A method for evaluating truck traffic intensity

Abstract

The present application relates to the technical field of traffic big data, and discloses a truck traffic intensity evaluation method, wherein the method comprises: obtaining traffic data corresponding to each vehicle in a target road network in a preset time period; based on license plate numbers and a public security traffic management motor vehicle database, target traffic data corresponding to a truck is selected from the traffic data; a traffic intensity comprehensive index of the truck in the target road network is determined based on a plurality of traffic intensity indexes calculated from the target traffic data; and the truck traffic intensity of the target road network is evaluated based on the traffic intensity comprehensive index and a preset interval. The present application provides basic data with strong continuity, wide coverage and high authenticity, and the basic data is analyzed in multiple dimensions, so that the truck traffic intensity obtained by the present embodiment is more accurate and has more reference value.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of traffic big data, and particularly relates to a truck traffic intensity evaluation method. BACKGROUND

[0002] Urban freight is an important part of the urban logistics system, and is the basic guarantee to meet the needs of residents' production and life, and is also the "bottom line of people's livelihood project" to ensure the normal operation of the core functions of the city and the normal circulation of the supply chain / industry chain. In recent years, urban truck traffic management has become the focus and difficulty of public security traffic management business, and actively provides decision-making reference for the formulation of urban truck traffic management and restriction policies, which meets the overall requirements of relieving congestion and ensuring smoothness, reducing and controlling the total amount, and has great significance for improving the level of urban logistics industry, improving the efficiency of freight traffic organization, improving urban traffic, and enhancing the competitiveness of the city.

[0003] The traditional vehicle traffic intensity analysis method adopts the map data and manual investigation and statistics method, but such a method not only needs to invest a large amount of manpower and material resources, but also has the problems of low sampling rate, great influence of weather on investigation day, inability to obtain continuous time data, inconsistent data dimensions, different ranges and the like, so that the final evaluation result has poor accuracy and weak reference significance. SUMMARY

[0004] Therefore, the present application provides a truck traffic intensity evaluation method to solve the problem of poor accuracy of the evaluation result in the related art.

[0005] In a first aspect, the present application provides a truck traffic intensity evaluation method, which comprises the following steps:

[0006] obtaining traffic data corresponding to each traffic vehicle in a target road network in a preset time period, the traffic data comprising a license plate number of the traffic vehicle, a card location of a traffic card passed by the traffic vehicle, and a traffic time corresponding to the traffic vehicle passing through each traffic card; based on the license plate number and a public security traffic management motor vehicle database, target traffic data corresponding to the traffic truck is selected from the traffic data; a traffic intensity comprehensive index of the truck in the target road network is determined based on a plurality of traffic intensity indexes calculated based on the target traffic data; and the traffic intensity of the truck in the target road network is evaluated based on the traffic intensity comprehensive index and a preset interval.

[0007] The evaluation method of truck passing strength provided in the embodiment combines the data collected by each traffic portal with the location information in the map data and the vehicle basic information in the public security traffic management database to obtain real truck data that is more continuous in the time and space dimensions and more comprehensive. The real truck data is analyzed and calculated from multiple dimensions to obtain multiple dimension corresponding passing strength indexes, so that the passing strength comprehensive index is obtained based on the multiple passing strength indexes, and the truck passing strength of the target road network in the preset time period is determined. Since the scheme provided in the embodiment has strong continuity, wide coverage, and high authenticity of the basic data, and the basic data is analyzed in multiple dimensions, the accuracy of the truck passing strength obtained by the embodiment is higher and has more reference significance.

[0008] In an optional implementation, the passing strength comprehensive index of the truck in the target road network is determined based on the multiple passing strength indexes calculated based on the target passing data, including:

[0009] The driving mileage and driving frequency of the passing truck are determined based on the target passing data; the passing strength index is determined based on the driving mileage, the driving frequency, the portal location, and the passing time; the passing strength index includes the truck flow strength, the truck trip mileage strength, the truck trip frequency strength, the truck on-road strength, the high-strength trip truck proportion, and the high-frequency trip truck proportion; and the passing strength comprehensive index is determined based on the passing strength index.

[0010] The embodiment integrates and calculates the original passing data to obtain multiple passing strength indexes corresponding to different dimensions respectively, and obtains the passing strength comprehensive index based on the multiple passing strength indexes corresponding to different dimensions respectively. In the above manner, the passing strength comprehensive index covers more dimensions and has stronger representativeness.

[0011] In an optional implementation, the determination manner of the truck flow strength includes:

[0012] The truck passing volume corresponding to each traffic portal is obtained based on the portal location of the traffic portal; the number of traffic portals in the target road network and the preset passing volume of each traffic portal are obtained; and the truck flow strength is determined based on the truck passing volume corresponding to each traffic portal in the target road network, the number of traffic portals, and the preset passing volume of each traffic portal.

[0013] In an optional implementation, the determination manner of the truck trip mileage strength includes:

[0014] The driving mileage, the number of preset unit time lengths in the truck passing time length, and the number of passing trucks in the target road network are obtained; and the truck trip mileage strength is determined based on the driving mileage, the number of passing trucks in the target road network, and the number of preset unit time lengths in the truck passing time length.

