A safe lane allocation method to improve left-turn efficiency for large vehicles

Abstract

A safe lane allocation method for improving the efficiency of left-turn traffic for large vehicles relates to the fields of traffic safety and management. The method involves obtaining traffic flow information, traffic environment information, the gross vehicle weight, location, and weather information of large vehicles; identifying large vehicles turning left at an intersection and determining the actual road capacity of each lane for left-turning large vehicles; obtaining the target vehicle's minimum turning radius and determining the lane the target vehicle can enter; and assessing the driving risk of the target vehicle in the accessible lane to generate the optimal lane and report it to the target vehicle's onboard communication terminal. By combining real-time data with artificial intelligence algorithms, the method takes into account multiple factors influencing left turns for large vehicles, resulting in an optimal lane that combines low risk with high actual capacity, effectively improving the actual road capacity and enhancing traffic efficiency.

Description

Technical Field

[0001] The present invention relates to the field of traffic safety and management, and in particular to a lane safety allocation method for improving the left-turn efficiency of large vehicles. Background Art

[0002] With economic and social development, the demand for large vehicles in today's urban transportation systems is increasing. However, the mutual influence and constraints between large vehicles and other vehicles have led to many problems. When traveling on urban roads, factors such as speed and weight limit their ability to pass. At the same time, the excessive road space occupied by large vehicles also affects the efficiency of other vehicles. Improving the efficiency of large left-turning vehicles has always been a hot topic. Because large left-turning vehicles typically have a longer body length and turning radius, they often occupy more lane space during their passage, which can easily lead to traffic congestion, increased accident risks, and reduced traffic efficiency.

[0003] Currently, several solutions have been proposed to address the issue of traffic efficiency for large left-turning vehicles. For example, some cities have adopted dedicated left-turn lanes or left-turn waiting areas to provide more turning space for large left-turning vehicles. However, this approach often requires more traffic resources and cannot completely solve the problem of low traffic efficiency for large vehicles. Other solutions attempt to use intelligent transportation systems to improve the traffic efficiency of large left-turning vehicles by optimizing traffic lights. However, since traffic light optimization often involves complex algorithms and real-time data processing, this method has certain challenges and limitations in practical application.

[0004] Based on this, the present invention aims to provide a lane safety allocation method for improving the left-turn efficiency of large vehicles, thereby improving traffic efficiency while ensuring safety. Summary of the Invention

[0005] In order to make up for the shortcomings of the existing technical problems, the purpose of the present invention is to provide a lane safety allocation method for improving the efficiency of left-turn traffic of large vehicles. The method combines real-time data and artificial intelligence algorithms, processes parameters such as traffic flow information, target vehicle information, road parameters, road environment parameters and weather information, and determines the lane that the target vehicle can enter through the minimum turning radius of the target vehicle. The actual traffic capacity and driving risk of the lane that the target vehicle can enter are evaluated, so that the optimal lane is generated with the characteristics of low risk and high actual traffic capacity, effectively improving the actual traffic capacity of the road and improving traffic efficiency.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] 1. A lane safety allocation method for improving left-turn efficiency of large vehicles, comprising the following steps:

[0008] (1) Traffic cameras and weight sensors are installed at road intersections to obtain traffic flow information, traffic environment information, and the total weight of large vehicles, and upload the information to the cloud server;

[0009] (2) The driving route and driver information are transmitted to the cloud server through the large vehicle information input system, and the current location of the large vehicle is determined through the vehicle positioning system of the large vehicle. The location information of the large vehicle is uploaded to the cloud server in real time. The cloud server stores road parameters and road environment parameters, and is loaded with weather information;

[0010] (3) Based on the driving route of the large vehicle, the large vehicle turning left at the intersection is determined, and the data processing system of the cloud server obtains the actual road capacity of each lane of the left-turning large vehicle exit according to the road parameters and traffic flow information;

[0011] (4) Utilizing the data processing system of the cloud server, respectively, the safe turning radius of the target vehicle under the influence of road factors, pedestrian crossing factors, traffic flow factors, and road environment factors when the target vehicle enters the current intersection is obtained, and the ideal safe turning radius of the target vehicle is obtained based on the safe turning radius under the above four factors;

