Method and device for determining road traffic capacity, storage medium and program product

Abstract

The embodiment of the invention provides a method and equipment for determining the traffic capacity of a road, a storage medium and a program product. The method comprises the following steps: determining at least one local area based on road network data, wherein the at least one local area comprises a plurality of roads of which the traffic capacity is to be determined; acquiring track information associated with a plurality of roads in the local area; determining passing willingness information and passing proportion information for the plurality of roads based on the track information; and determining traffic capacity information about the plurality of roads based on the traffic willingness information and the traffic proportion information. Through the method, the efficiency of path planning is optimized, and the operation quality of the whole traffic network is effectively improved.

Description

Technical Field

[0001] Example embodiments of the present disclosure generally relate to the field of vehicle navigation, and more particularly to a method, device, storage medium, and program product for determining road capacity. Background Art

[0002] As the basic feature data in navigation planning and guidance reminders, road accessibility plays a vital role in path selection. Traditional road accessibility evaluation is usually based on the binary judgment of "whether it is passable", but when the destination remains unchanged and users face multiple optional paths, choosing a relatively more convenient road can significantly improve the user experience. For example, in a residential area composed of low-level roads, although there is usually no obvious detour problem between any two roads, if the roads are generally narrow and difficult to pass, providing a relatively easier-to-pass road can better meet user needs. Summary of the invention

[0003] In a first aspect of the present disclosure, a method for determining road capacity is provided. The method comprises: determining at least one local area based on road network data, the at least one local area including multiple roads whose capacity is to be determined; obtaining trajectory information associated with the multiple roads in the local area; determining traffic willingness information and traffic ratio information for the multiple roads based on the trajectory information; and determining traffic capacity information about the multiple roads based on the traffic willingness information and the traffic ratio information.

[0004] In some embodiments, determining at least one local area includes: determining morphological points of medium and high-level roads equal to or higher than a predetermined level based on road network data; determining tile data of at least two levels corresponding to the morphological points; determining an envelope area of ​​the medium and high-level roads based on the tile data; and determining at least one local area based on the envelope area.

[0005] In some embodiments, acquiring the trajectory information includes: determining at least one connection segment of each of a plurality of roads in the local area; and acquiring the trajectory information associated with the at least one connection segment based on the at least one connection segment.

[0006] In some embodiments, the trajectory information includes at least one of the following: planned order volume, in-order traffic volume, trajectory traffic volume, and order deviation volume.

[0007] In some embodiments, determining the willingness to pass information includes: determining the planned achievement information associated with at least one connection segment based on the communication volume within the order and the planned order volume; determining the deviation information based on the order deviation volume and the traffic volume within the order; and determining the willingness to pass information based on the planned achievement information and the deviation information.

[0008] In some embodiments, determining traffic ratio information includes: determining the traffic volume of multiple connecting segments in a local area within a predetermined time period based on trajectory information; determining quantile information of the traffic volume of a target connecting segment in the local area based on multiple traffic volumes of multiple connecting segments; and determining the traffic ratio information based on the quantile information.

[0009] In some embodiments, determining the traffic capacity information about the plurality of roads includes: determining, based on the traffic willingness information and the traffic ratio information, a route planning weight associated with at least one connection segment having traffic ratio information within a predetermined range in the local area.

[0010] In a second aspect of the present disclosure, an electronic device is provided. The electronic device includes at least one processing unit; and at least one memory, the at least one memory is coupled to the at least one processing unit and stores instructions for execution by the at least one processing unit. When the instructions are executed by the at least one processing unit, the device performs the method according to the first aspect of the present disclosure.

[0011] In a third aspect of the present disclosure, a computer-readable storage medium is provided, wherein a computer program is stored on the computer-readable storage medium, and the computer program can be executed by a processor to implement the method according to the first aspect of the present disclosure.

[0012] In a fourth aspect of the present disclosure, a computer program product is provided, which includes computer executable instructions, and when the instructions are executed by a processor, the method according to the first aspect of the present disclosure is implemented.

