A method and device for optimizing road section congestion, an electronic device, and a storage medium

Abstract

The present application discloses a method and apparatus for optimizing road section congestion, an electronic device, and a storage medium. The method includes: dividing the vehicles passing through the road section to be optimized during the congestion-related period into normal driving vehicles and parked vehicles; analyzing the passing vehicle data of the normal driving vehicles and parked vehicles during the congestion-related period to obtain the congestion degree of the road section to be optimized, where the passing vehicle data includes time information of passing through the road section to be optimized; and performing a first optimization process on the road section to be optimized according to the congestion degree. The above solution can intuitively reflect the traffic operation status of the road section and timely solve the road congestion situation.

Description

Technical Field

[0001] The present application relates to the field of traffic information processing, and particularly to a method and device for optimizing road congestion, an electronic device, and a storage medium. Background Art

[0002] With the rapid construction and development of urban traffic and the rapid growth of the vehicle ownership, the urban traffic condition has deteriorated day by day. Especially in the traffic of large and medium-sized cities, the congestion is becoming more and more serious, which has seriously affected the daily life of urban residents. Among them, during the school dismissal time of primary and middle schools, most parents will choose to drive to pick up their children after school. This time period generally overlaps with the evening rush hour of office workers, resulting in a large number of vehicles pouring into the adjacent roads of the school, causing severe congestion in the adjacent roads of the school. If corresponding measures are not taken in time to relieve the congestion, it may lead to a more serious phenomenon of vehicle queue overflow at intersections.

[0003] The cause of this kind of congestion is essentially different from the congestion caused by large traffic flow during the morning and evening rush hours on roads. And the existing methods for judging road congestion cannot intuitively reflect the real operation condition of the road section in this scenario. Summary of the Invention

[0004] The present application provides at least a method and device for optimizing road congestion, an electronic device, and a storage medium, which can intuitively reflect the traffic operation condition of the road section and timely solve the road congestion situation.

[0005] In the first aspect of the present application, a method for optimizing road congestion is provided. The method includes: dividing the vehicles passing through the road section to be optimized during the congestion-related time period into normal driving vehicles and parked vehicles; analyzing the passing vehicle data of the normal driving vehicles and parked vehicles during the congestion-related time period to obtain the congestion degree of the road section to be optimized, where the passing vehicle data includes the time information of passing through the road section to be optimized; performing a first optimization process on the road section to be optimized according to the congestion degree.

[0006] Among them, analyzing the passing vehicle data of the normal driving vehicles and parked vehicles during the congestion-related time period to obtain the congestion degree of the road section to be optimized includes: using the passing vehicle data of the normal driving vehicles to obtain at least one first congestion characterization value, where the first congestion characterization value represents the congestion degree of the road section to be optimized from the dimension of the normal driving vehicles; using the passing vehicle data of the parked vehicles to obtain at least one second congestion characterization value, where the second congestion characterization value represents the congestion degree of the road section to be optimized from the dimension of the parked vehicles; and obtaining the congestion degree of the road section to be optimized based on the first congestion characterization value and the second congestion characterization value.

[0007] Among them, at least one first congestion characterization value is obtained by using the passing vehicle data of normally traveling vehicles, including: determining the statistical speed of normally traveling vehicles during the congestion-related period by using the time information of each normally traveling vehicle passing through the section to be optimized; obtaining the first congestion characterization value by using the gap between the statistical speed and the reference speed, where the reference speed represents the vehicle passing speed of the section to be optimized under normal traffic conditions.

[0008] Among them, the reference speed is the ratio of the length of the section to be optimized to the normal passing time, and the normal passing time is the first average passing time of each vehicle within the preset time period of the section to be optimized; determining the statistical speed of normally traveling vehicles during the congestion-related period by using the time information of each normally traveling vehicle passing through the section to be optimized includes: obtaining the second average passing time of the normally traveling vehicles passing through the section to be optimized during the congestion-related period by using the time information of each normally traveling vehicle passing through the section to be optimized; obtaining the ratio of the length of the section to be optimized to the second average passing time as the statistical speed; and / or obtaining the first congestion characterization value by using the gap between the statistical speed and the reference speed includes: obtaining the ratio of the statistical speed to the reference speed as the speed ratio; selecting the minimum value from the first multiple of the speed ratio and the first multiple of the first preset congestion value as the first congestion characterization value, where the first multiple is an integer greater than or equal to 1.

[0009] Among them, at least one second congestion characterization value is obtained by using the passing vehicle data of parked vehicles, including any one or more of the following steps: obtaining the statistical parking duration of parked vehicles by using the time information of each parked vehicle passing through the section to be optimized, and obtaining a second congestion characterization value based on the statistical parking duration; obtaining a second congestion characterization value by using the number of parked vehicles in the passing vehicle data of parked vehicles.

[0010] Among them, by using the time information of each parked vehicle passing through the section to be optimized, the statistical parking duration of the parked vehicles is obtained, including: by using the time information of each parked vehicle passing through the section to be optimized, the third average passing time of the parked vehicle passing through the section to be optimized during the congestion-related period is obtained as the statistical parking duration; and / or, a second congestion characterization value is obtained based on the statistical parking duration, including: obtaining the duration difference between the statistical parking duration and the normal passing duration; obtaining a first parking congestion value corresponding to the duration difference, and selecting the maximum value from the first parking congestion value and a second preset congestion value as the second congestion characterization value, where the first parking congestion value is negatively correlated with the duration difference; and / or, by using the number of parked vehicles in the passing vehicle data of the parked vehicle, a second congestion characterization value is obtained, including: using the number of parked vehicles and the vehicle length of the parked vehicle to obtain the total vehicle parking length; obtaining a second parking congestion value corresponding to the total vehicle parking length, and selecting the maximum value from the second parking congestion value and a third preset congestion value as the second congestion characterization value, where the second parking congestion characterization value is negatively correlated with the total vehicle parking length.

[0011] Among them, obtaining a first parking congestion characterization value corresponding to the duration difference includes: obtaining a second multiple of the difference between the preset value and the duration difference as the first parking congestion characterization value, where the second multiple is an integer greater than or equal to 1; obtaining a second parking congestion characterization value corresponding to the total vehicle parking length includes: obtaining the length difference between the length of the section to be optimized and the total vehicle parking length, and taking a third multiple of the ratio of the length difference to the length of the section to be optimized as the second parking congestion characterization value, where the third multiple is an integer greater than or equal to 1.

[0012] Among them, based on the first congestion characterization value and the second congestion characterization value, the congestion degree of the section to be optimized is obtained, including: weighting each first congestion characterization value and each second congestion characterization value to obtain the congestion degree of the section to be optimized.

