Road traffic condition prediction method, device, electronic equipment and computer program product

Abstract

The embodiment of the present application provides a method, device, electronic device, and computer program product for predicting road traffic conditions, wherein the method for predicting road traffic conditions includes: obtaining static traffic data and real-time dynamic data of the road; obtaining the congestion status of the road according to the static traffic data and road condition data; obtaining the event control data of the road according to the static traffic data and traffic event data; obtaining the congestion trend of the road according to the congestion status, event control data, and planning navigation data; generating weather warning information corresponding to the road based on the static traffic data, traffic event data, and meteorological data; predicting the traffic condition of the road according to the congestion trend and weather warning information of the road. The solution provided by the present application can accurately predict the traffic condition of the road and meet the requirements of fine, accurate, and real-time traffic management.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of traffic management technology, and in particular to a road traffic condition prediction method, device, electronic device and computer program product. Background Art

[0002] As transportation gradually develops, people's travel is becoming more and more convenient, for example, they can reach their destination quickly and conveniently by driving. However, as the number of vehicles on the road continues to increase, how to better ensure road safety and smooth traffic has become a focus of attention. The main reasons affecting safety and smooth traffic include traffic accidents, vehicle breakdowns, road maintenance, etc. In general, the reasons affecting road smoothness can be obtained through road condition observation, manual reporting, etc., and based on this, early warning reminders can be issued to vehicles that are heading or may head towards such roads. For example, if a road is congested due to a traffic accident, it is recommended that the driver change the route in advance to ensure the road is smooth as much as possible.

[0003] However, the data obtained through road condition observation or manual reporting is discrete and delayed, making the early warning alerts based on such data inaccurate and ineffective, and unable to meet the current needs of sophisticated, accurate and real-time traffic management. Summary of the invention

[0004] In view of this, an embodiment of the present application provides a road traffic condition prediction solution to at least partially solve the above-mentioned problem.

[0005] According to a first aspect of an embodiment of the present application, a method for predicting road traffic conditions is provided, comprising: obtaining static traffic data and real-time dynamic data of a road, wherein the real-time dynamic data includes road condition data, traffic event data, planning navigation data and meteorological data of the area where the road is located; obtaining a congestion state of the road according to the static traffic data and the road condition data; obtaining event control data of the road according to the static traffic data and the traffic event data; obtaining a congestion trend of the road according to the congestion state, the event control data and the planning navigation data; generating weather warning information corresponding to the road based on the static traffic data, the traffic event data and the meteorological data; and predicting the traffic condition of the road according to the congestion trend of the road and the weather warning information.

[0006] According to the second aspect of an embodiment of the present application, a road traffic condition prediction device is provided, comprising: a data acquisition module, used to acquire static traffic data and real-time dynamic data of a road, wherein the real-time dynamic data includes road condition data, traffic event data, planning navigation data and meteorological data of the area where the road is located; a state determination module, used to obtain the congestion state of the road according to the static traffic data and the road condition data, and to obtain event control data of the road according to the static traffic data and the traffic event data; a trend determination module, used to obtain the congestion trend of the road according to the congestion state, the event control data and the planning navigation data; a weather determination module, used to generate weather warning information corresponding to the road based on the static traffic data, the traffic event data and the meteorological data; a prediction module, used to predict the traffic condition of the road according to the congestion trend of the road and the weather warning information.

[0007] According to the third aspect of an embodiment of the present application, there is provided an electronic device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; the memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the road traffic condition prediction method described in the first aspect.

[0008] According to a fourth aspect of an embodiment of the present application, a computer program product is provided, on which a computer program is stored, and when the program is executed by a processor, the road traffic condition prediction method as described in the first aspect is implemented.

