Method, device, and medium for determining vehicle path

Abstract

The present invention relates to the field of intelligent transportation technology, and discloses a method, device, and medium for determining a vehicle path. The method uses real traffic to reflect the real situation of a target road section, and supplements the traffic demand lost due to missed or misdetected vehicles. A traffic residual network is constructed using the real physical connections between road sections, the corresponding road section IDs, and the residual between the real traffic and the current traffic, and the gain is calculated using the constructed network. When the gain is in a first state, a new path is generated by traversing the road section to form an updated path set. The traffic residual network is updated with the updated path set, and the gain is calculated cyclically until the gain is in a second state, that is, no new path is generated, and the vehicle path information of the target road section is determined. The solution of the present invention uses real traffic to reflect the real situation of the target road section, and uses the traffic residual network to supplement the missed or misdetected paths, thereby accurately reflecting the traffic travel demand of the target road section and improving the accuracy of vehicle path determination.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation technology, and in particular to a method, device, and medium for determining a vehicle path. Background Art

[0002] Vehicle routing can recreate the travel demand characteristics of motor vehicles with high confidence, specifically capturing regional traffic flow and travel paths. Acquiring regional traffic flow and travel paths can typically be achieved by deploying intelligent connected devices. Leveraging the rich vehicle trajectories provided by the intelligent connected environment, theoretically complete traffic demand modeling can be achieved.

[0003] In related technologies, vehicle paths are typically determined through image recognition techniques, such as by identifying license plates or vehicle ID information. However, this approach often suffers from variations in algorithms or external environmental factors, such as missed or false detections caused by occlusion of the vehicle or license plate or signal loss, leading to the loss of corresponding vehicle paths. Consequently, the low accuracy of vehicle paths leads to insufficient traffic demand modeled using these paths, failing to reflect actual traffic demand. Therefore, there is a need in related technologies for determining completed vehicle paths. Summary of the Invention

[0004] In view of this, the present invention provides a method, device, and medium for determining a vehicle path to solve the technical problems of missed detection and false detection of vehicle paths in related technologies.

[0005] In a first aspect, the present invention provides a method for determining a vehicle path, the method comprising: determining the initial segment sequence information and the current flow of the current path set based on the initial path set of the target segment; constructing a flow residual network based on the initial path set, the actual physical connection between the segments, the current flow, and the actual turning flow of the target segment within a preset time interval; based on the flow residual network, determining the gain between each segment using the actual turning flow, the current flow, and the initial segment sequence information; and determining the vehicle path information of the completed target segment based on the gain.

[0006] In combination with the first aspect, in a possible implementation of the first aspect, based on the gain, the vehicle path information of the completed target road section is determined, including: in response to the gain being in a first state, forming an updated path set according to the gain, updating the traffic residual network based on the updated path set and the real physical connection between the road sections, and returning the steps of determining the gain between each road section based on it; in response to the gain being in a second state, determining the vehicle path information of the target road section based on the latest updated path set.

[0007] In combination with the first aspect, in a possible implementation of the first aspect, a traffic residual network is constructed based on the real physical connection between the sections of the initial path set, the current traffic, and the real traffic of the target section within a preset time interval, including: determining multiple sections corresponding to the target section based on the initial path set; determining the traffic residual based on the real traffic and the current traffic; determining the physical connection relationship between the sections, the sampling probability corresponding to each section, the ID of each section, and the traffic residual corresponding to the physical connection relationship based on the multiple sections and the traffic residual; constructing the traffic residual network based on the physical connection relationship between the sections, the sampling probability, the ID of each section, and the traffic residual corresponding to the physical connection relationship.

[0008] In combination with the first aspect, in a possible implementation method of the first aspect, based on the traffic residual network, the gain between each road section is determined by utilizing the actual traffic, current traffic, and initial road section sequence information, including: based on the traffic residual, utilizing the physical connection relationship between the road sections corresponding to the initial road section sequence information and the sampling probability corresponding to each road section, to determine the gain between each road section.