[0015] In an optional implementation, the determination of the truck travel frequency intensity includes:

[0016] The travel frequency is obtained, and the truck travel frequency intensity is determined based on the travel frequency, the number of trucks passing through the target road network, and the number of preset unit time periods within the truck travel duration.

[0017] In an optional implementation, the determination of the truck travel frequency intensity includes:

[0018] The truck traffic volume in the target road network within a preset time period is obtained, the truck traffic volume in the target road network at each time within the preset time period is obtained, and the truck travel intensity is determined based on the truck traffic volume in the target road network at each time, the preset truck population of the target road network, the truck traffic volume in the target road network within the preset time period, and the preset motor vehicle population of the target road network.

[0019] In an optional implementation, the determination of the high-intensity travel truck proportion includes:

[0020] The number of trucks with a travel duration exceeding a preset duration threshold within each preset unit time period within a preset time period is determined based on the travel time, and the high-intensity travel truck proportion is determined based on the number of trucks exceeding the preset duration threshold in each preset unit time period and the preset truck population of the target road network.

[0021] The heat map generated in this embodiment can reflect the travel intensity of each place in the target road network, thereby providing travel reference and guidance for users, and reducing the occurrence of traffic congestion and traffic accidents.

[0022] In an optional implementation, the method further includes:

[0023] The positions of the traffic checkpoints through which the passing trucks pass are connected in the order of the travel time of the passing trucks when passing through the traffic checkpoints, to form the travel track corresponding to the passing trucks, and a heat map in the target road network is generated based on the travel tracks of all the passing trucks in the target road network within a preset time period.

[0024] In an optional implementation, the determination of the travel intensity comprehensive index based on the travel intensity indicators includes:

[0025] The average value of the travel intensity indicators is determined based on the travel intensity indicators, and the average value is determined as the travel intensity comprehensive index.

[0026] In an optional implementation, the method further includes:

[0027] A heat map in a preset range is generated based on the travel intensity comprehensive index corresponding to each road network in the preset range.

[0028] The intensity contrast between each road network in the preset range can be observed through the generated heat map, and the user can be globally guided and advised relative to the heat map in the target road network. BRIEF DESCRIPTION OF DRAWINGS

[0029] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the specific embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0030] Figure 1 is a flowchart of a truck passing intensity evaluation method according to an embodiment of the present application;

[0031] Figure 2 is a flowchart of another truck passing intensity evaluation method according to an embodiment of the present application;

[0032] Figure 3 is a flowchart of still another truck passing intensity evaluation method according to an embodiment of the present application;

[0033] Figure 4 is a structural block diagram of a truck passing intensity evaluation device according to an embodiment of the present application;

[0034] Figure 5 is a hardware structure schematic diagram of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0036] Before the present application is described in detail, a brief description of the current way of evaluating truck traffic intensity is given. In the past, the evaluation method of truck intensity usually adopts the way of map data and manual investigation and statistics, for example, intercepting a video monitoring image of a certain toll gate in a certain period of time, manually counting the number of truck passing vehicles by artificial method, or defining a certain range through navigation map, and calculating the reference number of trucks driving in a certain range through data center, but such a way not only needs to consume a large amount of manual cost, but also the collected data is discontinuous, not comprehensive, and the evaluation dimension is single, so that the accuracy of the final evaluation result is poor, and thus the evaluation result cannot provide decision reference for the formulation of urban truck traffic management and restriction policy. Therefore, the present application provides a truck traffic intensity evaluation method, which obtains comprehensive and continuous basic traffic data by combining map data and data in the public security traffic management database, and then processes the basic traffic data from multiple dimensions to achieve a high accuracy effect.

[0037] According to the embodiments of the present application, a truck traffic intensity evaluation method is provided. It should be noted that the steps shown in the flowchart can be executed in a computer system such as a set of computer executable instructions, and although the 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.

[0038] In the present embodiment, a truck traffic intensity evaluation method is provided, which can be used in a computer device, Figure 1 The flowchart of the truck traffic intensity evaluation method according to the embodiments of the present application is shown in FIG. 1, which includes the following steps: Figure 1 As shown in the figure, the flowchart includes the following steps:

[0039] In step S101, the traffic data corresponding to each passing vehicle in the target road network in a preset time period is obtained.

[0040] Specifically, the traffic data includes the license plate number of the passing vehicle, the toll gate position of the passing vehicle passing through the traffic toll gate, and the passing time corresponding to the passing vehicle passing through each traffic toll gate.

[0041] Specifically, the length of the preset time period is not limited here and can be selected based on actual needs. For example, the preset time period can be nearly one year or nearly three months, etc.

[0042] Exemplarily, first, the respective basic traffic data corresponding to each traffic checkpoint in the target road network in a preset time period is collected. Each traffic checkpoint corresponds to a plurality of basic traffic data, and each basic traffic data includes the checkpoint identifier of the traffic checkpoint, the passing time corresponding to a vehicle passing through the traffic checkpoint, and the license plate number of the vehicle. Second, based on the checkpoint identifier, the checkpoint position of the traffic checkpoint to which the checkpoint identifier belongs is found from the map data, and the checkpoint position is added to the basic traffic data corresponding to the checkpoint identifier. Finally, taking the license plate number as an index, the checkpoint positions of the traffic checkpoints passed through by the vehicle to which the license plate number belongs are obtained from all the basic traffic data after the checkpoint positions are added, and the passing time of the vehicle to which the license plate number belongs when passing through each traffic checkpoint is obtained. Based on the license plate number of each vehicle, the checkpoint positions of all traffic checkpoints passed through by each vehicle in the preset time period, and the passing time corresponding to each vehicle passing through each traffic checkpoint, the passing data is formed.

