Method and system for estimating traffic state of urban expressway on-ramp weaving area

Abstract

This invention discloses a method and system for estimating traffic conditions in weaving zones at urban expressway entrance ramps. On one hand, it calculates the number of vehicles in the weaving zone based on information transmitted between vehicles and an edge control center; on the other hand, it calculates the number of vehicles in the weaving zone based on vehicle information collected by video devices in the weaving zone. A fusion algorithm is used to combine the two vehicle counts calculated within the same time period to obtain the final vehicle count as the traffic condition estimation result. This invention utilizes 5G, edge computing, vehicle-road cooperative technology, BeiDou high-precision positioning, and high-definition video processing technology to achieve traffic condition estimation in weaving zones at urban expressway entrance ramps under a vehicle-road cooperative environment, providing a more accurate basis for optimizing vehicle traffic control in these weaving zones.

Description

Technical Field

[0001] This invention relates to the field of intelligent transportation technology, specifically to a method and system for estimating traffic conditions in weaving areas of urban expressway entrance ramps. Background Technology

[0002] With the development and maturation of 5G wireless communication technology, next-generation Internet technology, BeiDou high-precision positioning, and video processing technology, important technical support has been provided for the rapid implementation and application of vehicle-road cooperative technology. In a vehicle-road cooperative environment, the communication capabilities of vehicles are further enhanced, enabling effective information exchange between vehicles and between vehicles and roadside facilities. This strengthens the real-time nature of vehicles acquiring and transmitting external information, providing crucial support for the estimation and prediction of road traffic conditions.

[0003] For a considerable period in the future, vehicles with and without communication capabilities will likely coexist in mixed traffic scenarios. Utilizing information from connected vehicles and integrating it with vehicle information collected by video devices to comprehensively estimate and predict road traffic conditions in these mixed traffic environments will be a crucial research direction. Generally, to ensure the "fast" function of urban expressways, the estimation and prediction of traffic conditions in weaving areas, especially at entrance ramps, is critical. Timely and accurate estimation of traffic conditions allows for timely implementation of appropriate control measures to ensure adjustments are made for vehicles traveling upstream and on entrance ramps, effectively improving the efficiency and utilization of weaving areas. Therefore, lane-level accurate traffic condition estimation and prediction in mixed traffic scenarios involving both connected and non-connected vehicles is of great significance for improving the intelligence of urban transportation.

[0004] This invention mainly relies on BeiDou high-precision positioning, and based on 5G's high-speed, low-latency, and more stable V2V and V2I communication technologies, edge computing technology, vehicle-road cooperative technology, and high-definition video processing technology, to propose a fusion method for intelligent estimation of traffic conditions in entrance ramp weaving areas under a vehicle-road cooperative environment, providing a more accurate basis for optimizing vehicle traffic control in entrance ramp weaving areas. Summary of the Invention

[0005] To address the aforementioned problems in the existing technology, this invention provides a method and system for estimating traffic conditions in weaving areas of urban expressway entrance ramps.

[0006] This invention discloses a traffic state estimation method for weaving areas at urban expressway entrance ramps. The weaving area consists of upstream and downstream road segments. The upstream road segments are the main road segment and auxiliary road segment before merging, and the downstream road segment is the main road segment after merging. When all vehicles on the urban expressway are connected vehicles, the traffic state estimation method includes:

[0007] Based on the information transmission between vehicles and the edge control center, the number N of vehicles in the weaving zone during different time periods is calculated. 车 ;

[0008] The number of vehicles N in the upstream road segment within the same time period was collected from two different locations. 上前 and N 上后 and the number of vehicles N in the downstream section 下前 and N 下后 For N respectively 上前 and N 上后 and N 下前 and N 下后 Weighted fusion is performed to obtain N 上视频 and N 下视频 For N 上视频 and N 下视频 Summing yields the number N of vehicles in the weaving zone. 视频 ;

[0009] N calculated for the same time period 车 and N 视频 Weighted fusion is performed to obtain the final number of vehicles N as the traffic state estimation result.