[0009] In combination with the first aspect, in a possible implementation of the first aspect, in response to the gain being in a first state, an updated path set is formed according to the gain, a flow residual network is updated based on the updated path set and the real physical connection between the road segments, and the steps of determining the gain between each road segment based on the return thereof include: in response to the existence of a gain exceeding a preset gain threshold, selecting a road segment sequence exceeding the preset gain threshold, and determining the sum of the gains of the corresponding path sequences; determining a gain target based on the gain sum; based on the gain target, using the gains, traversing adjacent road segments in the road segment sequence information to form a new path; adding the new path to the path set to form an updated path set; updating the flow residual network based on the updated path set and the real physical connection between the road segments, and determining the step of determining the gain between each road segment based on the return thereof.

[0010] In combination with the first aspect, in a possible implementation of the first aspect, the method further includes: parsing vehicle trajectory data based on vehicle identification information to determine a matching trajectory; and forming an initial path set based on trajectory points in the matching trajectory.

[0011] In combination with the first aspect, in a possible implementation of the first aspect, vehicle trajectory data is parsed based on vehicle identification information to determine a matching trajectory, including: extracting a trajectory sequence based on the vehicle identification information; mapping the trajectory sequence to a road segment, and filtering the trajectory sequence based on the road segment to determine a matching trajectory.

[0012] In combination with the first aspect, in a possible implementation of the first aspect, an initial path set is formed based on the trajectory points in the matching trajectory, including: calculating the vehicle delays between adjacent trajectory points in time sequence based on the trajectory points; determining multiple corresponding paths based on each vehicle delay; and forming an initial path set based on the multiple paths.

[0013] In a second aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the vehicle path determination method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0014] In a third aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the vehicle path determination method of the first aspect or any corresponding embodiment thereof.

[0015] The technical solution of the present invention has the following advantages:

[0016] The present invention provides a method, device, and medium for determining a vehicle path. This method uses an initial path set for a target road segment to determine initial segment sequence information and current traffic flow. Using the initial path set and the actual physical connections between segments, a flow residual network is constructed. The flow residual network then determines the gain between each segment based on actual traffic flow, current traffic flow, and initial segment sequence information. When the gain is in a first state, an updated path set is formed using the gain. The flow residual network is then reconstructed based on the updated path set until all gains are in a second state, thereby determining the vehicle path information for the target road segment. This process utilizes actual traffic flow to reflect the actual situation of the target road segment. A flow residual network is constructed based on the actual physical connections between segments, corresponding segment IDs, and the residual between actual and current traffic flow. The constructed network is then used to calculate the gain, complete the path, and determine the vehicle path information for the completed target road segment. Therefore, based on the actual situation of the target section, the missed or misdetected paths are supplemented through the traffic residual network to determine the vehicle path information of the target section. The supplemented vehicle path information of the target section can supplement the missed or misdetected vehicle paths, thereby accurately reflecting the traffic travel demand of the target section and improving the accuracy of vehicle path determination. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 is a flow chart of a method for determining a vehicle path according to an embodiment of the present invention;

[0019] Figure 2 The determination of the vehicle path is a schematic diagram of the hardware structure of the computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0021] According to an embodiment of the present invention, an embodiment of a method for determining a vehicle path is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0022] This embodiment provides a method for determining a vehicle path, such as Figure 1 As shown, the method includes the following steps:

[0023] S101 : Based on an initial path set of a target road segment, determine initial road segment sequence information and current traffic volume of a current path set.

[0024] Specifically, the initial path set of the target road section refers to a preset initial path set; or, the initial path set is formed by parsing the vehicle trajectory data through the vehicle identification information and using the trajectory points in the vehicle trajectory data. The path set includes multiple path sequences of the target road section, the frequency of the corresponding path sequence, and the distribution of vehicle models in the corresponding path sequence. Each road section sequence is recorded as R , R ={ R 1, R 2,…, R p}, the frequency of the corresponding path sequence is recorded asC , C ={ C 1, C 2,…, C p},in, R p Indicates the p A sequence of paths, C p Indicates the p The frequency of the path sequence, the distribution of the corresponding path sequence is recorded as T , T ={ T 1, T 2,…, T p}, T p Indicates the p The distribution of vehicles in the path sequence, for example, {car:0.7,bus:0.3}. It should be understood that R There are no duplicate items in . If there are identical road segment sequences, the corresponding frequency increases, that is, the frequency represents the number of times the corresponding road segment sequence appears.

