Intelligent traffic system high-speed monitoring point layout optimization method and system

Abstract

The invention belongs to the technical field of high-speed monitoring point optimization, and provides an intelligent traffic system high-speed monitoring point layout optimization method and system, and the method comprises the steps: obtaining the information of a target monitoring road; according to road characteristics and a traffic jam model, determining a preliminary area set by each monitoring point; taking a congestion point identification increasing quantity and a traffic flow variable quantity after an emergency lane is started as positive indexes, taking monitoring cost as a negative index, and constructing a corresponding benefit evaluation function to form a target function; and solving by utilizing a quantum particle swarm optimization algorithm, or obtaining a plurality of optimization schemes by adjusting the number of monitoring points and uniformly distributing the monitoring points, evaluating the obtained optimization schemes based on a multiple linear regression model, and selecting a final optimization scheme. According to the invention, reasonable design of monitoring points can be ensured, effective monitoring of traffic congestion is facilitated, and whether an emergency lane is temporarily started or not is considered.

Description

Technical Field

[0001] The present invention belongs to the technical field of high-speed monitoring point optimization, and in particular relates to a high-speed monitoring point layout optimization method and system for an intelligent transportation system. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] When constructing highways, emergency lanes are often added to some sections to meet the needs of emergency vehicles such as engineering rescue, fire rescue, and medical rescue. Normally, emergency lanes are life-saving channels and cannot be occupied at will, but if they are used reasonably, for example, if the monitoring of traffic flow in the upper, middle, and lower reaches of a certain section finds that congestion is likely to occur, and there is no accident on the section, the emergency lane is allowed to be used to reduce traffic density in a timely manner, which is likely to avoid a major congestion.

[0004] At the same time, monitoring points on highways are generally deployed at set distances, and the distances between each monitoring point are relatively far, without considering issues such as regional convergence, effective monitoring of traffic congestion, and temporary activation of emergency lanes. Summary of the invention

[0005] In order to solve the above problems, the present invention proposes a method and system for optimizing the layout of high-speed monitoring points in an intelligent transportation system. The present invention takes indicators such as the increase in the number of congestion point identifications, the reduction in congestion time, and the reduction rate of congestion events as positive indicators, and the monitoring cost as a negative indicator, establishes an objective function, and solves it. This can ensure that the monitoring points are reasonably designed, help to effectively monitor traffic congestion, and consider whether to temporarily activate the emergency lane.

[0006] According to some embodiments, the present invention adopts the following technical solutions:

[0007] A method for optimizing the layout of high-speed monitoring points in an intelligent transportation system comprises the following steps:

[0008] Obtain information of target monitoring road;

[0009] Determine the preliminary area for setting up each monitoring point based on road characteristics and traffic congestion model;

[0010] The increase in the number of congestion points identified and the change in traffic flow after the emergency lane is activated are used as positive indicators, and the monitoring cost is used as a negative indicator. The corresponding benefit evaluation function is constructed to form the objective function.

[0011] The number of monitoring points and the positional relationship of the existing monitoring points are used as constraints of the objective function;

[0012] In the preliminary region, based on the objective function and the constraint conditions, a quantum particle swarm optimization algorithm is used to solve the problem and obtain a first optimization solution;

[0013] In the preliminary area, several optimization schemes are obtained by adjusting the number of monitoring points and distributing them evenly, and the obtained optimization schemes are evaluated based on a multivariate linear regression model to obtain a second optimization scheme;

[0014] According to the monitoring optimization target, the first optimization scheme or the second optimization scheme is selected as the final optimization scheme.

[0015] As an optional implementation manner, the preliminary areas for setting up each monitoring point include traffic bottleneck areas, areas with a change in the number of lanes, and sharp turns or ramp areas.

[0016] As an optional implementation, the preliminary area for determining each monitoring point setting also includes a monitoring blind area.

[0017] As an optional implementation, the positive indicator of traffic flow change after the emergency lane is activated includes the reduction in congestion time, the reduction rate of congestion events and the average speed increase rate, which is obtained based on the increase in the number of emergency lanes activated and the comparison of data before and after the emergency lane is activated.

[0018] As an optional implementation, the objective function is Max·ρBenifit-λ·Cost;

[0019] Among them, Benefit represents the positive indicator, ρ represents the positive indicator coefficient, Cost represents the cost, and λ represents the negative indicator coefficient.

[0020] As an optional implementation, the benefit evaluation function of the positive indicator is:

[0021] Benefits = a·(Z point )+b·(Δt)+c·(Δf)+d·(ΔS);

[0022] Among them, Z point The number of congestion points identified increases, Δt is the total congestion time reduced by optimizing the distribution of points, Δf is the reduction rate of congestion events, ΔS is the average speed improvement rate, and a, b, c, and d are the weight coefficients of the impact of these four benefits on the total benefit.

