Method, device and equipment for predicting road congestion and computer storage medium

Abstract

The invention provides a method, a device and equipment for predicting road congestion, and a computer storage medium, relates to the technical field of traffic, and is used for improving the speed of discovering congestion of a sensing blind area road section. The method comprises the steps that vehicle driving information corresponding to an upstream area and vehicle driving information corresponding to a downstream area of a target road section are continuously obtained, the upstream area is a vehicle driving-in area relative to the target road section, and the downstream area is a vehicle driving-in area relative to the target road section; wherein the following operations are executed every time the vehicle driving information of the downstream area at a first historical moment and the vehicle driving information of the upstream area at a second historical moment meet a preset flow condition, and it is determined that the target road section is congested, when the vehicle driving information of the downstream area at the first historical moment and the vehicle driving information of the upstream area at the second historical moment meet the preset flow condition; wherein the difference value between the first historical moment and the second historical moment is not greater than a preset time range.

Description

Technical Field

[0001] The present application relates to the field of traffic technology, and in particular to a method, device and equipment for predicting road congestion, and a computer storage medium. Background Art

[0002] Digital twins are a technical means of creating a virtual entity of a physical entity in a digital way, using historical data, real-time data, and algorithm models to simulate, verify, predict, and control the entire life cycle of a physical entity.

[0003] Digital twins can establish a virtual parallel world for highways, and map the environment, vehicles, events and other elements of the physical world of highways in real time and completely. Through the sensor data distributed in the highway, they can fully perceive and dynamically monitor, forming an accurate information expression and mapping of the virtual road to the physical road in the information dimension, so that managers can grasp the overall situation of the highway without being on the highway site, solving the problems of difficult monitoring of the entire road section, delayed event discovery, and difficult event review. It should not only have simulation capabilities, but also prediction and control capabilities.

[0004] In the commonly used digital twin method for highways, traffic accidents and other traffic events are detected through sensing devices (such as cameras). If congestion occurs in Figure 1 In the perception blind spot section shown, the perception equipment can only perceive that congestion occurs somewhere in the downstream blind spot when the congestion spreads to the upstream perception coverage area, but it cannot judge the time, location and intensity of the congestion, which will make it impossible to take the best response measures, which will aggravate the congestion. In addition, before the upstream perception area can observe the spreading congestion, it is impossible to infer that there is congestion in the blind spot. Being able to shorten the time to detect congestion and take measures in advance is also the key to alleviating congestion.

[0005] Therefore, how to improve the speed of detecting congestion in blind spots is a technical problem that needs to be solved urgently. Summary of the invention

[0006] The present application provides a method, device, equipment and computer storage medium for predicting road congestion, so as to improve the speed of discovering congestion in a perception blind spot section.

[0007] In a first aspect, the present application provides a method for predicting road congestion, the method comprising:

[0008] Continuously obtain vehicle driving information corresponding to the upstream area and the downstream area of ​​the target road section, wherein the upstream area is the vehicle approaching area compared to the target road section, and the downstream area is the vehicle heading area compared to the target road section; wherein each time the acquisition is performed, the following operations are performed:

[0009] When the vehicle driving information in the downstream area at the first historical moment and the vehicle driving information in the upstream area at the second historical moment satisfy the preset flow condition, it is determined that there is congestion on the target road section; wherein, the difference between the first historical moment and the second historical moment is not greater than the preset time range.

[0010] In a second aspect, the present application provides a device for predicting road congestion, and the device includes:

[0011] An acquisition module, configured to continuously acquire the vehicle driving information corresponding to the upstream area and the downstream area of the target road section respectively, where the upstream area is the area where vehicles approach compared to the target road section, and the downstream area is the area where vehicles drive away compared to the target road section;

[0012] A processing module, configured to perform the following operations each time an acquisition is made: when the vehicle driving information in the downstream area at the first historical moment and the vehicle driving information in the upstream area at the second historical moment satisfy the preset flow condition, it is determined that there is congestion on the target road section; wherein, the difference between the first historical moment and the second historical moment is not greater than the preset time range.

[0013] In a possible implementation manner, after determining that there is congestion on the target road section, the processing module is further configured to:

[0014] Obtain a first observation moment when it is determined that there is congestion on the target road section;

[0015] When the vehicle driving information corresponding to the upstream area satisfies the preset congestion condition, record a second observation moment when the vehicle driving information satisfies the preset congestion condition;

[0016] Based on the first observation moment and the second observation moment, and in combination with the vehicle driving information corresponding to the upstream area and the downstream area respectively, predict the road congestion information corresponding to the target road section.

[0017] In a possible implementation manner, the preset congestion condition is that the vehicle density in the upstream area is not less than the density threshold.

[0018] In a possible implementation manner, when the processing module is configured to predict the road congestion information corresponding to the target road section based on the first observation moment and the second observation moment, and in combination with the vehicle driving information corresponding to the upstream area and the downstream area respectively, it is specifically configured to:

[0019] Determine the propagation speed at which the congestion occurring on the target road section is transmitted to the upstream area based on the vehicle driving information of the upstream area within a preset historical time period before the second observation moment and the vehicle driving information of the upstream area at the second observation moment;

[0020] Predict the road congestion information corresponding to the target road section based on the propagation speed and the vehicle driving information corresponding to the downstream area, in combination with the first observation moment and the second observation moment.

[0021] In a possible implementation manner, when the processing module is used to determine the propagation speed at which the congestion occurring on the target road section is transmitted to the upstream area based on the vehicle driving information of the upstream area within a preset historical time period between the second moments and the vehicle driving information of the upstream area at the second observation moment, it is specifically used for:

[0022] Based on the vehicle driving information of the upstream area within a preset historical time period before the second observation moment, obtain the historical average traffic flow and historical average traffic density corresponding to the upstream area;

[0023] Based on the vehicle driving information of the upstream area at the second observation moment, obtain the congested traffic flow and congested traffic density corresponding to the upstream area at the second observation moment;

[0024] Determine the propagation speed based on the difference between the historical average traffic flow and the congested traffic flow, and the difference between the historical average traffic density and the congested traffic density.

[0025] In a possible implementation manner, the road congestion information at least includes: the congestion occurrence time;

[0026] Then, when the processing module is used to predict the road congestion information corresponding to the target road section based on the propagation speed and the vehicle driving information corresponding to the downstream area, in combination with the first observation moment and the second observation moment, it is specifically used for:

[0027] Based on the propagation speed, the road length corresponding to the target road section, and the vehicle driving speed corresponding to the downstream area within the preset historical time period, in combination with the first observation moment and the second observation moment, obtain the corresponding first propagation time and second propagation time; wherein, the first propagation time is: the time period from the occurrence of congestion on the target road section to the observation of congestion characteristics in the downstream area; the second propagation time is: the time period from the occurrence of congestion on the target road section to the observation of congestion characteristics in the upstream area;

[0028] Predict the congestion occurrence time corresponding to the target road segment based on the first propagation time and the first characteristic moment; or, predict the congestion occurrence time corresponding to the target road segment based on the second propagation time and the second characteristic moment.

[0029] In a possible implementation manner, the road congestion information at least includes: the congestion occurrence location;

[0030] When the processing module is used to predict the road congestion information corresponding to the congestion existing in the target road segment based on the propagation speed and the vehicle driving information corresponding to the downstream area, in combination with the first observation moment and the second observation moment, it is specifically used for:

[0031] Based on the propagation speed, the road length corresponding to the target road segment, and the vehicle driving speed corresponding to the downstream area within the preset historical time period, in combination with the first observation moment and the second observation moment, obtain the corresponding first propagation time and second propagation time; wherein, the first propagation time is: the time period from the occurrence of congestion in the target road segment to the observation of congestion characteristics in the downstream area; the second propagation time is: the time period from the occurrence of congestion in the target road segment to the observation of congestion characteristics in the upstream area;

[0032] Based on the first propagation time and the vehicle driving speed corresponding to the downstream area within the preset historical time period, predict the congestion occurrence location corresponding to the congestion existing in the target road segment; or, based on the second propagation time and the propagation speed, predict the congestion occurrence location corresponding to the target road segment.

[0033] In a possible implementation manner, the difference between the first historical moment and the second historical moment is: the time for the vehicle to drive through the target road segment at a preset speed.

[0034] In a third aspect, the present application provides an electronic device, which includes a processor and a memory, wherein the memory stores program code, and when the program code is executed by the processor, the processor is enabled to implement the steps of any of the above methods.

[0035] In a fourth aspect, the present application further provides a computer-readable storage medium, which includes program code, and when the program code runs on an electronic device, the program code is used to enable the electronic device to execute the steps of any of the above methods.

