Road traffic capacity determination method, device, equipment, medium and product

Abstract

The invention provides a road traffic capacity determination method, device, equipment, medium and product, and relates to the technical field of smart traffic, the method comprises the following steps: determining the first traffic capacity of each sub-road section in a target road section according to historical data reported by sensing equipment, each sub-road section corresponding to a road scene category; the road scene category comprises at least one of a basic road section scene, a ramp scene and a tunnel scene; for each sub-road section, determining the influence coefficient of the current traffic capacity of the sub-road section according to the road scene category corresponding to the sub-road section and the category of the currently monitored influence factor; and determining the current second traffic capacity of the target road section according to the first traffic capacity of each sub road section and the influence coefficient. According to the scheme, comprehensive measurement and calculation of the traffic capacity of the whole road section of the expressway can be realized, data support is provided for traffic management, and decision control is assisted.

Description

Technical Field

[0001] The present application relates to the field of intelligent transportation technology, and in particular to a method, device, equipment, medium and product for determining road capacity. Background Art

[0002] With the rapid development of the national economy, the number of cars continues to increase, the demand for transportation has grown rapidly, and the contradiction between transportation supply and demand has become increasingly prominent, bringing about traffic congestion problems. It not only reduces the traffic efficiency of highways, but also affects the public's travel experience, and derives a series of problems such as energy loss and environmental pollution.

[0003] As one of the core indicators of highway operation, traffic capacity has an important impact on highway operation. Traffic capacity refers to the maximum ability of traffic facilities to divert traffic flow under normal operation levels. It can effectively reflect the road capacity level and serve as an important basis for monitoring and issuing control measures for highway operation efficiency. It is a crucial indicator for solving problems such as traffic congestion, traffic planning, and road construction.

[0004] The intelligence and information level of existing highways are continuously improving. Especially under the vehicle-road collaborative technology system of the Internet of Things and the Internet of Vehicles, various sensing devices will generate massive amounts of traffic operation data. How to use massive amounts of operation data to achieve comprehensive measurement of the traffic capacity of highway sections, improve highway operation efficiency and management service level, so as to solve the serious traffic congestion problem caused by the rapid growth of transportation demand and the increasingly prominent contradiction between transportation supply and demand due to the continuous increase in the number of cars. Summary of the Invention

[0005] The embodiments of the present application provide a method, device, equipment, medium and product for determining road capacity, which solves the current problem of being unable to comprehensively measure the capacity of an entire road section.

[0006] In a first aspect, to achieve the above-mentioned objectives, embodiments of the present application provide a method for determining road capacity, which is applied to a cloud platform and includes:

[0007] Determine, based on historical data reported by the sensing device, a first traffic capacity of each sub-segment in the target road segment, wherein each sub-segment corresponds to a road scene category; the road scene category includes at least one of a basic road segment scenario, a ramp scenario, and a tunnel scenario;

[0008] For each of the sub-road sections, determining an influence coefficient of the current traffic capacity of the sub-road section according to the road scene category corresponding to the sub-road section and the category of the currently monitored influencing factors;

[0009] The current second traffic capacity of the target road section is determined according to the first traffic capacity of each of the sub-road sections and the influence coefficient.

[0010] The first traffic capacity of each sub-section in the target road section is determined based on the historical data reported by the sensing device, including:

[0011] Obtaining historical data reported by a plurality of sensing devices spaced apart within a target sub-section within a first time period; wherein the historical data includes an average vehicle speed and number of vehicles sensed by the sensing devices, and the target sub-section is any sub-section within the target sub-section;

[0012] determining a first length of an overlapping area between two adjacent sensing devices according to performance parameters and layout parameters of the sensing devices arranged in the target sub-section;

[0013] The first traffic capacity of the target sub-section is determined according to the first length, the road width corresponding to the target sub-section, the average vehicle speed and the number of vehicles reported by each of the sensing devices at the same time.

[0014] Determining a first traffic capacity of the target sub-segment based on the first length, the road width corresponding to the target sub-segment, the average vehicle speed and the number of vehicles reported by each of the sensing devices at the same time includes:

[0015] Obtaining, from the performance parameters of the sensing devices deployed on the target sub-road section, a second length of a radiation area of ​​each of the sensing devices, wherein the second length is a length of the radiation area along an extension direction of the target sub-road section;

[0016] determining the number of vehicles in each overlapping area according to the first length, the second length, the road width, and the number of vehicles reported by each sensing device;

[0017] The first traffic capacity of the target sub-section is determined according to the number of vehicles in the overlapping area, the second length, the average vehicle speed reported by each of the sensing devices, and the number of vehicles.

[0018] Determining the number of vehicles in each overlapping area according to the first length, the second length, the road width, and the number of vehicles reported by each sensing device includes:

[0019] For each of the sensing devices, determining the area of ​​the radiation area according to the second length of the radiation area of ​​the sensing device and the road width;

[0020] Determining the number of vehicles per unit area sensed by the sensing device based on the number of vehicles reported by the sensing device and the area of ​​the radiation area;

[0021] Determining an average number of vehicles per unit area in the target sub-segment according to the number of vehicles per unit area sensed by each of the sensing devices;

[0022] The number of vehicles in each overlapping area is determined according to the average number of vehicles per unit area, the road width, and the first length of each overlapping area.

[0023] Determining the first traffic capacity of the target sub-section according to the number of vehicles in the overlapping area, the second length, the average vehicle speed reported by each of the sensing devices, and the number of vehicles includes:

[0024] Among the plurality of sensing devices, two sensing devices having overlapping areas are divided into a sensing device group;

[0025] For each of the sensing device groups, the traffic capacity corresponding to the first sensing device is determined based on the second length corresponding to the first sensing device, the average vehicle speed and the number of vehicles reported by the first sensing device; the traffic capacity corresponding to the second sensing device is determined based on the second length corresponding to the second sensing device, the number of vehicles in the overlapping area corresponding to the first and second sensing devices, and the average vehicle speed and the number of vehicles reported by the second sensing device; wherein the first and second sensing devices are sensing devices arranged sequentially along the direction of vehicle travel on the target road section;

[0026] The first traffic capacity of the target sub-section is determined according to the traffic capacity corresponding to the first sensing device and the traffic capacity corresponding to the second sensing device in each of the sensing device groups.

[0027] Determining the traffic capacity corresponding to the second sensing device based on the second length corresponding to the second sensing device, the number of vehicles in the overlapping area corresponding to the first sensing device and the second sensing device, and the average vehicle speed and the number of vehicles reported by the second sensing device includes:

[0028] Calculating a first difference between the number of vehicles reported by the second sensing device and the number of vehicles in an overlapping area between the first sensing device and the second sensing device;

[0029] Calculating a product of the first difference and the average speed of the vehicle reported by the second sensing device;

[0030] The traffic capacity corresponding to the second sensing device is determined according to the ratio of the product and the second length corresponding to the second sensing device.

[0031] The determining of the influence coefficient of the current traffic capacity of the sub-road section according to the road scene category corresponding to the sub-road section and the category of the currently monitored influencing factors includes:

[0032] Obtaining target influencing factors corresponding to the target sub-segment according to the road scene category corresponding to the target sub-segment, the target influencing factors including weather and / or traffic events;

[0033] Determine the impact factor coefficient corresponding to the target impact factor according to the category of the target impact factor currently monitored;

[0034] The influence coefficient is determined according to the influence factor coefficient.

