Road condition determination method, device and computer readable storage medium

Abstract

The disclosed embodiment discloses a road condition determination method, device and computer-readable storage medium, the method comprising: determining a target road section; obtaining historical characteristics and real-time characteristics of the target road section; determining an abnormal congestion index of the target road section based on the historical characteristics and real-time characteristics of the target road section; and determining whether the target road section is abnormally congested based on the abnormal congestion index. The technical solution can achieve timely determination of abnormal congestion without the need for interaction with traffic participants, thereby effectively reducing disturbances to traffic participants, and is conducive to improving the experience of traffic participants and the quality of travel services.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of dynamic traffic technology, and more particularly to a road condition determination method, device, and computer-readable storage medium. Background Art

[0002] With the development and progress of society, there are more and more vehicles on the road. Many traffic participants will check the real-time traffic data through applications with map navigation functions before traveling to decide the travel time, travel mode or travel route. In the current real-time traffic data, regular congestion caused by conventional reasons such as large traffic volume, road design, traffic lights, special road facilities, special points of interest, etc. has obvious periodicity. Dynamic traffic services can accurately predict regular congestion, but due to the strong randomness of abnormal congestion caused by abnormal reasons such as temporary inspections, vehicle accidents, temporary traffic control, etc., dynamic traffic services usually find it difficult to predict the occurrence of abnormal congestion. At present, the judgment of abnormal congestion needs to be obtained through interaction with traffic participants, but this will disturb traffic participants and affect the service experience. Summary of the invention

[0003] Embodiments of the present disclosure provide a road condition determination method, device, and computer-readable storage medium.

[0004] In a first aspect, a method for determining road conditions is provided in an embodiment of the present disclosure.

[0005] Specifically, the road condition determination method includes:

[0006] Determine the target road section;

[0007] Acquire historical characteristics and real-time characteristics of the target road section;

[0008] Determining an abnormal congestion index of the target road section based on historical characteristics and real-time characteristics of the target road section;

[0009] Whether the target road section is abnormally congested is determined based on the abnormal congestion index.

[0010] In combination with the first aspect, in a first implementation of the first aspect of the embodiment of the present disclosure, the historical features of the target road segment include one or more of the following features:

[0011] The first average duration for objects to pass through the target section during congestion, the first average speed for objects to pass through the target section during congestion, the first number of objects passing through the target section during congestion, the second number of objects predicted to pass through the target section after a preset duration during congestion, the second average speed for objects to pass through the target section during smooth traffic, the third number of objects passing through the target section during smooth traffic, and the fourth number of objects predicted to pass through the target section after a preset duration during smooth traffic.

[0012] In combination with the first aspect and the first implementation of the first aspect, in a second implementation of the first aspect of the embodiment of the present disclosure, the real-time features of the target road section include one or more of the following features:

[0013] The fifth number of objects passing through the target section within a unit time, the sixth number of objects predicted to pass through the target section after a preset time, the third average speed passing through the target section within the current preset time, and the second average time for objects to pass through the target section within the current preset time.

[0014] In combination with the first aspect and the above implementation of the first aspect, in a third implementation of the first aspect of the present disclosure, determining the abnormal congestion index of the target road section based on the historical characteristics and real-time characteristics of the target road section includes:

[0015] Determining a congestion mark value of the target road section according to the first average duration, the first average speed, and the second average speed;

[0016] Determining a historical traffic threshold of the target road section according to the congestion mark value, the first average speed, and the first average duration of the target road section;

[0017] Determining a real-time traffic threshold of the target road section according to the congestion mark value of the target road section, the third average speed, and the second average duration;

[0018] determining an object threshold according to the first number of objects and the third number of objects, and determining a predicted object threshold according to the second number of objects and the fourth number of objects;

[0019] Determine a low speed level value, an object level value, and a traffic level value according to the historical traffic threshold, the real-time traffic threshold, the object threshold, the predicted object threshold, the fifth number of objects, the sixth number of objects, the first average speed, the third average speed, the first average duration, and the second average duration;

[0020] The abnormal congestion index of the target road section is determined according to the low speed level value, the object level value and the traffic level value.

[0021] In combination with the first aspect and the above implementation of the first aspect, in a fourth implementation of the first aspect of the present disclosure, determining the congestion mark value of the target road section according to the first average duration, the first average speed, and the second average speed includes:

[0022] Compare the first average time with a preset duration, or compare the first average speed with the difference between the second average speed and a preset speed value;

[0023] If the first average duration is less than a preset duration, or the first average speed is less than the difference between the second average speed and a preset speed value, the congestion mark value of the target road section is a first preset congestion mark value; otherwise, it is a second preset congestion mark value.

[0024] In combination with the first aspect and the above implementation of the first aspect, in a fifth implementation of the first aspect of the present disclosure, determining the historical traffic threshold of the target road section according to the congestion mark value, the first average speed and the first average duration of the target road section includes:

[0025] When the congestion mark value of the target road section is a first preset congestion mark value, determining the number of objects whose first average speed is lower than a preset speed threshold as the historical traffic threshold;

[0026] When the congestion mark value of the target road section is a second preset congestion mark value, the number of objects whose first average duration is higher than a preset duration threshold is determined as the historical passage threshold.

[0027] In combination with the first aspect and the above implementation of the first aspect, in a sixth implementation of the first aspect of the present disclosure, determining the real-time traffic threshold of the target road section according to the congestion mark value, the third average speed, and the second average duration of the target road section includes:

[0028] When the congestion mark value of the target road section is a first preset congestion mark value, determining the number of objects whose third average speed is lower than a preset speed threshold as the real-time traffic threshold;

[0029] When the congestion mark value of the target road section is a second preset congestion mark value, the number of objects whose second average duration is higher than a preset duration threshold is determined as the real-time passage threshold.

[0030] In combination with the first aspect and the foregoing implementation manner of the first aspect, in a seventh implementation manner of the first aspect of the present disclosure, determining the object threshold according to the first object quantity and the third object quantity includes:

[0031] Adding the third object number to one fourth of the difference between the first object number and the third object number to obtain the object threshold;

[0032] The step of determining the predicted object threshold according to the second object quantity and the fourth object quantity includes:

[0033] The predicted object threshold is obtained by adding the fourth object number to one fourth of the difference between the second object number and the fourth object number.

[0034] In combination with the first aspect and the above implementation manner of the first aspect, in an eighth implementation manner of the first aspect of the present disclosure, determining the low speed level value, the object level value, and the traffic level value according to the historical traffic threshold, the real-time traffic threshold, the object threshold, the predicted object threshold, the fifth number of objects, the sixth number of objects, the first average speed, the third average speed, the first average duration, and the second average duration includes:

[0035] Determine the minimum value of the ratio of the real-time passing threshold to the historical passing threshold and the first preset value as the low-speed grade value;

[0036] determining a maximum value of a ratio of the fifth number of objects to the first number of objects and a ratio of the sixth number of objects to the second number of objects, and a minimum value of a second preset value as the object level value;

[0037] When the congestion mark value of the target road section is the first preset congestion mark value, the ratio of the sum of the first average speed and the third preset value to the third average speed is determined as the traffic level value; when the congestion mark value of the target road section is the second preset congestion mark value, the product of the ratio of the second average duration to the first average duration and the fourth preset value is determined as the traffic level value.