[0012] (5) A comparison table of vehicle turning radius and vehicle weight is stored in the cloud server. The data processing system of the cloud server compares the vehicle turning radius corresponding to the target vehicle weight with the ideal safe turning radius, and selects the larger value between the two as the minimum turning radius of the target vehicle;

[0013] (6) The data processing system of the cloud server determines the lane that the target vehicle can enter based on the target vehicle positioning information, road parameters, and the minimum turning radius of the target vehicle;

[0014] (7) The data processing system of the cloud server determines the driving risk assessment of the target vehicle in the lane it can enter by using the expert scoring method. The driving risk assessment factors are vehicle safety, road conditions, driver status, and road environment.

[0015] (8) Based on the actual traffic capacity of the lanes that the target vehicle can enter and the driving risk assessment, the optimal lane is generated, which ensures high traffic efficiency and low risk, with driving risk as the main evaluation indicator;

[0016] (9) The cloud server broadcasts the generated optimal lane to the target vehicle’s onboard communication terminal.

[0017] In step (3), based on the target vehicle's driving route, the cloud server's data processing system obtains the actual road capacity of each lane of the road the target vehicle enters based on road parameters and traffic flow information, as follows:

[0018] Basic lane capacity C 基 The calculation formula is:

[0019]

[0020] t0 is the minimum headway between vehicles, in seconds; v is the average speed of large vehicles turning left at the intersection, in km / h; S0 is the minimum headway between vehicles, in meters;

[0021] S0=S 反 +S 制 +S 安 +S 车 (2);

[0022] in, S 反 is the distance traveled by the vehicle during the driver's reaction time, in meters; S 制 is the braking distance of the car; S 安 is the safe distance of the car; S 车 is the length of the car body; t is the average reaction time of the driver; is the longitudinal adhesion coefficient;

[0023] The actual capacity of the lane will be affected by the vehicle type and lane width. Therefore, it is necessary to correct the basic capacity of the lane to obtain the actual capacity C of the lane. 实 :

[0024] C 实 =C 基 *f w *f h (3);

[0025] Among them, f w is the lane width correction factor; f h is the vehicle proportion correction coefficient.

[0026] The lane width correction factor f w Determination method: Through a large amount of data collection and investigation, the saturated headway time on sections of different road widths is obtained, and the proportion of large vehicles in the section needs to be recorded. After collecting enough data, a linear regression model is established to determine the correction coefficient by comparing the average headway time with the lane width and the proportion of large vehicles.

[0027] For the same type of vehicles, a linear regression model of average headway and lane width is established. The fitting model of lane width w and average saturated headway h is:

[0028] h=-0.098w+2.554 (4);

[0029]

[0030] According to the above formulas (4) and (5), the lane width correction coefficient is:

[0031]

[0032] Among them, S 基 The basic saturation flow rate is generally increased or decreased with 50 as the basic unit, and is taken as 1650pcu / (h·dao); S p is the actual saturation flow rate;

[0033] For lanes of the same width, establish the average headway h and the ratio of large vehicles P 大型车 The linear regression model of large vehicle proportion P 大型车 The fitting model with the average headway h is:

[0034] h=2.137P 大型车 +2.219 (7);

[0035]

[0036] According to the above formulas (7) and (8), the vehicle ratio correction coefficient is obtained as follows:

[0037]

[0038] The calculation method of the ideal safe turning radius R in step (4) is:

[0039] R=w p ×R p +w v ×R v +w r ×R r +w e ×R e (10);

[0040] R r 、R p 、R v 、R e are the safe turning radius of the vehicle under the influence of road factors, pedestrian crossing factors, traffic flow factors, and road environment factors, respectively. r 、w p 、wv 、w e R r 、R p 、R v 、R e Weighted value.

[0041] The safe turning radius R of the vehicle under the influence of road factors r The calculation method is as follows:

[0042] Obtain the historical information of vehicle speeds at each intersection, eliminate the vehicle data that does not meet the design speed of the corresponding intersection, and sort the speeds of each intersection from small to large. The 85% speed is used as the actual operating speed V0 of the vehicle at the corresponding intersection; then the safe turning radius R of the vehicle under the influence of road factors is r As follows:

[0043]

[0044] g is the acceleration due to gravity, μ is the lateral force coefficient, and i0 is the cross slope of the road.

