Road congestion evaluation method based on multi-portal road intersection between adjacent portals

Abstract

The invention discloses a road congestion evaluation method based on a multi-portal road intersection between adjacent portals, and relates to the technical field of traffic data processing, and the method comprises the steps: obtaining each portal interval road section, analyzing the feature index of each portal interval road section, carrying out the adaptive adjustment of a traffic monitoring device, and judging the congestion level of each portal interval road section. And obtaining optical fiber link state information applied to each portal interval road section, and dynamically adjusting the optical fiber link. According to the invention, by analyzing the data of the intersection of the adjacent portal sections and the data of each portal interval section, the complex traffic condition at the intersection of the multi-portal road is comprehensively considered, the congestion level of each portal interval section can be accurately judged, the adaptive adjustment of the traffic monitoring device and the dynamic optimization of the optical fiber link are realized, and the traffic monitoring efficiency is improved. The method provides a numerical basis for traffic management, better adapts to a complex traffic environment, ensures smooth operation of a traffic system, and promotes reasonable allocation of traffic resources.

Description

Technical Field

[0001] The invention relates to the technical field of traffic data processing, and in particular to a road congestion evaluation method based on the existence of multi-gantry road intersections between adjacent gantries. Background Art

[0002] With the acceleration of urbanization, urban population and motor vehicle ownership continue to grow rapidly. Road intersections are key nodes for traffic flow convergence and diversion. When there are multi-gantry road intersections between adjacent gantries, the traffic situation becomes more complicated.

[0003] The prior art, such as the invention patent with announcement number: CN108153827B, is a method and device for determining a traffic congestion area, the method comprising: obtaining the location information of at least one vehicle; obtaining the number of vehicles in at least one preset area in the current map according to the location information of at least one vehicle; determining that a preset area with a number of vehicles greater than a first preset threshold is an area where traffic congestion will occur. The number of vehicles in a preset area can be detected by obtaining the location information of at least one vehicle, and then by judging whether the number of vehicles in the preset area is greater than the first preset threshold, when the number of vehicles in the preset area is greater than the first preset threshold, the preset area is determined to be an area where traffic congestion will occur.

[0004] The prior art, such as the invention patent with announcement number: CN109241938B, is a road congestion detection method and terminal, the method comprising: obtaining historical video image information of the road to be detected; learning the characteristics of the vehicle based on the historical video image information to obtain a learning result; obtaining current video image information of the road to be detected; detecting the vehicle in the current video image information based on the learning result to obtain a detection result; and judging whether the road to be detected is congested based on the detection result.

[0005] It can be seen from the above scheme that the current traditional traffic congestion evaluation mainly focuses on indicators such as traffic flow and vehicle speed of a single road section. However, due to the special geographical location and traffic function of multi-gantry road intersections, the traffic flows at the intersections have complex mutual influences. Vehicles at these intersections may be affected by multiple factors such as the merging and diversion rules of different lanes. Only considering the road section itself cannot accurately reflect the actual situation of traffic congestion, which may cause congestion to worsen, thereby affecting the operating efficiency of the overall transportation system. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a road congestion evaluation method based on the presence of multi-gantry road intersections between adjacent gantries, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented by the following technical solutions: The present invention provides a road congestion evaluation method based on the presence of multi-gantry road intersections between adjacent gantries, comprising the following steps:

[0008] S1, obtain adjacent gantry sections, count multiple road intersections between adjacent gantry sections, and simultaneously obtain the sections between the road intersections and adjacent gantries, which are recorded as the interval sections between each gantry.

[0009] S2, collecting traffic data of each gantry interval section through the traffic monitoring device, analyzing and processing to obtain characteristic indicators of each gantry interval section, and adaptively adjusting the traffic monitoring device accordingly.

[0010] S3, based on the characteristic indicators of each gantry interval section, and synchronously collects the basic data of each gantry interval section and the historical traffic characterization data for comprehensive processing, determines the congestion level of each gantry interval section.

[0011] S4, based on the traffic data transmission optical fiber link, obtains the optical fiber link status information of each gantry interval section, and dynamically adjusts the optical fiber link in combination with the congestion level of each gantry interval section, and uploads the road congestion information for early warning processing.

[0012] Compared with the prior art, the embodiments of the present invention have at least the following beneficial effects:

[0013] (1) The present invention provides a road congestion evaluation method based on the existence of multi-gantry road intersections between adjacent gantries, obtains and analyzes data on intersections between adjacent gantry sections and sections between gantry sections, comprehensively considers the complex traffic conditions at multi-gantry road intersections, and can accurately determine the congestion level of each gantry section. It can achieve adaptive adjustment of traffic monitoring devices and dynamic optimization of optical fiber links, provide numerical basis for traffic management, better adapt to complex traffic environments, ensure smooth operation of the traffic system, and promote reasonable allocation of traffic resources.

[0014] (2) The present invention obtains characteristic indicators of each gantry interval section through analysis and processing, which can accurately characterize the traffic activity level of each section, provide a scientific numerical basis for the subsequent adaptive adjustment of the traffic monitoring device, make the monitoring frequency of the monitoring device more in line with the actual traffic conditions of the section, ensure the accuracy and efficiency of traffic data collection, and thus more comprehensively and accurately grasp the road operation status, thereby improving the operation efficiency and stability of the entire traffic system.

[0015] (3) The present invention obtains the optical fiber link adjustment demand index applied to each gantry interval section through the optical fiber link state indicator coefficient applied to each gantry interval section and the transmission priority factor analysis of each gantry interval section. When road congestion occurs, the traffic conditions of each gantry interval section deteriorate, the amount of traffic data will increase sharply and need to be transmitted more frequently, which makes the data transmission pressure of the optical fiber link rise sharply and requires higher transmission capacity of the optical fiber link. Dynamically adjusting the optical fiber link according to the optical fiber link adjustment demand index applied to each gantry interval section can ensure efficient and stable transmission of traffic data, improve the reliability and real-time performance of the traffic monitoring system, and effectively avoid negative negative impacts such as delays on timely management of traffic due to data transmission bottlenecks, ensure that monitoring data is transmitted to the management center in a timely and accurate manner, and improve road traffic efficiency.