[0013] Among them, performing a first optimization process on the section to be optimized according to the congestion degree includes any one or more of the following steps: in response to the congestion degree meeting the first congestion prompt requirement, sending a detour suggestion for the section to be optimized to the user through the navigation system; extending the time when the signal light at the upstream intersection of the section to be optimized is in the first indication state, where the first indication state is used to indicate vehicles to enter the section to be optimized; extending the time when the signal light at the downstream intersection of the section to be optimized is in the second indication state, where the second indication state is used to indicate vehicles to leave the section to be optimized.

[0014] After classifying the vehicles passing through the section to be optimized during the congestion-related period into normal-moving vehicles and parked vehicles, the method further includes: obtaining the vehicle queuing situation based on the ratio between the total vehicle parking length and the length of the section to be optimized, where the total vehicle parking length is determined based on the vehicle lengths of the parked vehicles and the number of parked vehicles in the passing vehicle data of the parked vehicles; in response to the vehicle queuing situation meeting the requirements of the second congestion prompt, sending a prompt to the control terminal that the section to be optimized requires on-site guidance, and / or notifying the navigation system to send a detour suggestion to the user for the section to be optimized.

[0015] Among them, classifying the vehicles passing through the section to be optimized during the congestion-related period into normal-moving vehicles and parked vehicles includes: obtaining the passing vehicle data of the vehicles passing through the section to be optimized during the congestion-related period; clustering the vehicles passing through the section to be optimized during the congestion-related period based on the passing vehicle data to obtain a clustering result, where the clustering result includes the first vehicle information belonging to the normal-moving vehicles and the second vehicle information belonging to the parked vehicles.

[0016] A second aspect of the present application provides a section congestion optimization device, including: an acquisition and classification module, configured to classify the vehicles passing through the section to be optimized during the congestion-related period into normal-moving vehicles and parked vehicles; an analysis module, configured to analyze the congestion degree of the section to be optimized by using the passing vehicle data of the normal-moving vehicles and the parked vehicles during the congestion-related period, where the passing vehicle data includes the time information of passing through the section to be optimized; an execution module, configured to perform a first optimization process on the section to be optimized according to the congestion degree.

[0017] A third aspect of the present application provides an electronic device, including a memory and a processor coupled to each other, where the processor is configured to execute program instructions stored in the memory to implement the section congestion optimization method in the first aspect above.

[0018] A fourth aspect of the present application provides a computer-readable storage medium, on which program instructions are stored, and when the program instructions are executed by a processor, the section congestion optimization method in the first aspect above is implemented.

[0019] In the above solution, first, the vehicles passing through the section to be optimized during the congestion-related period are classified into normal-moving vehicles and parked vehicles, and the congestion degree of the section to be optimized is analyzed by using the time information of the normal-moving vehicles and the parked vehicles passing through the section to be optimized during the congestion-related period. The congestion degree determined based on the passing vehicle situations of the two types of vehicles, namely normal-moving vehicles and parked vehicles, can intuitively reflect the traffic operation status of the section to be optimized. Then, the section to be optimized is optimized according to the congestion degree, so as to be able to timely solve the congestion situation of the section to be optimized.

[0020] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, rather than limiting the present application. Description of the Drawings

[0021] The drawings herein are incorporated into the specification and constitute a part of this specification. These drawings show embodiments consistent with the present application and, together with the specification, are used to explain the technical solutions of the present application.

[0022] Figure 1 is a schematic flowchart of an embodiment of the method for optimizing road congestion in the present application;

[0023] Figure 2 is a schematic flowchart of step S110 in another embodiment of the method for optimizing road congestion in the present application;

[0024] Figure 3 is a schematic flowchart of step S120 in yet another embodiment of the method for optimizing road congestion in the present application;

[0025] Figure 4 is a schematic flowchart of another embodiment of the method for optimizing road congestion in the present application.

[0026] Figure 5 is a schematic framework diagram of an embodiment of the device for optimizing road congestion in the present application;

[0027] Figure 6 is a schematic framework diagram of an embodiment of the electronic device in the present application;

[0028] Figure 7 is a schematic framework diagram of an embodiment of the computer-readable storage medium in the present application. Detailed Embodiments

[0029] The following describes the solutions of the embodiments of the present application in detail with reference to the drawings in the specification.

[0030] In the following description, specific details such as specific system architectures, interfaces, and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the present application.

[0031] The term "and / or" in this article is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after. In addition, "multiple" in this article means two or more than two. In addition, the term "at least one" in this article represents any one of multiple or any combination of at least two of multiple. For example, including at least one of A, B, and C can represent including any one or more elements selected from the set composed of A, B, and C.

[0032] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of an embodiment of the method for optimizing road congestion in this application. Specifically, it may include the following steps:

[0033] Step S110: Divide the vehicles passing through the road section to be optimized during the congestion-related period into normal driving vehicles and parked vehicles.

[0034] The congestion-related period mentioned in this article refers to the congestion time period of the road section to be optimized. Specifically, the congestion-related period may be the school dismissal time period, the morning and evening rush hours for commuting, or other road congestion time periods, etc. Normal driving vehicles are vehicles that stay or make a short stop on the road due to external reasons. For example, vehicles that drive slowly due to road congestion, or vehicles that suddenly stall and continue to drive after re-igniting are all normal driving vehicles. Parked vehicles are vehicles that intentionally stay on the road for a relatively long time. For example, vehicles that stay near the school to pick up or drop off children.

[0035] In some embodiments, the clustering division method can be used to divide the vehicles passing through the road section to be optimized during the congestion-related period. Please refer to Figure 2 , Figure 2 which is a schematic flowchart of step S110 in another embodiment of the method for optimizing road congestion in this application. The specific steps are as follows:

[0036] Step S111: Obtain the passing vehicle data of the vehicles passing through the road section to be optimized during the congestion-related period.

[0037] In some embodiments, the electronic capture devices at the intersections of the road section to be optimized can be used to obtain the passing vehicle data. Among them, using the electronic capture devices to obtain the passing vehicle data of vehicles may include license plate numbers, passing times, driving directions, etc.

[0038] In some other embodiments, checkpoints can be set at the intersections of the road section to be optimized to obtain the passing vehicle data.

[0039] It can be understood that the method for obtaining the passing vehicle data of the vehicles passing through the road section to be optimized during the congestion-related period is not specifically limited here.

[0040] Step S112: Based on the passing vehicle data, cluster the vehicles passing through the road section to be optimized during the congestion-related period to obtain a clustering result, and the clustering result includes the first vehicle information belonging to normal driving vehicles and the second vehicle information belonging to parked vehicles.

[0041] In some embodiments, the K-means algorithm can be used to cluster the vehicles passing through the section to be optimized during the congestion-related period. Specifically, the passing times of the vehicles passing through the section to be optimized during the congestion-related period are obtained, and two cluster centers are selected from the passing times of all vehicles. Taking these two cluster centers as the central values of two subsets, which are the normal driving vehicle set and the parked vehicle set, the Euclidean distance is used to calculate the distance between the passing time of each vehicle passing through the section to be optimized during the congestion-related period and the central values of these two subsets. If the distance between the passing time of a vehicle and the central value of the normal driving vehicle set is the smallest, then this vehicle is classified into the normal driving vehicle set; if the distance between the passing time of a vehicle and the central value of the parked vehicle set is the smallest, then this vehicle is classified into the parked vehicle set. Among them, the normal driving vehicle set can be the first vehicle information of normal driving vehicles, and the parked vehicle set can be the second vehicle information of parked vehicles.

