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

Abstract

Embodiments of the present disclosure provide a method, device, storage medium, and program product for determining road capacity. The method includes: 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 for the multiple roads based on the traffic willingness information and traffic ratio information. This method not only optimizes the efficiency of route planning but also effectively improves the operational quality of the overall transportation network.

Description

Technical Field

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

[0002] Road accessibility, as fundamental feature data for navigation planning and guidance, plays a crucial role in route selection. Traditional road accessibility assessments are typically based on a binary judgment of "is it accessible?" However, when a user faces multiple options for the same destination, choosing a more convenient route can significantly improve the user experience. For example, in a residential area consisting of low-grade roads, while any two roads typically present no significant detours, if the roads are generally narrow and difficult to navigate, providing a more accessible route 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 for which capacity is to be determined; obtaining trajectory information associated with the multiple roads in the local area; determining travel willingness information and travel ratio information for the multiple roads based on the trajectory information; and determining capacity information for the multiple roads based on the travel willingness information and travel 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, obtaining the trajectory information includes: determining at least one connecting segment of each of a plurality of roads in the local area; and obtaining trajectory information associated with the at least one connecting segment based on the at least one connecting 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 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 coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit. When executed by the at least one processing unit, the instructions cause the device to perform 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, which, when executed by a processor, implement the method according to the first aspect of the present disclosure.

[0013] It should be understood that the content described in this summary section is not intended to limit the key features or important features of the embodiments of the present disclosure, nor is it 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 according to an embodiment of the present disclosure is shown;

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

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

[0019] The following describes embodiments of the present disclosure in more detail 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 limited to the embodiments described herein. Rather, 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 for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0020] It should be noted that the titles of any section / subsection provided herein are not limiting. Various embodiments are described throughout this document, and any type of embodiment may be included under any section / subsection. Furthermore, 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 in 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 solutions in this specification and the embodiments, if involving the processing of personal information, will be processed on the premise of having a legal basis (such as 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 scope of regulations or agreements. 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) must comply with the requirements of relevant laws, regulations and relevant provisions.

[0023] It is understandable that before using the technical solutions disclosed in the various embodiments of this disclosure, the type, scope of use, usage scenarios, etc. of the personal information involved in this 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 a user's active request, a prompt message is sent to the user to clearly remind 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 the electronic device, application, server or storage medium and other software or hardware that performs the operation 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 a user's active request, a prompt message may be sent to the user, for example, in the form of a pop-up window, in which the prompt message may be presented in text form. Furthermore, the pop-up window may also include a selection control for the user to select "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 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 corresponding inputs and outputs from training data, so that after training is completed, corresponding outputs can be generated for given inputs. 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", and these terms are used interchangeably in this article. A model can also include different types of processing units or networks.

[0028] Furthermore, 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 correlated with the time when the event occurs or the condition is satisfied. For example, in some cases, a subsequent action may be executed immediately upon the occurrence of the event or the satisfaction of the condition; in other cases, the subsequent action may be executed some time after the occurrence of the event or the satisfaction of the condition.

[0029] Road accessibility is a fundamental feature commonly used in navigation planning and guidance, and plays a crucial role in route selection within navigation systems. Traditional road accessibility assessments often focus on a binary judgment of "passable or not." However, in real-world applications, especially when the destination remains unchanged, users often face multiple possible routes. In such situations, selecting a relatively convenient and efficient route can significantly improve the user experience.

[0030] For example, when a user enters a large residential area consisting of multiple low-grade 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 high. 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-to-pass road within the available options is what users care about most.

[0031] This situation often occurs on low-grade roads. Traditional road accessibility assessment methods mine historical traffic trajectory data to construct a national road network model, then score the accessibility of each road based on this data. Ultimately, these scores are applied to navigation route selection. While this method can quantify the accessibility of each road, in some areas, especially those with a concentration of similar low-grade roads, the distinction is often poor, and even all roads in the area may be marked as "difficult to pass." This makes it impossible to effectively provide optimized route selection, preventing users from choosing the best route within these areas.

[0032] Traditional methods typically model the entire national road network, though some scenarios may be narrowed down to the city level. This method constructs a road accessibility model by collecting historical vehicle traffic behavior data, such as speed and traffic volume, as well as identifying obstacles and determining road widths through image acquisition. However, this approach has inherent limitations, primarily in the following two aspects: First, medium- and high-grade roads generally have good accessibility under normal circumstances, unless under special circumstances such as construction or road occupation. However, low-grade roads generally have poorer accessibility than high-grade roads. This method is also limited in its effectiveness in selecting low-grade roads, especially in certain scenarios where low-grade roads often fail to provide valuable navigation guidance. For example, when a driver enters a town or village, if all roads in the area are low-accessible, traditional evaluation methods will be unable to provide effective route selection recommendations for end-to-end navigation.

