A signal optimization result evaluation method based on multiple indicators

Abstract

The present invention provides a signal optimization result evaluation method based on multiple indicators, including: in response to a target signal, obtaining the optimization target of the last traffic intersection signal timing from historical records; determining multiple evaluation indicators corresponding to the traffic intersection signal timing optimization target based on the optimization target of the last traffic intersection signal timing; obtaining parameters corresponding to the evaluation indicators based on the multiple evaluation indicators, the parameters being obtained based on traffic flow and / or pedestrian flow data at the traffic intersection; and evaluating the traffic intersection signal timing optimization result based on the multiple evaluation indicators and their corresponding parameters to obtain the current evaluation result. By implementing the present invention, the automation of verification is improved, making it easier to timely discover or adjust the optimization direction of signal timing, readjust the signal timing, and improve traffic efficiency. The evaluation is also more targeted, and the multiple evaluation indicators make the evaluation more comprehensive and the evaluation results more accurate.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and in particular relates to a signal optimization result evaluation method based on multiple indicators. Background Art

[0002] In modern urban traffic management, optimizing traffic signal timing is a key approach to improving road efficiency, reducing traffic congestion, and enhancing driving and pedestrian safety. With the rapid development of urbanization and increasing traffic volume, traffic flow has increased significantly. To alleviate this pressure, signal timing optimization methods have been proposed to optimize signal timing to adapt to dynamically changing traffic demands. However, existing technologies fail to verify whether these methods meet current traffic conditions. This can lead to inappropriate signal timing and reduced traffic efficiency. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a signal optimization result evaluation method based on multiple indicators to meet the needs of verifying whether the signal light timing optimization method meets the current traffic conditions.

[0004] In order to achieve the above object, the present invention provides the following technical solutions:

[0005] According to a first aspect, the present invention provides a signal optimization result evaluation method based on multiple indicators, comprising: in response to a target signal, obtaining the optimization target of the last traffic intersection signal light timing from historical records; determining a corresponding plurality of evaluation indicators based on the optimization target of the last traffic intersection signal light timing; based on the plurality of evaluation indicators, obtaining parameters corresponding to the evaluation indicators, the parameters being obtained based on the traffic flow and / or pedestrian flow data at the traffic intersection; evaluating the signal light timing optimization result of the traffic intersection based on the plurality of evaluation indicators and their corresponding parameters to obtain this evaluation result.

[0006] Optionally, the method of determining the corresponding multiple evaluation indicators based on the optimization target of the last traffic intersection signal light timing includes: performing natural language processing on the optimization target to obtain at least one keyword; when the number of keywords is less than the target number, expanding the keywords to obtain multiple related recommendation words; matching the corresponding multiple first evaluation indicators according to the multiple keywords and / or related recommendation words; inputting the parameters corresponding to the multiple first evaluation indicators into the digital twin model to simulate the traffic intersection situation; adjusting the traffic intersection signal light timing to obtain the correlation between the various first evaluation indicators; merging the multiple first evaluation indicators whose correlation exceeds a first preset threshold to obtain the multiple evaluation indicators corresponding to the signal light timing optimization target of the traffic intersection.

[0007] Optionally, after the correlation calculation of the multiple first evaluation indicators is performed, it includes: marking the multiple first evaluation indicators whose correlation calculation is lower than the second preset threshold to obtain the target first evaluation indicator, and evaluating the signal light timing optimization result of the traffic intersection based on the multiple evaluation indicators and their corresponding parameters to obtain the current evaluation result, including: when there are multiple target first evaluation indicators, establishing a strategy variable combination of multiple target first evaluation indicators according to the particle swarm optimization algorithm; constructing a target space according to the strategy variable combination, the target space contains all strategy variable combinations; identifying the Pareto front in the target space; determining the best decision result in the Pareto front according to preset constraints; quantifying the difference between the parameters corresponding to the target first evaluation indicator of the best decision result and the parameters corresponding to the target first evaluation indicator in the optimization target to obtain a differentiated result; and determining the final result of the optimization target of the signal light timing of the last traffic intersection in this evaluation as the current evaluation result according to the differentiated result.

[0008] Optionally, the difference quantification of the parameters corresponding to the target first evaluation indicator of the optimal decision result and the parameters corresponding to the target first evaluation indicator in the optimization target to obtain a differentiated result includes: taking the parameters corresponding to the target first evaluation indicator of the optimal decision result as the first sequence, and taking the parameters corresponding to the target first evaluation indicator in the optimization target as the second sequence; performing dimensionless processing on the data in the first sequence and the data in the second sequence; determining the difference value between each data in the second sequence after dimensionless processing and the data in the first sequence according to the maximum difference and the minimum difference between the data in the first sequence after dimensionless processing and the data in the second sequence; and obtaining a differentiated result according to the difference value and its corresponding weight.

