to be adjusted; When it is necessary to adjust the variable lane direction setting parameters, the direction distribution characteristics of the vehicle flow data of the upstream and downstream traffic analysis units within the target control section are analyzed to determine the target driving direction of the variable lane, and the variable lane direction setting parameters including the direction identifier and the effective period parameter are generated; When lane opening and closing state parameters need to be adjusted, the number and locations of lanes that need to be temporarily closed are determined based on the area continuous length parameter in the congested area distribution characteristics, and the width compensation parameters of the remaining open lanes are set to generate lane opening and closing state parameters including lane identification, state identification and width compensation parameters; When lane priority allocation parameters need to be adjusted, corresponding lane access rights are allocated to areas with different congestion levels based on the congestion level classification results in the congestion level assessment feature. Associated lanes in areas with high congestion levels are set to a priority access state, and lane priority allocation parameters are generated, including lane identification, priority level, and access permission validation conditions. The variable lane direction setting parameters, the lane opening and closing state parameters and the lane traffic priority allocation parameters are integrated to generate an initial lane control strategy including control parameter type, target road section identification and execution timing information.

5. The lane control strategy optimization method for traffic congestion according to claim 4, characterized in that: When the variable lane direction setting parameters need to be adjusted, the directional distribution characteristics of the vehicle flow data of the upstream and downstream traffic analysis units within the target control section are analyzed to determine the target driving direction of the variable lane, and the variable lane direction setting parameters including the direction identifier and the effective period parameter are generated, including: Extracting vehicle flow data of an upstream traffic analysis unit within the target controlled road section, and counting the number of vehicles leaving the upstream traffic analysis unit per unit time as the upstream outgoing flow; Extracting vehicle flow data of the downstream traffic analysis unit within the target controlled road section, and counting the number of vehicles entering the downstream traffic analysis unit per unit time as the downstream incoming flow; Calculating a ratio of the upstream outgoing flow to the downstream incoming flow to obtain a directional flow ratio parameter; if the directional flow ratio parameter is greater than a preset directional threshold, determining that the main traffic flow direction is from upstream to downstream; and if the directional flow ratio parameter is less than the inverse of the preset directional threshold, determining that the main traffic flow direction is from downstream to upstream; Determining a target driving direction of the variable lane according to the main traffic flow direction, when the main traffic flow direction is from upstream to downstream, the target driving direction is set to be consistent with the main flow direction; when the main traffic flow direction is from downstream to upstream, the target driving direction is set to be opposite to the main flow direction; Analyzing the time series variation characteristics of vehicle flow data in the real-time traffic status data set, identifying the start time point and the end time point of the traffic peak period, and using the start time point and the end time point as the effective period start parameter and the effective period end parameter of the variable lane direction setting parameter; In combination with the target driving direction, the effective period start parameter and the effective period end parameter, variable lane direction setting parameters including direction identification, effective period start parameter and effective period end parameter are generated, wherein the direction identification adopts a text description method from the start point to the end point or the end point to the starting point of the road section.

6. The lane control strategy optimization method for traffic congestion according to claim 1, characterized in that: The sending of the initial lane control strategy to the road control device to execute the lane control operation, and continuously collecting the real-time traffic status data set after the implementation as the feedback traffic status data set, includes: Sending the initial lane control strategy to a road control device corresponding to the target road segment identifier, the road control device comprising a lane indicator light control unit, a lane barrier control unit, and a traffic sign display unit; After receiving the initial lane control strategy, the road control device controls the road control device to sequentially activate the lane indicator light control unit to adjust the lane indication direction, activate the lane barrier control unit to switch the lane isolation state, and activate the traffic sign display unit to update lane traffic information according to the execution timing information, thereby completing the execution of the lane control operation; During the lane control operation, the traffic detection equipment corresponding to the target road section mark continuously collects the vehicle flow data, vehicle speed data and road space occupancy data after the implementation; The collected vehicle flow data, vehicle speed data and road space occupancy data after implementation are timestamp aligned, and the vehicle flow data, vehicle speed data and road space occupancy data after timestamp alignment are combined into a feedback traffic status data set.

