Intelligent Traffic Signal Management and Control System Based on Machine Learning

Abstract

The present invention relates to the technical field of traffic signal management, and particularly to an intelligent traffic signal management and control system based on machine learning, which includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the following steps are implemented: for any phase of the target intersection at the moment to be predicted, at least one pre-intersection of the target intersection in any phase is obtained, and according to the possibility index of the traffic flow of each pre-intersection passing through the target intersection at the moment to be predicted, the traffic flow prediction value of any phase at the moment to be predicted is obtained. By combining the pedestrian flow prediction value of any phase at the moment to be predicted, the total flow prediction value is obtained; the total flow prediction value of each phase of the target intersection at the moment to be predicted is obtained, and the green light duration of each phase of the target intersection at the moment to be predicted is dynamically adjusted, which ensures the timeliness of traffic signal management and reduces the occurrence of traffic congestion problems.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic signal management, and particularly to an intelligent traffic signal management and control system based on machine learning. Background Art

[0002] Traffic problems have always been an important part of public issues. Especially with the increase in private vehicles, the fixed traffic signal mode can no longer maintain road smoothness and ensure traffic safety. Therefore, traffic signal management systems have become increasingly intelligent and can dynamically adjust the signal light mode according to the current road congestion situation.

[0003] Existing intelligent traffic signal management systems usually extend or shorten the green light duration according to the currently monitored traffic flow. For example, when there are continuously passing vehicles at an intersection, the green light duration will be extended by a fixed unit until a threshold is reached. Although existing intelligent traffic signal management systems can, to a certain extent, solve the traffic congestion problem caused by insufficient green light duration, the adjustment accuracy is relatively low. It may occur that the traffic flow decreases after the duration is increased, resulting in a decrease in the utilization rate of the green light. And the adjustment is also made after the traffic flow is recognized to be large, which may also lead to vehicle congestion due to untimely dredging.

[0004] Therefore, how to improve the dynamic adjustment accuracy of the signal light mode has become an urgent problem to be solved. Summary of the Invention

[0005] In view of this, an embodiment of the present invention provides an intelligent traffic signal management and control system based on machine learning to solve the problem of how to improve the dynamic adjustment accuracy of the signal light mode.

[0006] An intelligent traffic signal management and control system based on machine learning provided in an embodiment of the present invention includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the following steps are implemented:

[0007] For any phase of a target intersection at a to-be-predicted moment, obtain at least one pre-intersection of the target intersection in the any phase, where, in the vehicle driving route, the pre-intersection is passed first and then the target intersection;

[0008] For any pre-intersection, obtain at least one adjacent intersection of the any pre-intersection, where the adjacent intersection refers to the intersection where the traffic flow of the any pre-intersection is diverted. According to the historical traffic flow monitoring values and historical weather monitoring data of each adjacent intersection, obtain the possibility index of the traffic flow of the any pre-intersection passing through the target intersection at the to-be-predicted moment;

[0009] Obtain the predicted traffic flow value of any phase at the prediction moment according to the possibility index of the traffic flow of each upstream intersection passing through the target intersection at the prediction moment. Obtain the predicted pedestrian flow value of any phase at the prediction moment according to the historical pedestrian flow monitoring values of any phase of the target intersection within a preset time period. Combine the predicted traffic flow value and the predicted pedestrian flow value to obtain the total flow predicted value of any phase of the target intersection at the prediction moment;

[0010] Obtain the total flow predicted value of each phase of the target intersection at the prediction moment, and dynamically adjust the green light duration of each phase of the target intersection at the prediction moment according to the total flow predicted value of each phase of the target intersection at the prediction moment.

[0011] The beneficial effects of the embodiments of the present invention compared with the prior art are as follows:

[0012] The present invention combines the influence characteristics of time and weather on traffic flow, analyzes the possibility of the traffic flow of each upstream intersection of the target intersection being diverted to different intersections, that is, the possibility index of the traffic flow of any upstream intersection passing through the target intersection at the prediction moment, fully considers the regular characteristics of people's daily travel, and then combines the traffic flow data of each upstream intersection to predict in advance the traffic flow data of the target intersection at the prediction moment, that is, the predicted traffic flow value of each phase of the target intersection at the prediction moment, improving the accuracy of traffic flow prediction. Since the traffic signal at the intersection not only needs to ensure the normal driving of vehicles but also consider the normal passage of pedestrians, therefore, further combine the predicted pedestrian flow value at the prediction moment of the target intersection to predict the overall traffic flow of the target intersection, which is used to represent the total flow predicted value of each phase of the target intersection at the prediction moment, so as to adjust in advance the green light duration of each phase of the target intersection according to the prediction situation of the target intersection to ensure the timeliness of traffic signal management. At the same time, the occurrence of traffic congestion problems is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0014] Figure 1 It is a method flow chart of an intelligent traffic signal management and control method based on machine learning provided in Embodiment 1 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0015] Embodiments of the present disclosure will be described in detail below. Examples of the embodiments are shown in the accompanying drawings. The embodiments described below by referring to the accompanying drawings are exemplary and are intended to explain the present disclosure, and should not be construed as a limitation of the present disclosure.