[0009] According to the road traffic condition prediction solution provided by the embodiment of the present application, by acquiring static traffic data and real-time dynamic data of the road, wherein the real-time dynamic data includes road condition data, traffic event data, planned navigation data and meteorological data of the area where the road is located; the congestion state of the road is obtained according to the static traffic data and the road condition data; the event control data of the road is obtained according to the static traffic data and the traffic event data; the congestion trend of the road is obtained according to the congestion state, the event control data and the planned navigation data. Since the planned navigation data can reflect the expected driving route and travel time of the vehicle, The driving time and other information can be used to determine the road that the vehicle enters and the time of entering the road according to the planned navigation data. Therefore, according to the congestion status, the event management data and the planned navigation data, the congestion trend of the road can be accurately obtained; then based on the static traffic data, the traffic event data and the meteorological data, the weather warning information corresponding to the road is generated; and according to the congestion trend of the road and the weather warning information, the traffic condition of the road is predicted. Therefore, based on the congestion trend of the road, the weather affecting the traffic condition of the road and other information, the traffic condition of the road can be accurately predicted to meet the needs of fine, accurate and real-time traffic management. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the embodiments of the present application. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0011] Figure 1A A flowchart of a method for predicting road traffic conditions according to Embodiment 1 of the present application;

[0012] Figure 1B for Figure 1A A schematic diagram of an example scenario in the illustrated embodiment;

[0013] Figure 2A A flowchart of a method for predicting road traffic conditions according to Embodiment 2 of the present application;

[0014] Figure 2B This is a schematic diagram of an interface for displaying road traffic condition prediction results according to the second embodiment of the present application;

[0015] Figure 3 This is a schematic diagram of the structure of a road traffic condition prediction device according to Embodiment 3 of the present application;

[0016] Figure 4This is a schematic diagram of the structure of a road traffic condition prediction device according to the fourth embodiment of the present application;

[0017] Figure 5 This is a schematic diagram of the structure of an electronic device according to Embodiment 5 of the present application. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the embodiments of the present application should fall within the scope of protection of the embodiments of the present application.

[0019] The specific implementation of the embodiment of the present application is further explained below in conjunction with the accompanying drawings of the embodiment of the present application.

[0020] See also Figure 1A , shows a flow chart of the steps of a traffic condition prediction method provided in Example 1 of the present application, such as Figure 1A As shown, it includes:

[0021] S101. Obtain static traffic data and real-time dynamic data of a road, wherein the real-time dynamic data includes traffic condition data, traffic event data, planning navigation data, and meteorological data of an area where the road is located.

[0022] In this embodiment, static traffic data refers to data that does not change with vehicles, etc., for example, the width and length of the road, the intersections at both ends of the road, etc. The static traffic data can be the electronic map data corresponding to the road, and the electronic map can be obtained from multiple map producers.

[0023] Optionally, the static traffic data may specifically include at least one of the following: mileage information of the road and road section information of the road. The mileage information may include information of preset milestones at intervals beside the road; the road section information may include information such as the length of the road, the number of lanes on the road, and the type of each lane.

[0024] When the solution of the embodiment of the present application is executed for roads within a certain area, the static traffic information may also include boundary information of the road.

[0025] In this embodiment, the real-time dynamic data refers to data that changes with vehicles, etc. In this embodiment, the real-time dynamic data includes road condition data, traffic event data, planning navigation data, and weather data of the area where the road is located.

[0026] Road condition data, driving planning navigation data, etc. can be obtained from the navigation database, traffic incident data can be obtained through driver reports or official information. Weather data can be obtained from the weather data server.

[0027] Traffic event data may include traffic accidents or road control events, etc. Road control events may include, for example, occupying the road for road repairs, setting up highway checkpoints, etc.

[0028] S102: Obtain the congestion status of the road according to the static traffic data and the road condition data.

[0029] In this embodiment, static traffic data generally includes spatial information corresponding to the road, and road condition data generally includes congestion location information and congestion time of a congestion event on the road. The static traffic data and road condition data can be integrated to obtain the congestion status of the road.

[0030] S103: Obtaining event management and control data of the road according to the static traffic data and the traffic event data.

[0031] In this embodiment, the event control data may include data related to control (such as flow restriction), accidents, construction and other events, which are not limited in this embodiment. The event control data may also include: the upper limit of the traffic flow corresponding to the event, the start and end time of the event, etc.

[0032] S104: Obtain a congestion trend of the road according to the congestion status, the event management data, and the planning navigation data.

[0033] Since the planned navigation data can reflect the expected driving route and driving time of the vehicle, the road the vehicle enters and the time of entering the road can be determined based on the planned navigation data. Combined with the congestion status and event control data, a more accurate road congestion trend can be obtained.

[0034] In this embodiment, the congestion trend of the road can be displayed in the interface in the form of text or charts. For example, a bar chart can be displayed, and the text content "the congestion situation of section A will be greatly reduced in the next hour" can be displayed.