[0013] It should be understood that the contents described in this content section are not intended to limit the key features or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0015] Figure 1 A block diagram illustrating an example environment in which embodiments of the present disclosure can be implemented;

[0016] Figure 2 A schematic diagram showing determining at least one local area according to a method of an embodiment of the present disclosure;

[0017] Figure 3 A flow chart showing a method for determining road capacity according to an embodiment; and

[0018] Figure 4 A schematic block diagram of an electronic device suitable for implementing an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0019] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0020] It should be noted that the titles of any sections / subsections provided herein are not restrictive. Various embodiments are described throughout this article, and any type of embodiment may be included under any section / subsection. In addition, the embodiments described in any section / subsection may be combined in any manner with any other embodiments described in the same section / subsection and / or different sections / subsections.

[0021] In the description of the embodiments of the present disclosure, the term "including" and similar terms should be understood as open inclusion, that is, "including but not limited to". The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The term "some embodiments" should be understood as "at least some embodiments". The following may also include other explicit and implicit definitions. The terms "first", "second", etc. may refer to different or the same objects. The following may also include other explicit and implicit definitions. The schemes in this specification and the embodiments, if involving the processing of personal information, will be processed on the premise of having a legal basis (for example, obtaining the consent of the subject of personal information, or being necessary for the performance of a contract, etc.), and will only be processed within the prescribed or agreed scope. The user's refusal to process personal information other than the necessary information required for basic functions will not affect the user's use of basic functions.

[0022] It is understandable that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and relevant provisions.

[0023] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, scope of use, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0024] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly prompt the user that the operation requested to be performed will require obtaining and using the user's personal information, so that the user can independently choose whether to provide personal information to software or hardware such as electronic devices, applications, servers or storage media that execute operations of the technical solution of the present disclosure based on the prompt message.

[0025] As an optional but non-limiting implementation, in response to receiving an active request from the user, the prompt information is sent to the user in a manner such as a pop-up window, in which the prompt information can be presented in text form. In addition, the pop-up window can also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0026] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet the relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0027] As used herein, the term "model" can learn the association between the corresponding input and output from the training data, so that after the training is completed, the corresponding output can be generated for a given input. The generation of the model can be based on machine learning technology. Deep learning is a machine learning algorithm that processes inputs and provides corresponding outputs by using multiple layers of processing units. In this article, "model" may also be referred to as "machine learning model", "machine learning network" or "network", which are used interchangeably in this article. A model may also include different types of processing units or networks.

[0028] In addition, the term "in response to" as used herein refers to a state in which a corresponding event occurs or a condition is satisfied. It will be understood that the timing of executing a subsequent action executed in response to the event or condition is not necessarily strongly related to the time when the event occurs or the condition is satisfied. For example, in some cases, the subsequent action may be executed immediately when the event occurs or the condition is satisfied; while in other cases, the subsequent action may be executed after a period of time after the event occurs or the condition is satisfied.

[0029] Road accessibility is a basic feature data commonly used in navigation planning and guidance reminders, and plays a vital role in path selection in navigation systems. Traditional road accessibility evaluation focuses on the binary judgment of "whether it is passable", but in real applications, especially when the destination does not change, users usually face multiple optional paths. In this case, choosing a relatively more convenient and efficient road for users can significantly improve the user experience.

[0030] For example, when a user enters a large residential area consisting of multiple low-level roads, there are many roads in the area, and choosing any two roads will usually not cause obvious detour problems because the area is small and the road network density is large. In this case, even if multiple roads are in good condition, any path selection will not have a negative impact on the user experience. However, when the roads in an area are generally narrow and difficult to pass, providing a "relatively" easier to pass road will have higher value. Compared with completely impassable roads, providing the easiest road within the optional range is the most concerned issue for users.