[0036] In a fifth aspect, the present application further provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0037] The beneficial effects of the present application are as follows:

[0038] The present application provides a method for predicting road congestion. This method predicts the possible congestion in the perceived blind spot section based on the traffic states in the upstream and downstream areas covered by the perceived devices corresponding to the perceived blind spot section, avoiding the lag in the process where congestion is only perceived by the sensing area after it spreads to the upstream area, effectively improving the speed of detecting congestion on the road, so that road managers can take corresponding countermeasures, reducing the spatio-temporal impact of congestion.

[0039] Furthermore, based on the vehicle driving information obtained by the sensing devices in the upstream and downstream areas, the road congestion information related to the congestion occurring in the target section is predicted to determine information such as the corresponding congestion occurrence time and congestion occurrence location, providing specific information about the congestion on the road for road managers, improving the coverage range of digital twin of the road, reducing the lag in the twin of the congestion events occurring in the perceived blind spot, and thus enabling more accurate provision of data basis for alleviating road congestion subsequently.

[0040] Other features and advantages of the present application will be described in the following specification, and part of them will become obvious from the specification or be understood by implementing the present application. The objectives and other advantages of the present application can be achieved and obtained through the structures specifically pointed out in the written specification, claims, and drawings. Description of the Drawings

[0041] Figure 1 It is a schematic diagram of a perceived blind spot section;

[0042] Figure 2 It is a schematic diagram of a traffic flow basic diagram based on flow-density;

[0043] Figure 3 It is a schematic diagram of a possible application scenario provided by an embodiment of the present application;

[0044] Figure 4 It is a flowchart of a method for predicting road congestion provided by an embodiment of the present application;

[0045] Figure 5 It is a schematic diagram of the relationship between a sensing area and a perceived blind spot provided by an embodiment of the present application;

[0046] Figure 6 It is a schematic diagram of a traffic flow basic diagram provided by an embodiment of the present application;

[0047] Figure 7 It is a schematic diagram of the relationship between a first historical moment and a second historical moment provided by an embodiment of the present application;

[0048] Figure 8Flowchart of a method for predicting road congestion information provided by an embodiment of the present application;

[0049] Figure 9 Schematic diagram of the relationship between a first observation moment and a first historical moment provided by an embodiment of the present application;

[0050] Figure 10 Flowchart of a method for predicting road congestion information provided by an embodiment of the present application;

[0051] Figure 11 Schematic diagram of a fundamental traffic flow diagram after congestion provided by the present application;

[0052] Figure 12 Flowchart of a method for determining propagation speed provided by an embodiment of the present application;

[0053] Figure 13 Schematic diagram of propagation speed provided by an embodiment of the present application;

[0054] Figure 14 Flowchart of a method for predicting congestion occurrence time provided by an embodiment of the present application;

[0055] Figure 15 Schematic diagram of the relationship between the congestion occurrence location and the upstream and downstream regions provided by an embodiment of the present application;

[0056] Figure 16A Flowchart of a method for predicting road congestion information provided by an embodiment of the present application;

[0057] Figure 16B Schematic diagram of a method for predicting road congestion provided by an embodiment of the present application;

[0058] Figure 17 Schematic diagram of the structure of a device for predicting road congestion provided by an embodiment of the present application;

[0059] Figure 18 Schematic diagram of a hardware composition structure of an electronic device in an embodiment of the present application;

[0060] Figure 19 Schematic diagram of a hardware composition structure of another electronic device in an embodiment of the present application. Detailed implementation manners

[0061] To make the objectives, technical solutions, and advantages of this application more clear and understandable, the following will describe the technical solutions in the embodiments of this application clearly and completely in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without making creative efforts fall within the scope of protection of this application. Without conflict, the embodiments in this application and the features in the embodiments can be combined arbitrarily with each other. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0062] In the embodiments of this application, the term "module" or "unit" refers to a computer program with a predetermined function or a part of a computer program, which works together with other related parts to achieve a predetermined goal, and can be implemented in whole or in part by using software, hardware (such as a processing circuit or a memory), or a combination thereof. Similarly, one processor (or multiple processors or memories) can be used to implement one or more modules or units. In addition, each module or unit can be a part of an overall module or unit that includes the function of that module or unit.

[0063] It can be understood that in the following specific embodiments of this application, data related to road maps, vehicle driving, etc. are involved. When the embodiments of this application are applied to specific products or technologies, relevant permissions or consents need to be obtained, and the collection, use, and processing of relevant data need to comply with the relevant laws, regulations, and standards of relevant countries and regions. For example, when relevant data needs to be obtained, relevant volunteers can be recruited and relevant agreements on authorizing data by volunteers can be signed, and then the data of these volunteers can be used for implementation; or, implementation can be carried out within the scope of an organization that has been authorized to permit, and the following embodiments can be implemented by using the data of the internal members of the organization to make relevant predictions for the internal members; or, the relevant data used in the specific implementation are all simulated data, for example, they can be simulated data generated in a virtual scenario.

[0064] To facilitate the understanding of the technical solutions provided by the embodiments of this application, some key terms used in the embodiments of this application are first explained here:

[0065] Fundamental Diagram of Traffic Flow: The fundamental diagram of traffic flow is a graph that shows the relationship between road traffic flow (vehicles per hour) and traffic density (vehicles per kilometer). Macroscopic traffic models involving traffic flow, traffic density, and speed form the basis of the fundamental diagram. It can be used to predict the capacity of a road system or its behavior when applying inflow regulation or speed limits. The main tool for graphically presenting information in the study of traffic flow is the fundamental diagram. The fundamental diagram consists of three different graphs: flow-density, speed-flow, and speed-density. These graphs are two-dimensional. All the graphs are related by the equation "Flow = Speed * Density"; this equation is the fundamental equation in traffic flow. The fundamental diagram is obtained by plotting on-site data points and providing the best-fit curve for these data points. With the help of the fundamental diagram, researchers can explore the relationships among speed, flow, and traffic density. In the embodiments of this application, as Figure 2 shown, this application embodiment is mainly introduced based on the flow-density fundamental diagram.

[0066] Free Flow Speed, abbreviated as Free Speed, refers to the vehicle speed naturally selected by a driver according to the road characteristics without the interference of other vehicles, obvious speed measurement law enforcement, and other external environmental factors. In other words, this is the speed at which the driver feels comfortable under the current road conditions.

[0067] A traffic shock wave is a phenomenon in which at a section near a road bottleneck or at the change of traffic density and flow, a wave in the opposite direction of traffic congestion, disorder, and even blockage will occur in the traffic flow. A tiny change that is hardly noticeable to a single vehicle on the road can lead to a significant congestion. When a driver encounters congestion while driving, they often think that there has been a traffic accident ahead, but when they drive out of the congested section, they find that "nothing has happened". Initially, it may be just because a vehicle slows down or changes lanes, forming a shock wave that propagates backward, and the following vehicles will inexplicably encounter traffic jams.

[0068] The following briefly introduces the design concept of the embodiments of this application:

[0069] The digital twin of a highway is to map the elements such as the environment, vehicles, and events in the physical world of the highway in real time and completely in a virtual parallel world. Through the sensor data distributed in the highway, it can be fully perceived and dynamically monitored, forming an accurate information expression and mapping of the virtual road to the physical road in the information dimension, enabling management personnel to master the overall situation of the highway without being on-site, and solving problems such as difficult full-section monitoring, lag in event discovery, and difficult event review. It should not only have simulation capabilities but also prediction and control capabilities.

[0070] In general digital twin methods for highways, traffic events are detected by sensing devices. However, for traffic events occurring on sections within the sensing blind spots, they can only be detected by the sensing devices until their impacts spread to the sensing area, and then an alarm is issued. This will cause road managers to be unable to take optimal countermeasures in a timely manner, thereby aggravating the adverse impacts of traffic events.

[0071] For example, assume that a congestion occurs in the sensing blind spot. Then, for a general sensing solution, the sensing device can only detect that a congestion has occurred somewhere in the downstream blind spot when the congestion spreads to the upstream sensing coverage area. However, it is impossible to determine the time, location, and intensity of the congestion, which will cause road managers to be unable to take optimal countermeasures, thereby aggravating the congestion. Moreover, before the spreading congestion can be observed in the upstream sensing area, it is impossible to infer the existence of congestion in the blind spot. And being able to shorten the time to detect congestion and take measures earlier is also the key to alleviating congestion.

[0072] In view of this, the present application provides a method, device, equipment, and computer storage medium for predicting road congestion to improve the speed of detecting congestion on sections within the sensing blind spots. The method divides the road into an upstream area where vehicles are approaching, a target section within the sensing blind spot, and a downstream area where vehicles are moving towards, and continuously obtains the vehicle driving information corresponding to the upstream area and the downstream area respectively. Each time the corresponding vehicle driving information is obtained, when it is determined that the traffic flow in the upstream area is significantly greater than the traffic flow in the downstream area within a certain period of time, it is determined that a congestion has occurred in the target section. The specific determination method is: when it is determined that the vehicle driving information in the downstream area at the first historical moment and the vehicle driving information in the upstream area at the second historical moment meet the preset flow condition, it is determined that there is congestion in the target section, where the difference between the first historical moment and the second historical moment is not greater than the preset time range.