[0016] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] See also Figure 1 As shown, an embodiment of the present invention provides a road congestion evaluation method based on the presence of a multi-gantry road intersection between adjacent gantries, comprising the following steps:

[0020] S1, obtain adjacent gantry sections, count multiple road intersections between adjacent gantry sections, and simultaneously obtain the sections between the road intersections and adjacent gantries, which are recorded as the interval sections between each gantry.

[0021] In a specific embodiment, adjacent gantry sections are obtained, multiple road intersections between adjacent gantry sections are counted, and sections between road intersections and adjacent gantries are simultaneously obtained and recorded as sections between gantry sections. The specific process is as follows:

[0022] The road network is marked as a network topology consisting of nodes and edges through the geographic information system.

[0023] The nodes include adjacent gantry position points and road intersections, and the edges are monitored road sections.

[0024] The connection path between the intersection node and the adjacent gantry node is obtained, thereby determining the interval section between each gantry.

[0025] S2, collecting traffic data of each gantry interval section through the traffic monitoring device, analyzing and processing to obtain characteristic indicators of each gantry interval section, and adaptively adjusting the traffic monitoring device accordingly.

[0026] In this embodiment, the traffic data of each gantry interval section includes the total number of vehicles passing through each gantry interval section within a preset sensing period, the total number of large vehicles passing through, the average vehicle speed, the standard deviation of the vehicle speed and the average vehicle passing time.

[0027] It should be noted that the total number of vehicles passing through each gantry interval section can be collected by the geomagnetic sensor in the traffic monitoring device, and the total number of large vehicles passing through can be collected by the geomagnetic sensor in the traffic monitoring device in combination with the camera.

[0028] The vehicle speed can be collected by the speedometer in the traffic monitoring device.

[0029] The passing time can be collected through a time recording system in a traffic monitoring device linked to a speedometer.

[0030] In this embodiment, the characteristic index of each gantry interval section is obtained by analysis and processing, and the specific analysis process is as follows:

[0031] Extract reference traffic data stored in the database.

[0032] The reference traffic data include the reference total number of passing vehicles on the road section, the reference proportion of passing large vehicles, the reference average vehicle speed, the reference vehicle speed standard deviation and the reference vehicle average passing time.

[0033] It should be noted that the reference traffic data is obtained by collecting a large amount of historical traffic data of each gantry interval section and performing mean processing before establishing the database.

[0034] Based on the traffic data of each gantry interval section and the reference traffic data, the characteristic indicators of each gantry interval section are obtained through analysis and processing.

[0035] The characteristic index of each gantry interval section is used to characterize the traffic activity level of each gantry interval section.

[0036] In a specific embodiment, the characteristic index of each gantry interval section is obtained in the following manner:

[0037]

[0038] Among them, A i is the characteristic index of the ith gantry interval section, a iis the total number of vehicles passing through the ith gantry interval section, a 0 is the total number of reference vehicles passing the road section, b i is the total number of large vehicles passing through the ith gantry interval section, b 0 is the reference ratio of large vehicles passing through the road section, c i is the average vehicle speed of the ith gantry interval section, c 0 is the reference average speed of the road section, d i is the standard deviation of vehicle speed in the ith gantry interval section, d 0 is the reference speed standard deviation of the road section, f i is the average vehicle passing time of the i-th gantry interval section, f 0 is the average passing time of the reference vehicle on the road section, i is the number of each gantry interval section, i=1,2,...,m, m is the number of gantry interval sections, and e is a natural constant.

[0039] It should be noted that the specific method for obtaining the vehicle speed standard deviation is as follows: Among them, d i is the standard deviation of vehicle speed in the ith gantry interval section, v ij is the speed of the jth vehicle in the i-th gantry interval section, c i is the average vehicle speed of the i-th gantry interval section, i is the number of each gantry interval section, i=1,2,...,m, m is the number of gantry interval sections, j is the number of each vehicle, j=1,2,...,n, n is the number of vehicles.

[0040] It should also be noted that the characteristic index of each gantry interval section is obtained by analyzing and processing the traffic data of each gantry interval section, taking into account the influence between these parameters. For example, when the total number of passing vehicles increases, if the road capacity is limited, the distance between vehicles will decrease, which is likely to lead to a decrease in vehicle speed, that is, a decrease in the average speed. For example, during peak hours, there are more vehicles on the road, the vehicles drive slowly, and the average speed is significantly reduced. On the contrary, when the average speed is high, the total number of vehicles passing in a certain period of time may increase, because the vehicles drive smoothly and the number of vehicles passing per unit time increases. Large vehicles have a large size, heavy mass, and relatively poor acceleration and braking performance. When they are driving on the road, they will affect the speed of surrounding vehicles. When the total number of large vehicles increases, other vehicles may reduce their speed in order to maintain a safe distance, resulting in a decrease in the average speed. The speed of large vehicles is relatively stable and usually lower than that of small vehicles. Their distribution on the road will affect the standard deviation of the speed. If the total number of large vehicles passing is large and unevenly distributed, the speed distribution will be more discrete and the standard deviation of the speed will increase. The speed standard deviation reflects the degree of dispersion of vehicle speed. When the speed standard deviation is large, it means that the speeds of vehicles on the road vary greatly. In this case, the average speed may not represent the overall traffic flow well, and a large speed standard deviation may increase the instability of traffic flow, which in turn affects the average speed. When the average speed is low, if the speed difference between vehicles is large, the speed standard deviation will also be large. For example, on congested roads, vehicles travel slowly and stop and go, and the start and stop times of different vehicles are different, resulting in large speed differences and increased speed standard deviation. The average vehicle passing time is closely related to the total number of passing vehicles and the average speed. The more the total number of passing vehicles, the longer the average vehicle passing time will be when the road capacity is constant. The lower the average speed, the longer it takes for vehicles to pass the same road section, and the average vehicle passing time will increase.