[0023] As an optional implementation, the function of the negative indicator calculates the total cost according to the number and type of monitoring points and the maintenance cost. Assuming that the installation and operation cost of each monitoring point is fixed, the total cost is expressed as:

[0024] Where n is the number of monitoring points, c is the fixed cost of each monitoring point, and m iis the maintenance cost of the ith monitoring point.

[0025] As an optional implementation, the process of using the monitoring quantity and the position relationship of the existing monitoring points as the constraint conditions of the objective function includes:

[0026] The total number of monitoring points on the target monitoring road, the maximum distance from the existing monitoring points, and the setting interval limit of the monitoring points are set as constraints.

[0027] As an optional implementation, the process of evaluating each optimization scheme based on the multivariate linear regression model includes: arranging all feasible optimization schemes, and calculating positive indicators and negative indicators of each optimization scheme respectively;

[0028] Construct a multiple linear regression model, use the entropy weight method to process the indicators, and obtain the weights of the multiple linear regression model;

[0029] A multivariate linear regression model is used to perform weighting based on weights, and finally a new calculation result of the indicator is obtained. The solution decision is made based on the calculation result, and the optimal solution is selected.

[0030] An intelligent transportation system high-speed monitoring point layout optimization system, comprising:

[0031] A monitoring point preliminary determination module is configured to obtain information of a target monitoring road and determine a preliminary area for setting each monitoring point according to road characteristics and a traffic congestion model;

[0032] The objective function construction module is configured to use the increase in the number of congestion point identifications and the change in traffic flow after the emergency lane is activated as positive indicators, and the monitoring cost as a negative indicator, and to construct a corresponding benefit evaluation function to form an objective function;

[0033] A constraint module is configured to use the monitoring quantity and the position relationship of the existing monitoring points as constraint conditions of the objective function;

[0034] The optimization evaluation module is configured to solve the problem in the preliminary area based on the objective function and the constraints using a quantum particle swarm optimization algorithm to obtain a first optimization solution; obtain several optimization solutions in the preliminary area by adjusting the number of monitoring points and distributing them evenly, and evaluate the obtained optimization solutions based on a multivariate linear regression model to obtain a second optimization solution; and select the first optimization solution or the second optimization solution as the final optimization solution according to the monitoring optimization target.

[0035] An electronic device comprises a memory and a processor, and computer instructions stored in the memory and executed on the processor. When the computer instructions are executed by the processor, the steps in the above method are completed.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] The present invention takes into account the temporary activation of the emergency lane, optimizes the layout of existing monitoring points or arranges new monitoring points while controlling costs, so as to improve the scientific nature of the decision on the temporary activation of the emergency lane on the road section between two monitoring points.

[0038] The present invention proposes a quantum particle swarm optimization algorithm (QPSO) under multi-objective optimization conditions and a linear regression model solution decision algorithm based on the entropy weight method, which optimizes the layout of video surveillance points, controls costs, and improves the scientific nature of decision-making.

[0039] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0041] Figure 1 is a flow chart of an optimization method of an embodiment;

[0042] Figure 2 This is a schematic diagram of a positive indicator acquisition idea of ​​an embodiment;

[0043] Figure 3 This is a schematic diagram of the solution result of a quantum particle swarm algorithm of an embodiment;

[0044] Figure 4 The diagram is a schematic diagram of the results of a multiple linear regression model of an embodiment. DETAILED DESCRIPTION

[0045] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0046] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0047] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0048] In the absence of conflict, the embodiments in this application and the features in the embodiments may be combined with each other.

[0049] As described in the background technology, the existing monitoring points on expressways are far away, taking 3km as an example, it is necessary to increase video monitoring points to improve the accuracy of decision-making and ensure the scientific nature of decision-making while controlling costs.

[0050] This embodiment provides a method for optimizing the layout of high-speed monitoring points in an intelligent transportation system, including the following steps:

[0051] Obtain information of target monitoring road;

[0052] Determine the preliminary area for setting up each monitoring point based on road characteristics and traffic congestion model;

[0053] The increase in the number of congestion points identified and the change in traffic flow after the emergency lane is activated are used as positive indicators, and the monitoring cost is used as a negative indicator. The corresponding benefit evaluation function is constructed to form the objective function.

