Target road congestion determination method and system in intelligent traffic

Abstract

The invention provides a target road congestion determination method and system in smart traffic, and relates to the technical field of road congestion determination, and the method comprises the steps: carrying out the meshing of a target region, so as to obtain a grid list A corresponding to the target region; traversing A, and determining grids which have overlapped areas with a target road on which the user vehicle travels as specified grids, so as to obtain a specified grid list B; obtaining a vehicle density list rho corresponding to the B; traversing rho, and if rho j is greater than lambda1, determining Bj as a congestion grid; if rhoj is greater than or equal to lambda2 and less than or equal to lambda1, determining Bj as an undetermined grid; determining whether each to-be-determined grid is a congested grid or not; determining whether the target road is congested according to the number of the congested grids and the distance between the actual area corresponding to the congested grids and the user vehicle; according to the invention, the computing power consumption can be reduced, and the efficiency and accuracy of determining the road congestion are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of road congestion determination, and in particular to a method and system for determining target road congestion in smart transportation. Background Art

[0002] With the rapid growth of urban traffic flow, road congestion has become a core issue affecting travel efficiency. Real-time and accurate judgment of the congestion status of the target road is of great significance for the intelligent navigation system to optimize path planning and improve the efficiency of urban traffic management. Traditional solutions based on fixed sensors are limited by the hardware deployment density and coverage, and it is difficult to dynamically capture the global traffic status of complex road networks, especially in scenarios such as road construction and temporary control. Monitoring blind spots are prone to occur. The analysis method based on vehicle GPS data requires analysis of the GPS data of all vehicles, which consumes a lot of computing power and is limited by the longitude of GPS positioning, which is prone to misjudgment. Therefore, how to quickly and accurately judge whether a road is congested has become a technical problem that needs to be solved urgently. Summary of the invention

[0003] In view of the above technical problems, the technical solution adopted by the present invention is: According to a first aspect of the present application, a method for determining target road congestion in smart transportation is provided, the method comprising the following steps: S100, gridding the target area to obtain a grid list A=(A1, A2, ..., A i , …, A n ), i=1, 2, …, n; A i is the i-th grid corresponding to the target area, and n is the number of grids corresponding to the target area; S200, traverse A, determine the grids that overlap with the target road where the user's vehicle is traveling as designated grids, so as to obtain a designated grid list B = (B1, B2, ..., B j , …, B m ), j = 1, 2, …, m; B j is the jth designated grid determined, and m is the number of designated grids determined; S300, obtaining the vehicle density in the actual area corresponding to each specified grid in B, so as to obtain a vehicle density list corresponding to B ρ = (ρ1, ρ2, ..., ρ j ,…,ρ m );ρ j For B j The vehicle density in the corresponding actual area; S400, traverse ρ, if ρ j >λ1, then B j Determined as a congested grid; λ1 is a first preset vehicle density threshold; S500, if λ2≤ρ j ≤λ1, then B j Determined as a pending grid; λ2 is a second preset vehicle density threshold; S600, determining whether each of the to-be-determined grids is a congested grid according to the relationship between the position of each vehicle in the actual area corresponding to each to-be-determined grid and the boundary of the target road; S700: Determine whether the target road is congested according to the number of congestion grids and the distance between the actual area corresponding to the congestion grid and the user vehicle.

[0004] According to another aspect of the present application, a system for determining target road congestion in smart transportation is also provided, the system comprising a non-transitory computer-readable storage medium and a processor; wherein the storage medium stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement a method for determining target road congestion in smart transportation as described in any one of the first aspects.

[0005] The present invention has at least the following beneficial effects: The method for determining target road congestion in smart transportation of the present invention divides the target area into several grids, determines the designated grids that the target road passes through in combination with the target road traveled by the user's vehicle, obtains the density of vehicles in the actual area corresponding to each designated grid, determines the designated grids whose vehicle density is greater than a first preset vehicle density threshold as congested grids, determines the designated grids whose vehicle density is greater than a second preset vehicle density threshold and less than the first preset vehicle density threshold as pending grids, and determines whether each pending grid is a congested grid based on the relationship between the position of each vehicle in the actual area corresponding to each pending grid and the boundary of the target road; thereby all the congested grids that the target road passes through are determined, and then whether the target road is congested is determined; in the process of determining whether the target road is congested, the positioning data of all vehicles are not analyzed, but only the vehicle density in the grids that the target road passes through is analyzed, thereby reducing computing power consumption and improving the efficiency and accuracy of determining road congestion. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0007] Figure 1 A flowchart of a method for determining target road congestion in smart transportation provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0008] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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 creative work are within the scope of protection of the present invention.

[0009] It should be noted that, based on the present disclosure, those skilled in the art should understand that an aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement a device and / or practice a method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement this device and / or practice this method.

[0010] The following will refer to Figure 1 The flowchart of the method for determining target road congestion in smart transportation shown is an introduction to a method for determining target road congestion in smart transportation.