[0041] In a specific embodiment, characteristic indicators of each gantry interval section are obtained through analysis and processing, which can accurately characterize the traffic activity of each section, provide a scientific numerical basis for the subsequent adaptive adjustment of the traffic monitoring device, make the monitoring frequency of the monitoring device more in line with the actual traffic conditions of the section, ensure the accuracy and efficiency of traffic data collection, and thus more comprehensively and accurately grasp the road operation status, thereby improving the operation efficiency and stability of the entire transportation system.

[0042] In this embodiment, the traffic monitoring device is adaptively adjusted, and the specific adjustment process is as follows:

[0043] The traffic monitoring device adjustment parameters corresponding to the characteristic index intervals of each road section stored in the database are extracted, and the traffic monitoring device adjustment parameters corresponding to the intervals of the characteristic index of each gantry interval section are mapped and extracted, and recorded as the traffic monitoring device adjustment parameters of each gantry interval section.

[0044] The traffic monitoring device adjustment parameters for each gantry interval section include the traffic monitoring device monitoring frequency adjustment value and the sensor sensitivity adjustment value.

[0045] Based on the traffic monitoring device adjustment parameters of each gantry interval section, the corresponding traffic monitoring device is adaptively adjusted. The specific adjustment process is as follows:

[0046] The monitoring frequency adjustment value and sensor sensitivity adjustment value of the traffic monitoring device corresponding to the interval of the characteristic index of each gantry interval section are extracted, and the traffic monitoring device is adaptively adjusted based on the monitoring frequency adjustment value and sensor sensitivity adjustment value of the traffic monitoring device on the basis of the current monitoring frequency and the current sensor sensitivity of the traffic monitoring device.

[0047] In a specific embodiment, for example, the characteristic index of a gantry interval section is 8.52, and the monitoring frequency adjustment value of the traffic monitoring device corresponding to the section is to double the monitoring frequency (such as changing the original data collection every 2 minutes to every 1 minute), and the sensor sensitivity adjustment value is moderately increased (for example, the camera image recognition confidence is increased by 15%). After the system obtains these adjustment values, it sends instructions to the traffic monitoring device. After receiving the instructions, the control module in the traffic monitoring device immediately adjusts the internal timer parameters to shorten the data collection interval to 1 minute, and adjusts the camera sensor parameters at the same time. The adjusted camera can more accurately identify the details of vehicles and traffic scenes, thereby collecting traffic data more comprehensively and accurately, providing a more reliable basis for subsequent traffic condition analysis and management, and realizing adaptive adjustment of the traffic monitoring device, so that it can better adapt to the complex and changeable traffic conditions of the section.

[0048] It should be noted that the traffic monitoring device involved in this implementation is a monitoring device installed on the gantry, rather than a traffic device for road administration.

[0049] It should also be noted that if the extracted traffic monitoring device adjustment parameter exceeds the maximum adjustment value of the traffic monitoring device, the traffic monitoring device shall be adjusted using its maximum tolerable adjustment parameter; if the extracted traffic monitoring device adjustment parameter is lower than the minimum adjustment value of the traffic monitoring device, the traffic monitoring device shall be adjusted using its minimum tolerable adjustment parameter.

[0050] In a specific embodiment, the road section characteristic index comprehensively reflects the traffic conditions in many aspects, such as traffic flow, vehicle speed, and vehicle type distribution. When the characteristic index is large, it means that the risk of traffic congestion is high and the traffic flow is complex. At this time, it is necessary to increase the monitoring frequency in order to grasp the traffic dynamics more timely, and at the same time, appropriately increase the sensor sensitivity to ensure accurate detection of the vehicle status, so it is necessary to obtain the corresponding adjustment parameters based on the characteristic index. By obtaining the traffic monitoring device adjustment parameters based on the road section characteristic index and adaptively adjusting the corresponding traffic monitoring device, accurate adaptive adjustment of the traffic monitoring device can be achieved, avoiding waste of resources due to excessive monitoring, or missing traffic information due to insufficient monitoring. Potential traffic problems can be discovered in a timely manner, improving traffic management efficiency.

[0051] S3, based on the characteristic indicators of each gantry interval section, and synchronously collects the basic data of each gantry interval section and the historical traffic characterization data for comprehensive processing, determines the congestion level of each gantry interval section.

[0052] In this embodiment, basic data of each gantry interval section is collected, and the specific collection process is as follows:

[0053] Extract the GIS mapping model of each gantry interval section from the database;

[0054] The basic data of each gantry interval section are extracted from the GIS mapping model, including the section length, average road width, average road slope and road flatness of each gantry interval section.

[0055] In this embodiment, the historical traffic characterization data includes the historical average traffic flow, the historical average vehicle speed, the historical vehicle speed fluctuation extreme value difference and the historical congestion occurrence times of each gantry interval section.

[0056] The extreme difference of historical vehicle speed fluctuation is obtained by dividing the maximum value of historical vehicle speed fluctuation by the minimum value of historical vehicle speed fluctuation.

[0057] In a specific embodiment, the historical traffic characterization data is extracted from a data processing center to which the traffic monitoring device belongs.

[0058] In this embodiment, the congestion level of each gantry interval section is determined, and the specific process is as follows:

[0059] According to the basic data of each gantry interval section and the historical traffic characterization data, the traffic characterization coefficient of each gantry interval section is obtained by analysis and processing. The traffic characterization coefficient of each gantry interval section is used to characterize the basic traffic conditions of each gantry interval section.

