Method, device and equipment for determining in-transit vehicles in road network

Abstract

The invention discloses a road network in-transit vehicle determination method. The method comprises the steps that first vehicle data passing through a target checkpoint in a first time period, a second time period and a third time period are acquired respectively; the second time period comprises the first time period, and the second time period extends forwards for a first preset duration by taking the starting moment of the first time period as a reference; the starting moment of the third time period is the starting moment of the second time period, and the ending moment of the third time period is the starting moment of the first time period; based on the first vehicle data corresponding to the second time period and the third time period, determining second vehicle data which does not pass through the target gate in the third time period and passes through the target gate in the first time period; and on the basis of the first vehicle data and the second vehicle data corresponding to the first time period, actual in-transit vehicle data at the T moment in the road network vehicles is determined, and the T moment is the starting moment of the first time period. Meanwhile, the invention also discloses a device and equipment for determining the in-transit vehicles in the road network.

Description

Technical Field

[0001] The present application relates to the field of traffic management, and particularly to a method, apparatus, and device for determining in-transit vehicles on a road network. Background Art

[0002] The in-transit volume of a road network refers to the total number of vehicles simultaneously present on the road network at a certain instantaneous moment. Since the introduction of this indicator, the in-transit volume of the road network, as an important indicator in fields such as traffic congestion governance and urban sustainable development, has been widely applied on platforms such as local public security traffic management command centers and urban intelligent brains. However, the in-transit volume indicator has always been unable to be accurately calculated. Summary of the Invention

[0003] In view of this, embodiments of the present application are expected to provide a method, apparatus, and device for determining in-transit vehicles on a road network to at least solve the above technical problems.

[0004] To achieve the above object, the technical solution of the present application is implemented as follows:

[0005] According to one aspect of the embodiments of the present application, a method for determining in-transit vehicles on a road network is provided. The method includes:

[0006] Obtaining first vehicle data passing through a target checkpoint in a first time period, a second time period, and a third time period respectively; wherein, the second time period includes the first time period, and the second time period extends forward from the starting moment of the first time period by a first preset duration; the starting moment of the third time period is the starting moment of the second time period, and the ending moment of the third time period is the starting moment of the first time period;

[0007] Based on the first vehicle data corresponding to the second time period and the third time period respectively, determining second vehicle data that did not pass through the target checkpoint in the third time period and passed through the target checkpoint in the first time period;

[0008] Based on the first vehicle data corresponding to the first time period and the second vehicle data, determining the actual in-transit vehicle data of the road network vehicles at moment T, where the moment T is the starting moment of the first time period.

[0009] In the above solution, the first vehicle data passing through the target checkpoint in the first time period includes a first set of vehicle data and a second set of vehicle data, where the first set of vehicle data represents the set of vehicles in transit at moment T and passing through the target checkpoint in the first time period; the second set of vehicle data represents the set of vehicles not in transit at moment T and passing through the target checkpoint in the first time period; wherein, the first set of vehicle data and the second set of vehicle data do not overlap with each other;

[0010] The second vehicle data includes a third set of vehicle data and a fourth set of vehicle data. Among them, the third set of vehicle data represents a set of vehicles that are not on the road during the third time period, newly hit the road during the first time period, and pass through the target checkpoint; the fourth set of vehicle data represents a set of vehicles that are on the road during the third time period but do not pass through the target checkpoint, and pass through the target checkpoint during the first time period; the third set of vehicle data and the fourth set of vehicle data do not overlap with each other;

[0011] Determining the actual road network in-transit vehicle data at time T based on the first vehicle data and the second vehicle data corresponding to the first time period includes:

[0012] Calculating a first vehicle error between the third set of vehicle data and the second set of vehicle data, where the first vehicle error represents the vehicle dissimilarity between the third set of vehicle data and the second set of vehicle data; among them, the first vehicle error is related to the first preset duration and the second preset duration, and the second preset duration is the duration between the start time and the end time of the first time period;

[0013] Calculating the vehicle sum between the first set of vehicle data and the first vehicle error, and calculating the vehicle difference between the vehicle sum and the fourth set of vehicle data;

[0014] Taking the vehicle difference as the actual in-transit vehicle data of the road network vehicles at time T.

[0015] In the above solution, the method further includes:

[0016] Determining the checkpoint distribution density and the average vehicle speed in the preset area according to the number of checkpoints and the road mileage in the preset area;

[0017] Obtaining third vehicle data passing through the target checkpoint during the fourth time period, where the fourth time period includes the third time period, and the fourth time period extends backward by a third preset duration based on the start time of the first time period, and the third preset duration is related to the checkpoint distribution density and the average vehicle speed;

[0018] Based on the first vehicle data and the third vehicle data corresponding to the third time period, determining a second vehicle error, where the second vehicle error represents a set of vehicles entering the road network within the third preset duration extending forward or backward based on time T;

[0019] Determining the actual in-transit vehicle data of the road network vehicles at time T based on the first vehicle data and the second vehicle data corresponding to the first time period includes:

[0020] Based on the first vehicle data, the second vehicle data and the second vehicle error corresponding to the first time period, actual on-the-way vehicle data at time T in the road network vehicles is determined.

[0021] In the above solution, determining the actual on-the-way vehicle data of the road network vehicles at time T based on the first vehicle data, the second vehicle data and the second vehicle error corresponding to the first time period includes:

[0022] The second vehicle error includes a first group of errors and a second group of errors, wherein the first group of errors represents vehicle errors that enter the road network within a third preset time period extending forward from the T moment, and the second group of errors represents vehicle errors that enter the road network within a third preset time period extending backward from the T moment;

[0023] When the first group of errors and the second group of errors meet an equal condition, calculating a vehicle difference between the first vehicle data and the second vehicle data corresponding to the first time period, and calculating a vehicle sum between the vehicle difference and the first group of errors or calculating a vehicle sum between the vehicle difference and the second group of errors;

[0024] The vehicles and are determined as the actual on-the-way vehicle data of the road network vehicles at time T.