[0038] In combination with the first aspect and the above implementation of the first aspect, in a ninth implementation of the first aspect of the present disclosure, determining the abnormal congestion index of the target road section according to the low speed grade value, the object grade value and the traffic grade value includes:

[0039] The abnormal congestion index of the target road section is obtained by multiplying the difference between the maximum value among the low speed level value, the object level value, the traffic level value and the fifth preset value and the sixth preset value by the seventh preset value.

[0040] In combination with the first aspect and the above implementation of the first aspect, in a tenth implementation of the first aspect of the present disclosure, determining whether the target road section is abnormally congested based on the abnormal congestion index includes:

[0041] When the abnormal congestion index exceeds an eighth preset value, it is determined that the target road section is abnormally congested.

[0042] In a second aspect, a road condition determination device is provided in an embodiment of the present disclosure.

[0043] Specifically, the road condition determination device includes:

[0044] A first determination module is configured to determine a target road section;

[0045] An acquisition module, configured to acquire historical features and real-time features of the target road section;

[0046] A second determination module is configured to determine the abnormal congestion index of the target road section based on the historical characteristics and real-time characteristics of the target road section;

[0047] The third determination module is configured to determine whether the target road section is abnormally congested based on the abnormal congestion index.

[0048] In combination with the second aspect, in a first implementation manner of the second aspect of the embodiment of the present disclosure, the historical features of the target road segment include one or more of the following features:

[0049] The first average duration for objects to pass through the target section during congestion, the first average speed for objects to pass through the target section during congestion, the first number of objects passing through the target section during congestion, the second number of objects predicted to pass through the target section after a preset duration during congestion, the second average speed for objects to pass through the target section during smooth traffic, the third number of objects passing through the target section during smooth traffic, and the fourth number of objects predicted to pass through the target section after a preset duration during smooth traffic.

[0050] In combination with the second aspect and the first implementation of the second aspect, in a second implementation of the second aspect of the embodiment of the present disclosure, the real-time features of the target road section include one or more of the following features:

[0051] The fifth number of objects passing through the target section within a unit time, the sixth number of objects predicted to pass through the target section after a preset time, the third average speed passing through the target section within the current preset time, and the second average time for objects to pass through the target section within the current preset time.

[0052] In combination with the second aspect and the above implementation of the second aspect, in a third implementation of the second aspect of the present disclosure, the part of the second determination module that determines the abnormal congestion index of the target road section based on the historical characteristics and real-time characteristics of the target road section is configured as follows:

[0053] Determining a congestion mark value of the target road section according to the first average duration, the first average speed, and the second average speed;

[0054] Determining a historical traffic threshold of the target road section according to the congestion mark value, the first average speed, and the first average duration of the target road section;

[0055] Determining a real-time traffic threshold of the target road section according to the congestion mark value of the target road section, the third average speed, and the second average duration;

[0056] determining an object threshold according to the first number of objects and the third number of objects, and determining a predicted object threshold according to the second number of objects and the fourth number of objects;

[0057] Determine a low speed level value, an object level value, and a traffic level value according to the historical traffic threshold, the real-time traffic threshold, the object threshold, the predicted object threshold, the fifth number of objects, the sixth number of objects, the first average speed, the third average speed, the first average duration, and the second average duration;

[0058] The abnormal congestion index of the target road section is determined according to the low speed level value, the object level value and the traffic level value.

[0059] In combination with the second aspect and the above implementation manner of the second aspect, in a fourth implementation manner of the second aspect of the present disclosure, the part of determining the congestion mark value of the target road section according to the first average duration, the first average speed, and the second average speed is configured as follows:

[0060] Compare the first average time with a preset duration, or compare the first average speed with the difference between the second average speed and a preset speed value;

[0061] If the first average duration is less than a preset duration, or the first average speed is less than the difference between the second average speed and a preset speed value, the congestion mark value of the target road section is a first preset congestion mark value; otherwise, it is a second preset congestion mark value.

[0062] In combination with the second aspect and the above implementation of the second aspect, in a fifth implementation of the second aspect of the present disclosure, the part of determining the historical traffic threshold of the target road section according to the congestion mark value, the first average speed and the first average duration of the target road section is configured as follows:

[0063] When the congestion mark value of the target road section is a first preset congestion mark value, determining the number of objects whose first average speed is lower than a preset speed threshold as the historical traffic threshold;

[0064] When the congestion mark value of the target road section is a second preset congestion mark value, the number of objects whose first average duration is higher than a preset duration threshold is determined as the historical passage threshold.

[0065] In combination with the second aspect and the above implementation manner of the second aspect, in a sixth implementation manner of the second aspect of the present disclosure, the part of determining the real-time pass threshold of the target road section according to the congestion mark value, the third average speed and the second average duration of the target road section is configured as follows:

[0066] When the congestion mark value of the target road section is a first preset congestion mark value, determining the number of objects whose third average speed is lower than a preset speed threshold as the real-time traffic threshold;

[0067] When the congestion mark value of the target road section is a second preset congestion mark value, the number of objects whose second average duration is higher than a preset duration threshold is determined as the real-time passage threshold.

[0068] In combination with the second aspect and the foregoing implementation manner of the second aspect, in a seventh implementation manner of the second aspect of the present disclosure, the part of determining the object threshold according to the first object number and the third object number is configured as follows:

[0069] Adding the third object number to one fourth of the difference between the first object number and the third object number to obtain the object threshold;

[0070] The step of determining the predicted object threshold according to the second object quantity and the fourth object quantity includes:

[0071] The predicted object threshold is obtained by adding the fourth object number to one fourth of the difference between the second object number and the fourth object number.

[0072] In combination with the second aspect and the above implementation manner of the second aspect, in an eighth implementation manner of the second aspect of the present disclosure, the part of determining the low speed level value, the object level value, and the pass level value according to the historical pass threshold, the real-time pass threshold, the object threshold, the predicted object threshold, the fifth number of objects, the sixth number of objects, the first average speed, the third average speed, the first average duration, and the second average duration is configured as follows:

[0073] Determine the minimum value of the ratio of the real-time passing threshold to the historical passing threshold and the first preset value as the low-speed grade value;

[0074] determining a maximum value of a ratio of the fifth number of objects to the first number of objects and a ratio of the sixth number of objects to the second number of objects, and a minimum value of a second preset value as the object level value;

[0075] When the congestion mark value of the target road section is the first preset congestion mark value, the ratio of the sum of the first average speed and the third preset value to the third average speed is determined as the traffic level value; when the congestion mark value of the target road section is the second preset congestion mark value, the product of the ratio of the second average duration to the first average duration and the fourth preset value is determined as the traffic level value.