[0060] In a specific embodiment, the traffic characterization coefficient of each gantry interval section is obtained in the following manner:

[0061]

[0062] Among them, B i is the traffic characterization coefficient of the ith gantry interval section, g i is the length of the section between the ith gantry, h i is the average road width of the i-th gantry interval section, k i is the average road slope of the ith gantry interval section, p i is the road roughness of the i-th gantry interval section, q i is the historical average traffic flow of the ith gantry interval section, r i is the historical average vehicle speed of the ith gantry interval section, s i is the extreme value difference of historical vehicle speed fluctuation in the i-th gantry interval section, t i is the number of historical congestion occurrences of the i-th gantry interval section, is the correction factor corresponding to the unit section length, is the correction factor corresponding to the average width of the unit road, is the correction factor corresponding to the average slope of the unit road, is the correction factor corresponding to the unit road flatness, is the correction factor corresponding to the unit historical average traffic flow, is the correction factor corresponding to the unit historical average vehicle speed, is the correction factor corresponding to the extreme value difference of unit historical vehicle speed fluctuation, is the correction factor corresponding to a single historical congestion, i is the number of each gantry interval section, i=1,2,...,m, m is the number of gantry interval sections.

[0063] It should be noted that the swish function is a built-in function in Pytnon.

[0064] It should be understood that the correction factor corresponding to the unit section length, the correction factor corresponding to the unit road average width, the correction factor corresponding to the unit road average slope, the correction factor corresponding to the unit road flatness, the correction factor corresponding to the unit historical average traffic flow, the correction factor corresponding to the unit historical average vehicle speed, the correction factor corresponding to the unit historical vehicle speed fluctuation extreme value difference and the correction factor corresponding to the single historical congestion respectively represent the numerical value of the influence of the section length, the average road width, the average road slope, the road flatness, the historical average traffic flow, the historical average vehicle speed, the historical vehicle speed fluctuation extreme value difference and the number of historical congestion occurrences on the traffic characterization coefficient of the gantry interval section. When used, the preset value can be directly extracted from the database, and the corresponding relationship can be a pre-set mapping relationship. For example, the section length, the average road width, the average road slope, the road flatness, the historical average traffic flow, the historical average vehicle speed, the historical vehicle speed fluctuation extreme value difference and the number of historical congestion occurrences are respectively preset in the database for the unit section length. A mapping set is formed by the corresponding correction factor corresponding to the unit average road width, the correction factor corresponding to the unit average road slope, the correction factor corresponding to the unit road flatness, the correction factor corresponding to the unit historical average traffic flow, the correction factor corresponding to the unit historical average vehicle speed, the correction factor corresponding to the unit historical speed fluctuation extreme value difference and the correction factor corresponding to a single historical congestion. The real-time road section length, average road width, average road slope, road flatness, historical average traffic flow, historical average vehicle speed, historical speed fluctuation extreme value difference and the number of historical congestion occurrences are respectively input into the mapping set to obtain the correction factor corresponding to the unit road section length, the correction factor corresponding to the unit average road width, the correction factor corresponding to the unit average road slope, the correction factor corresponding to the unit road flatness, the correction factor corresponding to the unit historical average traffic flow, the correction factor corresponding to the unit historical average vehicle speed, the correction factor corresponding to the unit historical speed fluctuation extreme value difference and the correction factor corresponding to a single historical congestion, and the mapping relationship is one-to-one corresponding.

[0065] It should be noted that the traffic characterization coefficient of each gantry interval section is obtained by analyzing and processing the basic data of each gantry interval section and the historical traffic characterization data, taking into account the influence between these parameters. For example, a large slope on a long section will increase vehicle energy consumption and unstable driving speed, especially when the vehicle speed is significantly reduced when going uphill, and the vehicle speed needs to be controlled when going downhill, thus affecting the average speed of the entire section. In addition, long sections with large slopes have higher requirements for road flatness. Poor flatness will aggravate the bumps and speed changes of vehicle driving. When the average width of the road is narrow, the average slope of the road will have a more significant impact on vehicle driving. The lateral space that can be adjusted when the vehicle is climbing or going downhill is small, and it is easy to have accidents such as scratches with other vehicles, affecting traffic smoothness. At the same time, narrow roads also have higher requirements for road flatness. Poor flatness will further reduce vehicle driving and lead to a reduction in vehicle speed. When the historical average traffic volume is high, the vehicles on the road are dense, and the mutual interference between vehicles increases, resulting in a reduction in the historical average speed. At the same time, the difference in vehicle driving speed will also increase, and the difference in the extreme value of historical speed fluctuations will become larger. Moreover, large traffic volume is prone to cause traffic congestion, and the number of historical congestion occurrences will increase accordingly. When the historical average speed is low, the number of vehicles passing through in a certain period of time is relatively small, which may lead to a decrease in the historical average traffic volume. However, if the road capacity is limited, low speed may cause vehicles to queue, thereby increasing the traffic volume in local time periods or sections, and at the same time increase the extreme value difference of historical speed fluctuations and the number of historical congestion occurrences. Large speed fluctuations may cause vehicles to frequently accelerate and decelerate, reduce the traffic efficiency of the road, easily cause traffic congestion, increase the number of historical congestion occurrences, and also affect the statistical results of historical average speed and traffic volume. Sections with many historical congestion occurrences usually have large traffic volume and low speed, unstable vehicle speed, and large extreme value difference of historical speed fluctuations. Congestion will increase the time vehicles stay on the road section, further affecting the historical average speed and traffic volume. Low speed is prone to cause traffic congestion, and the number of historical congestion occurrences will increase. A large difference in the extreme value of historical speed fluctuations indicates that the vehicle speed changes frequently and with a large amplitude, which will cause the historical average speed to be unstable and reduced. Frequent speed fluctuations will interfere with the normal operation of traffic flow, increase conflicts between vehicles, easily cause traffic congestion, and increase the number of historical congestion occurrences. For sections with short section lengths, narrow average road widths, large average road slopes, and poor road flatness, it is difficult for vehicles to drive, the speed is limited, and the traffic capacity is low. The historical average speed will be low, and the extreme value difference of historical speed fluctuations is large. Such sections are prone to traffic congestion, and the number of historical congestion occurrences will increase, thereby affecting the historical average traffic volume, reducing it during congestion periods, and peak traffic may occur during non-congestion periods. Sections with low historical average speeds may be due to factors such as long section lengths and poor road conditions (such as narrow width, large slopes, and poor flatness), which lead to slow vehicle driving, which in turn attracts more vehicles to choose other routes, further affecting the distribution of historical average traffic volume.At the same time, low speeds and complex road conditions will increase speed fluctuations, affecting the historical speed fluctuation extreme value difference and the number of historical congestion occurrences. Road sections with large historical speed fluctuation extreme value differences may be due to complex road infrastructure conditions, and vehicles need to adjust speeds frequently, which will reduce the historical average speed, affect the road's traffic capacity, cause changes in traffic flow distribution, and increase the number of historical congestion occurrences.