[0073] Furthermore, after determining that there is congestion on the target road section, the road congestion information corresponding to the congestion existing on the target road section can be predicted according to the respective vehicle driving information in the upstream area and the downstream area, including predicting the congestion occurrence time, congestion occurrence location, etc. When predicting the congestion occurrence time, first, the first observation moment when it is determined that there is congestion on the target road section needs to be obtained, and the second observation moment when the vehicle driving information observed in the upstream area meets the preset congestion condition needs to be obtained. Based on the first observation moment and the second observation moment, and combined with the respective vehicle driving information in the upstream area and the downstream area, the congestion occurrence time is predicted accordingly. Specifically, when the second observation moment is obtained, the propagation speed of the congestion spreading to the upstream area can be determined through the vehicle driving information at the second observation moment and the vehicle driving information in the historical time period before the second observation moment. Then, through this propagation speed, the length of the target road section, and the vehicle driving speed corresponding to the downstream area in the preset historical event period, the first propagation time from the occurrence of the congestion to the observation of the congestion characteristics in the downstream area and the second propagation time from the occurrence of the congestion to the observation of the congestion characteristics in the upstream area are obtained. And according to the first propagation time or the second propagation time, the congestion occurrence time corresponding to the target road section is predicted.

[0074] Regarding the congestion occurrence location, after obtaining the first propagation time and the second propagation time in a manner similar to the above, the distance between the congestion occurrence location and the upstream area can be obtained through the first propagation time and the propagation speed obtained above, so as to determine the congestion occurrence location.

[0075] The method for predicting road congestion provided by the embodiments of the present application relates to an Intelligent Traffic System (ITS), also known as an Intelligent Transportation System. This system effectively and comprehensively applies advanced scientific and technological means (information technology, computer technology, data communication technology, sensor technology, electronic control technology, automatic control theory, operations research, artificial intelligence, etc.) to transportation, service control, and vehicle manufacturing, strengthening the connection among vehicles, roads, and users, thereby forming an integrated transportation system that ensures safety, improves efficiency, improves the environment, and saves energy. Or;

[0076] The Intelligent Vehicle Infrastructure Cooperative Systems (IVICS), also known as the vehicle-road collaborative system, is a development direction of the Intelligent Transportation System (ITS). The vehicle-road collaborative system uses advanced wireless communication and new-generation Internet technologies to comprehensively implement dynamic real-time information interaction between vehicles and between vehicles and roads. Based on the collection and fusion of dynamic traffic information in the whole space-time, it conducts active vehicle safety control and road collaborative management, fully realizing the effective collaboration of people, vehicles and roads, ensuring traffic safety, improving traffic efficiency, and thus forming a safe, efficient and environmentally friendly road traffic system.

[0077] The following briefly introduces the application scenarios applicable to the technical solutions of the embodiments of this application. It should be noted that the application scenarios introduced below are only for explaining the embodiments of this application rather than limiting them. In the specific implementation process, the technical solutions provided by the embodiments of this application can be flexibly applied according to actual needs.

[0078] See Figure 3 , which is a schematic diagram of a possible application scenario provided by the embodiments of this application. In this scenario, it may include a terminal device 301 and a server 302.

[0079] The terminal device 301 can be devices such as mobile phones, tablet computers (PADs), personal computers (PCs), wearable devices, in-vehicle terminals, etc., or devices such as cameras and video cameras. The server 302 can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, Content Delivery Network (CDN), and big data and artificial intelligence platforms.

[0080] The server 302 may include one or more processors 3021, a memory 3022, and an I / O interface 3023 for interacting with the terminal, etc. In addition, the server 302 can also be configured with a database 3024, which can be used for information such as road images, vehicle driving information, and various time information. Among them, the memory 3022 of the server 302 can also store program instructions for the method of predicting road congestion provided by the embodiments of this application. When these program instructions are executed by the processor 3021, they can be used to implement the steps of the method of predicting road congestion provided by the embodiments of this application to quickly discover the congestion existing in the perception blind area section.

[0081] A direct or indirect communication connection can be established between the terminal device 301 and the server 302 through one or more communication networks 303. The communication network 303 can be a wired network or a wireless network. For example, the wireless network can be a mobile cellular network or a Wireless-Fidelity (WIFI) network. Of course, it can also be other possible networks, and the embodiments of the present application do not limit this.

[0082] It should be noted that the method for predicting road congestion in the embodiments of the present application can be executed by a computer device, which can be the terminal device 301 or the server 302. That is, this method can be executed independently by the terminal device 301 or the server 302, or jointly executed by the terminal device 301 and the server 302.

[0083] For example, when the terminal device 301 independently executes the method for predicting road congestion provided in the present application, the terminal device 301 can continuously obtain the vehicle driving information corresponding to the upstream area and the downstream area from the upstream and downstream sensing devices, and determine whether congestion has occurred in the target road section based on the vehicle driving information in the upstream and downstream areas at different times, and further predict the congestion information.

[0084] Another example is that when the terminal device 301 and the server 302 jointly execute the method for predicting road congestion provided in the present application, the terminal device 301 can continuously obtain the vehicle driving information corresponding to the upstream area and the downstream area, and transmit this information to the server communicatively connected to it. Then, the server determines whether the vehicle age driving information at the first historical moment in the downstream area and the second historical moment in the upstream area meets the preset traffic conditions according to the program instructions configured in it, and determines whether there is congestion in the target road section based on this judgment.

[0085] It should be noted that Figure 3 The above is only an example. In fact, the number and communication methods of the terminal device and the server are not limited, and no specific limitations are made in the embodiments of the present application.

[0086] Next, the method for predicting road congestion provided by the exemplary embodiments of the present application will be described in combination with the above-described application scenarios and the subsequent reference drawings. It should be noted that the above application scenarios are only shown for the convenience of understanding the spirit and principle of the present application, and the embodiments of the present application are not limited in this regard.

[0087] See Figure 4, which is a flowchart of a method for predicting road congestion provided by an embodiment of the present application. The execution subject of this method can be the above-described terminal device or server, etc. For the convenience of subsequent description, the server will be taken as the execution subject as an example to introduce this method and related methods. As Figure 4 shown, the specific implementation steps of this method are as follows:

[0088] Step S401: Continuously obtain the vehicle driving information corresponding to the upstream area and the downstream area of the target section respectively, where the upstream area is the area where vehicles come from compared with the target section, and the downstream area is the area where vehicles are going compared with the target section.

[0089] Before specifically introducing the implementation details of the method steps, first introduce the specific real-world scenario to which this method is applied. As Figure 5 shown, the method provided by the embodiment of the present application can be applied to the main line of an expressway without ramps. For such a road, due to the interval of sensing devices, the road can be divided by whether it can be sensed by the sensing devices, resulting in alternately appearing sensing areas and sensing blind spots. This means that for any sensing blind spot, there are areas that can be sensed by the sensing devices before and after it. For the convenience of distinction, in the present application, according to the driving direction of the vehicle, the two perceivable areas before and after a sensing blind spot are denoted as the upstream area and the downstream area, and the sensing blind spot in the upstream area and the downstream area is denoted as the target section.

[0090] On the other hand, since it is located on the main line of an expressway without ramps, for the upstream area and the downstream area separated by a target section, the vehicles driving out of the upstream area will inevitably pass through the target section and drive into the downstream area after a period of time.

[0091] Based on the above two situations, when the method provided by the embodiment of the present application continuously obtains the vehicle driving information corresponding to the upstream area and the downstream area of the target section, the following operations can be further performed each time:

[0092] Step S402: When the vehicle driving information of the downstream area at the first historical moment and the vehicle driving information of the upstream area at the second historical moment meet the preset traffic flow condition, it is determined that there is congestion in the target section, where the difference between the first historical moment and the second historical moment is not greater than the preset time range.

[0093] First, an explanation is given for the continuous acquisition of vehicle driving information as described above: When the sensing devices sense the upstream area and the downstream area, the server can directly obtain the road information corresponding to the upstream area and the downstream area from these sensing devices. When the server processes these road images, it can extract information such as traffic flow, density, and speed in the road information at certain time intervals. For example, it can collect, analyze, and statistically process the vehicle data passing through the upstream area and the downstream area every minute to determine the average flow, density, and speed per minute. In this way, continuous acquisition of vehicle information corresponding to the upstream area and the downstream area can be achieved.

[0094] Meanwhile, when the server continuously acquires vehicle driving information, it can also determine whether there is congestion in the target section according to whether the vehicle driving information in the upstream area and the downstream area within a certain time range meets the preset flow conditions. Among them, the method for the server to judge congestion can be as follows:

[0095] Exemplarily, if the road is in a smooth condition, then the vehicles on this road will be Figure 6 as shown in state A in the fundamental diagram of traffic flow.