[0025] Specifically, determining the initial road segment sequence information based on the initial path set refers to determining the path sequences contained in the initial path set, the frequencies of the corresponding path sequences, and the distribution of vehicle types in the corresponding path sequences.

[0026] Specifically, based on the initial path set of the target road segment, determining the current flow of the current path set means determining the driving trajectory of each road segment in the road segment sequence that the vehicle passes through within the target road segment and the traffic flow of each road segment through the initial path set, thereby forming the current flow. The current flow is recorded as f now For example, the road segment sequences that the vehicles pass through during the preset time interval are { l 2, l 3, l 5}、{ l 1, l 2, l 3}, where l 1. l 2. l 3. l 5 represents section 1, section 2, section 3, and section 5 respectively, so the current flow is l 1, l 2:1, l 2, l 3:2, l 3, l5:1, which means that there is one vehicle traveling from section 1 to section 2, two vehicles traveling from section 2 to section 3, and one vehicle traveling from section 3 to section 5.

[0027] It should be understood that the current path set includes an initial path set and an updated path set. The difference between the initial path set and the updated path set is that the updated path set is formed by adding the updated paths to the initial path set after the paths are updated. For example, if the initial path set is q, and the current path set is q, the calculated gain is in the first state, and the updated path determined based on the gain is q1, then the updated path set at this time is: q + q1, and the current path set is now q + q1. If q + q1 is used as the updated path set, the calculated gain is still in the first state, and the updated path determined based on the gain is q2, then the updated path set at this time is: q + q1 + q2, and so on, and no further explanation is given.

[0028] S102: Construct a traffic residual network based on the initial path set, the actual physical connections between the road sections, the current traffic, and the actual turning traffic of the target road section within a preset time interval.

[0029] Specifically, the real physical connection between road sections refers to the connected areas between road sections connected in the target road section. Taking the road section sequence in the above embodiment as an example, the real physical connection between road sections exists between road section one and road section two, road section two and road section three, and road section three and road section five.

[0030] Specifically, based on the initial path set, the real physical connection between road sections, the current traffic, and the real traffic of the target road section within a preset time interval, constructing a traffic residual network means determining multiple road sections corresponding to the target road section through the initial path set, and using multiple road sections to determine the physical connection relationship between the road sections, the sampling probability corresponding to each road section, and the ID, so as to use the physical connection relationship between the road sections as the edge and the ID corresponding to each road section to construct a traffic residual network, wherein each edge has three corresponding characteristics, namely, the current traffic, the real traffic turned to and the traffic residual, and the traffic residual is the difference between the real traffic turned to and the current traffic.

[0031] S103 : Based on the flow residual network, the gain between each road section is determined by using the actual turning flow, the current flow, and the initial road section sequence information.

[0032] Specifically, based on the traffic residual network, the gain between each section is determined by using the real traffic, current traffic, and initial section sequence information. This means determining the traffic residual by using the real traffic and current traffic, and then determining the gain between each section through the traffic residual using the physical connection relationship corresponding to the initial section sequence information and each sampling probability.

[0033] Specifically, the determination of the actual turning flow of the target road section within the preset time interval is based on the intelligent network equipment, and the actual turning flow of the target road section within the preset time interval is obtained. The preset time interval refers to the unit time interval, which can be set according to the actual working conditions, usually 5 to 15 minutes. Intelligent network equipment refers to equipment such as cameras, lidars, signal collectors, etc. set on the side of the road, and the corresponding equipment can obtain information including vehicle ID, vehicle model, departure location, departure time, arrival location, vehicle coordinates, road section sequence, etc. The actual turning flow is recorded as f real .