[0010] As a further improvement of the present invention, the calculation of the number of vehicles in the weaving zone within different time periods based on information transmission between the vehicle and the edge control center includes:

[0011] Vehicle i that enters the vicinity of the weaving zone actively sends vehicle information to the edge control center; wherein, the vehicle information includes vehicle location, speed and license plate information;

[0012] The edge control center determines whether the vehicle is in the weaving zone based on the vehicle position and speed information sent by vehicle i.

[0013] If vehicle i is in the weaving zone, record the vehicle information;

[0014] If vehicle i is not within the weaving zone, the edge control center will not collect information about that vehicle.

[0015] The number of vehicles N in the weaving zone during different time periods was statistically determined. 车 .

[0016] This invention also discloses a traffic state estimation method for weaving areas at urban expressway entrance ramps, wherein the weaving area consists of upstream and downstream road segments, the upstream road segments being the main road segment and auxiliary road segment before merging, and the downstream road segment being the main road segment after merging; when the vehicles on the urban expressway are partially connected vehicles and the remaining are ordinary vehicles without connected vehicle functionality, the traffic state estimation method includes:

[0017] Within the buffer zone upstream of the upstream road segment, a convoy consisting of two adjacent connected vehicles in each lane, designated as the lead and tail vehicles, is called a lane cluster unit. Based on the vehicle information of the lead and tail vehicles in each lane cluster unit, the number of vehicles N in the weaving zone during different time periods is calculated. 车 ;

[0018] The number of vehicles N in the upstream road segment within the same time period was collected from two different locations. 上前 and N 上后 and the number of vehicles N in the downstream section 下前 and N 下后 For N respectively 上前 and N 上后 and N 下前 and N 下后 Weighted fusion is performed to obtain N 上视频 and N 下视频 For N 上视频 and N 下视频 Summing yields the number N of vehicles in the weaving zone. 视频 ;

[0019] N calculated for the same time period 车 and N 视频 Weighted fusion is performed to obtain the final number of vehicles N as the traffic state estimation result.

[0020] As a further improvement of the present invention, the number N of vehicles in the weaving zone during different time periods is calculated based on the vehicle information of the first and last vehicles in each lane cluster unit. 车 ;include:

[0021] Based on the head vehicle position P of a single lane cluster unit i i-头 (x i-头 ,y i-头 ) and the position of the rear vehicle P i-尾 (x i-尾 ,y i-尾 ), calculate the convoy length as L i-车队 :

[0022]

[0023] Based on the head vehicle speed V of a single lane cluster unit ii-头 and the speed of the last car V i-尾 Calculate the time t1 when the lead vehicle enters the upstream section of the weaving zone and the time t2 when the tail vehicle leaves the downstream section, and calculate the average speed of a single lane cluster unit i in the weaving zone.

[0024]

[0025] Based on the average speed of a single lane cluster unit i in the weaving zone Given the vehicle's maximum deceleration *a*, estimate the minimum safe travel distance for each vehicle as *L*. i-安全 And calculate the number of vehicles N in each lane cluster unit i. i-车队 :

[0026]

[0027]

[0028] Calculate the number of vehicles N in the weaving zone during different time periods. 车 :

[0029]

[0030] In the formula, Q is the number of lanes in a certain time period, and n is the number of lane cluster units in a single lane in a certain time period.

[0031] As a further improvement of the present invention

[0032] The pair N 上前 and N 上后 Weighted fusion is performed to obtain N 上视频 ,include:

[0033] Assuming the weights of the vehicle counts captured by video at two different locations before and after the upstream road segment are η1 and η2 respectively, the weighted sum of the vehicle counts in the upstream road segment is:

[0034]

[0035] Calculate N respectively 上前 and N 上后 and The difference Δ 上前 and Δ 上后 ,Right now

[0036]

[0037]

[0038] The calculation is performed iteratively within each sampling period to find the weight combination pair {η1, η2} corresponding to the minimum standard deviation, which is then used as the final weight:

[0039]

[0040] Based on the obtained weights η1 and η2, calculate the number of vehicles N in the upstream section. 上视频 :

[0041] N 上视频 =η1N 上前 +η2N 上后

[0042] The pair N 下前 and N 下后 Weighted fusion is performed to obtain N 下视频 ,include:

[0043] Assuming the weights of the vehicle counts captured by video at two different locations before and after the downstream road segment are η3 and η4 respectively, the weighted sum of the vehicle counts in the downstream road segment is:

[0044]

[0045] Calculate N respectively 下前 and N 下后 and The difference Δ 下前 and Δ 下后 ,Right now

[0046]

[0047]

[0048] The calculation is performed iteratively within each sampling period to find the weight combination pair {η3, η4} corresponding to the minimum standard deviation, which is then used as the final weight:

[0049]

[0050] Based on the obtained weights η3 and η4, calculate the number of vehicles N in the downstream road segment. 下视频 :

[0051] N 下视频 =η3N 下前 +η4N 下后 .

[0052] As a further improvement of the present invention, the N calculated for the same time period 车 and N 视频 The weighted fusion is performed to obtain the final number of vehicles N; including:

[0053] Assume N 车 and N 视频 The weights are λ1 and λ2, respectively. The weighted summation of the traffic conditions in the weaving zone is:

[0054] N * =λ1N 车 +λ2N 视频

[0055] Calculate N respectively 车 and N 视频 With N * The difference Δ 车 and Δ 视频 ,Right now

[0056] Δ 车 =N 车 -N *

[0057] Δ 视频 =N 视频 -N *

[0058] The calculation is performed iteratively within each sampling period to find the weight combination pair {λ1, λ2} corresponding to the minimum standard deviation, which is then used as the final weight:

[0059]

[0060] Based on the obtained weights λ1 and λ2, calculate the final number of vehicles N:

[0061] N=λ1N 车 +λ2N 视频 .

[0062] This invention also discloses a traffic state estimation system for weaving areas at urban expressway entrance ramps, used to implement the aforementioned traffic state estimation method for weaving areas at urban expressway entrance ramps. The weaving area at the urban expressway entrance ramps consists of upstream and downstream road segments. The upstream road segments are the main road segment and auxiliary road segment before merging, and the downstream road segment is the main road segment after merging. The traffic state estimation system includes:

[0063] 5G base stations are used to provide communication between vehicles, between vehicles and roadside facilities, between vehicles and edge control centers, between vehicles and video devices, and between video devices and edge control centers.

[0064] The edge control center is used to receive vehicle information sent by connected vehicles, receive vehicle information sent by video devices, and perform edge computing functions.

[0065] The first front-view video device and the first rear-view video device are respectively installed at both ends of the upstream road section;

[0066] The second front-view video device and the second rear-view video device are respectively installed at both ends of the downstream road section.

[0067] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0068] Based on 5G, edge computing, vehicle-road cooperative technology, BeiDou high-precision positioning and high-definition video processing technology, this invention realizes traffic state estimation of the weaving area of ​​urban expressway entrance ramps in a vehicle-road cooperative environment, providing a more accurate basis for optimizing vehicle traffic control in the weaving area of ​​entrance ramps. Attached Figure Description

[0069] Figure 1 This is a schematic diagram of the traffic state estimation system for the weaving area of ​​an urban expressway entrance ramp, as disclosed in one embodiment of the present invention.

[0070] In the picture:

[0071] 1. 5G base station; 2. Edge control center; 3. First front-facing video device; 4. First rear-facing video device; 5. Traffic lights at the entrance ramp; 6. Second front-facing video device; 7. Second rear-facing video device. Detailed Implementation

[0072] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0073] The present invention will now be described in further detail with reference to the accompanying drawings:

[0074] like Figure 1 As shown, this invention provides a traffic state estimation system for the weaving area of ​​an urban expressway entrance ramp. The weaving area of ​​the urban expressway entrance ramp consists of upstream and downstream road segments. The upstream road segments are the main road segment and auxiliary road segment before merging, and the downstream road segment is the main road segment after merging. The traffic state estimation system includes: a 5G base station 1, an edge control center 2, a first front-end video device 3, a first rear-end video device 4, traffic lights of the entrance ramp 5, a second front-end video device 6, and a second rear-end video device 7; wherein,

[0075] 5G base station 1 is used to provide communication between vehicles (with network communication capabilities), between vehicles and roadside facilities, between vehicles and the edge control center, between vehicles and video devices, and between video devices and the edge control center;

[0076] Edge Control Center 2 mainly receives information such as location, speed and license plate number sent by connected vehicles, receives vehicle information sent by video devices, and performs edge computing functions.