[0033] Therefore, existing assessment methods are unable to achieve accurate route recommendations within fine-grained regions. This is especially true for assessing the traversability of low-level roads, leaving significant room for improvement. A method is urgently needed to assess the traversability of low-level roads within a localized area. This method should be able to provide more differentiated traversability information by deeply analyzing the traffic intention and traffic ratio of low-level roads. This would improve the navigation system's route planning accuracy in complex road networks and meet users' demands for precise navigation and efficient driving.

[0034] According to an embodiment of the present disclosure, a method for determining road capacity is provided, particularly suitable for evaluating capacity in areas with densely populated low-level roads to optimize navigation system path planning. This method involves determining at least one local area based on road network data, the local area comprising multiple roads for which capacity is to be determined; obtaining trajectory information associated with the multiple roads in the local area; determining travel willingness information and travel ratio information for the multiple roads based on the trajectory information; and determining capacity information for the multiple roads based on the travel willingness information and travel ratio information.

[0035] In other words, 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 into multiple smaller areas based on medium and high-grade roads, and each area is regarded as a comparison range for the accessibility of low-grade roads. In the second stage, characteristic information such as planning data and traffic volume is extracted from the neighboring roads of each area to characterize the road's accessibility. This information is then compared with other roads in the area to ultimately determine the relative accessibility of the road within the area.

[0036] In subsequent route planning, the system can assign corresponding route planning weights to each road connection segment based on comprehensive capacity information. In actual application, the navigation system will select routes based on these weights, giving priority to road segments with higher capacity, thereby improving the reliability of the navigation route and the user's driving experience. At the same time, the system will appropriately avoid road segments with lower capacity during route planning, preventing drivers from taking unnecessary driving risks due to poor road accessibility. Through this method, the system not only optimizes the efficiency of route planning, but also effectively improves the operational quality of the overall transportation network.

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

[0038] The method according to an embodiment of the present disclosure will be described in detail below. According to the method according to an embodiment of the present disclosure, at least one local area is first determined based on road network data. The specific steps include preprocessing the nationwide road network data to screen multiple roads whose traffic capacity is to be determined. These roads may include low-grade roads that are below a predetermined grade. These low-grade roads are typically located in areas such as towns, villages, and residential areas, where trafficability is uncontrollable and they are 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 this 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 layer corresponds to a zoom level. The higher the number of layers, the higher the resolution and details of the map.

[0040] According to the method of the embodiment of the present disclosure, continuous high-grade road areas are formed by clustering tile data of two levels of medium and high-grade roads. These areas form envelope areas, which are subsequently used as reference areas for 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 illustrating how a local area is determined according to an embodiment of the present disclosure, 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 or a highway in a city. 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 clustered to form an envelope area 220 of the high-level roads, such as Figure 2 As shown. Then, excluding the envelope area formed by these high-grade roads, the remaining area 240 of low-grade roads 230 is the local area 240 that needs to be further divided. This division method not only effectively identifies the distribution of low-grade roads, but also ensures that the road capacity assessment within each local area has a high degree of differentiation, avoiding the inaccurate assessment problem caused by placing high-grade and low-grade roads in the same comparison domain in traditional methods.

[0042] After completing the area division, the method according to the disclosed embodiments proceeds to the acquisition and processing of 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, tracked traffic volume, and order deviation. 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 "edges" in the road network, while the nodes are "points". Links 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 disclosed embodiment extracts relevant trajectory information from historical data for each connection segment. This data comes from sources such as, but is not limited to, planned orders from navigation systems and / or ride-hailing platforms, order volume, track volume, and driver deviation records. By summarizing and analyzing this data, the system can fully understand the actual usage of each connection segment and the driver's driving intentions.

[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 traffic actually traveled through the target connection segment by the driver during the driving process related to the order; track throughput refers to the amount of traffic actually traveled through the target connection segment by the driver during all driving processes; and yaw records refer to yaw records related to the order and the target connection segment.

[0046] The method according to the disclosed embodiment then further calculates various trafficability characteristics, such as the planned-to-actual ratio, yaw rate, and heat quantile. These characteristic indicators can quantitatively reflect the traffic willingness and traffic ratio of each link, providing basic data for subsequent traffic capacity assessment.

[0047] Specifically, the "actual-to-plan ratio" refers to the ratio of the number of trips a driver has made to the number of planned trips, also known as plan achievement information. By calculating the actual-to-plan ratio for each link, the system assesses drivers' reliance on and trust in that route. For example, an 80% actual-to-plan ratio means that most drivers follow the navigation plan, indicating a high willingness to use that route.

[0048] Furthermore, in some embodiments, the method according to the disclosed embodiments determines yaw information by analyzing the yaw rate of each link segment, where the yaw amount can be the ratio of the order yaw amount to the traffic volume within the order. This yaw information allows the system to understand the driver's adjustments to the navigation route during driving. A high yaw rate may indicate that the driver has concerns or dissatisfaction with the road's drivability.