[0066] In a specific embodiment, by analyzing the traffic characterization coefficient of each gantry interval section, the traffic condition of the section can be accurately reflected, providing a numerical basis for the traffic characterization correction coefficient of each gantry interval section.

[0067] The traffic characterization correction coefficients corresponding to each traffic characterization coefficient interval stored in the database are extracted, and the traffic characterization correction coefficients corresponding to the intervals of the traffic characterization coefficients of each gantry interval section are mapped and extracted, and recorded as the traffic characterization correction coefficients of each gantry interval section.

[0068] According to the characteristic index of each gantry interval section and the traffic characterization correction coefficient of each gantry interval section, the congestion characteristic parameters of each gantry interval section are obtained through comprehensive analysis and processing.

[0069] In a specific embodiment, the congestion characteristic parameters of each gantry interval section are obtained in the following specific method:

[0070]

[0071] Among them, C i is the congestion characteristic parameter of the i-th gantry interval section, A i is the characteristic index of the ith gantry interval section, α i is the traffic characterization correction coefficient of the i-th gantry interval section, i is the number of each gantry interval section, i=1,2,...,m, m is the number of gantry interval sections, and e is a natural constant.

[0072] It should be understood that the softplus function is a built-in function in Python, softplus(x) = lg(1+e x ).

[0073] It should be noted that the characteristic index of each gantry interval section reflects the real-time traffic activity of the current section, such as the total number of vehicles, speed, etc., reflecting the dynamic characteristics of the traffic flow. The traffic characterization correction coefficient is based on the basic data of the section and the historical traffic characterization data, and comprehensively considers the long-term traffic carrying capacity and historical operation status of the section. It plays a role in correcting and supplementing the characteristic index. In a specific embodiment, the long-term traffic carrying capacity of the section determines its operation stability under different traffic flow levels. For example, a section with a narrow road width and a large slope has limited carrying capacity. Even if the current traffic flow does not reach a very high value, it may also be congested due to road conditions. At this time, information such as the number of congestion occurrences in the historical traffic conditions can supplement the congestion tendency of the section and correct the judgment based only on characteristic indicators such as real-time traffic flow. Data such as the extreme value of vehicle speed fluctuation in the historical operation status can reflect the speed change characteristics of the section in different time periods and traffic scenarios. If the historical vehicle speed fluctuation is large, even if the current average vehicle speed is acceptable, it is necessary to consider potential traffic instability factors, so as to more accurately assess the congestion risk. This correction and supplement can make the judgment of the congestion status of the road section more realistic and provide a more reliable basis for traffic management and planning. According to the characteristic indicators of each gantry interval section and the traffic characterization correction coefficient of each gantry interval section, the congestion characteristic parameters of each gantry interval section are obtained through comprehensive analysis and processing, which can more accurately assess its congestion status and provide a more targeted numerical basis for the traffic management department, which helps to understand the congestion distribution law of the road network, provide strong support for long-term road planning and resource allocation, improve the operation efficiency of the traffic system, and reduce traffic congestion.

[0074] The road section congestion level corresponding to the congestion characteristic parameter interval of each road section stored in the database is extracted, and the road section congestion level corresponding to the interval where the congestion characteristic parameter of each gantry interval section is located is mapped and extracted, and recorded as the congestion level of each gantry interval section.

[0075] S4, based on the traffic data transmission optical fiber link, obtains the optical fiber link status information of each gantry interval section, and dynamically adjusts the optical fiber link in combination with the congestion level of each gantry interval section, and uploads the road congestion information for early warning processing.

[0076] In this embodiment, the optical fiber link status information applied to each gantry interval section includes the bandwidth utilization, transmission delay duration, bit error rate and optical power loss of the optical fiber link.

[0077] It should be noted that the optical fiber link status information can be collected by an optical fiber link integrated sensing device, which includes an optical power meter, a bit error meter, an optical pulse analyzer and a vector network analyzer.

[0078] In this embodiment, the optical fiber link is dynamically adjusted, and the specific process is as follows:

[0079] Based on the optical fiber link status information applied to each gantry interval section, the optical fiber link status indicator coefficient applied to each gantry interval section is analyzed and processed, and the optical fiber link status indicator coefficient applied to each gantry interval section is used to characterize the optical fiber link transmission status applied to each gantry interval section.

[0080] In a specific embodiment, the optical fiber link status indicator coefficient applied to each gantry interval section is obtained in the following manner:

[0081] The optical fiber link reference state information stored in the database is extracted, where the optical fiber link reference state information includes a reference bandwidth utilization rate, a reference transmission delay time, a reference bit error rate, and a reference optical power loss of the optical fiber link.

[0082]

[0083] Among them, D i is the fiber link status indicator coefficient applied to the i-th gantry interval section, β i is the bandwidth utilization of the optical fiber link applied to the i-th gantry interval section, γ i is the transmission delay of the optical fiber link applied to the i-th gantry interval section, δ i is the bit error rate of the optical fiber link applied to the i-th gantry interval section, ε i is the optical power loss of the optical fiber link applied to the i-th gantry interval section, β 0 is the reference bandwidth utilization of the optical fiber link, γ 0 is the reference transmission delay of the optical fiber link, δ 0 is the reference bit error rate of the optical fiber link, ε 0 is the reference optical power loss of the optical fiber link, x 1 is the bandwidth utilization weight, x 2 is the transmission delay weight, x 3 is the bit error rate weight, x 4 is the optical power loss weight, i=1,2,...,m, m is the number of gantry interval sections, and e is a natural constant.