[0077] The first front-facing video device 3 and the first rear-facing video device 4 are respectively set at both ends of the upstream road segment to collect vehicle information of vehicles on the upstream road segment from both front and rear directions; the length of L2 is the length of the upstream road segment, and the length of L1 is the length of the vehicle buffer section. After the vehicle enters the upstream road segment, it can communicate with the edge control center 2 in real time. The edge control center 2 informs the vehicles on the upstream road segment of the traffic status of the entrance ramp weaving area in advance, so that the vehicles can decelerate and change lanes in a timely manner.

[0078] Traffic light 5 at the entrance ramp mainly controls vehicles from the auxiliary road to enter the main road;

[0079] The second front-view video device 6 and the second rear-view video device 7 are respectively set at both ends of the downstream road segment to collect vehicle information of vehicles on the downstream road segment from both front and rear directions; the length of L3 is the length of the downstream road segment. The upstream road segment and the downstream road segment form the weaving area of ​​the urban expressway entrance ramp. By estimating the traffic conditions of the two road segments, corresponding control measures can be taken in a timely manner for vehicles coming from the upstream and vehicles coming from the entrance ramp.

[0080] This invention defines the road segments within the length range of L2 and L3 as the intersecting area of ​​urban expressway entrance ramps. The traffic status of this area is the number of vehicles within the road segment of length L2+L3.

[0081] Based on the aforementioned system, the main concept of this invention is to address the traffic conditions within the weaving zone of urban expressway entrance ramps. On one hand, it calculates the number of vehicles in the weaving zone based on information transmitted between vehicles and the edge control center; on the other hand, it calculates the number of vehicles in the weaving zone based on vehicle information collected by video devices in the weaving zone. Considering that communication latency and packet loss can affect the active transmission of vehicle information, and that vehicle occlusion can affect the accuracy of video device acquisition, a fusion algorithm is employed to combine the two calculations to obtain a more accurate traffic condition. In the estimation process, this invention classifies vehicles into two categories: 1. All vehicles have network communication capabilities; 2. Some vehicles have network communication capabilities, while the remaining vehicles do not. The implementation scheme is described according to these two scenarios based on vehicle type.

[0082] Example 1

[0083] This invention provides a traffic state estimation method for weaving areas at urban expressway entrance ramps. When all vehicles on the urban expressway are connected vehicles, the traffic state estimation method includes:

[0084] S11. Based on the information transmission between the vehicle and the edge control center, calculate the number of vehicles N in the weaving zone during different time periods. 车 ;

[0085] Specifically, it includes:

[0086] Since all vehicles are connected to the network, they can achieve real-time communication between vehicles and between vehicles and the edge control center. Therefore, vehicle i entering the vicinity of the weaving zone can proactively send vehicle information to the edge control center. This information includes vehicle location, speed, and license plate information. Based on the vehicle location and speed information sent by vehicle i, the edge control center determines whether the vehicle is within the weaving zone (the L2+L3 road segment). If vehicle i is within the weaving zone, its information is recorded; otherwise, the edge control center does not record its information. Based on this, the number N of vehicles in the weaving zone during different time periods is calculated. 车 .

[0087] S12. Collect the number of vehicles N in the upstream road segment within the same time period as S11 from two different video positions. 上前 and N 上后 and the number of vehicles N in the downstream section 下前 and N 下后 For N respectively 上前 and N 上后 and N 下前 and N 下后 Weighted fusion is performed to obtain N 上视频 and N 下视频 For N 上视频 and N 下视频 Summing yields the number N of vehicles in the weaving zone. 视频 ;

[0088] Specifically, it includes:

[0089] 1) The first front video device 3 and the first rear video device 4 respectively collect video from two different positions to capture the number N of vehicles in the upstream road section during the same time period as S11. 上前 and N 上后 The second front video device 6 and the second rear video device 7 respectively collect video from two different positions to capture the number N of vehicles in the downstream road section during the same time period as S11. 下前 and N 下后 ;