[0076] In combination with the second aspect and the above implementation of the second aspect, in a ninth implementation of the second aspect of the present disclosure, the part of determining the abnormal congestion index of the target road section according to the low speed grade value, the object grade value and the traffic grade value is configured as follows:

[0077] The abnormal congestion index of the target road section is obtained by multiplying the difference between the maximum value among the low speed level value, the object level value, the traffic level value and the fifth preset value and the sixth preset value by the seventh preset value.

[0078] In combination with the second aspect and the above implementation manner of the second aspect, in a tenth implementation manner of the second aspect of the present disclosure, the part of the second determination module that determines whether the target road section is abnormally congested based on the abnormal congestion index is configured as follows:

[0079] When the abnormal congestion index exceeds an eighth preset value, it is determined that the target road section is abnormally congested.

[0080] On the third aspect, an embodiment of the present disclosure provides a navigation method, wherein a navigation route planned based on at least a starting point, an end point and real-time traffic conditions is obtained, navigation guidance is performed based on the navigation route, and during the navigation guidance process, real-time traffic events are reported, wherein abnormal congestion in the real-time traffic conditions is determined based on any one of the above methods.

[0081] In a fourth aspect, an embodiment of the present disclosure provides an electronic device, including a memory and a processor, wherein the memory is used to store one or more computer instructions that support a road condition determination device to execute the above-mentioned road condition determination method, and the processor is configured to execute the computer instructions stored in the memory. The road condition determination device may also include a communication interface for the road condition determination device to communicate with other devices or a communication network.

[0082] In a fifth aspect, an embodiment of the present disclosure provides a computer-readable storage medium for storing computer instructions used by a road condition determination device, which includes computer instructions involved in the road condition determination device for executing the above-mentioned road condition determination method.

[0083] In a sixth aspect, an embodiment of the present disclosure provides a computer program product, including a computer program / instructions, wherein the computer program / instructions, when executed by a processor, implement the steps of the above-mentioned road condition determination method.

[0084] The technical solution provided by the embodiments of the present disclosure may have the following beneficial effects:

[0085] The above technical solution uses the historical characteristics and real-time characteristics of the target road section to determine whether the target road section is in an abnormally congested state. The technical solution can achieve timely determination of abnormal congestion without the need for interaction with traffic participants, thereby effectively reducing the disturbance to traffic participants, which is conducive to improving the experience of traffic participants and the quality of travel services.

[0086] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the embodiments of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Other features, purposes and advantages of the embodiments of the present disclosure will become more apparent through the following detailed description of non-limiting embodiments in conjunction with the accompanying drawings. In the accompanying drawings:

[0088] Figure 1 A flow chart showing a method for determining a road condition according to an embodiment of the present disclosure;

[0089] Figure 2 A structural block diagram of a road condition determination device according to an embodiment of the present disclosure is shown;

[0090] Figure 3 A structural block diagram of an electronic device according to an embodiment of the present invention is shown;

[0091] Figure 4 It is a structural diagram of a computer system suitable for implementing a road condition determination method according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0092] Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily implement them. In addition, for the sake of clarity, parts not related to the description of the exemplary embodiments are omitted in the accompanying drawings.

[0093] In the embodiments of the present disclosure, it should be understood that terms such as "including" or "having" are intended to indicate the existence of features, numbers, steps, behaviors, components, parts, or a combination thereof disclosed in this specification, and are not intended to exclude the possibility of one or more other features, numbers, steps, behaviors, components, parts, or a combination thereof existing or being added.

[0094] It should also be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present disclosure may be combined with each other. The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0095] The technical solution provided by the disclosed embodiment determines whether the target road section is in an abnormally congested state by means of the historical characteristics and real-time characteristics of the target road section. The technical solution can achieve timely determination of abnormal congestion without the need for interaction with traffic participants, thereby effectively reducing disturbances to traffic participants, and is conducive to improving the experience of traffic participants and the quality of travel services.

[0096] Figure 1 A flow chart showing a method for determining a road condition according to an embodiment of the present disclosure is shown as follows: Figure 1 As shown, the road condition determination method includes the following steps S101-S104:

[0097] In step S101, a target road section is determined;

[0098] In step S102, historical features and real-time features of the target road section are obtained;

[0099] In step S103, based on the historical characteristics and real-time characteristics of the target road section, an abnormal congestion index of the target road section is determined;

[0100] In step S104, it is determined whether the target road section is abnormally congested based on the abnormal congestion index.

[0101] As mentioned above, with the development and progress of society, there are more and more vehicles on the road. Many traffic participants will check the real-time traffic data through application software with map navigation function before traveling to decide the travel time, travel mode or travel route. In the current real-time traffic data, regular congestion caused by conventional reasons such as large traffic volume, road design, traffic lights, special road facilities, special points of interest, etc. has obvious periodicity. Dynamic traffic services can accurately predict regular congestion, but due to the strong randomness of abnormal congestion caused by abnormal reasons such as temporary inspections, vehicle accidents, temporary traffic control, etc., dynamic traffic services usually find it difficult to predict the occurrence of abnormal congestion. At present, the judgment of abnormal congestion needs to be obtained through interaction with traffic participants, but this will disturb traffic participants and affect the service experience.

[0102] In view of the above problems, in this embodiment, a road condition determination method is proposed, which uses the historical characteristics and real-time characteristics of the target road section to determine whether the target road section is in an abnormally congested state. This technical solution can achieve timely determination of abnormal congestion without the need for interaction with traffic participants, thereby effectively reducing the disturbance to traffic participants, which is conducive to improving the experience of traffic participants and improving the quality of travel services.

[0103] In one embodiment of the present disclosure, the road condition determination method may be applicable to a computer, a computing device, an electronic device, a server, a service cluster, etc. that can perform road condition determination.

[0104] In one embodiment of the present disclosure, the target road section refers to a road section whose road condition needs to be determined and whether it is currently in an abnormally congested state. The target road section may be a road section in the road network data.

[0105] In one embodiment of the present disclosure, the historical feature of the target road section refers to feature data that is obtained based on relevant historical data of the target road section and can characterize historical feature information of the target road section.

[0106] Among them, the historical characteristics of the target road section may include one or more of the following characteristics: a first average duration of time for objects to pass through the target road section during congestion, a first average speed of objects to pass through the target road section during congestion, a first number of objects to pass through the target road section during congestion, a second number of objects to pass through the target road section after a preset duration predicted during congestion, a second average speed of objects to pass through the target road section during smooth traffic, a third number of objects to pass through the target road section during smooth traffic, a fourth number of objects to pass through the target road section after a preset duration predicted during smooth traffic, and so on.