[0054] The number of monitoring points and the positional relationship of the existing monitoring points are used as constraints of the objective function;

[0055] In the preliminary region, based on the objective function and the constraint conditions, a quantum particle swarm optimization algorithm is used to solve the problem and obtain a first optimization solution;

[0056] In the preliminary area, several optimization schemes are obtained by adjusting the number of monitoring points and distributing them evenly, and the obtained optimization schemes are evaluated based on a multivariate linear regression model to obtain a second optimization scheme;

[0057] According to the monitoring optimization target, the first optimization scheme or the second optimization scheme is selected as the final optimization scheme.

[0058] First, analyze the road section characteristics and monitoring needs. It is necessary to determine whether there are service area ramps, slopes and sharp turns. Vehicles tend to gather in these areas, causing traffic congestion.

[0059] For example, a monitoring point can be arranged about 1 km near the ramp of the service area.

[0060] Then, the monitoring layout problem within 3km is converted into the monitoring layout problem within 3-x distance, where x refers to the distance between the two monitoring points. Since there are no special road conditions at other locations, in some examples, the monitoring points can be evenly distributed within the 3-x distance range. However, this solution generally does not consider the temporary use of emergency lanes in emergency situations and the correct monitoring of congestion.

[0061] In this embodiment, Figure 1 As shown in the figure, we take the increase in the number of congestion point identification, the reduction in congestion time, and the reduction rate of congestion events as positive indicators, and the monitoring cost as a negative indicator to establish the objective function. At the same time, we take benefit evaluation, cost, and monitoring quantity as our constraints.

[0062] The quantum swarm optimization algorithm (QPSO) is used to solve the optimization goal of the objective function. In addition, a linear regression model based on the entropy weight method is used to evaluate the various solutions, and the results obtained by different methods are compared to obtain the final optimization result.

[0063] Each step is described in detail below.

[0064] (1) Determine the location of the monitoring point

[0065] According to the road characteristics and traffic congestion model, monitoring points should be set up at the following key locations:

[0066] Traffic bottleneck areas (for example, the entrance and exit of ramps): Since vehicles tend to gather at the intersection of ramps, resulting in increased traffic flow, it is recommended to set up monitoring points near the ramps or at the entrances of the ramps to monitor traffic conditions in real time and detect congestion in a timely manner.

[0067] Areas where the number of lanes changes: In some sections of road, the number of lanes may change, especially where emergency lanes may be converted into regular lanes, monitoring points should be set up to monitor the impact of lane changes on traffic flow.

[0068] Sharp turns or ramp areas: These areas may affect vehicle speed and traffic density, and vehicles may slow down, resulting in increased traffic density. Therefore, these places are also the focus of monitoring.

[0069] (2) Layout density of monitoring points

[0070] Even distribution: If monitoring points are already set at the start and end of a road section, monitoring points can be appropriately set up in the middle section according to the road characteristics. For example, a monitoring point can be set at a certain distance (about 500 meters to 1000 meters) to ensure that the entire road section is within the monitoring range.

[0071] Covering monitoring blind spots: If there are monitoring blind spots due to terrain undulations or other factors on the road (such as tunnels or mountain obstructions), additional monitoring points should be added near these blind spots to ensure the integrity of the monitoring data.

[0072] The final placement strategy of the monitoring points can be determined by maximizing the positive index while controlling the cost. This can be achieved by optimizing the following objective function:

[0073] Max·ρBenifit-λ·Cost

[0074] Among them, Benefit represents positive indicators, including the increase in the number of congestion point identifications, the reduction in congestion time, and the reduction rate of congestion events, ρ represents the positive indicator coefficient, Cost represents the cost, and λ represents the negative indicator coefficient.

[0075] In this embodiment, the emergency lane can be opened based on the congestion observation status. After the emergency lane is opened, it can be quantified to obtain quantitative indicators such as average speed improvement rate, traffic density improvement rate, traffic flow improvement rate, congestion time reduction, and congestion event reduction rate. These quantitative indicators are used as positive indicators, such as Figure 2 shown.

[0076] Assume that the number of monitoring points is n, and the following positive indicators can be established at a certain moment.

[0077] a. Increase the number of congestion point identifications:

[0078] Z point =n·P average ·Y all

[0079] Among them, Z point refers to the number of increased congestion points, n is the number of increased monitoring points, P average is the average probability (average accuracy) that the monitoring point can identify the congestion point, Y all The total number of actual congestion points.

[0080] b. Increase in the number of emergency lanes

[0081] if.M(KNN z ,SVM z ,LWR z )>1,F=1

[0082]

[0083] The above formula describes the voting mechanism formula of the KNN, SVM, and LWR models based on random forest.