[0016] It should be noted that the terms "first", "second", etc. in the specification of the present disclosure and the above-mentioned accompanying drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present disclosure described here can be implemented in an order other than those illustrated or described here. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present disclosure.

[0017] In order to illustrate the technical solution of the present invention, it will be described below through specific embodiments.

[0018] The specific scenario targeted by the present invention is as follows: In the process of the existing traffic signal management system dynamically adjusting the signal light duration according to the traffic flow situation, when traffic congestion is recognized and then the green light duration is adjusted, it is often impossible to dredge vehicles and people in time, resulting in a poor solution to the traffic congestion problem after adjustment, and the control of the green light duration is not precise enough, and still relies on the assistance of traffic police.

[0019] An embodiment of the present invention provides an intelligent traffic signal management and control system based on machine learning, including a processor and a memory. The processor executes a computer program stored in the memory to implement an intelligent traffic signal management and control method based on machine learning, as Figure 1 shown. The intelligent traffic signal management and control method based on machine learning includes the following steps:

[0020] Step S101, for any phase of the target intersection at the moment to be predicted, obtain at least one pre-intersection of the target intersection in any phase, where the vehicle driving route passes through the pre-intersection first and then passes through the target intersection.

[0021] The existing traffic signal management system ensures smooth vehicle flow at intersections and allows pedestrians to cross the road in a timely manner by adjusting the duration of green and red lights. Therefore, in the embodiments of the present invention, it is necessary to monitor the traffic flow and pedestrian flow at each intersection in real time to facilitate the subsequent dynamic regulation of traffic lights. Also, since different lanes at an intersection represent different directions of vehicle turning, that is, the places where the vehicles will continue to drive after passing through are different. In order to predict the traffic flow at the intersection based on the traffic flow and the possible driving routes of vehicles, it is necessary to monitor the traffic flow of each lane at the intersection separately. Therefore, in the embodiments of the present invention, geomagnetic sensors are installed on each lane at the intersection, and the number of vehicles passing through is counted every 10 seconds, which is recorded as the traffic flow monitoring value. At the same time, thermal imagers are installed at the entrance of each crosswalk at the intersection, and the number of pedestrians passing through is also counted every 10 seconds, which is recorded as the pedestrian flow monitoring value.

[0022] In the embodiments of the present invention, any intersection is denoted as the target intersection and used as the analysis object for dynamically regulating its traffic lights. Since each phase at an intersection has a fixed direction and not all other intersections can reach any phase of the target intersection, but different intersections are interconnected. When a vehicle passes through a previous intersection, it is very likely to pass through other intersections connected to it. Therefore, the traffic flow situation of the target intersection can be analyzed based on the traffic flow monitoring value of the previous intersection connected to the target intersection. Then, the previous intersections adjacent to each phase of the target intersection on the vehicle driving route are obtained respectively, that is, the vehicle passes through the previous intersection first and then the target intersection on the driving route, and thus the traffic flow situation of the target intersection can be predicted based on the traffic flow situation of the previous intersection.

[0023] It should be noted that phase (signal phase) is a term in the traffic system. During a signal cycle, if one or several vehicle flows obtain exactly the same signal light color display at any moment, then the continuous time sequence in which they obtain different light colors (green light, yellow light, full red) is called a signal phase.

[0024] Since there are at least two phases at each intersection, in the embodiments of the present invention, any phase of the target intersection is taken as an example for analysis, and the analysis process for other phases is the same. At the same time, in order to avoid the situation of adjusting the green light duration after traffic congestion is identified, in the embodiments of the present invention, the current moment is used as the time point for prediction at the moment to be predicted. Among them, the current moment refers to the previous sampling moment of the moment to be predicted, that is, the current moment is t and the moment to be predicted is t + 1. Therefore, for any phase of the target intersection at the moment to be predicted, first, at least one previous intersection of the target intersection in any phase is obtained for subsequent analysis of the possibility that the vehicle passing through the previous intersection will then pass through the target intersection.

[0025] Step S102. For any pre - intersection, obtain at least one adjacent intersection of any pre - intersection. An adjacent intersection refers to an intersection where the traffic flow of any pre - intersection is diverted to. According to the historical traffic flow monitoring values and historical weather monitoring data of each adjacent intersection, obtain the possibility index that the traffic flow of any pre - intersection passes through the target intersection at the moment to be predicted.

[0026] Since the traffic flow of a pre - intersection may be diverted to different intersections, the different intersections where the traffic flow is diverted to are used as the adjacent intersections of the pre - intersection. Among them, the adjacent intersections of the pre - intersection also include the target intersection. Therefore, it is necessary to evaluate the traffic flow situation of each adjacent intersection where the traffic flow of the pre - intersection is diverted according to the historical traffic flow situation of the adjacent intersections of the pre - intersection, so as to predict the possibility that the traffic flow of each pre - intersection passes through the target intersection at the moment to be predicted.