[0011] The method for determining target road congestion in smart transportation may include the following steps: S100, gridding the target area to obtain a grid list A=(A1, A2, ..., A i , …, A n ), i=1, 2, …, n; A i is the i-th grid corresponding to the target area, and n is the number of grids corresponding to the target area.

[0012] In this embodiment, the target area can be the area occupied by a city. When the target area is gridded, the size of the grid can be determined in combination with the road density; for example, the area with a larger road density corresponds to a smaller grid size, and the area with a smaller road density corresponds to a larger grid size; thereby, the efficiency of division and subsequent calculation can be improved while ensuring the accuracy of judgment; the grid can be of any shape and can be set according to actual needs.

[0013] S200, traverse A, determine the grids that overlap with the target road where the user's vehicle is traveling as designated grids, so as to obtain a designated grid list B = (B1, B2, ..., B j , …, B m ), j = 1, 2, …, m; B j is the jth designated grid determined, and m is the number of designated grids determined.

[0014] In this embodiment, the user's vehicle plans its itinerary through navigation software before departure, and the target road is a road composed of several sub-roads corresponding to the itinerary of the user's vehicle; the boundary coordinates of the target road and the boundary coordinates of each grid can be obtained through data from the map software, and the grids that overlap with the target road traveled by the user's vehicle can be determined by traversal, thereby obtaining B.

[0015] S300, obtaining the vehicle density in the actual area corresponding to each specified grid in B, so as to obtain a vehicle density list corresponding to B ρ = (ρ1, ρ2, ..., ρ j ,…,ρ m );ρ j For B j The corresponding vehicle density in the actual area.

[0016] In this embodiment, the vehicle density in the actual area corresponding to each designated grid can be determined by image recognition, or by GPS positioning. It should be noted that those skilled in the art can use existing vehicle density determination methods to determine the vehicle density in the actual area corresponding to each designated grid in B according to actual needs, which will not be elaborated here.

[0017] S400, traverse ρ, if ρ j >λ1, then B j Determined as a congested grid; λ1 is the first preset vehicle density threshold.

[0018] In this embodiment, if ρ j >λ1, indicating that the vehicle density in the actual area corresponding to the j-th specified grid is relatively large, and the vehicles almost fill the actual area corresponding to the j-th specified grid. In this case, the grid can be directly determined as a congested grid, that is, there is congestion in the section of the target road passing through the area corresponding to the grid. λ1 can be obtained based on the historical data analysis of a large number of congested sections. For example, the average vehicle density of several congested sections is determined as λ1.

[0019] S500, if λ2≤ρ j ≤λ1, then B j The grid is determined to be undetermined, and λ2 is the second preset vehicle density threshold.

[0020] In this embodiment, the designated grid with a large vehicle density can be directly determined as a congested grid, while the designated grid with a medium vehicle density cannot be directly determined as a congested grid or a non-congested grid, because if λ2≤ρ j≤λ1, there will be a variety of uncertain situations, such as: vehicles are concentrated in a small corner of the actual area corresponding to the specified grid, or vehicles are evenly distributed in the actual area corresponding to the specified grid, or vehicles are concentrated in the area corresponding to the target road in the specified grid; therefore, B j Determined as pending grid.

[0021] Furthermore, after step S500 and before step S600, the method further includes the following steps: S510, if ρ j <λ2, then B j Determined as a non-congested grid.

[0022] In this embodiment, if ρ j <λ2, indicating that the vehicle density in the actual area corresponding to the jth specified grid is small, and no matter how the vehicles are distributed, they will not cause congestion on the target road.

[0023] S600, determining whether each to-be-determined grid is a congested grid according to the relationship between the position of each target vehicle in the actual area corresponding to each to-be-determined grid and the boundary of the target road.

[0024] Further, step S600 includes the following steps: S610, obtaining the position coordinates of each vehicle in the actual area corresponding to each undetermined grid, so as to obtain a position coordinate list set W=(W1, W2, ..., W a , …, W b ), a=1, 2, …, b; W a is the position coordinate list corresponding to the ath undetermined grid, b is the number of undetermined grids; W a =(W a，1 , W a，2 , …, W a，x , …, W a，f（a） ), x=1, 2,..., f(a); W a，x is the position coordinate of the xth vehicle in the actual area corresponding to the ath undetermined grid, and f(a) is the number of vehicles in the actual area corresponding to the ath undetermined grid.

[0025] S620, traverse W to determine the number of position coordinates within the boundary of the target road to obtain a quantity list H corresponding to W = (H1, H2, ..., H a , …, H b ); H a W a The number of position coordinates in that are within the target road boundary.

[0026] S630, traverse H, if H a / f(a)>H', the ath undetermined grid is determined to be a congested grid; otherwise, the ath undetermined grid is determined to be a non-congested grid.

[0027] In this embodiment, the target road passes through the area corresponding to the pending grid, and not all vehicles in the area corresponding to the pending grid are necessarily on the target road. The above steps can determine the number of vehicles on the target road, and then determine whether the pending grid is a congested grid, so that it is more accurate to determine whether the pending grid is a congested grid; H' can be obtained by analyzing a large amount of vehicle data on congested sections.