[0043] For example, the target road network includes three traffic checkpoints A1, A2, and A3, wherein A1, A2, and A3 are the respective checkpoint identifiers of the traffic checkpoints. The respective basic traffic data corresponding to each traffic checkpoint is collected. After obtaining the basic traffic data of each traffic checkpoint, the checkpoint position of the traffic checkpoint to which the checkpoint identifier belongs is found from the map data according to the checkpoint identifier, such as the checkpoint position corresponding to A1 is (x1, y1), the checkpoint position corresponding to A2 is (x2, y2), and the checkpoint position corresponding to A3 is (x3, y3). After adding the checkpoint position to the basic traffic data corresponding to the checkpoint identifier, the basic traffic data shown in Table 1 is obtained.

[0044] Table 1

[0045]

[0046] Taking the license plate number as an index, the passing data corresponding to the vehicle to which the license plate number belongs is extracted from the above basic traffic data. The passing data is shown in Table 2.

[0047] Table 2

[0048]

[0049] In step S102, based on the license plate number and the public security traffic management motor vehicle database, the target passing data corresponding to the passing truck is selected from the passing data.

[0050] Exemplarily, the passing data obtained in step S101 is the passing data corresponding to all vehicles passing through the target road network in a preset time period, and these vehicles include trucks, buses, and private cars. Therefore, it is necessary to select the target passing data corresponding to the passing truck from all the passing data. The selection process of the target passing data is as follows:

[0051] First, the vehicle information of the vehicle to which the license plate number belongs is found from the public security traffic management motor vehicle database with the license plate number as the index. The vehicle information includes the license plate number, the vehicle owner, the vehicle type, the vehicle color, the registration time, etc.

[0052] Then, the target license plate number corresponding to the truck is extracted from all the vehicle information.

[0053] Finally, the target traffic data corresponding to the vehicle to which the target license plate number belongs is filtered from the traffic data with the target license plate number as the index.

[0054] Step S103, determining the traffic intensity comprehensive index of the truck in the target road network based on the multiple traffic intensity indexes calculated from the target traffic data.

[0055] Specifically, the target traffic data is analyzed and calculated in multiple dimensions to obtain multiple traffic intensity indexes corresponding to the respective dimensions, and the traffic intensity comprehensive index of the truck in the target road network is determined based on the multiple traffic intensity indexes.

[0056] Step S104, evaluating the truck traffic intensity of the target road network based on the traffic intensity comprehensive index and the preset interval.

[0057] Specifically, the preset interval is obtained based on historical actual data, and the preset intervals corresponding to the respective road networks are not completely the same.

[0058] Specifically, the traffic intensity comprehensive index and the preset interval are matched to determine the target preset interval to which the traffic intensity comprehensive index belongs, and the traffic intensity corresponding to the target preset interval is taken as the truck traffic intensity of the target road network.

[0059] For example, the preset interval has three intervals, (0, 0.3], (0.3, 0.7], and (0.7, 1.0], and the truck traffic intensities corresponding to the three intervals are low, medium, and high, respectively, wherein low indicates smooth operation, medium indicates basically smooth operation, and high indicates slow operation and congestion. For example, the traffic intensity comprehensive index 0.5 calculated is matched with the three intervals, and it is determined that 0.5 belongs to the second interval, so the truck traffic intensity "medium" corresponding to the second interval is taken as the truck traffic intensity of the target road network.

[0060] The truck passing strength evaluation method provided in the embodiment can obtain more continuous and more comprehensive real truck data in the time and space dimensions by combining the data collected by each traffic portal with the location information in the map data and the vehicle basic information in the public security traffic management database. The real truck data is analyzed and calculated from multiple dimensions to obtain multiple dimension corresponding passing strength indexes, so that the passing strength comprehensive index is obtained based on the multiple passing strength indexes, and the truck passing strength of the target road network in the preset time period is determined. Since the scheme provided in the embodiment has strong continuity, wide coverage, and high authenticity of the basic data, and the basic data is analyzed from multiple dimensions, the accuracy of the truck passing strength obtained by the embodiment is higher, and the truck passing strength is more meaningful.

[0061] In the embodiment, a truck passing strength evaluation method is provided, which can be used for a computer device, Figure 2 The flowchart of the truck passing strength evaluation method according to the embodiment of the application is shown in Figure 2 The flowchart includes the following steps:

[0062] In step S201, the passing data corresponding to each passing vehicle in the target road network in a preset time period is obtained. For details, refer to step S101 of the embodiment shown in Figure 1 The details are not repeated here.

[0063] In step S202, the target passing data corresponding to the passing truck is selected from the passing data based on the license plate number and the public security traffic management motor vehicle database. For details, refer to step S102 of the embodiment shown in Figure 1 The details are not repeated here.

[0064] In step S203, the passing strength comprehensive index of the truck in the target road network is determined based on the multiple passing strength indexes calculated from the target passing data.

[0065] Specifically, step S203 includes:

[0066] In step S2031, the driving mileage and driving frequency of the passing truck are determined based on the target passing data.

