Road traffic attribute updating method, device, equipment and storage medium

Abstract

The present application provides a method, device, electronic device and storage medium for updating the traffic attributes of a road, which are applied to various scenarios such as cloud technology, artificial intelligence, smart transportation, and assisted driving. The method includes: obtaining vehicle trajectory data and road network data; based on the trajectory matching result between the vehicle trajectory data and the road network data, determining a first candidate cycling road that matches the vehicle trajectory data from the road network data; the trajectory matching result includes the trajectory attribute information of the trajectory point in the vehicle trajectory data; based on the trajectory attribute information, the first candidate cycling road is screened for vehicle roads to obtain a target road; based on the preset vehicle attribute information, the traffic attribute information of the target road is updated. The embodiment of the present application can realize the update of the traffic attributes of a cycling road with vehicle traffic attributes using trajectory information, and the update accuracy of the road traffic attributes is high, the update cost is low, and the scope of application is wide.

Description

Technical Field

[0001] The present application belongs to the field of computer technology, and specifically relates to a method, device, equipment and storage medium for updating road traffic attributes. Background Art

[0002] In the field of road renewal, the relevant technologies mainly add roads to supplement missing roads, but cannot update existing roads. For example, the original road type is a walking or cycling road, but there are actually vehicle tracks passing through it, which has vehicle driving capabilities, but the relevant technologies cannot handle this situation. In addition, the features and credibility calculations used by the relevant technologies are relatively simple, and they cannot well identify and filter problem tracks, and the accuracy of road updates is low. In addition, the relevant technologies usually use images to identify and update roads. The cost of acquiring image data is high, and most roads do not have image information, which makes the cost of road updates high and the scope of application small. Summary of the invention

[0003] In order to solve the above technical problems, the present application provides a method, device, equipment and storage medium for updating road traffic attributes.

[0004] On the one hand, the present application proposes a method for updating road traffic attributes, the method comprising:

[0005] Obtain vehicle trajectory data and road network data;

[0006] Based on the trajectory matching result between the vehicle trajectory data and the road network data, a first candidate cycling road matching the vehicle trajectory data is determined from the road network data; the first candidate cycling road includes at least one of a walking road and a cycling road, and the trajectory matching result includes trajectory attribute information of a trajectory point in the vehicle trajectory data;

[0007] Based on the trajectory attribute information, the first candidate cycling road is screened for vehicle traffic to obtain a target road;

[0008] The traffic attribute information of the target road is updated based on preset vehicle traffic attribute information; the vehicle traffic attribute information is used to indicate that the road has the attribute of vehicle traffic.

[0009] On the other hand, an embodiment of the present application provides a device for updating road traffic attributes, the device comprising:

[0010] A data acquisition module, used to acquire vehicle trajectory data and road network data;

[0011] A first candidate cycling road determining module is configured to determine a first candidate cycling road matching the vehicle trajectory data from the road network data based on a trajectory matching result between the vehicle trajectory data and the road network data; the first candidate cycling road includes at least one of a walking road and a cycling road, and the trajectory matching result includes trajectory attribute information of a trajectory point in the vehicle trajectory data;

[0012] a target road determination module, configured to screen the first candidate cycling road for vehicles based on the trajectory attribute information to obtain a target road;

[0013] An updating module is used to update the traffic attribute information of the target road based on preset vehicle traffic attribute information; the vehicle traffic attribute information is used to indicate that the road has the attribute of vehicle traffic.

[0014] On the other hand, the present application proposes an electronic device for updating road traffic attributes, the electronic device comprising a processor and a packager, the packager packaging at least one instruction or at least one program, the at least one instruction or at least one program being loaded and executed by the processor to implement the method for updating road traffic attributes as described above.

[0015] On the other hand, the present application proposes a computer-readable storage medium, which packages at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by a processor to implement the road traffic attribute updating method as described above.

[0016] On the other hand, the present application proposes a computer program product, which, when executed by a processor, implements the method for updating road traffic attributes as described above.

[0017] The method, device, equipment and storage medium for updating the road traffic attributes proposed in the embodiments of the present application obtain vehicle trajectory data and road network data, and based on the trajectory matching result between the vehicle trajectory data and the road network data, determine the first candidate cycling road that matches the vehicle trajectory data from the road network data, the trajectory matching result includes the trajectory attribute information of the trajectory point in the vehicle trajectory data, the first candidate cycling road is screened for vehicle traffic based on the trajectory attribute information to obtain the target road, and the traffic attribute information of the target road is updated based on the preset vehicle attribute information, so as to realize the update of the traffic attributes of the cycling road with vehicle traffic attributes by using the trajectory information so that it has the vehicle traffic attributes. Since the update of the road traffic attributes can be realized according to the attribute information, the update accuracy of the road traffic attributes is high, the update cost is low, and the scope of application is wide. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 It is a schematic diagram of an implementation environment of a method for updating road traffic attributes according to an exemplary embodiment.

[0020] Figure 2 The figure is a flowchart of a method for updating road traffic attributes according to an exemplary embodiment.

[0021] Figure 3 The present invention is a flowchart showing a method of determining a first candidate cycling road that matches vehicle trajectory data according to an exemplary embodiment.

[0022] Figure 4 The present invention is a flowchart of obtaining a target road according to an exemplary embodiment.

[0023] Figure 5 The present invention is a flowchart showing a method of determining vehicle traffic characteristics of a first candidate cycling road according to an exemplary embodiment.

[0024] Figure 6 The present invention is a flowchart of obtaining a target road according to an exemplary embodiment.

[0025] Figure 7 The present invention is a flowchart of performing vehicle road screening on a second candidate cycling road to obtain a target road according to an exemplary embodiment.

[0026] Figure 8 It is a schematic diagram showing a method of upgrading a walking or cycling road to a vehicle road according to an exemplary embodiment.

[0027] Fig. 9 The present invention is a block diagram of a device for updating road traffic attributes according to an exemplary embodiment.

[0028] Fig.10 It is a hardware structure block diagram of a server for updating road traffic attributes provided according to an exemplary embodiment. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution 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. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0030] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technology in computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence. Artificial intelligence is to study the design principles and implementation methods of various intelligent machines so that machines have the functions of perception, reasoning and decision-making.

[0032] Artificial intelligence technology is a comprehensive discipline that covers a wide range of fields, including both hardware-level and software-level technologies. Basic artificial intelligence technologies generally include sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operating / interactive systems, mechatronics, and other technologies. Artificial intelligence software technologies mainly include computer vision technology, speech processing technology, natural language processing technology, machine learning / deep learning, and autonomous driving.