[0028] Furthermore, W a，x It can be obtained by following the steps below: In this embodiment, when analyzing the positioning data of the vehicle corresponding to the grid to be determined, it is necessary to ensure the accuracy of the vehicle positioning data, and GPS positioning has errors. Therefore, W is determined by the following steps: a，x .

[0029] S611, obtaining the GPS positioning coordinates Z1 and the base station positioning coordinates Z2 corresponding to the x-th target vehicle in the actual area corresponding to the a-th undetermined grid at the current moment.

[0030] In this embodiment, the target vehicle is preset with a GPS positioning module and a base station positioning module. Through wireless network communication, the GPS positioning coordinates Z1 and the base station positioning coordinates Z2 corresponding to the target vehicle can be obtained; wherein Z1 and Z2 both include longitude coordinates and latitude coordinates; GPS positioning and base station positioning are two different modes of positioning methods.

[0031] S612: Determine a first distance ΔL1 between Z1 and Z2 according to Z1 and Z2.

[0032] In this embodiment, after obtaining Z1 and Z2, the first distance ΔL1 between Z1 and Z2 can be determined; it should be noted that those skilled in the art can use the existing distance calculation method between longitude and latitude coordinates to determine the first distance ΔL1 between Z1 and Z2 according to actual needs, which will not be elaborated here.

[0033] S613, if ΔL1>L, then obtain m preset first influencing factors that affect the GPS positioning accuracy but not the base station positioning accuracy at the current moment and n preset second influencing factors that affect the base station positioning accuracy but not the GPS positioning accuracy; wherein the center point coordinates of the range corresponding to the first influencing factor are Z2, and the center point coordinates of the range corresponding to the second influencing factor are Z1; L is the preset first distance threshold.

[0034] In this embodiment, if ΔL1>L, it means that the difference between the positioning coordinates obtained by the two modes of positioning modules on the target vehicle is large, and the positioning coordinates of at least one mode are inaccurate; there are several first influencing factors that affect the GPS positioning accuracy but do not affect the base station positioning accuracy, such as: the number of satellites communicating with the GPS module on the target vehicle, the terrain type of the area where the target vehicle is located, and the performance level of the GPS module on the target vehicle; similarly, there are several second influencing factors that affect the base station positioning accuracy but do not affect the GPS positioning accuracy, such as: the number of base stations in the area where the target vehicle is located, the idle bandwidth of the base station, and the performance level of the base station; L can be determined based on the maximum error of GPS positioning and the maximum error of base station positioning, for example: the sum of the maximum error of GPS positioning and the maximum error of base station positioning is determined as L.

[0035] It should be noted that when obtaining m first influencing factors, it is assumed that Z2 positioning is accurate, therefore, the target vehicle is considered to be within the range of Z2 as the center point coordinates; therefore, it is necessary to obtain m first influencing factors within the range of Z2 as the center point coordinates; similarly, it is also necessary to obtain n second influencing factors within the range of Z1 as the center point coordinates; the sizes of the range of Z1 as the center point coordinates and the range of Z2 as the center point coordinates can be set based on experience.

[0036] Furthermore, after step S613 and before step S614, the method further includes the following steps: If ΔL1≤L, the average positioning coordinates corresponding to Z1 and Z2 are determined as the real coordinates corresponding to the target vehicle at the current moment.

[0037] In this embodiment, if ΔL1≤L, it means that the GPS positioning coordinates and the base station positioning coordinates are within the corresponding error range. Therefore, it can be determined that Z1 and Z2 are both accurate. Therefore, the average positioning coordinates corresponding to Z1 and Z2 can be determined as the real coordinates corresponding to the target vehicle at the current moment; or, according to the proportional relationship between the GPS positioning error and the base station positioning error, the weights corresponding to Z1 and Z2 are set, and the real coordinates corresponding to the target vehicle at the current moment are determined by weighted summation.

[0038] S614, substitute the normalized first influencing factor values ​​corresponding to the m first influencing factors into the preset GPS positioning deviation function to obtain the GPS positioning deviation ΔG; and substitute the normalized second influencing factor values ​​corresponding to the n second influencing factors into the preset base station positioning deviation function to obtain the base station positioning deviation ΔH.

[0039] Further, step S614 may include the following steps: S61, obtain a preset GPS positioning deviation function; wherein the GPS positioning deviation function includes a GPS positioning longitude deviation function f1 and a GPS positioning latitude deviation function f2; the inputs of f1 and f2 are dependent variables corresponding to m first influencing factors, the output of f1 is the GPS positioning longitude deviation value corresponding to the m dependent variables, and the output of f2 is the GPS positioning latitude deviation value corresponding to the m dependent variables.

[0040] In this embodiment, f1, f2, f3 and f4 are obtained according to the GPS positioning data and base station positioning data corresponding to the historical vehicles.