[0034] Specifically, based on intelligent network devices, obtaining the actual turning traffic volume of the target road section within a preset time interval means using the departure position, arrival position, vehicle coordinates, and road section sequence obtained by the roadside collection device to determine the driving trajectory of each road section in the road section sequence passed by the vehicle within the target road section and the traffic volume of each road section, thereby forming the actual turning traffic volume. For example, the road section sequence passed by the vehicle on the target road section within the preset time interval is { l 1, l 2, l 3}、{ l 1, l 2, l 4}, where l 1. l 2. l 3. l 4 represents road sections 1 to 4 respectively, so the actual turning flow is l 1, l 2:2, l 2, l 3:1, l 3, l 4:1, which means that there are two vehicles traveling from section 1 to section 2, one vehicle traveling from section 2 to section 3, and one vehicle traveling from section 3 to section 4.

[0035] S104: Determine vehicle path information of the target road section based on the gain.

[0036] Specifically, determining the vehicle path information for the target road segment based on the gain involves completing the vehicle path according to the gain's state until no new paths are generated, at which point the complete path information at the current moment serves as the vehicle path information for the target road segment. The completed vehicle path corresponds to the gain being in a first state. When the gain is in the first state, new paths are generated by traversing the road segment to form an updated path set. The traffic residual network is then updated with the updated path set, and the gain is repeatedly calculated until the gain is in a second state, i.e., no new paths are generated, at which point the complete path information at the current moment serves as the vehicle path information for the target road segment.

[0037] The present invention provides a method, device, and medium for determining a vehicle path. This method uses an initial path set for a target road segment to determine initial segment sequence information and current traffic flow. Using the initial path set and the actual physical connections between segments, a flow residual network is constructed. The flow residual network then determines the gain between each segment based on actual traffic flow, current traffic flow, and initial segment sequence information. When the gain is in a first state, an updated path set is formed using the gain. The flow residual network is then reconstructed based on the updated path set until all gains are in a second state, thereby determining the vehicle path information for the target road segment. This process utilizes actual traffic flow to reflect the actual situation of the target road segment. A flow residual network is constructed based on the actual physical connections between segments, corresponding segment IDs, and the residual between actual and current traffic flow. The constructed network is then used to calculate the gain, complete the path, and determine the vehicle path information for the completed target road segment. Therefore, based on the actual situation of the target section, the missed or misdetected paths are supplemented through the traffic residual network to determine the vehicle path information of the target section. The supplemented vehicle path information of the target section can supplement the missed or misdetected vehicle paths, thereby accurately reflecting the traffic travel demand of the target section and improving the accuracy of vehicle path determination.

[0038] In an optional implementation, determining the vehicle path information of the completed target road segment based on the gain includes:

[0039] In response to the gain being in a first state, an updated path set is formed according to the gain, a flow residual network is updated based on the updated path set and the real physical connection between the road segments, and the gain between each road segment is determined based on the return thereof; in response to the gain being in a second state, vehicle path information of the target road segment is determined based on the latest updated path set.

[0040] Specifically, the gain being in the first state means that the gain exceeds a preset gain threshold.

[0041] Specifically, forming an updated path set based on gain means selecting a sequence of road sections that exceeds a preset gain threshold, determining the sum of the gains of the corresponding path sequences, determining a gain target based on the sum of the gains, and constructing new paths based on the gain target and the gains, thereby adding the new paths to the original path set to form an updated path set.

[0042] Specifically, the step of updating the flow residual network based on the updated path set and the real physical connection between the road segments, and returning and determining the gain between each road segment based on it refers to bringing the updated path set into the flow residual network again, that is, updating the flow residual network with the updated path set, and determining the gain between each road segment, thereby repeating this cycle until the gains are all in the second state.