[0009] Optionally, when there are multiple unmarked first evaluation indicators, the final result of the optimization target of the traffic light timing at the previous traffic intersection in this evaluation is determined as the evaluation result of this evaluation based on the differentiation result, including: determining the position of the corresponding parameters of the multiple unmarked first evaluation indicators in the evaluation indicator parameter interval preset for the optimization target; determining the evaluation values ​​of the multiple unmarked first evaluation indicators based on the position of the corresponding parameters of the multiple unmarked first evaluation indicators in the interval; obtaining the evaluation results of the multiple unmarked first evaluation indicators based on the preset first evaluation indicator weights and the corresponding evaluation values; obtaining the differentiated evaluation results based on the differentiation result and its corresponding first evaluation indicator weights; obtaining the final result of the optimization target of the traffic light timing at the previous traffic intersection in this evaluation as the evaluation result of this evaluation based on the evaluation results of the multiple unmarked first evaluation indicators and the differentiated evaluation results.

[0010] Optionally, a signal optimization result evaluation method based on multiple indicators also includes: obtaining multiple evaluation results; performing trend analysis on the multiple evaluation results to obtain a trend analysis graph, wherein the trend analysis graph contains points corresponding to the multiple evaluation results and the overall trend; and determining the applicability of the optimization target of the signal timing of the previous traffic intersection under the current traffic intersection conditions based on the trend analysis graph.

[0011] Optionally, the trend analysis graph includes points corresponding to multiple evaluation results and overall trend data, and determining the applicability of the optimization target of the signal timing of the previous traffic intersection under the current traffic intersection based on the trend analysis results includes: obtaining points corresponding to multiple evaluation results of the trend analysis graph, and determining the slope data of the line between the point and the adjacent point in the trend analysis graph; determining a first suitability parameter based on the slope data of the line between the point and the adjacent point in the trend analysis graph; determining a second suitability parameter based on the overall trend data; and determining the applicability of the optimization target of the signal timing of the previous traffic intersection under the current traffic intersection based on the first suitability parameter and the second suitability parameter.

[0012] According to the second aspect, an embodiment of the present invention provides a signal optimization result evaluation device based on multiple indicators, including: a target acquisition module, used to obtain the optimization target of the signal timing of the last traffic intersection from historical records in response to a target signal; an indicator determination module, used to determine the corresponding multiple evaluation indicators based on the optimization target of the signal timing of the last traffic intersection; a parameter acquisition module, used to obtain parameters corresponding to the evaluation indicators based on multiple evaluation indicators, and the parameters are obtained based on the traffic flow and / or pedestrian flow data at the traffic intersection; a result determination module, used to evaluate the signal timing optimization result of the traffic intersection based on multiple evaluation indicators and their corresponding parameters to obtain this evaluation result.

[0013] According to the third aspect, an embodiment of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and runnable on the processor, wherein the processor executes the steps of the signal optimization result evaluation method based on multiple indicators described in the first aspect or any embodiment of the first aspect.

[0014] According to the fourth aspect, an embodiment of the present invention provides a computer storage medium having computer instructions stored thereon, which, when executed by a processor, implement the steps of the signal optimization result evaluation method based on multiple indicators described in the first aspect or any embodiment of the first aspect.

[0015] The present invention provides a multi-metric signal optimization result evaluation method. Upon receiving a target signal, the method automatically verifies whether the signal timing optimization method meets current traffic conditions. This improves the automation of verification, facilitates timely identification or adjustment of signal timing optimization directions, and allows for readjustment of signal timing to improve traffic efficiency. Furthermore, the present invention determines corresponding evaluation indicators based on the optimization goals of the previous intersection signal timing, making the evaluation more targeted. The use of multiple evaluation indicators results in a more comprehensive and accurate evaluation.

[0016] Other advantages, objectives and features of the present invention will be described in the following description and will be apparent to those skilled in the art to some extent, or those skilled in the art can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to make the purpose, technical solutions and beneficial effects of the present invention more clear, the present invention provides the following drawings for illustration:

[0018] Figure 1 This is a specific example flow chart of a signal optimization result evaluation method based on multiple indicators of the present invention;

[0019] Figure 2 This is a principle block diagram of a specific example of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0021] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "installed," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; internal connections between two components; wireless connections or wired connections. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0022] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0023] This embodiment provides a signal optimization result evaluation method based on multiple indicators, such as Figure 1 As shown, including:

[0024] S101, in response to a target signal, obtaining an optimization target for the last traffic intersection signal timing from historical records;

[0025] S102, determining corresponding multiple evaluation indicators based on the optimization goal of the traffic light timing at the previous traffic intersection;

[0026] S103, based on the multiple evaluation indicators, obtaining parameters corresponding to the evaluation indicators, where the parameters are obtained based on vehicle flow and / or pedestrian flow data at the traffic intersection;

[0027] S104: Evaluate the traffic light timing optimization result at the traffic intersection based on the multiple evaluation indicators and their corresponding parameters to obtain the evaluation result.