[0027] Since the vehicle driving route is highly correlated with the intersection location, time, and weather conditions, the possibility that the traffic flow of each pre - intersection passes through the target intersection at the moment to be predicted can be analyzed by combining the time and weather conditions corresponding to the historical traffic flow monitoring values. In the embodiment of the present invention, taking any pre - intersection of the target intersection as an example, first obtain at least one adjacent intersection of any pre - intersection. Then, for any adjacent intersection, according to the historical traffic flow monitoring values and historical weather monitoring data within one month before the moment to be predicted of any adjacent intersection, obtain the historical average traffic flow of any adjacent intersection, which is used to characterize the diversion characteristics of any pre - intersection to any adjacent intersection before the moment to be predicted. The larger the historical average traffic flow, the larger the traffic flow of any adjacent intersection at the moment to be predicted.

[0028] Considering that time is a key factor affecting traffic flow and vehicle driving routes, that is, the traffic flow situation at different times of a day will have obvious differences. For example, the traffic flow during commuting hours will increase significantly, while the traffic flow during noon and evening rest times will decrease greatly. Then, the closer the time point between the historical traffic flow monitoring value of any adjacent intersection within one month before the moment to be predicted and the moment to be predicted, the closer the historical traffic flow monitoring value is to the traffic flow situation at the moment to be predicted. In addition, since the traffic flow change rules at different times of a week are also different. Generally speaking, people's travel activities from Monday to Friday are related to work and school hours, while weekend activities do not conform to the daily work and school hours. Therefore, the more consistent the week number corresponding to the historical traffic flow monitoring value and the week number corresponding to the moment to be predicted, the more the historical traffic flow monitoring value can reflect the traffic flow situation at the moment to be predicted. Therefore, in the embodiment of the present invention, according to the historical traffic flow monitoring values at each sampling moment within a preset historical period (within one month before the moment to be predicted) of any adjacent intersection, respectively obtain the first similarity degree in time between each historical traffic flow monitoring value and the traffic flow at the moment to be predicted.

[0029] Among them, the method for obtaining the first similarity degree in time between each historical traffic flow monitoring value of any adjacent intersection and the traffic flow at the moment to be predicted is as follows:

[0030] For any historical traffic flow monitoring value of the any adjacent intersection within a preset historical period, record the sampling moment corresponding to the any historical traffic flow monitoring value as the target moment, calculate the absolute value of the time difference between the target moment and the moment to be predicted, obtain the first ratio between the absolute value of the time difference and the maximum moment, and use the difference between the constant 1 and the first ratio as the degree of closeness in time;

[0031] Obtain the week number to which the target moment belongs and the week number to which the moment to be predicted belongs, calculate the absolute value of the difference in week numbers between the week number to which the target moment belongs and the week number to which the moment to be predicted belongs, obtain the second ratio between the absolute value of the difference in week numbers and the maximum week number, and use the difference between the constant 1 and the second ratio as the degree of consistency in week numbers;

[0032] Take the product of the degree of closeness in time and the degree of consistency in week numbers as the first similarity degree in time between the any historical traffic flow monitoring value and the traffic flow at the moment to be predicted.

[0033] In an embodiment, taking the i-th historical traffic flow monitoring value of the c-th adjacent intersection as an example, the calculation formula for the first similarity degree in time between the i-th historical traffic flow monitoring value and the traffic flow at the moment to be predicted is:

[0034]

[0035] Among them, represents the first similarity degree in time between the i-th historical traffic flow monitoring value of the c-th adjacent intersection and the traffic flow at the moment to be predicted, 1 represents a constant, represents the sampling moment of the i-th historical traffic flow monitoring value of the c-th adjacent intersection (i.e., what time and minute within a day, such as 9:10 am), represents the moment to be predicted, represents the maximum sampling moment ( ), | | represents the absolute value symbol, represents the week number to which the i-th historical traffic flow monitoring value of the c-th adjacent intersection belongs (such as Monday, Tuesday, etc.), represents the week number to which the moment to be predicted belongs, represents the maximum week number ( ).

[0036] It should be noted that, It is used to characterize the closeness of the sampling moment of the i-th historical traffic flow monitoring value to the moment to be predicted at a specific time point, that is, the moment closeness. The smaller the value of , the closer the sampling moment of the i-th historical traffic flow monitoring value is to the moment to be predicted at the time point, and the greater the first similarity degree in time between the i-th historical traffic flow monitoring value and the traffic flow at the moment to be predicted. It is used to characterize whether the week number of the i-th historical traffic flow monitoring value is the same as the week number of the moment to be predicted. The smaller the value of , the more consistent the week number of the i-th historical traffic flow monitoring value is with the week number of the moment to be predicted, and the greater the first similarity degree in time between the i-th historical traffic flow monitoring value and the traffic flow at the moment to be predicted.