[0049] The method according to the embodiment of the present disclosure also combines the plan completion information and the deviation information to form a comprehensive pass willingness score. This score reflects the driver's overall pass 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 traffic ratio information. Specifically, the method according to the embodiment of the present disclosure calculates the traffic volume of each connecting segment within a predetermined time period based on the trajectory information, reflecting the frequency of road use and traffic pressure. The length of this predetermined time period can be freely set according to actual circumstances and is not limited by the embodiment of the present disclosure.

[0051] By analyzing the traffic volume of multiple connecting segments within a region using a quantile analysis, the system determines the traffic share ranking of each road within the region. For example, a road with a traffic volume in the 70th percentile within a region indicates a high traffic share. Based on this quantile information, the system assigns a traffic share score to each road, reflecting its traffic pressure and frequency of use within 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 determining the willingness and traffic ratio information, the method according to the disclosed embodiment enters the comprehensive capacity assessment phase. The core of this phase is to combine the above two types of information to generate a capacity score for each road and assign it a corresponding route planning weight.

[0054] In some embodiments, the method according to embodiments of the present disclosure assigns a weight to at least one link whose traffic ratio information falls within a predetermined range based on the traffic willingness information and traffic ratio information. For example, weighting may be omitted for links with traffic ratio information indicating a traffic percentile above 75%, as these links are often small in number and have a high traffic ratio. Furthermore, links with traffic percentiles below 25% are also omitted, as these roads are often difficult to navigate.

[0055] For multiple connecting segments with pass percentiles between 25% and 75%, according to embodiments of the present disclosure, weights for future route planning are further determined based on pass willingness information and pass ratio information. Of course, it should be understood that the above-mentioned pass percentiles of 25% and 75% are merely illustrative and are not intended to limit the scope of protection of the present disclosure. Any other appropriate range can be selected according to actual needs according to the methods of the present disclosure.

[0056] The method according to the disclosed embodiments can dynamically adjust the weight of each link in route planning based on its capacity score. Furthermore, the system can adjust the weight allocation algorithm and parameters based on actual application needs to ensure flexibility and adaptability in route planning. For example, real-time traffic data and environmental monitoring information can be incorporated to dynamically adjust the weight allocation strategy to address real-time changes in road capacity.

[0057] Based on the method according to the embodiments of the present disclosure, when planning routes, the server or ride-hailing platform will prioritize road segments with high capacity scores and high weights, thereby ensuring the accessibility and safety of the driving route. The system will try to avoid road segments with low capacity scores and low weights during route planning, reducing the driving risks and inconveniences for drivers caused by 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 within a city, the road network data contains multiple roads of varying grades. In several local areas, low-grade roads dominate, with unclassified accessibility. To determine the capacity of these unclassified roads within these local areas, the system first filters out all low-grade roads based on a predetermined road grade threshold (e.g., grade below 4). The system then divides the local areas into multiple zones, combining the morphological points and tile data of medium- and high-grade roads. The low-grade roads within each local area serve as the primary target for capacity assessment.

[0060] Next, the system obtains historical trajectory information associated with these low-level roads, including data such as the driver's planned order volume, actual traffic volume, trajectory traffic volume, and order deviation volume. By analyzing this data, the system calculates the traffic willingness information and traffic ratio information for each road. For example, on a certain 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. Based on this, the system will calculate that the planned 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 this information, the system combines traffic willingness information with traffic ratio information to generate a capacity score for the road and assigns it a corresponding route planning weight. During navigation route planning, the system prioritizes roads with higher capacity scores to ensure the accessibility and safety of the driver's route. For example, a low-grade road with a high capacity score may be included in the preferred route, while another road with a lower capacity score may be avoided. This optimizes the overall route and enhances 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-grade 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-grade roads.

[0063] Specifically, the method according to the disclosed embodiments utilizes morphological points and tile data for medium- and high-grade roads, enabling the system to accurately delineate localized areas of low-grade roads, ensuring high accuracy and discrimination in capacity assessment. Furthermore, by segmenting connected segments and collecting multi-dimensional trajectory information, the method according to the disclosed embodiments enables the system to fully understand drivers' driving behavior and willingness to travel, providing accurate capacity information and dynamically adjusting path planning weights, thereby improving the navigation system's path planning effectiveness and user experience.

[0064] Furthermore, the method according to the disclosed embodiments can integrate other data sources, such as real-time traffic flow monitoring, meteorological data, and road sensor data, in addition to historical trajectory data, to further enhance the accuracy and comprehensiveness of capacity assessments. By integrating multi-source data, the system can gain a more comprehensive understanding of road conditions and provide more accurate capacity scores and route planning recommendations.