[0084] Where M is the voting function for the three algorithms, that is, the results of the three algorithms are logically calculated. When the KNN model predicts congestion, KNN z Set to 1, otherwise set to 0. SVM z ,LWR z Similarly, if the M voting function is greater than 1, F is set to 1, indicating that the emergency lane is open, otherwise it is 0 and the emergency lane is not open. z It refers to the increase in the number of emergency lanes that are put into use.

[0085] In addition, this embodiment provides a method for determining the activation of an emergency lane, comprising the following steps:

[0086] Obtain historical surveillance videos of each monitoring point and perform video processing on the historical surveillance videos;

[0087] Extracting vehicle information from the processed video, determining the number and speed of each type of vehicle based on the extracted vehicle information, redefining and calculating traffic flow parameters, the traffic flow parameters including vehicle flow density, speed and traffic flow, and determining the changing trend of the traffic flow parameters over time;

[0088] According to the traffic flow parameters, the initial speed threshold for the opening of the emergency lane is analyzed, and the parameters of the congestion probability distribution are estimated to build a traffic flow interruption probability model, calculate the probability that the road capacity is less than the observed flow, and determine the final threshold for the opening of the emergency lane;

[0089] Based on traffic flow parameters and final thresholds, multiple classification models are trained;

[0090] According to the traffic flow parameters, the traffic congestion warning model is trained;

[0091] Obtain surveillance video of the target monitoring point within the target time period, perform video processing and data extraction, and use the trained traffic congestion warning model to process based on the calculated real-time traffic flow parameters to predict traffic density and warning points;

[0092] Based on the prediction results, a variety of pre-trained classification models are used to decide whether to open the emergency lane. The results of each decision are integrated to obtain the final judgment result on whether the emergency lane needs to be opened.

[0093] The process of extracting vehicle information from the processed video includes: extracting vehicle information from the processed video using a target detection algorithm, and introducing a Kalman filter algorithm to improve recognition accuracy and target tracking accuracy.

[0094] In this embodiment, the process of redefining and calculating traffic flow parameters includes: the traffic flow density is the total number of vehicles appearing in one frame;

[0095] The speed calculation process includes: calculating the speed of the same vehicle by tracking the position change in consecutive frames, that is, assigning a unique ID to each vehicle and recording its position in consecutive frames, and obtaining the speed of the vehicle by calculating the displacement of the vehicle between consecutive frames and dividing the time interval;

[0096] The traffic flow is the number of vehicles passing through the corresponding monitoring point per unit time.

[0097] In this embodiment, the warning process of the traffic congestion warning model includes: evaluating whether the current traffic density is close to the critical density based on the traffic density data recorded by the relevant monitoring points, and issuing a traffic congestion alarm if the relevant monitoring points all reach the adjacent interval of the critical density and the density growth exceeds the set value;

[0098] By calculating the rate of change of the flow at relevant monitoring points, if the flow rate drops by more than a threshold and the density increases, the risk of congestion increases, and a traffic congestion alarm is issued;

[0099] Set the prediction time, and combine it with the current traffic density to determine whether congestion will occur within the set prediction time. If so, issue a traffic congestion alarm.

[0100] In this embodiment, the traffic congestion warning model is an LWR model. After the traffic congestion warning model is trained, it is cross-validated using the TPI-LSTM method. The specific process includes: based on dynamic vehicle position information, the operating speed of roads with different functional levels is obtained, and the weight of the road in the entire network is calculated according to the different road functions and traffic data. Finally, the index indicator value is converted through human perception and judgment of congestion. Based on the calculation method of the sliding window, the index indicator value is divided to obtain training samples. Based on the training samples, the long short-term memory network neural network model is used in combination with historical data to predict whether a continuous warning state will occur, and multiple prediction results are obtained.

[0101] The prediction results of the LWR model can be used within a certain period of time, and the prediction results of the LSTM model can be used for predictions exceeding the set period of time.

[0102] In this embodiment, the process of determining the final threshold for opening the emergency lane includes: establishing a congestion index model based on traffic flow parameters, analyzing the initial speed threshold for opening and activating the emergency lane, estimating the parameters of the congestion probability distribution through the maximum likelihood estimation function, thereby constructing a traffic flow interruption probability model, calculating the probability that the road capacity is less than the observed flow, and visualizing the relationship between the congestion probability and the traffic flow, so as to determine the traffic flow for activating the emergency lane.

[0103] In this embodiment, the process of establishing a congestion index model includes: grading the traffic status and road operation status according to the traffic density, traffic flow and speed, determining the traffic congestion index, calculating the index indicator value, and drawing a visual line graph of its change over time.