[0045] Safe turning radius R affected by pedestrian crossing factors p Refers to the length L of pedestrian crossing p The influence of the safety turning radius R p With the construction of pedestrian crossing length L p Models between:

[0046] R p =AL p +C (12);

[0047] Model parameters A and C are affected by the speed of pedestrians crossing the intersection x1, the number of pedestrians crossing the intersection x2, the traffic volume at the intersection x3, the average speed at the intersection x4, and the number of lanes at the intersection x5. A multiple linear regression model is established with x1, x2, x3, x4, and x5 as independent variables and A and C as dependent variables:

[0048] A=b1x1+b2x2+b3x3+b4x4+b5x5+θ (13);

[0049] C=d1x1+d2x2+d3x3+d4x4+d5x5+β (14);

[0050] Among them, b1, b2, b3, b4, b5, d1, d2, d3, d4, and d5 are the regression coefficients of the multiple linear regression model, and θ and β are the constant terms of the multiple linear regression model.

[0051] The safe turning radius R of a vehicle under the influence of traffic flow factors v The calculation method is as follows:

[0052] The proportion of large vehicles in the traffic flow is P l The vehicle running speed V sv The influence of different large vehicle ratios was studied to obtain the vehicle running speed V sv The relationship between traffic volume q and traffic capacity C is as follows:

[0053]

[0054] Among them, V f is the initial speed of traffic flow;

[0055] but,

[0056] The safe turning radius R of a vehicle under the influence of road environmental factors e The calculation method is as follows:

[0057] The road environment factors include illegal roadside parking, the presence of moving pedestrians, the presence of non-motor vehicles, and pedestrians stationed in the lane. The impact of the above factors on the vehicle running speed is investigated, and the impact value of each factor on the running speed is determined by the expert scoring method, thereby obtaining the current intersection running speed reduction and the intersection running speed reduction value V. k , combined with formula (1) to calculate the intersection turning radius R under the influence of this factor e , the formula is as follows:

[0058]

[0059] The safe turning radius R of the vehicle under the influence of road factors r The calculation method is as follows:

[0060] Driving risk can be calculated using the following formula:

[0061] P=w1×S1+w2×S2+w3×S3+w4×S4 (18);

[0062] Among them, w1-w4 are weight coefficients, which are determined by expert scoring method. S1-S4 are vehicle safety score, road form score, traffic flow score, and environment score respectively. The specific scores are scored by experts.

[0063] Compared with the prior art, the present invention has the following beneficial effects:

[0064] 1. The present invention obtains information on current traffic flow, traffic environment, and vehicles that need to turn left, and obtains the ideal safe turning radius and the actual road capacity in real time, thereby allocating lanes for left-turning vehicles. While ensuring safety, it selects lanes with higher traffic efficiency, thereby improving traffic efficiency.

[0065] 2. The present invention combines real-time data with intelligent algorithms to incorporate factors that affect vehicle traffic, such as road factors that affect the ideal safe turning radius, pedestrian crossing factors, traffic flow factors, road environment factors, vehicle safety, road conditions, driver status and road environment factors that affect driving risks. By comprehensively considering these factors, lane allocation is made more reasonable, thereby improving the safety and efficiency of lane allocation and reducing the occurrence of traffic accidents and traffic congestion.

[0066] 3. The present invention dynamically optimizes lane allocation through the interaction of the intersection's data acquisition system, the vehicle-mounted system, and the cloud server, and transmits the allocation results to the driver through the vehicle-mounted communication terminal to achieve the purpose of safe lane allocation. This interactive mode realizes lane allocation quickly and effectively, and is real-time. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0068] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0069] like Figure 1 As shown, a lane safety allocation method for improving the left-turn efficiency of large vehicles includes the following steps:

[0070] (1) Traffic cameras and weight sensors are installed at road intersections to obtain traffic flow information, traffic environment information, and the total weight of large vehicles, and upload the information to the cloud server;