[0035] S105: Generate weather warning information corresponding to the road based on the static traffic data, the traffic event data and the meteorological data.

[0036] As described above, static traffic data generally includes spatial information corresponding to roads. By combining static traffic data and meteorological data, it is possible to determine the road sections whose traffic conditions are specifically affected by the weather. For example, it is possible to determine whether a certain road section will have icy roads, low visibility, etc. On the basis of determining that the traffic conditions of the road section are affected by the weather, the occurrence of a traffic incident may further affect the traffic conditions of the road section, or the cause of the traffic incident may be icy roads, low visibility, etc. Therefore, in this embodiment, more accurate weather warning information corresponding to the road can be generated based on the static traffic data, the traffic incident data, and the meteorological data.

[0037] For example, the weather warning information may specifically be: road icing occurs on section A, a traffic accident occurs at *** meters of section A due to road icing, and it is recommended to switch to other roads. Alternatively, the weather warning information may specifically be: road icing occurs on section A, no traffic accident occurs near section A, and it is recommended to drive slowly.

[0038] It should be noted that step S105 of this embodiment can be executed after step S101 and before step S106, and this embodiment does not limit the execution order of step S105 and steps S102-S104; step S105 can be executed in parallel or serially with steps S102-S104, all of which are within the protection scope of this application.

[0039] S106: predicting the traffic condition of the road according to the congestion trend of the road and the weather warning information.

[0040] For example, in this embodiment, it can be determined that a certain section of the road will continue to be congested based on the congestion trend of the road, etc.; or, it can be determined that a certain section of the road will have icy road conditions in the next hour based on the congestion trend of the road and weather warning information, and a large number of vehicles will pass through the section of the road in the subsequent hour, so it can be predicted that the section of the road is likely to be congested in the next hour.

[0041] like Figure 1B As shown, the solution of this embodiment is exemplified below through a specific usage scenario.

[0042] Assuming that it is necessary to predict the traffic conditions of the roads in City A, the static traffic data of each road in City A can be obtained based on the electronic map, and the real-time dynamic data of City A can be obtained. The real-time dynamic data includes the road condition data, traffic event data, planning navigation data and meteorological data of the area where the roads are located.

[0043] Based on static traffic data and road condition data, the real-time congestion status of each road in City A can be obtained.

[0044] Obtaining event control data on each road in city A based on static traffic data and traffic event data;

[0045] According to the congestion status of each road in city A, the event control data on each road, and the planning navigation data corresponding to the vehicles traveling in city A, the congestion trend of each road in city A is obtained;

[0046] Based on the static traffic data, traffic event data and meteorological data of city A, the weather warning information of city A can be obtained.

[0047] According to the congestion trends of various roads in city A and the weather warning information obtained above, the traffic conditions of various roads in city A can be predicted. If it is predicted that congestion may occur, or that the road may be icy or the road visibility may be low, corresponding warning information can be generated, and unobstructed roads in city A can be recommended to the driver.

[0048] According to the road traffic condition prediction solution provided by the embodiment of the present application, by obtaining static traffic data and real-time dynamic data of the road, wherein the real-time dynamic data includes road condition data, traffic event data, planned navigation data and meteorological data of the area where the road is located; the congestion state of the road is obtained according to the static traffic data and the road condition data; the event control data of the road is obtained according to the static traffic data and the traffic event data; the congestion trend of the road is obtained according to the congestion state, the event control data and the planned navigation data. Since the planned navigation data can reflect the expected driving route and The information such as driving time can be determined according to the planned navigation data, and the road that the vehicle enters and the time of entering the road. Therefore, according to the congestion status, the event management data and the planned navigation data, the congestion trend of the road can be accurately obtained; then based on the static traffic data, the traffic event data and the meteorological data, the weather warning information corresponding to the road is generated; and according to the congestion trend of the road and the weather warning information, the traffic condition of the road is predicted. Therefore, based on the congestion trend of the road, the weather affecting the traffic condition of the road and other information, the traffic condition of the road can be accurately predicted to meet the needs of fine, accurate and real-time traffic management.

[0049] The road traffic condition prediction method of this embodiment can be executed by any appropriate electronic device with data processing capabilities, including but not limited to: a server, a mobile terminal (such as a mobile phone, a PAD, etc.) and a PC, etc.