[0096] In the fundamental diagram of traffic flow (hereinafter simply referred to as the fundamental diagram) as Figure 6 shown, it represents that as the traffic density increases, the traffic flow will gradually increase to the maximum point Q m , and then gradually decrease from the maximum point Q m to 0, that is, transition from the free flow state to the congestion state; among them, the slope of the line segment from the origin to point Q m is the free flow speed V corresponding to this road f1 . And this fundamental diagram is determined by the attributes of its corresponding road itself. These attributes can include the road grade, number of lanes, radius of curvature, etc. Through these road attributes, the key parameters of the fundamental diagram can be calibrated to determine the fundamental diagram of the corresponding section. Therefore, before the traffic flow reaches the peak, the vehicles on the road will all travel at the free flow speed.

[0097] It should be understood that Figure 6 state A in

[0098] is only used to indicate that the vehicles on the road are in an uncongested state, rather than specifically limiting the road corresponding to this fundamental diagram to a certain traffic density and traffic flow.

[0099] Therefore, based on this situation, this application proposes that after obtaining the vehicle driving information of the upstream area and the downstream area, it is necessary to compare the vehicle driving information of the downstream area corresponding to the first historical moment with the vehicle driving information of the upstream area corresponding to the second historical moment within a certain time range, and determine whether it meets the preset traffic flow condition. If it meets, it can be determined that there is congestion on the target road section between the upstream area and the downstream area. Among them, the preset traffic flow condition can be: the difference between the traffic flow of the upstream area at the second historical moment and the traffic flow of the downstream area at the first historical moment is greater than the traffic flow threshold. Among them, the difference between the first historical moment and the second historical moment is not greater than the preset time range, and this preset time range can be specifically set according to the road conditions.

[0100] In this way, when there is no congestion on the target road section, the traffic flow of the downstream area at the first historical moment should not differ much from the traffic flow of the upstream area at the second historical moment. Once the difference between the two is too large, it can be determined that there is congestion on the target road section.

[0101] Optionally, in order to further improve the accuracy of the judgment on whether there is congestion on the target road section, for the difference between the first historical moment and the second historical moment, further restrictions can be imposed on the original preset time range.

[0102] Specifically, the difference between the first historical moment and the second historical moment can be restricted to: the time for the vehicle to travel through the target road section at the preset speed. The determination of this preset speed can be the average driving speed of the vehicle within a certain period of time statistically obtained by the server when obtaining the vehicle driving information corresponding to the upstream area and the downstream area respectively, or the free flow speed of the vehicle traveling on the target road section shown in the basic map corresponding to the target road section in the non-congested state.

[0103] Exemplarily, as Figure 7 shown, in the time horizontal axis, record the first historical moment as T1 and the second historical moment as T2. Then, the time interval between T1 and T2 is the time T consumed for the vehicle to travel through the length of the target road section at the free flow speed. 12 。

[0104] In this way, it can be ensured as much as possible that in the vehicle driving information corresponding to the downstream area at the first historical moment and the upstream area at the second historical moment, it is the vehicle driving information corresponding to the same batch of vehicles, thereby ensuring the accuracy of the judgment on whether there is congestion on the target road section.

[0105] In this solution, based on the traffic states in the upstream and downstream areas covered by the sensed devices corresponding to the blind spots of perception, the congestion that may occur in the blind spots of perception is predicted, avoiding the lag in the process where the congestion is only sensed by the sensing area after spreading to the upstream area, effectively improving the speed of detecting congestion on the road, so that road managers can take corresponding countermeasures and reduce the spatio-temporal impact of congestion.

[0106] The above introduced a method for judging whether there is congestion on the target road section based on the vehicle driving information corresponding to the upstream and downstream areas. After determining that there is congestion on the target road section, as for how to predict the relevant road congestion information corresponding to the congestion on the target road section, it can be completed through the following method.

[0107] Optionally, after determining that there is congestion on the target road section, the server can also continue to execute one or more of the following methods:

[0108] See Figure 8 , which is a flowchart of a method for predicting road congestion information provided by an embodiment of the present application. As Figure 8 shown, the specific implementation steps of this method are as follows:

[0109] Step S801: Obtain the first observation moment when it is determined that there is congestion on the target road section.

[0110] When the server determines that there is congestion on the target road section based on the relationship between the vehicle driving information at the first historical moment in the downstream area and the vehicle driving information at the second historical moment in the upstream area, the server can simultaneously obtain the first observation moment when it is determined that there is congestion on the target road section at this time, as the basic data for subsequent continuous prediction of road congestion information. At the same time, for the convenience of subsequent introduction and understanding, the event that the server determines that there is congestion on the target road section will be recorded as the first feature hereinafter.

[0111] It should be clear that although the gap between the first observation moment and the first historical moment may be very small, the first observation moment cannot be directly regarded as the first historical moment. As Figure 9 shown, in the time horizontal axis, since there may be a certain lag when the server obtains the vehicle driving information in the downstream area. In other words, the server may obtain the vehicle driving information corresponding to the moment of 20:00 at 20:01. Therefore, there may be a certain time interval between the first historical moment (T1) and the first observation moment (t1), and this time interval is the lag of the server in obtaining the vehicle information corresponding to the downstream area.

[0112] Step S802: When the vehicle driving information corresponding to the upstream area meets the preset congestion condition, record the second observation moment when the vehicle driving information meets the preset congestion condition.

[0113] When there is congestion on the target road section, the congestion situation will gradually spread upstream against the vehicle flow direction. Therefore, in the vehicle driving information in the upstream area, it will directly show that there is congestion in the area that can be sensed by this sensing device in the upstream area. For example, the traffic flow drops to a certain threshold, or the vehicle density increases to a certain threshold, etc. Specifically, the preset congestion condition can be that the traffic flow in the upstream area is not greater than the flow threshold, or the vehicle density in the upstream area is not less than the density threshold. Among them, the determination of the flow threshold and the density threshold can be obtained through pre-configuration, and this application does not limit this. It should be noted that the flow threshold, the density threshold, etc. are all related to the road attributes of the target road section itself. Their specific values can be configured by the road manager according to management experience, or can be obtained by the computer through deep learning based on the historical data of a certain target road section. This application also does not limit this.

[0114] At this time, the server can determine whether it meets the preset congestion condition according to the vehicle driving information corresponding to the upstream area, and then determine whether there is congestion in the upstream area; if so, the second observation moment when the vehicle driving information meets the preset congestion condition can be recorded. Similarly, subsequently, the event that the server determines that the vehicle driving information in the upstream area meets the preset congestion condition will also be recorded as the second feature.

[0115] After obtaining the first observation moment and the second observation moment, the server has already completely obtained the basic data required to predict the road congestion information. Then, the server can continue to perform the following operations:

[0116] Step S803: Based on the first observation moment and the second observation moment, combined with the vehicle driving information corresponding to the upstream area and the downstream area respectively, predict the road congestion information corresponding to the target road section.

[0117] Through the first observation moment corresponding to the first feature and the second observation moment corresponding to the second feature, and then combined with the vehicle driving information corresponding to the upstream area and the downstream area respectively within a period of time, the server can predict the road congestion information corresponding to the target road section.

[0118] Specifically, in the process of the server predicting the road congestion information corresponding to the target road section, first, it is necessary to obtain the propagation speed at which the congestion existing in the target road section spreads to the upstream area.

[0119] See Figure 10 , which is a flowchart of a method for predicting road congestion information provided by an embodiment of this application. As Figure 10 shown, the specific implementation steps of this method are as follows:

[0120] Step S1001: Based on the vehicle driving information in the preset historical time period between the second observation moments in the upstream area, and the vehicle driving information in the upstream area at the second observation moment, determine the propagation speed at which the congestion occurring on the target road section spreads to the upstream area.

[0121] It should be clear that the process in which the congestion gradually spreads from the target road section to the upstream, as described above, can be understood as the traffic flow shock wave or traffic flow shock wave proposed above, and its corresponding propagation speed can also be understood as the propagation speed of the shock wave, and this propagation speed can also be correspondingly reflected in the fundamental diagram:

[0122] See Figure 11 , which is a schematic diagram of the fundamental diagram of traffic flow after congestion provided by this application. In Figure 11 , the state of the fundamental diagram corresponding to the target road section when there is no congestion is shown by the solid line, while the dotted line shows the state of the fundamental diagram corresponding to the target road section after congestion occurs.