[0090] 2) For N 上前 and N 上后 Weighted fusion is performed to obtain N 上视频 Specifically, it includes:

[0091] Assuming the weights of the vehicle counts captured by video at two different locations before and after the upstream road segment are η1 and η2 respectively, the weighted sum of the vehicle counts in the upstream road segment is:

[0092]

[0093] Calculate N respectively 上前 and N 上后 and The difference Δ 上前 and Δ 上后 ,Right now

[0094]

[0095]

[0096] The calculation is performed iteratively within each sampling period to find the weight combination pair {η1, η2} corresponding to the minimum standard deviation, which is then used as the final weight:

[0097]

[0098] Based on the obtained weights η1 and η2, calculate the number of vehicles N in the upstream section. 上视频 :

[0099] N 上视频 =η1N 上前 +η2N 上后

[0100] 3) For N 下前 and N 下后 Weighted fusion is performed to obtain N 下视频 Specifically, it includes:

[0101] Assuming the weights of the vehicle counts captured by video at two different locations before and after the downstream road segment are η3 and η4 respectively, the weighted sum of the vehicle counts in the downstream road segment is:

[0102]

[0103] Calculate N respectively 下前 and N 下后 and The difference Δ 下前 and Δ 下后 ,Right now

[0104]

[0105]

[0106] The calculation is performed iteratively within each sampling period to find the weight combination pair {η3, η4} corresponding to the minimum standard deviation, which is then used as the final weight:

[0107]

[0108] Based on the obtained weights η3 and η4, calculate the number of vehicles N in the downstream road segment. 下视频 :

[0109] N 下视频 =η3N 下前 +η4N 下后

[0110] 4) For N 上视频 and N 下视频 Summing yields the number N of vehicles in the weaving zone. 视频 :

[0111] N 视频 =N 上视频 +N 下视频

[0112] S13, N calculated for the same time period 车 and N 视频 Weighted fusion is performed to obtain the final number of vehicles N as the traffic state estimation result;

[0113] Specifically, it includes:

[0114] 1) Assume N 车 and N 视频 The weights are λ1 and λ2, respectively. The weighted summation of the traffic conditions in the weaving zone is:

[0115] N * =λ1N 车 +λ2N 视频

[0116] 2) Calculate N respectively 车 and N 视频 With N * The difference Δ 车 and Δ 视频 ,Right now

[0117] Δ 车 =N 车 -N *

[0118] Δ 视频 =N 视频 -N *

[0119] 3) Perform iterative calculations within each sampling period to find the weight combination pair {λ1, λ2} corresponding to the minimum standard deviation, which will be used as the final weights:

[0120]

[0121] 4) Calculate the final number of vehicles N based on the obtained weights λ1 and λ2:

[0122] N=λ1N 车 +λ2N 视频

[0123] Example 2

[0124] This invention provides a traffic state estimation method for weaving areas at the entrance ramps of urban expressways. When the vehicles on the urban expressway are partially connected vehicles and the remaining vehicles are ordinary vehicles without connected vehicle capabilities, the traffic state estimation method includes:

[0125] S21. In the buffer zone upstream of the upstream road segment, two adjacent connected vehicles in each lane are designated as the lead and tail vehicles, with several ordinary vehicles between them, forming a convoy called a lane cluster unit. Based on the distribution of connected vehicles, a lane can have several lane cluster units. Lane cluster units within the same lane can exchange information through the connected vehicles at the head and tail of the convoy. The onboard sensors of the connected vehicles at the head and tail of each lane cluster unit can calculate the speed of adjacent ordinary vehicles in the same lane. Based on this, the number of vehicles in each lane cluster unit is estimated according to the minimum safe distance. Simultaneously, the average speed of the entire convoy is estimated, and based on this, the speed of all lane cluster units can be calculated. Based on this, the number of vehicles N in the weaving zone during each time period can be calculated. 车 ;

[0126] Specifically, it includes:

[0127] 1) Based on the head vehicle position P of a single lane cluster unit i i-头 (x i-头 ,y i-头 ) and the position of the rear vehicle P i-尾 (x i-尾 ,y i-尾 ), calculate the convoy length as L i-车队 :

[0128]

[0129] 2) Based on the head vehicle speed V of a single lane cluster unit i i-头 and the speed of the last car V i-尾 Calculate the time t1 when the lead vehicle enters the upstream section of the weaving zone and the time t2 when the tail vehicle leaves the downstream section, and calculate the average speed of a single lane cluster unit i in the weaving zone.