[0042] It can be understood that, in addition to the K-means algorithm, other clustering algorithms can also be used to cluster the vehicles passing through the section to be optimized during the congestion-related period, and specific limitations are not made here.

[0043] Step S120: Analyze the passing vehicle data of normal driving vehicles and parked vehicles during the congestion-related period to obtain the congestion degree of the section to be optimized.

[0044] Among them, the passing vehicle data includes the time information of passing through the section to be optimized.

[0045] In some embodiments, the number of normal driving vehicles and the number of parked vehicles passing through the section to be optimized during the congestion-related period can be collected, and the number of normal driving vehicles and the number of parked vehicles are analyzed to obtain the ratio of the number of normal driving vehicles to the number of parked vehicles. If the ratio is smaller, the proportion of the number of parked vehicles is larger, and the congestion degree of the section to be optimized is more serious; if the ratio is larger, the proportion of the number of parked vehicles is smaller, and the congestion degree of the section to be optimized is lighter.

[0046] In other embodiments, the congestion degree of the section to be optimized can be obtained by using the characterization value of the passing vehicle data of normal driving vehicles and the characterization value of the passing vehicle data of parked vehicles. Please refer to Figure 3 , Figure 3 is a schematic flowchart of step S120 in another embodiment of the road congestion optimization method of the present application. The specific steps are as follows:

[0047] Step S121: Use the passing vehicle data of normal driving vehicles to obtain at least one first congestion characterization value, and the first congestion characterization value represents the congestion degree of the section to be optimized from the dimension of normal driving vehicles.

[0048] In some embodiments, the time information of normal driving vehicles passing through the section to be optimized can be utilized to obtain a first congestion characterization value. The specific steps are as follows:

[0049] Step S1211: Determine the statistical speed of normal driving vehicles during the congestion-related period by using the time information of each normal driving vehicle passing through the section to be optimized.

[0050] In some embodiments, by using the time information of each normal driving vehicle passing through the section to be optimized, the second average passing time of normal driving vehicles passing through the section to be optimized during the congestion-related period is obtained, as shown in Formula 1:

[0051]

[0052] where t′ ia is the time when a normal driving vehicle passes through the upstream intersection of the section to be optimized during the congestion-related period, t′ ib is the time when a normal driving vehicle passes through the downstream intersection of the section to be optimized during the congestion-related period, n2 is the total number of normal driving vehicles passing through the section to be optimized during the congestion-related period, and T2 is the second average passing time.

[0053] After obtaining the second average passing time, the ratio between the length of the section to be optimized and the second average passing time is used as the statistical speed, as shown in Formula 2:

[0054]

[0055] where L is the length of the section to be optimized and V2 is the statistical speed.

[0056] In some other embodiments, calculate the time difference between the upstream intersection and the downstream intersection of the section to be optimized when a normal driving vehicle passes through during the congestion-related period as the passing time, and use the ratio between the length of the section to be optimized and the passing time as the speed of the normal driving vehicle passing through the section to be optimized. Then calculate the speeds of all normal driving vehicles passing through the section to be optimized during the congestion-related period and take the average value to obtain the statistical speed.

[0057] Step S1212: Obtain the first congestion characterization value by using the gap between the statistical speed and the reference speed, where the reference speed represents the vehicle passing speed of the section to be optimized under normal traffic conditions.

[0058] In some embodiments, the reference speed is the ratio between the length of the section to be optimized and the normal passing time, where the normal passing time is the first average passing time of each vehicle within a preset time period of the section to be optimized. Specifically, the preset time period can be selected as the off-peak time period of the section to be optimized, and the first average passing time of normal driving vehicles during the off-peak time period is collected, as shown in Formula 3.

[0059]

[0060] where t ia is the time when a normally traveling vehicle passes through the upstream intersection of the section to be optimized during a preset time period, and t ib is the time when a normally traveling vehicle passes through the downstream intersection of the section to be optimized during a preset time period. n1 is the total number of normally traveling vehicles passing through the section to be optimized during the preset time period, and T1 is the first average passing time.

[0061] Use the ratio between the length of the section to be optimized and the first average passing time as the reference speed, as shown in Equation 4,

[0062]

[0063] where V1 is the reference speed.

[0064] Obtain the ratio between the statistical speed in the above step S1211 and the reference speed as the speed ratio. Select the minimum value from the first multiple of the speed ratio and the first multiple of the first preset congestion value as the first congestion characterization value, where the first multiple is an integer greater than or equal to 1. As shown in Equation 5,

[0065]

[0066] where e1 is the first preset congestion value, f1 is the first multiple, and Score1 is the first congestion characterization value.

[0067] It can be understood that the settings of the first multiple and the first preset congestion value can be determined according to specific situations and are not specifically limited here.

[0068] In some other embodiments, calculate the time difference between when a parked vehicle passes through the upstream intersection and the downstream intersection of the section to be optimized during the congestion-related period as the passing time, and use the ratio between the length of the section to be optimized and the passing time as the speed at which the parked vehicle passes through the section to be optimized. Then calculate the speeds of all parked vehicles passing through the section to be optimized during the congestion-related period and take the average value to obtain the reference speed. Obtain the ratio between the statistical speed and the reference speed as the speed ratio, and select the minimum value from the first multiple of the speed ratio and the first multiple of the first preset congestion value as the first congestion characterization value, where the first multiple is an integer greater than or equal to 1.

[0069] It can be understood that in this embodiment, the ratio between the statistical speed and the reference speed is used to represent the gap between the statistical speed and the reference speed. Other methods can also be used to represent the gap between the statistical speed and the reference speed, which can be determined according to specific situations and are not specifically limited here.

[0070] In some other embodiments, the number of normal driving vehicles passing through the section to be optimized may also be used to obtain a first congestion characterization value. Therefore, the specific method for obtaining the first congestion characterization value is not specifically limited herein.

[0071] Step S122: Use the passing vehicle data of the parked vehicles to obtain at least one second congestion characterization value, where the second congestion characterization value represents the congestion degree of the section to be optimized from the dimension of the parked vehicles.

[0072] In some embodiments, the time information of each parked vehicle passing through the section to be optimized may be used to obtain the statistical parking duration of the parked vehicles, and a second congestion characterization value may be obtained based on the statistical parking duration. To obtain the statistical parking duration, the time information of each parked vehicle passing through the section to be optimized may be used to obtain the third average passing time of the parked vehicles passing through the section to be optimized during the congestion-related period as the statistical parking duration.