[0065] Furthermore, the method according to the disclosed embodiments can also collect user feedback data to understand user satisfaction with route planning and driving experience, further optimizing the capacity scoring and weighting strategy. Through personalized optimization, the system can provide navigation services that better meet the needs of different users, improving their driving experience and satisfaction.

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

[0068] At block 320, server 150 obtains trajectory information associated with a plurality of roads in a local area. At block 330, server 150 determines traffic willingness information and traffic ratio information for the plurality of roads based on the trajectory information. At block 340, server 150 determines traffic capacity information for 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, obtaining the trajectory information includes: determining at least one connecting segment of each of a plurality of roads in the local area; and obtaining trajectory information associated with the at least one connecting segment based on the at least one connecting 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 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 can be the server or other appropriate device mentioned above that communicates with the 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. Various programs and data required for device operation can also be stored in RAM 403. 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 magnetic disk, optical disk, etc.; and a communication unit 409, such as a network card, modem, wireless communication transceiver, etc. The communication unit 409 allows the device to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0076] The various processes and procedures described above, such as the aforementioned processes, may be performed by processing unit 401. For example, in some embodiments, the aforementioned methods or processes may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed onto 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 aforementioned methods or processes may 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 media can be a tangible device that can hold and store the instructions used by the instruction execution device. Computer-readable storage media 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 media 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 disc 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. Computer-readable storage media used herein is not interpreted as a transient signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagated by waveguides or other transmission media (for example, light pulses by fiber optic cables), or electrical signals transmitted by wires.

[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, fiber optic transmission, wireless transmission, 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 to be stored in the computer-readable storage medium in each computing / processing device.

[0080] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent 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++, 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 entirely 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., utilizing 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 personalized by utilizing the state information of the computer-readable program instructions. 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 flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, 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 such that when these instructions are executed by the processing unit of the computer or other programmable data processing device, a device is generated that implements the functions / actions specified in one or more blocks in the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, where these instructions cause the computer, programmable data processing device, and / or other device to operate in a specific manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing various aspects of the functions / actions specified in one or more blocks 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 operational 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 blocks in the flowchart and / or block diagram.

[0084] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present disclosure. In this regard, each 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 prescribed logical function. In some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two consecutive 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 box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart, can be implemented by a dedicated hardware-based system that performs the prescribed function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0085] While various implementations of the present disclosure have been described above, the foregoing description is intended to be illustrative, not exhaustive, and not limited to the disclosed implementations. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described implementations. The terminology used herein is selected to best explain the principles of the implementations, their practical applications, or improvements to existing technologies, or to enable others skilled 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; obtaining trajectory information associated with the plurality of roads in the local area; Determining, based on the trajectory information, traffic intention information and traffic ratio information for the plurality of roads, wherein the traffic intention information is determined based on the plan achievement information and the deviation information, and the traffic ratio information is determined by performing a traffic quantile analysis on traffic volumes of a plurality of connecting segments within the at least one local area; 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: Determining, based on the road network data, morphological points of medium and high-level roads that are equal to or higher than a predetermined level; determining tile data of at least two levels corresponding to the morphological point; Determining an 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, in-order traffic volume, trajectory traffic volume, and order deviation volume.

5. The method according to claim 4, wherein determining the passing intention information comprises: determining plan achievement information associated with the at least one connection segment based on the in-order communication volume and the planned order volume; Determining yaw information based on the order yaw 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 traffic ratio information comprises: determining the traffic volume of a plurality of connecting segments in the local area within a predetermined time period based on the trajectory information; Determining quantile information of the traffic volume of the target link in the local area based on the multiple traffic volumes of the multiple link; 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; obtaining trajectory information associated with the plurality of roads in the local area; Determining, based on the trajectory information, traffic intention information and traffic ratio information for the plurality of roads, wherein the traffic intention information is determined based on the plan achievement information and the deviation information, and the traffic ratio information is determined by performing a traffic quantile analysis on traffic volumes of a plurality of connecting segments within the at least one local area; 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: Determining, based on the road network data, morphological points of medium and high-level roads that are equal to or higher than a predetermined level; determining tile data of at least two levels corresponding to the morphological point; Determining an 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 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 to 10, wherein the trajectory information comprises at least one of the following: planned order quantity, traffic volume within the order, track traffic volume, and order deviation volume.

12. The electronic device according to claim 11, wherein determining the passing intention information comprises: determining plan achievement information associated with the at least one connection segment based on the in-order communication volume and the planned order volume; Determining yaw information based on the order yaw 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: determining the traffic volume of a plurality of connecting segments in the local area within a predetermined time period based on the trajectory information; Determining quantile information of the traffic volume of the target link in the local area based on the multiple traffic volumes of the multiple link; 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 to 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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