[0025] In the above solution, the actual on-the-way vehicle data includes: a fifth group of vehicle data and a sixth group of vehicle data, wherein the fifth group of vehicle data represents a set of vehicles that are on the road at time T and have not passed through the target checkpoint within the first time period; the sixth group of vehicle data is the same as the first group of vehicle data;

[0026] The fifth set of vehicle data includes a first part of data and a second part of data, wherein the first part of data represents a set of vehicles that are on the road at time T and have been on the road during the first time period but have not passed through the target checkpoint; the second part of data represents a set of vehicles that are on the road at time T but have left the road network at a certain time during the first time period and have not passed through the target checkpoint;

[0027] The first portion of data is related to a second preset duration, and the second portion of data is related to a mount density.

[0028] According to a second aspect of the present application, a device for determining vehicles on the road network is provided, the device comprising:

[0029] An acquisition unit is configured to respectively acquire first vehicle data passing through a target checkpoint within a first time period, a second time period, and a third time period; wherein, the second time period includes the first time period, and the second time period extends forward from the start time of the first time period by a first preset duration; the start time of the third time period is the start time of the second time period, and the end time of the third time period is the start time of the first time period;

[0030] A determination unit is configured to determine, based on the first vehicle data respectively corresponding to the second time period and the third time period, second vehicle data that did not pass through the target checkpoint within the third time period but passed through the target checkpoint within the first time period; and is configured to determine, based on the first vehicle data corresponding to the first time period and the second vehicle data, actual in-transit vehicle data of road network vehicles at time T, where the time T is the start time of the first time period.

[0031] In the above solution, the first vehicle data of vehicles passing through the target checkpoint within the first time period includes a first set of vehicle data and a second set of vehicle data, where the first set of vehicle data represents a set of vehicles that were in transit at time T and passed through the target checkpoint within the first time period; the second set of vehicle data represents a set of vehicles that were not in transit at time T and passed through the target checkpoint within the first time period; wherein, the first set of vehicle data and the second set of vehicle data do not overlap with each other;

[0032] The second vehicle data includes a third set of vehicle data and a fourth set of vehicle data, where the third set of vehicle data represents a set of vehicles that were not in transit within the third time period, newly entered the road and passed through the target checkpoint within the first time period; the fourth set of vehicle data represents a set of vehicles that were in transit within the third time period but did not pass through the target checkpoint and passed through the target checkpoint within the first time period; the third set of vehicle data and the fourth set of vehicle data do not overlap with each other;

[0033] The apparatus further includes:

[0034] A calculation unit is configured to calculate a first vehicle error between the third set of vehicle data and the second set of vehicle data, where the first vehicle error represents the vehicle dissimilarity between the third set of vehicle data and the second set of vehicle data; wherein, the first vehicle error is related to the first preset duration and a second preset duration, and the second preset duration is the duration between the start time and the end time of the first time period; and is configured to calculate the sum of vehicles between the first set of vehicle data and the first vehicle error, and calculate the difference between the sum of vehicles and the fourth set of vehicle data;

[0035] The determining unit is configured to use the vehicle difference as the actual in-route vehicle data of the road network vehicles at time T.

[0036] In the above solution, the determining unit is further configured to determine the distribution density of the checkpoints and the average vehicle speed within the preset area according to the number of checkpoints and the road mileage within the preset area;

[0037] The obtaining unit is further configured to obtain the third vehicle data passing through the target checkpoint within the fourth time period, where the fourth time period includes the third time period and the fourth time period extends backward from the starting moment of the first time period by a third preset duration, and the third preset duration is related to the checkpoint distribution density and the average vehicle speed;

[0038] The calculating unit is further configured to calculate a second vehicle error based on the first vehicle data and the third vehicle data corresponding to the third time period, where the second vehicle error represents a set of vehicles entering the road network within the third preset duration extending forward or backward from time T;

[0039] The determining unit is configured to determine the actual in-route vehicle data of the road network vehicles at time T based on the first vehicle data, the second vehicle data, and the second vehicle error corresponding to the first time period.

[0040] In the above solution, the second vehicle error includes a first set of errors and a second set of errors, where the first set of errors represents the vehicle error of the vehicles entering the road network within the third preset duration extending forward from time T, and the second set of errors represents the vehicle error of the vehicles entering the road network within the third preset duration extending backward from time T;

[0041] The calculating unit is further configured to calculate the vehicle difference between the first vehicle data and the second vehicle data corresponding to the first time period, and calculate the vehicle sum between the vehicle difference and the first set of errors and / or calculate the vehicle sum between the vehicle difference and the second set of errors when the first set of errors and the second set of errors meet the equal condition;

[0042] The determining unit is configured to determine the vehicle sum as the actual in-route vehicle data of the road network vehicles at time T.

[0043] According to the third aspect of the present application, there is provided a device for determining in-route vehicles of a road network, the device including:

[0044] A memory for storing a computer program;

[0045] At least one processor, when running the computer program, executes the method for determining the in-route vehicles on the road network according to any one of the above.