[0041] Furthermore, f1 and f2 can be obtained by the following steps: S01, obtaining each first historical positioning coordinate group corresponding to the historical vehicle to obtain a first historical positioning coordinate group list C = (C1, C2, ..., C p , …, C q ), p = 1, 2, …, q; where C p is the pth first historical positioning coordinate group obtained, q is the number of first historical positioning coordinate groups obtained; C p =(C p，1 , C p，2 ); C p，1 is the GPS positioning coordinates corresponding to the pth first historical positioning coordinate group, C p，2 is the base station positioning coordinates corresponding to the pth first historical positioning coordinate group; C p，1 Inaccurate, C. p，2 precise.

[0042] In this embodiment, historical positioning coordinate groups of several historical vehicles can be obtained through historical data records, and each historical positioning coordinate group includes GPS positioning coordinates and base station positioning coordinates. The accuracy of the GPS positioning coordinates and the base station positioning coordinates is also known. Therefore, the historical positioning coordinate group with accurate GPS positioning coordinates and inaccurate base station positioning coordinates can be determined as the first historical positioning coordinate group by traversal; the GPS positioning coordinates are inaccurate and the base station positioning coordinates are accurate. The corresponding scenario is that the GPS positioning coordinates are affected by the corresponding first influencing factors, while the base station positioning coordinates are not affected by the corresponding second influencing factors. The base station positioning coordinates can be used as accurate positioning coordinates. In the case of several groups of corresponding first influencing factors and inaccurate GPS positioning coordinates, the GPS positioning longitude deviation function f1 and the GPS positioning latitude deviation function f2 can be fitted according to C subsequently.

[0043] S02, obtaining the historical first influencing factor group corresponding to each first historical positioning coordinate group in C, so as to obtain the historical first influencing factor group list LC corresponding to C = (LC1, LC2, ..., LC p ,…,LCq ); where LC p C p The corresponding historical first influencing factor group; LC p =(LC p，1 , L.C. p，2 ,…,LC p，j ,…,LC p，m ), j = 1, 2, …, m; LC p，j C p The corresponding j-th historical first influencing factor; LC p，j It affects the GPS positioning accuracy but does not affect the base station positioning accuracy.

[0044] In this embodiment, it can be understood that when each historical first historical positioning coordinate group is generated, a historical first influencing factor group is also recorded, and the historical first influencing factor group is obtained within a range centered on the GPS positioning coordinates.

[0045] S03, normalize each historical first influencing factor in LC to obtain a historical first influencing factor value group list LC'=(LC'1, LC'2, ..., LC' p ,…,LC' q ); where LC' p For LC p The corresponding historical first influencing factor value group; LC' p =(LC' p，1 , LC' p，2 ,…,LC' p，j ,…,LC' p，m );LC' p，j For LC p，j The corresponding historical first influencing factor value.

[0046] In this embodiment, each historical first influencing factor can be normalized. For the historical first influencing factor with a specific value, a standard value can be set, which is obtained by dividing the specific value corresponding to the historical first influencing factor by the set standard value; for the historical first influencing factor with a type, a corresponding value can be set for each type; it should be noted that those skilled in the art can use the existing normalization method to normalize each historical first influencing factor in LC to obtain the historical first influencing factor value group list LC' corresponding to LC, which will not be elaborated here.

[0047] S04, LC' p and C p As a group, function fitting is performed to obtain the GPS positioning longitude deviation function f1 and the GPS positioning latitude deviation function f2.

[0048] In this embodiment, LC' p and C p As a group, LC' p is the independent variable, C p C p，1 and C p，2 As the dependent variable and standard value, C p，1 and C p，2 Both contain longitude coordinates and latitude coordinates. By fitting functions to the longitude and latitude respectively, we can obtain the GPS positioning longitude deviation function f1 and the GPS positioning latitude deviation function f2. It should be noted that the output of f1 is the longitude deviation value of GPS positioning under the influence of the corresponding m first influencing factors, and the output of f2 is the latitude deviation value of GPS positioning under the influence of the corresponding m first influencing factors. The output values ​​of f1 and f2 have positive and negative values, corresponding to positive adjustment and negative adjustment respectively.

[0049] It should be noted that those skilled in the art can use existing function fitting methods according to actual needs to convert LC' p and C p As a group, function fitting is performed to obtain the GPS positioning longitude deviation function f1 and the GPS positioning latitude deviation function f2, which will not be described in detail here.

[0050] S62, obtain a preset base station positioning deviation function; wherein the base station positioning deviation function includes a base station positioning longitude deviation function f3 and a base station positioning latitude deviation function f4; the inputs of f3 and f4 are dependent variables corresponding to n second influencing factors, the output of f3 is the base station positioning longitude deviation value corresponding to the n dependent variables, and the output of f4 is the base station positioning latitude deviation value corresponding to the n dependent variables.