[0028] For example, the target signal can be when sensors and monitoring equipment deployed at a traffic intersection detect a traffic accident or severe congestion. Severe congestion can be characterized by the duration of congestion, for example, if the congestion lasts for more than 20 minutes. This means that when a traffic accident or severe congestion occurs at the intersection, the evaluation algorithm is activated to evaluate the results of the traffic light timing optimization. The target signal can also be when a timer reaches its set time, for example, one week. When the timer reaches one week, the target signal is issued to activate the evaluation algorithm and evaluate the results of the traffic light timing optimization.

[0029] The optimization target of the last traffic intersection signal timing can be one or more combinations of 100% driving safety, 70% traffic capacity, 100% pedestrian safety, public transportation priority, etc. This embodiment does not limit the optimization target, and those skilled in the art can determine it according to actual conditions.

[0030] According to the optimization target of the signal light timing at the traffic intersection last time, determining the multiple evaluation indicators corresponding to the optimization target of the signal light timing at the traffic intersection can be by retrieving a mapping relationship table of optimization targets and evaluation indicators pre-stored in the system. Multiple evaluation indicators are obtained according to the mapping relationship table. The method of obtaining the parameters corresponding to the evaluation indicators can be to obtain the traffic flow and / or pedestrian flow data at the traffic intersection through sensors or monitoring equipment, and then clean the obtained data to obtain the parameters corresponding to the evaluation indicators. The parameters corresponding to the evaluation indicators represent the parameters used to calculate the results corresponding to the evaluation indicators. For example, when the evaluation indicator is traffic capacity, its parameters are the number of vehicles passing through the traffic intersection within a specific time.

[0031] After obtaining multiple evaluation indicators and corresponding parameters, the evaluation method for each evaluation indicator is determined. In this embodiment, the difference between the parameter corresponding to the evaluation indicator and the parameter of the evaluation indicator preset for the optimization target can be used to perform normalization based on the difference to obtain the evaluation result corresponding to the evaluation indicator. For example, for the optimization target of achieving 70% traffic capacity, the parameter is the number of vehicles passing through the intersection within a specific time period, which is a quantifiable parameter. Taking the number of vehicles passing through the intersection within a specific time period as an example, the evaluation result of the traffic capacity indicator is calculated by first determining the maximum traffic capacity of the intersection. For example, if the maximum number of vehicles that can pass through the intersection within a specific time period is 800, then at least 560 vehicles must pass through to achieve the optimization target. For a traffic capacity of 380 vehicles, the evaluation result of the traffic capacity indicator is calculated by dividing the actual number of vehicles passing through the intersection within the specific time period by the minimum number of vehicles required to pass through the intersection to achieve the optimization target, which is 0.68. After obtaining the evaluation results of all evaluation indicators according to the above method, the evaluation result is obtained by performing a weighted sum of each evaluation indicator result and the corresponding weight. This embodiment uses the above evaluation result as an example for explanation, and can also be implemented using technologies such as neural networks. This embodiment does not limit the specific method for obtaining the evaluation result.

[0032] The present invention provides a multi-metric signal optimization result evaluation method. Upon receiving a target signal, the method automatically verifies whether the signal timing optimization method meets current traffic conditions. This improves the automation of verification, facilitates timely identification or adjustment of signal timing optimization directions, and allows for readjustment of signal timing to improve traffic efficiency. Furthermore, the present invention determines corresponding evaluation indicators based on the optimization goals of the previous intersection signal timing, making the evaluation more targeted. The use of multiple evaluation indicators results in a more comprehensive and accurate evaluation.

[0033] As an optional implementation, multiple corresponding evaluation indicators are determined based on the optimization target of the last traffic intersection signal timing, including: performing natural language processing on the optimization target to obtain at least one keyword; when the number of keywords is less than the target number, expanding the keywords to obtain multiple related recommendation words; matching multiple first evaluation indicators corresponding to the multiple keywords and / or related recommendation words; inputting the parameters corresponding to the multiple first evaluation indicators into the digital twin model to simulate the traffic intersection situation and adjust the traffic intersection signal timing to obtain the correlation between the various first evaluation indicators; merging multiple first evaluation indicators whose correlation exceeds a first preset threshold to obtain multiple evaluation indicators corresponding to the traffic intersection signal timing optimization target.

[0034] For example, in order to improve the intelligence and automation level of determining the evaluation indicators, this embodiment no longer relies on the pre-set mapping relationship table between the optimization objectives and the evaluation indicators. Instead, it is proposed to capture the keywords of the optimization objectives proposed by the user through natural language processing. When the number of keywords is not enough to cover all the optimization objectives, the keywords are expanded through the recommendation algorithm to ensure the comprehensiveness of the evaluation indicators and further improve the accuracy of the evaluation. In addition, it should be noted that in this embodiment, the use of natural language processing to extract keywords from the optimization objectives can enhance the convenience and interactivity of the user, that is, it is applicable to various language habits contained in any optimization objective. The keyword capture process is one of the mature natural language processing technologies and will not be described in detail in this embodiment.