[0043] Specifically, the gain being in the second state means that the gains are all below a preset gain threshold. Determining the vehicle path information for the target segment based on the most recently updated path set means using the paths in the updated path set when the gain is in the second state as the vehicle path information for the target segment, and outputting the information in the format of <vehicle ID, vehicle model, departure location, departure time, arrival location, arrival time, segment sequence>.

[0044] In an optional embodiment, a flow residual network is constructed based on the initial path set, the actual physical connections between the road segments, the current flow rate, and the actual flow rate of the target road segment within a preset time interval, including:

[0045] Based on the initial path set, multiple road sections corresponding to the target road section are determined; based on the actual steering flow and the current flow, the flow residual is determined; based on the multiple road sections and the flow residual, the physical connection relationship between the road sections, the sampling probability corresponding to each road section, the ID of each road section, and the flow residual corresponding to the physical connection relationship are determined; based on the physical connection relationship between the road sections, the sampling probability, the ID of each road section, and the flow residual corresponding to the physical connection relationship, a flow residual network is constructed.

[0046] Specifically, determining multiple road segments corresponding to the target road segment based on the initial path set refers to matching each road segment sequence in the initial path set with the target road segment to determine multiple road segments corresponding to the target road segment.

[0047] Specifically, based on the actual flow and the current flow, determining the flow residual means using the difference between the actual flow and the current flow to determine the flow residual, which is recorded as f real - f now .

[0048] Specifically, based on multiple road segments and traffic residuals, determining the physical connection relationship between road segments, the sampling probability corresponding to each road segment, the ID of each road segment, and the traffic residual corresponding to the physical connection relationship means taking the connected areas between road segments connected in the target road segment as edges to perform the first-level traversal. For each edge, further traverse all road segment sequences passing through the corresponding edge in multiple road segments to form a road segment sequence set, and calculate the sampling probability of each road segment sequence in the road segment sequence set and the ID of each road segment. Among them, each edge is recorded as E , E ∈( l m , l n ), m 、 n are positive integers, l m 、 l n Respectively represent m Section 1 n The road segment sequence set is recorded as R `, R `={ R `1, R `2… R ` s}, R ` s Indicates the first s The path sequence is sampled with probability R prob . And because each edge in the flow residual network has three corresponding characteristics, namely the current flow, the actual flow turned to, and the flow residual, after determining the physical connection relationship between the road sections, the flow residual corresponding to the physical connection relationship can be determined. It should be understood that the multiple road sections determined by the initial path set here are based on the initial road section sequence information, and the multiple road sections corresponding to the target road section are determined, thereby performing a two-layer traversal based on the initial road section sequence information, so that the traversal process is based on the initial road section sequence information, reducing the complexity of data traversal, thereby improving the calculation rate. And in determining the corresponding E and R prob Afterwards, it is necessary to count the distribution of vehicle models again to provide a data basis for the subsequent construction of new routes.

[0049] Specifically, the sampling probability is calculated by the proportion of the frequency of the corresponding road segment in the corresponding road segment sequence, for example, { l 1, l 2, l The frequency of {3} is 100 vehicles, l 2, l 3,l The frequency of {4} is 200 vehicles, l 3, l 4} has a frequency of 250 vehicles. If the first layer traverses to the second segment, then all the segment sequences that pass through the second segment are determined. l 1, l 2, l 3}、{ l 2, l 3, l 4}. And then { l 1, l 2, l 3} and { l 2, l 3, l The sampling probabilities of 4} are calculated as: 100 / (100+200) and 200 / (100+200).

[0050] Specifically, based on the physical connection relationship between road segments, sampling probability, and the ID flow residual of each road segment, the flow residual network is constructed by taking the connected areas between the road segments connected in the target road segment as edges and using the road segment ID corresponding to each road segment to construct the flow residual network. The constructed flow residual network is recorded as G res ( L , E ), where L represents the road segment ID corresponding to each road segment, and each edge E has a corresponding flow residual.

[0051] In an optional embodiment, based on the flow residual network, using the actual flow, current flow, and initial segment sequence information, the gain between each segment is determined, including:

[0052] Based on the traffic residual, the gain between each road segment is determined by utilizing the physical connection relationship between the road segments corresponding to the initial road segment sequence information and the sampling probability corresponding to each road segment.