[0084] It should be noted that the bandwidth utilization weight, transmission delay time weight, bit error rate weight and optical power loss weight represent the numerical values ​​of the influence of bandwidth utilization, transmission delay time, bit error rate weight and optical power loss on the optical fiber link state indicator coefficient applied to the gantry interval section. When used, the preset bandwidth utilization weight, transmission delay time weight, bit error rate weight and optical power loss weight can be directly obtained from the database, and the corresponding relationship can be a pre-set mapping relationship. For example, the bandwidth utilization, transmission delay time weight, bit error rate weight and optical power loss are respectively used with the bandwidth utilization weight, transmission delay time weight, bit error rate weight and optical power loss weight preset in the database to construct a mapping set, and the real-time bandwidth utilization, transmission delay time, bit error rate weight and optical power loss are respectively input into the mapping set to obtain the corresponding bandwidth utilization weight, transmission delay time weight, bit error rate weight and optical power loss weight, and the mapping relationship is one-to-one.

[0085] It should also be noted that the optical fiber link status indicator coefficients applied to each gantry interval section are obtained by analyzing and processing the optical fiber link status information applied to each gantry interval section, taking into account the mutual influence between these parameters. For example, when the bandwidth utilization is high, it means that the amount of data transmitted in the optical fiber link is close to or reaches its carrying capacity. At this time, there will be a queue for data transmission, just like when there are more vehicles on a narrow road, there will be congestion, resulting in an increase in the time for each vehicle to pass, and the delay time of data transmission will increase. Under high bandwidth utilization, the link is in a high load state, and the possibility of interference during signal transmission increases. Because the amount of data is too large, it may cause mutual interference between signals, thereby increasing the bit error rate. When the bandwidth utilization is high, the device needs to continue to operate at a higher power to handle a large number of data transmission tasks, which will increase the optical power loss. A longer transmission delay may mean that the signal takes longer to transmit in the link, and the time it is interfered with by the outside world is also increased accordingly. In this process, the quality of the signal is more likely to decline, resulting in an increase in the bit error rate. When the bit error rate is high, in order to ensure the accurate transmission of data, the system may take some error correction measures, such as retransmitting data. These operations will increase the workload of the device and lead to increased optical power loss. Large optical power loss leads to reduced signal quality, which may cause unstable data transmission and increase transmission delay.

[0086] In a specific embodiment, by analyzing the optical fiber link status indicator coefficient applied in the gantry interval section, the overall transmission status is accurately reflected. This helps to determine whether the link is in a good operating state, or is facing transmission pressure, performance degradation and other problems. This avoids the situation where traffic data transmission is not smooth due to link problems, which in turn causes delays or errors in traffic management decisions, greatly improving the timeliness and effectiveness of traffic management.

[0087] The transmission priority factors corresponding to the congestion levels of each road section stored in the database are extracted, and the transmission priority factors corresponding to the congestion levels of each gantry interval section are mapped and extracted, and marked as the transmission priority factors of each gantry interval section.

[0088] The congestion level of a road section is an important indicator for measuring road traffic conditions. The higher the congestion level, the worse the traffic conditions on that road section, and the more important the real-time and accuracy of traffic data are. The transmission priority factor reflects the priority of data transmission. The larger the value, the higher the priority should be for data transmission on that road section.

[0089] When the congestion level of a road section is high, it is necessary to grasp the traffic data of the road section more timely and accurately so as to take effective measures quickly. By setting a positive correlation between the congestion level and the transmission priority factor, that is, the higher the congestion level, the greater the transmission priority factor, it can be ensured that during the data transmission process, the data of the seriously congested road sections are transmitted first. In this way, the information of these key sections, such as traffic volume and speed, can be obtained more quickly, which improves the road traffic efficiency and alleviates the congestion. At the same time, it helps to optimize the data transmission order of the entire traffic monitoring system, avoid decision-making errors caused by untimely or chaotic data transmission, and ensure the efficient operation of the traffic system.

[0090] According to the optical fiber link status indicator coefficient applied to each gantry interval section and the transmission priority factor of each gantry interval section, the optical fiber link adjustment demand index applied to each gantry interval section is obtained through comprehensive analysis and processing.

[0091] The optical fiber link adjustment demand index applied to each gantry interval section is used to characterize the degree of optical fiber link adjustment demand applied to each gantry interval section.

[0092] In a specific embodiment, the optical fiber link adjustment demand index applied to each gantry interval section is obtained in the following manner:

[0093]

[0094] Among them, F i is the fiber link adjustment demand index applied to the i-th gantry interval section, D i is the fiber link status indicator coefficient applied to the i-th gantry interval section, is the transmission priority factor of the i-th gantry interval section, i=1,2,...,m, and m is the number of gantry interval sections.

[0095] The optical fiber link adjustment demand index applied to each gantry interval section is obtained by comprehensive analysis based on the optical fiber link status indicator coefficient and the transmission priority factor of each gantry interval section. It combines the actual transmission status of the optical fiber link and the consideration of data transmission priority under traffic congestion on the section. It can accurately quantify the adjustment demand of the optical fiber link in each section and provide a numerical basis for traffic data transmission. When the road is congested, the amount of traffic data increases sharply and needs to be transmitted more frequently, and the pressure on the optical fiber link increases. The index can analyze the degree of demand adjustment through the transmission pressure of the optical fiber link, providing a numerical basis for the system to make rapid adjustments.