[0035] If the number of keywords is less than the target number, the keywords are expanded. The target number can be 3, which can be set based on actual conditions and is not limited in this embodiment. The keyword expansion method can be to use a transportation dictionary or online database to search for co-occurring words with the keywords. Co-occurring words refer to words that frequently appear with the keywords. Then, using a network analysis tool, important co-occurring words are identified and used as relevant recommended words.

[0036] The first evaluation indicators include traffic flow, delay, queue length, traffic capacity, etc. The specific method of matching the corresponding multiple first evaluation indicators according to multiple keywords and / or related recommended words can be to establish an evaluation indicator library to classify and describe the evaluation indicators commonly used in various fields. When keywords and recommended words are input, the most relevant evaluation indicators can be found through matching algorithms (such as vector matching, string similarity matching, etc.); or a machine learning method, such as a classification algorithm, can be used to input keywords and recommended words as features, and the evaluation indicators learned through training data are used as output. When keywords and recommended words are used as input, the model can predict their corresponding evaluation indicators. This embodiment does not limit the specific method of matching the corresponding multiple first evaluation indicators according to multiple keywords and / or related recommended words, and those skilled in the art can determine it as needed.

[0037] A digital twin model of a traffic intersection is constructed using geometric models, semantic models, and associative mapping. By integrating multi-source data, comprehensive and accurate geometric information is obtained, enabling the construction of a complex geometric model with multi-scale features and full-factor characteristics. Using the digital twin model to simulate traffic intersection conditions allows for the virtual testing of different signal timing schemes, saving the cost and time of actual testing. By adjusting the signal timing at the intersection within the digital twin, correlations between various primary evaluation indicators are determined. For example, when one of the first evaluation indicators is traffic flow and the other first evaluation indicator is queue length, when the first evaluation indicator is traffic flow, its parameter is the traffic flow data obtained by the sensor or monitor, and when the first evaluation indicator is queue length, its parameter is the queue length of each lane obtained by the sensor or monitor in a certain period of time. The obtained traffic flow data and queue length are input into the digital twin model. At the same time, the traffic light timing at the traffic intersection is adjusted. The change in the traffic light timing at the traffic intersection affects the parameters of each first evaluation indicator, and the correlation between the first evaluation indicators is obtained. For example, when the red light time of the lane is extended by adjusting the traffic light at the traffic intersection, the queue length will increase, and the average increase in length will be recorded. The traffic flow will decrease, and the reduction in traffic flow will be recorded. Then, based on the relationship between the average increase in queue length and the reduction in traffic flow, the correlation between the queue length indicator and the traffic flow indicator can be obtained. The Pearson correlation coefficient can be used to obtain it, which will not be elaborated here. After determining the correlation, in order to reduce the subsequent amount of calculation, multiple first evaluation indicators whose correlation exceeds the first preset threshold are merged, that is, similar evaluation indicators are merged to obtain multiple evaluation indicators corresponding to the signal timing optimization target of the traffic intersection. The first preset threshold can be 0.5. This embodiment does not limit the first preset threshold and can be determined according to the needs of technical personnel in this field.

[0038] As an optional implementation, after calculating the correlation of the plurality of first evaluation indicators, the method includes: marking the plurality of first evaluation indicators whose correlation is lower than a second preset threshold value to obtain a target first evaluation indicator; and evaluating the signal light timing optimization result of the traffic intersection based on the plurality of evaluation indicators and their corresponding parameters to obtain a current evaluation result, including:

[0039] When there are multiple target first evaluation indicators, strategy variable combinations of multiple target first evaluation indicators are established according to the particle swarm optimization algorithm; based on the strategy variable combinations, a target space is constructed, and the target space contains all strategy variable combinations; the Pareto front is identified in the target space; the best decision result is determined in the Pareto front according to the preset constraints; the parameters corresponding to the target first evaluation indicator of the best decision result and the parameters corresponding to the target first evaluation indicator in the optimization target are quantified to obtain a differentiated result; based on the differentiated result, the final result of the optimization target of the traffic light timing at the previous traffic intersection in this evaluation is determined as the result of this evaluation.

[0040] Exemplarily, the correlation between multiple first evaluation indicators is lower than the second preset threshold, indicating that the multiple first evaluation indicators are relatively independent. In this embodiment, the relatively independent first evaluation indicators are referred to as target first evaluation indicators. For the target first evaluation indicators, this embodiment first obtains multiple optimization objectives, then determines multiple target first evaluation indicators, and then creates an initial particle swarm, where each particle represents a set of possible solutions, that is, a combination of strategy variables, specifically a combination of traffic light timing parameters. According to the strategy variable combinations and their corresponding evaluation indicators, a target space is constructed. This space contains all possible combinations of strategy variables, and each combination is a potential solution. The PSO algorithm is used to iteratively find the optimal solution. In the PSO algorithm, the speed and position of each particle are adjusted according to the update formula, and the speed update formula is as follows:

[0041]

[0042] The position update formula is as follows:

[0043]

[0044] Among them, V id is the velocity of particle i in dimension d, X id is the position of particle i in dimension d, ω is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers, P id is the individual optimal position of particle i, P gd is the global optimal position, and k is the kth iteration.