[0050] See also Figure 2A , shows a flow chart of the steps of a traffic condition prediction method provided in the second embodiment of the present application, such as Figure 2AAs shown, it includes:

[0051] S201. Obtain static traffic data and real-time dynamic data of the road.

[0052] In this embodiment, the static traffic data includes at least one of the following: mileage information of the road, section information of the road. The static data can be obtained from a geographic space database or electronic sub-map data corresponding to the road.

[0053] In this embodiment, the real-time dynamic data includes the road condition data, traffic event data, planning navigation data and meteorological data of the area where the road is located.

[0054] S202: Obtain the congestion status of the road according to the static traffic data and the road condition data.

[0055] The specific implementation method of this step refers to the above step S102 and will not be repeated here.

[0056] S203, performing spatiotemporal fusion on the static traffic data and the traffic event data to obtain event control data corresponding to the road, and determining the impact degree data of the traffic event according to the event control data and the planning navigation data.

[0057] In this embodiment, traffic events may include traffic accidents, road control events, etc. Road control events may include, for example, road occupation for road repairs, setting up highway checkpoints, etc. Compared with other traffic events such as traffic accidents, road control events have higher certainty, for example, the location of the event, the duration of the event, etc. may be predetermined. Therefore, in this embodiment, road control events are analyzed separately. For example, the event control data and impact degree data corresponding to the road control event may be determined, and the event control data and impact degree data corresponding to the traffic accident may be determined.

[0058] Optionally, in this embodiment, the static traffic data and traffic event data are temporally and spatially fused to obtain the event control data corresponding to the road, which may include: clustering and fusing the static traffic data and the traffic event data based on the geographic location information of the static traffic data, and the effective time and geographic location information of the traffic event data; and obtaining the event control data corresponding to the road according to the clustering and fusion results. By performing clustering and fusion based on the effective time and geographic location information, the obtained real-time dynamic data can be deduplicated, thereby reducing the amount of calculation. In addition, in this embodiment, temporal and spatial fusion can also be performed according to the category of traffic events to further deduplicate the obtained real-time dynamic data. The category of traffic events can be, for example, control, accident, construction, etc., which is not limited in this embodiment.

[0059] Since part of the traffic event data is obtained through official information and part of it is obtained through drivers' reports, there is some overlap in the traffic time data obtained, that is, multiple pieces of traffic event data may correspond to the same traffic event. In this embodiment, data can be deduplicated through spatiotemporal fusion.

[0060] In this embodiment, the data can be further deduplicated in combination with the planned navigation data of the road. For example, if it is determined based on the driving navigation data that the locations and times at which two or more drivers report traffic event data are relatively close, then it can be determined that the traffic event data reported by the multiple drivers correspond to the same traffic event.

[0061] In this embodiment, the impact degree data of the traffic event can also be determined based on the event control data and the planning navigation data. For example, based on the impact of road construction, setting up highway checkpoints, etc. on the upper limit of traffic flow, the number of people (or vehicles) affected by the road control event can be determined based on the planning navigation data, and the calculation result can be used as the impact degree data of the road control event.

[0062] Specifically, if a traffic incident occurs, the relevant information of the traffic incident can be determined based on the planned navigation data and pushed to the drivers that may be affected by the traffic incident, and the number of drivers that need to be pushed can be used as the number of people (or vehicles) affected by the traffic incident. The specific method of determining the drivers that need to be pushed can refer to the relevant technology, which will not be repeated here.

[0063] In this embodiment, after determining the event control data and the impact degree data of the traffic event, they can be displayed on the interface. Figure 2B As shown, a map corresponding to the road can be displayed in the middle of the interface, and traffic events can be marked on the map through labels. The impact degree data corresponding to the traffic events can also be reflected through the size of the labels.

[0064] In addition, since road control events are more certain, Figure 2B As shown, the number of specific road control events and impact data can be displayed on one side of the interface, so that relevant personnel have a more intuitive and accurate understanding of the road control events.

[0065] S204: Obtain the congestion trend of the road according to the congestion status, the event control data, the planning navigation data and the impact degree data of the traffic event.

[0066] In this step, the congestion trend of the road can be obtained directly based on the impact degree data of the traffic event determined in the above steps, combined with the congestion status, the event management data and the planning navigation data.