[0096] In a specific embodiment, when the optical fiber link adjustment demand index applied to the gantry interval section is high, problems such as insufficient optical fiber link bandwidth can be discovered in time, prompting the system to quickly increase bandwidth, ensuring that a large amount of data collected by the traffic monitoring device is transmitted to the management center in real time and accurately, avoiding traffic management decision-making errors due to data transmission delays or losses, and effectively improving the timeliness and accuracy of traffic management. When traffic congestion occurs, fast data transmission helps the management department to quickly grasp the road conditions, take timely diversion measures, improve road traffic efficiency, and alleviate congestion. At the same time, reasonable adjustment demand index analysis also helps to optimize the allocation of optical fiber link resources, avoid resource waste, reduce operating costs, and ensure the long-term stable and reliable operation of the traffic monitoring system.

[0097] Extract the preset optical fiber link demand adjustment threshold in the database.

[0098] The optical fiber link adjustment demand index applied to each gantry interval section is subtracted from the optical fiber link adjustment demand threshold to obtain the optical fiber link adjustment demand deviation factor applied to each gantry interval section.

[0099] Difference processing refers to subtracting the fiber optic link demand adjustment threshold from the fiber optic link adjustment demand index applied to each gantry interval section. The difference processing result can be greater than zero, less than zero or equal to zero.

[0100] Based on the optical fiber link demand adjustment deviation factor applied to each gantry interval section, the optical fiber link is dynamically adjusted. The specific adjustment method is as follows: the bandwidth adjustment value corresponding to each demand adjustment deviation factor interval is extracted, and the bandwidth adjustment value of the interval in which the optical fiber link demand adjustment deviation factor applied to each gantry interval section is extracted is mapped and recorded as the optical fiber link bandwidth adjustment value applied to each gantry interval section, and the optical fiber link is adjusted according to the optical fiber link bandwidth adjustment value applied to each gantry interval section.

[0101] It should be noted that the dynamic adjustment of the optical fiber link refers to adjusting the bandwidth of the optical fiber link. In a specific embodiment, for example, if a certain gantry interval section is severely congested during peak hours, its optical fiber link adjustment demand index is calculated to be 0.8. The preset optical fiber link demand adjustment threshold of 0.5 for this type of section is extracted from the database. Difference processing is performed, that is, 0.8-0.5=0.3, and the obtained optical fiber link demand adjustment deviation factor is 0.3 (greater than zero). This indicates that the optical fiber link of the current section needs to increase the bandwidth to meet the data transmission demand. According to the correspondence between the preset optical fiber link demand adjustment deviation factor applied to each gantry interval section and the optical fiber link bandwidth adjustment value applied to each gantry interval section, the bandwidth adjustment amount corresponding to the deviation factor 0.3 is an increase of 50Mbps. The system will automatically send instructions to the optical fiber link device, adjust its configuration parameters, and increase the bandwidth of the optical fiber link of the section by 50Mbps to ensure that traffic data can be transmitted quickly and stably, avoid traffic management decision lags due to poor data transmission, thereby ensuring the efficient operation of the traffic system and alleviating traffic pressure caused by congestion.

[0102] It should also be noted that if the extracted bandwidth adjustment value exceeds the maximum value of the fiber link bandwidth adjustment, the fiber link is adjusted with its maximum tolerable bandwidth; if the extracted bandwidth adjustment value is lower than the minimum value of the fiber link bandwidth adjustment, the fiber link is adjusted with its minimum tolerable bandwidth.

[0103] In a specific embodiment, the optical fiber link adjustment demand index applied to each gantry interval section is obtained by analyzing the optical fiber link state indicator coefficient applied to each gantry interval section and the transmission priority factor of each gantry interval section. When road congestion occurs, the traffic conditions of each gantry interval section deteriorate, the amount of traffic data will increase sharply and need to be transmitted more frequently, which makes the data transmission pressure of the optical fiber link rise sharply and requires higher transmission capacity of the optical fiber link. Dynamic adjustment of the optical fiber link according to the optical fiber link adjustment demand index applied to each gantry interval section can ensure efficient and stable transmission of traffic data, improve the reliability and real-time performance of the traffic monitoring system, and effectively avoid negative negative impacts such as delays on timely management of traffic due to data transmission bottlenecks, ensure that monitoring data is transmitted to the management center in a timely and accurate manner, and improve road traffic efficiency.

[0104] In a specific embodiment, the road congestion information is uploaded for early warning processing, and the specific process is as follows:

[0105] Upload road congestion information to the traffic control system for early warning display.

[0106] In a specific embodiment, the warning display content may be a visual presentation of the congestion level of each gantry interval section, while displaying the specific location and traffic data of the congested section.

[0107] In a specific embodiment, by providing a road congestion evaluation method based on the existence of multi-gantry road intersections between adjacent gantries, data of intersections between adjacent gantry sections and sections between gantry sections are obtained and analyzed, and the complex traffic conditions at the multi-gantry road intersections are comprehensively considered. The congestion level of each gantry interval section can be accurately determined, and the congestion level of each gantry interval section can be accurately determined. The adaptive adjustment of the traffic monitoring device and the dynamic optimization of the optical fiber link can be realized, providing a numerical basis for traffic management, better adapting to the complex traffic environment, ensuring the smooth operation of the traffic system, and promoting the rational allocation of traffic resources.

[0108] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0109] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. As long as they do not deviate from the structure of the present invention or exceed the scope defined by the present invention, they should all belong to the protection scope of the present invention.

Claims

1. A road congestion evaluation method based on the presence of multi-gantry road intersections between adjacent gantries, characterized by: The following steps are involved: S1, obtain adjacent gantry sections, count multiple road intersections between adjacent gantry sections, and simultaneously obtain sections between road intersections and adjacent gantries, which are recorded as the interval sections between each gantry; S2, collecting traffic data of each gantry interval section through a traffic monitoring device, analyzing and processing to obtain characteristic indicators of each gantry interval section, and adaptively adjusting the traffic monitoring device accordingly; S3, based on the characteristic indicators of each gantry interval section, and synchronously collecting basic data and historical traffic characterization data of each gantry interval section for comprehensive processing, determine the congestion level of each gantry interval section; S4, based on the traffic data transmission optical fiber link, obtains the optical fiber link status information of each gantry interval section, and dynamically adjusts the optical fiber link in combination with the congestion level of each gantry interval section, and uploads the road congestion information for early warning processing.