[0033] Autonomous driving technology usually includes high-precision maps, environmental perception, behavioral decision-making, path planning, motion control and other technologies.

[0034] Specifically, the embodiments of the present application relate to high-precision map technology in autonomous driving.

[0035] First, the technical terms involved in the embodiments of the present application are introduced:

[0036] Walking and cycling roads: roads where only walking and cycling are allowed and no vehicles are allowed.

[0037] Carriageway: a road on which cars and other vehicles can pass.

[0038] Figure 1 FIG. 1 is a schematic diagram of an implementation environment of a method for updating road traffic attributes according to an exemplary embodiment. Figure 1 As shown, the implementation environment may include at least a terminal 01 and a server 02. The terminal 01 and the server 02 may be directly or indirectly connected via wired or wireless communication, which is not limited in the present embodiment.

[0039] Specifically, the terminal 01 can be used to collect vehicle trajectory data and road network data. Optionally, the terminal 01 can include but is not limited to mobile phones, computers, intelligent voice interaction devices, smart home appliances, vehicle terminals, aircraft, etc. The embodiments of the present application can be applied to various scenarios, including but not limited to cloud technology, artificial intelligence, smart transportation, assisted driving, etc.

[0040] Specifically, the server 02 can be used to obtain vehicle trajectory data and road network data; and to determine, from the road network data, a first candidate cycling road that matches the vehicle trajectory data based on a trajectory matching result between the vehicle trajectory data and the road network data; the trajectory matching result includes trajectory attribute information of the trajectory points in the vehicle trajectory data; and to screen the first candidate cycling road based on the trajectory attribute information to obtain a target road; and to update the traffic attribute information of the target road based on preset vehicle attribute information.

[0041] Optionally, the server 02 may be an independent physical device or a physical server. It may also be a server cluster or a distributed system composed of multiple physical servers. It may also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms.

[0042] It should be noted that Figure 1 This is just an example. In other scenarios, other implementation environments may also be included.

[0043] Figure 2 is a flow chart of a method for updating road traffic attributes according to an exemplary embodiment. The method can be used Figure 1In the implementation environment of the embodiment. This specification provides the method operation steps as described in the embodiment or flowchart, but may include more or fewer operation steps based on routine or non-creative work. The order of steps listed in the embodiment is only one way of executing the steps among many steps, and does not represent the only execution order. When the actual system or server product is executed, it can be executed in the order of the method shown in the embodiment or the figure or in parallel (for example, in a parallel processor or multi-threaded processing environment). Specifically, Figure 2 As shown, the method may include:

[0044] S101. Obtain vehicle trajectory data and road network data.

[0045] Optionally, the vehicle trajectory data can be obtained through Figure 1 For example, the terminal is a vehicle-mounted device, and the vehicle trajectory data is collected in real time through the vehicle-mounted device. Exemplarily, the vehicle trajectory data can be composed of multiple (Global Positioning System, GPS) points, and each vehicle trajectory contains multiple GPS points with time series information, which can reflect the moving speed and driving position of the terminal.

[0046] Optionally, the electronic road network data includes roads across the country, and each road in the electronic road network has a specific location and attributes, such as a vehicle attribute, which is used to indicate that the road has a vehicle traffic attribute, that is, vehicles are allowed to pass.

[0047] S103. Based on the trajectory matching result between the above-mentioned vehicle trajectory data and the above-mentioned road network data, determine the first candidate cycling road that matches the above-mentioned vehicle trajectory data from the above-mentioned road network data; the above-mentioned first candidate cycling road includes at least one of a walking road and a cycling road, and the above-mentioned trajectory matching result includes the trajectory attribute information of the trajectory point in the above-mentioned vehicle trajectory data.

[0048] In a feasible embodiment, the vehicle trajectory data can be matched with the road network data to obtain a trajectory matching result. Since the vehicle trajectory data is composed of multiple GPS points and the road network data contains roads across the country, the trajectory matching result obtained by matching the two can include results in two dimensions, one is the trajectory attribute information in the trajectory point dimension, and the other is the road matching information in the road dimension.

[0049] Optionally, the track attribute information may include the location information, motion state information, matching degree information, etc. of each GPS point in each vehicle track data. Exemplarily, the location information may be the coordinates of the GPS point, the motion state information may include the motion speed and motion direction of the GPS point, etc., and the matching degree information may represent the matching degree between the track point and the matched first candidate cycling road, for example, matching confidence, the higher the matching confidence, the higher the matching degree.

[0050] Optionally, the road matching information may represent the road information of the road in the road network data that matches the vehicle trajectory data, for example, the road set in the electronic road network that the vehicle trajectory data passes through, and the road (IdentityDocument, ID) sequence.

[0051] It should be noted that the closer the GPS point is to the road and the more consistent the direction of movement is, the higher the confidence of matching the road is. If the GPS point does not match any road, the GPS is an isolated point.

[0052] In an alternative embodiment, Figure 3 is a flowchart showing a method of determining a first candidate cycling road matching vehicle trajectory data according to an exemplary embodiment. Figure 3 As shown, in the above step S103, the above trajectory matching result includes road matching information, and the above road matching information represents the road information of the road in the above road network data that matches the above vehicle trajectory data. The above based on the trajectory matching result between the above vehicle trajectory data and the road network data, determining the first candidate riding road that matches the above vehicle trajectory data from the above road network data includes:

[0053] S1031. Filter the vehicle trajectory data based on at least one of the trajectory attribute information and the road matching information to obtain candidate trajectory data.

[0054] S1033. Obtain multiple walking and cycling roads from the above road network data.

[0055] S1035. Use the walking and cycling road that matches the candidate trajectory data among the multiple walking and cycling roads as the first candidate walking and cycling road.