[0067] like Figure 2B As shown, the congestion trend can be displayed in the trend display area on the left side of the interface in the form of text or charts. For example, a bar chart can be displayed, and the text content "the congestion situation on section A will be greatly reduced in the next hour" can be displayed.

[0068] In this embodiment, if the road is an expressway, then obtaining the congestion trend of the road according to the congestion status, the event control data and the planned navigation data includes: obtaining the real-time congestion mileage and the congestion mileage change trend of the expressway according to the congestion status, the event control data and the planned navigation data, and obtaining the real-time congestion mileage, congestion duration and congestion duration change trend of the toll station of the expressway.

[0069] The congestion mileage change trend may include the change trend of the congestion mileage ratio, where the congestion mileage ratio = congestion section mileage / total road mileage*100%. If the solution provided in this embodiment is calculated for all roads in a region, the congestion mileage ratio = congestion section mileage in the region / total road mileage in the region*100%.

[0070] In this embodiment, the congestion trend can be determined based on a collection of multiple historical real-time congestion situations and possible congestion situations determined based on planned navigation data. The specific determination method can refer to related technologies and will not be repeated here.

[0071] Specifically, the congestion mileage change trend is calculated by the following method: obtaining the congestion mileage corresponding to several time points in the target area, and determining the proportion of several congestion journeys to the total mileage in the target area; determining the congestion mileage change trend according to several time points and the corresponding proportions.

[0072] In this embodiment, the several time points may be historical time points or future time points. The congested mileage corresponding to the historical time points may be directly obtained from historical data, and the congested mileage corresponding to the future time points may be obtained from planned navigation data.

[0073] In this embodiment, the real-time congestion mileage data of the toll station of the expressway=∑ the length of the road section before the toll station in congestion, where ∑ represents the sum.

[0074] In this embodiment, the sum of the lengths of all congested sections before toll stations in a certain area can be used as the real-time congestion mileage data of the toll stations of the expressway, or the sum of the lengths of all congested sections before toll stations on a certain expressway can be used as the real-time congestion mileage data of the toll stations of the expressway, but this embodiment does not limit this.

[0075] In this embodiment, the congestion duration data of the toll station of the expressway=∑the duration of each congestion at the toll station.

[0076] In this embodiment, the sum of the congestion durations of all toll booths in a certain area that are in congestion can be used as the congestion duration data of the toll booths on the expressway. In addition, the time from the beginning of congestion to the unblocked road can be used as the congestion duration.

[0077] In this embodiment, the method for determining the changing trend of the congestion duration at the toll station of the expressway is similar to the method for calculating the changing trend of the congestion mileage described above, and will not be described in detail here.

[0078] Optionally, in this embodiment, a distance threshold of a traffic event can be determined based on the impact data of the traffic event; an association is established between a traffic event whose distance from the congestion location is less than the distance threshold and the occurred congestion; and the congestion trend of the road is obtained based on the traffic events that are associated with the occurred congestion and their impact data, as well as the congestion status and the planned navigation data.

[0079] In this embodiment, the distance threshold of the traffic event is determined based on the impact degree data of the traffic event, such as the distance range of the impact degree, etc. The impact degree of each type of traffic event is different. If there are many traffic events occurring within a certain range, the impact degree corresponding to the traffic event is also different. In this embodiment, the distance threshold of the traffic event can be determined based on the impact degree data of the traffic event and can also be combined with historical traffic event data.

[0080] A correlation relationship is established between traffic events whose distance from the congestion location is less than a distance threshold and the occurred congestion, and the congestion trend of the road can be obtained based on the traffic events associated with the occurred congestion and their impact data, as well as the congestion status and the planned navigation data.

[0081] In this embodiment, traffic events whose distance from the congestion location is less than the distance threshold can be displayed together with the congestion time through the interface, for example Figure 2B As shown, it can be displayed in the traffic event / congestion location display area on the right side of the interface, and a distance prompt can be sent, such as "the distance between event B and *** congestion is X meters". In combination with the planned navigation data, it can be determined that there are many vehicles passing through event B in the next hour. It can be determined that the section where event B is located is very likely to be congested, and the congestion situation at a distance of X meters may worsen.

[0082] In this embodiment, the relationship between traffic events and the congestion that occurs can also be analyzed based on the congestion mileage, congestion time, traffic event occurrence time, etc. in the historical traffic data, and the determined relationship can be learned so that the possible congestion can be predicted more accurately based on the traffic time in the subsequent process.