[0035] Wherein, determining the impact factor coefficient corresponding to the target impact factor according to the category of the target impact factor currently monitored includes:

[0036] Determining the severity of the target influencing factor according to the category of the target influencing factor;

[0037] The impact factor coefficient is determined according to the severity and the attenuation coefficient.

[0038] Wherein, determining the influence coefficient according to the influence factor coefficient includes:

[0039] When there is only one target influencing factor, according to the formula determining the influence coefficient;

[0040] Alternatively, when the target influencing factors include multiple factors, according to the formula determining the influence coefficient;

[0041] Where x and y represent the impact factor coefficients respectively.

[0042] The step of determining the current second traffic capacity of the target road section according to the first traffic capacity of each of the sub-road sections and the influence coefficient includes:

[0043] Determining a weight coefficient for each sub-segment according to the influence coefficient corresponding to each sub-segment;

[0044] Based on the weight coefficient, the first traffic capacity of each sub-section is weightedly summed to obtain the second traffic capacity.

[0045] Wherein, determining the weight coefficient of each sub-segment according to the influence coefficient corresponding to each sub-segment includes:

[0046] Summing the reciprocals of the influence coefficients corresponding to the sub-sections to obtain a first value;

[0047] The weight coefficient of the target sub-road section is determined according to the ratio of the inverse of the influence coefficient of the target sub-road section to the first value, wherein the target sub-road section is any one of the sub-road sections.

[0048] In a second aspect, to achieve the above-mentioned objectives, embodiments of the present application provide a road capacity determination device, applied to a cloud platform, comprising:

[0049] A first determination module is configured to determine a first traffic capacity of each sub-segment in the target road segment based on historical data reported by the sensing device, wherein each sub-segment corresponds to a road scene category; the road scene category includes at least one of a basic road segment scenario, a ramp scenario, and a tunnel scenario;

[0050] A second determination module is configured to determine, for each of the sub-road sections, an influence coefficient of the current traffic capacity of the sub-road section according to the road scene category corresponding to the sub-road section and the category of the currently monitored influencing factors;

[0051] The third determining module is configured to determine the current second traffic capacity of the target road section according to the first traffic capacity of each of the sub-road sections and the influence coefficient.

[0052] In the third aspect, in order to achieve the above-mentioned purpose, an embodiment of the present application provides a road capacity determination device, including a transceiver, a processor, a memory, and a program stored on the memory and runnable on the processor; when the processor executes the program, the road capacity determination method as described in the first aspect is implemented.

[0053] In a fourth aspect, in order to achieve the above-mentioned purpose, an embodiment of the present application provides a readable storage medium on which a program or instruction is stored, and when the program or instruction is executed by a processor, the road capacity determination method as described in the first aspect is implemented.

[0054] In a fifth aspect, in order to achieve the above-mentioned purpose, an embodiment of the present application provides a computer program product, including computer instructions, which, when executed by a processor, implement the road capacity determination method as described in the first aspect.

[0055] The beneficial effects of the above technical solution of this application are as follows:

[0056] In an embodiment of the present application, first, based on the historical data reported by the sensing device, the first traffic capacity of each sub-section in the target section is determined, wherein each sub-section corresponds to a road scene category; the road scene category includes at least one of a basic section scene, a ramp scene and a tunnel scene; secondly, for each of the sub-sections, the influence coefficient of the current traffic capacity of the sub-section is determined according to the road scene category corresponding to the sub-section and the category of the currently monitored influencing factors; finally, based on the first traffic capacity of each of the sub-sections and the influence coefficient, the current second traffic capacity of the target section is determined. In this way, real-time measurement of the traffic capacity of the entire section of the highway is achieved to comprehensively reflect the actual traffic capacity of the entire section, provide a basis for traffic management, planning and control decisions, thereby reducing the probability of traffic congestion and improving the public's travel experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A flowchart of a method for determining road capacity according to an embodiment of the present application;

[0058] Figure 2 Schematic diagram of the radar coverage area in an embodiment of the present application;

[0059] Figure 3 This is a schematic diagram of IoT perception transmission according to an embodiment of the present application;

[0060] Figure 4 This is a schematic structural diagram of a device for determining road capacity according to an embodiment of the present application;

[0061] Figure 5 This is a schematic diagram of the structure of the road capacity determination device according to an embodiment of the present application. DETAILED DESCRIPTION

[0062] In order to make the technical problems, technical solutions and advantages to be solved by this application clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0063] It should be understood that references throughout this specification to "one embodiment" or "an embodiment" mean that a particular feature, structure, or characteristic associated with the embodiment is included in at least one embodiment of the present application. Therefore, the appearances of "in one embodiment" or "in an embodiment" throughout this specification do not necessarily refer to the same embodiment. Furthermore, these particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0064] In the various embodiments of the present application, it should be understood that the size of the serial numbers of the following processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0065] Additionally, the terms "system" and "network" are often used interchangeably herein.

[0066] In the embodiments provided herein, it should be understood that "B corresponding to A" means that B is associated with A and B can be determined based on A. However, it should also be understood that determining B based on A does not mean determining B based solely on A; B can also be determined based on A and / or other information.

[0067] Before describing the embodiments of the present application, the following technical points are first described by way of example:

[0068] 1. Capacity calculation method based on car-following theory:

[0069] The car-following theory-based capacity calculation method analyzes the dynamic behavior of vehicles on a single lane where overtaking is prohibited, leveraging the interactions between vehicles to assess road capacity. This method first establishes a car-following model to describe the behavior of a following vehicle following a leading vehicle. It then identifies performance metrics such as free-flow speed and average trip speed. Traffic flow characteristics are then analyzed to examine vehicle behavior under varying speeds and densities. Finally, based on the relationship between headway and speed, the basic capacity of a one-way traffic segment is derived.

[0070] However, the driving behavior variables currently considered in the car-following model are mostly information such as the speed, speed difference, vehicle spacing, and acceleration between the following vehicle and the preceding vehicle. These data are difficult to collect during the operation of existing highways, making the model's practical application more difficult.

[0071] 2. Capacity calculation method based on traffic simulation:

[0072] The application of simulation methods relies on the construction of simulation scenarios and the establishment of vehicle operation rules. The former requires accurate knowledge of static information such as the road network's location, mileage, lane width, and network structure. The larger the road network, the more road and environmental information needs to be collected, and the higher the accuracy requirements, because the degree of simulation environment restoration directly affects the accuracy of the simulation results. The latter requires accurate knowledge of traffic operation rules (such as lane change probability and overtaking probability) at different times and in different spaces. However, actual traffic operation is significantly influenced by objective factors and the subjective behavior of drivers, and is subject to randomness and self-organization. Traffic operation rules are not fixed but rather uncertain and randomly changing, so simulation results are bound to differ from reality.

[0073] However, this method requires accurate knowledge of traffic operation rules at different time periods and in different spaces, including lane change probability and overtaking probability, to build an operating environment for capacity calculation. However, in actual road operation, the above behaviors are greatly affected by subjective factors, which will cause certain differences between the simulation results and the actual situation.