[0056] Since there are multiple vehicle trajectory data, invalid trajectories, messy trajectories, parked trajectories, walking and cycling trajectories, etc. are mostly invalid information. In order to improve the matching accuracy of the trajectory and the road in the road network data, in the above step S1031, invalid trajectories, messy trajectories, parked trajectories, walking and cycling trajectories, etc. can be filtered out from the multiple vehicle trajectory data to obtain candidate trajectory data (i.e., valid trajectory data), wherein the above multiple refers to at least two. Among them, invalid trajectory refers to a trajectory without GPS information; messy trajectory refers to a trajectory in which the direction of the GPS point changes too quickly during movement; parked trajectory refers to a trajectory in which most adjacent trajectory points are close to each other; drift trajectory refers to a trajectory in which most GPS points are far from the matched matching road; too short trajectory refers to a trajectory whose trajectory length is less than a first preset distance threshold (e.g., 100 meters) and whose trajectory duration is less than a preset time threshold (e.g., 3 seconds). A walking cycling trajectory refers to a trajectory whose length is less than or equal to a second preset distance threshold and a preset number of trajectory points whose movement speed is less than or equal to a preset speed threshold, for example, a trajectory is less than or equal to 1 km, and more than 10% of the trajectory points have a speed less than or equal to 20 km / h.

[0057] In one implementation, in the above step S1031, a track without GPS information may be screened out from a plurality of vehicle trajectory data according to the location information in the trajectory attribute information, and the track may be filtered.

[0058] In another embodiment, according to the movement direction in the movement state information in the trajectory attribute information, the trajectory with the GPS point direction changing too fast can be screened out from multiple vehicle trajectory data and filtered, wherein the direction change can be determined by the movement direction of the GPS point at different times.

[0059] In another embodiment, based on the position information in the trajectory attribute information, the trajectory with most adjacent trajectory points being close to each other can be selected from the vehicle trajectory data and the trajectory can be filtered. For example, if the distances between most adjacent trajectory points in a vehicle trajectory data are close to each other, the trajectory can be filtered.

[0060] In another embodiment, based on the location information in the trajectory attribute information and the matching road and road ID in the road matching information, a trajectory in which most of the GPS points are far from the matched matching road can be screened out from the multiple vehicle trajectory data, and the trajectory can be filtered. For example, in a certain vehicle trajectory data, if most of the GPS points are far from the matched matching road, the trajectory is filtered.

[0061] In another embodiment, the length information of the vehicle trajectory data can also be determined based on the first position information of the first trajectory point and the last position information of the last trajectory point in the vehicle trajectory data, and the trajectory whose length information is less than a preset distance threshold is screened out from multiple vehicle trajectory data, and the trajectory is filtered.

[0062] In another embodiment, the continuous collection time of the vehicle trajectory data can also be determined according to the difference between the collection time of the first trajectory point and the collection time of the last trajectory point, and the trajectory with the continuous collection time less than the preset time threshold can be screened out from the multiple vehicle trajectory data, and the trajectory can be filtered.

[0063] In another embodiment, according to the trajectory length information and the movement speed of the trajectory points, a trajectory whose trajectory length is less than or equal to a second preset distance threshold and a preset number of trajectory points with a movement speed less than or equal to a preset speed threshold can be screened out from multiple vehicle trajectory data, and the trajectory can be filtered, thereby effectively filtering out some trajectories that are disguised as vehicle movements but are actually walking trajectories.

[0064] In one implementation, in the above step S1033, information of all walking and cycling roads may be extracted from the road network data to obtain a plurality of walking and cycling roads.

[0065] In one implementation, after invalid trajectories are filtered, in the above step S1035, the cyclist's road that matches the candidate trajectory data obtained by screening in step S1031 among the above multiple cyclist's roads may be used as the first candidate cyclist's road.

[0066] In the embodiment of the present application, due to the existence of invalid trajectories, messy trajectories, parked trajectories, walking and cycling trajectories and other trajectories, the matching accuracy of the first candidate walking and cycling road will be reduced, thereby reducing the determination accuracy of the target road, and further reducing the update accuracy of the road traffic attribute; in addition, the existence of invalid trajectories, messy trajectories, parked trajectories, walking and cycling trajectories and other trajectories will also increase the matching calculation amount of the candidate walking and cycling roads. Therefore, pre-filtering these trajectories can not only screen the trajectories from multiple angles and improve the update accuracy of the road traffic attribute, but also reduce the consumption of system resources in the update process of the road traffic attribute, thereby reducing the update cost of the road traffic attribute.

[0067] S105. Screen the first candidate cycling road for vehicles based on the trajectory attribute information to obtain a target road.

[0068] Optionally, in the embodiment of the present application, the first candidate cycling road is screened based on the trajectory attribute information to obtain a target road in a variety of ways, which are not specifically limited here.

[0069] In an alternative embodiment, Figure 4 is a flow chart showing a method of obtaining a target road according to an exemplary embodiment. Figure 4 As shown, in the above step S105, the above-mentioned first candidate cycling road is screened based on the above-mentioned trajectory attribute information to obtain the target road, which may include:

[0070] S1051. Based on the above trajectory attribute information, determine the vehicle traffic characteristics of the above first candidate cycling road.

[0071] S1053. Screen the first candidate cycling road based on the vehicle traffic characteristics to obtain a target road.

[0072] For example, when there are multiple first candidate cycling roads, in the above step S1051, feature statistics can be performed on each first candidate cycling road according to the trajectory attribute information, that is, the vehicle traffic features of each first candidate cycling road can be counted. And in the above step S1053, according to the vehicle traffic features of each first candidate cycling road, each first candidate cycling road is screened for vehicle traffic to obtain a target road.

[0073] For example, when there is only one first candidate cycling road, the first candidate cycling road can be directly used as the target road.

[0074] In another optional embodiment, in the above step S105, an image corresponding to a road in the road network data may be captured, and the first candidate cycling road may be screened for vehicle traffic through image recognition and a method combining images and trajectories to obtain a target road. For example, through image recognition and a method combining images and trajectories, it may be identified whether the first candidate cycling road is open to traffic, and if so, the road is used as the target road. Exemplarily, it may be identified through images whether the road is paved with cement, whether there are vehicles passing by, whether the road width meets preset conditions, etc., and if so, it may be open to traffic, etc.

[0075] In the embodiment of the present application, the first candidate cycling road is screened for vehicle traffic characteristics based on the trajectory attribute information, thereby utilizing the trajectory information to screen out target roads with vehicle traffic attributes. The target roads are determined with high accuracy, low cost, and a wide range of applications.

[0076] In one possible embodiment, Figure 5 FIG. 1 is a flow chart showing a method of determining vehicle traffic characteristics of a first candidate cycling road according to an exemplary embodiment. Figure 5As shown, the track attribute information includes position information, motion state information and matching degree information, and the matching degree information represents the matching degree between the track point and the first candidate cycling road. In step S1051, determining the vehicle traffic characteristics of the first candidate cycling road based on the track attribute information may include:

[0077] S10511. Based on the position information, determine the penetration amount of the first candidate cycling road; the penetration amount represents the number of vehicle trajectory data that penetrate the first candidate cycling road.