[0053] Specifically, the gain between each road segment is calculated using formula (1):

[0054] (1)

[0055] in, Represents the sum of the gains of all road segment sequences in the path S, Rs represents the road segment sequence, which is used to describe all road segments passed by a vehicle path. Represents the current flow residual, represents the original flow residual, Indicates the s The sampling probability of a path sequence.

[0056] Specifically, the original flow residual For update R ` s The previous flow residual and the current flow residual For update R ` s The residual flow after R ` s Update refers to the process of generating new paths based on the gain to form an updated path set. Therefore, the original flow residual Indicates the flow residual before updating the flow residual network with the updated path set in this cycle, the current flow residual Represents the flow residual after updating the flow residual network with the updated path set in this cycle.

[0057] Specifically, in response to the gain being below a preset gain threshold, determining the vehicle path information for the target segment means saving each path in the form of <vehicle ID, vehicle type, departure location, departure time, arrival location, arrival time, segment sequence> when the gain between each segment in the traffic residual network constructed by updating the path set is below the preset gain threshold as the vehicle path information for the target segment. The preset gain threshold can be set based on actual operating conditions and is not specifically limited in this embodiment. Typically, zero is selected as the preset gain threshold.

[0058] In an optional embodiment, in response to the gain being in the first state, forming an updated path set according to the gain, updating the flow residual network based on the updated path set and the actual physical connections between the road segments, and returning the steps of determining the gain between each road segment based on the updated path set, includes:

[0059] In response to the presence of a gain exceeding a preset gain threshold, a sequence of road segments exceeding the preset gain threshold is selected, and a sum of gains of the corresponding path sequences is determined; a gain target is determined based on the sum of gains; based on the gain target, adjacent road segments in the road segment sequence information are traversed using the gains to construct a new path; the new path is added to a path set to form an updated path set; based on the updated path set and the actual physical connections between the road segments, a flow residual network is updated, and based on the updated path set, a step of returning to determine the gain between each road segment is performed.

[0060] Specifically, in response to the existence of a gain exceeding a preset gain threshold, a sequence of road sections exceeding the preset gain threshold is selected, and the sum of the gains of the corresponding path sequences is determined. Determining the gain target means that in the traffic residual network updated by updating the path set, when there is a gain between road sections exceeding the preset gain threshold, a sequence of road sections exceeding the preset gain threshold is selected, the sum of the gains of the corresponding road sections is calculated, and the gain target is determined.

[0061] Specifically, selecting the road segment sequence exceeding the preset gain threshold includes selecting the road segment sequence with the largest corresponding gain or selecting a road segment sequence in a weighted sampling manner according to a value corresponding to the gain.

[0062] Specifically, determining a gain target based on the summed gain means using the gain corresponding to the selected segment sequence as the gain target. It should be understood that in this embodiment, since the number of segment sequences selected that exceed the preset gain threshold constitutes one segment sequence, the gain corresponding to the selected segment sequence is numerically the same as the summed gain in this embodiment. The number of segment sequences selected that exceed the preset gain threshold can be selected based on actual operating conditions and will not be further elaborated in this embodiment.

[0063] Specifically, based on the gain target, using the gain, traversing adjacent road segments in the road segment sequence information to form a new path means generating a random number in the range of [0, gain target), traversing the gain between each road segment according to the weight ratio, accumulating the weight until the accumulated weight exceeds or equals the generated random number, selecting the road segment corresponding to the currently traversed gain as the new road segment sequence, and randomly generating a vehicle ID in combination with the vehicle type distribution counted again in the above embodiment, thereby forming a new path.

[0064] Specifically, adding a new path to the path set to form an updated path set means that if the original path set is q, in response to the current path set being q, the calculated gain is in the first state, and the updated path determined according to the gain is q1, then the updated path set at this time is: q+q1, and the current path set at this time is q+q1. If q+q1 is used as the updated path set, the calculated gain is still in the first state, and the updated path determined according to the gain is q2, then the updated path set at this time is: q+q1+q2, and so on, which will not be repeated.