[0104] In this embodiment, a traffic flow interruption probability model is constructed to calculate the probability that the road capacity is less than the observed traffic flow, and the process of visualizing the relationship between the congestion probability and the traffic flow includes: establishing a traffic flow interruption probability model:

[0105]

[0106] Where q is the number of vehicles passing through each lane within a set time, c is the capacity, i.e., the traffic capacity; P(c, q) is the probability that the capacity is less than the observed traffic; q i is the traffic flow observed at interval i, which represents the traffic flow before the speed decreases; ki refers to q>q i The number of time intervals; d i It means that the traffic flow reaches q i The number of time intervals when each congestion is considered separately, d i =1; B{is the set of congestion intervals B1, B2, ...};

[0107] The distribution parameters are estimated by applying the maximum likelihood function;

[0108] Based on historical data, the relationship between traffic flow parameters is determined and a scatter plot is drawn. Based on the relationship, the speed threshold is preliminarily estimated, and the traffic flow interruption probability model is solved to calculate the congestion probability. A scatter plot of the relationship between the congestion probability and the flow rate is drawn. Based on the relationship scatter plot, the threshold for activating the emergency lane is determined.

[0109] In this embodiment, the process of training multiple classification models includes:

[0110] The final threshold determined by the traffic flow interruption probability model is used as the training set and as the input of the KNN model, SVM model and LWR traffic flow model for decision training.

[0111] The process of fusing the decision results includes: using the idea of ​​random forest voting, fusing the decision results of the KNN model, SVM model and LWR traffic flow model, and completing the judgment of whether to open the emergency lane.

[0112] c. Reduction in congestion time, reduction rate of congestion incidents, and increase rate of average speed

[0113] According to the established model, after obtaining the data on the increase in the number of emergency lanes activated, the corresponding indicators such as the reduction in congestion time and the reduction rate of congestion events can be obtained. The quantified indicators are used as positive indicators of the optimization objective function.

[0114] According to the above indicators such as the increase in the number of congestion point identifications, the increase in the number of emergency lanes activated, the reduction in congestion time, the reduction rate of congestion incidents, and the average speed improvement rate, a corresponding benefit evaluation function can be written as the first term of the objective function.

[0115] Benefits = a·(Z point )+b·(Δt)+c·(Δf)+d·(ΔS)

[0116] Among them, Δt is the total congestion time reduced by optimizing the layout, Δf is the reduction rate of congestion events after improvement, and a, b, c, and d are the weight coefficients of the impact of these four benefits on the total benefit.

[0117] Negative indicator model establishment

[0118] The cost function can calculate the total cost based on the number and type of monitoring points and maintenance costs. Assuming that the installation and operation costs of each monitoring point are fixed, the total cost can be expressed as:

[0119]

[0120] Where n is the number of monitoring points, c is the fixed cost of each monitoring point, and m i is the maintenance cost of the ith monitoring point.

[0121] Constraint setting:

[0122] It is known that at least one camera needs to be installed within a distance of 1Km±200m near the original fourth monitoring point, the monitoring interval within the remaining distance needs to be greater than 500m, and the number of cameras between the original third monitoring point and the fourth monitoring point is less than or equal to 4. The above positive indicator function and negative indicator function are also constraints of the objective function.

[0123] In order to ensure the persuasiveness and accuracy of the results, we use another idea and method to further solve and verify the results, namely the multiple linear regression scheme evaluation method.

[0124] In this approach, the problem is simplified and basic simplifications and assumptions are made:

[0125] 1. According to the analysis of terrain characteristics, a monitoring point is set at about 1km±200m near the ramp of the fourth point. This continuity condition is simplified to at least one monitoring point at 1.2km, 1km, and 0.8km near the fourth point.

[0126] 2. Based on the above assumptions and simplifications, the remaining 3km of road is divided into three cases: 1.8km, 2km, and 2.2km. Since the existing monitoring points are approximately 1km apart, in order to control costs, it is assumed that there are at most 2 cameras in this remaining road.

[0127] Based on the above assumptions, several optimization schemes can be obtained, and each scheme is listed below.

[0128] Solution 1: Set up one monitoring point 0.8 km near the fourth point, and two monitoring points evenly distributed on the remaining sections;

[0129] Solution 2: Set up one monitoring point 0.8 km near the fourth point, and evenly distribute one monitoring point on the remaining sections;

[0130] Solution 3: Set up one monitoring point 1km away from the fourth point, and evenly distribute two monitoring points on the remaining sections;

[0131] Solution 4: Set up one monitoring point 1km away from the fourth point, and evenly distribute one monitoring point on the remaining sections;

[0132] Solution 5: Set up one monitoring point 1.2 km near the fourth point, and two monitoring points evenly distributed on the remaining sections

[0133] Plan 6: Set up one monitoring point 1.2 km near the fourth point, and evenly distribute one monitoring point on the remaining sections;

[0134] Based on the above schemes, the positive and negative indicators of each scheme can be calculated.