[0046] The method, device and equipment for determining the in-route vehicles on the road network provided by this application is a solution for statistically calculating the first vehicle data passing through a checkpoint at a certain moment and the second vehicle data not passing through the checkpoint in different time periods based on checkpoint data, and calculating the in-route quantity of the road network based on the first vehicle data and the second vehicle data. Specifically, the solution is as follows: respectively obtain the first vehicle data passing through the target checkpoint in the first time period, the second time period, and the third time period; wherein, the second time period includes the first time period, and the second time period extends forward by a first preset duration based on the starting moment of the first time period; the starting moment of the third time period is the starting moment of the second time period, and the ending moment of the third time period is the starting moment of the first time period; based on the first vehicle data corresponding to the second time period and the third time period respectively, determine the second vehicle data that does not pass through the target checkpoint in the third time period and passes through the target checkpoint in the first time period; based on the first vehicle data and the second vehicle data corresponding to the first time period, determine the actual in-route vehicle data of the road network vehicles at moment T, where moment T is the starting moment of the first time period. In this way, the number of in-route vehicles on the road network at a certain instant can be accurately calculated, which helps the public security traffic management department to more accurately grasp the road operation status and improve the traffic congestion situation. Brief Description of the Drawings

[0047] Figure 1 It is a schematic diagram of the process implementation of the method for determining the in-route vehicles on the road network in this application;

[0048] Figure 2 It is a schematic diagram of the structural composition of the device for determining the in-route vehicles on the road network in this application;

[0049] Figure 3 It is a schematic diagram of the structural composition of the equipment for determining the in-route vehicles on the road network in this application. Detailed Description of the Embodiments

[0050] The technical solutions of this application will be further elaborated in detail below in conjunction with the accompanying drawings of the specification and specific embodiments.

[0051] For each specific technical feature in each of the embodiments described in the detailed description, various combinations can be made without contradiction. For example, different embodiments can be formed by combining different specific technical features. To avoid unnecessary repetition, various possible combination methods of each specific technical feature in this application will not be described separately.

[0052] It should be noted that the terms "first / second / third" involved in the embodiments of the present application are only used to distinguish similar objects and do not represent a specific order for the objects. It can be understood that "first / second / third" can be interchanged with a specific order or sequence when permitted. It should be understood that the objects distinguished by "first / second / third" can be interchanged appropriately so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein.

[0053] Figure 1 Schematic diagram for implementing the method for determining in-route vehicles in the road network of the present application, as Figure 1 shown, the method includes:

[0054] Step 101: Obtain first vehicle data passing through the target checkpoint in the first time period, the second time period, and the third time period respectively; wherein, the second time period includes the first time period, and the second time period extends forward by a first preset duration based on the starting moment of the first time period; the starting moment of the third time period is the starting moment of the second time period, and the ending moment of the third time period is the starting moment of the first time period;

[0055] Here, the acquisition method of the first vehicle data is not limited, including but not limited to acquisition through checkpoint devices and in-vehicle devices.

[0056] Suppose the goal of the present application is to calculate the in-route quantity Z of the road network at time T, where T is any instantaneous moment; define t as any time length, such as 30 min, 60 min; define the first preset duration as x, representing any time length, such as 15 min.

[0057] Then: The first vehicle data corresponding to the first time period can be defined as K, representing the set of vehicles passing through the target checkpoint within the time period [T, T + t];

[0058] The first vehicle data corresponding to the second time period can be defined as K2, representing the set of vehicles passing through the target checkpoint within the time period [T - x, T + t];

[0059] The first vehicle data corresponding to the third time period can be defined as K1, representing the set of vehicles passing through the target checkpoint within the time period [T - x, T].

[0060] Step 102: Based on the first vehicle data corresponding to the second time period and the third time period respectively, determine second vehicle data that did not pass through the target checkpoint within the third time period and passed through the target checkpoint within the first time period;

[0061] Here, the second vehicle data can be determined by the vehicle difference between the first vehicle data corresponding to the second time period and the first vehicle data corresponding to the third time period. As shown in formula (1):

[0062] K2 - K1 = ΔK (1)

[0063] Wherein, ΔK represents the set of vehicles that did not pass through the target checkpoint during the time period [T - x, T] and passed through the target checkpoint during the time period [T, T + t].

[0064] Step 103: Based on the first vehicle data and the second vehicle data corresponding to the first time period, determine the actual in - transit vehicle data of the road network vehicles at time T, where time T is the starting time of the first time period.

[0065] Here, based on the vehicle difference between the first vehicle data and the second vehicle data corresponding to the first time period, the actual in - transit vehicle data of the road network vehicles at time T can be determined. As shown in formula (2):

[0066] K - ΔK = Z (2)

[0067] Wherein, Z represents the set of all in - transit road network vehicles at time T.

[0068] By using the method for determining in - transit road network vehicles provided in this application, the statistical accuracy of the in - transit volume of the road network can be improved, vehicle errors can be reduced, enabling the public security traffic management department to more accurately grasp the road operation conditions and improve the traffic congestion phenomenon.

[0069] In this application, the first vehicle data K passing through the target checkpoint within the first time period includes a first set of vehicle data and a second set of vehicle data. Among them, the first set of vehicle data can be defined as B, representing the set of vehicles in transit at time T and passing through the target checkpoint during the time period [T, T + t]; the second set of vehicle data can be defined as C, representing the set of vehicles not in transit at time T and passing through the target checkpoint during the time period [T, T + t]. As shown in formulas (3) and (4) below:

[0070] K = B ∪ C (3)

[0071]

[0072] It can be seen that the union of B and C is K, that is, B and C are a partition of K, and B and C do not overlap with each other.

[0073] In this application, the second vehicle data includes a third set of vehicle data and a fourth set of vehicle data. Among them, the third set of vehicle data can be defined as ΔK a, representing the set of vehicles that are not on the road during the time period [T - x, T], newly enter the road during the time period [T, T + t], and pass through the target checkpoint; this fourth set of vehicle data can be defined as ΔK b , representing the set of vehicles that are on the road but have not passed through the target checkpoint during the time period [T - x, T] and pass through the target checkpoint during the time period [T, T + t]; the third set of vehicle data and the fourth set of vehicle data do not overlap. As shown in formulas (5) and (6):

[0074] ΔK = ΔK a ∪ΔK b (5)

[0075]

[0076] It can be seen that the union of ΔK a and ΔK b is ΔK, that is, ΔK a and ΔK b is a partition of ΔK, and ΔK a and ΔK b do not overlap.