[0031] This situation usually occurs on low-level roads. Traditional road accessibility assessment methods mine historical traffic trajectory data, build a national road network model, and score the accessibility of each road based on this, and finally apply these scores to navigation path selection. Although this method can quantify the accessibility of each road, in some areas, especially in areas where low-level roads of similar levels are concentrated, the distinction is often small, and even all roads in the area will be marked as "difficult to pass", which cannot effectively provide optimized path selection, resulting in users being unable to choose the best path in these areas.

[0032] The modeling scope of traditional methods usually covers the entire national road network, and some scenarios may be reduced to the city dimension. This method constructs a road accessibility model by collecting historical vehicle traffic behavior data, such as traffic speed, traffic heat, and obstacle identification and road width determination through image acquisition. However, this method has natural limitations, which are mainly reflected in the following two aspects: First, medium and high-grade roads usually have good accessibility under normal circumstances, unless there are special circumstances such as construction and road occupation, but low-grade roads are generally less accessible than high-grade roads. In the selection of low-grade roads, this method has limited effect, especially in some scenarios, low-grade roads often cannot provide valuable navigation guidance. For example, when a driver enters a town or village, if all roads in the area are low-access roads, the evaluation of traditional methods cannot provide effective path selection suggestions in terminal navigation.

[0033] Therefore, the existing evaluation methods cannot achieve accurate path recommendations in fine-grained areas, especially for the evaluation of the passability of low-level roads, which has a large room for improvement. At present, there is an urgent need for a method that can refine the evaluation of the passability of low-level roads in local areas. This method should be able to provide more differentiated passability information by deeply analyzing the willingness to pass and the traffic ratio of low-level roads, thereby improving the path planning accuracy of the navigation system in a complex road network environment and meeting the user's needs for accurate navigation and efficient driving.

[0034] According to an embodiment of the present disclosure, a method for determining road capacity is provided, which is particularly suitable for evaluating the capacity of low-level road-dense areas to optimize the path planning effect of the navigation system. According to the scheme, at least one local area is determined based on road network data, and the at least one local area includes multiple roads whose capacity is to be determined; trajectory information associated with the multiple roads in the local area is obtained; based on the trajectory information, the traffic willingness information and traffic ratio information for the multiple roads are determined; and the traffic capacity information about the multiple roads is determined based on the traffic willingness information and the traffic ratio information.

[0035] That is to say, the method according to the embodiment of the present disclosure is generally divided into two stages: in the first stage, the road network is first divided by medium and high-level roads, the entire road network is subdivided into multiple smaller areas, and each area is regarded as the accessibility comparison range of low-level roads. In the second stage, characteristic information such as planning data and traffic volume are extracted from the neighborhood roads of each area to characterize the accessibility of the road, and this information is compared with other roads in the area, and finally the relative accessibility of the road in the area is obtained.

[0036] In subsequent path planning, the system can assign corresponding route planning weights to the connecting segments of each road based on the comprehensive capacity information. In actual applications, the navigation system will select paths based on these weights, giving priority to road segments with stronger capacity, thereby improving the reliability of the navigation path and the user's driving experience. At the same time, for road segments with weaker capacity, the system will appropriately avoid them in path planning to prevent drivers from taking unnecessary driving risks due to poor road permeability. In this way, the system not only optimizes the efficiency of path planning, but also effectively improves the operating quality of the overall transportation network.

[0037] In the following, we will first refer to Figure 1 An example environment is described in which embodiments according to the present disclosure can be implemented. Figure 1 A simplified schematic diagram of a scenario to which the method according to an embodiment of the present disclosure can be applied is shown. Figure 1 In the example environment shown, a server 150 (also referred to as an electronic device) for determining road capacity may be included. The server 150 may process the road network data and the trajectory information, and perform predetermined processing to ultimately determine the capacity information about the plurality of roads. Subsequently, the server 150 plans a navigation path for the vehicle 110 based on the capacity information.