[0073] Specifically, obtain the times when the parked vehicles pass through the upstream intersection and the downstream intersection of the section to be optimized during the congestion-related period, calculate the passing time of the parked vehicles passing through the section to be optimized therefrom, count the passing times of all parked vehicles, and take the average value as the third average passing time, as shown in Formula 6.

[0074]

[0075] where, t″ ia is the time when the parked vehicle passes through the upstream intersection of the section to be optimized during the congestion-related period, t″ ib is the time when the parked vehicle passes through the downstream intersection of the section to be optimized during the congestion-related period, n3 is the total number of parked vehicles passing through the section to be optimized during the congestion-related period, and T3 is the third average passing time.

[0076] Take the reciprocal of the third average passing time as the second congestion characterization value.

[0077] In some other embodiments, the time information of each parked vehicle passing through the section to be optimized may be used to obtain the statistical parking duration of the parked vehicles; obtain the duration difference between the statistical parking duration and the normal passing duration; obtain the first parked vehicle congestion value corresponding to the duration difference, and select the maximum value from the first parked vehicle congestion value and the second preset congestion value as the second congestion characterization value, where the first parked vehicle congestion value is negatively correlated with the duration difference.

[0078] Specifically, obtain the second multiple of the difference between the preset value and the duration difference as the first parked vehicle congestion characterization value, where the second multiple is an integer greater than or equal to 1. As shown in Formula 7,

[0079] Score 21= max(e2, (m - (T3 - T1)) × f2) (7)

[0080] Wherein, e2 is the second preset congestion value, m is a preset value, f2 is the second multiple, and Score 21 is a second congestion characterization value, and (m - (T3 - T1)) is the first stop congestion value.

[0081] In some other embodiments, the number of parked vehicles in the passing vehicle data of the parked vehicles can be used to obtain a second congestion characterization value. The total vehicle stop length is obtained by using the number of parked vehicles and the vehicle length of the parked vehicles. The vehicle length of the parked vehicles can be the actually measured length or a preset value; a second stop congestion value corresponding to the total vehicle stop length is obtained, and the maximum value is selected from the second stop congestion value and the third preset congestion value as the second congestion characterization value, and the second stop congestion characterization value is negatively correlated with the total vehicle stop length.

[0082] Specifically, the electronic capture device at the upstream intersection of the road section to be optimized can be used to obtain the vehicles passing through the road section to be optimized during the congestion-related period, the number of parked vehicles is obtained from the vehicles passing through the road section to be optimized by using the clustering algorithm, and the total vehicle stop length is obtained according to the preset vehicle length of the parked vehicles. The length difference between the length of the road section to be optimized and the total vehicle stop length is obtained, and the third multiple of the ratio of the length difference to the length of the road section to be optimized is used as the second stop congestion characterization value, wherein the third multiple is an integer greater than or equal to 1, as shown in formula 8

[0083]

[0084] Wherein, e3 is the third preset congestion value, L is the length of the road section to be optimized, l is the vehicle length of the parked vehicles, f3 is the third multiple is the second stop congestion value, and Score 22 is another second congestion characterization value.

[0085] Step S123: Based on the first congestion characterization value and the second congestion characterization value, obtain the congestion degree of the road section to be optimized.

[0086] In some embodiments, the sum of the first congestion characterization value and the second congestion characterization value can be used as the congestion degree of the road section to be optimized.

[0087] In some other embodiments, each first congestion characterization value and each second congestion characterization value can be weighted to obtain the congestion degree Score of the road section to be optimized.

[0088] Specifically, as shown in formula 9

[0089] Score = α1 × Score 11+…+α m ×Score 1m +β1×Score 21 +…+β n ×Score 2n (9)

[0090] where Score 11 , …, Score 1m are the first congestion characterization values, Score 21 , …, Score 2n are the second congestion characterization values, α1, …, α m are the weights corresponding to the first congestion characterization values, β1, …, β n are the weights corresponding to the second congestion characterization values, and α1+…+α m +β1+…+β n = 1.

[0091] It can be understood that the method of obtaining the congestion degree of the road section to be optimized by using the first congestion characterization value and the second congestion characterization value can be methods such as weighted summation and weighted average, depending on the specific situation, and is not specifically limited herein.

[0092] Step S130: Perform a first optimization process on the road section to be optimized with respect to the congestion degree.

[0093] In some embodiments, before performing the first optimization process on the road section to be optimized, a first congestion prompt requirement can be preset. If the analyzed congestion degree meets the first congestion prompt requirement, a suggestion to bypass the road section to be optimized is sent to the user through the navigation system, reducing the traffic flow passing through the road section to be optimized during the congestion-related time period, thereby alleviating the congestion degree of the road section to be optimized.

[0094] In other embodiments, the signal timing of the intersections upstream and downstream of the road section to be optimized can be first optimized according to the obtained congestion degree of the road section to be optimized. For example, the time when the signal light at the upstream intersection of the road section to be optimized is in the first indication state is extended. The first indication state is used to indicate that vehicles enter the road section to be optimized. Generally, the first indication state is a red light; or, the time when the signal light at the downstream intersection of the road section to be optimized is in the second indication state is extended. The second indication state is used to indicate that vehicles leave the road section to be optimized. Generally, the second indication state is a green light. When the congestion degree of the road section to be optimized is very serious, the time when the signal light at the downstream intersection of the road section to be optimized is in the first indication state and the time when the signal light at the downstream intersection of the road section to be optimized is in the second indication state can be simultaneously extended, reducing the vehicles entering the road section to be optimized and accelerating the vehicles leaving the road section to be optimized, so as to achieve the purpose of quickly improving the congestion degree of the road section to be optimized.

[0095] In some embodiments, the road section to be optimized may be further optimized according to the queuing situation of parked vehicles. Based on the ratio between the total parked vehicle length and the length of the road section to be optimized, the vehicle queuing situation is obtained. The total parked vehicle length is determined based on the vehicle length of parked vehicles and the number of parked vehicles in the passing vehicle data of parked vehicles, as shown in Formula 10.

[0096]

[0097] Wherein, l is the vehicle length of parked vehicles, n3 is the total number of parked vehicles passing through the road section to be optimized during the congestion-related period, L is the length of the road section to be optimized, and q is the vehicle queuing situation of parked vehicles.

[0098] A second congestion prompt requirement may be preset for the vehicle queuing situation of parked vehicles. If the obtained vehicle queuing situation of parked vehicles meets the second congestion prompt requirement, a second optimization process is performed on the road section to be optimized. The second optimization process includes: sending a prompt to the control terminal that on-site guidance is required for the road section to be optimized; notifying the navigation system to send a detour suggestion to users for the road section to be optimized. If the vehicle queuing situation q is close to or equal to 1, it can be regarded that the road section to be optimized has reached the edge of queuing overflow, and an optimization process can be simultaneously adopted, including sending a prompt to the control terminal that on-site guidance is required for the road section to be optimized and notifying the navigation system to send a detour suggestion to users for the road section to be optimized.