[0130]

[0131] 3) Based on the average speed of a single lane cluster unit i in the weaving zone Given the vehicle's maximum deceleration a (constant), estimate the minimum safe travel distance for each vehicle as L. i-安全 And calculate the number of vehicles N in each lane cluster unit i. i-车队 :

[0132]

[0133]

[0134] 4) Calculate the number of vehicles N in the weaving zone during different time periods. 车 :

[0135]

[0136] In the formula, Q is the number of lanes in a certain time period, and n is the number of lane cluster units in a single lane in a certain time period.

[0137] S22. Collect the number of vehicles N in the upstream road segment within the same time period as S21 from two different locations. 上前 and N 上后 and the number of vehicles N in the downstream section 下前 and N 下后 For N respectively 上前 and N 上后 and N 下前 and N 下后 Weighted fusion is performed to obtain N 上视频 and N 下视频 For N 上视频 and N 下视频 Summing yields the number N of vehicles in the weaving zone. 视频 ;

[0138] Specifically, it includes:

[0139] 1) The first front video device 3 and the first rear video device 4 respectively collect video from two different positions to collect the number N of vehicles in the upstream road section during the same time period as S21. 上前 and N 上后 The second front video device 6 and the second rear video device 7 respectively collect video from two different positions to capture the number N of vehicles in the downstream road section during the same time period as S21. 下前 and N 下后 ;

[0140] 2) For N 上前 and N 上后 Weighted fusion is performed to obtain N 上视频 Specifically, it includes:

[0141] Assuming the weights of the vehicle counts captured by video at two different locations before and after the upstream road segment are η1 and η2 respectively, the weighted sum of the vehicle counts in the upstream road segment is:

[0142]

[0143] Calculate N respectively 上前 and N 上后 and The difference Δ 上前 and Δ 上后 ,Right now

[0144]

[0145]

[0146] The calculation is performed iteratively within each sampling period to find the weight combination pair {η1, η2} corresponding to the minimum standard deviation, which is then used as the final weight:

[0147]

[0148] Based on the obtained weights η1 and η2, calculate the number of vehicles N in the upstream section. 上视频 :

[0149] N 上视频 =η1N 上前 +η2N 上后

[0150] 3) For N 下前 and N 下后 Weighted fusion is performed to obtain N 下视频 Specifically, it includes:

[0151] Assuming the weights of the vehicle counts captured by video at two different locations before and after the downstream road segment are η3 and η4 respectively, the weighted sum of the vehicle counts in the downstream road segment is:

[0152]

[0153] Calculate N respectively 下前 and N 下后 and The difference Δ 下前 and Δ 下后 ,Right now

[0154]

[0155]

[0156] The calculation is performed iteratively within each sampling period to find the weight combination pair {η3, η4} corresponding to the minimum standard deviation, which is then used as the final weight:

[0157]

[0158] Based on the obtained weights η3 and η4, calculate the number of vehicles N in the downstream road segment. 下视频 :

[0159] N 下视频 =η3N 下前 +η4N 下后

[0160] 4) For N 上视频 and N 下视频 Summing yields the number N of vehicles in the weaving zone. 视频 :

[0161] N 视频 =N 上视频 +N 下视频

[0162] S23, N calculated for the same time period 车 and N 视频 Weighted fusion is performed to obtain the final number of vehicles N as the traffic state estimation result;

[0163] Specifically, it includes:

[0164] 1) Assume N 车 and N 视频 The weights are λ1 and λ2, respectively. The weighted summation of the traffic conditions in the weaving zone is:

[0165] N * =λ1N 车 +λ2N 视频

[0166] 2) Calculate N respectively 车 and N 视频 With N * The difference Δ 车 and Δ 视频 ,Right now