[0038] The method according to the embodiment of the present disclosure will be described in detail below. According to the method according to the embodiment of the present disclosure, at least one local area is first determined based on the road network data. The specific steps include preprocessing the road network data across the country and screening out multiple roads whose traffic capacity is to be determined. These roads may include low-level roads below a predetermined level. These low-level roads are usually located in towns, villages, and areas around communities, where trafficability is uncontrollable and densely distributed.

[0039] In order to improve the accuracy of regional division, the system uses the morphological points and tile data of medium and high-level roads for assistance. A morphological point can refer to the center point along the road, and each morphological point corresponds to two levels of tiles - high-level tiles and low-level tiles. In the present disclosure, "tile" refers to dividing the entire map area into several small, fixed-size square tiles (usually 256×256 pixels). This tiling method helps to improve the loading speed, rendering efficiency and user interaction experience of the map. The tile system adopts a hierarchical (layered) structure, and each level corresponds to a zoom level. The higher the number of levels, the higher the resolution and detail of the map.

[0040] According to the method of the embodiment of the present disclosure, by clustering the tile data of two levels of medium and high-level roads, continuous high-level road areas are formed. These areas form envelope areas, which are subsequently used as reference areas with high trafficability.

[0041] Next, the system determines the local area of ​​the low-level road based on the envelope area of ​​the high-level road. Figure 2 A schematic diagram showing how to determine a local area according to an embodiment of the present disclosure is shown, wherein the dotted line in the figure represents a high-level road 210 whose road level is higher than or equal to a predetermined level, such as a ring road in a city, a highway, etc. The process of determining a local area includes: first, based on the morphological points of the medium and high-level roads, the corresponding tile data is calculated, and clustering is performed to form an envelope area 220 of the high-level road, such as Figure 2 As shown. Then, excluding the envelope area formed by these high-level roads, the remaining area 240 of low-level roads 230 is the local area 240 that needs to be further divided. This division method can not only effectively identify the distribution of low-level roads, but also ensure that the road capacity evaluation in each local area has a high degree of differentiation, avoiding the inaccurate evaluation problem caused by placing high-level and low-level roads in the same comparison domain in the traditional method.

[0042] After completing the area division, the method according to the embodiment of the present disclosure will proceed to the stage of obtaining and processing trajectory information. Trajectory information refers to historical driving data associated with low-level roads, including but not limited to planned order volume, actual traffic volume, trajectory traffic volume, and order deviation volume. The specific acquisition steps are as follows.

[0043] First, the link is determined or obtained. The system subdivides each low-level road into multiple links. In the embodiment of the present disclosure, a link refers to a clear road segment that connects two nodes (such as intersections, junctions or other important geographical locations), which can be understood as an "edge" in the road network, while the node is a "point". A link can be unidirectional or bidirectional. A complete road usually includes multiple links. This subdivision method can capture the driver's driving behavior and willingness to pass on different road segments in more detail. In some embodiments, the road network data may include link segment data for each road.

[0044] Next, the method according to the embodiment of the present disclosure extracts relevant trajectory information from historical data based on each connection segment. These data sources include but are not limited to the following data: planned orders, order traffic, trajectory traffic, and driver deviation records of the navigation system and / or online car-hailing platform. By summarizing and analyzing these data, the system can fully understand the actual usage of each connection segment and the driver's driving intention.

[0045] According to an embodiment of the present disclosure, a planned order refers to an order related to a connection segment planned by a navigation system and / or an online car-hailing platform; order throughput refers to the amount of the driver actually driving through the target connection segment during the driving process related to the order; track throughput refers to the amount of the driver actually driving through the target connection segment during all driving processes; and deviation records represent deviation records related to the order and the target connection segment.

[0046] The method according to the embodiment of the present disclosure then further calculates various traffic characteristics, such as the actual travel planning ratio, yaw rate, and heat quantile. These characteristic indicators can quantitatively reflect the traffic willingness and traffic ratio of each connection segment, providing basic data for subsequent traffic capacity evaluation.