[0083] In addition, since information such as the location and duration of control events is relatively certain, it is possible to focus on associating congestion with control events so that more accurate traffic condition predictions can be made based on the known control events.

[0084] S205: Generate weather warning information corresponding to the road based on the static traffic data, the traffic event data and the meteorological data.

[0085] In this embodiment, the weather warning information may include warning information for roads in a certain area, and the weather warning information may also include section-level dangerous road surface prediction warning information and section-level low visibility prediction warning information.

[0086] Optionally, after step S205, the method provided in this embodiment may further include: generating a recommended control strategy for vehicles expected to pass through the weather warning location based on the weather warning information and the planned navigation data associated with the weather warning location, the recommended control strategy including at least one of the following: a recommended control strategy for dangerous roads and a recommended control strategy for low visibility.

[0087] For example, if section-level dangerous road surface prediction and warning information is generated for a certain section of the road, it means that the section may have icy road conditions, and it is necessary to limit the vehicle speed on the section. Therefore, the appropriate driving speed can be determined according to the road conditions, and a recommended dangerous road surface control strategy can be generated and sent to relevant personnel to suggest that relevant personnel pay special attention to the section, and the recommended driving speed requirement for the section can be sent to the driver.

[0088] In this embodiment, corresponding control suggestion events can also be generated according to the weather warning information to suggest relevant personnel to control roads where danger may occur.

[0089] S206: predicting the traffic condition of the road according to the congestion trend of the road and the weather warning information.

[0090] In this embodiment, based on the congestion situation data, the congestion trend, the weather warning information, the event control data and the impact data of road control events, the traffic conditions of the road can be observed in multiple dimensions, thereby more accurately predicting the traffic conditions of the road.

[0091] The road traffic condition prediction method of this embodiment can be executed by any appropriate electronic device with data processing capabilities, including but not limited to: a server, a mobile terminal (such as a mobile phone, a PAD, etc.) and a PC, etc.

[0092] See also Figure 3 , shows a schematic diagram of the structure of a traffic condition prediction device provided in the third embodiment of the present application, such as Figure 3 As shown, it includes:

[0093] The data acquisition module 301 is used to acquire static traffic data and real-time dynamic data of the road, wherein the real-time dynamic data includes road condition data, traffic event data, planning navigation data and meteorological data of the area where the road is located;

[0094] A state determination module 302, configured to obtain the congestion state of the road according to the static traffic data and the road condition data, and to obtain event control data of the road according to the static traffic data and the traffic event data;

[0095] A trend determination module 303, configured to obtain a congestion trend of the road according to the congestion state, the event control data and the planning navigation data;

[0096] A weather determination module 304, configured to generate weather warning information corresponding to the road based on the static traffic data, the traffic event data and the meteorological data;

[0097] The prediction module 305 is used to predict the traffic condition of the road according to the congestion trend of the road and the weather warning information.

[0098] The road traffic condition prediction device of this embodiment is used to implement the corresponding road traffic condition prediction methods in the aforementioned multiple method embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be described in detail here. In addition, the functional implementation of each module in the road traffic condition prediction device of this embodiment can refer to the description of the corresponding parts in the aforementioned method embodiments, which will not be described in detail here.

[0099] See also Figure 4 , shows a schematic diagram of the structure of a traffic condition prediction device provided in Embodiment 4 of the present application, such as Figure 4 As shown, it includes:

[0100] The static traffic data acquisition module is used to obtain static traffic data of the road, and can be specifically used to obtain mileage post data, area boundary data, and road section data.

[0101] The real-time dynamic data acquisition module is used to acquire real-time dynamic data of the road, wherein the real-time dynamic data includes road condition data, traffic event data, planning navigation data and meteorological data of the area where the road is located.

[0102] The control intelligence status determination module is used to determine the event control data corresponding to the road and the impact degree data of the traffic event based on the static traffic data and the traffic event data.

[0103] The control intelligence status determination module can specifically integrate the control event data obtained from the official in time and space, and the traffic event data obtained from the Internet (such as reported by drivers). It can also determine the number of people (vehicles) affected by the control based on the integrated road control events and driving navigation data, and can provide reminders for road control events and conduct a global analysis of the impact of road control events in a certain area.