[0107] Among them, the first average duration of objects passing through the target road section during congestion refers to the average time taken by one or more objects to pass through the target road section when the target road section is in congestion; the first average speed of objects passing through the target road section during congestion refers to the average speed of one or more objects when passing through the target road section when the target road section is in congestion; the first number of objects passing through the target road section during congestion refers to the total number of objects passing through the target road section when the target road section is in congestion; the second number of objects passing through the target road section after the preset duration during congestion refers to the total number of objects passing through the target road section after the preset duration is predicted when the target road section is in congestion, with the help of navigation data or other methods. The total number of objects that will pass through the target section after a preset time period predicted by the road condition and object data; the second average speed of objects passing through the target section during the unblocked period refers to the average speed of one or more objects passing through the target section when the target section is in a unblocked period; the third number of objects passing through the target section during the unblocked period refers to the total number of objects passing through the target section when the target section is in a unblocked period; the fourth number of objects passing through the target section after a preset time period predicted during the unblocked period refers to the total number of objects that will pass through the target section after a preset time period predicted by navigation data or other road condition and object data when the target section is in a unblocked period. Among them, the preset time period can be set according to the needs of the actual application, such as 5 minutes, 10 minutes, 15 minutes, 20 minutes, 30 minutes, etc.

[0108] In one embodiment of the present disclosure, the object refers to a sample object that can generate a behavior of passing through a certain road section and can be used to measure whether a certain road section is congested or unobstructed. The object can be, for example, a vehicle. Of course, in other application scenarios, it can also be a pedestrian, a bicycle or other observable and countable object.

[0109] In one embodiment of the present disclosure, the real-time feature of the target road section refers to feature data that is obtained based on relevant real-time data of the target road section and can characterize the real-time feature information of the target road section.

[0110] Among them, the real-time characteristics of the target section may include one or more of the following characteristics: the fifth number of objects passing through the target section within a unit time length, the sixth number of objects predicted to pass through the target section after a preset time length, the third average speed passing through the target section within the current preset time length, and the second average time for objects to pass through the target section within the current preset time length.

[0111] Among them, the fifth number of objects passing through the target section within the unit time refers to the total number of objects passing through the target section within the unit time; the sixth number of objects predicted to pass through the target section after the preset time refers to the total number of objects that will pass through the target section after the preset time, which is predicted by using navigation data or other road conditions and object data; the third average speed passing through the target section within the current preset time refers to the average speed of one or more objects passing through the target section within the current preset time; the second average time for objects passing through the target section within the current preset time refers to the average time taken by one or more objects to pass through the target section within the current preset time. Among them, the unit time can be set according to the needs of actual application, such as 1 minute, etc.; the current preset time refers to the time obtained by presetting the time forward or backward from the current time, for example, if the current time is 11:00 and the preset time is 5 minutes, the current preset time can be either 10:55-11:00 or 11:00-11:05.

[0112] In one embodiment of the present disclosure, the abnormal congestion index refers to an index used to characterize whether the target road section is abnormally congested. According to the abnormal congestion index, it can be determined whether the target road section is in an abnormally congested state.

[0113] In the above implementation, the target road section is first determined, then the historical characteristics and real-time characteristics of the target road section are obtained, and then the abnormal congestion index of the target road section is determined based on the historical characteristics and real-time characteristics of the target road section. Then, whether the target road section is in an abnormally congested state can be determined based on the abnormal congestion index.

[0114] In one embodiment of the present disclosure, the step of determining the abnormal congestion index of the target road section based on the historical characteristics and real-time characteristics of the target road section in step S103 may include the following steps:

[0115] Determining a congestion mark value of the target road section according to the first average duration, the first average speed, and the second average speed;

[0116] Determining a historical traffic threshold of the target road section according to the congestion mark value, the first average speed, and the first average duration of the target road section;

[0117] Determining a real-time traffic threshold of the target road section according to the congestion mark value of the target road section, the third average speed, and the second average duration;

[0118] determining an object threshold according to the first number of objects and the third number of objects, and determining a predicted object threshold according to the second number of objects and the fourth number of objects;

[0119] Determine a low speed level value, an object level value, and a traffic level value according to the historical traffic threshold, the real-time traffic threshold, the fifth number of objects, the sixth number of objects, the first average speed, the third average speed, the first average duration, and the second average duration;

[0120] The abnormal congestion index of the target road section is determined according to the low speed level value, the object level value and the traffic level value.

[0121] In this embodiment, the congestion mark value, historical traffic threshold, real-time traffic threshold, object threshold, predicted object threshold, low speed level value, object level value and traffic level value are determined based on the historical characteristics and real-time characteristics of the target road section, and finally the abnormal congestion index of the target road section is determined.

[0122] Specifically:

[0123] In one embodiment of the present disclosure, the step of determining the congestion mark value of the target road segment according to the first average duration, the first average speed, and the second average speed may include the following steps:

[0124] Compare the first average time with a preset duration, or compare the first average speed with the difference between the second average speed and a preset speed value;

[0125] If the first average duration is less than the preset duration, or the first average speed is less than the difference between the second average speed and the preset speed value, the congestion mark value of the target road section is the first preset congestion mark value, indicating that the probability that the target road section is in an abnormally congested state is high; otherwise, it is the second preset congestion mark value, indicating that the probability that the target road section is in an abnormally congested state is low.

[0126] When the preset time length is 30 seconds and the preset speed value is 15, the congestion mark value can be determined by the formula:

[0127]

[0128] Among them, red label Indicates the congestion mark value, red time Indicates the first average duration, red speed Indicates the first average speed, green speedIndicates the second average speed, 0 is the first preset congestion mark value, and 1 is the second preset congestion mark value.

[0129] In one embodiment of the present disclosure, the step of determining the historical traffic threshold of the target road section according to the congestion mark value, the first average speed and the first average duration of the target road section may include the following steps:

[0130] When the congestion mark value of the target road section is a first preset congestion mark value, determining the number of objects whose first average speed is lower than a preset speed threshold as the historical traffic threshold;

[0131] When the congestion mark value of the target road section is a second preset congestion mark value, the number of objects whose first average duration is higher than a preset duration threshold is determined as the historical passage threshold.

[0132] When the first preset congestion mark value is set to 0 and the second preset congestion mark value is set to 1, the determination of the historical traffic threshold can be expressed by the formula:

[0133]

[0134] Among them, slow limit represents the historical traffic threshold, speed[i] represents the first average speed sequence that is lower than the preset speed threshold, i is the traffic participant mark, for example, it can be set to 1, and time[i] represents the first average duration sequence that is higher than the preset duration threshold.

[0135] In one embodiment of the present disclosure, the step of determining the real-time traffic threshold of the target road section according to the congestion mark value, the third average speed and the second average duration of the target road section may include the following steps:

[0136] When the congestion mark value of the target road section is a first preset congestion mark value, determining the number of objects whose third average speed is lower than a preset speed threshold as the real-time traffic threshold;

[0137] When the congestion mark value of the target road section is a second preset congestion mark value, the number of objects whose second average duration is higher than a preset duration threshold is determined as the real-time passage threshold.

[0138] When the first preset congestion mark value is set to 0 and the second preset congestion mark value is set to 1, the determination of the real-time traffic threshold can be expressed by the formula:

[0139]

[0140] Among them, slow online Indicates the real-time passing threshold, speed *[i] represents the third average speed sequence below the preset speed threshold, time * [i] represents a second average duration sequence that is higher than a preset duration threshold.

[0141] In one embodiment of the present disclosure, the step of determining the object threshold according to the first object number and the third object number may include the following steps:

[0142] The object threshold is obtained by adding the third object number to one fourth of the difference between the first object number and the third object number.