[0047] Specifically, the actual travel ratio refers to the ratio of the driver's order volume to the planned order volume, also known as the planning achievement information. By calculating the actual travel ratio of each connecting segment, the system evaluates the driver's reliance on and trust in the road. For example, an actual travel ratio of 80% means that most drivers drive according to the navigation plan, indicating that the willingness to travel on this road is high.

[0048] Furthermore, in some embodiments, the method according to the embodiment of the present disclosure determines the yaw information by analyzing the yaw rate of each connection segment, wherein the yaw amount may be the ratio of the order yaw amount to the amount of traffic within the order. The yaw information can be used to systematically understand the driver's adjustment of the navigation path during driving. A higher yaw rate may indicate that the driver has doubts or dissatisfaction with the trafficability of the road.

[0049] The method according to the embodiment of the present disclosure also combines the planning achievement information and the deviation information to form a comprehensive passing willingness score. The score reflects the driver's overall passing willingness for each road and helps to distinguish the relative passability of roads in the area.

[0050] The method according to the embodiment of the present disclosure further determines the traffic ratio information. Specifically, the method according to the embodiment of the present disclosure counts the traffic volume of each connection segment within a predetermined time period based on the trajectory information, reflecting the frequency of use and traffic pressure of the road. The length of the predetermined time period can be freely set according to actual conditions, and the embodiment of the present disclosure does not limit this.

[0051] By performing a quantile analysis on the traffic volume of multiple connecting sections in the region, the system determines the traffic ratio ranking of each road in the region. For example, the traffic volume of a road is at the 70th percentile in the region, indicating that its traffic ratio is high. Based on the quantile information, the system assigns a traffic ratio score to each road, reflecting its traffic pressure and usage frequency in the region.

[0052] The determination of traffic willingness information and traffic ratio information provides the necessary basic data for subsequent traffic capacity assessment, enabling the system to comprehensively and accurately assess the traffic capacity of each low-level road.

[0053] After the determination of the traffic willingness information and the traffic ratio information is completed, the method according to the embodiment of the present disclosure enters the comprehensive evaluation stage of the traffic capacity information. The core of this stage is to combine the above two types of information to generate the traffic capacity score of each road and assign corresponding route planning weights to it.

[0054] In some embodiments, the method according to the embodiment of the present disclosure assigns a weight to at least one connection segment whose traffic ratio information is within a predetermined range according to the traffic willingness information and the traffic ratio information. For example, weighted processing may not be performed for the traffic ratio information indicating that the traffic percentile is above 75%, because the number of these connection segments is often small and the traffic ratio is relatively high. In addition, connection segments whose traffic percentile is below 25% are not processed because these roads are often difficult to pass.

[0055] For multiple connecting segments with pass percentiles ranging from 25% to 75%, according to the embodiment of the present disclosure, the weight of future route planning will be further determined based on the pass willingness information and the pass ratio information. Of course, it should be understood that the above-mentioned pass percentiles of 25% and 75% are only illustrative and are not intended to limit the scope of protection of the present disclosure. According to the method of the present disclosure, any other appropriate range can be selected according to actual needs.

[0056] According to the method of the embodiment of the present disclosure, the weight of each connection segment in path planning can be dynamically adjusted according to the capacity score of each connection segment. In addition, the system can also adjust the algorithm and parameters of weight allocation according to actual application requirements to ensure the flexibility and adaptability of path planning. For example, real-time traffic data and environmental monitoring information can be introduced to dynamically adjust the weight allocation strategy to cope with real-time changes in road capacity.

[0057] Based on the method according to the embodiment of the present disclosure, when planning a route, the server or online car-hailing platform will give priority to road segments with higher capacity scores and higher weights, thereby ensuring the accessibility and safety of the driving route. For road segments with lower capacity scores and lower weights, the system will try to avoid them in route planning, reducing the driving risks and inconveniences caused to drivers due to poor road accessibility.

[0058] In order to more clearly illustrate the specific implementation of the present invention, a specific example is described in detail below.