[0071] (2) The driving route and driver information are transmitted to the cloud server through the large vehicle information input system, and the current location of the large vehicle is determined through the vehicle positioning system of the large vehicle. The location information of the large vehicle is uploaded to the cloud server in real time. The cloud server stores road parameters and road environment parameters, and is loaded with weather information;

[0072] (3) Based on the driving route of the large vehicle, the large vehicle turning left at the intersection is determined. The data processing system of the cloud server obtains the actual traffic capacity of each lane of the left-turning large vehicle exit according to the road parameters and traffic flow information; the details are as follows:

[0073] Assume that the cars on the road have the same technical performance, the speed of the cars is constant, and the cars maintain the same safe distance. The basic traffic capacity of the lane is C 基 The calculation formula is:

[0074]

[0075] t0 is the minimum headway between vehicles, in seconds; v is the average speed of large vehicles turning left at the intersection, in km / h; S0 is the minimum headway between vehicles, in meters;

[0076] S0=S 反 +S 制 +S 安 +S 车 (2);

[0077] in, S 反 is the distance traveled by the vehicle during the driver's reaction time, in meters; S 制 is the braking distance of the car; S 安 is the safe distance of the car; S 车 is the length of the car body; t is the driver's reaction time; is the longitudinal adhesion coefficient;

[0078] The actual capacity of the lane will be affected by the vehicle type and lane width. Therefore, it is necessary to correct the basic capacity of the lane to obtain the actual capacity C of the lane. 实 :

[0079] C 实 =C 基 *f w *f h (3);

[0080] Among them, f w is the lane width correction factor; f h is the vehicle proportion correction coefficient.

[0081] The lane width correction factor f w Determination method: Through a large amount of data collection and investigation, the saturated headway time on sections of different road widths is obtained, and the proportion of large vehicles in the section needs to be recorded. After collecting enough data, a linear regression model is established to determine the correction coefficient by comparing the average headway time with the lane width and the proportion of large vehicles.

[0082] For the same type of vehicles, a linear regression model of average headway and lane width is established. The fitting model of lane width w and average saturated headway h is:

[0083] h=-0.098w+2.554 (4);

[0084]

[0085] According to the above formulas (4) and (5), the lane width correction coefficient is:

[0086]

[0087] Among them, S 基 The basic saturation flow rate is generally increased or decreased with 50 as the basic unit, and is taken as 1650pcu / (h·dao); S p is the actual saturation flow rate;

[0088] For lanes of the same width, establish the average headway h and the ratio of large vehicles P 大型车 The linear regression model of large vehicle proportion P 大型车 The fitting model with the average headway h is:

[0089] h=2.137P 大型车 +2.219 (7);

[0090]

[0091] According to the above formulas (7) and (8), the vehicle ratio correction coefficient is obtained as follows:

[0092]

[0093] (4) Utilizing the data processing system of the cloud server, respectively, the safe turning radius of the target vehicle under the influence of road factors, pedestrian crossing factors, traffic flow factors, and road environment factors when the target vehicle enters the current intersection is obtained, and the ideal safe turning radius of the target vehicle is obtained based on the safe turning radius under the above four factors;

[0094] The calculation method of the ideal safe turning radius R is:

[0095] R=w p ×R p +w v ×R v +w r ×R r +w e ×R e (10)

[0096] R r 、R p 、R v 、R eare the safe turning radius of the vehicle under the influence of road factors, pedestrian crossing factors, traffic flow factors, and road environment factors, respectively. r 、w p 、w v 、w e R r 、R p 、R v 、R e Weighted value.

[0097] The safe turning radius R of the vehicle under the influence of road factors r The calculation method is as follows:

[0098] Obtain the historical information of vehicle speeds at each intersection, eliminate the vehicle data that does not meet the design speed of the corresponding intersection, and sort the speeds of each intersection from small to large. The 85% speed is used as the actual operating speed V0 of the vehicle at the corresponding intersection; then the safe turning radius R of the vehicle under the influence of road factors is r As follows:

[0099]

[0100] g is the acceleration due to gravity, μ is the lateral force coefficient, and i0 is the cross slope of the road.