[0123] When congestion occurs, since it is often caused by a traffic accident on the road, generally speaking, the fundamental diagram at the accident location will change due to the reduction of the number of available lanes, which is reflected in Figure 11 as: the fundamental diagram corresponding to the target road section changes from the solid line state to the dotted line state, that is, the maximum traffic flow decreases (reflected in Figure 11 as Q m1 >Q m2 ), the free flow speed decreases (reflected in Figure 11 as V f1 >V f2 ), etc. And because congestion will block one or several lanes, the vehicles in the lanes blocked by congestion upstream will change to the unblocked lanes, which will cause the traffic flow to not reach the theoretical maximum traffic flow Q m2 , but be at Q m2 less than Q b , which is reflected in Figure 11 as: the vehicle driving state on the target road section changes from state A on the solid line before congestion to state B on the dotted line after congestion occurs.

[0124] At this time, since the traffic flow passing through the congestion location does not change, therefore, when the congestion spreads to the upstream area, its traffic flow will be the same as the traffic flow at the congestion location, which is shown in Figure 11 as: after congestion appears upstream, its state will change from state A when there is no congestion to state C with the same traffic flow as state B.

[0125] In summary, in Figure 11Among them, state A represents the vehicle driving state of the upstream area and the target road section when there is no congestion, state B represents the vehicle driving state after congestion appears in the target road section, and state C represents the vehicle driving state of the upstream area after the congestion in the target road section spreads to the upstream area.

[0126] Based on the fundamental diagram property and shock wave propagation theory, it can be obtained that the propagation speed of the shock wave is the slope between two traffic states. The propagation speed of the shock wave generated by the accident upstream is w AC , and its slope is less than 0, indicating that its propagation direction is opposite to the traffic flow movement direction and is propagating upstream.

[0127] Therefore, when determining the propagation speed of the congestion that occurs in the target road section to the upstream area, the acquisition of the propagation speed can be completed through the following method:

[0128] See Figure 12 , which is a flowchart of a method for determining the propagation speed provided by an embodiment of the present application. As Figure 12 shown, the specific implementation steps of this method are as follows:

[0129] Step S1201: Based on the vehicle driving information of the upstream area within a preset historical time period before the second observation moment, obtain the historical average traffic flow and historical average traffic density corresponding to the upstream area.

[0130] Among them, the specific value of the preset historical time period can be configured according to actual needs, and the present application does not limit this. In fact, since the second observation moment corresponds to the moment when the server discovers the second feature, that is, the moment when the congestion spreads to the upstream, therefore, within the preset historical time period before the second observation moment, the upstream area is still not congested. Therefore, at this time, the vehicle driving state corresponding to the upstream area is still state A as Figure 11 shown. Then, for the historical average traffic flow and historical average traffic density obtained in step S1201, they can be represented by Q A and K A respectively.

[0131] Step S1202: Based on the vehicle driving information of the upstream area at the second observation moment, obtain the congested traffic flow and congested traffic density corresponding to the upstream area at the second observation moment.

[0132] Similar to step S1201, since at the second observation moment, the congestion has spread to the upstream area, then for the upstream area, the traffic flow and traffic density shown in the vehicle driving information at this time are both in the congested state. Therefore, corresponding to Figure 11 , from the perspective that the upstream area is in state C at the second observation moment, Q C and KC Indicates the congested traffic flow and congested traffic density obtained in step 1202.

[0133] After completing Q A and K A and Q C and K C After the acquisition, the server can complete the acquisition of the propagation speed through the following steps:

[0134] Step S1203: Determine the propagation speed based on the difference between the historical average traffic flow and the congested traffic flow, and the difference between the historical average density and the congested traffic density.

[0135] Among them, the propagation speed is positively correlated with the difference between the historical average traffic flow and the congested traffic flow, and negatively correlated with the difference between the historical average density and the congested traffic density.

[0136] Exemplarily, the relationship between the propagation speed (w AC ) and the historical average traffic flow Q A , the historical average vehicle density K A , the congested traffic flow Q C , the congested vehicle density K C can be shown by the following formula 1:

[0137] w AC =(Q A -Q C ) / (K A -K C ) (Formula 1)

[0138] In this way, the propagation speed at which the congestion in the target section spreads to the upstream area can be obtained, and the performance of this propagation speed in Figure 13 is the slope of the straight line between state A and state C.

[0139] After the acquisition of the propagation speed is completed, the server can continue to execute the following steps to predict the corresponding road congestion information:

[0140] Step S1002: Predict the road congestion information corresponding to the target section based on the propagation speed and the vehicle driving information corresponding to the downstream area, in combination with the first observation moment and the second observation moment.

[0141] Among them, the road congestion information includes one or more of the following information: the congestion occurrence time, the congestion occurrence location, and the congestion degree.

[0142] For these items, their specific prediction methods are also different. Therefore, next, the acquisition methods corresponding to different road congestion information will be introduced.

[0143] First, when the road congestion information includes at least the congestion occurrence time, the road congestion information can be predicted in the following manner.

[0144] See Figure 14 , which is a flowchart of a method for predicting the congestion occurrence time provided by an embodiment of the present application. As Figure 14 shown, the specific implementation steps of this method are as follows:

[0145] Step S1401: Based on the propagation speed, the road length corresponding to the target section, and the vehicle driving speed corresponding to the downstream area within a preset historical time period, and in combination with the first observation moment and the second observation moment, obtain the corresponding first propagation time and second propagation time;

[0146] Among them, the first propagation time is the time period from the occurrence of congestion in the target section to the observation of congestion characteristics in the downstream area; the second propagation time is the time period from the occurrence of congestion in the target section to the observation of congestion characteristics in the upstream area; in other words, as Figure 15 shown, the first propagation time is the time from the start of congestion occurrence in the target section to the end of the time when the first characteristic is observed, and the second propagation time is the time from the start of congestion occurrence in the target section to the end of the time when the second characteristic is observed.

[0147] It is pointed out in step S1401 that the acquisition of the first propagation time and the second propagation time needs to be completed based on the foregoing data, and specifically how to obtain the first propagation time and the second propagation time through these data will be introduced next.

[0148] First, as Figure 15 shown, assume that L1 is the distance between the congestion occurrence location and the edge of the downstream area, and L2 is the distance between the congestion occurrence location and the edge of the upstream area, where the edge of the downstream area and the edge of the upstream area are the edges closest to the target section in their respective areas.

[0149] When the traffic flow at the congestion occurrence location of the target section is restricted by the number of lanes reduced at the congestion point, although the traffic flow decreases, the vehicles leaving the congestion area will travel towards the downstream area at the free flow speed at state A corresponding to the target section, and this speed is the same as the free flow speed of the vehicles in the downstream area. Therefore, the vehicle driving speed corresponding to the downstream area in step S1401 can be represented by the free flow speed V at state A f1 , and, at this time, when observing the reduced traffic flow after the occurrence of congestion in the downstream area, the time (T f ) required for the corresponding first propagation speed is the distance L1 divided by the free flow speed V f1 , that is: T f = L1 / V f1 .

[0150] Correspondingly, the shock wave corresponding to the congestion on the target road section will propagate upstream, and the time (T b ) required for it to propagate to the upstream area is the distance L2 divided by the propagation speed w AC , that is: T b = L2 / w AC .

[0151] In this way, by solving the system of equations corresponding to the following Formula 2 and Formula 3, the first propagation time and the second propagation time can be solved:

[0152] V f1 *T f + w AC *T b = L1 + L2 (Formula 2)

[0153] T f - T b = Δt (Formula 3)

[0154] where Δt is the time difference between the first observation moment and the second observation moment.

[0155] After solving for T f and T b , the server determines the values of the first propagation time and the second propagation time. In this way, the server can continue to perform the following operations:

[0156] Step S1402: Predict the congestion occurrence time corresponding to the target road section based on the first propagation time and the first characteristic moment; or, predict the congestion occurrence time corresponding to the target road section based on the second propagation time and the second characteristic moment.

[0157] After obtaining the two propagation times, the server can determine the congestion occurrence time according to the lengths of the propagation times and the times when the first characteristic and the second characteristic are respectively observed.

[0158] Specifically, assuming that the first observation moment is t1, then the congestion occurrence time can be the difference between the first observation moment and the first propagation time, that is, t1 - T f .

[0159] Or, assuming that the second observation moment is t2, then the congestion occurrence time can be the difference between the second observation moment and the second propagation time, that is, t2 - Tb.

[0160] In this way, the prediction of the congestion occurrence time can be completed.

[0161] Second, when the road congestion information includes at least the congestion occurrence location, the road congestion information can be predicted in the following way.

[0162] First, similar to step S1401, the server can first obtain the first propagation time and the second propagation time. After obtaining the two propagation times, it can then choose to predict the congestion occurrence location according to the first propagation time or the second propagation time respectively.

[0163] Specifically, the congestion occurrence location corresponding to the congestion existing in the target road section can be predicted according to the first propagation time and the vehicle driving speed corresponding to the downstream area within a preset historical time period; or, the congestion occurrence location corresponding to the congestion existing in the target section can be predicted according to the second propagation time and the propagation speed.