[0074] 3. Capacity calculation method based on measured data:

[0075] At present, the basic capacity values ​​specified in the road capacity manuals of various countries are mostly directly taken from actual observation results, that is, using measured traffic flow data with sufficient sample size that includes information on various traffic conditions to construct a speed-flow relationship model to deduce road capacity.

[0076] However, this method relies on actual observation data of highways, and the measured data cannot reflect changes in traffic conditions in a timely manner, resulting in deviations between the calculated results and the actual situation.

[0077] In addition, the above-mentioned methods mainly calculate the traffic capacity based on a single section or scenario, lack the calculation of the traffic capacity of the entire road section, and cannot fully reflect the actual traffic conditions of the entire road section, which in turn affects traffic management, planning and control decisions.

[0078] Furthermore, IoT technology is underutilized in calculating highway capacity. Traditional methods rely on single devices or offline data, failing to form an IoT system with interconnected devices. This results in fragmented data collection, high transmission latency, and the inability to achieve real-time dynamic monitoring. For example, radar, camera, or meteorological sensor data is often processed independently, lacking collaborative integration based on cloud platforms. This can lead to problems such as duplicate vehicle counting or delayed responses to environmental factors. In other words, existing technologies fail to fully utilize the cloud-edge architecture of Cellular Vehicle to Everything (C-V2X). Devices lack intelligent connectivity, preventing data from being shared and processed in real time, making it difficult to adapt to dynamic changes in complex scenarios.

[0079] In view of this, the embodiment of the present application provides a method for determining highway capacity, which is applied to a cloud platform, specifically, for example, Figure 3 The V2X center cloud platform shown in Figure 1 As shown, the method includes:

[0080] Step 101, based on the historical data reported by the sensing device, determine the first traffic capacity of each sub-section in the target section, wherein each sub-section corresponds to a road scene category; the road scene category includes at least one of a basic section scene, a ramp scene and a tunnel scene; wherein, first, the sensing device is a device deployed at intervals on the roadside of the target section, such as a radar, wherein the sensing device belongs to a device in the perception layer of the Internet of Things; in addition, the road scene categories corresponding to different sub-sections can be different or the same; second, the first traffic capacity is, for example, the maximum traffic capacity of the target section, wherein the "first traffic capacity" is, for example, the maximum number of vehicles that can pass through the target section per unit time.

[0081] Step 102: for each of the sub-road sections, determine the influence coefficient of the current traffic capacity of the sub-road section according to the road scene category corresponding to the sub-road section and the category of the currently monitored influencing factors; wherein, the influencing factors corresponding to different road scene categories may be at least partially the same, or may be different, and may be specifically determined according to actual conditions. For example, the influencing factors corresponding to the basic road section scene may include weather and / or traffic events, the influencing factors corresponding to the ramp scene may be the same as the influencing factors corresponding to the basic road section scene, both including weather and / or traffic events; the influencing factors corresponding to the tunnel scene may only include traffic events; in addition, the category of the influencing factor corresponds to the degree of influence on the traffic capacity.

[0082] Step 103 determines the current second capacity of the target road segment based on the first capacity of each sub-segment and the influence coefficient. In other words, the current second capacity of the road segment is related to the type of the current influencing factor. For example, the second capacity is the number of vehicles that can pass through the target road segment per unit time.

[0083] In the road capacity determination method of the embodiment of the present application, first, based on the historical data reported by the sensing device, the first capacity of each sub-section in the target road section is determined, wherein each sub-section corresponds to a road scene category; the road scene category includes at least one of a basic road section scene, a ramp scene and a tunnel scene; secondly, for each sub-section, the influence coefficient of the current capacity of the sub-section is determined according to the road scene category corresponding to the sub-section and the category of the currently monitored influencing factor; finally, based on the first capacity of each sub-section and the influence coefficient, the current second capacity of the target road section is determined. In this way, the maximum capacity of the target road section is determined using historical data, and the influence coefficient is determined based on the category of the factors affecting the capacity monitored in real time, so that the real-time capacity of the target road section is determined based on the maximum capacity and the influence coefficient, so as to reflect the current actual capacity of the entire road section in real time and comprehensively, provide a basis for traffic management, planning and control decisions, thereby reducing the probability of traffic congestion and improving the public's travel experience.

[0084] As an optional implementation, step 101 includes:

[0085] Obtain historical data reported by multiple sensing devices spaced apart within a target sub-section within a first time period; wherein the historical data includes the average vehicle speed and number of vehicles sensed by the sensing devices, and the target sub-section is any sub-section within the target section; that is, this step is: for each sub-section, obtain the average vehicle speed and number of vehicles reported by the sensing devices deployed within the sub-section within the same time period.

[0086] The sensing device reports the average vehicle speed and number of vehicles in real time, or in other words, the sensing device reports the average vehicle speed and number of vehicles in a very short reporting cycle, for example, a reporting cycle of 1 second. In addition, at least two sensing devices can be deployed in the target sub-section.

[0087] According to the performance parameters and layout parameters of the sensing devices arranged in the target sub-segment, a first length of an overlapping area between two adjacent sensing devices is determined, wherein the first length is the length of the overlapping area in the extension direction of the sub-segment.

[0088] Here, it should be noted that in a networked environment, sensing devices (such as radars) are generally deployed in opposite directions (such as Figure 2 The above arrangement will cause overlapping coverage areas of adjacent sensing devices (such as Figure 2 There are overlapping coverage areas between radar 1 and radar 2, as well as radar 3 and radar 4 ( Figure 2When counting the number of vehicles in the coverage area of ​​multiple sensing devices, duplicate counting may occur. Therefore, in the above steps, the first length of the overlapping area is determined based on the performance parameters and layout parameters of the sensing devices to eliminate the duplicated number of vehicles during statistics.

[0089] The first capacity of the target sub-segment is determined based on the first length, the road width corresponding to the target sub-segment, the average vehicle speed reported simultaneously by each sensing device, and the number of vehicles. Since there are overlapping areas between different sensing devices, it is necessary to exclude vehicles in the overlapping areas that have been repeatedly reported when determining the first capacity. Therefore, in this step, the number of vehicles repeatedly counted, combined with the first length of the overlapping areas, is also determined when determining the first capacity to improve the accuracy of the first capacity calculation. The specific implementation of this step will be described later.

[0090] As a specific implementation, the step of “determining the first traffic capacity of the target sub-segment based on the first length, the road width corresponding to the target sub-segment, the average vehicle speed and the number of vehicles reported by each of the sensing devices at the same time” includes:

[0091] 1) From the performance parameters of the sensing devices deployed on the target sub-road section, obtain the second length of the radiation area of ​​each of the sensing devices, wherein the second length is the length of the radiation area along the extension direction of the target sub-road section.