[0143] In this embodiment, the determination of the object threshold can be expressed by the formula:

[0144]

[0145] Among them, user limit represents the object threshold, u g Indicates the number of third objects, u r Indicates the first object quantity.

[0146] In one embodiment of the present disclosure, the step of determining the predicted object threshold according to the second object number and the fourth object number may include the following steps:

[0147] The predicted object threshold is obtained by adding the fourth object number to one fourth of the difference between the second object number and the fourth object number.

[0148] In this embodiment, the determination of the prediction object threshold can be expressed by the formula:

[0149]

[0150] Among them, etau limit represents the prediction object threshold, e g Indicates the number of the fourth object, e r Indicates the number of the second object.

[0151] In one embodiment of the present disclosure, the step of determining the low speed level value, the object level value, and the pass level value according to the historical pass threshold, the real-time pass threshold, the object threshold, the predicted object threshold, the fifth object number, the sixth object number, the first average speed, the third average speed, the first average duration, and the second average duration may include the following steps:

[0152] Determine the minimum value of the ratio of the real-time passing threshold to the historical passing threshold and the first preset value as the low-speed grade value;

[0153] Determine the maximum value of the ratio of the fifth object number to the object threshold and the ratio of the sixth object number to the predicted object threshold, and the minimum value of the second preset value as the object level value;

[0154] When the congestion mark value of the target road section is the first preset congestion mark value, the ratio of the sum of the first average speed and the third preset value to the third average speed is determined as the traffic level value; when the congestion mark value of the target road section is the second preset congestion mark value, the product of the ratio of the second average duration to the first average duration and the fourth preset value is determined as the traffic level value.

[0155] In this embodiment, when the first preset value is 2.7, the determination of the low speed level value can be expressed by the formula:

[0156]

[0157] Among them, v0 represents the low speed level value.

[0158] When the second preset value is 1.7, the object level value can be determined by the formula:

[0159]

[0160] Among them, v1 represents the object level value, user online Indicates the fifth object quantity, etau online Indicates the number of the sixth object, user limit Indicates the object threshold, etau limit Indicates the prediction object threshold.

[0161] When the third preset value is 15 and the fourth preset value is 2, the determination of the traffic level value can be expressed by the formula:

[0162]

[0163] Among them, v2 represents the access level value.

[0164] In one embodiment of the present disclosure, the step of determining the abnormal congestion index of the target road section according to the low speed level value, the object level value and the traffic level value may include the following steps:

[0165] The abnormal congestion index of the target road section is obtained by multiplying the difference between the maximum value among the low speed level value, the object level value, the traffic level value and the fifth preset value and the sixth preset value by the seventh preset value.

[0166] When the fifth preset value is 0, the sixth preset value is 1, and the seventh preset value is 100, the abnormal congestion index can be determined by the formula:

[0167] red level =(max{v0, v1, v2, 1}-1)*100,

[0168] Among them, red level Indicates abnormal congestion index.

[0169] Furthermore, in order to limit the upper limit of the abnormal congestion index, the formula for determining the abnormal congestion index can be modified as follows:

[0170] red level =min((max{v0, v1, v2, 1}-1)*100, 500).

[0171] In one embodiment of the present disclosure, the step of determining whether the target road section is abnormally congested based on the abnormal congestion index in step S103 may include the following steps:

[0172] When the abnormal congestion index exceeds an eighth preset value, it is determined that the target road section is abnormally congested.

[0173] In this embodiment, whether the target road section is abnormally congested is determined according to the size of the abnormal congestion index. For example, a preset value is first determined. If the abnormal congestion index exceeds the preset value, it can be determined that the target road section is in an abnormally congested state. Otherwise, it is not in an abnormally congested state. Among them, the size of the eighth preset value can be set according to the needs of actual application, and the present disclosure does not make specific restrictions on it. For example, the size of the preset value can be determined according to different road section levels, whether there are traffic lights on the road section, whether the current time period belongs to a high-frequency and congested time period, etc. For example, if the target road section belongs to a high-flow road section, has a traffic light, and the current time period belongs to a high-frequency and congested time period, the eighth preset value can be set to a higher value, such as 300, 320, etc., otherwise, the eighth preset value can be set to a lower value, such as 260, 280, etc.

[0174] The following are embodiments of the apparatus of the present disclosure, which can be used to execute embodiments of the method of the present disclosure.

[0175] Figure 2 FIG. 1 is a block diagram showing a road condition determination device according to an embodiment of the present disclosure. The device can be implemented as part or all of an electronic device through software, hardware, or a combination of both. Figure 2 As shown, the road condition determination device includes:

[0176] A first determination module 201 is configured to determine a target road section;

[0177] An acquisition module 202 is configured to acquire historical features and real-time features of the target road segment;

[0178] The second determination module 203 is configured to determine the abnormal congestion index of the target road section based on the historical characteristics and real-time characteristics of the target road section;

[0179] The second determination module 204 is configured to determine whether the target road section is abnormally congested based on the abnormal congestion index.

[0180] As mentioned above, with the development and progress of society, there are more and more vehicles on the road. Many traffic participants will check the real-time traffic data through application software with map navigation function before traveling to decide the travel time, travel mode or travel route. In the current real-time traffic data, regular congestion caused by conventional reasons such as large traffic volume, road design, traffic lights, special road facilities, special points of interest, etc. has obvious periodicity. Dynamic traffic services can accurately predict regular congestion, but due to the strong randomness of abnormal congestion caused by abnormal reasons such as temporary inspections, vehicle accidents, temporary traffic control, etc., dynamic traffic services usually find it difficult to predict the occurrence of abnormal congestion. At present, the judgment of abnormal congestion needs to be obtained through interaction with traffic participants, but this will disturb traffic participants and affect the service experience.

[0181] In view of the above problems, in this embodiment, a road condition determination device is proposed, which determines whether the target road section is in an abnormally congested state by means of the historical characteristics and real-time characteristics of the target road section. This technical solution can achieve timely determination of abnormal congestion without the need for interaction with traffic participants, thereby effectively reducing the disturbance to traffic participants, which is conducive to improving the experience of traffic participants and improving the quality of travel services.

[0182] In one embodiment of the present disclosure, the road condition determination device may be implemented as a computer, a computing device, an electronic device, a server, a service cluster, etc. that can perform road condition determination.

[0183] In one embodiment of the present disclosure, the target road section refers to a road section whose road condition needs to be determined and whether it is currently in an abnormally congested state. The target road section may be a road section in the road network data.

[0184] In one embodiment of the present disclosure, the historical feature of the target road section refers to feature data that is obtained based on relevant historical data of the target road section and can characterize historical feature information of the target road section.

[0185] Among them, the historical characteristics of the target road section may include one or more of the following characteristics: a first average duration of time for objects to pass through the target road section during congestion, a first average speed of objects to pass through the target road section during congestion, a first number of objects to pass through the target road section during congestion, a second number of objects to pass through the target road section after a preset duration predicted during congestion, a second average speed of objects to pass through the target road section during smooth traffic, a third number of objects to pass through the target road section during smooth traffic, a fourth number of objects to pass through the target road section after a preset duration predicted during smooth traffic, and so on.