[0164] For example, when predicting the congestion occurrence location corresponding to the congestion existing in the target road section according to the first propagation time and the vehicle driving speed corresponding to the downstream area within a preset historical time period, since the preset historical time period is the time period before the congestion propagates to the upstream area, it can be determined that the vehicle driving speed corresponding to the downstream area within the preset historical time period will be the same as the free flow speed in state A. Therefore, in this case, the distance between the edge of the downstream area and the congestion occurrence location of the target road section is the product of the first propagation time and the free flow speed in state A, that is: L1 = V f1 *T f . Thus, after obtaining the distance between the edge of the downstream area and the congestion occurrence location of the target road section, the congestion occurrence location corresponding to the congestion in the target road section can be determined according to the position information of the edge of the downstream area.

[0165] Another example, when predicting the congestion occurrence location corresponding to the congestion existing in the target section according to the second propagation time and the propagation speed, the distance between the edge of the upstream area and the congestion occurrence location of the target road section can be directly determined by multiplying the obtained propagation speed above by the second propagation time. Then, according to the position information of the edge of the upstream area, the congestion occurrence location corresponding to the congestion in the target can be determined.

[0166] In this way, the prediction of the congestion occurrence location can be completed.

[0167] Thirdly, when the road congestion information includes at least the congestion degree, the road congestion information can be predicted in the following way.

[0168] According to general regulations, road traffic accidents are generally divided into four levels according to factors such as their severity and influence scope: Level I (extraordinarily serious), Level II (serious), Level III (relatively large), and Level IV (general). Research shows that when one lane is occupied by congestion on a two-lane highway, the traffic capacity of this section is only 35% of the original, or a 65% decrease. If one lane is occupied by congestion on a three-lane highway, the traffic capacity will drop to 49% of the original; when two lanes are occupied, the traffic capacity is only 17% of the original. In the method provided in this application, the severity of the accident can be judged by comparing the traffic flow at the first historical moment in the downstream area with the traffic flow at the second historical moment in the upstream area. That is, different classifications can be made according to different degrees of traffic flow decrease to make a preliminary judgment on the severity of the accident.

[0169] Exemplarily, the traffic flow passing index δ = Q1(T1) / Q2(T2) can be defined to judge the severity of the accident according to this index, where Q1(T1) is the traffic flow at the first historical moment in the downstream area, and Q2(T2) is the traffic flow at the second historical moment in the upstream area. In a possible situation, it can be set that when δ < 0.4, the congestion level is a general accident; when δ < 0.2, the congestion level is a serious accident, where the judgment threshold can be set in combination with specific road conditions, and this application does not limit this.

[0170] The above introduces the method for predicting road congestion provided by the embodiments of this application and its various possible specific implementation manners. In order to facilitate clarifying various possible combination manners of the above methods, the above methods will be introduced through a complete embodiment below.

[0171] See Figure 16A , which is a flowchart of a method for predicting road congestion information provided by the embodiments of this application. As Figure 16A shown, the specific implementation steps of this method are as follows:

[0172] Step S1601: Continuously obtain the vehicle driving information corresponding to the upstream area and the downstream area respectively.

[0173] Among them, both the upstream area and the downstream area are sections of a certain road.

[0174] Step S1602: Judge whether the current moment is the moment when the traffic states of the upstream and downstream areas need to be determined. If so, continue to execute the subsequent steps. If not, wait for the time to continue to advance until the determined time reaches the predetermined moment.

[0175] Step S1603: Obtain the vehicle driving information in the downstream area at the first historical moment and the vehicle driving information in the upstream area at the second historical moment, and determine the state of vehicle driving on the road, denoted as state A.

[0176] Step S1604: Determine whether the traffic flow in the downstream area at the first historical moment and the traffic flow in the upstream area at the second historical moment meet the preset traffic flow condition (in other words, determine whether the first feature is observed); if so, execute Step S1605, otherwise, execute Step S1606.

[0177] Step S1605: Determine that there is congestion in the target section between the upstream area and the downstream area, and continue to execute Step S1606.

[0178] Step S1606: Determine whether there is congestion in the upstream area (in other words, determine whether the second feature is observed); if so, execute Step S1607, otherwise, execute Step S1608.

[0179] Step S1607: According to the vehicle driving information in the upstream area during congestion, determine the state of vehicle driving on the upstream area at this time, denoted as state C, and continue to execute Step S1608.

[0180] Step S1608: Determine whether both the first feature and the second feature are observed; if so, execute Step S1609, otherwise, execute Step 1610.

[0181] Step S1609: According to the vehicle driving information corresponding to state A and state C, predict the road congestion information corresponding to the target section, and send an alarm message to the road manager.

[0182] Step S1610: Determine whether the first feature is observed; if so, execute Step S1611, otherwise, execute Step S1612.

[0183] Step S1611: Determine that there is congestion in the target section, send an alarm message to the road manager, and continue to execute Step S1602.

[0184] Step S1612: Determine whether the second feature is observed; if so, execute Step S1613, otherwise, continue to execute Step S1602.

[0185] Step S1613: Determine that there is congestion in the target section, send an alarm message to the road manager, and continue to execute Step S1602.

[0186] The above steps introduce the execution process of the method for predicting road congestion information provided by the present application in the specific implementation process. The process of predicting the road congestion information corresponding to the target road section based on the vehicle driving information corresponding to state A and state C in step S1609 can refer to the introduction of predicting road congestion information in the foregoing part of the present application, and its specific implementation manner is the foregoing content.

[0187] For the sake of facilitating the overall understanding of the above-mentioned proposed solution, the overall implementation logic of the solution will be introduced below through Figure 16B introducing the overall implementation logic of the solution.

[0188] As Figure 16B shown, on the road where the vehicle is driving, the road can be divided into an upstream area, a target area, and a downstream area according to whether it can be sensed by the sensing device and the driving direction of the vehicle. Among them, the target area is the sensing blind area that is not covered by the sensing device.

[0189] When congestion occurs in the target road section, in order to improve the sensing speed of the congestion in the sensing blind area, the server can determine whether there is congestion in the target road section based on the vehicle driving information in the downstream area at the first historical moment and the vehicle driving information in the upstream area at the second historical moment.

[0190] If it is determined that there is congestion, the server can continue to predict the road congestion information corresponding to the target road section based on this vehicle driving information. Specifically, the server can obtain the first observation moment corresponding to the first feature and the second observation moment corresponding to the second feature, and combine the obtained vehicle driving information to determine the propagation speed at which the congestion spreads to the upstream area, and then determine the first propagation time and the second propagation time corresponding to the congestion in the target road section, and finally obtain the road congestion information corresponding to this congestion.

[0191] In this way, after the road congestion information is obtained, the server can display this information to the road manager, so that the road manager can timely obtain the congestion situation that occurs in the sensing blind area section, and then provide corresponding countermeasures for this congestion, thereby reducing the spatio-temporal impact that the congestion may cause.

[0192] Based on the same inventive concept, the embodiment of the present application also provides a device for predicting road congestion information. Refer to Figure 17 , which is a schematic structural diagram of a device for predicting road congestion information provided by the embodiment of the present application. This device can be the above-mentioned terminal device or server, or a chip or integrated circuit therein, etc. This device includes modules / units / technical means for executing the methods executed by the terminal device or server in the above method embodiments.

[0193] Exemplarily, the device 1700 includes:

[0194] An acquisition module 1701, configured to continuously acquire the vehicle driving information corresponding to the upstream area and the downstream area of the target road section respectively, where the upstream area is the vehicle approaching area compared with the target road section, and the downstream area is the vehicle driving area compared with the target road section;

[0195] A processing module 1702, configured to perform the following operations every time an acquisition is made: when the vehicle driving information of the downstream area at the first historical moment and the vehicle driving information of the upstream area at the second historical moment meet a preset traffic flow condition, it is determined that there is congestion on the target road section; wherein, the time difference between the first historical moment and the second historical moment is not greater than a preset time range.

[0196] In a possible implementation manner, after determining that there is congestion on the target road section, the processing module 1702 is further configured to:

[0197] Acquire a first observation moment when it is determined that there is congestion on the target road section;

[0198] When the vehicle driving information corresponding to the upstream area meets a preset congestion condition, record a second observation moment when the vehicle driving information meets the preset congestion condition;

[0199] Based on the first observation moment and the second observation moment, and combining the vehicle driving information corresponding to the upstream area and the downstream area respectively, predict the road congestion information corresponding to the target road section.

[0200] In a possible implementation manner, the preset congestion condition is: the vehicle density in the upstream area is not less than a density threshold.

[0201] In a possible implementation manner, when the processing module 1702 is configured to predict the road congestion information corresponding to the target road section based on the first observation moment and the second observation moment, and combining the vehicle driving information corresponding to the upstream area and the downstream area respectively, it is specifically configured to:

[0202] Based on the vehicle driving information of the upstream area within a preset historical time period before the second observation moment, and the vehicle driving information of the upstream area at the second observation moment, determine the propagation speed at which the congestion occurring on the target road section spreads to the upstream area;

[0203] Based on the propagation speed and the vehicle driving information corresponding to the downstream area, and combining the first observation moment and the second observation moment, predict the road congestion information corresponding to the target road section.