[0067] Specifically, the target passing data includes the license plate number of the passing truck, the portal position of each traffic portal passed by the passing truck, and the passing time of each traffic portal passed by the passing truck.

[0068] Firstly, the positions of the traffic posts through which the truck passes are sorted according to the order of the passing time of the truck at the traffic posts. Still taking the example in step S101, if the truck license plate is 0000001, the positions of the traffic posts are sorted according to the order of the passing time 12:00, 12:15 and 12:30, and the sorted positions are (x1, y1), (x2, y2) and (x3, y3).

[0069] Secondly, the mileage of the truck is determined based on the positions of the traffic posts.

[0070] Thirdly, the frequency of the truck is determined based on the passing time of the truck at the traffic posts.

[0071] If the preset time length is 10 minutes, the passing time from 12:00 to 12:15 is one trip, and the passing time from 12:15 to 12:30 is one trip, so the frequency of the truck in the preset time period is 2. If the preset time length is 1 hour, the passing time of the truck A in the preset time period is 12:00, 12:48, 14:00 and 14:30, the time period formed by 12:00 and 12:48 is less than 1 hour, so it is not one trip, the time period formed by 12:48 and 14:00 is more than 1 hour, so the passing time from 12:00 to 14:00 is one trip, the time period formed by 14:00 and 14:30 is less than 1 hour, so it is not one trip, and the passing time from 12:00 to 14:30 is one trip.

[0072] If the time period formed by any two adjacent passing times of the truck does not exceed the preset time length, the whole passing time is one trip. For example, the passing time of the truck B in the preset time period is 12:00, 12:30, 13:15 and 14:00, and the time period formed by any two adjacent passing times is less than 1 hour, so the passing time from 12:00 to 14:00 is one trip.

[0073] Finally, the driving data is composed of the frequency of the truck, the mileage of the truck, the positions of the traffic posts through which the truck passes, and the passing time of the truck at the traffic posts.

[0074] In step S2032, the traffic intensity index is determined based on the driving mileage, driving frequency, location of the traffic checkpoint, and passing time.

[0075] Specifically, the traffic intensity index includes truck traffic flow intensity, truck driving mileage intensity, truck driving frequency intensity, truck on-road intensity, high-intensity driving truck proportion, and high-frequency driving truck proportion.

[0076] In some optional embodiments, in step S2032, the determination of the truck traffic flow intensity includes the following steps.

[0077] In step a1, the truck traffic volume corresponding to each traffic checkpoint is obtained based on the location of the traffic checkpoint.

[0078] For example, the truck traffic volume corresponding to each traffic checkpoint in the target road network is D1, …, D i , …, D n .

[0079] In step a2, the number of traffic checkpoints in the target road network and the preset traffic volume of each traffic checkpoint are obtained.

[0080] For example, the number of traffic checkpoints in the target road network is n, and the preset traffic volume of each traffic checkpoint is C1, …, C i , …, C n . It should be noted that the preset traffic volume of the traffic checkpoint is the total number of vehicles that can pass through the traffic checkpoint in the preset time period. The preset traffic volume is related to the number of lanes of the traffic checkpoint, and therefore, it is not specifically limited here, and those skilled in the art can determine it according to the actual road conditions.

[0081] In step a3, the truck traffic flow intensity is determined based on the truck traffic volume corresponding to each traffic checkpoint in the target road network, the number of traffic checkpoints, and the preset traffic volume of each traffic checkpoint.

[0082] For example, the single-point truck traffic flow intensity S1 i is determined based on the truck traffic volume D i of the traffic checkpoint and the preset traffic volume C i of the traffic checkpoint. The determination method of S1 i is as follows:

[0083]

[0084] Based on the truck traffic volume D1, …, D i , …, D n corresponding to each traffic checkpoint and the number n of traffic checkpoints in the target road network, the average truck traffic flow intensity S v of the target road network is determined. The determination method of S v is as follows:

[0085]

[0086] Based on the single-point truck flow intensity S1, …, S u , …, S n and the target road network truck average flow intensity S v , the truck flow intensity ε is determined. The determination method of ε is as follows:

[0087]

[0088]

[0089] In the above step S2032, the determination method of the truck travel mileage intensity includes the following steps:

[0090] Step b1, obtaining the travel mileage, the number of preset unit time lengths contained in the truck travel time length, and the number of trucks passing through the target road network.

[0091] Specifically, the preset unit time length is set based on actual needs, which is not limited here. For example, it can be 1 day, 1 month, etc. The number of preset unit time lengths contained in the truck travel time length is determined by the travel time of the passing truck and the preset unit time length. For example, the difference between the end travel time and the initial travel time of a vehicle in a preset time period is determined as the truck travel time, and the number of preset unit time lengths contained in the truck travel time is determined based on the truck travel time and the preset unit time.

[0092] Exemplarily, the number of trucks passing through the target road network in the preset time period is q. In the preset time period, the travel mileage of all trucks passing through the target road network is L1, …, L i , …, L q , respectively. The number of preset unit time lengths contained in each truck travel time is t1, …, t i , …, r q .

[0093] Step b2, determining the truck travel mileage intensity based on the travel mileage, the number of trucks passing through the target road network, and the number of preset unit time lengths contained in the truck travel time.