[0092] 2) Determine the number of vehicles within each overlapping area based on the first length, the second length, the road width, and the number of vehicles reported by each sensing device; the number of vehicles within the overlapping area may be the product of the number of vehicles per unit area and the area of ​​the overlapping area. For example, this step may include the following sub-steps:

[0093] Sub-step 1: For each of the sensing devices, determine the area of ​​the radiation area according to the second length of the radiation area of ​​the sensing device and the road width; wherein the area of ​​the radiation area is the product of the second length and the road width;

[0094] Sub-step 2: Determine the number of vehicles per unit area sensed by the sensing device based on the number of vehicles reported by the sensing device and the area of ​​the radiation area; wherein the number of vehicles per unit area is related to the ratio of the number of vehicles reported by the sensing device to the area of ​​the radiation area of ​​the sensing device, wherein "related" here can be understood as: when the ratio is an integer, the number of vehicles per unit area is the ratio; when the ratio is a decimal, the number of vehicles per unit area is the integer obtained by rounding the ratio;

[0095] Sub-step three: determining the average number of vehicles per unit area within the target sub-segment based on the number of vehicles per unit area sensed by each of the sensing devices; wherein the average number of vehicles per unit area is correlated with the mean of the ratios of the number of vehicles reported by each sensing device to the area of ​​the radiation area; similarly, the term "correlated" here can be understood as follows: when the mean is an integer, the average number of vehicles per unit area is the mean; when the mean is a decimal, the average number of vehicles per unit area is the integer obtained by rounding the mean;

[0096] Sub-step four: Determine the number of vehicles in each overlapping area based on the average number of vehicles per unit area, the road width, and the first length of each overlapping area; wherein the number of vehicles in the overlapping area is the product of the average number of vehicles per unit area and the area of ​​the overlapping area (the product of the first length and the road width).

[0097] 3) Determining a first traffic capacity of the target sub-section based on the number of vehicles in the overlapping area, the second length, the average vehicle speed reported by each of the sensing devices, and the number of vehicles; illustratively, this step may include the following sub-steps:

[0098] Sub-step 1: Among the plurality of sensing devices, two sensing devices with overlapping areas are divided into a sensing device group; Figure 2 For example, radar 1 and radar 2 are one equipment group, and radar 3 and radar 4 are another equipment group. Radar 2 and radar 3 are deployed at the same location, but the radiation area of ​​radar 2 is on its left, and the radiation area of ​​radar 3 is on its right.

[0099] Sub-step 2: For each of the sensing device groups, determine the traffic capacity corresponding to the first sensing device based on the second length corresponding to the first sensing device, the average vehicle speed and the number of vehicles reported by the first sensing device; determine the traffic capacity corresponding to the second sensing device based on the second length corresponding to the second sensing device, the number of vehicles in the overlapping area corresponding to the first and second sensing devices, and the average vehicle speed and the number of vehicles reported by the second sensing device; wherein the first sensing device and the second sensing device are sensing devices arranged sequentially along the direction of vehicle travel on the target road section;

[0100] That is to say, for two devices in a device group, for the sensing device that the vehicle passes first, its corresponding traffic capacity is the ratio of the product of the average vehicle speed and the number of vehicles reported by it to the second length of its coverage area. For the sensing device that the vehicle passes later, the number of vehicles in the overlapping area needs to be eliminated when calculating its corresponding traffic capacity. In this way, repeated calculation of vehicles in the overlapping area is avoided, thereby improving the accuracy of the traffic capacity calculation.

[0101] Sub-step three: Determine the first traffic capacity of the target sub-section based on the traffic capacity corresponding to the first sensing device and the traffic capacity corresponding to the second sensing device in each of the sensing device groups. Here, the first traffic capacity of the target sub-section is, for example, the average of the traffic capacities corresponding to each sensing device calculated in sub-step two.

[0102] As a more specific implementation, in the above sub-step 2, "determining the traffic capacity corresponding to the second sensing device based on the second length corresponding to the second sensing device, the number of vehicles in the overlapping area corresponding to the first sensing device and the second sensing device, and the average vehicle speed and the number of vehicles reported by the second sensing device" includes:

[0103] Calculating a first difference between the number of vehicles reported by the second sensing device and the number of vehicles in an overlapping area between the first sensing device and the second sensing device;

[0104] Calculating a product of the first difference and the average speed of the vehicle reported by the second sensing device;

[0105] The traffic capacity corresponding to the second sensing device is determined according to the ratio of the product and the second length corresponding to the second sensing device.

[0106] The following uses a radar as an example to illustrate the implementation process of determining the first traffic capacity using the above-mentioned implementation methods:

[0107] First, the average speed vkm / h, the number of vehicles q, and the radar coverage length lkm reported by the roadside radar equipment are obtained in real time. Therefore, the actual hourly capacity C of a single radar coverage area is related to v, q, and l as follows:

[0108]

[0109] Based on the above relationship, we can know that:

[0110] Secondly, the overlapping areas of the radars are eliminated; here, it should be noted that, firstly, as mentioned above, in a networked environment, radars are generally set up in basic sections, ramps, tunnels and other locations in a counter-arranged manner, and adjacent radars may have overlapping coverage areas. Therefore, when counting the number of vehicles in multiple radar coverage areas, there will be repeated counting, so vehicles in the overlapping areas need to be eliminated. Secondly, in order to accurately calculate the traffic capacity of a single scene / sub-section, generally at least two consecutive radar observation data in the scene are taken as the data source. It is known that the reporting frequency of each radar is 1hz, and the number of vehicles in the overlapping areas is 1hz. Figure 2 For example, the number of vehicles reported by radar 1 in real time is q1, the coverage length of radar 1 is l1km, and the average speed of vehicles reported is v1. The number of vehicles reported by radar 2 in real time is q2, the coverage length of radar 2 is l2km, and the average speed of vehicles reported is v2. The length of the overlapping area of ​​the two adjacent radar radiations is l c , the road width is l w , there are x radar devices in this scenario. Based on this, the average number of vehicles per unit area in a single sub-segment / scenario is expressed as:

[0111]

[0112] Furthermore, the number of vehicles in the overlapping area is: q c =q v *l c *l w

[0113] Next, calculate the capacity of a single scene / sub-section. Specifically, select data reported by multiple consecutive radars in a single scene / sub-section to calculate the scene capacity. The number of vehicles in the overlapping areas between adjacent radars must be eliminated to calculate the capacity of each radar deployment point. The capacity of the single scene is then averaged. In other words, the capacity of a single scene / sub-section can be expressed as:

[0114]

[0115] The above algorithm calculates the real-time traffic capacity of each scene based on the historical data reported by the radar. The radar reports the road traffic operation data once every second. In order to calculate the traffic capacity of the scene under normal road conditions, while taking into account road reconstruction and seasonal factors, it is necessary to collect the data reported by the radar within the past year and take the maximum traffic capacity C from the historical data. max The actual road capacity in the current scenario is the first capacity.

[0116] The above-mentioned calculation method for determining the actual traffic capacity of a single scene / sub-section of a highway in a networked environment uses Internet of Things technology to realize intelligent interconnection of devices to solve the problem of traffic capacity calculation errors caused by repeated counting of vehicles in the coverage area of ​​multiple sensing devices (such as radars). The traffic capacity of a single deployment point is calculated by using indicators such as radar coverage length, traffic flow, and vehicle speed. At the same time, in order to accurately calculate the traffic capacity of a single scene, the reported data of multiple consecutive radars in a single scene will be statistically analyzed. At the same time, the number of vehicles in the overlapping area covered by two adjacent radars will be eliminated to ensure that the vehicles under radar coverage are not counted repeatedly, and the actual traffic capacity of a single scene will be calculated. In this way, the accuracy and real-time performance of the actual traffic capacity assessment of highways are significantly improved, providing accurate decision-making support for the dynamic management and control of intelligent transportation systems.