[0204] In a possible implementation, when the processing module 1702 is used to determine the propagation speed at which the congestion occurring in the target road section is transmitted to the upstream area based on the vehicle driving information in the preset historical time period between the second moments in the upstream area and the vehicle driving information in the upstream area at the second observation moment, it is specifically used for:

[0205] Based on the vehicle driving information in the preset historical time period before the second observation moment in the upstream area, obtain the historical average traffic flow and historical average traffic density corresponding to the upstream area;

[0206] Based on the vehicle driving information in the upstream area at the second observation moment, obtain the congested traffic flow and congested traffic density corresponding to the upstream area at the second observation moment;

[0207] Based on the difference between the historical average traffic flow and the congested traffic flow, and the difference between the historical average traffic density and the congested traffic density, determine the propagation speed.

[0208] In a possible implementation, the road congestion information at least includes: the congestion occurrence time;

[0209] Then, when the processing module 1702 is used to predict the road congestion information corresponding to the target road section based on the propagation speed and the vehicle driving information corresponding to the downstream area, in combination with the first observation moment and the second observation moment, it is specifically used for:

[0210] Based on the propagation speed, the road length corresponding to the target road section, and the vehicle driving speed corresponding to the downstream area in the preset historical time period, in combination with the first observation moment and the second observation moment, obtain the corresponding first propagation time and second propagation time; wherein, the first propagation time is: the time period from the occurrence of congestion in the target road section to the observation of congestion characteristics in the downstream area; the second propagation time is: the time period from the occurrence of congestion in the target road section to the observation of congestion characteristics in the upstream area;

[0211] Based on the first propagation time and the first characteristic moment, predict the congestion occurrence time corresponding to the target road section; or, based on the second propagation time and the second characteristic moment, predict the congestion occurrence time corresponding to the target road section.

[0212] In a possible implementation, the road congestion information at least includes: the congestion occurrence location;

[0213] When the processing module 1702 is used to predict the road congestion information corresponding to the congestion existing in the target road section based on the propagation speed and the vehicle driving information corresponding to the downstream area, in combination with the first observation moment and the second observation moment, it is specifically used for:

[0214] Based on the propagation speed, the road length corresponding to the target road section, and the vehicle driving speed corresponding to the downstream area within the preset historical time period, in combination with the first observation moment and the second observation moment, obtain the corresponding first propagation time and second propagation time; wherein, the first propagation time is the time period from the occurrence of congestion in the target road section to the observation of congestion characteristics in the downstream area; the second propagation time is the time period from the occurrence of congestion in the target road section to the observation of congestion characteristics in the upstream area.

[0215] Based on the first propagation time and the vehicle driving speed corresponding to the downstream area within the preset historical time period, predict the congestion occurrence location corresponding to the congestion existing in the target road section; or, based on the second propagation time and the propagation speed, predict the congestion occurrence location corresponding to the target road section.

[0216] In a possible implementation manner, the difference between the first historical moment and the second historical moment is the time for the vehicle to drive through the target road section at a preset speed.

[0217] Based on the same inventive concept, an embodiment of the present application further provides an electronic device. In a possible implementation manner, the electronic device may be a server, such as Figure 1 the server 102 shown. In this embodiment, the structure of the electronic device 1800 is as Figure 18 shown, and may at least include a memory 1801, a communication module 1803, and at least one processor 1802.

[0218] The memory 1801 is used to store the computer program executed by the processor 1802. The memory 1801 may mainly include a program storage area and a data storage area. Among them, the program storage area may store an operating system and programs required to run the instant messaging function, etc.; the data storage area may store various instant messaging information and operation instruction sets, etc.

[0219] The memory 1801 can be a volatile memory, such as a random-access memory (RAM); the memory 1801 can also be a non-volatile memory, such as a read-only memory, a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD); or the memory 1801 is any other medium that can be used to carry or store a desired computer program in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 1801 can be a combination of the above memories.

[0220] The processor 1802 can include one or more central processing units (CPUs) or be a digital processing unit, etc. The processor 1802 is used to implement the above method for predicting road congestion when calling the computer program stored in the memory 1801.

[0221] The communication module 1803 is used to communicate with the terminal device and other servers.

[0222] In the embodiments of the present application, the specific connection medium between the above memory 1801, communication module 1803 and processor 1802 is not limited. In the embodiments of the present application Figure 18 it is described that the memory 1801 and the processor 1802 are connected through a bus 1804, and the bus 1804 is described by a thick line in Figure 18 The connection manners between other components are only for illustrative purposes and are not limited thereto. The bus 1804 can be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience of description, Figure 18 only a thick line is used to describe it in

[0223] The memory 1801 stores a computer storage medium, and the computer storage medium stores computer-executable instructions for implementing the method for predicting road congestion in the embodiments of the present application. The processor 1802 is used to execute the above method for predicting road congestion.

[0224] In another embodiment, the electronic device can also be other electronic devices, such as Figure 2 the terminal device 201 shown. In this embodiment, the structure of the electronic device can be as shown in Figure 19As shown, it includes components such as a communication component 1910, a memory 1920, a display unit 1930, a camera 1940, a sensor 1950, an audio circuit 1960, a Bluetooth module 1970, a processor 1980, etc.

[0225] The communication component 1910 is used to communicate with a server. In some embodiments, it may include a Wireless Fidelity (WiFi) module. The WiFi module belongs to short - range wireless transmission technology, and through the WiFi module, the electronic device can help an object send and receive information.

[0226] The memory 1920 can be used to store software programs and data. The processor 1980 executes various functions and data processing of the terminal device 201 by running the software programs or data stored in the memory 1920. The memory 1920 may include high - speed random access memory, and may also include non - volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non - volatile solid - state storage devices. The memory 1920 stores an operating system that enables the terminal device 201 to run. In this application, the memory 1920 can store the operating system and various application programs, and can also store the computer program for implementing the method of predicting road congestion in the embodiments of this application.

[0227] The display unit 1930 can also be used to display information input by an object or information provided to the object, as well as the graphical user interface (GUI) of various menus of the terminal device 201. Specifically, the display unit 1930 may include a display screen 1932 disposed on the front of the terminal device 201. Among them, the display screen 1932 can be configured in the form of a liquid crystal display, a light - emitting diode, etc. The display unit 1930 can be used to display the road interface, result display interface, etc. in the embodiments of this application.

[0228] The display unit 1930 can also be used to receive input digital or character information, and generate signal inputs related to the settings and function controls of the object of the terminal device 101. Specifically, the display unit 1930 may include a touch screen 1931 disposed on the front of the terminal device 201, which can collect touch operations of an object on or near it, such as clicking buttons, dragging scroll boxes, etc.

[0229] Among them, the touch screen 1931 can cover the display screen 1932, or the touch screen 1931 and the display screen 1932 can be integrated to implement the input and output functions of the physical terminal device 201. After integration, it can be simply referred to as a touch display screen. In this application, the display unit 1930 can display application programs and corresponding operation steps.

[0230] The camera 1940 can be used to capture static images, and the object can publish the images captured by the camera 1940 through the application. There can be one or more cameras 1940. The object generates an optical image through the lens and projects it onto the photosensitive element. The photosensitive element can be a charge coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor. The photosensitive element converts the optical signal into an electrical signal and then transmits the electrical signal to the processor 1980 to be converted into a digital image signal.

[0231] The physical terminal device may further include at least one sensor 1950, such as an acceleration sensor 1951, a distance sensor 1952, a fingerprint sensor 1953, and a temperature sensor 1954. The terminal device may also be configured with other sensors such as a gyroscope, a barometer, a hygrometer, a thermometer, an infrared sensor, a light sensor, and a motion sensor.

[0232] The audio circuit 1960, the speaker 1961, and the microphone 1962 can provide an audio interface between the object and the terminal device 101. The audio circuit 1960 can transmit the electrical signal converted from the received audio data to the speaker 1961, and the speaker 1961 converts it into a sound signal for output. The physical terminal device 101 may also be configured with a volume button for adjusting the volume of the sound signal. On the other hand, the microphone 1962 converts the collected sound signal into an electrical signal, which is received by the audio circuit 1960 and converted into audio data, and then the audio data is output to the communication component 1910 to be sent to, for example, another physical terminal device 201, or the audio data is output to the memory 1920 for further processing.

[0233] The Bluetooth module 1970 is used to interact with other Bluetooth devices with Bluetooth modules through the Bluetooth protocol. For example, the physical terminal device can establish a Bluetooth connection with a wearable electronic device (such as a smart watch) that also has a Bluetooth module through the Bluetooth module 1970 to perform data interaction.