[0045] The Pareto front represents a set of solutions that strike a balance between multiple objectives. These solutions are non-dominated in the objective space, meaning no other solution simultaneously outperforms them on all objectives. By comparing the fitness of each particle, the individual optimal solution and the global optimal solution are updated. In the search for the optimal solution, practical constraints such as traffic light cycles and minimum and maximum green light times can be considered. Within the Pareto front, the optimal solution is selected based on the pre-set constraints and the decision maker's preferences, resulting in the optimal decision.

[0046] The difference between the parameters corresponding to the target first evaluation indicator of the optimal decision result and the parameters corresponding to the target first evaluation indicator in the optimization target is quantified to obtain a differentiated result. Specifically, first, the target first evaluation indicator parameters of the optimization strategy obtained from the previous optimization target are collected through sensors or monitors. Then, the target first evaluation indicator parameters corresponding to the optimal decision result are determined. Specifically, the optimal decision result is input into a pre-established digital twin model to obtain the parameters corresponding to the target first evaluation indicator when the optimal decision result is implemented at the traffic intersection. After obtaining the target first evaluation indicator parameters corresponding to the optimal decision result and the target first evaluation indicator parameters of the optimization strategy obtained from the previous optimization target, the impact of the difference between the optimal decision result and the optimization result of the previous optimization target on the target first evaluation indicator parameters is determined. For example, if the optimal decision result indicates a red light time of 45 seconds, and the optimization strategy obtained from the previous optimization target is a red light time of 30 seconds, in this case, the average values ​​of the queue length and traffic flow evaluation indicators before and after the parameter change are recorded, and the difference is calculated. The differences in the changes in multiple target first evaluation indicators caused by the difference between the optimal decision result and the optimization result of the previous optimization target are weighted summed to obtain a quantified differentiated result. Based on the differentiation results, the final result of the optimization target of the traffic light timing at the previous traffic intersection in this evaluation is obtained.

[0047] An embodiment of the present invention provides a signal optimization result evaluation method based on multiple indicators. The best decision result is obtained through a particle swarm optimization method. The best decision result is simulated by a digital twin to obtain the parameters corresponding to the target first evaluation indicator corresponding to the best decision result. The parameters are compared with the target first evaluation indicator parameters when the optimization strategy obtained by implementing the previous optimization target is compared to determine the difference. The final result of this evaluation is determined based on the differentiated results. Through this method, the difference with the best decision result is used as a reference for the evaluation result, thereby improving the reliability and accuracy of the evaluation.

[0048] As an optional implementation, the parameters corresponding to the target first evaluation index of the optimal decision result and the parameters corresponding to the target first evaluation index in the optimization target are differentially quantified to obtain a differentiated result, including: taking the parameters corresponding to the target first evaluation index of the optimal decision result as the first sequence, and taking the parameters corresponding to the target first evaluation index in the optimization target as the second sequence; performing dimensionless processing on the data in the first sequence and the data in the second sequence; determining the difference value between each data in the dimensionless second sequence and the data in the first sequence according to the maximum difference and the minimum difference between the data in the first sequence after dimensionless processing and the data in the second sequence; and obtaining a differentiated result according to the difference value and its corresponding weight.

[0049] Specifically, a first evaluation indicator corresponding to the optimal decision result is determined, and the parameters corresponding to the first evaluation indicator are used as the first sequence. The parameters corresponding to the first evaluation indicator in the optimization target are used as the second sequence. The first sequence data and the second sequence data are then dimensionlessly processed to eliminate the effects of different dimensions and orders of magnitude. Dimensionless processing can include range normalization (maximum-minimum method), averaging, initialization, etc.

[0050] Then, based on the maximum and minimum differences between the data in the first sequence after dimensionless processing and the data in the second sequence, the difference between each data in the second sequence after dimensionless processing and each data in the first sequence is determined. The specific formula is as follows:

[0051]

[0052] Where χi represents the difference value of the first evaluation index of the i-th target, n represents the number of data points in the first sequence or the second sequence corresponding to the first evaluation index of the i-th target, Δ min is the minimum difference between two sequences, Δ max is the maximum difference between the two sequences, ρ is the resolution coefficient, which is 0.5, X 1i (k) is the first sequence corresponding to the first evaluation index of the i-th target, X 2i (k) is the second sequence corresponding to the first evaluation indicator of the i-th target.

[0053] When there are multiple target first evaluation indicators, differentiated results are obtained based on the difference values ​​of each target first evaluation indicator and the corresponding weights. The specific weights can be set based on expert opinions or analysis of historical data, and this embodiment does not impose any restrictions on the weights.