[0135] The multiple linear regression model based on entropy weight method is used for scheme evaluation and decision making.

[0136] The expression of the multiple linear regression model is:

[0137] f(x)=k T x+b

[0138] y=k 1 x 1 +k 2 x 2 +...+k d x d +b×1

[0139] Among them, x is the input vector, which contains multiple features (independent variables); f(x) is the output or response of the model (the predicted target variable); k is the feature weight; b is the intercept or bias of the model; our goal is to make f(x) as close as possible to the true observation y by learning k and b.

[0140] Determination of the weight of each indicator feature:

[0141] In order to determine the feature weight k, we use the entropy weight method to process the indicators and obtain the weights. The entropy weight method is a method that processes known data to obtain the weights of influencing factors. Its basic idea is to determine the objective weights based on the variability of the indicators. The advantage of the entropy weight method is that it determines the indicator weights based on the degree of variation of the indicator values ​​of each indicator. It is an objective weighting method that avoids the deviation caused by human factors. Compared with those subjective assignment methods, it has higher accuracy and stronger objectivity, and can better explain the results obtained.

[0142] In the process of model building, we first need to make the indicators positive, because different indicators have different meanings. Some indicators are better the bigger they are, which are called positive indicators; some indicators are better the smaller they are, which are called negative indicators; and some indicators are best at a certain point or interval, which are called moderate indicators. For the convenience of evaluation, all indicators should be processed in the same direction.

[0143] Among them, positive indicators:

[0144] x′ ij =x ij

[0145] Negative indicators:

[0146] x i ' j =max(x ij )-x ij

[0147] Moderation indicators:

[0148]

[0149] In this question, the increase in the number of congestion point identifications, the reduction in congestion time, and the reduction rate of congestion events are positive indicators, and the cost is a negative indicator.

[0150] Secondly, data standardization is carried out, and indicators are scaled so that they fall within a specific range, thereby removing the impact of the dimension and allowing indicators of different units or magnitudes to be compared and weighted.

[0151]

[0152] Finally, calculate the information entropy:

[0153]

[0154] Finally, a multivariate linear regression model is used to perform weighting and finally obtain new indicators for program decision-making.

[0155] In this embodiment, it is assumed that the installation and subsequent maintenance cost of each camera is RMB 100,000. The indicator comparison table of each solution is shown in Table 1.

[0156] Table 1 Result indicators

[0157]

[0158] After solving the total index of entropy weight method and multivariate linear regression, in the evaluation of six traffic congestion relief plans, Plan 1 won with the highest score of 0.2269, showing the best comprehensive benefit. Despite the high cost, it is effective in reducing congestion points and time. Plans 3 and 5 also performed well, with scores of 0.2150 and 0.1910 respectively, and they found a good balance between cost and benefit. In contrast, Plan 6 scored the lowest, 0.1136, and the overall benefit was not ideal. Plans 2 and 4 also scored relatively low, 0.1326 and 0.1205 respectively, indicating that a simple low-cost strategy does not always bring the highest benefit.

[0159] In summary, Option 1 is the most efficient option. Figure 4 These assessment results emphasize that when formulating mitigation measures, costs and benefits should be considered comprehensively to achieve the best allocation of resources and efficient optimization of traffic management.

[0160] According to the results obtained by the above method, three additional monitoring points should be installed between the third point and the fourth point, and their intervals from the third point are 766m, 766m, 766m (evenly distributed) and 700m respectively.

[0161] Based on the quantum particle swarm algorithm, the core of the QPSO algorithm is to simulate the behavior of quantum particles and use quantum superposition and quantum tunneling effects to enhance the search process. In QPSO, particles no longer have a definite position and speed, but exist in the search space in the form of a probability cloud. This means that particles can exist in multiple locations at the same time, thereby increasing the coverage of the search space.

[0162] The specific process includes:

[0163] 1. Initialization: Randomly initialize the position and probability distribution of each particle in the particle swarm.

[0164] 2. Evaluate fitness: calculate the fitness value of each particle.

[0165] 3. Update individual extreme values: If the fitness of the current particle is better than the previously recorded individual extreme value, update the individual extreme value.

[0166]

[0167] Where μ is the current position of the particle, σ is the standard deviation, and x is the new position.

[0168] 4. Update the global extreme value: If the fitness of the current particle is better than the global extreme value, update the global extreme value.

[0169] 5. Update probability distribution: Update the probability distribution of each particle based on individual extreme values ​​and global extreme values.