[0101] Vehicle safe turning radius R p Pedestrian crossing length L p The influence of the safety turning radius R p With the construction of pedestrian crossing length L p Models between:

[0102] R p =AL p +C (12);

[0103] Model parameters A and C are affected by the speed of pedestrians crossing the intersection x1, the number of pedestrians crossing the intersection x2, the traffic volume at the intersection x3, the average speed at the intersection x4, and the number of lanes at the intersection x5. A multiple linear regression model is established with x1, x2, x3, x4, and x5 as independent variables and A and C as dependent variables:

[0104] A=b1x1+b2x2+b3x3+b4x4+b5x5+θ (13);

[0105] C=d1x1+d2x2+d3x3+d4x4+d5x5+β (14);

[0106] Among them, b1, b2, b3, b4, b5, d1, d2, d3, d4, and d5 are the regression coefficients of the multiple linear regression model, and θ and β are the constant terms of the multiple linear regression model.

[0107] The safe turning radius R of a vehicle under the influence of traffic flow factors v The calculation method is as follows:

[0108] The proportion of large vehicles in the traffic flow is P l The vehicle running speed V sv The influence of different large vehicle ratios was studied to obtain the vehicle running speed V sv The relationship between traffic volume q and traffic capacity C is as follows:

[0109]

[0110] Among them, V f is the initial speed of traffic flow;

[0111] but,

[0112] The safe turning radius R of a vehicle under the influence of road environmental factors e The calculation method is as follows:

[0113] The road environment factors include illegal roadside parking, the presence of moving pedestrians, the presence of non-motor vehicles, and pedestrians stationed in the lane. The impact of the above factors on the vehicle running speed is investigated, and the impact value of each factor on the running speed is determined by the expert scoring method, thereby obtaining the current intersection running speed reduction and the intersection running speed reduction value V. k , combined with formula (1) to calculate the intersection turning radius R under the influence of this factor e , the formula is as follows:

[0114]

[0115] The safe turning radius R of the vehicle under the influence of road factors r The calculation method is as follows:

[0116] Driving risk can be calculated using the following formula:

[0117]

[0118] Among them, w1-w4 are weight coefficients, which are determined by expert scoring. S1-S4 are vehicle safety score, road form score, traffic flow score, and environment score, respectively. The specific scores are scored by experts and are as follows:

[0119] ①: Establish a judgment matrix: Use expert scoring to determine the impact of different influencing factors (vehicle safety, road conditions, driver status, and environment) on driving risk. The comparison scale is as follows:

[0120]

[0121] So the judgment matrix is ​​as follows:

[0122]

[0123] S1-S4 in the table are different influencing factors, and the judgment matrix a ij is the relative importance, so the values ​​should have the following relationship:

[0124] And a ii =1

[0125] ② Calculate the relative entropy weight: After establishing the judgment matrix, it is necessary to judge each sub-factor in the judgment matrix and calculate the maximum eigenvector of each matrix to determine the relative entropy weight of each sub-factor. First, it is necessary to calculate the product M of each row element. i , and then calculate its nth root right Perform normalization and determine the relative weight coefficient w i , as shown in the following table:

[0126]

[0127] (5) A vehicle turning radius and vehicle weight comparison table (as shown below) is stored in the cloud server. The data processing system of the cloud server compares the vehicle turning radius corresponding to the target vehicle weight with the ideal safe turning radius and selects the larger value between the two as the minimum turning radius of the target vehicle;

[0128]

[0129] (6) The data processing system of the cloud server determines the lane that the target vehicle can enter based on the target vehicle positioning information, road parameters, and the minimum turning radius of the target vehicle;

[0130] (7) The data processing system of the cloud server uses an expert scoring method to determine the driving risk assessment of the target vehicle in the lanes it can enter. The driving risk assessment factors are vehicle safety, road conditions, driver status, and road environment. The specific method is as follows: before system integration, accident analysis experts from the transportation industry are invited to score the basic road conditions of different lanes on different roads (congested, normal, idle), target vehicle safety (scored according to vehicle types, including freight trucks, buses, garbage trucks, cement mixers, engineering vehicles, etc.), target vehicle driver status (driver driving experience, age, whether there have been any violations in the past month), and different environments (rainy days, sunny days, snowy days, foggy days, etc.) (on a 100-point scale). Through multiple simulation training and corrections, classification can eventually be achieved through the system algorithm. Specifically, in actual application, computer vision and image recognition technology can be used to directly derive the specific scores for different target vehicle safety, road conditions, driver status, and environment.