[0051] Furthermore, f3 and f4 can be obtained by the following steps: S05, obtaining each second historical positioning coordinate group corresponding to the historical vehicle to obtain a second historical positioning coordinate group list D=(D1, D2, ..., D u , …, D v ), u = 1, 2, …, v; where D u is the uth second historical positioning coordinate group obtained, v is the number of the second historical positioning coordinate groups obtained; D u =(D u，1 , D u，2 );D u，1 is the GPS positioning coordinates corresponding to the u-th second historical positioning coordinate group, D u，2 is the base station positioning coordinates corresponding to the u-th second historical positioning coordinate group; D u，1 Accurate, D u，2 Inaccurate.

[0052] In this embodiment, historical positioning coordinate groups of several historical vehicles can be obtained through historical data records, and each historical positioning coordinate group includes GPS positioning coordinates and base station positioning coordinates. The accuracy of GPS positioning coordinates and base station positioning coordinates is also known. Therefore, the historical positioning coordinate group with inaccurate GPS positioning coordinates and accurate base station positioning coordinates can be determined as the second historical positioning coordinate group by traversal; the GPS positioning coordinates are accurate and the base station positioning coordinates are inaccurate, and the corresponding scenario is that the GPS positioning coordinates are not affected by the corresponding first influencing factors, while the base station positioning coordinates are affected by the corresponding second influencing factors. The GPS positioning coordinates can be used as accurate positioning coordinates. In the case of several groups of corresponding second influencing factors and inaccurate base station positioning coordinates, the base station positioning longitude deviation function f3 and the base station positioning latitude deviation function f4 can be fitted according to D subsequently.

[0053] S06, obtaining the historical second influencing factor group corresponding to each second historical positioning coordinate group in D, so as to obtain the historical second influencing factor group list LD corresponding to D = (LD1, LD2, ..., LD u ,…,LD v ); where LD u D u The corresponding historical second influencing factor group; LD u =(LD u，1 , LD u，2 ,…,LD u，i ,…,LD u，n ), i=1, 2, …, n; LD u，i D u The corresponding second historical influencing factor of the ith period; LD u，i It does not affect the GPS positioning accuracy, but affects the base station positioning accuracy.

[0054] In this embodiment, it can be understood that when each historical second historical positioning coordinate group is generated, a historical second influencing factor group is also recorded, and the historical second influencing factor group is obtained within a range centered on the base station positioning coordinates.

[0055] S07, normalize each historical second influencing factor in LD to obtain a historical second influencing factor value group list LD'= (LD'1, LD'2, ..., LD' u ,…,LD' v ); where LD' u LD u The corresponding historical second influencing factor value group; LD' u =(LD' u，1 , LD' u，2 ,…,LD' u，i,…,LD' u，n );LD' u，i LD u，i The corresponding historical second influencing factor value.

[0056] S08, LD' u and D u As a group, function fitting is performed to obtain the base station positioning longitude deviation function f3 and the base station positioning latitude deviation function f4.

[0057] In this embodiment, f3 and f4 can be obtained by using the method for obtaining f1 and f2 in the above steps, which will not be described in detail here.

[0058] S63, substituting the normalized first influencing factor values ​​corresponding to the m first influencing factors into f1 and f2 to obtain the GPS positioning longitude deviation value ΔG1 and the GPS positioning latitude deviation value ΔG2.

[0059] In this embodiment, ΔG1 and ΔG2 are the longitude deviation and latitude deviation caused by m first influencing factors to GPS positioning, and Z1 can be reversely corrected by ΔG1 and ΔG2.

[0060] S64, substituting the normalized second influencing factor values ​​corresponding to the n second influencing factors into f3 and f4 to obtain the base station positioning longitude deviation value ΔH1 and the base station positioning latitude deviation value ΔH2.

[0061] In this embodiment, ΔH1 and ΔH2 are the longitude deviation and latitude deviation caused by n second influencing factors to the base station positioning, and Z2 can be reversely corrected by ΔH1 and ΔH2.

[0062] S615, adjusting Z1 according to ΔG to obtain an adjusted positioning coordinate Z1' corresponding to Z1; and adjusting Z2 according to ΔH to obtain an adjusted positioning coordinate Z2' corresponding to Z2.

[0063] Further, step S615 may include the following steps: Get ΔG = (ΔG1, ΔG2), Z1 = (Z 1，x , Z 1，y ), ΔH = (ΔH1, ΔH2) and Z2 = (Z 2，x , Z 2，y ); where Z 1，x and Z 1，y are the longitude and latitude corresponding to Z1, Z 2，x and Z 2，y are the longitude and latitude corresponding to Z2 respectively.

[0064] According to ΔG, Z1 is adjusted to obtain the adjusted positioning coordinates Z1'= (Z 1，x+ΔG1,Z 1，y +ΔG2).

[0065] According to ΔH, Z2 is adjusted to obtain the adjusted positioning coordinates Z2'=(Z 2，x +ΔH1,Z 2，y +ΔH2).