[0094] Exemplarily, based on the travel mileage L i of each passing truck and the number of trucks passing through the target road network q, the average travel mileage S r of each truck is determined. The determination method of S r is as follows:

[0095]

[0096] Based on the truck's driving mileage L i The number of pre-set unit time periods (t) included in the truck travel time. i Determine the average mileage S2 of the truck within a preset unit of time. i S2 i The determination method is as follows:

[0097]

[0098] Based on the average driving distance S2 of each truck within a preset unit time period i And the average trip distance S per truck r Determine the truck travel mileage intensity τ. The method for determining τ is as follows:

[0099]

[0100]

[0101] In step S2032 above, the method for determining the frequency intensity of truck trips includes the following steps:

[0102] Step c1: Obtain driving frequency.

[0103] For example, taking the embodiment of step b1 as an example, the number of freight trucks traveling in the target road network within a preset time period is q. The travel frequency of each freight truck within the preset time period is F1, ..., F1. i F q .

[0104] Step c2: Determine the frequency intensity of truck travel based on driving frequency, the number of trucks traveling in the target road network, and the number of preset unit durations included in the truck travel time.

[0105] For example, taking the embodiment of step b1 as an example, the number of preset unit time periods included in the travel time corresponding to each passing freight truck are t1, ..., t2, respectively. i 、…、t q .

[0106] Based on the driving frequency F of each freight truck i Given the number of freight trucks q in the target road network, determine the average trip frequency S of each freight truck within a preset time period. x S x The determination method is as follows:

[0107]

[0108] Based on the frequency of truck trips F i The number of pre-set unit time periods (t) included in the truck travel time. idetermining an average driving frequency S3 of the truck in a preset unit time period i S3 i is determined in the following manner:

[0109]

[0110] determining a truck travel mileage intensity γ based on the average driving frequency S3 of the truck in the preset unit time period i and the average travel frequency S of each truck in the preset time period x The determination manner of γ is as follows:

[0111]

[0112]

[0113] In the above step S2032, the determination manner of the truck road entering intensity includes the following steps:

[0114] Step d1, obtaining a total truck passing amount in the target road network in a preset time period, and obtaining truck passing amounts in the target road network at each time point in the preset time period.

[0115] Exemplarily, the total truck passing amount in the target road network in the preset time period is Me, the preset time period contains m preset time points, and the truck passing amounts in the target road network at each time point in the preset time period are Mr1, …, Mr i , …, Mr m .

[0116] Step d2, determining the truck road entering intensity based on the truck passing amounts in the target road network at each time point, the preset truck population of the target road network, the total truck passing amount in the target road network in the preset time period, and the preset motor vehicle population of the target road network.

[0117] Exemplarily, based on the truck passing amounts Mr i in the target road network at each time point in the preset time period and the preset truck population Md of the target road network, the road entering rate lu i of the truck in the target road network at each time point is calculated. It should be noted that the preset truck population Md of the target road network is the number of trucks that have completed registration and registration in the target road network. lu i is determined in the following manner:

[0118]

[0119] Based on the total truck passing amount Me in the target road network in the preset time period and the preset motor vehicle population My of the target road network, the truck passing proportion lp in the target road network is determined. It should be noted that the preset motor vehicle population My of the target road network is the number of motor vehicles that have completed registration and registration in the target road network. lp is determined in the following manner:

[0120]

[0121] the truck on-road rate lu of the target road network at each time point in a preset time period i and the truck passing proportion lp in the target road network to determine the truck on-road intensity ω. The determination method of ω is:

[0122]

[0123]

[0124] In the above step S2032, the determination method of the high-intensity travel truck proportion includes the following steps:

[0125] Step e1, based on the passing time, determine the number of trucks with driving time exceeding the preset time threshold in each preset unit time in the preset time period.

[0126] Specifically, the preset unit time in the present embodiment is consistent with the preset unit time in step b1, so in the present embodiment, the number of preset unit times in the preset time period is still q.

[0127] For example, if the preset unit time is one day and the preset time threshold is 10h, then according to the passing time in the driving data, the vehicles with driving time exceeding 10h in one day are filtered out, and the number of trucks with driving time exceeding the preset time threshold in each preset time is obtained Mh1, …, Mh i , …Mh q .

[0128] Step e2, based on the number of trucks with driving time exceeding the preset time threshold in each preset time Mh i and the preset truck population of the target road network to determine the high-intensity travel truck proportion.

[0129] Specifically, the travel with driving time exceeding the preset time threshold is defined as high-intensity travel, such as in the present embodiment, the travel with driving time exceeding 10h is called high-intensity travel. It should be noted that the driving time corresponding to high-intensity can be determined according to actual conditions, which is not limited here.

[0130] For example, based on the number of trucks with driving time exceeding the preset time threshold in the preset time Mh i and the preset truck population Md of the target road network to determine the high-intensity travel truck proportion lh i in each preset unit time. The determination method of lh i is:

[0131]

[0132] Determine the high-intensity travel truck proportion in the target road network in the preset time period based on the high-intensity travel truck proportion in each preset unit time length.

[0133]

[0134] In the step S2032, the determination manner of the high-frequency travel truck proportion includes the following steps:

[0135] Step f1, determine the number of trucks with travel frequency exceeding the preset frequency threshold in each preset unit time length in the preset time period based on the passing time.