[0099] Please refer to Figure 4 , Figure 4 which is a schematic flowchart of another embodiment of the road section congestion optimization method of the present application. The specific steps are as follows:

[0100] Step S410: Obtain the vehicles passing through the road section to be optimized during the congestion-related period.

[0101] Step S420: Analyze the vehicles passing through the road section to be optimized during the congestion-related period to obtain normal driving vehicles and parked vehicles.

[0102] This step is the same as the above step S110, and will not be elaborated again here.

[0103] Step S430: Based on the passing vehicle data of normal driving vehicles and parked vehicles during the congestion-related period, obtain the first congestion degree.

[0104] This step is the same as the above step S120, and will not be elaborated again here.

[0105] Step S440: Based on the vehicle information of parked vehicles, obtain the second congestion degree.

[0106] In some embodiments, a second congestion level is obtained based on the ratio between the overall vehicle stop length and the length of the section to be optimized. The overall vehicle stop length is determined based on the vehicle lengths of the stopped vehicles and the number of stopped vehicles in the passing vehicle data of the stopped vehicles.

[0107] Step S450: Optimize the section to be optimized based on the first congestion level and the second congestion level.

[0108] In some embodiments, a first congestion prompt requirement may be preset for the first congestion level. If the first congestion level meets the first congestion prompt requirement, a suitable processing solution can be selected from the first optimization plan. Specifically, the first optimization plan includes: Solution 1: Send a detour suggestion for the section to be optimized to the user through the navigation system; Solution 2: Extend the time when the signal light at the upstream intersection of the section to be optimized is in the first indication state, and the first indication state is used to indicate vehicles to enter the section to be optimized; Solution 3: Extend the time when the signal light at the downstream intersection of the section to be optimized is in the second indication state, and the second indication state is used to indicate vehicles to drive out of the section to be optimized, etc.

[0109] A second congestion prompt requirement may be preset for the second congestion level. If the second congestion level meets the second congestion prompt requirement, a suitable processing solution can be selected from the second optimization plan. Specifically, the second optimization plan includes: Solution 4: Send a prompt to the control terminal that on-site guidance is required for the section to be optimized; Solution 5: Notify the navigation system to send a detour suggestion for the section to be optimized to the user, etc.

[0110] It can be understood that for the processing solutions available in the above first optimization plan and second optimization plan, they can be changed according to the specific road conditions, and can be increased or decreased, and no specific limitation is made here.

[0111] By comprehensively analyzing the first congestion level and the second congestion level, and according to the processing methods for the section to be optimized based on the first congestion level and the second congestion level, a suitable plan can be selected from the first optimization plan and the second optimization plan for combined processing and optimization, so that the processing method for the section to be optimized is more targeted and more in line with actual operations. Among them, the combined processing plan can be a one-to-one combined plan or a many-to-many combined plan, and the combined plan can be selected according to the specific situation, and no specific limitation is made here.

[0112] In a specific application scenario, the road sections adjacent to primary and secondary schools are optimized, where the congestion-related period is the school dismissal period, and the vehicles parked are vehicles picking up children. The dismissal time for primary and secondary schools is 5 pm. The C-means clustering algorithm is used to classify the vehicles passing through the road sections adjacent to the school between 4:30 and 5 pm. The C-means clustering algorithm divides the vehicles in this time period into two subsets based on the similarity measure of the data, namely, the normal driving vehicle set and the child picking up vehicle set. At the same time, the passing data of the normal driving vehicles and the child picking up vehicles are collected. Using the passing data of the normal driving vehicles, a first congestion characterization value is obtained, and using the passing data of the child picking up vehicles, two second congestion characterization values ​​are obtained. The congestion level Score of the road section adjacent to the school based on the first congestion characterization value and the second congestion characterization value can be obtained through Formula 9.

[0113] Score=α×Score1+β×Score2+γ×Score3

[0114] Among them, Score1 is the first congestion characterization value, also known as the normal driving vehicle speed score, and α is the weight of the normal driving vehicle speed score; Score2 is the second congestion characterization value, also known as the average stay time score of vehicles picking up children, and β is the weight of the average stay time score of vehicles picking up children; Score3 is another second congestion characterization value, also known as the number score of vehicles picking up children, and γ is the weight of the number score of vehicles picking up children.

[0115] In order to obtain the actual value of the congestion level Score of the road sections adjacent to the school, it is necessary to calculate Score1 (score of the speed of normal driving vehicles), Score2 (score of the average stay time of vehicles picking up children) and Score3 (score of the number of vehicles picking up children) respectively.

[0116] First, calculate Score1 (speed score of normal driving vehicles). Using the time information of each normal driving vehicle passing through the road section adjacent to the school, through formulas 1 and 2, determine the statistical speed V2 of normal driving vehicles during the school dismissal period. The statistical speed V2 is the average speed of normal driving vehicles passing through the road section adjacent to the school during the school dismissal period. Using the ratio between the length of the road section adjacent to the school and the first average passing time, through formulas 3 and 4, calculate the reference speed V1. The reference speed V1 is the speed of normal driving vehicles in the road section adjacent to the school under unobstructed conditions. Among them, the first average passing time is the time difference between normal driving vehicles passing the upstream and downstream intersections of the road section adjacent to the school during the off-peak period of the day.

[0117] Obtain the ratio between the statistical speed and the reference speed as the speed ratio. From the first multiple of the speed ratio and the first multiple of the first preset congestion value, select the minimum value as the normal driving vehicle speed score Score1. The first preset congestion value is preset to 1, and the first multiple is preset to 100. The normal driving vehicle speed score Score1 can be obtained through Formula 5.

[0118]

[0119] Then calculate Score2 (the average stay duration score of the pick-up vehicles). Obtain the times when the pick-up vehicles pass through the upstream intersection and the downstream intersection of adjacent sections of the school during the school dismissal period. Through Formula 6, calculate the statistical stay duration. Obtain the duration difference between the statistical stay duration and the normal passing duration; obtain the second multiple of the difference between the preset value and the duration difference as the first stay congestion characterization value. From the first stay congestion value and the second preset congestion value, select the maximum value as the average stay duration score Score2 of the pick-up vehicles through Formula 7. Among them, the second preset congestion value is set to 0, the preset value is set to 20, and the second multiple is set to 5.

[0120] Score2 = max(0, (20 - (T3 - T1)) × 5).

[0121] Then calculate Score3 (the score of the number of pick-up vehicles). Utilize the number of parked vehicles and the vehicle lengths of the parked vehicles to obtain the total vehicle parking length; obtain the length difference between the length of the section to be optimized and the total vehicle parking length. The third multiple of the ratio of the length difference to the length of the section to be optimized is used as the second stay congestion characterization value. From the second stay congestion value and the third preset congestion value, select the maximum value as the score of the number of pick-up vehicles through Formula 8. Among them, the third preset congestion value is set to 0, the third multiple is set to 100, and the vehicle length of the pick-up vehicle is the distance between the tail of the nth vehicle and the tail of the (n + 1)th vehicle parked on the adjacent section of the school, which is set to 5m.