[0078] S10513. Based on the penetration amount and the motion state information, determine the traffic flow characteristics of the first candidate cycling road.

[0079] S10515. Based on the above matching degree information and the above position information, determine the trajectory point features of the above first candidate walking and cycling path.

[0080] S10517. Use the above-mentioned traffic flow characteristics and the above-mentioned trajectory point characteristics as the above-mentioned vehicle traffic characteristics.

[0081] Optionally, when there are multiple first candidate cycling roads, the vehicle traffic characteristics of each first candidate cycling road can be counted from multiple perspectives. For example, the first candidate cycling road can be counted from two perspectives: traffic flow characteristics and trajectory point characteristics.

[0082] Optionally, in the above step S10511, the penetration amount of the first candidate cycling road can be determined according to the position information. The penetration amount can represent the number of vehicle trajectory data that penetrate the first candidate cycling road. That is, the number of trajectories of the vehicle trajectory data that pass through the first candidate cycling road Ri from beginning to end on that day, that is, how many vehicle trajectory data pass through the preceding road Ri-1 and the succeeding road Ri+1 of the first candidate cycling road Ri. Ri-1 can be understood as the road whose end is connected to the beginning of Ri in the road network data, and Ri+1 can be understood as the road whose beginning is connected to the end of Ri in the road network data. The specific method for determining the penetration amount can be: traversing each first candidate cycling road, obtaining multiple vehicle trajectory data matching each first candidate cycling road, if the first candidate cycling road is the first road or the last road matched by a certain vehicle trajectory data, it is considered that the certain vehicle trajectory data does not penetrate the first candidate cycling road, if the first candidate cycling road is not the first road or the last road matched by a certain vehicle trajectory data, and the first candidate cycling road Ri does not have a U-turn, it is considered that the certain vehicle trajectory data penetrates the first candidate cycling road Ri. It should be noted that, in the case where the vehicle trajectory data is filtered in advance to obtain the candidate trajectory data, the multiple vehicle trajectory data matching each first candidate cycling road refers to the trajectory data matching each first candidate cycling road in the candidate trajectory data.

[0083] Optionally, the motion state information may include speed distribution. The speed distribution records the speed value distribution of the GPS point in the vehicle trajectory data that matches the first candidate cycling road. For example, the speed value distribution can be divided into 12 levels from 0 to 60 km / h, and the higher the level, the faster the speed. It should be noted that, when the vehicle trajectory data is filtered in advance to obtain the candidate trajectory data, the GPS point in the above vehicle trajectory data that matches the first candidate cycling road refers to the GPS point in the candidate trajectory data that matches the first candidate cycling road.

[0084] Optionally, in the above step S10513, the penetration rate and speed distribution may be used as the traffic flow characteristics of the first candidate cycling road.

[0085] Optionally, in the above step S10515, the matching degree information and the location information can be directly used as the track point features of the first candidate cycling path. It can be understood that the track point features record the matching degree information of all GPS points matched to the first candidate cycling path, the location information of the GPS points, and the location features of the location information of the GPS points relative to the first candidate cycling path. It should be noted that the more high-confidence GPS points there are, the more accurate the matching result of the first candidate cycling path is.

[0086] In the embodiment of the present application, the vehicle flow characteristics and trajectory point characteristics are determined by the position information, motion state information and matching degree information in the trajectory attribute information, and the characteristics of the vehicle trajectory data can be statistically analyzed from multiple angles, which can improve the determination accuracy of the vehicle flow characteristics and trajectory point characteristics. Moreover, the vehicle flow characteristics and trajectory point characteristics are both determined by the trajectory attribute information, and the determination cost of the vehicle flow characteristics and trajectory point characteristics is low, and the scope of application is wide. Using the vehicle flow characteristics and trajectory point characteristics as the vehicle traffic characteristics of the target road screening benchmark can realize the use of trajectory information to screen out the target road with the attributes of vehicle traffic, and the determination accuracy of the target road is high, the determination cost is low, and the scope of application is wide.

[0087] In a feasible embodiment, an image of the road may be collected, and the target road obtained in step S105 may be filtered through image recognition. For example, the target road may be identified through image recognition to determine whether it is paved with cement, whether there are vehicles passing through, whether the road width meets preset conditions, etc. If so, it may be the target road, if not, it is considered not to be the target road and may be deleted from the target roads.

[0088] In another feasible embodiment, images of roads may be collected, and roads in the road network data may be checked and supplemented through image recognition. For example, through image recognition, whether roads other than the target road in the road network data are paved with cement, whether there are vehicles passing through, whether the road width meets the preset conditions, etc., if so, it is used as the target road.

[0089] In an alternative embodiment, Figure 6 is a flow chart showing a method of obtaining a target road according to an exemplary embodiment. Figure 6 As shown, in the above step S1053, the above-mentioned vehicle road screening for the above-mentioned first candidate cycling road based on the above-mentioned vehicle traffic characteristics to obtain the target road may include:

[0090] S10531. According to the above vehicle traffic characteristics, determine a walking and cycling road that meets preset conditions from the above first candidate walking and cycling roads.

[0091] S10533. Filter the cycling roads that meet the preset conditions from the first candidate cycling roads to obtain second candidate cycling roads.

[0092] In the embodiment of the present application, if there are multiple first candidate cycling roads, the cycling road with the most accurate result and the most likely to be a vehicle-traffic road can be selected from the multiple first candidate cycling roads according to the vehicle traffic characteristics corresponding to each of the multiple first candidate cycling roads to obtain the second candidate cycling road.

[0093] Optionally, in the embodiment of the present application, in the above step S10531, a plurality of methods may be used to determine a cycling road whose vehicle traffic characteristics meet preset conditions from the above first candidate cycling roads, which are not specifically limited here.

[0094] In one embodiment, based on the above-mentioned penetration amount, the above-mentioned matching degree information and the above-mentioned position information, a candidate cycling road can be determined from the above-mentioned first candidate cycling road, whose penetration amount satisfies the first preset condition, whose matching degree information satisfies the second preset condition, and whose distribution of position information satisfies the third preset condition, to obtain the above-mentioned cycling road that meets the preset conditions.