[0136] Specifically, the preset unit time length can be 3 hours or 1 day, which can be determined according to actual needs. For example, if the preset unit time length is one day and the preset frequency threshold is 5 times, then according to the passing time in the travel data, the vehicles with travel frequency exceeding 5 times in one day are screened out.

[0137] Exemplarily, in the present embodiment, the preset time period contains q preset unit time lengths, and the number of trucks with travel frequency exceeding the preset frequency threshold in each preset unit time length in the preset time period is Fd1, …, Fd i , …, Fd q .

[0138] Step f2, determine the high-frequency travel truck proportion based on the number of trucks with travel frequency exceeding the preset frequency threshold in each preset unit time length and the truck population in the target road network in the preset unit time length.

[0139] Exemplarily, the truck population in the target road network in each preset unit time length is Mj1, …, Mj i , …, Mj q

[0140] Determine the high-frequency travel truck proportion lb i in each preset unit time length based on the number of trucks Fd i with travel frequency exceeding the preset frequency threshold in the preset unit time length and the truck population Mj i in the target road network in the preset unit time length. i The determination manner of lb i is as follows:

[0141]

[0142] Determine the high-frequency travel truck proportion in the target road network in the preset time period based on the high-frequency travel truck proportion lb i in each preset unit time length in the preset time period.

[0143]

[0144] Step S2033, determining a traffic intensity comprehensive index based on the traffic intensity indexes.

[0145] In some optional embodiments, the step S2032 comprises:

[0146] Step g1, determining an average value of the traffic intensity indexes based on the traffic intensity indexes.

[0147] Exemplarily, the traffic intensity comprehensive index is denoted as σ, and the traffic intensity comprehensive index is determined in the following manner:

[0148]

[0149] wherein, n is the number of the traffic intensity indexes. In the embodiment, n = 4.

[0150] Step g2, determining the average value as the traffic intensity comprehensive index.

[0151] Specifically, the value of σ is taken as the traffic intensity comprehensive index of the target road network in the preset time period.

[0152] Step S204, evaluating the truck traffic intensity of the target road network based on the traffic intensity comprehensive index and the preset interval. For details, refer to the step S104 of the embodiment shown in Figure 1 The evaluation method of the truck traffic intensity provided in the embodiment makes the characterization of the truck traffic intensity in the final target road network more real and more accurate by analyzing the truck traffic intensity from multiple different dimensions.

[0153] An evaluation method of truck traffic intensity is provided in the embodiment, which can be used in a computer device, Figure 3 The flowchart of the evaluation method of the truck traffic intensity according to the embodiment of the application is shown in Figure 3 The flowchart comprises the following steps:

[0154] Step S301, obtaining traffic data respectively corresponding to each traffic vehicle in a target road network in a preset time period. For details, refer to the step S101 of the embodiment shown in Figure 1 The step S101 of the embodiment shown in

[0155] Step S302, screening out target traffic data corresponding to the traffic trucks from the traffic data based on the license plate numbers and the public security traffic management motor vehicle database. For details, refer to the step S102 of the embodiment shown in Figure 1 The step S102 of the embodiment shown in

[0156] Step S303, determining a traffic intensity comprehensive index of the trucks in the target road network based on multiple traffic intensity indexes calculated based on the target traffic data. For details, refer to​Figure 1 Step S103 of the illustrated embodiment, which will not be repeated here.

[0157] Step S304, evaluating the truck traffic intensity of the target road network based on the traffic intensity comprehensive index and the preset interval. For details, please refer to Figure 1 Step S104 of the illustrated embodiment, which will not be repeated here.

[0158] Step S305, connecting the positions of the traffic cards through which the truck passes in the order of the corresponding traffic time, to form the driving track corresponding to the truck.

[0159] Specifically, according to the traffic time of each truck passing through each traffic card in the driving data, the positions of the traffic cards corresponding to each traffic time are connected in order to form the driving track of the truck. In the above manner, the driving track corresponding to each truck is generated.

[0160] Step S306, generating a heat map in the target road network based on the driving tracks of all the trucks in the target road network within a preset time period.

[0161] Specifically, based on the driving track of each truck, a heat map corresponding to the truck traffic intensity is drawn by using GIS, MATLAB, etc. The heat map can reflect the traffic intensity of each place in the target road network, providing reference and guidance for the user's truck operation in the target road network.

[0162] Step S307, generating a heat map in the preset range based on the traffic intensity comprehensive index corresponding to each road network in the preset range.

[0163] Specifically, the preset range can be a region containing at least two road networks. Based on the traffic intensity comprehensive index corresponding to each road network, a truck traffic intensity heat map in the preset range can be obtained by using GIS, MATLAB, etc. Through the heat map, the intensity contrast between the road networks in the preset range can be observed, which can provide global guidance and suggestions to the user compared with the heat map in the target road network.

[0164] In this embodiment, an evaluation device for truck traffic intensity is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments, and will not be repeated here. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware, or a combination of software and hardware is also possible and is contemplated.