[0059] Assume that in a certain urban area, the road network data contains multiple roads of different levels, among which in multiple local areas, low-level roads dominate and the trafficability is not subdivided. In order to determine the capacity information of these roads in the local areas whose trafficability is not subdivided, the system first screens out all low-level roads based on a predetermined road level threshold (for example, the level is lower than level 4), and divides multiple local areas by combining the morphological points and tile data of medium and high-level roads. The low-level roads in each local area will be the main object of trafficability evaluation.

[0060] Next, the system obtains historical trajectory information associated with these low-level roads, including the driver's planned order volume, actual traffic volume, trajectory traffic volume, and order deviation volume. By analyzing these data, the system calculates the traffic willingness information and traffic ratio information for each road. For example, on a low-level road, the planned order volume is 1,000 orders, the actual traffic volume is 800 orders, and the deviation volume is 200 orders. The system will calculate that the planning achievement rate of the road is 80% and the deviation rate is 20%. At the same time, by analyzing the percentile of the trajectory traffic volume of the road in the area, the system determines its traffic ratio information, such as the traffic volume of the road is at the 60th percentile in the area.

[0061] Based on the above information, the system combines the willingness to travel information with the traffic ratio information to generate a capacity score for the road and assign it a corresponding route planning weight. During the navigation route planning process, the system will give priority to roads with higher capacity scores to ensure the accessibility and safety of the driver's driving route. For example, a low-level road is included in the preferred route because of its high capacity score, while another road with a lower capacity score is avoided, thereby optimizing the overall driving route and improving the practicality and user experience of the navigation system.

[0062] The method according to the embodiment of the present disclosure is not only applicable to areas with dense low-level roads such as towns or villages, but also has good scalability and can be applied to other complex road network environments that require refined capacity assessment. Through the detailed description of the above specific implementation methods, it can be seen that the method according to the embodiment of the present disclosure has significant advantages in refining the capacity assessment of low-level roads.

[0063] Specifically, the method according to the embodiment of the present disclosure utilizes the morphological points and tile data of medium and high-level roads, and the system can accurately divide the local area of ​​the low-level road to ensure the high accuracy and discrimination of the capacity assessment. In addition, the method according to the embodiment of the present disclosure, through the segmentation of the connection segment and the collection of multi-dimensional trajectory information, the system can fully understand the driver's driving behavior and willingness to pass, provide accurate capacity information, and dynamically adjust the path planning weight to improve the path planning effect and user experience of the navigation system.

[0064] In addition, in addition to historical trajectory data, the method according to the embodiment of the present disclosure can also integrate other data sources, such as real-time traffic flow monitoring, meteorological data, road sensor data, etc., to further improve the accuracy and comprehensiveness of traffic capacity assessment. Through multi-source data fusion, the system can have a more comprehensive understanding of road traffic conditions and provide more accurate traffic capacity scores and path planning suggestions.

[0065] Furthermore, the method according to the embodiment of the present disclosure can also collect user feedback data to understand the user's satisfaction with route planning and driving experience, and further optimize the traffic capacity score and weight allocation strategy. Through personalized optimization, the system can provide different users with navigation services that better meet their needs, thereby improving the user's driving experience and satisfaction.

[0066] Figure 3 A flow chart of a process 300 for determining road capacity according to some embodiments of the present disclosure is shown. In some embodiments, the process 300 may be implemented based on the server 150. It should be understood that the process 300 may include additional actions not shown and / or may omit the actions shown, and the scope of the present disclosure is not limited in this respect.

[0067] In block 310 , the server 150 determines at least one local area based on the road network data, wherein the at least one local area includes a plurality of roads for which capacity is to be determined.

[0068] At block 320, the server 150 acquires trajectory information associated with a plurality of roads in the local area. At block 330, the server 150 determines traffic willingness information and traffic ratio information for the plurality of roads based on the trajectory information. At block 340, the server 150 determines traffic capacity information about the plurality of roads based on the traffic willingness information and traffic ratio information.