[0065] In an optional embodiment, the method further includes:

[0066] Based on the vehicle identification information, the vehicle trajectory data is parsed to determine the matching trajectory; based on the trajectory points in the matching trajectory, an initial path set is formed.

[0067] Specifically, parsing the vehicle trajectory data based on the vehicle identification information and determining the matching trajectory refers to extracting a trajectory sequence based on the vehicle identification information, screening the trajectory sequence, and determining the matching trajectory.

[0068] Specifically, forming an initial path set based on the trajectory points in the matching trajectory means calculating the vehicle delays between adjacent trajectory points in the calculation time sequence through the trajectory points, dividing the matching trajectory into trip chains based on the vehicle delays, and determining multiple paths, thereby forming an initial path set.

[0069] In an optional embodiment, based on the vehicle identification information, parsing the vehicle trajectory data and determining the matching trajectory includes:

[0070] Based on the vehicle identification information, the trajectory sequence is extracted; the trajectory sequence is mapped to the road segment, and the trajectory sequence is filtered based on the road segment to determine the matching trajectory.

[0071] Specifically, based on vehicle identification information, extracting trajectory sequences means using the vehicle license plate as identification information to extract the trajectory sequences corresponding to each vehicle. Each trajectory sequence corresponding to a vehicle contains multiple trajectory points, and the set of trajectory points is recorded as S , S ={ s 1, s 2, s 3, s 4,…, sn},in, sn Indicates the n Track points.

[0072] Specifically, mapping the trajectory sequence to the road segment and filtering the trajectory sequence based on the road segment to determine the matching trajectory means mapping the trajectory points to each road segment of the target road segment, removing duplicates by road segment, and only retaining the first trajectory point of the trajectory on the road segment to obtain a new matching trajectory. Snet , Snet The expression can be: s 1- edge 1, s 2- edge 1, s 3- edge 2, s 4- edge 2,…, sn - edgen}, the matching trajectory is recorded as S`net , Snet The expression can be: s 1- edge 1, s 3- edge 2,… sn - edgem}.

[0073] In an optional embodiment, forming an initial path set based on the track points in the matching track includes:

[0074] Based on the trajectory points, the vehicle delays between adjacent trajectory points in time are calculated; based on each vehicle delay, multiple corresponding paths are determined; based on the multiple paths, an initial path set is formed.

[0075] Specifically, the vehicle delay between adjacent trajectory points in time sequence is calculated using formula (2):

[0076] (2)

[0077] Among them, timeloss represents the vehicle delay between adjacent trajectory points in time sequence. l s1,s2 represents the shortest travel distance between adjacent trajectory points, v represents the average velocity between adjacent trajectory points, t 1. t 2 respectively represent the time corresponding to the two trajectory points.

[0078] Specifically, based on each vehicle delay, determining multiple corresponding paths means that if the vehicle delay between adjacent trajectory points in time exceeds a vehicle delay threshold, the corresponding matching trajectory is divided into a trip chain to form multiple corresponding paths.

[0079] Specifically, as shown in Table 1, Table 1 exemplarily shows a plurality of corresponding paths formed after the corresponding matches are divided into itinerary chains.

[0080] Table 1

[0081]

[0082] Among them, Edge1, Edge2, and Edge5 respectively represent the sections in the target section.

[0083] Specifically, forming an initial path set based on multiple paths means saving each path according to <vehicle id, vehicle model, departure location, departure time, arrival location, arrival time, road section sequence> to form an initial path set.

[0084] The present invention also provides a computer device. Figure 2 , Figure 2 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 2As shown, the computer device includes: one or more processors 201, memory 202, and the interface for connecting each component, including a high-speed interface and a low-speed interface. Each component utilizes different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instruction executed in the computer device, including the instruction stored in the memory or on the memory to display the graphic information of the GUI on an external input / output device (such as, being coupled to the display device of the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer devices can be connected, and each device provides the necessary operation of part (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 2 A processor 201 is taken as an example.