[0077] Here, ΔK b is related to the length of the time period x. When x is too small, ΔK b will increase significantly; when x continues to increase, ΔK b will decrease to a stable value and then remain stable. ΔK b exists as an error term all the time and cannot be eliminated because the phenomenon that vehicles enter the road network immediately in a short time before T exists objectively and is difficult to be recorded by the checkpoint. ΔK b As an error term, its influence is also acceptable because most of its elements are vehicles that enter the road network in a very short time before T. If a vehicle enters the road network a long time before T, when the distribution density of checkpoint equipment meets the standard, the probability of not passing through the checkpoint for a long time is very low. Therefore, the size of the set ΔK b is small and the error influence is acceptable.

[0078] It should be noted that ΔK a is very similar to the definition of C, but ΔK a has more strict requirements for the out-of-road time period. The error between the two lies in the set of vehicles that are on the road during the time period [T - x, T], not on the road at T, and pass through the target checkpoint during the time period [T, T + t]. This set of vehicles can be defined as ΔK ε .

[0079] Therefore, in the present application, a first vehicle error between the third set of vehicle data and the second set of vehicle data can also be calculated. The first vehicle error characterizes the vehicle dissimilarity between the third set of vehicle data and the second set of vehicle data, as shown in the following formulas (7) and (8):

[0080] ΔK a ∪ΔK ε =C (7)

[0081] C-ΔK a =ΔK ε (8);

[0082] Wherein, the first vehicle error ΔK ε is related to the first preset duration x and the second preset duration t. Wherein, the second preset duration t is the duration between the start time and the end time of the first time period, such as 30 min, 60 min. That is to say, when the vehicle is driving on the road, the behavior of leaving the road network and then re-entering the road network within a short period of time. When x meets the preset conditions (for example: x is less than 15 minutes), it can be determined that there are very few error vehicles that meet the conditions. Therefore, the set C and the set ΔK a can be regarded as approximately equal, and both are the set of vehicles that have just entered the road network and passed through the target checkpoint within the time period [T, T + t], C≈ΔK a .

[0083] Then, calculate the vehicle sum between the first set of vehicle data and the first vehicle error, and calculate the vehicle difference between the vehicle sum and the fourth set of vehicle data; use the vehicle difference as the actual in-transit vehicle data of the road network at time T. As shown in the following formula (9):

[0084] K-ΔK=B+ΔK ε -ΔK b (9)

[0085] In the present application, the actual in-transit vehicle data includes: the fifth set of vehicle data and the sixth set of vehicle data. Wherein, the fifth set of vehicle data can be defined as A, representing the set of vehicles in transit at time T and not passing through the target checkpoint within the time period [T, T + t]; the sixth set of vehicle data is the same as the first set of vehicle data and can be defined as B, representing the set of vehicles in transit at time T and passing through the target checkpoint within the time period [T, T + t]. As shown in the following formulas (10) and (11):

[0086] Z=A∪B (10)

[0087]

[0088] It can be seen that the union of A and B is Z, that is, A and B are a partition of Z, and A and B do not overlap with each other.

[0089] In this application, the fifth group of vehicle data further includes first part data and second part data. Among them, the first part data represents the set of vehicles that are on the way at time T and have been on the way during the first time period but have not passed through the target checkpoint; the second part data represents the set of vehicles that are on the way at time T but leave the road network at a certain moment during the first time period and have not passed through the target checkpoint. Among them, the first part data is related to a second preset duration, and the second part data is related to the checkpoint density. Specifically, as shown in the following formulas (12) and (13):

[0090] A = A1 ∪ A2 (12)

[0091]

[0092] Among them, A represents the set of vehicles that are on the way at time T and have not passed through the target checkpoint during the time period [T, T + t]. A1 represents the set of vehicles that are on the way at time T and have been on the way during the time period [T, T + t] but have not passed through the target checkpoint. A2 represents the set of vehicles that are on the way at time T and leave the road network at a certain moment during the time period [T, T + t] and have not passed through the target checkpoint.

[0093] Here, since checkpoint devices (electronic police, monitors) are distributed everywhere in the city, if a vehicle has only been driving on the road for 2 minutes, it is entirely possible that it has not encountered a checkpoint within these 2 minutes; however, if a vehicle has been driving continuously on the road for half an hour, the probability of not encountering a checkpoint even once within half an hour is very small because there are many checkpoints in the city. Therefore, the size of A1 is related to the length of the time period t. The larger t is, the smaller A1 is. When the distribution density of the checkpoint devices reaches the average level, when t is large enough, A1 will approach 0 infinitely.

[0094] Here, the size of A2 is related to the distribution density of the checkpoint devices. The greater the distribution density of the checkpoint devices, the smaller |A2| is. This is because the greater the density of the checkpoint devices, the easier it is for vehicles to encounter checkpoint devices when driving on the road. Therefore, the probability of not passing through the checkpoint before leaving the road network is smaller, and the number of such vehicles is fewer.

[0095] In this application, the distribution density of checkpoints and the average vehicle speed within a preset area can also be determined based on the number of checkpoints and the road mileage within the preset area; third vehicle data passing through the target checkpoint within a fourth time period is obtained, where the fourth time period includes the third time period and extends backward from the starting moment of the first time period by a third preset duration, and the third preset duration is related to the checkpoint distribution density and the average vehicle speed; based on the first vehicle data and the third vehicle data corresponding to the third time period, a second vehicle error is determined, and the second vehicle error can be defined as ε′, representing the set of vehicles entering the road network within the third preset duration extending forward or backward from the T moment.

[0096] Here, the third preset duration can be defined as y, representing the duration of vehicles entering the road network shortly before the T moment or leaving the road network shortly after the T moment. When the checkpoint density in the city is relatively high, the vehicle speed is slower during peak hours, and y can be set to 2 minutes; when the vehicle speed is faster during off-peak hours, y can be set to 1 minute; when the checkpoint density in the city is average, the vehicle speed is slower during peak hours, and y can be set to 5 minutes; when the vehicle speed is faster during off-peak hours, y can be set to 2 minutes.