[0165] The present embodiment provides an evaluation device for truck traffic intensity, which is used to implement the above-mentioned embodiments and preferred embodiments, and will not be repeated here. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware, or a combination of software and hardware is also possible and is contemplated. Figure 4As shown, comprising:

[0166] The acquisition module 401 is configured to acquire target road network passing data of each passing vehicle in a preset time period, the target road network passing data including a license plate number of the passing vehicle, a card location of a traffic card passed by the passing vehicle, and a passing time corresponding to the passing vehicle when passing through each traffic card.

[0167] The screening module 402 is configured to screen target passing data of the passing truck from the target road network passing data based on the license plate number and the public security traffic management motor vehicle database.

[0168] The determination module 403 is configured to determine a passing strength comprehensive index of the truck in the target road network based on a plurality of passing strength indexes calculated based on the target passing data.

[0169] The evaluation module 404 is configured to evaluate the passing strength of the truck in the target road network based on the passing strength comprehensive index and a preset interval.

[0170] In some optional embodiments, the determination module 403 comprises:

[0171] The first determination submodule is configured to determine a driving mileage and a driving frequency of the passing truck based on the target passing data.

[0172] The second determination submodule is configured to determine a passing strength index based on the driving mileage, the driving frequency, the card location, and the passing time, the passing strength index including a truck flow strength, a truck trip mileage strength, a truck trip frequency strength, a truck on-road strength, a high-strength trip truck proportion, and a high-frequency trip truck proportion.

[0173] The third determination submodule is configured to determine the passing strength comprehensive index based on the passing strength index.

[0174] In some optional embodiments, the second determination submodule comprises:

[0175] The first acquisition unit is configured to acquire a truck passing volume corresponding to each traffic card based on the card location of the traffic card.

[0176] The second acquisition unit is configured to acquire a number of traffic cards in the target road network and a preset passing volume of each traffic card.

[0177] The first determination unit is configured to determine the truck flow strength based on the truck passing volume corresponding to each traffic card in the target road network, the number of traffic cards, and the preset passing volume of each traffic card.

[0178] In some optional embodiments, the second determination submodule comprises:

[0179] The third obtaining unit is configured to obtain a driving mileage, a quantity of preset unit time lengths contained in a truck passing time length, and a quantity of trucks passing through the target road network.

[0180] The second determining unit is configured to determine a truck travel mileage intensity based on the driving mileage, the quantity of trucks passing through the target road network, and the quantity of preset unit time lengths contained in the truck passing time length.

[0181] In some optional embodiments, the second determining sub-module includes:

[0182] The fourth obtaining unit is configured to obtain a driving frequency.

[0183] The third determining unit is configured to determine a truck travel frequency intensity based on the driving frequency, the quantity of trucks passing through the target road network, and the quantity of preset unit time lengths contained in the truck passing time length.

[0184] In some optional embodiments, the second determining sub-module includes:

[0185] The fifth obtaining unit is configured to obtain a truck passing amount in the target road network in a preset time period, and obtain a truck passing amount in the target road network at each time in the preset time period.

[0186] The fourth determining unit is configured to determine a truck on-road intensity based on the truck passing amount in the target road network at each time, a preset truck population of the target road network, the truck passing amount in the target road network in the preset time period, and a preset motor vehicle population of the target road network.

[0187] In some optional embodiments, the second determining sub-module includes:

[0188] The fifth determining unit is configured to determine, based on a passing time, a quantity of trucks with a driving time longer than a preset time threshold in each preset unit time length in the preset time period.

[0189] The sixth determining unit is configured to determine a high-intensity travel truck proportion based on the quantity of trucks with the driving time longer than the preset time threshold in each preset unit time length and the preset truck population of the target road network.

[0190] In some optional embodiments, the determining module 403 includes:

[0191] The first determining sub-module is configured to determine an average value of the passing intensity indexes based on the passing intensity indexes.

[0192] The second determining sub-module is configured to determine the average value as a passing intensity comprehensive index.

[0193] In some optional embodiments, the truck passing intensity evaluation device further includes:

[0194] The sorting module is used to connect the checkpoints of the passing trucks according to the order of their passage times, forming the driving trajectory of the passing trucks.

[0195] The first generation module is used to generate a heat map of the target road network based on the driving trajectories of all passing freight trucks in the target road network within a preset time period.

[0196] In some optional embodiments, the truck traffic intensity evaluation device further includes:

[0197] The second generation module is used to generate a heat map within a preset range based on the comprehensive traffic intensity index corresponding to each road network within the preset range.

[0198] Further functional descriptions of the various modules and units described above are the same as those in the corresponding embodiments described above, and will not be repeated here. The truck traffic intensity evaluation device is presented in the form of functional units. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0199] This invention also provides a computer device having the above-described features. Figure 4 The device shown is for evaluating the traffic intensity of trucks.

[0200] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 5 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 5 Take a processor 10 as an example.

[0201] The processor 10 can be a central processor, a network processor, or a combination thereof. The processor 10 can further include hardware chips. The hardware chips can be application specific integrated circuits, programmable logic devices, or a combination thereof. The programmable logic devices can be complex programmable logic devices, field programmable logic gate arrays, general array logic, or any combination thereof.

[0202] The memory 20 stores instructions executable by the at least one processor 10 to cause the at least one processor 10 to perform the methods illustrated by the above embodiments.