[0186] Among them, the first average duration of objects passing through the target road section during congestion refers to the average time taken by one or more objects to pass through the target road section when the target road section is in congestion; the first average speed of objects passing through the target road section during congestion refers to the average speed of one or more objects when passing through the target road section when the target road section is in congestion; the first number of objects passing through the target road section during congestion refers to the total number of objects passing through the target road section when the target road section is in congestion; the second number of objects passing through the target road section after the preset duration during congestion refers to the total number of objects passing through the target road section after the preset duration is predicted when the target road section is in congestion, with the help of navigation data or other methods. The total number of objects that will pass through the target section after a preset time period predicted by the road condition and object data; the second average speed of objects passing through the target section during the unblocked period refers to the average speed of one or more objects passing through the target section when the target section is in a unblocked period; the third number of objects passing through the target section during the unblocked period refers to the total number of objects passing through the target section when the target section is in a unblocked period; the fourth number of objects passing through the target section after a preset time period predicted during the unblocked period refers to the total number of objects that will pass through the target section after a preset time period predicted by navigation data or other road condition and object data when the target section is in a unblocked period. Among them, the preset time period can be set according to the needs of the actual application, such as 5 minutes, 10 minutes, 15 minutes, 20 minutes, 30 minutes, etc.

[0187] In one embodiment of the present disclosure, the object refers to a sample object that can generate a behavior of passing through a certain road section and can be used to measure whether a certain road section is congested or unobstructed. The object can be, for example, a vehicle. Of course, in other application scenarios, it can also be a pedestrian, a bicycle or other observable and countable object.

[0188] In one embodiment of the present disclosure, the real-time feature of the target road section refers to feature data that is obtained based on relevant real-time data of the target road section and can characterize the real-time feature information of the target road section.

[0189] Among them, the real-time characteristics of the target section may include one or more of the following characteristics: the fifth number of objects passing through the target section within a unit time length, the sixth number of objects predicted to pass through the target section after a preset time length, the third average speed passing through the target section within the current preset time length, and the second average time for objects to pass through the target section within the current preset time length.

[0190] Among them, the fifth number of objects passing through the target section within the unit time refers to the total number of objects passing through the target section within the unit time; the sixth number of objects predicted to pass through the target section after the preset time refers to the total number of objects that will pass through the target section after the preset time, which is predicted by using navigation data or other road conditions and object data; the third average speed passing through the target section within the current preset time refers to the average speed of one or more objects passing through the target section within the current preset time; the second average time for objects passing through the target section within the current preset time refers to the average time taken by one or more objects to pass through the target section within the current preset time. Among them, the unit time can be set according to the needs of actual application, such as 1 minute, etc.; the current preset time refers to the time obtained by presetting the time forward or backward from the current time, for example, if the current time is 11:00 and the preset time is 5 minutes, the current preset time can be either 10:55-11:00 or 11:00-11:05.

[0191] In one embodiment of the present disclosure, the abnormal congestion index refers to an index used to characterize whether the target road section is abnormally congested. According to the abnormal congestion index, it can be determined whether the target road section is in an abnormally congested state.

[0192] In the above implementation, the target road section is first determined, then the historical characteristics and real-time characteristics of the target road section are obtained, and then the abnormal congestion index of the target road section is determined based on the historical characteristics and real-time characteristics of the target road section. Then, whether the target road section is in an abnormally congested state can be determined based on the abnormal congestion index.

[0193] In one embodiment of the present disclosure, the part of the second determination module 203 that determines the abnormal congestion index of the target road section based on the historical characteristics and real-time characteristics of the target road section may be configured as follows:

[0194] Determining a congestion mark value of the target road section according to the first average duration, the first average speed, and the second average speed;

[0195] Determining a historical traffic threshold of the target road section according to the congestion mark value, the first average speed, and the first average duration of the target road section;

[0196] Determining a real-time traffic threshold of the target road section according to the congestion mark value of the target road section, the third average speed, and the second average duration;

[0197] determining an object threshold according to the first number of objects and the third number of objects, and determining a predicted object threshold according to the second number of objects and the fourth number of objects;

[0198] Determine a low speed level value, an object level value, and a traffic level value according to the historical traffic threshold, the real-time traffic threshold, the fifth number of objects, the sixth number of objects, the first average speed, the third average speed, the first average duration, and the second average duration;

[0199] The abnormal congestion index of the target road section is determined according to the low speed level value, the object level value and the traffic level value.

[0200] In this embodiment, the congestion mark value, historical traffic threshold, real-time traffic threshold, object threshold, predicted object threshold, low speed level value, object level value and traffic level value are determined based on the historical characteristics and real-time characteristics of the target road section, and finally the abnormal congestion index of the target road section is determined.

[0201] Specifically:

[0202] In one embodiment of the present disclosure, the part of determining the congestion mark value of the target road segment according to the first average duration, the first average speed and the second average speed may be configured as follows:

[0203] Compare the first average time with a preset duration, or compare the first average speed with the difference between the second average speed and a preset speed value;

[0204] If the first average duration is less than the preset duration, or the first average speed is less than the difference between the second average speed and the preset speed value, the congestion mark value of the target road section is the first preset congestion mark value, indicating that the probability that the target road section is in an abnormally congested state is high; otherwise, it is the second preset congestion mark value, indicating that the probability that the target road section is in an abnormally congested state is low.

[0205] When the preset time length is 30 seconds and the preset speed value is 15, the congestion mark value can be determined by the formula:

[0206]

[0207] Among them, red label Indicates the congestion mark value, red time Indicates the first average duration, red speed Indicates the first average speed, green speedIndicates the second average speed, 0 is the first preset congestion mark value, and 1 is the second preset congestion mark value.

[0208] In one embodiment of the present disclosure, the part of determining the historical traffic threshold of the target road segment according to the congestion mark value, the first average speed and the first average duration of the target road segment may be configured as follows:

[0209] When the congestion mark value of the target road section is a first preset congestion mark value, determining the number of objects whose first average speed is lower than a preset speed threshold as the historical traffic threshold;

[0210] When the congestion mark value of the target road section is a second preset congestion mark value, the number of objects whose first average duration is higher than a preset duration threshold is determined as the historical passage threshold.

[0211] When the first preset congestion mark value is set to 0 and the second preset congestion mark value is set to 1, the determination of the historical traffic threshold can be expressed by the formula:

[0212]

[0213] Among them, slow limit represents the historical traffic threshold, speed[i] represents the first average speed sequence that is lower than the preset speed threshold, i is the traffic participant mark, for example, it can be set to 1, and time[i] represents the first average duration sequence that is higher than the preset duration threshold.

[0214] In one embodiment of the present disclosure, the part of determining the real-time traffic threshold of the target road section according to the congestion mark value, the third average speed and the second average duration of the target road section may be configured as follows:

[0215] When the congestion mark value of the target road section is a first preset congestion mark value, determining the number of objects whose third average speed is lower than a preset speed threshold as the real-time traffic threshold;

[0216] When the congestion mark value of the target road section is a second preset congestion mark value, the number of objects whose second average duration is higher than a preset duration threshold is determined as the real-time passage threshold.