[0122]

[0123] Through the above steps, the congestion degree score Score of the adjacent section of the school can be calculated. By calculating the real-time congestion degree score of the adjacent section of the school, the traffic condition of the adjacent section of the school can be evaluated.

[0124] Among them, the sum of the weights α, β, and γ in the congestion degree score of the adjacent section of the school is 1, and different values can be set according to the weights of each influence.

[0125] Meanwhile, under normal circumstances, the vehicles picking up children usually choose to temporarily park in the rightmost lane of the road. If all the vehicles picking up children stay in the rightmost lane, it is very likely to cause the queue in the right lane to overflow, affecting the operation efficiency of the entire section. At this time, the approximate queue situation q of the vehicles picking up children in the right lane can be calculated using Formula 10 based on the length of the adjacent section of the school and the above-mentioned index data.

[0126]

[0127] According to the calculated Score and the queue situation q of the vehicles picking up children, the traffic operation condition of the section can be improved by optimizing the signal timing of the two ends of the adjacent section of the school in real time.

[0128] When the calculated Score is lower, it proves that the operation condition of the adjacent section of the school is worse. At this time, the signal duration of the upstream intersection indicating vehicles to enter the adjacent section of the school should be extended to reduce the frequency of vehicle flow in the lane leading to the adjacent section of the school, or the signal duration of the downstream intersection indicating vehicles to drive out of the adjacent section of the school should be extended to allow more green light release time for this lane.

[0129] For a normal crossroads, the traffic flow in the lane leading to the adjacent section of the school comes from the straight, left-turn and right-turn traffic flows in the other three directions respectively. For example, if the lane of the adjacent section of the school is on the east side, the traffic flow in the lane leading to the adjacent section of the school is the straight traffic flow from the west intersection, the right-turn traffic flow from the south intersection and the left-turn traffic flow from the north intersection. The green light duration in this direction is adjusted in real time according to the road congestion degree score to control the incoming vehicles. At the same time, the green light duration of the lane flow driving out of the adjacent section of the school at the downstream intersection of the section is controlled to improve the traffic capacity of the section.

[0130] When the queue situation q of the vehicles picking up children in the rightmost lane of the adjacent section of the school reaches 95%, it proves that the right section is extremely congested and has reached the edge of queue overflow. At this time, the effect of relieving road congestion by signal timing adjustment may be slow. When the section is in this situation, the corresponding warning mechanism can be triggered to send a prompt to the control terminal that the section to be optimized needs on-site guidance, reminding that the section needs to be controlled. It can be guided on-site by traffic commanders or linked with the navigation to remind the vehicle owners about to enter this section that the road ahead is severely congested and it is recommended to change the route.

[0131] In this application, vehicles passing through the section to be optimized during the congestion-related period are classified into normally traveling vehicles and parked vehicles by a clustering algorithm; the statistical speed of the normally traveling vehicles during the congestion-related period is determined by using the time information of the normally traveling vehicles passing through the section to be optimized; a first congestion characterization value is obtained by using the gap between the statistical speed and the reference speed; at least one second congestion characterization value is obtained by using the passing vehicle data of the parked vehicles; the congestion degree of the section to be optimized is obtained based on the first congestion characterization value and the second congestion characterization value; a first optimization process corresponding to the congestion degree is performed on the section to be optimized. Thereby, the traffic operation status of the section can be intuitively reflected, and the road congestion situation can be optimized in real time.

[0132] Please refer to Figure 5 , Figure 5 FIG. is a schematic framework diagram of an embodiment of a section congestion optimization device 500 according to the present application. The section congestion optimization device 500 includes: an acquisition and classification module 510, an analysis module 520, and an execution module 530. Among them, the acquisition and classification module 510 is configured to classify vehicles passing through the section to be optimized during the congestion-related period into normally traveling vehicles and parked vehicles; the analysis module 520 is configured to analyze the congestion degree of the section to be optimized by using the passing vehicle data of the normally traveling vehicles and the parked vehicles during the congestion-related period, where the passing vehicle data includes the time information of passing through the section to be optimized; the execution module 530 is configured to perform a first optimization process on the section to be optimized corresponding to the congestion degree.

[0133] In some embodiments, when the analysis module 520 executes to analyze the congestion degree of the section to be optimized by using the passing vehicle data of the normally traveling vehicles and the parked vehicles during the congestion-related period, it specifically includes: obtaining at least one first congestion characterization value by using the passing vehicle data of the normally traveling vehicles, where the first congestion characterization value represents the congestion degree of the section to be optimized from the dimension of the normally traveling vehicles; obtaining at least one second congestion characterization value by using the passing vehicle data of the parked vehicles, where the second congestion characterization value represents the congestion degree of the section to be optimized from the dimension of the parked vehicles; obtaining the congestion degree of the section to be optimized based on the first congestion characterization value and the second congestion characterization value.

[0134] In some embodiments, when the analysis module 520 executes to obtain at least one first congestion characterization value by using the passing vehicle data of the normally traveling vehicles, it specifically includes: determining the statistical speed of the normally traveling vehicles during the congestion-related period by using the time information of each normally traveling vehicle passing through the section to be optimized; obtaining the first congestion characterization value by using the gap between the statistical speed and the reference speed, where the reference speed represents the vehicle passing speed of the section to be optimized under normal traffic conditions.

[0135] In some embodiments, the reference speed is the ratio between the length of the section to be optimized and the normal passing time, where the normal passing time is the first average passing time of each vehicle within a preset time period of the section to be optimized; the analysis module 520 executes to determine the statistical speed of normal driving vehicles during the congestion-related period by using the time information of each normal driving vehicle passing through the section to be optimized, including: obtaining the second average passing time of normal driving vehicles passing through the section to be optimized during the congestion-related period by using the time information of each normal driving vehicle passing through the section to be optimized; obtaining the ratio between the length of the section to be optimized and the second average passing time as the statistical speed; and / or, executing to obtain a first congestion characterization value by using the gap between the statistical speed and the reference speed, including: obtaining the ratio between the statistical speed and the reference speed as the speed ratio; selecting the minimum value from the first multiple of the speed ratio and the first multiple of the first preset congestion value as the first congestion characterization value, where the first multiple is an integer greater than or equal to 1.

[0136] In some embodiments, the analysis module 520 executes to obtain at least one second congestion characterization value by using the passing vehicle data of parked vehicles, including any one or more of the following steps: obtaining the statistical parking duration of parked vehicles by using the time information of each parked vehicle passing through the section to be optimized, and obtaining a second congestion characterization value based on the statistical parking duration; obtaining a second congestion characterization value by using the number of parked vehicles in the passing vehicle data of parked vehicles.