[0037] Considering that weather is another important factor affecting traffic flow, and rainfall is the main influencing factor, because rainfall will greatly affect people's travel modes and travel decisions. For example, compared with sunny days, people are more inclined to drive when it rains heavily, and their willingness to engage in non-essential outdoor activities will be greatly reduced. Therefore, in the embodiments of the present invention, according to the historical rainfall at each sampling moment of any adjacent intersection within a preset historical period (within one month before the moment to be predicted), the second similarity degree in weather between each historical traffic flow monitoring value and the traffic flow at the moment to be predicted is obtained respectively.

[0038] Among them, the method for obtaining the second similarity degree in weather between any historical traffic flow monitoring value and the traffic flow at the moment to be predicted is as follows: according to the historical rainfall at each sampling moment of the any adjacent intersection within the preset historical period, obtain the rainfall range between the maximum historical rainfall and the minimum historical rainfall; calculate the absolute value of the rainfall difference between the historical rainfall at the target moment and the rainfall at the moment to be predicted, calculate the third ratio between the absolute value of the rainfall difference and the rainfall range, and use the difference between the constant 1 and the third ratio as the second similarity degree in weather between the any historical traffic flow monitoring value and the traffic flow at the moment to be predicted, where the rainfall at the moment to be predicted refers to the rainfall at the previous sampling moment of the moment to be predicted.

[0039] In an embodiment, considering that the time difference between the moment to be predicted and the current moment is small, and generally the rainfall does not change greatly in a short time, the rainfall at the moment to be predicted is set to the rainfall at the current moment. Taking the i-th historical traffic flow monitoring value of the c-th adjacent intersection as an example, the calculation formula for the second similarity degree in weather between the i-th historical traffic flow monitoring value and the traffic flow at the moment to be predicted is:

[0040]

[0041] Among them, represents the second similarity degree in terms of weather between the i-th historical traffic flow monitoring value of the c-th adjacent intersection and the traffic flow at the moment to be predicted, 1 represents a constant, represents the historical rainfall at the sampling moment (i.e., the target moment) of the i-th historical traffic flow monitoring value of the c-th adjacent intersection, represents the rainfall at the moment to be predicted, | | represents the absolute value symbol, represents the maximum historical rainfall within a month, represents the minimum historical rainfall within a month.

[0042] It should be noted that, The smaller the value of, the more similar the weather conditions at the sampling moment of the i-th historical traffic flow monitoring value and the moment to be predicted, that is, the closer the rainfall, and the greater the second similarity degree in terms of weather between the i-th historical traffic flow monitoring value and the traffic flow at the moment to be predicted.

[0043] Similarly, the first similarity degree in terms of time and the second similarity degree in terms of weather between each historical traffic flow monitoring value of any adjacent intersection and the traffic flow at the moment to be predicted can be obtained. Considering that the influencing degrees of time and weather on traffic flow may not be exactly the same, therefore, it is also necessary to further calculate the influencing weights of time and weather on traffic flow respectively. If the influencing degree of a certain factor on traffic flow is large, it indicates that the correlation between the change of traffic flow and the change of this factor is strong. Furthermore, when the traffic flow is at a certain level, the corresponding characteristic value of this factor should also be similar. In the embodiment of the present invention, according to the first similarity degree and the second similarity degree corresponding to each historical traffic flow monitoring value of any adjacent intersection, the time influencing weight and the weather influencing weight are respectively obtained.

[0044] Among them, the methods for obtaining the time influencing weight and the weather influencing weight are as follows:

[0045] Using the Min - Max normalization method, all historical traffic flow monitoring values are normalized to obtain normalized traffic flow. Taking the normalized traffic flow as the horizontal axis, the first similarity degree as the vertical axis, and the second similarity degree as the vertical axis, a three - dimensional space is constructed. All historical traffic flow monitoring values are mapped in the three - dimensional space. A historical traffic flow monitoring value corresponds to a data point in the three - dimensional space. K - means clustering is performed on all data points in the three - dimensional space to obtain a preset number of clustering clusters; where k = 6, that is, the preset number is 6, and there is no limitation here, which can be set according to the implementation scenario.

[0046] For any clustering cluster, obtain the standard deviation of the first similarity degree and the standard deviation of the second similarity degree of all data points in the any clustering cluster, denoted as the time standard deviation and the weather standard deviation respectively; accumulate the time standard deviations of all clustering clusters to obtain the accumulated value of the time standard deviation, normalize the reciprocal of the accumulated value of the time standard deviation to obtain the time influence weight; accumulate the weather standard deviations of all clustering clusters to obtain the accumulated value of the weather standard deviation, normalize the reciprocal of the accumulated value of the weather standard deviation to obtain the weather influence weight.

[0047] In one embodiment, the calculation formula of the time influence weight is:

[0048]

[0049] Wherein, represents the time influence weight of the c-th adjacent intersection, represents the normalization function, represents the number of clustering clusters, represents the standard deviation of the first similarity degree of all data points in the o-th clustering cluster corresponding to the c-th adjacent intersection, that is, the time standard deviation, and 1 represents a constant.