[0217] When the first preset congestion mark value is set to 0 and the second preset congestion mark value is set to 1, the determination of the real-time traffic threshold can be expressed by the formula:

[0218]

[0219] Among them, slow online Indicates the real-time passing threshold, speed *[i] represents the third average speed sequence below the preset speed threshold, time * [i] represents a second average duration sequence that is higher than a preset duration threshold.

[0220] In one embodiment of the present disclosure, the part of determining the object threshold according to the first object quantity and the third object quantity may be configured as follows:

[0221] The object threshold is obtained by adding the third object number to one fourth of the difference between the first object number and the third object number.

[0222] In this embodiment, the determination of the object threshold can be expressed by the formula:

[0223]

[0224] Among them, user limit represents the object threshold, u g Indicates the number of third objects, u r Indicates the first object quantity.

[0225] In one embodiment of the present disclosure, the part of determining the predicted object threshold according to the second object number and the fourth object number may be configured as follows:

[0226] The predicted object threshold is obtained by adding the fourth object number to one fourth of the difference between the second object number and the fourth object number.

[0227] In this embodiment, the determination of the prediction object threshold can be expressed by the formula:

[0228]

[0229] Among them, etau limit represents the prediction object threshold, e g Indicates the number of the fourth object, e r Indicates the number of the second object.

[0230] In one embodiment of the present disclosure, the part of determining the low speed level value, the object level value and the pass level value according to the historical pass threshold, the real-time pass threshold, the object threshold, the predicted object threshold, the fifth number of objects, the sixth number of objects, the first average speed, the third average speed, the first average duration and the second average duration may be configured as follows:

[0231] Determine the minimum value of the ratio of the real-time passing threshold to the historical passing threshold and the first preset value as the low-speed grade value;

[0232] determining a maximum value of a ratio of the fifth number of objects to the first number of objects and a ratio of the sixth number of objects to the second number of objects, and a minimum value of a second preset value as the object level value;

[0233] When the congestion mark value of the target road section is the first preset congestion mark value, the ratio of the sum of the first average speed and the third preset value to the third average speed is determined as the traffic level value; when the congestion mark value of the target road section is the second preset congestion mark value, the product of the ratio of the second average duration to the first average duration and the fourth preset value is determined as the traffic level value.

[0234] In this embodiment, when the first preset value is 2.7, the determination of the low speed level value can be expressed by the formula:

[0235]

[0236] Among them, v0 represents the low speed level value.

[0237] When the second preset value is 1.7, the object level value can be determined by the formula:

[0238]

[0239] Among them, v1 represents the object level value.

[0240] When the third preset value is 15 and the fourth preset value is 2, the determination of the traffic level value can be expressed by the formula:

[0241]

[0242] Among them, v2 represents the access level value.

[0243] In one embodiment of the present disclosure, the part of determining the abnormal congestion index of the target road section according to the low speed level value, the object level value and the traffic level value may be configured as follows:

[0244] The abnormal congestion index of the target road section is obtained by multiplying the difference between the maximum value among the low speed level value, the object level value, the traffic level value and the fifth preset value and the sixth preset value by the seventh preset value.

[0245] When the fifth preset value is 0, the sixth preset value is 1, and the seventh preset value is 100, the abnormal congestion index can be determined by the formula:

[0246] red level =(max{v0, v1, v2, 1}-1)*100,

[0247] Among them, red level Indicates abnormal congestion index.

[0248] Furthermore, in order to limit the upper limit of the abnormal congestion index, the formula for determining the abnormal congestion index can be modified as follows:

[0249] red level =min((max{v0, v1, v2, 1}-1)*100, 500).

[0250] In one embodiment of the present disclosure, the part of the second determination module 203 that determines whether the target road section is abnormally congested based on the abnormal congestion index may be configured as follows:

[0251] When the abnormal congestion index exceeds an eighth preset value, it is determined that the target road section is abnormally congested.

[0252] In this embodiment, whether the target road section is abnormally congested is determined according to the size of the abnormal congestion index. For example, a preset value is first determined. If the abnormal congestion index exceeds the preset value, it can be determined that the target road section is in an abnormally congested state. Otherwise, it is not in an abnormally congested state. Among them, the size of the eighth preset value can be set according to the needs of actual application, and the present disclosure does not make specific restrictions on it. For example, the size of the preset value can be determined according to different road section levels, whether there are traffic lights on the road section, whether the current time period belongs to a high-frequency and congested time period, etc. For example, if the target road section belongs to a high-flow road section, has a traffic light, and the current time period belongs to a high-frequency and congested time period, the eighth preset value can be set to a higher value, such as 300, 320, etc., otherwise, the eighth preset value can be set to a lower value, such as 260, 280, etc.

[0253] The disclosed embodiment also discloses a navigation method, wherein a navigation route planned based on at least a starting point, an end point and real-time traffic conditions is obtained, navigation guidance is performed based on the navigation route, and during the navigation guidance process, real-time traffic events are reported, wherein abnormal congestion in the real-time traffic conditions is determined based on any one of the above methods.

[0254] The present disclosure also discloses an electronic device, Figure 3 A structural block diagram of an electronic device according to an embodiment of the present disclosure is shown. Figure 3 As shown, the electronic device 300 includes a memory 301 and a processor 302; wherein,

[0255] The memory 301 is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor 302 to implement the above method steps.

[0256] Figure 4 It is a structural diagram of a computer system suitable for implementing a road condition determination method according to an embodiment of the present disclosure.

[0257] like Figure 4 As shown, the computer system 400 includes a processing unit 401, which can perform various processes in the above-mentioned embodiments according to a program stored in a read-only memory (ROM) 402 or a program loaded from a storage part 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the computer system 400 are also stored. The processing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0258] The following components are connected to the I / O interface 405: an input section 406 including a keyboard, a mouse, etc.; an output section 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. The drive 410 is also connected to the I / O interface 405 as needed. Removable media 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., are installed on the drive 410 as needed, so that the computer program read therefrom is installed into the storage section 408 as needed. Among them, the processing unit 401 can be implemented as a processing unit such as a CPU, a GPU, a TPU, an FPGA, an NPU, etc.

[0259] In particular, according to an embodiment of the present disclosure, the method described above can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program tangibly contained on a readable medium, and the computer program includes a program code for executing the road condition determination method. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 409, and / or installed from the removable medium 411.

[0260] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the road map or block diagram can represent a part of a module, program segment or code, and the part of the module, program segment or code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a different order from the order marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0261] The units or modules involved in the embodiments described in the present disclosure may be implemented by software or hardware. The units or modules described may also be set in a processor, and the names of these units or modules do not constitute limitations on the units or modules themselves in some cases.

[0262] As another aspect, the embodiments of the present disclosure further provide a computer-readable storage medium, which may be a computer-readable storage medium included in the device described in the above-mentioned embodiment; or a computer-readable storage medium that exists independently and is not assembled into the device. The computer-readable storage medium stores one or more programs, and the programs are used by one or more processors to execute the methods described in the embodiments of the present disclosure.