[0170] 6. Repeat steps 2-5 until the termination condition (such as number of iterations, fitness threshold, etc.) is met.

[0171] Assume that the particle population size is N, the iteration process is the tth step, the particle moves in the D-dimensional space, the potential well of the particle in the d-dimensional space is Pid(t), and the update equation of the particle x(t) is as follows:

[0172]

[0173] p id (t) = φ(t)·p id (t)+(1-φ(t))·G d (t)

[0174]

[0175] Where P(t) is the current optimal position of the particle, G(t) and C(t) are the global optimal position and average optimal position of the population. and u(t) are uniformly distributed random numbers in the interval [0, 1]: β is the contraction-expansion factor and is the only control parameter of QPSO.

[0176] Through quantum superposition and quantum tunneling effects, QPSO can explore the global search space more effectively and reduce the risk of falling into local optimality. QPSO allows particles to exist in multiple locations at the same time, thereby improving search efficiency. QPSO reduces the possibility of premature convergence through quantum behavior, allowing the algorithm to maintain good diversity during the iteration process.

[0177] Table 1 QPSO iterative process parameters display

[0178]

[0179] As can be seen from the table, the quantum particle swarm optimization (QPSO) algorithm demonstrated its strong ability in parameter optimization after 50 iterations. At the beginning of the algorithm, the value of parameter C was large, while the value of parameter gamma was small, which may reflect the algorithm's extensive exploration of the parameter space in the initial stage. As the iterations deepened, parameter C gradually decreased, while gamma remained at a low level, indicating that the algorithm was gradually narrowing the search range and looking for more accurate parameter values. During the iteration process, the fitness (accuracy) of the algorithm steadily increased, and finally reached the highest point at the 49th and 50th iterations, with an accuracy of 84.07%. This result not only proves the effectiveness of the QPSO algorithm in finding the optimal parameter combination, but also shows its stability and convergence during the iteration process. Finally, the algorithm converged to parameters C = 6.557312481545432 and gamma = 0.001, which represents the best solution found in the given optimization problem. This performance of the QPSO algorithm highlights its potential and value in solving complex optimization problems.

[0180] like Figure 3 As shown, according to the solution results of the quantum ion swarm optimization algorithm (QPSO), four monitoring points should be added at the third and fourth points. Starting from the third point, their intervals are 639m, 639m, 579m, 611m, and 532m respectively.

[0181] The results obtained by the two methods are compared, as shown in Table 3:

[0182] Table 3 Method comparison

[0183]

[0184] Taking all factors into consideration, if the budget is sufficient and higher efficiency and effect are sought, the quantum particle swarm optimization algorithm under multi-objective optimization may be a better choice, because it is superior to method 2 in terms of the number of monitoring points, the number of increased congestion point identification, and the reduction in congestion time. However, if the budget is limited and a balance needs to be found between cost and efficiency, the multivariate linear regression model based on the entropy weight method provides a cost-effective solution.

[0185] An intelligent transportation system high-speed monitoring point layout optimization system, comprising:

[0186] A monitoring point preliminary determination module is configured to obtain information of a target monitoring road and determine a preliminary area for setting each monitoring point according to road characteristics and a traffic congestion model;

[0187] The objective function construction module is configured to use the increase in the number of congestion point identifications and the change in traffic flow after the emergency lane is activated as positive indicators, and the monitoring cost as a negative indicator, and to construct a corresponding benefit evaluation function to form an objective function;

[0188] A constraint module is configured to use the monitoring quantity and the position relationship of the existing monitoring points as constraint conditions of the objective function;

[0189] The optimization evaluation module is configured to solve the problem in the preliminary area based on the objective function and the constraints using a quantum particle swarm optimization algorithm to obtain a first optimization solution; obtain several optimization solutions in the preliminary area by adjusting the number of monitoring points and distributing them evenly, and evaluate the obtained optimization solutions based on a multivariate linear regression model to obtain a second optimization solution; and select the first optimization solution or the second optimization solution as the final optimization solution according to the monitoring optimization target.

[0190] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0191] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0192] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0193] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0194] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made by those skilled in the art within the spirit and principle of the present invention without creative labor shall be included in the protection scope of the present invention.