[0095] For example, a cycling road with a high confidence ratio (for example, high confidence means confidence>0.9; a high confidence ratio means that among the GPS points matched to the first candidate cycling road, more than 90% of the points have high confidence), a penetration amount greater than or equal to a preset number (for example, the preset number is 1, that is, at least one track passes through the road from beginning to end), and a uniform distribution of GPS points can be regarded as a road that does not meet the preset conditions, and the roads other than the roads that do not meet the preset conditions in the first candidate cycling road can be regarded as cycling roads that meet the preset conditions, that is, a cycling road with a non-high confidence ratio, a penetration amount less than a preset number, and an uneven distribution of GPS points can be regarded as a cycling road that meets the preset conditions. A uniform distribution of GPS points means that the road is relatively smooth, avoiding a large number of U-turns at both ends of the road. The method for determining whether a vehicle often turns around at the end of a road can be as follows: the road can be divided into several sections according to its length, the number of GPS points distributed in these sections is calculated, and then the ratio of the number of GPS points in the first and last sections to the number of GPS points in the middle section is calculated. If the ratio is greater than a preset number of times (for example, 50 times), it means that the vehicle often turns around at the beginning and end of the road, and it is generally an impassable road. The road is then regarded as a walking and cycling road that meets the preset conditions. For example, if the number of sections is 7 sections, then the sum of the number of GPS points in the first section and the number of GPS points in the seventh section is calculated to see if the ratio of the number of GPS points in the fourth section is greater than 50 times. If so, the road is a walking and cycling road that meets the preset conditions. Otherwise, the road is a walking and cycling road that does not meet the preset conditions.

[0096] In another implementation, the candidate cycling roads whose area meets the fourth preset condition may be determined from the first candidate cycling roads according to the location information to obtain the cycling roads that meet the preset condition.

[0097] For example, the regions to which each of the plurality of first candidate cycling roads belongs can be determined according to the location information in the trajectory point feature, and when the regions to which they belong meet the fourth preset condition, it is indicated that the first candidate cycling road is a cycling road that meets the preset condition. Exemplarily, the regions to which they belong meet the fourth preset condition, for example, the regions mentioned above can be places such as residential areas, schools, and hospitals.

[0098] In another embodiment, based on the above-mentioned motion state information and the above-mentioned penetration amount, a candidate cycling road can be determined from the above-mentioned first candidate cycling road, whose motion state information satisfies the fifth preset condition and whose penetration amount satisfies the sixth preset condition with the penetration amount of the connected road, to obtain the above-mentioned cycling road that meets the preset conditions.

[0099] Optionally, the motion state information may be speed information. For example, for a road with a low trajectory speed (all matching trajectory points have speeds less than 20 km / h), if its penetration is much less than one percent of the penetration of the attached road, the road is considered inconvenient to pass and is not suitable as a vehicle road, and is used as a cycling path that meets the preset conditions. The attached road refers to a road in the road network data that is connected to the road with a low trajectory speed.

[0100] In another embodiment, a candidate cycling road whose head end or end is connected to a road in the road network data can be determined from the first candidate cycling road based on the location information to obtain the cycling road that meets the preset conditions.

[0101] Optionally, since one-way roads are of little significance for vehicle traffic and navigation, roads that have only one-way traffic capacity, that is, roads with only one end connected to other roads and the other end being a dead end (that is, candidate walking and cycling roads whose head or end is connected to the roads in the above road network data), are used as walking and cycling roads that meet the preset conditions.

[0102] Optionally, after the cycling roads satisfying the preset conditions are found in the above step S10531, in the above step S10533, the cycling roads satisfying the preset conditions may be filtered to obtain second candidate cycling roads.

[0103] S10535. Based on the connection relationship between the second candidate cycling road and the roads in the road network data, the second candidate cycling road is screened for vehicle traffic to obtain the target road.

[0104] In the embodiment of the present application, after filtering to obtain the second candidate cycling road, the second candidate cycling road can be connected by cycling, that is, the prerequisite for a second candidate cycling road to be upgraded to a vehicle road is that it can be opened to traffic from beginning to end after the upgrade, that is, both ends of the second candidate cycling road can be connected to the vehicle roads in the road network data, otherwise the second candidate cycling road is likely not to be a complete vehicle road, or it is not important, and even if it is upgraded, it is of little help for actual vehicle guidance. Based on this, based on the connection relationship between the second candidate cycling road and the road in the road network data, the second candidate cycling road can be connected, that is, the vehicle road can be screened to obtain the target road.

[0105] In the embodiment of the present application, according to the vehicle traffic characteristics including the traffic flow characteristics and the trajectory point characteristics, the cycling roads whose vehicle traffic characteristics meet the preset conditions are determined from the first candidate cycling roads, and the cycling roads that meet the preset conditions are filtered from the first candidate cycling roads to obtain the second candidate cycling roads, so that the second candidate cycling roads with the attribute of vehicle traffic can be preliminarily screened out by using the trajectory information, and the determination accuracy of the second candidate cycling roads is high, the cost is low, and the application scope is wide; in addition, based on the connection relationship between the second candidate cycling roads and the roads in the road network data, the second candidate cycling roads are connected by cycling, and complete vehicle roads or vehicle roads that are helpful for actual vehicle driving guidance can be screened out, thereby improving the accuracy of target road determination and reducing the impact of incomplete vehicle roads or vehicle roads that are not helpful for actual vehicle driving guidance on the updated map.

[0106] In a feasible embodiment, in the above S10535, based on the connection relationship between the above second candidate cycling road and the road in the above road network data, the above second candidate cycling road is screened for vehicle traffic to obtain the above target road, which may include:

[0107] From the second candidate walking and cycling roads, walking and cycling roads whose two ends are respectively connected to the vehicle roads in the road network data are selected to obtain the target road.

[0108] Optionally, when there are multiple second candidate cycling roads, the road network data can be used to search for the intersections at both ends of each second candidate cycling road to see if they are connected to other vehicle roads. If so, the second candidate cycling road with both end intersections connected to other vehicle roads is used as the target road.

[0109] In an embodiment of the present application, if both ends of a road are respectively connected to the vehicle roads in the road network data, it means that the road can be opened to traffic from beginning to end. Taking this road as a target road with vehicle traffic attributes can not only improve the determination accuracy of the target road, but also provide assistance for actual vehicle driving guidance after the traffic attributes of the road are updated.