2. The road congestion evaluation method based on the presence of a multi-gantry road intersection between adjacent gantries according to claim 1, characterized in that: The traffic data of each gantry interval section includes the total number of vehicles passing through each gantry interval section within a preset sensing period, the total number of large vehicles passing through, the average vehicle speed, the standard deviation of the vehicle speed and the average vehicle passing time.

3. The road congestion evaluation method based on the presence of a multi-gantry road intersection between adjacent gantries according to claim 2, characterized in that: The analysis process obtains characteristic indicators of each gantry interval section, and the specific analysis process is as follows: extracting reference traffic data stored in a database; Based on the traffic data of each gantry interval section and the reference traffic data, the characteristic index of each gantry interval section is obtained by analysis and processing; The characteristic index of each gantry interval section is used to characterize the traffic activity level of each gantry interval section.

4. The road congestion evaluation method based on the presence of a multi-gantry road intersection between adjacent gantries according to claim 3 is characterized by: The traffic monitoring device is adaptively adjusted, and the specific adjustment process is as follows: Extract the traffic monitoring device adjustment parameters corresponding to the characteristic index intervals of each road section stored in the database, and map and extract the traffic monitoring device adjustment parameters corresponding to the intervals where the characteristic indexes of each gantry interval section are located, and record them as the traffic monitoring device adjustment parameters of each gantry interval section; The traffic monitoring device adjustment parameters of each gantry interval section include a traffic monitoring device monitoring frequency adjustment value and a sensor sensitivity adjustment value; Based on the traffic monitoring device adjustment parameters of each gantry interval section, the corresponding traffic monitoring device is adaptively adjusted.

5. The road congestion evaluation method based on the presence of multi-gantry road intersections between adjacent gantries according to claim 1, characterized in that: The basic data of each gantry interval section is collected, and the specific collection process is as follows: Extract the GIS mapping model of each gantry interval section from the database; The basic data of each gantry interval section are extracted from the GIS mapping model, including the section length, average road width, average road slope and road flatness of each gantry interval section.

6. The road congestion evaluation method based on the presence of a multi-gantry road intersection between adjacent gantries according to claim 1, characterized in that: The historical traffic characterization data include the historical average traffic flow, the historical average vehicle speed, the historical vehicle speed fluctuation extreme value difference and the historical congestion occurrence times of each gantry interval section.

7. The road congestion evaluation method based on the presence of a multi-gantry road intersection between adjacent gantries according to claim 6, characterized in that: The specific process of determining the congestion level of each gantry interval section is as follows: According to the basic data of each gantry interval section and the historical traffic characterization data, the traffic characterization coefficient of each gantry interval section is obtained by analyzing and processing, and the traffic characterization coefficient of each gantry interval section is used to characterize the basic traffic conditions of each gantry interval section; Extract the traffic characterization correction coefficient corresponding to each traffic characterization coefficient interval stored in the database, and map and extract the traffic characterization correction coefficient corresponding to the interval of the traffic characterization coefficient of each gantry interval section, and record it as the traffic characterization correction coefficient of each gantry interval section; According to the characteristic index of each gantry interval section and the traffic characterization correction coefficient of each gantry interval section, the congestion characteristic parameters of each gantry interval section are obtained through comprehensive analysis and processing; The road section congestion level corresponding to the congestion characteristic parameter interval of each road section stored in the database is extracted, and the road section congestion level corresponding to the interval of the congestion characteristic parameter of each gantry interval road section is mapped and extracted, and recorded as the congestion level of each gantry interval road section.

8. The road congestion evaluation method based on the presence of multi-gantry road intersections between adjacent gantries according to claim 1, characterized in that: The optical fiber link status information applied to each gantry interval section includes the bandwidth utilization, transmission delay duration, bit error rate and optical power loss of the optical fiber link.

9. The road congestion evaluation method based on the presence of a multi-gantry road intersection between adjacent gantries according to claim 8, characterized in that: The specific process of dynamically adjusting the optical fiber link is as follows: Based on the optical fiber link status information applied to each gantry interval section, the optical fiber link status indicator coefficient applied to each gantry interval section is analyzed and processed, and the optical fiber link status indicator coefficient applied to each gantry interval section is used to characterize the optical fiber link transmission status applied to each gantry interval section; Extract the transmission priority factors corresponding to the congestion levels of each road section stored in the database, and map and extract the transmission priority factors corresponding to the congestion levels of each gantry interval section, and mark them as the transmission priority factors of each gantry interval section; According to the optical fiber link status indicator coefficient and the transmission priority factor of each gantry interval section, the optical fiber link adjustment demand index applied to each gantry interval section is obtained by comprehensive analysis and processing; The optical fiber link adjustment demand index applied to each gantry interval section is used to characterize the optical fiber link adjustment demand degree applied to each gantry interval section; Extracting the optical fiber link demand adjustment threshold preset in the database; The optical fiber link regulation demand index applied to each gantry interval section is processed with the optical fiber link regulation demand threshold to obtain the optical fiber link regulation demand deviation factor applied to each gantry interval section; The optical fiber link is dynamically adjusted based on the optical fiber link demand adjustment deviation factor applied to each gantry interval section.

10. The road congestion evaluation method based on the presence of multi-gantry road intersections between adjacent gantries according to claim 7, characterized in that: The specific method for obtaining the congestion characteristic parameters of each gantry interval section is as follows: Among them, C i is the congestion characteristic parameter of the i-th gantry interval section, B i is the characteristic index of the ith gantry interval section, α i is the traffic characterization correction coefficient of the i-th gantry interval section, i is the number of each gantry interval section, i=1,2,...,m, m is the number of gantry interval sections, and e is a natural constant.

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