[0085] Processor 201 may be a central processing unit, a network processor, or a combination thereof. Processor 201 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0086] The memory 202 stores instructions that can be executed by at least one processor 201, so that the at least one processor 201 executes the method shown in the above embodiment.

[0087] The memory 202 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 202 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 202 may optionally include a memory remotely located relative to the processor 201, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0088] The memory 202 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, a hard disk, or a solid-state drive; the memory 202 may also include a combination of the aforementioned types of memory. The computer device also includes a communication interface 203 for communicating with other devices or a communication network.

[0089] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0090] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for determining a vehicle path, characterized in that: The method comprises: Based on the initial path set of the target road segment, determining the initial road segment sequence information and the current flow of the current path set; Determining multiple road segments corresponding to the target road segment based on the initial path set, and constructing a flow residual network based on flow residuals, actual physical connections between the road segments, and the multiple road segments; wherein the flow residuals are determined based on the actual turn flow of the target road segment within a preset time interval and the current flow; Determining, based on the flow residual network, a gain between each of the road segments according to the flow residual, the physical connection relationship between the road segments corresponding to the initial road segment sequence information, and the sampling probability corresponding to each of the road segments; The vehicle path is completed based on the state of the gain until no new path is generated, and the vehicle path information of the completed target road section is determined based on all path information at the current moment.

2. The method according to claim 1, characterized in that The vehicle path information of the target road section after completion is determined, including: In response to the gain being in a first state, forming an updated path set according to the gain, updating a flow residual network based on the updated path set and the real physical connections between the road segments, and returning to the step of determining the gain between each of the road segments based on the updated path set; In response to the gain being in the second state, vehicle path information of the completed target road segment is determined based on the latest updated path set.

3. The method according to claim 1, characterized in that The constructing of the traffic residual network includes: Based on the plurality of road segments and the flow residuals, determining a physical connection relationship between the road segments, a sampling probability corresponding to each of the road segments, an ID of each of the road segments, and a flow residual corresponding to the physical connection relationship; A flow residual network is constructed based on the physical connection relationship between the road sections, the sampling probability, the ID of each road section, and the flow residual corresponding to the physical connection relationship.

4. The method according to claim 2, characterized in that In response to the gain being in the first state, forming an updated path set according to the gain, updating the flow residual network based on the updated path set and the real physical connections between the road segments, and returning the step of determining the gain between each of the road segments based on the updated path set, comprises: In response to the existence of the gain exceeding a preset gain threshold, selecting a road segment sequence exceeding the preset gain threshold, and determining a sum of gains of the corresponding path sequence; determining a gain target based on the gain sum; Based on the gain target, using the gain, traversing adjacent road segments in the road segment sequence information to form a new path; Adding the newly added path to the path set to form an updated path set; Based on the updated path set and the actual physical connections between the road segments, the flow residual network is updated, and based on it, the step of determining the gain between each of the road segments is returned.

5. The method according to claim 1, wherein The method further comprises: Based on the vehicle identification information, the vehicle trajectory data is parsed to determine the matching trajectory; An initial path set is formed based on the trajectory points in the matching trajectory.

6. The method according to claim 5, characterized in that The method of parsing the vehicle trajectory data based on the vehicle identification information and determining the matching trajectory includes: Extract trajectory sequence based on vehicle identification information; The trajectory sequence is mapped to a road segment, and the trajectory sequence is filtered based on the road segment to determine a matching trajectory.

7. The method according to claim 5, characterized in that The forming of an initial path set based on the trajectory points in the matching trajectory includes: Based on the trajectory points, the vehicle delay between adjacent trajectory points in time sequence is calculated; determining a plurality of corresponding routes based on each of the vehicle delays; Based on the plurality of paths, an initial path set is formed.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the vehicle path determination method according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method for determining a vehicle path according to any one of claims 1 to 6.

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