[0167] Δ 车 =N 车 -N *

[0168] Δ 视频 =N 视频 -N *

[0169] 3) Perform iterative calculations within each sampling period to find the weight combination pair {λ1, λ2} corresponding to the minimum standard deviation, which will be used as the final weights:

[0170]

[0171] 4) Calculate the final number of vehicles N based on the obtained weights λ1 and λ2:

[0172] N=λ1N 车 +λ2N 视频

[0173] The advantages of this invention are:

[0174] Based on 5G, edge computing, vehicle-road cooperative technology, BeiDou high-precision positioning and high-definition video processing technology, this invention realizes traffic state estimation of the weaving area of ​​urban expressway entrance ramps in a vehicle-road cooperative environment, providing a more accurate basis for optimizing vehicle traffic control in the weaving area of ​​entrance ramps.

[0175] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for estimating traffic conditions in a weaving zone at an urban expressway entrance ramp, wherein the weaving zone consists of an upstream road segment and a downstream road segment, the upstream road segment being the main road segment and auxiliary road segment before merging, and the downstream road segment being the main road segment after merging; characterized in that, When all vehicles on urban expressways are connected vehicles, the traffic state estimation method includes: Based on the information transmission between vehicles and the edge control center, the number N of vehicles in the weaving zone during different time periods is calculated. 车 ; The number of vehicles N in the upstream road segment within the same time period was collected from two different locations. 上前 and N 上后 and the number of vehicles N in the downstream section 下前 and N 下后 For N respectively 上前 and N 上后 and N 下前 and N 下后 Weighted fusion is performed to obtain N 上视频 and N 下视频 For N 上视频 and N 下视频 Summing yields the number N of vehicles in the weaving zone. 视频 ; N calculated for the same time period 车 and N 视频 Weighted fusion is performed to obtain the final number of vehicles N as the traffic state estimation result.

2. The traffic state estimation method for weaving areas at urban expressway entrance ramps as described in claim 1, characterized in that, The number of vehicles in the weaving zone during different time periods is calculated based on the information transmission between the vehicle and the edge control center. include: Vehicle i that enters the vicinity of the weaving zone actively sends vehicle information to the edge control center; wherein, the vehicle information includes vehicle location, speed and license plate information; The edge control center determines whether the vehicle is in the weaving zone based on the vehicle position and speed information sent by vehicle i. If vehicle i is in the weaving zone, record the vehicle information; If vehicle i is not within the weaving zone, the edge control center will not collect information about that vehicle. The number of vehicles N in the weaving zone during different time periods was statistically determined. 车 .

3. A method for estimating traffic conditions in a weaving zone at an urban expressway entrance ramp, wherein the weaving zone consists of an upstream road segment and a downstream road segment, the upstream road segment being the main road segment and auxiliary road segment before merging, and the downstream road segment being the main road segment after merging; characterized in that, When the vehicles on urban expressways are partially connected vehicles and the remaining vehicles are ordinary vehicles without connected vehicle capabilities, the traffic state estimation method includes: Within the buffer zone upstream of the upstream road segment, a convoy consisting of two adjacent connected vehicles in each lane, designated as the lead and tail vehicles, is called a lane cluster unit. Based on the vehicle information of the lead and tail vehicles in each lane cluster unit, the number of vehicles N in the weaving zone during different time periods is calculated. 车 ; The number of vehicles N in the upstream road segment within the same time period was collected from two different locations. 上前 and N 上后 and the number of vehicles N in the downstream section 下前 and N 下后 For N respectively 上前 and N 上后 and N 下前 and N 下后 Weighted fusion is performed to obtain N 上视频 and N 下视频 For N 上视频 and N 下视频 Summing yields the number N of vehicles in the weaving zone. 视频 ; N calculated for the same time period 车 and N 视频 Weighted fusion is performed to obtain the final number of vehicles N as the traffic state estimation result.