[0110] In another possible embodiment, Figure 7 FIG. 1 is a flowchart showing a method of screening a second candidate cycling road to obtain a target road according to an exemplary embodiment. Figure 7 As shown, in the above S10535, based on the connection relationship between the above second candidate cycling road and the road in the above road network data, the above second candidate cycling road is screened for vehicle traffic to obtain the above target road, which may include:

[0111] S105351. Obtain a preset number of candidate cycling roads to be connected from the second candidate cycling roads mentioned above.

[0112] S105353. Connect the preset number of candidate cycling roads to be connected to obtain a target candidate cycling road.

[0113] S105355. When both ends of the target candidate walking and cycling road are respectively connected to the vehicle roads in the road network data, the preset number of candidate walking and cycling roads to be connected are used as the target roads.

[0114] Optionally, in the case where there are multiple second candidate step cycling roads, if a preset number of roads among the multiple second candidate step cycling roads are connected (the number of the preset number of roads is less than or equal to the number of the multiple second candidate step cycling roads), a target candidate step cycling road is obtained. If both ends of the target candidate step cycling road can be connected to vehicle roads, then the preset number of roads are considered to be connected and can be upgraded together as target roads. Exemplarily, graph theory techniques can be used to mark several connected (first-order connected) second candidate step cycling roads and second candidate step cycling roads connected to these first-order candidate roads (second-order connected, ..., n-order connected) starting from a certain vehicle road intersection in the road network data. The connected second candidate step cycling roads are considered to be open to traffic, that is, indirectly connected. These directly or indirectly connected second candidate step cycling roads are retained as target roads that need to be upgraded to vehicle roads in the end.

[0115] For example, there are five second candidate cycling roads, from which three candidate cycling roads to be connected can be obtained. These three candidate cycling roads to be connected are connected to obtain the target candidate cycling road. If both ends of the target candidate cycling road can be respectively connected to the vehicle roads in the above road network data, then the three candidate cycling roads to be connected will be used as the target roads together.

[0116] In an embodiment of the present application, if a second candidate cycling road is indirectly connected to a vehicle road in the road network, it can also be used as a target road that needs to be upgraded to a vehicle road, which can not only improve the determination accuracy of the target road, but also expand the determination range of the target road; in addition, after the traffic attributes of the road are updated, it can also provide assistance for actual vehicle driving guidance.

[0117] In another feasible embodiment, when there is only one second candidate cycling road, the second candidate cycling road can be directly used as the target road.

[0118] S107. Update the traffic attribute information of the target road based on preset vehicle traffic attribute information; the vehicle traffic attribute information is used to indicate that the road has the attribute of vehicle traffic.

[0119] Optionally, after obtaining the target road, the target road can be upgraded. For example, in the case of multiple target roads, the target roads that need to be upgraded to vehicle roads can be processed one by one, and the types of vehicles such as "passenger car", "truck", and "taxi" can be added to the vehicle types of each target road, and updated to the electronic map data at the same time to complete this round of upgrade.

[0120] In one embodiment, in the above step S107, the traffic attribute information of the target road can be updated to the preset vehicle attribute information based on the preset vehicle attribute information, that is, the updated traffic attribute information of the target road is the vehicle attribute information used to indicate that the road has the attribute of vehicle traffic.

[0121] In another embodiment, in the above step S107, the target road's traffic attribute information can be updated to the preset vehicle attribute information based on the preset vehicle attribute information, and the target road also includes walking and cycling attribute information. That is, the updated target road can have vehicle attributes or retain walking and cycling attributes.

[0122] The embodiment of the present application can automatically upgrade the pedestrian and bicycling roads to vehicle roads according to the vehicle trajectory data, perform feature calculation and screening on the matching results of the vehicle trajectory data from multiple angles, and finally effectively obtain a set of pedestrian and bicycling roads that can be opened to traffic and automatically put them online. By calculating the vehicle trajectory data of a single week, the efficiency of upgrading pedestrian and bicycling roads to vehicle roads is greater than 90%, and it can be automatically put online and operated. In the case of weekly automated upgrades, the accumulated intelligence and mileage increase week by week, and the electronic map data is continuously updated to improve the efficiency and accuracy of the electronic map data. Figure 8 is a schematic diagram showing a method of upgrading a walking or cycling road to a driving road according to an exemplary embodiment, such as Figure 8 As shown in the figure, from the actual scene on the left, it can be seen that the two roads forward and to the right corresponding to the two pedestrian and cycling roads are both cement roads, and the width meets the conditions for traffic. Then the two pedestrian and cycling roads can be upgraded to vehicle roads, that is, the traffic attribute information of the two roads forward and to the right is updated based on the preset vehicle attribute information.

[0123] In an exemplary embodiment, the vehicle trajectory data, road network data in the above step S101, and the vehicle traffic characteristics in step S10151 can be stored in the blockchain system.

[0124] Fig. 9 FIG. 1 is a block diagram of a device for updating road traffic attributes according to an exemplary embodiment. Fig. 9 As shown, the device may include at least:

[0125] The data acquisition module 201 is used to acquire vehicle trajectory data and road network data.

[0126] The first candidate cycling road determination module 203 is used to determine the first candidate cycling road that matches the above-mentioned vehicle trajectory data from the above-mentioned road network data based on the trajectory matching result between the above-mentioned vehicle trajectory data and the above-mentioned road network data; the above-mentioned first candidate cycling road includes at least one of a walking road and a cycling road, and the above-mentioned trajectory matching result includes the trajectory attribute information of the trajectory point in the above-mentioned vehicle trajectory data.

[0127] The target road determination module 205 is used to screen the first candidate cycling road based on the trajectory attribute information to obtain a target road.

[0128] The updating module 207 is used to update the traffic attribute information of the target road based on preset vehicle traffic attribute information; the vehicle traffic attribute information is used to indicate that the road has the attribute of vehicle traffic.

[0129] In an optional embodiment, the target road determination module 205 includes:

[0130] The vehicle traffic feature determination submodule is used to determine the vehicle traffic feature of the first candidate cycling road based on the trajectory attribute information.

[0131] The screening submodule is used to screen the first candidate cycling road based on the vehicle traffic characteristics to obtain a target road.

[0132] In an optional embodiment, the trajectory attribute information includes position information, motion state information and matching degree information, the matching degree information represents the matching degree between the trajectory point and the first candidate cycling road, and the vehicle traffic feature determination submodule includes:

[0133] The penetration amount determination unit is used to determine the penetration amount of the first candidate cycling road based on the position information; the penetration amount represents the amount of vehicle trajectory data that penetrates the first candidate cycling road.