[0069] In some embodiments, determining at least one local area includes: determining morphological points of medium and high-level roads equal to or higher than a predetermined level based on road network data; determining tile data of at least two levels corresponding to the morphological points; determining an envelope area of ​​the medium and high-level roads based on the tile data; and determining at least one local area based on the envelope area.

[0070] In some embodiments, acquiring the trajectory information includes: determining at least one connection segment of each of a plurality of roads in the local area; and acquiring the trajectory information associated with the at least one connection segment based on the at least one connection segment.

[0071] In some embodiments, determining the willingness to pass information includes: determining the planned achievement information associated with at least one connection segment based on the communication volume within the order and the planned order volume; determining the deviation information based on the order deviation volume and the traffic volume within the order; and determining the willingness to pass information based on the planned achievement information and the deviation information.

[0072] In some embodiments, determining traffic ratio information includes: determining the traffic volume of multiple connecting segments in a local area within a predetermined time period based on trajectory information; determining quantile information of the traffic volume of a target connecting segment in the local area based on multiple traffic volumes of multiple connecting segments; and determining the traffic ratio information based on the quantile information.

[0073] In some embodiments, determining the traffic capacity information about the plurality of roads includes: determining, based on the traffic willingness information and the traffic ratio information, a route planning weight associated with at least one connection segment having traffic ratio information within a predetermined range in the local area.

[0074] Figure 4 Schematic block diagram of an electronic device suitable for implementing an embodiment of the present disclosure is shown. Figure 4As shown, according to an electronic device provided by the present disclosure, the electronic device 400 may be a server or other appropriate device mentioned above that communicates with a vehicle. The electronic device 400 includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) 402 or computer program instructions loaded from a storage unit into a random access memory (RAM) 403. In RAM 403, various programs and data required for device operation may also be stored. CPU 401, ROM 402, and RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0075] Multiple components in the device are connected to the I / O interface 405, including: an input unit 406, such as a touch screen, buttons, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a disk, an optical disk, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the device to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0076] The various processes and processing described above, such as the processes mentioned above, can be performed by the processing unit 401. For example, in some embodiments, the above methods or processes can be implemented as computer software programs, which are tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program can be loaded and / or installed on the device via ROM 402 and / or communication unit 409. When the computer program is loaded into RAM 403 and executed by CPU 401, one or more actions of the above methods or processes can be performed.

[0077] Embodiments of the present disclosure relate to methods, electronic devices, and / or computer program products. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present disclosure.

[0078] Computer readable storage medium can be a tangible device that can hold and store instructions used by an instruction execution device. Computer readable storage medium can be, for example, (but not limited to) an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (non-exhaustive list) of computer readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, for example, a punch card or a convex structure in a groove on which instructions are stored, and any suitable combination thereof. The computer readable storage medium used here is not interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated by a waveguide or other transmission medium (for example, a light pulse by an optical fiber cable), or an electrical signal transmitted by a wire.

[0079] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0080] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0081] Various aspects of the present disclosure are described herein with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0082] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processing unit of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0083] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0084] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of a module, program segment or instruction includes one or more executable instructions for realizing the specified logical function. In some alternative implementations, the function marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of special hardware and computer instructions.

[0085] The above descriptions of various implementations of the present disclosure are exemplary, non-exhaustive, and not limited to the disclosed implementations. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described implementations. The selection of terms used herein is intended to best explain the principles of the implementations, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the various implementations disclosed herein.

Claims

1. A method for determining road capacity, comprising: Determine at least one local area based on the road network data, wherein the at least one local area includes a plurality of roads whose traffic capacity is to be determined; Acquiring trajectory information associated with the plurality of roads in the local area; Determining traffic intention information and traffic ratio information for the plurality of roads based on the trajectory information; as well as Traffic capacity information about the plurality of roads is determined based on the traffic willingness information and the traffic ratio information.