[0050] It should be noted that if the influence degree of time on the traffic flow is large, then the traffic flow data corresponding to the data points with similar first similarity degree will also be relatively similar, so the data points will be relatively concentrated and easier to be clustered into the same category; if the influence degree of time on the traffic flow is small, then the distribution of data points with similar time corresponding to the same traffic flow level is relatively scattered, that is, more likely to be clustered into different categories. Therefore, the smaller the time standard deviation of each clustering cluster, the closer the time corresponding to the data points in the clustering cluster, the greater the influence degree of time on the traffic flow, and the greater the corresponding time influence weight.

[0051] Similarly, the calculation formula of the weather influence weight is:

[0052]

[0053] Wherein, represents the weather influence weight of the c-th adjacent intersection, represents the normalization function, represents the number of clustering clusters, represents the standard deviation of the second similarity degree of all data points in the o-th clustering cluster corresponding to the c-th adjacent intersection, that is, the weather standard deviation, and 1 represents a constant.

[0054] After determining the first similarity degree and the second similarity degree corresponding to each historical traffic flow monitoring value of any adjacent intersection, as well as the time influence weight and the weather influence weight of any adjacent intersection, analyze the conditional similarity between each historical traffic flow monitoring value and the traffic flow at the moment to be predicted. Specifically: for any historical traffic flow monitoring value, perform a weighted sum of the first similarity degree and the second similarity degree of the any historical traffic flow monitoring value according to the time influence weight and the weather influence weight to obtain the conditional similarity between the any historical traffic flow monitoring value and the traffic flow at the moment to be predicted.

[0055] In one embodiment, the calculation formula for the conditional similarity between the i-th historical traffic flow monitoring value of the c-th adjacent intersection and the traffic flow at the moment to be predicted is:

[0056]

[0057] Wherein, represents the conditional similarity between the i-th historical traffic flow monitoring value of the c-th adjacent intersection and the traffic flow at the moment to be predicted, represents the first similarity degree in terms of time between the i-th historical traffic flow monitoring value of the c-th adjacent intersection and the traffic flow at the moment to be predicted, represents the second similarity degree in terms of weather between the i-th historical traffic flow monitoring value of the c-th adjacent intersection and the traffic flow at the moment to be predicted, represents the time influence weight of the c-th adjacent intersection, represents the weather influence weight of the c-th adjacent intersection.

[0058] Similarly, the conditional similarity between each historical traffic flow monitoring value of any adjacent intersection and the traffic flow at the moment to be predicted can be obtained. Furthermore, according to the conditional similarity between each historical traffic flow monitoring value and the traffic flow at the moment to be predicted, the historical average traffic flow of any adjacent intersection is obtained. The specific method is: perform an averaging operation on the product of each historical traffic flow monitoring value and its corresponding conditional similarity to obtain the historical average traffic flow of the any adjacent intersection.

[0059] In one embodiment, the calculation formula for the historical average traffic flow of the c-th adjacent intersection is:

[0060]

[0061] Wherein, represents the historical average traffic flow of the c-th adjacent intersection, represents the i-th historical traffic flow monitoring value of the c-th adjacent intersection, represents the conditional similarity between the i-th historical traffic flow monitoring value of the c-th adjacent intersection and the traffic flow at the moment to be predicted, Indicates the number of historical traffic flow monitoring values of the c-th adjacent intersection.

[0062] According to the method for obtaining the historical average traffic flow of the c-th adjacent intersection described above, the historical average traffic flow of each adjacent intersection of any pre-intersection can be obtained. The more the historical average traffic flow of each intersection, the more likely it is that vehicles will pass through this intersection after passing through the pre-intersection. Therefore, the possibility that vehicles passing through the pre-intersection reach the target intersection is calculated. The specific calculation method is as follows: Accumulate the historical average traffic flows of all adjacent intersections to obtain the accumulated historical traffic flow value; obtain the historical average traffic flow of the target intersection, and calculate the ratio of the historical average traffic flow of the target intersection to the accumulated historical traffic flow value as the possibility index that the traffic flow of any pre-intersection passes through the target intersection at the moment to be predicted.

[0063] In an embodiment, the calculation formula for the possibility index that the traffic flow of the a-th pre-intersection passes through the target intersection at the moment to be predicted is:

[0064]

[0065] Wherein, Indicates the possibility index that the traffic flow of the a-th pre-intersection passes through the target intersection at the moment to be predicted, Indicates the number of adjacent intersections of the a-th pre-intersection, Indicates the historical average traffic flow of the c-th adjacent intersection, Indicates the historical average traffic flow of the target intersection.

[0066] Similarly, the possibility index that the traffic flow of each pre-intersection on any phase of the target intersection passes through the target intersection at the moment to be predicted can be obtained.