[0134] The vehicle flow characteristic determination unit is used to determine the vehicle flow characteristic of the first candidate cycling road based on the penetration amount and the motion state information.

[0135] A trajectory point feature determination unit is used to determine the trajectory point features of the first candidate walking and cycling path based on the matching degree information and the position information.

[0136] The vehicle traffic feature determination unit is used to use the above-mentioned vehicle flow feature and the above-mentioned trajectory point feature as the above-mentioned vehicle traffic feature.

[0137] In an optional embodiment, the above screening submodule includes:

[0138] The walking and cycling road determining unit is used to determine a walking and cycling road that meets a preset condition from the first candidate walking and cycling roads according to the vehicle traffic characteristics.

[0139] The second candidate cycling road determining unit is used to filter the cycling roads that meet the preset conditions from the first candidate cycling roads to obtain second candidate cycling roads.

[0140] The screening unit is used to screen the second candidate cycling road for vehicle traffic based on the connection relationship between the second candidate cycling road and the roads in the road network data to obtain the target road.

[0141] In an optional embodiment, the screening unit comprises:

[0142] The screening subunit is used to screen out the cycling roads whose two ends are respectively connected to the vehicle roads in the road network data from the second candidate cycling roads to obtain the target road.

[0143] In an optional embodiment, the screening unit comprises:

[0144] The to-be-connected road acquisition subunit is used to acquire a preset number of to-be-connected candidate cycling roads from the second candidate cycling roads.

[0145] The connection subunit is used to connect the preset number of candidate cycling roads to be connected to obtain a target candidate cycling road.

[0146] The target road determination subunit is used to use the preset number of candidate walking and cycling roads to be connected as the target roads when both ends of the target candidate walking and cycling roads are respectively connected to the vehicle roads in the road network data.

[0147] In an optional embodiment, the trajectory matching result includes road matching information, and the road matching information represents road information of a road in the road network data that matches the vehicle trajectory data. The first candidate cycling road determination module 203 includes:

[0148] The trajectory filtering unit is used to filter the vehicle trajectory data based on at least one of the trajectory attribute information and the road matching information to obtain candidate trajectory data.

[0149] The walking and cycling road acquisition unit is used to acquire a plurality of walking and cycling roads from the above road network data.

[0150] The first candidate cycling road determining unit is configured to use a cycling road among the plurality of cycling roads that matches the candidate trajectory data as the first candidate cycling road.

[0151] It should be noted that the device embodiment provided in the embodiments of the present application and the above-mentioned method embodiment are based on the same inventive concept.

[0152] An embodiment of the present application also provides an electronic device for updating road traffic attributes, the electronic device including a processor and a memory, the memory storing at least one instruction or at least one program, the at least one instruction or at least one program being loaded and executed by the processor to implement a method for updating road traffic attributes as provided in any of the above embodiments.

[0153] An embodiment of the present application also provides a computer-readable storage medium, which can be set in a terminal to store at least one instruction or at least one program for implementing a method for updating road traffic attributes in a method embodiment, and the at least one instruction or at least one program is loaded and executed by a processor to implement the method for updating road traffic attributes provided in the above method embodiment.

[0154] Optionally, in the embodiment of this specification, the storage medium may be located in at least one of the multiple network servers of the computer network. Optionally, in this embodiment, the above storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store program codes.

[0155] The memory of the embodiment of this specification can be used to store software programs and modules. The processor executes various functional applications and updates the road traffic attributes by running the software programs and modules stored in the memory. The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, applications required for functions, etc.; the data storage area may store data created according to the use of the device, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Accordingly, the memory may also include a memory controller to provide the processor with access to the memory.

[0156] The embodiment of the present application also provides a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the method for updating the road traffic attribute provided in the above method embodiment.

[0157] The method for updating the road traffic attribute provided in the embodiment of the present application can be executed in a terminal, a computer terminal, a server or a similar computing device. Taking running on a server as an example, Fig.10 FIG. 1 is a hardware structure block diagram of a server for updating road traffic attributes according to an exemplary embodiment. Fig.10As shown, the server 300 may have relatively large differences due to different configurations or performances, and may include one or more central processing units (CPU) 310 (the central processing unit 310 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 330 for storing data, and one or more storage media 320 (such as one or more mass storage devices) for storing application programs 323 or data 322. Among them, the memory 330 and the storage medium 320 can be short-term storage or permanent storage. The program stored in the storage medium 320 may include one or more modules, each of which may include a series of instruction operations on the server. Furthermore, the central processing unit 310 can be configured to communicate with the storage medium 320 and execute a series of instruction operations in the storage medium 320 on the server 300. The server 300 may also include one or more power supplies 360, one or more wired or wireless network interfaces 350, one or more input and output interfaces 340, and / or one or more operating systems 321, such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, etc.

[0158] The input / output interface 340 may be used to receive or send data via a network. The specific example of the network may include a wireless network provided by a communication provider of the server 300. In one example, the input / output interface 340 includes a network adapter (Network Interface Controller, NIC), which may be connected to other network devices via a base station so as to communicate with the Internet. In one example, the input / output interface 340 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0159] It can be understood by those skilled in the art that Fig.10 The structure shown is only for illustration and does not limit the structure of the above electronic device. Fig.10 More or fewer components as shown, or with Fig.10 Different configurations are shown.

[0160] It should be noted that the above-mentioned sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above-mentioned specific embodiments of this specification are described. Other embodiments are within the scope of the attached claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0161] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device and server embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0162] A person skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware, or by a program to instruct the relevant hardware, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0163] The above are only preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for updating road traffic attributes, characterized in that: The method comprises: Obtain vehicle trajectory data and road network data; Based on the trajectory matching result between the vehicle trajectory data and the road network data, a first candidate cycling road matching the vehicle trajectory data is determined from the road network data; the first candidate cycling road includes at least one of a walking road and a cycling road, and the trajectory matching result includes trajectory attribute information of a trajectory point in the vehicle trajectory data; Filtering the candidate cycling roads whose penetration quantity meets the first preset condition, whose matching degree information meets the second preset condition, and whose position information distribution meets the third preset condition from the first candidate cycling roads to obtain the second candidate cycling roads; Based on the connection relationship between the second candidate cycling road and the roads in the road network data, the second candidate cycling road is screened for vehicle traffic to obtain a target road; The traffic attribute information of the target road is updated based on preset vehicle traffic attribute information; the vehicle traffic attribute information is used to indicate that the road has the attribute of vehicle traffic.