[0066] In this embodiment, after Z1 and Z2 are adjusted, the obtained Z1' and Z2' are the corrected coordinates after eliminating the influence of the first influencing factor and the second influencing factor.

[0067] S616, obtaining a second distance ΔL2 between Z1' and Z2 and a third distance ΔL3 between Z2' and Z1.

[0068] It should be noted that those skilled in the art can use the existing distance calculation method between longitude and latitude coordinates to obtain the second distance ΔL2 between Z1' and Z2 and the third distance ΔL3 between Z2' and Z1 according to actual needs, which will not be elaborated here.

[0069] S617, if ΔL2<L', determine that Z2 is accurate; wherein L' is a preset second distance threshold.

[0070] S618, if ΔL3<L', it is determined that Z1 is accurate.

[0071] In this embodiment, assuming that Z1 is inaccurate and Z2 is accurate, then Z1' obtained after adjusting Z1 should be closer to Z2. Based on this, if ΔL2<L', Z2 is determined to be accurate; similarly, if ΔL3<L', Z1 is determined to be accurate; L' is an empirical value and can also be determined comprehensively based on the positioning errors of GPS and base stations. For example, the sum of the maximum error of GPS positioning and the maximum error of base station positioning is determined as L'.

[0072] In this embodiment, if ΔL2≥L' and ΔL3≥L', at this time, it is possible that both Z1 and Z2 are inaccurate, or it is possible that at least one of Z1 and Z2 is accurate, and the result judgment is wrong due to calculation problems; therefore, in order to further improve the accuracy of the result, a secondary verification is performed through the above method; the third distance threshold is an empirical value and can be specifically set according to actual needs, for example: the third distance threshold can be set according to the average vehicle speed, the greater the average vehicle speed, the greater the third distance threshold; if any of the distance between ER and Z1 and the distance between ER and Z2 is less than or equal to the preset third distance threshold, it means that the judgment result conflicts with the judgment result in the above embodiment and needs to be manually determined manually.

[0073] In this embodiment, when the first distance between the corresponding GPS positioning coordinates and the base station positioning coordinates on the target vehicle is greater than the preset first distance threshold, it indicates that there is inconsistency between the positioning coordinates of the two modes. At this time, m preset first influencing factors and n preset second influencing factors are obtained, wherein the center point of the range corresponding to the first influencing factor is Z2, and the center point of the range corresponding to the second influencing factor is Z1; since the first influencing factor only affects the GPS positioning accuracy, and the second influencing factor only affects the base station positioning accuracy, the GPS positioning deviations corresponding to the m first influencing factors can be obtained according to the m first influencing factors and the GPS positioning deviation function; and the base station positioning deviations corresponding to the n second influencing factors can be obtained according to the n preset second influencing factors and the base station positioning deviation function; the GPS positioning coordinates and the base station positioning coordinates are adjusted respectively according to the positioning deviations. If Z1 or Z2 is accurate, it must meet ΔL2<L' or ΔL3<L', and then it is judged that Z1 or Z2 is accurate; the positioning coordinates of the two modes are combined with their corresponding influencing factors to determine the accurate positioning coordinates, so that the positioning of the target vehicle is more accurate.

[0074] S700: Determine whether the target road is congested according to the number of congestion grids and the distance between the actual area corresponding to the congestion grid and the user vehicle.

[0075] Furthermore, step S700 includes the following steps: S710, if NUM / n>θ1, the target road is determined to be a congested road; otherwise, the target road is determined to be a non-congested road; NUM is the number of congested grids, and θ1 is a first preset road congestion threshold.

[0076] In this embodiment, if NUM / n>θ1, it means that the number of congested grids is large, and the target road can be directly determined to be congested.

[0077] S720, if θ2≤NUM / n≤θ1, then obtain the distance between the area corresponding to each congestion grid and the user's vehicle to obtain a distance list QL=(QL1, QL2, ..., QL p , …, QL q ), p = 1, 2, …, q; QL p is the distance between the area corresponding to the pth congestion grid and the user's vehicle, q is the number of congestion grids; θ2 is the second preset road congestion threshold.

[0078] In this embodiment, if θ2≤NUM / n≤θ1, it means that the number of congested grids is at a medium level. At this time, it is not possible to directly determine that the target road is congested, and further judgment is required; the distance between the area corresponding to each congested grid and the user's vehicle can be obtained.

[0079] S730: Determine the proximity between each congested grid and the user vehicle according to the QL to obtain a proximity list ω=(ω1, ω2, …, ω p ,…,ω q );ω p is the proximity between the pth congested grid and the user’s vehicle; ω p =QL p / QL'; QL' is the distance between the starting point and the end point of the target road.

[0080] S740, if ∑ q p=1 ω p <ω', the target road is determined to be congested; otherwise, the target road is determined to be uncongested; ω' is a preset proximity threshold.

[0081] In this embodiment, if ∑ q p=1 ω p <ω', indicating that the distances between the congested grids and the user's vehicle are relatively small, and the target road can be directly determined to be congested; otherwise, it indicates that the distances between the congested grids and the user's vehicle are relatively large. When the user's vehicle reaches the congested grid, the area corresponding to the congested grid is highly likely to have been unblocked. Therefore, it is determined that the target road is not congested.