[0203] The memory 20 can include a program storage area and a data storage area. The program storage area can store an operating system, application programs required by at least one function, and the like. The data storage area can store data created according to the use of the computer device, and the like. In addition, the memory 20 can include a high-speed random access memory, and can further include a non-transitory memory such as at least one disk storage device, a flash memory device, or other non-transitory solid state memory device. In some alternative embodiments, the memory 20 can optionally include a memory disposed remotely from the processor 10, which can be connected to the computer device through a network. Examples of the network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0204] The memory 20 can include a volatile memory such as a random access memory, and can further include a non-volatile memory such as a flash memory, a hard disk, or a solid state disk, and a combination thereof.

[0205] The computer device further includes a communication interface 30 for communication of the computer device with other devices or communication networks.

[0206] The embodiments of the present application also provide a computer readable storage medium. The above method according to the embodiments of the present application can be implemented in hardware, firmware, or recorded in a storage medium, or stored in a remote storage medium or a non-transitory machine readable storage medium and downloaded from a network and stored in a local storage medium by computer code, so that the method described herein can be processed by such software on a storage medium using a general purpose computer, a special purpose processor, or programmable or special purpose hardware. The storage medium can be a disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid state disk, etc. Further, the storage medium can include a combination of the above-mentioned memories. It can be understood that the computer, the processor, the microprocessor controller, or the programmable hardware includes a storage component that can store or receive software or computer code, which, when accessed and executed by the computer, the processor, or the hardware, implements the method illustrated by the above embodiments.

[0207] While embodiments of the present application have been described in conjunction with the appended drawings, various modifications and changes are possible within the spirit and scope of the present application, and such modifications and changes are intended to fall within the scope of the appended claims.

Claims

1. A method for evaluating truck traffic intensity, characterized by, The method comprises: acquiring passing data corresponding to each passing vehicle in a target road network in a preset time period, the passing data comprising a license plate number of the passing vehicle, a position of a traffic checkpoint passed by the passing vehicle, and a passing time corresponding to the passing vehicle when passing each traffic checkpoint; screening target passing data corresponding to a passing truck from the passing data based on the license plate number and a public security traffic management motor vehicle database; determining a passing intensity comprehensive index of the truck in the target road network based on a plurality of passing intensity indexes calculated based on the target passing data; evaluating the truck passing intensity of the target road network based on the passing intensity comprehensive index and a preset interval; determining the passing intensity comprehensive index of the truck in the target road network based on the plurality of passing intensity indexes calculated based on the target passing data, comprising: determining a driving mileage and a driving frequency of the passing truck based on the target passing data; determining the passing intensity indexes including a truck traffic flow intensity, a truck trip mileage intensity, a truck trip frequency intensity, a truck on-road intensity, a high-intensity trip truck proportion, and a high-frequency trip truck proportion based on the driving mileage, the driving frequency, the position of the traffic checkpoint, and the passing time; determining the passing intensity comprehensive index based on each of the passing intensity indexes; the determination method of the truck on-road intensity, comprising: acquiring a truck passing volume in the target road network in the preset time period, and acquiring a truck passing volume in the target road network at each time in the preset time period; determining the truck on-road intensity based on the truck passing volume in the target road network at each time, a preset truck population of the target road network, the truck passing volume in the target road network in the preset time period, and a preset motor vehicle population of the target road network; determining the passing intensity comprehensive index based on each of the passing intensity indexes, comprising: determining an average value of the passing intensity indexes based on each of the passing intensity indexes; determining the average value as the passing intensity comprehensive index.

2. The method of claim 1, wherein, the determination method of the truck traffic flow intensity, comprising: acquiring a truck passing volume corresponding to each traffic checkpoint based on the position of the traffic checkpoint; acquiring a number of the traffic checkpoints in the target road network and a preset passing volume of each traffic checkpoint; determining the truck traffic flow intensity based on the truck passing volume corresponding to each traffic checkpoint in the target road network, the number of the traffic checkpoints, and the preset passing volume of each traffic checkpoint.

3. The method of claim 1, wherein, the determination method of the truck trip mileage intensity, comprising: acquiring the driving mileage, a number of preset unit time lengths in a truck passing time length, and a number of the passing trucks in the target road network; determining the truck trip mileage intensity based on the driving mileage, the number of the passing trucks in the target road network, and the number of preset unit time lengths in the truck passing time length.

4. The method of claim 3, wherein, the determination method of the truck trip frequency intensity, comprising: acquiring the driving frequency; Determine the truck travel frequency intensity based on the truck travel frequency, the number of the passing trucks in the target road network, and the number of preset unit time periods within the truck passing time period.

5. The method of claim 1, wherein, The determination method of the high-intensity travel truck proportion includes: Determine the number of trucks with travel time exceeding the preset time threshold in each preset unit time period within the preset time period based on the passing time. Determine the high-intensity travel truck proportion based on the number of trucks exceeding the preset time threshold in each preset unit time period and the preset truck population of the target road network.

6. The method of claim 1, wherein, The method further includes: Connect the positions of the traffic checkpoints passed by the passing trucks in the order of the passing time of the passing trucks passing through each traffic checkpoint to form the travel track corresponding to the passing trucks. Generate a heat map of the target road network based on the travel tracks of all the passing trucks in the target road network within the preset time period.

7. The method of claim 1, wherein, The method further includes: Generate a heat map of the preset range based on the passing intensity comprehensive index corresponding to each road network in the preset range.

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