[0117] In other words, considering the situation where overlapping area data may be reported repeatedly when adjacent sensing devices are deployed, affecting capacity calculations, the present embodiment utilizes the computing power of the intelligent connected cloud platform to achieve accurate data deduplication by quantifying the relationship between the area of ​​overlapping areas and the number of vehicles. Specifically, radar data is first used to calculate the number of vehicles per unit area under radar coverage. The number of vehicles in the overlapping area is then calculated by multiplying the area of ​​the overlapping area by the number of vehicles per unit area. Finally, when calculating the capacity of a single scenario, the relationship between the number of vehicles and the area of ​​the overlapping area is quantified to achieve accurate correction of the capacity calculation.

[0118] As an optional implementation, step 102 includes:

[0119] 1) According to the road scene category corresponding to the target sub-section, the target influencing factors corresponding to the target sub-section are obtained, and the target influencing factors include weather and / or traffic events. Among them, the actual capacity of the basic section and ramp is mainly affected by weather, traffic events, etc., and the actual capacity of the tunnel is mainly affected by traffic events, which affects the capacity of the expressway. Therefore, the target influencing factors of the basic section scene and the ramp scene include weather and traffic events, and the target influencing factor of the ramp section scene is traffic events. Exemplarily, the weather includes sunny days, slight rain / snowfall, light rain / light snow, moderate rain / moderate snow, heavy rain / heavy snow, heavy rain / heavy snow, heavy rain / heavy snow, light fog, heavy fog, dense fog or very dense fog, etc.; traffic events include, for example, traffic accidents, abnormal parking, road spillage, road construction or no events, etc.

[0120] 2) Determine the impact factor coefficient corresponding to the target impact factor based on the category of the target impact factor currently being monitored. This step can be: using roadside sensing equipment (meteorological sensors and event detection cameras) to obtain the category of the target impact factor currently being monitored in real time, and using the edge computing unit as an IoT node to report the category of the target impact factor to the V2X central cloud platform, so that the V2X central cloud platform can determine the impact factor coefficient corresponding to the target impact factor. For example, this step can be implemented through the following two sub-steps:

[0121] Sub-step 1: Determine the severity of the target influencing factor based on the category of the target influencing factor. A pre-configured correspondence between the category of the influencing factor and the severity can be used to obtain the severity corresponding to the category of the target influencing factor through a table lookup. For example, using weather and traffic events as influencing factors, a standardized severity grading table across weather types can be pre-established, mapping meteorological parameters such as precipitation intensity and visibility into continuous numerical values. Furthermore, a pre-established mapping relationship between event classification and severity can be established.

[0122] Sub-step 2: Determine the impact factor coefficient based on the severity and the attenuation coefficient. The attenuation coefficient can be predetermined based on experience for different impact factors, and the attenuation coefficients for different impact factors can be the same or different. For example, for highways, the recommended attenuation coefficient for weather factors is 0.3, and the recommended attenuation coefficient for traffic incident factors is 0.25.

[0123] Based on the above two sub-steps, it can be concluded that the impact factor coefficient corresponding to the weather factor can be expressed as: Among them, f(w) represents the influence factor coefficient of the weather factor, α represents the attenuation coefficient of the weather factor, and s(w) represents the weather severity index; that is, the weather severity index and the influence factor coefficient of the weather factor have a negative correlation, so that the attenuation algorithm of the weather impact degree can be implemented.

[0124] The influencing factor coefficient corresponding to traffic incident factors can be expressed as: Among them, f(e) represents the influencing factor coefficient of the traffic incident factor, β represents the attenuation coefficient of the traffic incident factor, and γ(e) represents the severity level of the incident. That is, the severity level of the traffic incident and the influencing factor coefficient of the traffic incident factor have a nonlinear mapping relationship. The influencing factor coefficient of the traffic incident factor can be realized by defining the severity of the traffic incident.

[0125] Of course, the above-mentioned influencing factor coefficients can also be directly realized by looking up a table. Among them, the influencing factor coefficients of weather factors are shown in Table 1 below, and the influencing factor coefficients of traffic incident factors are shown in Table 2 below:

[0126] Table 1 Influencing factor coefficients of weather factors

[0127]

[0128] Table 2 Influencing factor coefficients of traffic incident factors

[0129]

[0130] That is to say, the impact factor coefficients corresponding to the above weather factors are based on the dynamic weather impact algorithm of the Internet of Things, which is updated in real time using the data of the meteorological sensors of the Internet of Things. Specifically, a standardized severity grading table across weather types can be established to map meteorological parameters such as precipitation intensity and visibility into continuous values, and the severity index s(w) and the impact factor can be defined. The negative correlation between the weather and the weather is realized by the attenuation algorithm.

[0131] The impact factor coefficients corresponding to the above traffic event factors are based on the dynamic algorithm of traffic event impact of the Internet of Things. Through the Internet of Things event detection devices such as cameras, the uploaded events are automatically identified and the impact coefficients are calculated. By establishing the relationship between event classification and weight mapping, The algorithm realizes the nonlinear mapping relationship between event severity and impact factor, and determines the highway traffic impact coefficient by defining the event severity r(e).

[0132] 3) Determining the influence coefficient according to the influence factor coefficient; illustratively, this step may include at least one of the following two sub-steps:

[0133] Sub-step 1: When there is only one target influencing factor, according to the formula Determine the influence coefficient; for example, if the target influencing factor corresponding to the ramp scenario is a traffic event, y represents the influence factor coefficient of the traffic event, that is, the aforementioned f(e); f(y) represents the influence coefficient corresponding to the ramp scenario.

[0134] In the case where the target influencing factors include multiple factors, according to the formula Determine the influence coefficient; for example, the target influencing factors corresponding to the basic road section scenario / tunnel scenario include weather and traffic events, then x represents the influence factor coefficient of weather, that is, the aforementioned f(w), y represents the influence factor coefficient of traffic events, that is, the aforementioned f(e); f(x, y) represents the influence coefficient corresponding to the basic road section scenario / tunnel scenario.

[0135] In other words, it is possible to integrate multi-source data from the Internet of Things to achieve adaptive adjustment of weight coefficients. For example, for basic road sections and ramps, an algorithm for weight coefficients of the actual highway capacity under the influence of dynamic factors such as weather and traffic events is proposed. Determine the weight coefficients of basic sections and ramps, so as to characterize the changes in the capacity of basic sections and ramps of highways. For tunnels, an algorithm for the weight coefficient of the actual capacity of highways under the influence of traffic events is proposed. Determine tunnel weight coefficients to characterize the changes in highway tunnel capacity. This allows for dynamic weight adjustments based on these coefficients.

[0136] As an optional implementation, step 103 includes:

[0137] Determine the weight coefficient of each sub-segment based on the influence coefficient corresponding to each sub-segment; wherein, this step may calculate the weight coefficient by an inverse weighting method. For example, the weight coefficient of each sub-segment may be the proportion of the reciprocal of its influence coefficient in the sum of the reciprocals of the influence coefficients of all sub-segments. Specifically, this step may also include the following sub-steps:

[0138] Sub-step 1: summing the reciprocals of the influence coefficients corresponding to the sub-sections to obtain a first value;

[0139] Sub-step 2: determining a weight coefficient of the target sub-segment according to a ratio of the inverse of the influence coefficient of the target sub-segment to the first value, wherein the target sub-segment is any one of the sub-segments.