[0067] Step S103, according to the possibility index that the traffic flow of each pre-intersection passes through the target intersection at the moment to be predicted, obtain the traffic flow prediction value of any phase at the moment to be predicted. According to the historical pedestrian flow monitoring value of any phase of the target intersection within a preset time period, obtain the pedestrian flow prediction value of any phase at the moment to be predicted. Combine the traffic flow prediction value and the pedestrian flow prediction value to obtain the total flow prediction value of any phase of the target intersection at the moment to be predicted.

[0068] The greater the likelihood that the traffic flow at the upstream intersection reaches the target intersection, the more likely it is that more vehicles will reach the target intersection at the moment to be predicted. Therefore, by combining the real-time traffic flow data of all upstream intersections at the current moment on any phase of the target intersection and the likelihood index of reaching the target intersection, the predicted traffic flow value of any phase of the target intersection at the moment to be predicted is obtained. Taking the u-th phase of the target intersection as an example, the real-time traffic flow monitoring values of each upstream intersection of the u-th phase at the current moment are obtained, and the real-time traffic flow monitoring value corresponding to each upstream intersection is multiplied by the likelihood index to obtain the multiplication value corresponding to each upstream intersection. The multiplication values are accumulated to obtain the predicted traffic flow value of the u-th phase at the moment to be predicted.

[0069] Among them, the calculation formula for the predicted traffic flow value of the u-th phase at the moment to be predicted is:

[0070]

[0071] Among them, represents the predicted traffic flow value of the u-th phase of the target intersection at the moment to be predicted, represents the number of upstream intersections on the u-th phase of the target intersection, represents the likelihood index that the traffic flow of the a-th upstream intersection of the u-th phase passes through the target intersection at the moment to be predicted, represents the real-time traffic flow monitoring value of the a-th upstream intersection of the u-th phase at the current moment.

[0072] When there are many pedestrians at the intersection, it is also necessary to appropriately extend the street lamp duration to ensure that pedestrians can pass normally within the green light time and reduce the congestion of pedestrians at the intersection. Therefore, on the basis of obtaining the predicted traffic flow value of the target intersection, it is also necessary to combine the pedestrian flow situation at the intersection to predict the total flow of each phase of the target intersection. Since the number of pedestrians at the intersection does not change significantly in a short period of time, the historical pedestrian flow monitoring values of each sampling moment within the preset duration before the moment to be predicted of the u-th phase of the target intersection are obtained, and the average value of all historical pedestrian flow monitoring values is calculated, which is recorded as the predicted pedestrian flow value of the u-th phase at the moment to be predicted . Furthermore, the sum value between the predicted traffic flow value and the predicted pedestrian flow value of the u-th phase of the target intersection at the moment to be predicted is recorded as the predicted total flow value of the u-th phase of the target intersection at the moment to be predicted .

[0073] Similarly, the predicted total flow value of the u-th phase of the target intersection at the moment to be predicted is obtained, and then the predicted total flow values of each phase of the target intersection at the moment to be predicted can be obtained.

[0074] Step S104: Obtain the total traffic flow prediction value of each phase at the target intersection at the moment to be predicted, and dynamically adjust the green light duration of each phase at the target intersection at the moment to be predicted according to the total traffic flow prediction value of each phase at the target intersection at the moment to be predicted.

[0075] Since, under the condition of ensuring the continuity of signal changes between different intersections, the signal cycle duration of each intersection is usually fixed, and the phase with more traffic flow requires a longer green light duration to reduce road congestion. Therefore, it is necessary to dynamically adjust the green light duration of each phase at the target intersection at the moment to be predicted according to the total traffic flow prediction value of each phase at the target intersection at the moment to be predicted. It should be noted that the signal cycle duration refers to the time required for the traffic signal to complete a full cycle of light colors (such as red - green - yellow - red), and the signal cycle duration should be set to 30 seconds to 150 seconds in the non - saturated traffic state and should not exceed 180 seconds in the saturated state.

[0076] Among them, dynamically adjusting the green light duration of each phase at the target intersection at the moment to be predicted according to the total traffic flow prediction value of each phase at the target intersection at the moment to be predicted includes:

[0077] Accumulate the total traffic flow prediction values of all phases at the target intersection at the moment to be predicted to obtain the total traffic flow accumulation value. For any phase at the target intersection, calculate the ratio between the total traffic flow prediction value of the any phase at the moment to be predicted and the total traffic flow accumulation value as the duration adjustment coefficient. Obtain the signal cycle duration of the target intersection, and take the product between the duration adjustment coefficient and the signal cycle duration as the target green light duration required for the any phase at the target intersection at the moment to be predicted, and adjust the green light duration of the any phase at the target intersection at the moment to be predicted to the target green light duration.

[0078] In an embodiment, taking the u - th phase of the target intersection as an example, the calculation formula for the target green light duration required for the u - th phase at the moment to be predicted is:

[0079]

[0080] Among them, represents the target green light duration required for the u - th phase of the target intersection at the moment to be predicted, represents the total traffic flow prediction value of the u - th phase at the moment to be predicted, represents the total traffic flow prediction value of the v - th phase at the moment to be predicted, represents the number of phases of the target intersection, represents the signal cycle duration of the target intersection.