2. The method according to claim 1, characterized in that: The method further comprises: Determining vehicle traffic characteristics of the first candidate cycling road based on the trajectory attribute information; The first candidate cycling road is screened for vehicles based on the vehicle traffic characteristics to obtain the target road.

3. The method according to claim 2, characterized in that The trajectory attribute information includes position information, motion state information and matching degree information, the matching degree information represents the matching degree between the trajectory point and the first candidate cycling road, and the determining of the vehicle traffic characteristics of the first candidate cycling road based on the trajectory attribute information includes: Based on the position information, determining the penetration amount of the first candidate cycling road; the penetration amount represents the number of vehicle trajectory data penetrating the first candidate cycling road; Determining a traffic flow characteristic of the first candidate cycling road based on the penetration amount and the motion state information; Determining the trajectory point features of the first candidate walking and cycling trail based on the matching degree information and the position information; The vehicle flow characteristics and the trajectory point characteristics are used as the vehicle traffic characteristics.

4. The method according to claim 1, characterized in that: The step of screening the second candidate cycling road for vehicles based on the connection relationship between the second candidate cycling road and the road in the road network data to obtain the target road includes: From the second candidate walking and cycling roads, walking and cycling roads whose two ends are respectively connected to the vehicle roads in the road network data are selected to obtain the target road.

5. The method according to claim 1, characterized in that The step of screening the second candidate cycling road for vehicles based on the connection relationship between the second candidate cycling road and the road in the road network data to obtain the target road includes: Obtaining a preset number of candidate cycling roads to be connected from the second candidate cycling roads; Connecting the preset number of candidate cycling roads to be connected to obtain a target candidate cycling road; When both ends of the target candidate walking and cycling road are respectively connected to the vehicle roads in the road network data, the preset number of candidate walking and cycling roads to be connected are used as the target roads.

6. The method according to any one of claims 1 to 5, characterized in that The trajectory matching result includes road matching information, wherein the road matching information represents road information of a road in the road network data that matches the vehicle trajectory data, and determining a first candidate cycling road that matches the vehicle trajectory data from the road network data based on the trajectory matching result between the vehicle trajectory data and the road network data includes: Filtering the vehicle trajectory data based on at least one of the trajectory attribute information and the road matching information to obtain candidate trajectory data; Acquire a plurality of walking and cycling roads from the road network data; A walking cycling road among the multiple walking cycling roads that matches the candidate trajectory data is used as the first candidate walking cycling road.

7. A device for updating road traffic attributes, characterized in that: The device comprises: A data acquisition module, used to acquire vehicle trajectory data and road network data; A first candidate cycling road determining module is configured to determine a first candidate cycling road matching the vehicle trajectory data from the road network data based on a trajectory matching result between the vehicle trajectory data and the road network data; the first candidate cycling road includes at least one of a walking road and a cycling road, and the trajectory matching result includes trajectory attribute information of a trajectory point in the vehicle trajectory data; a target road determination module, configured to filter, from the first candidate cycling roads, candidate cycling roads whose penetration quantity satisfies a first preset condition, whose matching degree information satisfies a second preset condition, and whose position information distribution satisfies a third preset condition, to obtain a second candidate cycling road; a screening unit, configured to screen the second candidate cycling road for vehicles based on the connection relationship between the second candidate cycling road and the roads in the road network data to obtain a target road; An updating module is used to update the traffic attribute information of the target road based on preset vehicle traffic attribute information; the vehicle traffic attribute information is used to indicate that the road has the attribute of vehicle traffic.

8. The device according to claim 7, characterized in that The device also includes: A vehicle traffic feature determination submodule, configured to determine a vehicle traffic feature of the first candidate cycling road based on the trajectory attribute information; The screening submodule is used to screen the first candidate cycling road for vehicles based on the vehicle traffic characteristics to obtain the target road.

9. The device according to claim 8, characterized in that The trajectory attribute information includes position information, motion state information and matching degree information, wherein the matching degree information represents the matching degree between the trajectory point and the first candidate cycling road. The vehicle traffic feature determination submodule includes: A penetration amount determination unit, configured to determine a penetration amount of the first candidate cycling road based on the position information; the penetration amount represents the amount of vehicle trajectory data penetrating the first candidate cycling road; a vehicle flow characteristic determination unit, configured to determine a vehicle flow characteristic of the first candidate cycling road based on the penetration amount and the motion state information; a trajectory point feature determination unit, configured to determine the trajectory point features of the first candidate walking and cycling path based on the matching degree information and the position information; The vehicle traffic feature determination unit is used to use the vehicle flow feature and the trajectory point feature as the vehicle traffic feature.

10. The device according to claim 7, characterized in that The screening unit comprises: The screening subunit is used to screen out the cycling roads whose two ends are respectively connected to the vehicle roads in the road network data from the second candidate cycling roads to obtain the target road.

11. The device according to claim 7, characterized in that The screening unit comprises: A to-be-connected road acquisition subunit is used to acquire a preset number of to-be-connected candidate cycling roads from the second candidate cycling roads; A connecting subunit, used for connecting the preset number of candidate cycling roads to be connected to obtain a target candidate cycling road; The target road determination subunit is used to use the preset number of candidate cycling roads to be connected as the target roads when both ends of the target candidate cycling road are respectively connected to the vehicle roads in the road network data.

12. The device according to claim 7, characterized in that The trajectory matching result includes road matching information, and the road matching information represents road information of a road in the road network data that matches the vehicle trajectory data. The first candidate cycling road determination module includes: A trajectory filtering unit, configured to filter the vehicle trajectory data based on at least one of the trajectory attribute information and the road matching information to obtain candidate trajectory data; A walking and cycling road acquisition unit, used to acquire a plurality of walking and cycling roads from the road network data; The first candidate cycling road determining unit is configured to use a cycling road among the plurality of cycling roads that matches the candidate trajectory data as the first candidate cycling road.

13. An electronic device for a method of updating road traffic attributes, characterized in that: The electronic device includes a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or at least one program is loaded and executed by the processor to implement the method for updating road traffic attributes as described in any one of claims 1 to 6.

14. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the method for updating road traffic attributes as described in any one of claims 1 to 6.

15. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for updating the road traffic attribute according to any one of claims 1 to 6 is implemented.

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