[0234] The processor 1980 is the control center of the physical terminal device, connecting various parts of the entire terminal through various interfaces and circuits. By running or executing software programs stored in the memory 1920 and invoking data stored in the memory 1920, it performs various functions of the terminal device and processes data. In some embodiments, the processor 1980 may include one or more processing units; the processor 1980 may also integrate an application processor and a baseband processor, where the application processor mainly processes the operating system, user interface, application programs, etc., and the baseband processor mainly processes wireless communication. It can be understood that the above baseband processor may not be integrated into the processor 1980. In this application, the processor 1980 can run the operating system, application programs, user interface display and touch response, as well as the method for predicting road congestion in the embodiments of this application. In addition, the processor 1980 is coupled to the display unit 1930.

[0235] In addition, it should be noted that in the specific implementation of this application, when it comes to object data related to the prediction road congestion model, etc., when the above embodiments of this application are applied to specific products or technologies, object permission or consent needs to be obtained, and the collection, use, and processing of relevant data need to comply with the relevant laws, regulations, and standards of relevant countries and regions.

[0236] In some possible implementation manners, each aspect of the method for predicting road congestion provided in this application can also be implemented in the form of a program product, which includes a computer program. When the program product runs on an electronic device, the computer program is used to cause the electronic device to execute the steps in the method for predicting road congestion according to various exemplary embodiments of this application described above in this specification.

[0237] The program product can adopt any combination of one or more readable media. The readable media can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0238] The program product of the embodiments of the present application may be a portable compact disc read-only memory (CD-ROM) and include a computer program, and may run on an electronic device. However, the program product of the present application is not limited thereto. In this document, a readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with a command execution system, apparatus, or device.

[0239] The readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a readable computer program is carried. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. The readable signal medium may also be any readable medium other than a readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with a command execution system, apparatus, or device.

[0240] The computer program contained on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0241] The computer program for performing the operations of the present application may be written in any combination of one or more programming languages. The programming languages include object-oriented programming languages such as Java, C++, etc., and also include conventional procedural programming languages such as the "C" language or similar programming languages. The computer program may be executed entirely on the user's electronic device, partially on the user's electronic device, executed as a stand-alone software package, partially on the user's electronic device and partially on a remote electronic device, or entirely on the remote electronic device. In the case of a remote electronic device, the remote electronic device may be connected to the user's electronic device through any type of network including a local area network (LAN) or a wide area network (WAN), or may be connected to an external electronic device (e.g., connected through the Internet using an Internet service provider).

[0242] It should be noted that although several units or subunits of the apparatus are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present application, the features and functions of the two or more units described above may be embodied in one unit. Conversely, the features and functions of one unit described above may be further divided and embodied by multiple units.

[0243] In addition, although the operations of the method of the present application are described in a specific order in the drawings, this does not require or imply that these operations must be performed in that specific order, or that all of the shown operations must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step for execution, and / or one step may be decomposed into multiple steps for execution.

[0244] Those skilled in the art should understand that the embodiments of the present application may be provided as a method, a system, or a computer program product. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0245] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices generate means for implementing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0246] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0247] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are performed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0248] Obviously, those skilled in the art can make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalent technologies, this application is also intended to include these modifications and variations.

Claims

1. A method for predicting road congestion, characterized in that, The method includes: Continuously obtaining the vehicle driving information corresponding to the upstream area and the downstream area of the target road section respectively. The upstream area is the area where vehicles come from compared to the target road section, and the downstream area is the area where vehicles are heading compared to the target road section. Wherein, each time it is obtained, the following operations are performed: When the vehicle driving information of the downstream area at the first historical moment and the vehicle driving information of the upstream area at the second historical moment meet the preset traffic flow condition, it is determined that there is congestion on the target road section. Wherein, the difference between the first historical moment and the second historical moment is not greater than the preset time range.

2. The method according to claim 1, characterized in that, After determining that there is congestion on the target road section, the method further includes: Obtaining the first observation moment when it is determined that there is congestion on the target road section; When the vehicle driving information corresponding to the upstream area meets the preset congestion condition, recording the second observation moment when the vehicle driving information meets the preset congestion condition; Based on the first observation moment and the second observation moment, and combining the vehicle driving information corresponding to the upstream area and the downstream area respectively, predicting the road congestion information corresponding to the target road section.

3. The method according to claim 2, wherein The preset congestion condition is: the vehicle density in the upstream area is not less than the density threshold.

4. The method according to claim 2, wherein The predicting the road congestion information corresponding to the target road section based on the first observation moment and the second observation moment, and combining the vehicle driving information corresponding to the upstream area and the downstream area respectively includes: Based on the vehicle driving information of the upstream area within the preset historical time period before the second observation moment and the vehicle driving information of the upstream area at the second observation moment, determining the propagation speed at which the congestion occurring on the target road section spreads to the upstream area; Based on the propagation speed and the vehicle driving information corresponding to the downstream area, and combining the first observation moment and the second observation moment, predicting the road congestion information corresponding to the target road section.

5. The method according to claim 4, characterized in that, The determining the propagation speed at which the congestion occurring on the target road section spreads to the upstream area based on the vehicle driving information of the upstream area within the preset historical time period between the second moments and the vehicle driving information of the upstream area at the second observation moment includes: Based on the vehicle driving information of the upstream area within the preset historical time period before the second observation moment, obtaining the historical average traffic flow and historical average traffic density corresponding to the upstream area; Based on the vehicle driving information of the upstream area at the second observation moment, obtaining the congested traffic flow and congested traffic density corresponding to the upstream area at the second observation moment; Based on the difference between the historical average traffic flow and the congested traffic flow, and the difference between the historical average traffic density and the congested traffic density, determining the propagation speed.

6. The method according to claim 4, wherein The road congestion information at least includes: the congestion occurrence time; Then the predicting the road congestion information corresponding to the target road section based on the propagation speed and the vehicle driving information corresponding to the downstream area, and combining the first observation moment and the second observation moment includes: Based on the propagation speed, the road length corresponding to the target road section, and the vehicle driving speed corresponding to the downstream area within the preset historical time period, in combination with the first observation moment and the second observation moment, obtain the corresponding first propagation time and second propagation time; wherein, the first propagation time is the time period from the occurrence of congestion in the target road section to the observation of congestion characteristics in the downstream area; the second propagation time is the time period from the occurrence of congestion in the target road section to the observation of congestion characteristics in the upstream area; Based on the first propagation time and the first characteristic moment, predict the congestion occurrence time corresponding to the target road section; or, based on the second propagation time and the second characteristic moment, predict the congestion occurrence time corresponding to the target road section.

7. The method according to claim 4, wherein The road congestion information at least includes: the congestion occurrence location; Then, the road congestion information corresponding to the congestion existing in the target road section predicted based on the propagation speed and the vehicle driving information corresponding to the downstream area, in combination with the first observation moment and the second observation moment, includes: Based on the propagation speed, the road length corresponding to the target road section, and the vehicle driving speed corresponding to the downstream area within the preset historical time period, in combination with the first observation moment and the second observation moment, obtain the corresponding first propagation time and second propagation time; wherein, the first propagation time is the time period from the occurrence of congestion in the target road section to the observation of congestion characteristics in the downstream area; the second propagation time is the time period from the occurrence of congestion in the target road section to the observation of congestion characteristics in the upstream area; Based on the first propagation time and the vehicle driving speed corresponding to the downstream area within the preset historical time period, predict the congestion occurrence location corresponding to the congestion existing in the target road section; or, based on the second propagation time and the propagation speed, predict the congestion occurrence location corresponding to the target road section.

8. The method according to any one of claims 1-7, characterized in that, The difference between the first historical moment and the second historical moment is: the time for the vehicle to drive through the target road section at a preset speed.

9. A device for predicting road congestion, characterized in that, The device includes: An acquisition module, configured to continuously acquire the vehicle driving information corresponding to the upstream area and the downstream area of the target road section respectively, where the upstream area is the vehicle approaching area compared to the target road section, and the downstream area is the vehicle driving area compared to the target road section; A processing module, configured to perform the following operations each time an acquisition is made: when the vehicle driving information of the downstream area at the first historical moment and the vehicle driving information of the upstream area at the second historical moment meet the preset traffic condition, determine that there is congestion in the target road section; wherein, the difference between the first historical moment and the second historical moment is not greater than the preset time range.

10. An electronic device, characterized in that, It includes a processor and a memory, wherein the memory stores program code, and when the program code is executed by the processor, the processor is caused to execute the steps of the method according to any one of claims 1-8.

11. A computer-readable storage medium, characterized in that, It includes program code which, when run on a computing device, causes the computing device to perform the steps of the method according to any one of claims 1-8.

12. A computer program product, characterized in that, It includes a computer program which, when executed by a processor, implements the steps of the method according to any one of claims 1-8.

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