[0082] Furthermore, after step S700, the method further includes the following steps: S800: If it is determined that the target road is congested, a prompt message is generated; wherein the prompt message is used to prompt the user to change the driving road.

[0083] In this embodiment, if it is determined that the target road is congested, a prompt message may be generated to prompt the user to change to another road to avoid congestion.

[0084] In this embodiment, the target area is divided into several grids, and the designated grids that the target road passes through are determined in combination with the target road traveled by the user's vehicle, and the density of vehicles in the actual area corresponding to each designated grid is obtained. The designated grids whose vehicle density is greater than the first preset vehicle density threshold are determined as congested grids, and the designated grids whose vehicle density is greater than the second preset vehicle density threshold and less than the first preset vehicle density threshold are determined as pending grids. According to the relationship between the position of each target vehicle in the actual area corresponding to each pending grid and the boundary of the target road, it is determined whether each pending grid is a congested grid; thereby, all the congested grids that the target road passes through are determined, and then it is determined whether the target road is congested; in the process of determining whether the target road is congested, the positioning data of all vehicles are not analyzed, but only the vehicle density in the grids that the target road passes through is analyzed, thereby reducing computing power consumption and improving the efficiency and accuracy of determining road congestion.

[0085] In an exemplary embodiment, a system for determining target road congestion in smart transportation is also provided, the system comprising a non-transitory computer-readable storage medium and a processor; wherein the storage medium stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the method for determining target road congestion in smart transportation as described in the above embodiments.

[0086] In addition, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0087] Although some specific embodiments of the present invention have been described in detail by way of example, it should be understood by those skilled in the art that the above examples are only for illustration, not for limiting the scope of the present invention. It should also be understood by those skilled in the art that various modifications may be made to the embodiments without departing from the scope and spirit of the present invention.

Claims

1. A method for determining target road congestion in smart transportation, characterized in that: The method comprises the following steps: S100, gridding the target area to obtain a grid list A corresponding to the target area = (A1, A2, ..., A i , …, A n ), i=1, 2, …, n; A i is the i-th grid corresponding to the target area, and n is the number of grids corresponding to the target area; S200, traverse A, determine the grids that overlap with the target road where the user's vehicle is traveling as designated grids, so as to obtain a designated grid list B = (B1, B2, ..., B j , …, B m ), j = 1, 2, …, m; B j is the jth designated grid determined, and m is the number of designated grids determined; S300, obtaining the vehicle density in the actual area corresponding to each specified grid in B, so as to obtain a vehicle density list corresponding to B ρ = (ρ1, ρ2, ..., ρ j ,…,ρ m );ρ j For B j The vehicle density in the corresponding actual area; S400, traverse ρ, if ρ j >λ1, then B j Determined as a congested grid; λ1 is a first preset vehicle density threshold; S500, if λ2≤ρ j ≤λ1, then B j Determined as a pending grid; λ2 is a second preset vehicle density threshold; S600, determining whether each of the pending grids is a congested grid according to the relationship between the position of each vehicle in the actual area corresponding to each of the pending grids and the boundary of the target road; S700: Determine whether the target road is congested according to the number of congestion grids and the distance between the actual area corresponding to the congestion grid and the user vehicle.

2. The method for determining target road congestion in smart transportation according to claim 1, characterized in that: Step S700 includes the following steps: S710, if NUM / n>θ1, the target road is determined to be a congested road; otherwise, the target road is determined to be a non-congested road; NUM is the number of congested grids, and θ1 is a first preset road congestion threshold; S720, if θ2≤NUM / n≤θ1, then obtain the distance between the area corresponding to each congestion grid and the user's vehicle to obtain a distance list QL=(QL1, QL2, ..., QL p , …, QL q ), p = 1, 2, …, q; QL p is the distance between the area corresponding to the pth congestion grid and the user's vehicle, q is the number of congestion grids; θ2 is the second preset road congestion threshold; S730: Determine the proximity between each congested grid and the user vehicle according to the QL to obtain a proximity list ω=(ω1, ω2, …, ω p ,…,ω q );ω p is the proximity between the pth congested grid and the user’s vehicle; ω p =QL p / QL'; QL' is the distance between the starting point and the end point of the target road; S740, if ∑ q p=1 ω p <ω', the target road is determined to be congested; otherwise, the target road is determined to be uncongested; ω' is a preset proximity threshold.

3. The method for determining target road congestion in smart transportation according to claim 1, characterized in that: After step S500 and before step S600, the method further includes the following steps: S510, if ρ j <λ2, then B j Determined as a non-congested grid.

4. The method for determining target road congestion in intelligent transportation according to claim 1, characterized in that: After step S700, the method further includes the following steps: S800: If it is determined that the target road is congested, a prompt message is generated; wherein the prompt message is used to prompt the user to change the driving road.