[0104] The congestion event warning module is used to obtain the congestion status and congestion trend of the road according to the static traffic data, road condition data, event control data and the planning navigation data. Specifically, the congestion event warning module can be used to analyze the congestion status and trend of the road section, that is, to determine the real-time congestion mileage data and congestion mileage change trend of the expressway; it can also be used to analyze the congestion status and trend of the toll station, that is, to determine the real-time congestion mileage data, congestion duration data and congestion duration change trend of the toll station of the expressway; and it can be used to perform congestion intelligence and control correlation analysis, that is, to establish a correlation between traffic events (including road control events) whose distance from the congestion location is less than the distance threshold and the congestion that occurs.

[0105] The severe weather intelligence warning module is used to generate weather warning information corresponding to the road based on the static traffic data, the traffic event data and the meteorological data.

[0106] The severe weather intelligence module can analyze the impact and trend of dangerous roads, that is, generate section-level dangerous road forecasts and warnings, and determine the corresponding recommended control strategies for dangerous roads; it can also analyze the impact and trend of low visibility, that is, generate section-level low visibility forecasts and warnings, and generate recommended control strategies for low visibility; it can also analyze the correlation between weather warnings and traffic events, that is, issue warnings for roads within the region (i.e., district and county level), and establish the correlation between weather warnings and road control events.

[0107] The global safety emergency event multi-dimensional warning module is used to predict the traffic conditions of the road based on the congestion situation data, the congestion trend, the weather warning information, the event control data and the impact degree data of the road control event.

[0108] The road traffic condition prediction device of this embodiment is used to implement the corresponding road traffic condition prediction methods in the aforementioned multiple method embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be described in detail here. In addition, the functional implementation of each module in the road traffic condition prediction device of this embodiment can refer to the description of the corresponding parts in the aforementioned method embodiments, which will not be described in detail here.

[0109] Reference Figure 5 , shows a schematic diagram of the structure of an electronic device according to Embodiment 5 of the present application. The specific embodiments of the present application do not limit the specific implementation of the electronic device.

[0110] like Figure 5 As shown, the electronic device may include: a processor (processor) 502 , a communication interface (Communications Interface) 504 , a memory (memory) 506 , and a communication bus 508 .

[0111] in:

[0112] The processor 502 , the communication interface 504 , and the memory 506 communicate with each other via a communication bus 508 .

[0113] The communication interface 504 is used to communicate with other electronic devices or servers.

[0114] The processor 502 is used to execute the program 510, and specifically can execute the relevant steps in the above-mentioned road traffic condition prediction method embodiment.

[0115] Specifically, the program 510 may include program codes, which include computer operation instructions.

[0116] The processor 502 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application. The one or more processors included in the smart device may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0117] The memory 506 is used to store the program 510. The memory 506 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0118] The specific implementation of each step in program 510 can refer to the corresponding description of the corresponding steps and units in the above-mentioned road traffic condition prediction method embodiment, which will not be repeated here. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described devices and modules can refer to the corresponding process description in the above-mentioned method embodiment, which will not be repeated here.

[0119] Another embodiment of the present application further provides a computer program product, on which a computer program is stored, and when the program is executed by a processor, the road traffic condition prediction method provided in the above embodiment is implemented.

[0120] It should be pointed out that, according to the needs of implementation, the various components / steps described in the embodiments of the present application can be split into more components / steps, or two or more components / steps or partial operations of components / steps can be combined into new components / steps to achieve the purpose of the embodiments of the present application.

[0121] The above-mentioned method according to the embodiment of the present application can be implemented in hardware, firmware, or implemented as software or computer code that can be stored in a recording medium (such as a CD ROM, RAM, floppy disk, hard disk or magneto-optical disk), or implemented as computer code originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded through a network and to be stored in a local recording medium, so that the method described herein can be stored in such software processing on a recording medium using a general-purpose computer, a dedicated processor or programmable or dedicated hardware (such as an ASIC or FPGA). It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by a computer, a processor or hardware, the road traffic condition prediction method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the road traffic condition prediction method shown herein, the execution of the code converts the general-purpose computer into a dedicated computer for executing the road traffic condition prediction method shown herein.

[0122] Those of ordinary skill in the art will appreciate that the units and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the embodiments of the present application.