[0097] Here, the third vehicle data passing through the target checkpoint within the fourth time period can be defined as K3, representing the set of all vehicles passing through the target checkpoint within the time period [T - x, T + y].

[0098] Based on the first vehicle data, the second vehicle data, and the second vehicle error corresponding to the first time period, the actual in-transit vehicle data of the road network vehicles at the T moment can be determined. As shown in formulas (14) and (15):

[0099] K3 - K1 = ε′ (14)

[0100] K - ΔK + ε′ = Z (15)

[0101] In this application, the second vehicle error ε′ includes a first group of errors and a second group of errors, where the first group of errors represents the vehicle errors of vehicles entering the road network within the third preset duration extending forward from the T moment, and the second group of errors represents the vehicle errors of vehicles entering the road network within the third preset duration extending backward from the T moment; when the first group of errors and the second group of errors satisfy the condition of being equal (representing relatively equal or absolutely equal), the vehicle difference between the first vehicle data and the second vehicle data corresponding to the first time period can be calculated, and the vehicle sum between the vehicle difference and the first group of errors or the vehicle sum between the vehicle difference and the second group of errors can be calculated; the vehicle sum is determined as the actual in-transit vehicle data of the road network vehicles at the T moment.

[0102] That is to say, ε′ can also be divided into two sets, namely, "the set of vehicles that are not on the way during the period of [T - x, T] and newly enter the road and pass through the target checkpoint during the period of [T, T + y]" and "the set of vehicles that are on the way but do not pass through the target checkpoint during the period of [T - x, T] and pass through the target checkpoint during the period of [T, T + y]". Among them, the former can be used to estimate the number of vehicles entering the road network in a short time after time T, and the latter can be used to estimate the number of vehicles entering the road network in a short time before time T. Assuming that the number of vehicles entering the road network is equal in a very short time before and after time T, then ε′ divided by 2 is the estimate of the number of vehicles entering the road network in a short time before time T, that is, the estimate of ΔK b Estimation.

[0103] In this application, the statistic K - ΔK is used as the estimate of the actual in - transit volume Z of the road network at time T. The estimation error ε is expressed by the following formula (16):

[0104] ε = Z - (K - ΔK) = A1 + A2 - ΔK ε +ΔK b (16)

[0105] When the time period t is relatively large (for example, t is more than 30 minutes), A1 will approach 0. Therefore, the estimation error in actual application is mainly composed of A2 - ΔK ε +ΔK b . Among them, A2 cannot be estimated and eliminated because the phenomenon that vehicles leave the road network in a short time after time T objectively exists and is difficult to be recorded by the checkpoints. However, as an error term, the influence of A2 is acceptable because most of its elements are vehicles that leave the road network in a very short time after time T. If the vehicle travels for a while before leaving the road network after time T, when the distribution density of checkpoint equipment meets the standard, the probability of not passing through the checkpoint for a long time is very low. Therefore, the size of set A2 is small and the error influence is acceptable.

[0106] Here, the error of ΔK b in the actual in - transit vehicle data is eliminated as shown in the following formula (17):

[0107] K - ΔK + ε′÷2 = Z(17)

[0108] In this way, by eliminating the error of ΔK b in the actual in - transit vehicle data, the statistical result can be made smaller than the actual in - transit volume of the road network, greatly improving the statistical accuracy of the actual in - transit vehicles on the road network.

[0109] For easy understanding, specific numerical calculations are used as examples. The following takes the calculation of the in - transit volume of the road network at 8:00 in the morning as an example to illustrate.

[0110] 1. First, select the length of time period t. The key point in selecting time period t is to make A1 (the set of vehicles that are in transit at time T and have not passed through the target checkpoint within the time period [T, T + t]) as small as possible. Here, t is taken as 30 minutes. When the distribution density of checkpoint devices in a city reaches the average level, the probability that a vehicle continuously travels on the road network for 30 minutes without passing through a checkpoint approaches 0. At the same time, to control the size of ΔK ε t cannot be taken too large. For various types of cities, it is recommended that t be taken as 30 minutes.

[0111] 2. Calculate K. That is, count the number of vehicles passing through the checkpoint during the time period (8:00, 8:30) through the checkpoint passing vehicle data. Since there are no duplicate elements in the set, when counting the number of vehicles based on license plate numbers here, it is necessary to remove duplicates from the license plate numbers (each license plate is only counted once).

[0112] 3. Calculate ΔK. At this time, it is necessary to first select the length of time period x. If the length of x is taken too small, it will cause the error term ΔK b to be too large; if the length of x is taken too large, it will cause the error term ΔK ε to be too large. Therefore, for various types of cities, it is recommended that the length of time period x be taken as 15 minutes. Count the number of vehicles passing through the checkpoint during (7:45, 8:30) and the number of vehicles passing through the checkpoint during (7:45, 8:00), and subtract the two. The result is ΔK.

[0113] 4. Set time period y and calculate ε′.

[0114] 5. Calculate K - ΔK + ε′÷2 = Z, and the result is the in-transit volume of the road network at 8:00.

[0115] The following takes a specific city as an example:

[0116] The number of vehicles K passing through the checkpoint device during the time period (8:00, 8:30) = 373684, the number of vehicles K2 passing through the checkpoint device during (7:45, 8:30) = 507536, the number of vehicles K1 passing through the checkpoint device during (7:45, 8:00) = 257797, and the difference between the two ΔK = 249739.

[0117] The checkpoint density of this city is relatively high, and it is the morning rush hour. Therefore, set time period y to 2 minutes. The number of vehicles K3 passing through the checkpoint during (7:45, 8:02) = 277123, and ε′÷2 = (K 3- - K1)÷2 = 9663.