[0054] As an optional implementation, when there are multiple unlabeled first evaluation indicators, the final result of the optimization target of the traffic light timing at the previous traffic intersection in this evaluation is determined as the result of this evaluation based on the differentiation result, including:

[0055] Determine the positions of the corresponding parameters of the unmarked multiple first evaluation indicators in the evaluation indicator parameter interval preset for the optimization target; determine the evaluation values ​​of the unmarked multiple first evaluation indicators based on the positions of the corresponding parameters of the unmarked multiple first evaluation indicators in the interval; obtain the evaluation results of the unmarked multiple first evaluation indicators based on the preset first evaluation indicator weights and the corresponding evaluation values; obtain the differentiated evaluation results based on the differentiated results and their corresponding first evaluation indicator weights; obtain the final result of the optimization target of the traffic light timing at the previous traffic intersection in this evaluation based on the evaluation results of the unmarked multiple first evaluation indicators and the differentiated evaluation results as the evaluation result of this evaluation.

[0056] Exemplarily, the parameters corresponding to the unlabeled multiple first evaluation indicators are compared with the preset evaluation indicator parameter interval to determine the position of each parameter within the interval. Based on the position of the evaluation indicator parameter within the preset interval, an evaluation value is determined for each unlabeled evaluation indicator. This evaluation value can be a direct mapping based on the parameter position, or it can be a result obtained by a pre-trained scoring model. Based on the preset first evaluation indicator weight and the corresponding evaluation value, the evaluation result of each unlabeled evaluation indicator is calculated using a weighted summation method. Based on the differentiated result and its corresponding first evaluation indicator weight, a differentiated evaluation result is calculated, for example, a weighted sum is performed on the differentiated result of each evaluation indicator. The evaluation results of the unlabeled multiple first evaluation indicators are combined with the differentiated evaluation result to obtain the final result of the optimization target of the traffic light timing at the previous traffic intersection in this evaluation. The final evaluation result is used as the output of this evaluation, and this result can be used to judge the applicability and effectiveness of the previous optimization target in the current situation.

[0057] An embodiment of the present invention provides a signal optimization result evaluation method based on multiple indicators, which combines the evaluation values ​​of multiple unlabeled first evaluation indicators and the evaluation values ​​of multiple labeled first evaluation indicators. Its evaluation dimensions are more complete and comprehensive, and the evaluation results are more accurate.

[0058] As an optional implementation, a signal optimization result evaluation method based on multiple indicators further includes:

[0059] Obtain multiple evaluation results; perform trend analysis on the multiple evaluation results to obtain a trend analysis graph, which includes points corresponding to the multiple evaluation results and the overall trend; and determine, based on the trend analysis graph, the applicability of the optimization target of the traffic light timing at the previous traffic intersection to the current traffic intersection situation.

[0060] Exemplarily, statistical software or programming language is used to perform trend analysis on the data. Use data analysis tools to draw a trend analysis graph, which should contain: data points, with the results of each evaluation as points on the graph; trend lines, which show the overall trend of the data, which can be linear or nonlinear. Observe the trend analysis graph, analyze the distribution of data points and the direction of the trend line, and determine the changing trend of the evaluation results over time. Evaluate whether the trend is significant, whether it continues to rise or fall, or whether there are fluctuations. Based on the results of the trend analysis graph, evaluate the applicability of the optimization goal of the traffic light timing at the last traffic intersection to the current traffic intersection situation: if the trend shows continued improvement, it may indicate that the current optimization goal is still applicable. If the trend shows that the performance indicator has not improved significantly or has declined, the optimization goal may need to be adjusted. If the trend is unstable or fluctuates greatly, further analysis of the data may be required to determine the effectiveness of the optimization goal.

[0061] As an optional implementation, the trend analysis graph includes points corresponding to multiple evaluation results and overall trend data, and determining the applicability of the optimization target of the signal timing of the previous traffic intersection under the current traffic intersection based on the trend analysis results, including: obtaining points corresponding to multiple evaluation results of the trend analysis graph, and determining the slope data of the line between the point and the adjacent point in the trend analysis graph; determining a first suitability parameter based on the slope data of the line between the point and the adjacent point in the trend analysis graph; determining a second suitability parameter based on the overall trend data; and determining the applicability of the optimization target of the signal timing of the previous traffic intersection under the current traffic intersection based on the first suitability parameter and the second suitability parameter.

[0062] For example, data points corresponding to each evaluation cycle are extracted from a trend analysis graph, representing the evaluation results at different time points. For each data point in the trend analysis graph, the slope of the line connecting it and the adjacent points is calculated. Based on the calculated slope data, a first fitness parameter is determined. This parameter may be the average slope. A second fitness parameter may be the slope of the overall trend line. Combining the first fitness parameter and the second fitness parameter, a comprehensive assessment is made of the fitness of the optimization target for the previous traffic intersection signal timing under the current circumstances. Specifically, the difference between the average slope and the slope of the overall trend line is calculated. When the difference between the average slope and the slope of the overall trend line is within a preset range, the corresponding fitness is searched in a mapping table between the slope of the overall trend line and fitness. In this mapping table, the larger the slope, the higher the corresponding fitness. The specific value of this mapping relationship can be determined based on actual conditions. When the difference between the average slope and the slope of the overall trend line exceeds the preset range, the fitness can be a fixed value, which indicates that the trend is unstable and serves to remind the user to adjust the optimization target.