[0140] The above two sub-steps can be expressed by the following formula:

[0141]

[0142] Among them, f i represents the influence coefficient of the ith sub-segment, j represents the total number of sub-segments, and represents the weight coefficient of the ith sub-segment.

[0143] Calculating the weight coefficient using the above two sub-steps can ensure that the scene with a smaller impact coefficient (worse capacity) has a higher weight, making the capacity calculation more accurate.

[0144] Based on the weight coefficient, the first traffic capacity of each sub-segment is weightedly summed to obtain the second traffic capacity. For example, if the target segment includes a sub-segment corresponding to a basic segment scenario, a sub-segment corresponding to a ramp scenario, and a sub-segment corresponding to a tunnel scenario, this step can be expressed by the following formula:

[0145] C 全路段 =W 基本路段 ·C 基本路段 +W 匝道 ·C 匝道 +W 隧道 ·C 隧道

[0146] In the above-mentioned method of the embodiment of the present application, in order to calculate the actual capacity of the entire road section, the road between the first and last points is divided into multiple scenarios / sub-segments, and a weight coefficient algorithm based on complex scenarios is established to calculate the actual capacity of the entire road section. This weight coefficient algorithm mainly considers factors such as weather and traffic events, combines the actual capacity of the road in a single scenario, and calculates the weight coefficient of the complex road traffic operating environment through reverse weighting, thereby obtaining the actual capacity of the entire road section.

[0147] The road capacity determination method according to the embodiment of the present application can bring the following beneficial effects:

[0148] (1) Simple and convenient calculation of the actual traffic capacity of a single scene: The actual traffic capacity of each scene is calculated in real time based on the data reported by the radar. Taking into account the problem of repeated reporting of data in the overlapping radiation area by adjacent radars, it is eliminated based on the unit area method. At the same time, considering that the instantaneous reported radar data cannot reflect the overall situation, the maximum value is obtained from the historical data within one year to represent the actual traffic capacity of the current scene.

[0149] (2) Consider the impact of weather, traffic events, etc. on the scene's traffic capacity: The actual traffic capacity of the highway will be affected by external factors, including weather, traffic events, etc., which will reduce the actual traffic capacity of the road. Therefore, different weather types, traffic event types, etc. are graded and classified, and the impact of these influencing factors on road traffic conditions is defined, so as to calculate the impact coefficient of weather types, traffic events, etc.

[0150] (3) Calculation of the traffic capacity of the entire road section based on complex scenarios: Based on the characteristics of each scenario, weight coefficients of basic road sections, ramps, tunnels and other scenarios are given. Using the wooden barrel principle, the weights are calculated by reverse weighting to give the actual traffic capacity of the entire road section, which can provide a basis for daily monitoring, control and scheduling, and planning implementation of the management department.

[0151] (4) Achieving full-chain intelligence through IoT technology: Utilizing intelligent connected sensing devices and networks, data can be collected and transmitted in real time, significantly improving the timeliness of capacity calculations (response within seconds). The V2X central cloud platform supports the fusion of multi-source data (radar, weather, events), reducing manual intervention and improving system robustness. It provides a dynamic decision-making basis for intelligent transportation systems. For example, traffic control strategies can be automatically triggered based on intelligent connected data, improving highway management efficiency.

[0152] In short, the embodiments of this application can build an intelligent highway connected perception system based on the real-time data collection and processing system of the V2X central cloud platform. This system includes nodes such as radar, weather sensors, and event detection equipment, interconnected through a unified communication protocol. The V2X central cloud platform enables big data analysis and historical data mining for optimizing and calculating traffic capacity models.

[0153] That is to say, the above method of the embodiment of the present application can be implemented based on the three-layer architecture of the Internet of Things (perception layer, network layer, application layer), specifically:

[0154] Perception layer: Deploy roadside IoT sensors (such as Figure 3 The radar, meteorological sensor, and camera in the edge computing system collect traffic flow data (number of vehicles, average speed, and coverage length), weather data, and traffic event data in real time, and transmit them through the edge computing unit as a node.

[0155] Network layer: Transmit sensor data to the cloud platform through wired / wireless communication technology to ensure low latency and high reliability transmission.

[0156] Application layer: Run the capacity calculation algorithm in the cloud, integrate dynamic factors such as weather and events, and output the capacity of the entire road section.

[0157] The embodiment of the present application also provides a road capacity determination device, such as Figure 4 Shown, including:

[0158] A first determining module 401 is configured to determine a first traffic capacity of each sub-segment in a target road segment based on historical data reported by the sensing device, wherein each sub-segment corresponds to a road scene category, and the road scene category includes at least one of a basic road segment scenario, a ramp scenario, and a tunnel scenario;

[0159] A second determining module 402 is configured to determine, for each of the sub-road sections, an influence coefficient of the current traffic capacity of the sub-road section according to the road scene category corresponding to the sub-road section and the category of the currently monitored influencing factors;

[0160] The third determining module 403 is configured to determine the current second traffic capacity of the target road section according to the first traffic capacity of each of the sub-road sections and the influence coefficient.

[0161] The first determining module 401 includes:

[0162] a first acquisition submodule, configured to acquire historical data reported by a plurality of sensing devices spaced apart within a target subsection within a first time period; wherein the historical data includes an average vehicle speed and number of vehicles sensed by the sensing devices, and the target subsection is any subsection within the target subsection;

[0163] A first determining submodule is configured to determine a first length of an overlapping area between two adjacent sensing devices according to performance parameters and layout parameters of the sensing devices deployed in the target sub-section;

[0164] The second determination submodule is used to determine the first traffic capacity of the target sub-section according to the first length, the road width corresponding to the target sub-section, the average vehicle speed and the number of vehicles reported by each of the sensing devices at the same time.

[0165] The second determining submodule includes:

[0166] A first acquiring unit is configured to acquire, from the performance parameters of the sensing devices deployed on the target sub-road section, a second length of a radiation area of ​​each of the sensing devices, wherein the second length is a length of the radiation area along an extending direction of the target sub-road section;

[0167] a first determining unit, configured to determine the number of vehicles in each overlapping area according to the first length, the second length, the road width, and the number of vehicles reported by each sensing device;

[0168] The second determining unit is used to determine the first traffic capacity of the target sub-section according to the number of vehicles in the overlapping area, the second length, the average vehicle speed reported by each of the sensing devices, and the number of vehicles.

[0169] The first determining unit includes:

[0170] A first determining subunit is configured to determine, for each of the sensing devices, an area of ​​the radiation area according to a second length of the radiation area of ​​the sensing device and the road width;

[0171] A second determining subunit is configured to determine the number of vehicles per unit area sensed by the sensing device according to the number of vehicles reported by the sensing device and the area of ​​the radiation area;

[0172] a third determining subunit, configured to determine an average number of vehicles per unit area in the target sub-segment according to the number of vehicles per unit area sensed by each of the sensing devices;

[0173] The fourth determining subunit is configured to determine the number of vehicles in each overlapping area according to the average number of vehicles per unit area, the road width, and the first length of each overlapping area.