[0137] In some embodiments, the analysis module 520 executes to obtain the statistical parking duration of parked vehicles by using the time information of each parked vehicle passing through the section to be optimized, including: obtaining the third average passing time of parked vehicles passing through the section to be optimized during the congestion-related period by using the time information of each parked vehicle passing through the section to be optimized as the statistical parking duration; and / or, executing to obtain a second congestion characterization value based on the statistical parking duration, including: obtaining the duration difference between the statistical parking duration and the normal passing duration; obtaining the first parking congestion value corresponding to the duration difference, and selecting the maximum value from the first parking congestion value and the second preset congestion value as the second congestion characterization value, where the first parking congestion value is negatively correlated with the duration difference; and / or, executing to obtain a second congestion characterization value by using the number of parked vehicles in the passing vehicle data of parked vehicles, including: obtaining the total vehicle parking length by using the number of parked vehicles and the vehicle length of parked vehicles; obtaining the second parking congestion value corresponding to the total vehicle parking length, and selecting the maximum value from the second parking congestion value and the third preset congestion value as the second congestion characterization value, where the second parking congestion characterization value is negatively correlated with the total vehicle parking length.

[0138] In some embodiments, the analysis module 520 executes to obtain a first residence congestion characterization value corresponding to the duration difference, including: obtaining a second multiple of the difference between a preset value and the duration difference as the first residence congestion characterization value, where the second multiple is an integer greater than or equal to 1; executing to obtain a second residence congestion characterization value corresponding to the overall vehicle residence length, including: obtaining the length difference between the length of the section to be optimized and the overall vehicle residence length, and taking a third multiple of the ratio of the length difference to the length of the section to be optimized as the second residence congestion characterization value, where the third multiple is an integer greater than or equal to 1.

[0139] In some embodiments, the analysis module 520 executes to obtain the congestion degree of the section to be optimized based on the first congestion characterization value and the second congestion characterization value, including: weighting each first congestion characterization value and each second congestion characterization value to obtain the congestion degree of the section to be optimized.

[0140] In some embodiments, the execution module 530 executes a first optimization process for the section to be optimized in accordance with the congestion degree, including any one or more of the following steps: in response to the congestion degree meeting the first congestion prompt requirement, sending a detour suggestion for the section to be optimized to the user through the navigation system; extending the time when the signal light at the upstream intersection of the section to be optimized is in the first indication state, where the first indication state is used to indicate vehicles to enter the section to be optimized; extending the time when the signal light at the downstream intersection of the section to be optimized is in the second indication state, where the second indication state is used to indicate vehicles to leave the section to be optimized.

[0141] In some embodiments, after the execution module 530 executes to divide the vehicles passing through the section to be optimized during the congestion-related period into normal-travel vehicles and residence vehicles, it further includes: obtaining the vehicle queue situation based on the ratio between the overall vehicle residence length and the length of the section to be optimized, where the overall vehicle residence length is determined based on the vehicle lengths of the residence vehicles and the number of residence vehicles in the passing vehicle data of the residence vehicles; in response to the vehicle queue situation meeting the second congestion prompt requirement, sending a prompt to the control terminal that the section to be optimized requires on-site guidance, and / or notifying the navigation system to send a detour suggestion for the section to be optimized to the user.

[0142] In some embodiments, the acquisition classification module 510 executes to divide the vehicles passing through the section to be optimized during the congestion-related period into normal-travel vehicles and residence vehicles, including: obtaining the passing vehicle data of the vehicles passing through the section to be optimized during the congestion-related period; clustering the vehicles passing through the section to be optimized during the congestion-related period based on the passing vehicle data to obtain a clustering result, where the clustering result includes first vehicle information belonging to normal-travel vehicles and second vehicle information belonging to residence vehicles.

[0143] Those skilled in the art can understand that in the above method of the specific implementation manner, the writing order of each step does not mean a strict execution order and does not impose any limitation on the implementation process. The specific execution order of each step should be determined according to its function and possible internal logic.

[0144] Please refer to Figure 6 , Figure 6 which is a schematic framework diagram of an embodiment of the electronic device 60 of the present application. The electronic device 60 includes a memory 61 and a processor 62 that are coupled to each other. The processor 62 is configured to execute program instructions stored in the memory 61 to implement the steps in any of the above-described method embodiments for optimizing road segment congestion. In a specific implementation scenario, the electronic device 60 may include, but is not limited to, a microcomputer and a server. In addition, the electronic device 60 may also include mobile devices such as a laptop computer and a tablet computer, which are not limited herein.

[0145] Specifically, the processor 62 is configured to control itself and the memory 61 to implement the steps in any of the above-described method embodiments for optimizing road segment congestion. The processor 62 may also be referred to as a CPU (Central Processing Unit). The processor 62 may be an integrated circuit chip with signal processing capabilities. The processor 62 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. Additionally, the processor 62 may be implemented jointly by integrated circuit chips.

[0146] Please refer to Figure 7 , Figure 7 which is a schematic framework diagram of an embodiment of the computer-readable storage medium 70 of the present application. The computer-readable storage medium 70 stores program instructions 701 that can be run by a processor. The program instructions 701 are used to implement the steps in any of the above-described method embodiments for optimizing road segment congestion.

[0147] In some embodiments, the functions or modules included in the device provided by the embodiments of the present disclosure can be used to execute the methods described in the above method embodiments. The specific implementation can refer to the description of the above method embodiments. For the sake of brevity, it will not be repeated here.

[0148] The descriptions of the various embodiments above tend to emphasize the differences between the various embodiments. For the similarities or resemblances among them, reference can be made to each other. For the sake of brevity, they will not be elaborated herein again.

[0149] In several embodiments provided in the present application, it should be understood that the disclosed methods and devices can be implemented in other ways. For example, the device implementation manners described above are only illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division manners. For example, units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in electrical, mechanical or other forms.

[0150] In addition, each functional unit in the various embodiments of the present application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0151] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods in the various implementation manners of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

Claims

1. A method for optimizing road section congestion, characterized in that, Including: Dividing the vehicles passing through the section to be optimized during the congestion-related period into normally traveling vehicles and parked vehicles; Analyzing the traffic congestion degree of the section to be optimized by using the passing vehicle data of the normally traveling vehicles and parked vehicles during the congestion-related period, wherein the passing vehicle data includes the time information of passing through the section to be optimized; the analyzing the traffic congestion degree of the section to be optimized by using the passing vehicle data of the normally traveling vehicles and parked vehicles during the congestion-related period includes: determining the statistical speed of the normally traveling vehicles during the congestion-related period by using the time information of each of the normally traveling vehicles passing through the section to be optimized; obtaining the ratio between the statistical speed and the reference speed as the speed ratio; selecting the minimum value from the first multiple of the speed ratio and the first multiple of the first preset congestion value as the first congestion characterization value, the first multiple being an integer greater than or equal to 1, the reference speed representing the vehicle passing speed of the section to be optimized under normal traffic conditions, and the first congestion characterization value representing the traffic congestion degree of the section to be optimized from the dimension of the normally traveling vehicles; obtaining at least one second congestion characterization value by using the passing vehicle data of the parked vehicles, the second congestion characterization value representing the traffic congestion degree of the section to be optimized from the dimension of the parked vehicles; obtaining the traffic congestion degree of the section to be optimized based on the first congestion characterization value and the second congestion characterization value; Performing a first optimization process on the section to be optimized according to the traffic congestion degree.