[0123] The above implementation methods are only used to illustrate the embodiments of the present application, and are not limitations on the embodiments of the present application. Ordinary technicians in the relevant technical field can make various changes and modifications without departing from the spirit and scope of the embodiments of the present application. Therefore, all equivalent technical solutions also belong to the scope of the embodiments of the present application. The scope of patent protection of the embodiments of the present application should be limited by the claims.

Claims

1. A method for predicting road traffic conditions, comprising: Acquire static traffic data and real-time dynamic data of the road, wherein the real-time dynamic data includes road condition data, traffic event data, planning navigation data and meteorological data of the area where the road is located; Obtaining a congestion state of the road according to the static traffic data and the road condition data; Obtaining event control data of the road according to the static traffic data and the traffic event data; Obtaining a congestion trend of the road according to the congestion status, the event control data, and the planning navigation data; generating weather warning information corresponding to the road based on the static traffic data, the traffic event data and the meteorological data; The traffic condition of the road is predicted according to the congestion trend of the road and the weather warning information.

2. The method according to claim 1, wherein: The obtaining of the event control data of the road according to the static traffic data and the traffic event data includes: Performing spatiotemporal fusion on the static traffic data and the traffic event data to obtain event control data corresponding to the road, and determining the impact degree data of the traffic event according to the event control data and the planning navigation data; The obtaining the congestion trend of the road according to the congestion state, the event control data and the planning navigation data includes: The congestion trend of the road is obtained according to the congestion status, the event control data, the planning navigation data and the impact degree data of the traffic event.

3. The method according to claim 2, wherein: The obtaining of the congestion trend of the road according to the congestion state, the event control data, the planning navigation data and the impact degree data of the traffic event includes: Determining a distance threshold of a traffic event according to the impact degree data of the traffic event; Establishing an association relationship between traffic events whose distance from the congestion location is less than a distance threshold and the congestion that occurs; The congestion trend of the road is obtained according to the traffic events associated with the congestion and the data of their impact, as well as the congestion status and the planned navigation data.

4. The method according to claim 2, wherein: The performing spatiotemporal fusion of the static traffic data and the traffic event data to obtain the event control data corresponding to the road includes: Based on the geographic location information of the static traffic data, and the effective time and geographic location information of the traffic event data, clustering and fusing the static traffic data and the traffic event data; According to the clustering fusion result, the event control data corresponding to the road is obtained.

5. The method according to claim 1, wherein: After generating weather warning information corresponding to the road based on the static traffic data and the meteorological data, the method further includes: Based on the weather warning information and the planned navigation data associated with the weather warning location, a recommended control strategy is generated for vehicles expected to pass through the weather warning location, and the recommended control strategy includes at least one of the following: a recommended control strategy for dangerous roads and a recommended control strategy for low visibility.

6. The method according to claim 1, wherein: If the road is a highway, obtaining the congestion trend of the road according to the congestion state, the event control data, and the planning navigation data includes: According to the congestion status, the event management data and the planned navigation data, the real-time congestion mileage and the congestion mileage change trend of the expressway are obtained, as well as the real-time congestion mileage, congestion duration and the congestion duration change trend of the toll stations of the expressway are obtained.

7. The method according to claim 6, wherein: The congestion mileage change trend is calculated by the following method: Obtain the congestion mileage corresponding to several time points in the target area, and determine the proportion of several congestion journeys to the total mileage in the target area; The congestion mileage change trend is determined according to a number of time points and corresponding ratios.

8. A road traffic condition prediction device, comprising: A data acquisition module, used to acquire static traffic data and real-time dynamic data of the road, wherein the real-time dynamic data includes road condition data, traffic event data, planning navigation data and meteorological data of the area where the road is located; A state determination module, configured to obtain the congestion state of the road according to the static traffic data and the road condition data, and to obtain event control data of the road according to the static traffic data and the traffic event data; A trend determination module, used for obtaining the congestion trend of the road according to the congestion state, the event control data and the planning navigation data; A weather determination module, configured to generate weather warning information corresponding to the road based on the static traffic data, the traffic event data and the meteorological data; The prediction module is used to predict the traffic condition of the road according to the congestion trend of the road and the weather warning information.

9. An electronic device, comprising: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the road traffic condition prediction method according to any one of claims 1-7.

10. A computer program product having a computer program stored thereon, wherein when the program is executed by a processor, the road traffic condition prediction method as claimed in any one of claims 1 to 7 is implemented.

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