Claims

1. A method for optimizing the layout of high-speed monitoring points in an intelligent transportation system, characterized in that: The following steps are involved: Obtain information of target monitoring road; Determine the preliminary area for setting up each monitoring point based on road characteristics and traffic congestion model; The increase in the number of congestion points identified and the change in traffic flow after the emergency lane is activated are used as positive indicators, and the monitoring cost is used as a negative indicator. The corresponding benefit evaluation function is constructed to form the objective function. The number of monitoring points and the positional relationship of the existing monitoring points are used as constraints of the objective function; In the preliminary region, based on the objective function and the constraint conditions, a quantum particle swarm optimization algorithm is used to solve the problem and obtain a first optimization solution; In the preliminary area, several optimization schemes are obtained by adjusting the number of monitoring points and distributing them evenly, and the obtained optimization schemes are evaluated based on a multivariate linear regression model to obtain a second optimization scheme; According to the monitoring optimization target, the first optimization scheme or the second optimization scheme is selected as the final optimization scheme.

2. The method for optimizing the layout of high-speed monitoring points in an intelligent transportation system according to claim 1, characterized in that: The preliminary areas for setting up each monitoring point include traffic bottleneck areas, areas with changed number of lanes, sharp turns or ramp areas, and monitoring blind spots.

3. The method for optimizing the layout of high-speed monitoring points in an intelligent transportation system as claimed in claim 1, characterized in that: The positive indicators of traffic flow changes after the emergency lane is activated include the reduction in congestion time, the reduction rate of congestion events and the average speed increase rate, which are obtained based on the increase in the number of emergency lanes activated and the comparison of data before and after the emergency lane is activated.

4. The method for optimizing the layout of high-speed monitoring points in an intelligent transportation system according to claim 1 or 3, characterized in that: The objective function is Max·ρBenifit-λ·Cost; Among them, Benefit represents the positive indicator, ρ represents the positive indicator coefficient, Cost represents the cost, and λ represents the negative indicator coefficient.

5. The method for optimizing the layout of high-speed monitoring points in an intelligent transportation system as claimed in claim 3, characterized in that: The benefit evaluation function of the positive indicator is: Benefits=a·(Z point )+b·(Δt)+c·(Δf)+d·(ΔS); Among them, Z point The number of congestion points identified increases, Δt is the total congestion time reduced by optimizing the distribution of points, Δf is the reduction rate of congestion events, ΔS is the average speed improvement rate, and a, b, c, and d are the weight coefficients of the impact of these four benefits on the total benefit.

6. The method for optimizing the layout of high-speed monitoring points in an intelligent transportation system as claimed in claim 1, characterized in that: The function of the negative indicator calculates the total cost based on the number and type of monitoring points and the maintenance cost. Assuming that the installation and operation costs of each monitoring point are fixed, the total cost is expressed as: Where n is the number of monitoring points, c is the fixed cost of each monitoring point, and m i is the maintenance cost of the ith monitoring point.

7. The method for optimizing the layout of high-speed monitoring points in an intelligent transportation system as claimed in claim 1, characterized in that: The process of using the monitoring quantity and the position relationship of the existing monitoring points as the constraint conditions of the objective function includes: The total number of monitoring points on the target monitoring road, the maximum distance from the existing monitoring points, and the setting interval limit of the monitoring points are set as constraints.

8. The method for optimizing the layout of high-speed monitoring points in an intelligent transportation system as claimed in claim 1, characterized in that: The process of evaluating each optimization scheme based on the multivariate linear regression model includes: arranging all feasible optimization schemes and calculating the positive and negative indicators of each optimization scheme respectively; Construct a multiple linear regression model, use the entropy weight method to process the indicators, and obtain the weights of the multiple linear regression model; A multivariate linear regression model is used to perform weighting based on weights, and finally a new calculation result of the indicator is obtained. The solution decision is made based on the calculation result, and the optimal solution is selected.

9. An intelligent transportation system high-speed monitoring point layout optimization system, characterized in that: include: A monitoring point preliminary determination module is configured to obtain information of a target monitoring road and determine a preliminary area for setting each monitoring point according to road characteristics and a traffic congestion model; The objective function construction module is configured to use the increase in the number of congestion point identifications and the change in traffic flow after the emergency lane is activated as positive indicators, and the monitoring cost as a negative indicator, and to construct a corresponding benefit evaluation function to form an objective function; A constraint module is configured to use the monitoring quantity and the position relationship of the existing monitoring points as constraint conditions of the objective function; The optimization evaluation module is configured to solve the problem in the preliminary area based on the objective function and the constraint conditions by using the quantum particle swarm optimization algorithm to obtain a first optimization solution; in the preliminary area, several optimization solutions are obtained by adjusting the number of monitoring points and distributing them evenly, and the obtained optimization solutions are evaluated based on a multivariate linear regression model to obtain a second optimization solution; According to the monitoring optimization target, the first optimization scheme or the second optimization scheme is selected as the final optimization scheme.

10. An electronic device, characterized in that: The method comprises a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the steps in the method according to any one of claims 1 to 8 are completed.

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