7. The lane control strategy optimization method for traffic congestion according to claim 6, characterized in that: During the lane control operation, the traffic detection equipment corresponding to the target road segment marker continuously collects the vehicle flow data, vehicle speed data, and road space occupancy data after the operation, including: Determine the layout location and device type of traffic detection equipment corresponding to the target road section identifier, wherein the traffic detection equipment includes a video detection unit, a microwave detection unit, and a coil detection unit; Sending an image acquisition instruction to the video detection unit, controlling the video detection unit to capture real-time traffic images of the target road section according to a preset sampling interval, extracting the number of vehicles and vehicle driving trajectory information from the real-time traffic images through an image recognition algorithm, and calculating the number of vehicles passing through the detection section per unit time as vehicle flow data after implementation; sending a radar detection instruction to the microwave detection unit to control the microwave detection unit to calculate the vehicle's running speed as the vehicle's running speed data after implementation; sending an induction signal acquisition instruction to the coil detection unit, controlling the coil detection unit to detect changes in electromagnetic induction generated when a vehicle passes, recording the time interval and duration of the vehicle passing, and calculating the time ratio of the road space occupied by the vehicle based on the layout length of the coil as the road space occupancy rate data after implementation; Cross-validate the data collected by the video detection unit, microwave detection unit, and coil detection unit. When the deviation value of the same type of data collected by different devices exceeds the preset deviation threshold, a weighted average method is used to fuse the data of multiple devices. The weight coefficient is determined based on the historical detection accuracy of the device. The vehicle flow data after implementation, the vehicle speed data after implementation and the road space occupancy rate data after implementation that have undergone cross-validation processing are stored in a temporary data cache area.

8. The lane control strategy optimization method for traffic congestion according to claim 1, characterized in that: constructing a strategy effect evaluation index based on the flow rate change, the speed change, and the occupancy rate change, and determining that the initial lane control strategy is effective for the corresponding time window unit when the speed change is positive and the occupancy rate change is negative; When the speed change is negative or the occupancy change is positive, the initial lane control strategy is determined to be invalid for the corresponding time window unit, including: A sign judgment is performed on the flow rate change. If the flow rate change is a positive value, it indicates that the vehicle flow rate has increased after the lane control strategy is implemented; if the flow rate change is a negative value, it indicates that the vehicle flow rate has decreased after the lane control strategy is implemented; A sign judgment is performed on the speed change. If the speed change is a positive value, it indicates that the vehicle speed increases after the lane control strategy is implemented; if the speed change is a negative value, it indicates that the vehicle speed decreases after the lane control strategy is implemented; A sign judgment is performed on the occupancy change. If the occupancy change is a positive value, it indicates that the road space occupancy ratio has increased after the lane control strategy is implemented; if the occupancy change is a negative value, it indicates that the road space occupancy ratio has decreased after the lane control strategy is implemented. Constructing the judgment rules of strategy effect evaluation indicators: When the speed change is positive and the occupancy change is negative, the initial lane control strategy is determined to be valid for the corresponding time window unit regardless of the sign of the flow change; When the speed change is negative, regardless of the signs of the occupancy change and the flow change, the initial lane control strategy is determined to be invalid for the corresponding time window unit; When the speed change is positive but the occupancy change is also positive, the sign of the flow change is further determined. If the flow change is positive and the increase exceeds the preset flow threshold, it is determined to be invalid. If the flow rate change is negative or the increase does not exceed the preset flow rate threshold, it is determined to be valid; The judgment result of each time window unit is marked as valid or invalid, and stored in association with the corresponding time window unit identifier to form a set of strategy effect evaluation results.

9. A lane control strategy optimization system for traffic congestion, characterized in that: It includes a processor and a memory, the memory is connected to the processor, the memory is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the memory to implement the lane control strategy optimization method for traffic congestion conditions as described in any one of claims 1 to 8.

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