[0118] Finally, calculate K - ΔK + ε′÷2, and the instantaneous in-transit volume of the road network of this city at 8:00 is 373684 - 249739 + 9663 = 133608 vehicles. That is, the instantaneous in-transit volume of the road network of this city at 8:00 is 133608 vehicles.

[0119] Thus, through the method for determining the in-route vehicles on the road network provided by this application, the number of in-route vehicles on the road network at a certain instant can be accurately calculated, which helps the public security traffic management department to more accurately grasp the road operation conditions and improve the traffic congestion situation.

[0120] Figure 2 It is a schematic structural composition diagram of the device for determining the in-route vehicles on the road network in this application, as Figure 2 shown. The device includes:

[0121] An acquisition unit 201, configured to respectively acquire first vehicle data passing through a target checkpoint within a first time period, a second time period, and a third time period; wherein, the second time period includes the first time period, and the second time period extends forward from the starting moment of the first time period by a first preset duration; the starting moment of the third time period is the starting moment of the second time period, and the ending moment of the third time period is the starting moment of the first time period;

[0122] A determination unit 202, configured to determine second vehicle data that did not pass through the target checkpoint within the third time period and passed through the target checkpoint within the first time period based on the first vehicle data respectively corresponding to the second time period and the third time period; and configured to determine the actual in-route vehicle data of the road network vehicles at moment T based on the first vehicle data corresponding to the first time period and the second vehicle data, wherein the moment T is the starting moment of the first time period.

[0123] Here, the first vehicle data passing through the target checkpoint within the first time period includes a first set of vehicle data and a second set of vehicle data, wherein the first set of vehicle data represents the set of vehicles that were in route at moment T and passed through the target checkpoint within the first time period; the second set of vehicle data represents the set of vehicles that were not in route at moment T and passed through the target checkpoint within the first time period; wherein, the first set of vehicle data and the second set of vehicle data do not overlap with each other;

[0124] The second vehicle data includes a third set of vehicle data and a fourth set of vehicle data, wherein the third set of vehicle data represents the set of vehicles that were not in route within the third time period, newly hit the road within the first time period, and passed through the target checkpoint; the fourth set of vehicle data represents the set of vehicles that were in route within the third time period but did not pass through the target checkpoint and passed through the target checkpoint within the first time period; the third set of vehicle data and the fourth set of vehicle data do not overlap with each other.

[0125] In a preferred solution of this application, the device further includes:

[0126] The calculation unit 203 is configured to calculate a first vehicle error between the third set of vehicle data and the second set of vehicle data, where the first vehicle error characterizes the vehicle dissimilarity between the third set of vehicle data and the second set of vehicle data; wherein the first vehicle error is related to the first preset duration and a second preset duration, and the second preset duration is the duration between the start time and the end time of the first time period; and is configured to calculate the sum of vehicles between the first set of vehicle data and the first vehicle error, and calculate the vehicle difference between the sum of vehicles and the fourth set of vehicle data.

[0127] The determination unit 202 is configured to use the vehicle difference as the actual in-transit vehicle data of the road network vehicles at time T.

[0128] In a preferred solution of the present application, the determination unit 202 is further configured to determine the distribution density of checkpoints and the average vehicle speed within the preset area according to the number of checkpoints and the road mileage within the preset area.

[0129] The acquisition unit 201 is further configured to acquire third vehicle data passing through the target checkpoint within a fourth time period, where the fourth time period includes the third time period and extends backward from the start time of the first time period by a third preset duration, and the third preset duration is related to the checkpoint distribution density and the average vehicle speed.

[0130] The calculation unit 203 is further configured to calculate a second vehicle error based on the first vehicle data and the third vehicle data corresponding to the third time period, where the second vehicle error characterizes the set of vehicles entering the road network within a third preset duration extending forward or backward from time T.

[0131] The determination unit 202 is specifically configured to determine the actual in-transit vehicle data of the road network vehicles at time T based on the first vehicle data, the second vehicle data, and the second vehicle error corresponding to the first time period.

[0132] Here, the second vehicle error includes a first set of errors and a second set of errors, where the first set of errors characterizes the vehicle error of the vehicles entering the road network within a third preset duration extending forward from the time T, and the second set of errors characterizes the vehicle error of the vehicles entering the road network within a third preset duration extending backward from the time T.

[0133] In a preferred embodiment of the present application, the calculation unit 203 is further configured to calculate the vehicle difference between the first vehicle data and the second vehicle data corresponding to the first time period, and calculate the vehicle sum between the vehicle difference and the first group of errors and / or calculate the vehicle sum between the vehicle difference and the second group of errors when the first group of errors and the second group of errors meet the equal condition;

[0134] The determination unit 202 is configured to determine the vehicle sum as the actual in-route vehicle data of the road network vehicles at time T.

[0135] It should be noted that the device for determining in-route road network vehicles provided in the above embodiments and the above Figure 1 The method for determining in-route road network vehicles belong to the same concept. The specific implementation process can refer to the above system embodiments and will not be elaborated here.

[0136] Through the device for determining in-route road network vehicles provided by the present application, the number of in-route road network vehicles at a certain instant can be accurately calculated, which helps the public security traffic management department to more accurately grasp the road operation conditions and improve the traffic congestion situation.

[0137] Figure 3 For the device for determining in-route road network vehicles in the present application, as Figure 3 shown, the device 300 for determining in-route road network vehicles includes at least one processor 301 and a memory 302 for storing a computer program that can run on the processor 301. When the processor 301 is used to run the computer program, it executes the method for determining in-route road network vehicles as prompted in the above embodiments of the present application. The device 300 for determining in-route road network vehicles further includes at least one network interface 304 and a user interface 303. Each component in the device 300 for determining in-route road network vehicles is coupled together through a bus system 305. It can be understood that the bus system 305 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 305 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clear illustration, in Figure 3 all kinds of buses are labeled as the bus system 305.