[0263] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A method for determining a road condition, comprising: Determine the target road section; Acquire historical features and real-time features of the target road section, wherein the historical features of the target road section include the following features: a first average duration of time for objects to pass through the target road section during congestion, a first average speed of objects to pass through the target road section during congestion, a first number of objects to pass through the target road section during congestion, a second number of objects to pass through the target road section after a preset duration predicted during congestion, a second average speed of objects to pass through the target road section during smooth traffic, a third number of objects to pass through the target road section during smooth traffic, and a fourth number of objects to pass through the target road section after a preset duration predicted during smooth traffic; and the real-time features of the target road section include the following features: a fifth number of objects to pass through the target road section within a unit duration, a sixth number of objects to pass through the target road section after a preset duration predicted, a third average speed to pass through the target road section within a current preset duration, and a second average duration of time for objects to pass through the target road section within a current preset duration; Determining a congestion mark value of the target road section according to the first average duration, the first average speed, and the second average speed; Determining a historical traffic threshold of the target road section according to the congestion mark value, the first average speed, and the first average duration of the target road section; Determining a real-time traffic threshold of the target road section according to the congestion mark value of the target road section, the third average speed, and the second average duration; determining an object threshold according to the first number of objects and the third number of objects, and determining a predicted object threshold according to the second number of objects and the fourth number of objects; Determine a low speed level value, an object level value, and a traffic level value according to the historical traffic threshold, the real-time traffic threshold, the object threshold, the predicted object threshold, the fifth number of objects, the sixth number of objects, the first average speed, the third average speed, the first average duration, and the second average duration; Determine the abnormal congestion index of the target road section according to the low speed grade value, the object grade value and the traffic grade value; Whether the target road section is abnormally congested is determined based on the abnormal congestion index.

2. The method according to claim 1, wherein determining the congestion mark value of the target road segment according to the first average duration, the first average speed, and the second average speed comprises: Compare the first average time with a preset duration, or compare the first average speed with the difference between the second average speed and a preset speed value; If the first average duration is less than a preset duration, or the first average speed is less than the difference between the second average speed and a preset speed value, the congestion mark value of the target road section is a first preset congestion mark value; otherwise, it is a second preset congestion mark value.

3. The method according to claim 2, wherein determining the historical traffic threshold of the target road section according to the congestion mark value, the first average speed and the first average duration of the target road section comprises: When the congestion mark value of the target road section is a first preset congestion mark value, determining the number of objects whose first average speed is lower than a preset speed threshold as the historical traffic threshold; When the congestion mark value of the target road section is a second preset congestion mark value, the number of objects whose first average duration is higher than a preset duration threshold is determined as the historical passage threshold.

4. The method according to claim 2, wherein determining the real-time traffic threshold of the target road section according to the congestion mark value, the third average speed and the second average duration of the target road section comprises: When the congestion mark value of the target road section is a first preset congestion mark value, determining the number of objects whose third average speed is lower than a preset speed threshold as the real-time traffic threshold; When the congestion mark value of the target road section is a second preset congestion mark value, the number of objects whose second average duration is higher than a preset duration threshold is determined as the real-time passage threshold.

5. The method according to any one of claims 1 to 4, wherein determining the object threshold according to the first object quantity and the third object quantity comprises: Adding the third object number to one fourth of the difference between the first object number and the third object number to obtain the object threshold; The step of determining a predicted object threshold according to the second object quantity and the fourth object quantity includes: The predicted object threshold is obtained by adding the fourth object number to one fourth of the difference between the second object number and the fourth object number.

6. The method according to any one of claims 2 to 4, wherein determining the low speed level value, the object level value and the traffic level value according to the historical traffic threshold, the real-time traffic threshold, the object threshold, the predicted object threshold, the fifth number of objects, the sixth number of objects, the first average speed, the third average speed, the first average duration and the second average duration comprises: Determine the minimum value of the ratio of the real-time passing threshold to the historical passing threshold and the first preset value as the low-speed grade value; Determine the maximum value of the ratio of the fifth number of objects to the first number of objects and the ratio of the sixth number of objects to the second number of objects, and the minimum value of the second preset value as the object level value; When the congestion mark value of the target road section is a first preset congestion mark value, a ratio of a sum of the first average speed and a third preset value to the third average speed is determined as the traffic grade value; When the congestion mark value of the target road section is the second preset congestion mark value, the product of the ratio of the second average duration to the first average duration and a fourth preset value is determined as the traffic grade value.

7. The method according to any one of claims 1 to 4, wherein determining the abnormal congestion index of the target road section according to the low speed grade value, the object grade value and the traffic grade value comprises: The abnormal congestion index of the target road section is obtained by multiplying the difference between the maximum value among the low speed level value, the object level value, the traffic level value and the fifth preset value and the sixth preset value by the seventh preset value.

8. The method according to any one of claims 1 to 4, wherein determining whether the target road section is abnormally congested based on the abnormal congestion index comprises: When the abnormal congestion index exceeds an eighth preset value, it is determined that the target road section is abnormally congested.

9. A road condition determination device, comprising: A first determination module is configured to determine a target road section; an acquisition module configured to acquire historical features and real-time features of the target road section, wherein the historical features of the target road section include the following features: a first average duration of time for objects to pass through the target road section during congestion, a first average speed of objects to pass through the target road section during congestion, a first number of objects to pass through the target road section during congestion, a second number of objects to pass through the target road section after a preset duration predicted during congestion, a second average speed of objects to pass through the target road section during smooth traffic, a third number of objects to pass through the target road section during smooth traffic, and a fourth number of objects to pass through the target road section after a preset duration predicted during smooth traffic; and the real-time features of the target road section include the following features: a fifth number of objects to pass through the target road section within a unit duration, a sixth number of objects to pass through the target road section after a preset duration predicted, a third average speed to pass through the target road section within a current preset duration, and a second average duration of time for objects to pass through the target road section within a current preset duration; The second determination module is configured to determine the congestion mark value of the target road section according to the first average duration, the first average speed and the second average speed; determine the historical traffic threshold of the target road section according to the congestion mark value of the target road section, the first average speed and the first average duration; determine the real-time traffic threshold of the target road section according to the congestion mark value of the target road section, the third average speed and the second average duration; determine the object threshold according to the first number of objects and the third number of objects, and determine the predicted object threshold according to the second number of objects and the fourth number of objects; determine the low speed grade value, the object grade value and the traffic grade value according to the historical traffic threshold, the real-time traffic threshold, the object threshold, the predicted object threshold, the fifth number of objects, the sixth number of objects, the first average speed, the third average speed, the first average duration and the second average duration; determine the abnormal congestion index of the target road section according to the low speed grade value, the object grade value and the traffic grade value; The third determination module is configured to determine whether the target road section is abnormally congested based on the abnormal congestion index.

10. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the computer instructions are executed by a processor, the method steps described in any one of claims 1 to 8 are implemented.

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