4. The traffic state estimation method for weaving areas at urban expressway entrance ramps as described in claim 3, characterized in that, The number of vehicles N in the weaving zone during different time periods is calculated based on the vehicle information of the first and last vehicles in each lane cluster unit. 车 ; include: Based on the head vehicle position P of a single lane cluster unit i i-头 (x i-头 ,y i-头 ) and the position of the rear vehicle P i-尾 (x i-尾 ,y i-尾 ), calculate the convoy length as L i-车队 : Based on the head vehicle speed V of a single lane cluster unit i i-头 and the speed of the last car V i-尾 Calculate the time t1 when the lead vehicle enters the upstream section of the weaving zone and the time t2 when the tail vehicle leaves the downstream section, and calculate the average speed of a single lane cluster unit i in the weaving zone. Based on the average speed of a single lane cluster unit i in the weaving zone Given the vehicle's maximum deceleration *a*, estimate the minimum safe travel distance for each vehicle as *L*. i-安全 And calculate the number of vehicles N in each lane cluster unit i. i-车队 : Calculate the number of vehicles N in the weaving zone during different time periods. 车 : In the formula, Q is the number of lanes in a certain time period, and n is the number of lane cluster units in a single lane in a certain time period.

5. The traffic state estimation method for weaving areas at urban expressway entrance ramps as described in claim 1 or 3, characterized in that, The pair N 上前 and N 上后 Weighted fusion is performed to obtain N 上视频 ,include: Assuming the weights of the vehicle counts captured by video at two different locations before and after the upstream road segment are η1 and η2 respectively, the weighted sum of the vehicle counts in the upstream road segment is: Calculate N respectively 上前 and N 上后 and The difference Δ 上前 and Δ 上后 ,Right now The calculation is performed iteratively within each sampling period to find the weight combination pair {η1, η2} corresponding to the minimum standard deviation, which is then used as the final weight: Based on the obtained weights η1 and η2, calculate the number of vehicles N in the upstream section. 上视频 : N 上视频 =η1N 上前 +η2N 上后 The pair N 下前 and N 下后 Weighted fusion is performed to obtain N 下视频 ,include: Assuming the weights of the vehicle counts captured by video at two different locations before and after the downstream road segment are η3 and η4 respectively, the weighted sum of the vehicle counts in the downstream road segment is: Calculate N respectively 下前 and N 下后 and The difference Δ 下前 and Δ 下后 ,Right now The calculation is performed iteratively within each sampling period to find the weight combination pair {η3, η4} corresponding to the minimum standard deviation, which is then used as the final weight: Based on the obtained weights η3 and η4, calculate the number of vehicles N in the downstream road segment. 下视频 : N 下视频 η3N 下前 +η4N 下后 。 6. The traffic state estimation method for weaving areas at urban expressway entrance ramps as described in claim 1 or 3, characterized in that, The N calculated for the same time period 车 and N 视频 Perform weighted fusion to obtain the final number of vehicles N; include: Assume N 车 and N 视频 The weights are λ1 and λ2, respectively. The weighted summation of the traffic conditions in the weaving zone is: N * =λ1N 车 +λ2N 视频 Calculate N respectively 车 and N 视频 With N * The difference Δ 车 and Δ 视频 ,Right now D 车 =N 车 -N * D 视频 =N 视频 -N * The calculation is performed iteratively within each sampling period to find the weight combination pair {λ1, λ2} corresponding to the minimum standard deviation, which is then used as the final weight: Based on the obtained weights λ1 and λ2, calculate the final number of vehicles N: N=λ1N 车 +λ2N 视频 。 7. A traffic state estimation system for weaving areas at urban expressway entrance ramps, used to implement the traffic state estimation method for weaving areas at urban expressway entrance ramps as described in any one of claims 1 to 6, characterized in that, The urban expressway entrance ramp weaving area consists of an upstream section and a downstream section. The upstream section is the main road section and auxiliary road section before the convergence, and the downstream section is the main road section after the convergence. The traffic state estimation system includes: 5G base stations are used to provide communication between vehicles, between vehicles and roadside facilities, between vehicles and edge control centers, between vehicles and video devices, and between video devices and edge control centers. The edge control center is used to receive vehicle information sent by connected vehicles, receive vehicle information sent by video devices, and perform edge computing functions. The first front-view video device and the first rear-view video device are respectively installed at both ends of the upstream road section; The second front-view video device and the second rear-view video device are respectively installed at both ends of the downstream road section.

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