[0131] (8) Based on the actual traffic capacity of the lanes that the target vehicle can enter and the driving risk assessment, the optimal lane is generated, which ensures high traffic efficiency and low risk, with driving risk as the main evaluation indicator; in specific road sections, a driving risk threshold is set according to the implementation situation. If the driving risks of all lanes that can be entered are lower than the threshold, the lane with high traffic efficiency is preferred; if the driving risks of all lanes that can be entered are higher than the threshold, the lane with the lowest driving risk is preferred; if the driving risks of the lanes that can be entered are both higher and lower than the threshold, the lane with the highest traffic efficiency below the threshold is selected.

[0132] (9) The cloud server broadcasts the generated optimal lane to the target vehicle’s onboard communication terminal.

[0133] The above is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solutions and inventive concepts of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A lane safety allocation method for improving the left-turn efficiency of large vehicles, characterized in that: The following steps are involved: (1) Traffic cameras and weight sensors are installed at road intersections to obtain traffic flow information, traffic environment information, and the total weight of large vehicles, and upload the information to the cloud server; (2) The driving route and driver information are transmitted to the cloud server through the large vehicle information input system, and the current location of the large vehicle is determined through the vehicle positioning system of the large vehicle. The location information of the large vehicle is uploaded to the cloud server in real time. The cloud server stores road parameters and road environment parameters, and is loaded with weather information; (3) Based on the driving route of the large vehicle, the large vehicle turning left at the intersection is determined, and the data processing system of the cloud server obtains the actual road capacity of each lane of the left-turning large vehicle exit according to the road parameters and traffic flow information; (4) Utilizing the data processing system of the cloud server, respectively, the safe turning radius of the target vehicle under the influence of road factors, pedestrian crossing factors, traffic flow factors, and road environment factors when the target vehicle enters the current intersection is obtained, and the ideal safe turning radius of the target vehicle is obtained based on the safe turning radius under the above four factors; (5) A comparison table of vehicle turning radius and vehicle weight is stored in the cloud server. The data processing system of the cloud server compares the vehicle turning radius corresponding to the target vehicle weight with the ideal safe turning radius, and selects the larger value between the two as the minimum turning radius of the target vehicle; (6) The data processing system of the cloud server determines the lane that the target vehicle can enter based on the target vehicle positioning information, road parameters, and the minimum turning radius of the target vehicle; (7) The data processing system of the cloud server determines the driving risk assessment of the target vehicle in the lane it can enter by using the expert scoring method. The driving risk assessment factors are vehicle safety, road conditions, driver status, and road environment. (8) Based on the actual traffic capacity of the lanes that the target vehicle can enter and the driving risk assessment, the optimal lane is generated, which ensures high traffic efficiency and low risk, with driving risk as the main evaluation indicator; (9) The cloud server broadcasts the generated optimal lane to the target vehicle’s onboard communication terminal; The calculation method of the ideal safe turning radius R in step (4) is: R=w p ×R p +w v ×R v +w r ×R r +w e ×R e (10); R r 、R p 、R v 、R e are the safe turning radius of the vehicle under the influence of road factors, pedestrian crossing factors, traffic flow factors, and road environment factors, respectively. r 、w p 、w v 、w e R r 、R p 、R v 、R e weighted value; The safe turning radius R of the vehicle under the influence of road factors r The calculation method is as follows: Obtain the historical information of vehicle speeds at each intersection, eliminate the vehicle data that does not meet the design speed of the corresponding intersection, and sort the speeds of each intersection from small to large. The 85% speed is used as the actual operating speed V0 of the vehicle at the corresponding intersection; then the safe turning radius R of the vehicle under the influence of road factors is r As follows: g is the acceleration of gravity, μ is the lateral force coefficient, and i0 is the cross slope of the road; Safe turning radius R affected by pedestrian crossing factors p Refers to the length L of pedestrian crossing p The influence of the safety turning radius R p With the construction of pedestrian crossing length L p Models between: <h2 style=";text-align:left;direction:ltr">R<h2 style=";text-align:left;direction:ltr"> p <h2 style=";text-align:left;direction:ltr"> (AL)<h2 style=";text-align:left;direction:ltr"> p <h2 style=";text-align:left;direction:ltr"> +C (12); Model parameters A and C are affected by the speed of pedestrians crossing the intersection x1, the number of pedestrians crossing the intersection x2, the traffic volume at the intersection x3, the average speed at the intersection x4, and the number of lanes at the intersection x5. A multiple linear regression model is established with x1, x2, x3, x4, and x5 as independent variables and A and C as dependent variables: A=b1x1+b2x2+b3x3+b4x4+b5x5+θ (13); C=d1x1+d2x2+d3x3+d4x4+d5x5+β (14); Among them, b1, b2, b3, b4, b5, d1, d2, d3, d4, and d5 are the regression coefficients of the multiple linear regression model, and θ and β are the constant terms of the multiple linear regression model.