[0081] According to the above formula for calculating the target green light duration, the target green light duration required for each phase of the target intersection at the moment to be predicted can be obtained. Furthermore, based on the target green light duration required for each phase at the moment to be predicted, the green light duration of each phase of the target intersection at the moment to be predicted can be adjusted in advance to the target green light duration, improving the timeliness and accuracy of traffic signal management.

[0082] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. An intelligent traffic signal management and control system based on machine learning, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, the following steps are implemented: For any phase of the target intersection at the moment to be predicted, at least one pre-intersection of the target intersection in the any phase is obtained, where the vehicle driving route passes through the pre-intersection first and then the target intersection; For any pre-intersection, at least one adjacent intersection of the any pre-intersection is obtained. The adjacent intersection refers to the intersection where the traffic flow of the any pre-intersection is diverted. According to the historical traffic flow monitoring values and historical weather monitoring data of each adjacent intersection, the possibility index of the traffic flow of the any pre-intersection passing through the target intersection at the moment to be predicted is obtained; According to the possibility index of the traffic flow of each pre-intersection passing through the target intersection at the moment to be predicted, the traffic flow prediction value of the any phase at the moment to be predicted is obtained. According to the historical pedestrian flow monitoring values of the any phase of the target intersection within a preset time period, the pedestrian flow prediction value of the any phase at the moment to be predicted is obtained. Combining the traffic flow prediction value and the pedestrian flow prediction value, the total flow prediction value of the any phase of the target intersection at the moment to be predicted is obtained; The total flow prediction value of each phase of the target intersection at the moment to be predicted is obtained. According to the total flow prediction value of each phase of the target intersection at the moment to be predicted, the green light duration of each phase of the target intersection at the moment to be predicted is dynamically regulated.

2. The intelligent traffic signal management and control system based on machine learning according to claim 1, characterized in that, The obtaining the possibility index of the traffic flow of the any pre-intersection passing through the target intersection at the moment to be predicted according to the historical traffic flow monitoring values and historical weather monitoring data of each adjacent intersection includes: For any adjacent intersection, according to the historical traffic flow monitoring values and historical rainfall amounts at each sampling moment within a preset historical period of the any adjacent intersection, the first similarity degree in time and the second similarity degree in weather between each historical traffic flow monitoring value and the traffic flow at the moment to be predicted are respectively obtained; According to the first similarity degree and the second similarity degree corresponding to each historical traffic flow monitoring value, the time influence weight and the weather influence weight are respectively obtained; According to the first similarity degree, the second similarity degree, the time influence weight and the weather influence weight corresponding to each historical traffic flow monitoring value, the historical average traffic flow of the any adjacent intersection is obtained; The historical average traffic flow of each adjacent intersection is obtained, and the historical traffic flow accumulation value is obtained by accumulating the historical average traffic flows of all adjacent intersections; The historical average traffic flow of the target intersection is obtained, and the ratio of the historical average traffic flow of the target intersection to the historical traffic flow accumulation value is calculated as the possibility index of the traffic flow of the any pre-intersection passing through the target intersection at the moment to be predicted.

3. The intelligent traffic signal management and control system based on machine learning according to claim 2, characterized in that The obtaining the first similarity degree in time and the second similarity degree in weather between each historical traffic flow monitoring value and the traffic flow at the moment to be predicted according to the historical traffic flow monitoring values and historical rainfall amounts at each sampling moment within a preset historical period of the any adjacent intersection includes: For any historical traffic flow monitoring value of any adjacent intersection within a preset historical period, record the sampling moment corresponding to the any historical traffic flow monitoring value as the target moment, calculate the absolute value of the time difference between the target moment and the moment to be predicted, obtain the first ratio between the absolute value of the time difference and the maximum moment, and take the difference between the constant 1 and the first ratio as the time proximity degree; Obtain the week number to which the target moment belongs and the week number to which the moment to be predicted belongs, calculate the absolute value of the week number difference between the week number to which the target moment belongs and the week number to which the moment to be predicted belongs, obtain the second ratio between the absolute value of the week number difference and the maximum week number, and take the difference between the constant 1 and the second ratio as the week number consistency degree; Take the product of the time proximity degree and the week number consistency degree as the first similarity degree in time between the any historical traffic flow monitoring value and the traffic flow at the moment to be predicted.

4. The intelligent traffic signal management and control system based on machine learning according to claim 3, characterized in that, The step of respectively obtaining the first similarity degree in time and the second similarity degree in weather between each historical traffic flow monitoring value and the traffic flow at the moment to be predicted according to the historical traffic flow monitoring values and historical rainfall amounts at each sampling moment of any adjacent intersection within a preset historical period further includes: According to the historical rainfall amounts at each sampling moment of any adjacent intersection within a preset historical period, obtain the rainfall range between the maximum historical rainfall amount and the minimum historical rainfall amount; calculate the absolute value of the rainfall difference between the historical rainfall amount at the target moment and the rainfall amount at the moment to be predicted, calculate the third ratio between the absolute value of the rainfall difference and the rainfall range, and take the difference between the constant 1 and the third ratio as the second similarity degree in weather between the any historical traffic flow monitoring value and the traffic flow at the moment to be predicted, where the rainfall amount at the moment to be predicted refers to the rainfall amount at the previous sampling moment of the moment to be predicted.