[0174] The second determining unit includes:

[0175] a grouping subunit, configured to group two of the sensing devices having overlapping areas into a sensing device group among the plurality of sensing devices;

[0176] a fifth determining subunit, configured to determine, for each of the sensing device groups, the traffic capacity corresponding to the first sensing device based on the second length corresponding to the first sensing device, the average vehicle speed and the number of vehicles reported by the first sensing device; and determine the traffic capacity corresponding to the second sensing device based on the second length corresponding to the second sensing device, the number of vehicles in the overlapping area corresponding to the first and second sensing devices, and the average vehicle speed and the number of vehicles reported by the second sensing device; wherein the first sensing device and the second sensing device are sensing devices arranged sequentially along the direction of vehicle travel on the target road section;

[0177] The sixth determining subunit is configured to determine the first traffic capacity of the target sub-section according to the traffic capacity corresponding to the first sensing device and the traffic capacity corresponding to the second sensing device in each of the sensing device groups.

[0178] The sixth determination subunit, when used to determine the traffic capacity corresponding to the second sensing device based on the second length corresponding to the second sensing device, the number of vehicles in the overlapping area corresponding to the first sensing device and the second sensing device, and the average vehicle speed and the number of vehicles reported by the second sensing device, is specifically configured to:

[0179] Calculating a first difference between the number of vehicles reported by the second sensing device and the number of vehicles in an overlapping area between the first sensing device and the second sensing device;

[0180] Calculating a product of the first difference and the average speed of the vehicle reported by the second sensing device;

[0181] The traffic capacity corresponding to the second sensing device is determined according to the ratio of the product and the second length corresponding to the second sensing device.

[0182] The second determining module 402 includes:

[0183] A second acquisition submodule is configured to acquire a target influencing factor corresponding to the target sub-segment according to the road scene category corresponding to the target sub-segment, wherein the target influencing factor includes weather and / or traffic events;

[0184] A third determining submodule is configured to determine an impact factor coefficient corresponding to the target impact factor according to the category of the target impact factor currently monitored;

[0185] The fourth determining submodule is configured to determine the influence coefficient according to the influence factor coefficient.

[0186] Wherein, the third determination submodule includes:

[0187] a third determining unit, configured to determine the severity of the target influencing factor according to the category of the target influencing factor;

[0188] The fourth determining unit is configured to determine the impact factor coefficient according to the severity and the attenuation coefficient.

[0189] The fourth determining submodule includes a fifth determining unit configured to perform any of the following:

[0190] When there is only one target influencing factor, according to the formula determining the influence coefficient;

[0191] Alternatively, when the target influencing factors include multiple factors, according to the formula determining the influence coefficient;

[0192] Where x and y represent the impact factor coefficients respectively.

[0193] The third determining module 403 includes:

[0194] a fifth determining submodule, configured to determine a weight coefficient of each of the sub-road sections according to the influence coefficient corresponding to each of the sub-road sections;

[0195] The third acquisition submodule is configured to perform weighted summation of the first traffic capacity of each of the sub-road sections based on the weight coefficient to obtain the second traffic capacity.

[0196] The fifth determining submodule includes:

[0197] A second obtaining unit is configured to sum the inverses of the influence coefficients corresponding to the sub-sections to obtain a first value;

[0198] The fifth determining unit is configured to determine the weight coefficient of the target sub-road section according to a ratio of the inverse of the influence coefficient of the target sub-road section to the first value, wherein the target sub-road section is any one of the sub-road sections.

[0199] It should be noted here that the above-mentioned road capacity determination device provided in the embodiment of the present application can implement all the method steps implemented in the above-mentioned road capacity determination method embodiment, and can achieve the same technical effect. The parts and beneficial effects of this embodiment that are the same as the method embodiment will not be described in detail here.

[0200] An embodiment of the present application also provides a road capacity determination device, including a transceiver 510, a processor 500, a memory 520, and a program stored on the memory 520 and executable on the processor 500; wherein, when the processor 500 executes the program, the road capacity determination method as described above is implemented.

[0201] The transceiver 510 is configured to receive and send data under the control of the processor 500 .

[0202] Among them, Figure 5 In the embodiment of the present invention, the bus architecture can include any number of interconnected buses and bridges, specifically linking various circuits such as one or more processors represented by processor 500 and memory represented by memory 520. The bus architecture can also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore not further described herein. The bus interface provides an interface. The transceiver 510 can be multiple components, namely, a transmitter and a receiver, providing a unit for communicating with various other devices over a transmission medium.

[0203] The processor 500 is responsible for managing the bus architecture and general processing, and the memory 520 can store data used by the processor 500 when performing operations.

[0204] The present application also provides a readable storage medium having a program stored thereon. When executed by a processor, the program implements the above-described method for determining road capacity and achieves the same technical effects. To avoid repetition, the details are not described here. The readable storage medium may be, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0205] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course, by hardware, but in many cases the former is a better embodiment. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD), and includes a number of instructions for executing the methods described in each embodiment of the present application.

[0206] Therefore, an embodiment of the present application also provides a computer program product, including computer instructions, which, when executed by a processor, implement the road capacity determination method as described above and can achieve the same technical effect. To avoid repetition, they will not be described here.

[0207] In embodiments of the present application, modules can be implemented in software so that they can be executed by various types of processors. For example, an identified executable code module can include one or more physical or logical blocks of computer instructions, for example, which can be constructed as objects, processes, or functions. Nevertheless, the executable code of the identified module does not need to be physically located together, but can include different instructions stored in different locations, which, when logically combined together, constitute the module and achieve the specified purpose of the module.

[0208] In fact, executable code module can be a single instruction or many instructions, and can even be distributed on a plurality of different code segments, distributed in the middle of different programs, and distributed across a plurality of memory devices.Similarly, operating data can be identified in the module, and can be implemented and organized in the data structure of any appropriate type according to any appropriate form.Described operating data can be collected as a single data set, or can be distributed in different locations (including on different storage devices), and can only be present on a system or network as an electronic signal at least in part.

[0209] When a module can be implemented using software, given the current state of hardware technology, those skilled in the art can build corresponding hardware circuits to implement the corresponding functions of the module, regardless of cost. The hardware circuits may include conventional very large scale integration (VLSI) circuits or gate arrays, as well as existing semiconductors such as logic chips and transistors, or other discrete components. Modules may also be implemented using programmable hardware devices, such as field programmable gate arrays, programmable array logic, or programmable logic devices.

[0210] The above exemplary embodiments are described with reference to the accompanying drawings. Many different forms and embodiments are possible without departing from the spirit and teachings of this application. Therefore, this application should not be construed as limited to the exemplary embodiments set forth herein. Rather, these exemplary embodiments are provided so that this application will be complete and impartial and will convey the scope of this application to those skilled in the art. In the drawings, component sizes and relative sizes may be exaggerated for clarity. The terminology used herein is for purposes of describing specific exemplary embodiments only and is not intended to be limiting. As used herein, unless the context clearly indicates otherwise, the singular forms "a," "an," and "the" are intended to encompass such plural forms. It will be further understood that the terms "comprising" and / or "including," when used in this specification, indicate the presence of the stated features, integers, steps, operations, components, and / or elements, but do not preclude the presence or addition of one or more other features, integers, steps, operations, components, elements, and / or groups thereof. Unless otherwise indicated, when stated, a range of values ​​includes the upper and lower limits of that range and any subranges therebetween.