5. The method for determining target road congestion in intelligent transportation according to claim 1, characterized in that: Step S600 includes the following steps: S610, obtaining the position coordinates of each vehicle in the actual area corresponding to each undetermined grid, so as to obtain a position coordinate list set W=(W1, W2, ..., W a , …, W b ), a=1, 2, …, b; W a is the position coordinate list corresponding to the ath undetermined grid, b is the number of undetermined grids; W a =(W a，1 , W a，2 , …, W a，x , …, W a，f（a） ), x=1, 2,…, f(a); W a，x is the position coordinate of the x-th vehicle in the actual area corresponding to the a-th undetermined grid, and f(a) is the number of vehicles in the actual area corresponding to the a-th undetermined grid; S620, traverse W to determine the number of position coordinates within the boundary of the target road to obtain a quantity list H corresponding to W = (H1, H2, ..., H a , …, H b ); H a W a The number of position coordinates in the target road boundary; S630, traverse H, if H a / f(a)>H', the ath undetermined grid is determined to be a congested grid; otherwise, the ath undetermined grid is determined to be a non-congested grid.

6. The method for determining target road congestion in intelligent transportation according to claim 5, characterized in that: W a，x Obtained through the following steps: S611, obtaining the GPS positioning coordinates Z1 and the base station positioning coordinates Z2 corresponding to the x-th vehicle in the actual area corresponding to the a-th undetermined grid at the current moment; S612, determining a first distance ΔL1 between Z1 and Z2 according to Z1 and Z2; S613, if ΔL1>L, then obtain m preset first influencing factors that affect the GPS positioning accuracy but not the base station positioning accuracy at the current moment and n preset second influencing factors that affect the base station positioning accuracy but not the GPS positioning accuracy; wherein the coordinates of the center point of the range corresponding to the first influencing factor are Z2, and the coordinates of the center point of the range corresponding to the second influencing factor are Z1; L is a preset first distance threshold; S614, substituting the normalized first influencing factor values ​​corresponding to the m first influencing factors into a preset GPS positioning deviation function to obtain a GPS positioning deviation ΔG; and substituting the normalized second influencing factor values ​​corresponding to the n second influencing factors into a preset base station positioning deviation function to obtain a base station positioning deviation ΔH; S615, adjusting Z1 according to ΔG to obtain an adjusted positioning coordinate Z1' corresponding to Z1; and adjusting Z2 according to ΔH to obtain an adjusted positioning coordinate Z2' corresponding to Z2; S616, obtaining a second distance ΔL2 between Z1' and Z2 and a third distance ΔL3 between Z2' and Z1; S617, if ΔL2<L', then determine that Z2 is accurate; wherein L' is a preset second distance threshold; S618, if ΔL3<L', it is determined that Z1 is accurate.

7. The method for determining target road congestion in intelligent transportation according to claim 6, characterized in that: Step S614 includes the following steps: S61, obtaining a preset GPS positioning deviation function; wherein the GPS positioning deviation function includes a GPS positioning longitude deviation function f1 and a GPS positioning latitude deviation function f2; the inputs of f1 and f2 are dependent variables corresponding to m first influencing factors, the output of f1 is the GPS positioning longitude deviation value corresponding to the m dependent variables, and the output of f2 is the GPS positioning latitude deviation value corresponding to the m dependent variables; S62, obtaining a preset base station positioning deviation function; wherein the base station positioning deviation function includes a base station positioning longitude deviation function f3 and a base station positioning latitude deviation function f4; the inputs of f3 and f4 are dependent variables corresponding to n second influencing factors, the output of f3 is the base station positioning longitude deviation value corresponding to the n dependent variables, and the output of f4 is the base station positioning latitude deviation value corresponding to the n dependent variables; S63, substituting the normalized first influencing factor values ​​corresponding to the m first influencing factors into f1 and f2 to obtain the GPS positioning longitude deviation value ΔG1 and the GPS positioning latitude deviation value ΔG2; S64, substituting the normalized second influencing factor values ​​corresponding to the n second influencing factors into f3 and f4 to obtain the base station positioning longitude deviation value ΔH1 and the base station positioning latitude deviation value ΔH2.

8. The method for determining target road congestion in intelligent transportation according to claim 7, characterized in that: f1, f2, f3 and f4 are obtained according to the GPS positioning data and base station positioning data corresponding to the historical vehicles.

9. The method for determining target road congestion in intelligent transportation according to claim 6, characterized in that: The first influencing factors include: the number of satellites communicating with the GPS module on the vehicle, the terrain type of the area where the vehicle is located, and the performance level of the GPS module on the vehicle; the second influencing factors include: the number of base stations in the area where the vehicle is located, the idle bandwidth of the base stations, and the performance level of the base stations.

10. A target road congestion determination system in smart transportation, characterized in that: The system includes a non-transitory computer-readable storage medium and a processor; wherein, the storage medium stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the method for determining target road congestion in smart transportation as described in any one of claims 1-9.

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