[0138] Among them, the user interface 303 may include a display, a keyboard, a mouse, a trackball, a click wheel, a button, a button, a touchpad, or a touch screen, etc.

[0139] It can be understood that the memory 302 can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a ferromagnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); the magnetic surface memory can be a disk memory or a tape memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), sync link dynamic random access memory (SLDRAM), direct rambus random access memory (DRRAM).The memory 302 described in the embodiments of the present application is intended to include, but is not limited to, these and any other suitable types of memories.

[0140] The memory 302 in the embodiments of the present application is used to store various types of data to support the operation of the on-road vehicle determination device 300 of the road network. Examples of such data include: any computer programs for operating on the on-road vehicle determination device 300 of the road network, such as the operating system 3021 and application programs 3022; wherein, the operating system 3021 contains various system programs, such as the framework layer, core library layer, driver layer, etc., for implementing various basic services and processing hardware-based tasks. The application programs 3022 may include various application programs, such as a Media Player, Browser, etc., for implementing various application services. The program for implementing the method of the embodiments of the present application may be included in the application programs 3022.

[0141] The processor 301 may be an integrated circuit chip with the ability to process signals. In the implementation process, each step of the above method may be completed by the integrated logic circuit in the hardware of the processor 301 or instructions in software form. The above-mentioned processor 301 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 301 may implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor or any conventional processor, etc. Combining the steps of the method disclosed in the embodiments of the present application, it may be directly embodied as being executed and completed by a hardware decoding processor, or executed and completed by a combination of the hardware and software modules in the decoding processor. The software module may be located in a storage medium, and this storage medium is located in the memory 302. The processor 301 reads the information in the memory 302 and combines its hardware to complete the steps of the foregoing method.

[0142] In an exemplary embodiment, the determining device 300 for vehicles on the road network can be implemented by one or more application specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field programmable gate arrays (FPGAs), general purpose processors, controllers, microcontroller units (MCUs), microprocessors, or other electronic components, and is used to execute the foregoing method.

[0143] In an exemplary embodiment, the embodiments of the present application further provide a computer-readable storage medium, such as a memory 302 including a computer program. The foregoing computer program can be executed by a processor 301 of the determining device 300 for vehicles on the road network to complete the steps described in the foregoing method. The computer-readable storage medium can be a FRAM, ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface memory, optical disc, or CD-ROM, etc.; it can also be various devices including one or any combination of the foregoing memories, such as a computer, a tablet device, a personal digital assistant, etc.

[0144] A computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, it executes the surge warning method of the compressor prompted by the foregoing embodiments of the present application.

[0145] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only illustrative. In addition, the features disclosed in several method or device embodiments provided by the present application can be combined arbitrarily without conflict to obtain new method embodiments or device embodiments.

[0146] As described above, the above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present application, and all should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for determining vehicles in transit on a road network, characterized in that, The method includes: Obtaining first vehicle data passing through a target checkpoint in a first time period, a second time period, and a third time period respectively; wherein, the second time period includes the first time period, and the second time period extends forward from the starting moment of the first time period by a first preset duration; the starting moment of the third time period is the starting moment of the second time period, and the ending moment of the third time period is the starting moment of the first time period; Based on the first vehicle data corresponding to the second time period and the third time period respectively, determining second vehicle data that did not pass through the target checkpoint in the third time period but passed through the target checkpoint in the first time period; Based on the first vehicle data corresponding to the first time period and the second vehicle data, determining the actual in-transit vehicle data of road network vehicles at time T, where the time T is the starting moment of the first time period.

2. The method according to claim 1, characterized in that, The first vehicle data of the vehicles passing through the target checkpoint in the first time period includes a first set of vehicle data and a second set of vehicle data, wherein the first set of vehicle data represents the set of vehicles in transit at time T and passing through the target checkpoint within the first time period; the second set of vehicle data represents the set of vehicles not in transit at time T and passing through the target checkpoint within the first time period; wherein, the first set of vehicle data and the second set of vehicle data do not overlap; The second vehicle data includes a third set of vehicle data and a fourth set of vehicle data, wherein the third set of vehicle data represents the set of vehicles not in transit in the third time period, newly on the road and passing through the target checkpoint in the first time period; the fourth set of vehicle data represents the set of vehicles in transit in the third time period but not passing through the target checkpoint and passing through the target checkpoint in the first time period; the third set of vehicle data and the fourth set of vehicle data do not overlap; The determining the actual in-transit vehicle data of the road network at time T based on the first vehicle data corresponding to the first time period and the second vehicle data includes: Calculating a first vehicle error between the third set of vehicle data and the second set of vehicle data, where the first vehicle error represents the vehicle dissimilarity between the third set of vehicle data and the second set of vehicle data; wherein, the first vehicle error is related to the first preset duration and a second preset duration, and the second preset duration is the duration between the starting moment and the ending moment of the first time period; Calculating the vehicle sum between the first set of vehicle data and the first vehicle error, and calculating the vehicle difference between the vehicle sum and the fourth set of vehicle data; Taking the vehicle difference as the actual in-transit vehicle data of the road network vehicles at time T.

3. The method according to claim 1, wherein The method further includes: Determining the checkpoint distribution density and the average vehicle speed within the preset area according to the number of checkpoints and the road mileage within the preset area; Acquire third vehicle data that passes through the target checkpoint within a fourth time period, wherein the fourth time period includes the third time period, and the fourth time period extends backward by a third preset time length based on the start time of the first time period, and the third preset time length is related to the checkpoint distribution density and the average speed of the vehicle; Determine a second vehicle error based on the first vehicle data and the third vehicle data corresponding to the third time period, wherein the second vehicle error represents a set of vehicles that enter the road network within the third preset time period extending forward or backward based on time T; The determining, based on the first vehicle data and the second vehicle data corresponding to the first time period, actual on-the-way vehicle data of the road network vehicles at time T includes: Based on the first vehicle data, the second vehicle data and the second vehicle error corresponding to the first time period, actual on-the-way vehicle data at time T in the road network vehicles is determined.