2. The method of claim 1 , wherein determining at least one local area comprises: Determine the morphological points of medium and high-level roads that are equal to or higher than a predetermined level based on the road network data; determining at least two levels of tile data corresponding to the morphological point; Determine the envelope area of ​​the medium and high-level roads based on the tile data; as well as The at least one local area is determined based on the envelope area.

3. The method according to claim 1, wherein obtaining trajectory information comprises: determining at least one connecting segment of each of the plurality of roads in the local area; as well as Based on the at least one connection segment, trajectory information associated with the at least one connection segment is acquired.

4. The method according to any one of claims 1-3, wherein the trajectory information includes at least one of the following: planned order quantity, traffic volume within the order, track traffic volume, and order deviation volume.

5. The method according to claim 4, wherein determining the passing intention information comprises: determining planning achievement information associated with the at least one connection segment based on the in-order communication volume and the planned order volume; Determine deviation information based on the order deviation amount and the in-order traffic amount; as well as The passing intention information is determined based on the plan achievement information and the deviation information.

6. The method according to claim 4, wherein determining the traffic ratio information comprises: Determine the traffic volume of multiple connecting segments in the local area within a predetermined time period based on the trajectory information; Determine quantile information of the traffic volume of the target link segment in the local area based on the multiple traffic volumes of the multiple link segments; as well as The traffic ratio information is determined based on the quantile information.

7. The method of claim 6, wherein determining capacity information about the plurality of roads comprises: Based on the traffic willingness information and the traffic ratio information, a route planning weight associated with at least one connecting segment in the local area whose traffic ratio information is within a predetermined range is determined.

8. An electronic device, comprising: a memory for storing one or more computer instructions; as well as A processor, configured to execute the one or more computer instructions stored in the memory to perform the following actions: Determine at least one local area based on the road network data, wherein the at least one local area includes a plurality of roads whose traffic capacity is to be determined; Acquiring trajectory information associated with the plurality of roads in the local area; Determining traffic intention information and traffic ratio information for the plurality of roads based on the trajectory information; as well as Traffic capacity information about the plurality of roads is determined based on the traffic willingness information and the traffic ratio information.

9. The electronic device of claim 8, wherein determining at least one local area comprises: Determine the morphological points of medium and high-level roads that are equal to or higher than a predetermined level based on the road network data; determining at least two levels of tile data corresponding to the morphological point; Determine the envelope area of ​​the medium and high-level roads based on the tile data; as well as The at least one local area is determined based on the envelope area.

10. The electronic device according to claim 8, wherein obtaining the trajectory information comprises: determining at least one connecting segment of each of the plurality of roads in the local area; as well as Based on the at least one connection segment, trajectory information associated with the at least one connection segment is acquired.

11. The electronic device according to any one of claims 8-10, wherein the trajectory information comprises at least one of the following: planned order quantity, traffic volume within an order, track traffic volume, and order deviation volume.

12. The electronic device according to claim 11, wherein determining the passing intention information comprises: determining planning achievement information associated with the at least one connection segment based on the in-order communication volume and the planned order volume; Determine deviation information based on the order deviation amount and the in-order traffic amount; as well as The passing intention information is determined based on the plan achievement information and the deviation information.

13. The electronic device according to claim 11, wherein determining the traffic ratio information comprises: Determine the traffic volume of multiple connecting segments in the local area within a predetermined time period based on the trajectory information; Determine quantile information of the traffic volume of the target link segment in the local area based on the multiple traffic volumes of the multiple link segments; as well as The traffic ratio information is determined based on the quantile information.

14. The electronic device of claim 13, wherein determining capacity information about the plurality of roads comprises: Based on the traffic willingness information and the traffic ratio information, a route planning weight associated with at least one connecting segment in the local area whose traffic ratio information is within a predetermined range is determined.

15. A computer-readable storage medium having one or more computer instructions stored thereon, wherein the one or more computer instructions are executed by a processor to implement the method according to any one of claims 1-7.

16. A computer program product comprising computer executable instructions, wherein the computer executable instructions, when executed by a processor, implement the method according to any one of claims 1 to 7.

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