[0211] The above is a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles described in the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for determining road capacity, characterized in that: Applied to a cloud platform, the method includes: Determine, based on historical data reported by the sensing device, a first traffic capacity of each sub-segment in the target road segment, wherein each sub-segment corresponds to a road scene category; the road scene category includes at least one of a basic road segment scenario, a ramp scenario, and a tunnel scenario; For each of the sub-road sections, determining an influence coefficient of the current traffic capacity of the sub-road section according to the road scene category corresponding to the sub-road section and the category of the currently monitored influencing factors; The current second traffic capacity of the target road section is determined according to the first traffic capacity of each of the sub-road sections and the influence coefficient.

2. The method according to claim 1, characterized in that Based on the historical data reported by the sensing devices, the first traffic capacity of each sub-section in the target section is determined, including: Obtaining historical data reported by a plurality of sensing devices spaced apart within a target sub-section within a first time period; wherein the historical data includes an average vehicle speed and number of vehicles sensed by the sensing devices, and the target sub-section is any sub-section within the target sub-section; determining a first length of an overlapping area between two adjacent sensing devices according to performance parameters and layout parameters of the sensing devices arranged in the target sub-section; The first traffic capacity of the target sub-section is determined according to the first length, the road width corresponding to the target sub-section, the average vehicle speed and the number of vehicles reported by each of the sensing devices at the same time.

3. The method according to claim 2, characterized in that Determining a first traffic capacity of the target sub-segment according to the first length, a road width corresponding to the target sub-segment, the average vehicle speed reported by each of the sensing devices at the same time, and the number of vehicles includes: Obtaining, from the performance parameters of the sensing devices deployed on the target sub-road section, a second length of a radiation area of ​​each of the sensing devices, wherein the second length is a length of the radiation area along an extension direction of the target sub-road section; determining the number of vehicles in each overlapping area according to the first length, the second length, the road width, and the number of vehicles reported by each sensing device; The first traffic capacity of the target sub-section is determined according to the number of vehicles in the overlapping area, the second length, the average vehicle speed reported by each of the sensing devices, and the number of vehicles.

4. The method according to claim 3, characterized in that Determining the number of vehicles in each overlapping area according to the first length, the second length, the road width, and the number of vehicles reported by each sensing device includes: For each of the sensing devices, determining the area of ​​the radiation area according to the second length of the radiation area of ​​the sensing device and the road width; Determining the number of vehicles per unit area sensed by the sensing device based on the number of vehicles reported by the sensing device and the area of ​​the radiation area; Determining an average number of vehicles per unit area in the target sub-segment according to the number of vehicles per unit area sensed by each of the sensing devices; The number of vehicles in each overlapping area is determined according to the average number of vehicles per unit area, the road width, and the first length of each overlapping area.

5. The method according to claim 3, characterized in that Determining a first traffic capacity of the target sub-section according to the number of vehicles in the overlapping area, the second length, the average vehicle speed reported by each of the sensing devices, and the number of vehicles includes: Among the plurality of sensing devices, two sensing devices having overlapping areas are divided into a sensing device group; For each of the sensing device groups, the traffic capacity corresponding to the first sensing device is determined based on the second length corresponding to the first sensing device, the average vehicle speed and the number of vehicles reported by the first sensing device; the traffic capacity corresponding to the second sensing device is determined based on the second length corresponding to the second sensing device, the number of vehicles in the overlapping area corresponding to the first and second sensing devices, and the average vehicle speed and the number of vehicles reported by the second sensing device; wherein the first and second sensing devices are sensing devices arranged sequentially along the direction of vehicle travel on the target road section; The first traffic capacity of the target sub-section is determined according to the traffic capacity corresponding to the first sensing device and the traffic capacity corresponding to the second sensing device in each of the sensing device groups.

6. The method according to claim 5, characterized in that Determining the traffic capacity corresponding to the second sensing device according to the second length corresponding to the second sensing device, the number of vehicles in the overlapping area corresponding to the first sensing device and the second sensing device, and the average vehicle speed and the number of vehicles reported by the second sensing device, including: Calculating a first difference between the number of vehicles reported by the second sensing device and the number of vehicles in an overlapping area between the first sensing device and the second sensing device; Calculating a product of the first difference and the average speed of the vehicle reported by the second sensing device; The traffic capacity corresponding to the second sensing device is determined according to the ratio of the product and the second length corresponding to the second sensing device.

7. The method according to claim 1, characterized in that Determining the influence coefficient of the current traffic capacity of the sub-road section according to the road scene category corresponding to the sub-road section and the category of the currently monitored influencing factors includes: Obtaining target influencing factors corresponding to the target sub-segment according to the road scene category corresponding to the target sub-segment, the target influencing factors including weather and / or traffic events; Determine the impact factor coefficient corresponding to the target impact factor according to the category of the target impact factor currently monitored; The influence coefficient is determined according to the influence factor coefficient.

8. The method according to claim 7, characterized in that Determine the impact factor coefficient corresponding to the target impact factor according to the category of the target impact factor currently monitored, including: Determining the severity of the target influencing factor according to the category of the target influencing factor; The impact factor coefficient is determined according to the severity and the attenuation coefficient.

9. The method according to claim 7, characterized in that Determining the influence coefficient according to the influence factor coefficient includes: When there is only one target influencing factor, according to the formula determining the influence coefficient; Alternatively, when the target influencing factors include multiple factors, according to the formula determining the influence coefficient; Where x and y represent the impact factor coefficients respectively.

10. The method according to claim 1, characterized in that Determining the current second traffic capacity of the target road section according to the first traffic capacity of each of the sub-road sections and the influence coefficient includes: Determining a weight coefficient for each sub-segment according to the influence coefficient corresponding to each sub-segment; Based on the weight coefficient, the first traffic capacity of each sub-section is weightedly summed to obtain the second traffic capacity.

11. The method according to claim 10, characterized in that Determining a weight coefficient of each sub-segment according to the influence coefficient corresponding to each sub-segment includes: Summing the reciprocals of the influence coefficients corresponding to the sub-sections to obtain a first value; The weight coefficient of the target sub-road section is determined according to the ratio of the inverse of the influence coefficient of the target sub-road section to the first value, wherein the target sub-road section is any one of the sub-road sections.

12. A device for determining road capacity, characterized in that: Applied to a cloud platform, the device includes: A first determination module is configured to determine a first traffic capacity of each sub-segment in the target road segment based on historical data reported by the sensing device, wherein each sub-segment corresponds to a road scene category; the road scene category includes at least one of a basic road segment scenario, a ramp scenario, and a tunnel scenario; A second determination module is configured to determine, for each of the sub-road sections, an influence coefficient of the current traffic capacity of the sub-road section according to the road scene category corresponding to the sub-road section and the category of the currently monitored influencing factors; The third determining module is configured to determine the current second traffic capacity of the target road section according to the first traffic capacity of each of the sub-road sections and the influence coefficient.

13. A road capacity determination device, characterized in that: The method comprises a transceiver, a processor, a memory and a program stored in the memory and executable on the processor; the method is characterized in that when the processor executes the program, the method for determining the road capacity as claimed in any one of claims 1 to 11 is implemented.

14. A readable storage medium having a program or instruction stored thereon, characterized in that: When the program or instruction is executed by a processor, the road capacity determination method according to any one of claims 1 to 11 is implemented.

15. A computer program product, characterized in that The method comprises computer instructions, which, when executed by a processor, implement the road capacity determination method according to any one of claims 1 to 11.

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