[0063] An embodiment of the present invention provides a signal optimization result evaluation method based on multiple indicators. By analyzing the trends of multiple evaluation results, the applicability is determined, thereby avoiding the one-sidedness caused by determining the applicability by a single evaluation, and avoiding the problem that the last optimization goal can be met most of the time, but the time of initiating the evaluation is a certain period of time, resulting in the evaluation result not meeting the last optimization goal, thereby improving the reliability of the applicability results.

[0064] This embodiment provides a signal optimization result evaluation device based on multiple indicators, including:

[0065] A target acquisition module is used to obtain the optimization target of the last traffic intersection signal timing from the historical records in response to the target signal;

[0066] An indicator determination module, configured to determine a plurality of corresponding evaluation indicators according to the optimization target of the last traffic intersection signal light timing;

[0067] a parameter acquisition module, configured to acquire parameters corresponding to the evaluation indicators based on the plurality of evaluation indicators, wherein the parameters are obtained based on vehicle flow and / or pedestrian flow data at the traffic intersection;

[0068] The result determination module is used to evaluate the signal light timing optimization result of the traffic intersection according to the multiple evaluation indicators and their corresponding parameters to obtain the evaluation result.

[0069] The present application also provides an electronic device, such as Figure 2 As shown, a processor 501 and a memory 502 , wherein the processor 501 and the memory 502 may be connected via a bus or other means.

[0070] The processor 501 may be a central processing unit (CPU). The processor 501 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips.

[0071] Memory 502, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the multi-metric signal optimization result evaluation method in the embodiments of the present invention. The processor executes the non-transitory software programs, instructions, and modules stored in the memory to perform various processor functions and data processing.

[0072] The memory 502 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created by the processor, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 502 may optionally include a memory remotely located relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0073] The one or more modules are stored in the memory 502 and when executed by the processor 501, perform the following steps: Figure 1 The signal optimization result evaluation method based on multiple indicators in the illustrated embodiment.

[0074] For details of the above electronic equipment, please refer to Figure 1 The corresponding descriptions and effects in the embodiments shown can be understood and will not be repeated here.

[0075] This embodiment further provides a computer storage medium storing computer-executable instructions capable of executing the multi-index-based signal optimization result evaluation method of any of the above-described method embodiments. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD), or a solid-state drive (SSD); the storage medium may also include a combination of the above-described types of memory.

[0076] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made in form and details without departing from the scope defined by the claims of the present invention.

Claims

1. A signal optimization result evaluation method based on multiple indicators, characterized in that: include: In response to the target signal, obtaining the optimization target of the previous traffic intersection signal timing from the historical records; Determining corresponding multiple evaluation indicators based on the optimization goal of the traffic light timing at the previous traffic intersection; Based on the plurality of evaluation indicators, obtaining parameters corresponding to the evaluation indicators, wherein the parameters are obtained based on vehicle flow and / or pedestrian flow data at the traffic intersection; Evaluate the signal light timing optimization result of the traffic intersection according to the plurality of evaluation indicators and their corresponding parameters to obtain the evaluation result; The method of determining a plurality of corresponding evaluation indicators based on the optimization target of the last traffic intersection signal light timing includes: Perform natural language processing on the optimization target to obtain at least one keyword; When the number of keywords is less than the target number, the keywords are expanded to obtain multiple related recommended words; Matching corresponding multiple first evaluation indicators according to multiple keywords and / or related recommendation words; Inputting parameters corresponding to the plurality of first evaluation indicators into the digital twin model to simulate traffic intersection conditions; Adjust the timing of traffic lights at traffic intersections to obtain the correlation between the first evaluation indicators; The multiple first evaluation indicators whose correlation exceeds the first preset threshold are combined to obtain multiple evaluation indicators corresponding to the signal light timing optimization target of the traffic intersection.

2. The signal optimization result evaluation method based on multiple indicators according to claim 1 is characterized in that: After calculating the correlation of the plurality of first evaluation indicators, the method includes marking the plurality of first evaluation indicators whose correlation is lower than a second preset threshold value to obtain a target first evaluation indicator. The method evaluates the signal light timing optimization result of the traffic intersection based on the plurality of evaluation indicators and their corresponding parameters to obtain a current evaluation result, including: When there are multiple target first evaluation indicators, a strategy variable combination of multiple target first evaluation indicators is established according to the particle swarm optimization algorithm; According to the strategy variable combination, a target space is constructed, where the target space includes all strategy variable combinations; identifying a Pareto front in the target space; Determine the best decision result in the Pareto front according to the preset constraints; quantifying the difference between the parameter corresponding to the first evaluation index of the target of the optimal decision result and the parameter corresponding to the first evaluation index of the target in the optimization target to obtain a differentiated result; According to the differentiation results, the final result of the optimization target of the traffic light timing at the previous traffic intersection in this evaluation is determined as the result of this evaluation.