5. The intelligent traffic signal management and control system based on machine learning according to claim 2, characterized in that, The step of respectively obtaining the time influence weight and the weather influence weight according to the first similarity degree and the second similarity degree corresponding to each historical traffic flow monitoring value includes: Perform normalization processing on all historical traffic flow monitoring values to obtain normalized traffic flows. Take the normalized traffic flows as the horizontal axis, the first similarity degree as the vertical axis, and the second similarity degree as the vertical axis to construct a three-dimensional space. Map all historical traffic flow monitoring values in the three-dimensional space. One historical traffic flow monitoring value corresponds to a data point in the three-dimensional space. Perform k-means clustering on all data points in the three-dimensional space to obtain a preset number of clustering clusters; For any clustering cluster, obtain the standard deviation of the first similarity degree and the standard deviation of the second similarity degree of all data points in the any clustering cluster, and denote them as the time standard deviation and the weather standard deviation respectively; accumulate the time standard deviations of all clustering clusters to obtain the accumulated time standard deviation value, perform normalization on the reciprocal of the accumulated time standard deviation value to obtain the time influence weight; accumulate the weather standard deviations of all clustering clusters to obtain the accumulated weather standard deviation value, perform normalization on the reciprocal of the accumulated weather standard deviation value to obtain the weather influence weight.

6. The intelligent traffic signal management and control system based on machine learning according to claim 2, characterized in that Obtaining the historical average traffic flow of any adjacent intersection according to the first similarity degree, second similarity degree, time influence weight, and weather influence weight corresponding to each historical traffic flow monitoring value includes: For any historical traffic flow monitoring value, perform a weighted sum of the first similarity degree and the second similarity degree of the any historical traffic flow monitoring value according to the time influence weight and the weather influence weight to obtain the conditional similarity between the any historical traffic flow monitoring value and the traffic flow at the moment to be predicted; Obtain the conditional similarity between each historical traffic flow monitoring value and the traffic flow at the moment to be predicted, and perform an averaging operation on the product of each historical traffic flow monitoring value and its corresponding conditional similarity to obtain the historical average traffic flow of any adjacent intersection.

7. The intelligent traffic signal management and control system based on machine learning according to claim 1, characterized in that, Obtaining the traffic flow prediction value of any phase at the moment to be predicted according to the possibility index of the traffic flow of each upstream intersection passing through the target intersection at the moment to be predicted includes: Denote the previous sampling moment of the moment to be predicted as the current moment, obtain the real-time traffic flow monitoring value of each upstream intersection at the current moment, multiply the real-time traffic flow monitoring value corresponding to each upstream intersection by the possibility index to obtain the multiplication value corresponding to each upstream intersection, and accumulate all the multiplication values to obtain the traffic flow prediction value of any phase at the moment to be predicted.

8. The intelligent traffic signal management and control system based on machine learning according to claim 1, characterized in that Obtaining the pedestrian flow prediction value of any phase at the moment to be predicted according to the historical pedestrian flow monitoring value of any phase of the target intersection within a preset duration includes: Obtain the historical pedestrian flow monitoring value of each sampling moment within the preset duration before the moment to be predicted for any phase of the target intersection, calculate the mean value of all the historical pedestrian flow monitoring values, and denote it as the pedestrian flow prediction value of any phase at the moment to be predicted.

9. The intelligent traffic signal management and control system based on machine learning according to claim 1, characterized in that, Combining the traffic flow prediction value and the pedestrian flow prediction value to obtain the total flow prediction value of any phase of the target intersection at the moment to be predicted includes: Denote the sum value between the traffic flow prediction value and the pedestrian flow prediction value as the total flow prediction value of any phase of the target intersection at the moment to be predicted.

10. The intelligent traffic signal management and control system based on machine learning according to claim 1, wherein, Dynamically adjusting the green light duration of each phase of the target intersection at the moment to be predicted according to the total flow prediction value of each phase of the target intersection at the moment to be predicted includes: Accumulate the total flow prediction values of all phases of the target intersection at the moment to be predicted to obtain the total flow accumulation value. For any phase of the target intersection, calculate the ratio between the total flow prediction value of the any phase at the moment to be predicted and the total flow accumulation value as the duration adjustment coefficient, obtain the signal light cycle duration of the target intersection, take the product of the duration adjustment coefficient and the signal light cycle duration as the target green light duration required for the any phase at the moment to be predicted, and adjust the green light duration of the any phase of the target intersection at the moment to be predicted to the target green light duration.

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