4. The method according to claim 3, wherein The determining, based on the first vehicle data, the second vehicle data and the second vehicle error corresponding to the first time period, actual on-the-way vehicle data of the road network vehicles at time T includes: The second vehicle error includes a first group of errors and a second group of errors, wherein the first group of errors represents vehicle errors that enter the road network within a third preset time period extending forward from the T moment, and the second group of errors represents vehicle errors that enter the road network within a third preset time period extending backward from the T moment; When the first group of errors and the second group of errors meet an equal condition, calculating a vehicle difference between the first vehicle data and the second vehicle data corresponding to the first time period, and calculating a vehicle sum between the vehicle difference and the first group of errors or calculating a vehicle sum between the vehicle difference and the second group of errors; The vehicles and are determined as the actual on-the-way vehicle data of the road network vehicles at time T.

5. The method according to claim 2, wherein The actual on-the-way vehicle data includes: a fifth group of vehicle data and a sixth group of vehicle data, wherein the fifth group of vehicle data represents a set of vehicles that are on-the-way at time T and have not passed through the target checkpoint within the first time period; the sixth group of vehicle data is the same as the first group of vehicle data; The fifth set of vehicle data includes a first part of data and a second part of data, wherein the first part of data represents a set of vehicles that are on the road at time T and have been on the road during the first time period but have not passed through the target checkpoint; the second part of data represents a set of vehicles that are on the road at time T but have left the road network at a certain time during the first time period and have not passed through the target checkpoint; The first portion of data is related to a second preset duration, and the second portion of data is related to a mount density.

6. A determining device for vehicles in transit on a road network, characterized in that, The device comprises: An acquisition unit for respectively acquiring first vehicle data passing through a target checkpoint within a first time period, a second time period, and a third time period; wherein, the second time period includes the first time period, and the second time period extends forward from the starting moment of the first time period by a first preset duration; the starting moment of the third time period is the starting moment of the second time period, and the ending moment of the third time period is the starting moment of the first time period; A determination unit for determining, based on the first vehicle data respectively corresponding to the second time period and the third time period, second vehicle data that did not pass through the target checkpoint within the third time period but passed through the target checkpoint within the first time period; and for determining, based on the first vehicle data corresponding to the first time period and the second vehicle data, the actual in-transit vehicle data of the road network vehicles at moment T, wherein the moment T is the starting moment of the first time period.

7. The device according to claim 6, characterized in that, The first vehicle data of the vehicles passing through the target checkpoint within the first time period includes a first set of vehicle data and a second set of vehicle data, wherein the first set of vehicle data represents the set of vehicles that were in transit at moment T and passed through the target checkpoint within the first time period; the second set of vehicle data represents the set of vehicles that were not in transit at moment T and passed through the target checkpoint within the first time period; wherein, the first set of vehicle data and the second set of vehicle data do not overlap; The second vehicle data includes a third set of vehicle data and a fourth set of vehicle data, wherein the third set of vehicle data represents the set of vehicles that were not in transit within the third time period, newly hit the road within the first time period, and passed through the target checkpoint; the fourth set of vehicle data represents the set of vehicles that were in transit within the third time period but did not pass through the target checkpoint and passed through the target checkpoint within the first time period; the third set of vehicle data and the fourth set of vehicle data do not overlap; The apparatus further includes: A calculation unit for calculating a first vehicle error between the third set of vehicle data and the second set of vehicle data, the first vehicle error representing the vehicle dissimilarity between the third set of vehicle data and the second set of vehicle data; wherein, the first vehicle error is related to the first preset duration and a second preset duration, and the second preset duration is the duration between the starting moment and the ending moment of the first time period; and for calculating the vehicle sum between the first set of vehicle data and the first vehicle error, and calculating the vehicle difference between the vehicle sum and the fourth set of vehicle data; The determination unit for using the vehicle difference as the actual in-transit vehicle data of the road network vehicles at moment T.

8. The device according to claim 7, characterized in that, The determination unit is further configured to determine the checkpoint distribution density and the average vehicle speed within the preset area according to the number of checkpoints and the road mileage within the preset area; The obtaining unit is further configured to obtain third vehicle data passing through the target checkpoint within a fourth time period, where the fourth time period includes the third time period and extends backward from the starting moment of the first time period by a third preset duration, and the third preset duration is related to the checkpoint distribution density and the average vehicle speed; The calculating unit is further configured to calculate a second vehicle error based on the first vehicle data and the third vehicle data corresponding to the third time period, where the second vehicle error represents a set of vehicles entering the road network within the third preset duration extending forward or backward from the T moment; The determining unit is configured to determine the actual in-transit vehicle data of the road network vehicles at the T moment based on the first vehicle data, the second vehicle data, and the second vehicle error corresponding to the first time period.

9. The device according to claim 8, wherein The second vehicle error includes a first set of errors and a second set of errors, where the first set of errors represents the vehicle error of the vehicles entering the road network within the third preset duration extending forward from the T moment, and the second set of errors represents the vehicle error of the vehicles entering the road network within the third preset duration extending backward from the T moment; The calculating unit is further configured to, when the first set of errors and the second set of errors meet the equality condition, calculate the vehicle difference between the first vehicle data and the second vehicle data corresponding to the first time period, and calculate the vehicle sum between the vehicle difference and the first set of errors and / or calculate the vehicle sum between the vehicle difference and the second set of errors; The determining unit is configured to determine the vehicle sum as the actual in-transit vehicle data of the road network vehicles at the T moment.

10. A determining device for vehicles in transit on a road network, characterized in that, The device includes: a memory for storing a computer program; at least one processor, configured to execute the method for determining in-transit road network vehicles according to any one of claims 1 to 5 when running the computer program.

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