3. The signal optimization result evaluation method based on multiple indicators according to claim 2 is characterized in that: The step of quantifying the difference between the parameter corresponding to the target first evaluation indicator of the optimal decision result and the parameter corresponding to the target first evaluation indicator in the optimization target to obtain a differentiated result includes: The parameters corresponding to the first evaluation index of the optimal decision result are used as the first sequence, and the parameters corresponding to the first evaluation index of the optimization target are used as the second sequence; performing dimensionless processing on the data in the first sequence and the data in the second sequence; Determining, based on the maximum difference and the minimum difference between the data in the first sequence after the dimensionless processing and the data in the second sequence, the difference between each data in the second sequence after the dimensionless processing and the data in the first sequence; A differentiation result is obtained according to the difference value and its corresponding weight.

4. The signal optimization result evaluation method based on multiple indicators according to claim 2 is characterized in that: When there are multiple unlabeled first evaluation indicators, determining, based on the differentiation results, the final result of the optimization target of the traffic light timing at the previous traffic intersection in this evaluation as the result of this evaluation, includes: Determine the positions of the unmarked first evaluation index corresponding parameters in the evaluation index parameter interval preset in the optimization target; Determining evaluation values ​​of the unmarked plurality of first evaluation indicators according to positions of the parameters corresponding to the unmarked plurality of first evaluation indicators in the interval; Obtaining evaluation results of multiple unlabeled first evaluation indicators according to preset first evaluation indicator weights and corresponding evaluation values; Obtaining a differentiated evaluation result according to the differentiated result and its corresponding first evaluation indicator weight; According to the evaluation results of the unlabeled plurality of first evaluation indicators and the differentiated evaluation result, a final result of the optimization target of the traffic light timing at the previous traffic intersection in this evaluation is obtained as the evaluation result of this evaluation.

5. The signal optimization result evaluation method based on multiple indicators according to claim 1 is characterized in that: Also includes: Obtain multiple evaluation results; Performing trend analysis on the multiple evaluation results to obtain a trend analysis graph, wherein the trend analysis graph includes points corresponding to the multiple evaluation results and an overall trend; The applicability of the optimization target of the traffic light timing at the previous traffic intersection to the current traffic intersection situation is determined based on the trend analysis diagram.

6. The signal optimization result evaluation method based on multiple indicators according to claim 5 is characterized in that: The trend analysis graph includes points corresponding to multiple evaluation results and overall trend data. The determination of the applicability of the optimization target of the traffic light timing at the previous traffic intersection under the current traffic intersection conditions based on the trend analysis results includes: Obtain points corresponding to multiple evaluation results of the trend analysis graph, and determine slope data of the lines connecting the points in the trend analysis graph and adjacent points; Determine a first fitness parameter based on slope data of a line connecting a point and adjacent points in the trend analysis graph; determining a second fitness parameter based on the overall trend data; The suitability of the optimization target of the traffic light timing at the previous traffic intersection under the current traffic intersection conditions is determined based on the first suitability parameter and the second suitability parameter.

7. A signal optimization result evaluation device based on multiple indicators, characterized in that: include: A target acquisition module is used to obtain the optimization target of the last traffic intersection signal timing from the historical records in response to the target signal; An indicator determination module, configured to determine a plurality of corresponding evaluation indicators according to the optimization target of the last traffic intersection signal light timing; a parameter acquisition module, configured to acquire parameters corresponding to the evaluation indicators based on the plurality of evaluation indicators, wherein the parameters are obtained based on vehicle flow and / or pedestrian flow data at the traffic intersection; A result determination module is used to evaluate the signal light timing optimization result of the traffic intersection according to the plurality of evaluation indicators and their corresponding parameters to obtain the evaluation result; The method of determining a plurality of corresponding evaluation indicators based on the optimization target of the last traffic intersection signal light timing includes: Perform natural language processing on the optimization target to obtain at least one keyword; When the number of keywords is less than the target number, the keywords are expanded to obtain multiple related recommended words; Matching corresponding multiple first evaluation indicators according to multiple keywords and / or related recommendation words; Inputting parameters corresponding to the plurality of first evaluation indicators into the digital twin model to simulate traffic intersection conditions; Adjust the timing of traffic lights at traffic intersections to obtain the correlation between the first evaluation indicators; The multiple first evaluation indicators whose correlation exceeds the first preset threshold are combined to obtain multiple evaluation indicators corresponding to the signal light timing optimization target of the traffic intersection.

8. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the steps of the signal optimization result evaluation method based on multiple indicators as described in any one of claims 1 to 6.

9. A computer storage medium having computer instructions stored thereon, characterized in that: When the instruction is executed by the processor, the steps of the signal optimization result evaluation method based on multiple indicators described in any one of claims 1 to 6 are implemented.

Images