2. The method according to claim 1, wherein The reference speed is the ratio between the length of the section to be optimized and the normal passing time, and the normal passing time is the first average passing time of each vehicle within the preset time period of the section to be optimized; The determining the statistical speed of the normally traveling vehicles during the congestion-related period by using the time information of each of the normally traveling vehicles passing through the section to be optimized includes: Obtaining the second average passing time of the normally traveling vehicles passing through the section to be optimized during the congestion-related period by using the time information of each of the normally traveling vehicles passing through the section to be optimized; Obtaining the ratio between the length of the section to be optimized and the second average passing time as the statistical speed.

3. The method according to claim 1, characterized in that The obtaining at least one second congestion characterization value by using the passing vehicle data of the parked vehicles includes any one or more of the following steps: Obtaining the statistical parking duration of the parked vehicles by using the time information of each of the parked vehicles passing through the section to be optimized, and obtaining one second congestion characterization value based on the statistical parking duration; Obtaining one second congestion characterization value by using the number of parked vehicles in the passing vehicle data of the parked vehicles.

4. The method according to claim 3, wherein The obtaining the statistical parking duration of the parked vehicles by using the time information of each of the parked vehicles passing through the section to be optimized includes: Obtaining the third average passing time of the parked vehicles passing through the section to be optimized during the congestion-related period by using the time information of each of the parked vehicles passing through the section to be optimized as the statistical parking duration; And / or, obtaining a second congestion characterization value based on the statistical residence duration includes: Obtaining the duration difference between the statistical residence duration and the normal passing duration; Obtaining a first residence congestion value corresponding to the duration difference, and selecting the maximum value from the first residence congestion value and a second preset congestion value as the second congestion characterization value, where the first residence congestion value is negatively correlated with the duration difference; And / or, obtaining a second congestion characterization value by using the number of parked vehicles in the passing data of the parked vehicles includes: Using the number of parked vehicles and the vehicle lengths of the parked vehicles to obtain the total vehicle residence length; Obtaining a second residence congestion value corresponding to the total vehicle residence length, and selecting the maximum value from the second residence congestion value and a third preset congestion value as the second congestion characterization value, where the second residence congestion characterization value is negatively correlated with the total vehicle residence length.

5. The method according to claim 4, wherein The obtaining the first residence congestion characterization value corresponding to the duration difference includes: Obtaining a second multiple of the difference between a preset value and the duration difference as the first residence congestion characterization value, where the second multiple is an integer greater than or equal to 1; The obtaining the second residence congestion characterization value corresponding to the total vehicle residence length includes: Obtaining the length difference between the length of the section to be optimized and the total vehicle residence length, and using a third multiple of the ratio of the length difference to the length of the section to be optimized as the second residence congestion characterization value, where the third multiple is an integer greater than or equal to 1.

6. The method according to claim 1, wherein Based on the first congestion characterization value and the second congestion characterization value, obtaining the congestion degree of the section to be optimized includes: Weighting each of the first congestion characterization values and each of the second congestion characterization values to obtain the congestion degree of the section to be optimized.

7. The method according to claim 1, wherein Performing a first optimization process on the section to be optimized according to the congestion degree includes any one or more of the following steps: In response to the congestion degree meeting the first congestion prompt requirement, sending a detour suggestion for the section to be optimized to the user through the navigation system; Extending the time when the signal light at the upstream intersection of the section to be optimized is in a first indication state, where the first indication state is used to indicate vehicles to enter the section to be optimized; Extending the time when the signal light at the downstream intersection of the section to be optimized is in a second indication state, where the second indication state is used to indicate vehicles to drive out of the section to be optimized.

8. The method according to claim 1, characterized in that After classifying the vehicles passing through the section to be optimized during the congestion-related period into normal driving vehicles and parked vehicles, the method further includes: Based on the ratio between the total vehicle residence length and the length of the section to be optimized, obtaining the vehicle queuing situation, where the total vehicle residence length is determined based on the vehicle lengths of the parked vehicles and the number of parked vehicles in the passing data of the parked vehicles; In response to the vehicle queuing situation meeting the second congestion prompt requirement, sending a prompt to the control terminal that the section to be optimized needs on-site guidance, and / or notifying the navigation system to send a detour suggestion for the section to be optimized to the user.

9. The method according to claim 1, wherein Dividing the vehicles passing through the section to be optimized during the congestion-related period into normal-traveling vehicles and staying vehicles includes: Obtaining the passing vehicle data of the vehicles passing through the section to be optimized during the congestion-related period; Based on the passing vehicle data, clustering the vehicles passing through the section to be optimized during the congestion-related period to obtain a clustering result, where the clustering result includes first vehicle information belonging to the normal-traveling vehicles and second vehicle information belonging to the staying vehicles.

10. A device for optimizing road section congestion, characterized in that, Includes: An acquisition classification module for dividing the vehicles passing through the section to be optimized during the congestion-related period into normal-traveling vehicles and staying vehicles; An analysis module for analyzing the congestion degree of the section to be optimized by using the passing vehicle data of the normal-traveling vehicles and staying vehicles during the congestion-related period, where the passing vehicle data includes time information of passing through the section to be optimized; the analyzing the congestion degree of the section to be optimized by using the passing vehicle data of the normal-traveling vehicles and staying vehicles during the congestion-related period includes: determining the statistical speed of the normal-traveling vehicles during the congestion-related period by using the time information of each normal-traveling vehicle passing through the section to be optimized; obtaining the ratio between the statistical speed and the reference speed as the speed ratio; selecting the minimum value from the first multiple of the speed ratio and the first multiple of the first preset congestion value as the first congestion characterization value, where the first multiple is an integer greater than or equal to 1, the reference speed represents the vehicle passing speed of the section to be optimized under normal traffic conditions, and the first congestion characterization value represents the congestion degree of the section to be optimized from the dimension of the normal-traveling vehicles; obtaining at least one second congestion characterization value by using the passing vehicle data of the staying vehicles, where the second congestion characterization value represents the congestion degree of the section to be optimized from the dimension of the staying vehicles; obtaining the congestion degree of the section to be optimized based on the first congestion characterization value and the second congestion characterization value; An execution module for performing a first optimization process on the section to be optimized according to the congestion degree.

11. An electronic device, characterized in that, Includes a memory and a processor coupled to each other, where the processor is configured to execute program instructions stored in the memory to implement the section congestion optimization method according to any one of claims 1 to 9.

12. A computer-readable storage medium having program instructions stored thereon, characterized in that, When the program instructions are executed by the processor, the section congestion optimization method according to any one of claims 1 to 9 is implemented.

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