2. The lane safety allocation method for improving left-turn efficiency of large vehicles according to claim 1 is characterized in that: In step (3), based on the target vehicle's driving route, the cloud server's data processing system obtains the actual road capacity of each lane of the road the target vehicle enters based on road parameters and traffic flow information, as follows: Basic lane capacity C 基 The calculation formula is: t0 is the minimum headway between vehicles, in seconds; v is the average speed of large vehicles turning left at the intersection, in km / h; S0 is the minimum headway between vehicles, in meters; S0=S 反 +S 制 +S 安 +S 车 (2); in, S 反 is the distance traveled by the vehicle during the driver's reaction time, in meters; S 制 is the braking distance of the car; S 安 is the safe distance of the car; S 车 is the length of the car body; t is the average reaction time of the driver; is the longitudinal adhesion coefficient; The actual capacity of the lane will be affected by the vehicle type and lane width. Therefore, it is necessary to correct the basic capacity of the lane to obtain the actual capacity C of the lane. 实 : C 实 =C 基 *f w *f h (3); Among them, f w is the lane width correction factor; f h is the vehicle proportion correction coefficient.

3. The lane safety allocation method for improving left-turn efficiency of large vehicles according to claim 2 is characterized in that: The lane width correction factor f w Determination method: Through a large amount of data collection and investigation, the saturated headway time on sections of different road widths is obtained, and the proportion of large vehicles in the section needs to be recorded. After collecting enough data, a linear regression model is established to determine the correction coefficient by comparing the average headway time with the lane width and the proportion of large vehicles. For the same type of vehicles, a linear regression model of average headway and lane width is established. The fitting model of lane width w and average saturated headway h is: h=-0.098w+2.554 (4); According to the above formulas (4) and (5), the lane width correction coefficient is: Among them, S 基 The basic saturation flow rate is generally increased or decreased with 50 as the basic unit, and is taken as 1650pcu / (h·dao); S p is the actual saturation flow rate; For lanes of the same width, establish the average headway h and the ratio of large vehicles P 大型车 The linear regression model of large vehicle proportion P 大型车 The fitting model with the average headway h is: h=2.137P 大型车 +2.219 (7); According to the above formulas (7) and (8), the vehicle ratio correction coefficient is obtained as follows:

4. The lane safety allocation method for improving left-turn efficiency of large vehicles according to claim 1 is characterized in that: The safe turning radius R of a vehicle under the influence of traffic flow factors v The calculation method is as follows: The proportion of large vehicles in the traffic flow is P l The vehicle running speed V sv The influence of different large vehicle ratios was studied to obtain the vehicle running speed V sv The relationship between traffic volume q and traffic capacity C is as follows: Among them, V f is the initial speed of traffic flow; but, 5. The lane safety allocation method for improving left-turn efficiency of large vehicles according to claim 1 is characterized in that: The safe turning radius R of a vehicle under the influence of road environmental factors e The calculation method is as follows: The road environment factors include illegal roadside parking, the presence of moving pedestrians, the presence of non-motor vehicles, and pedestrians stationed in the lane. The impact of the above factors on the vehicle running speed is investigated, and the impact value of each factor on the running speed is determined by the expert scoring method, thereby obtaining the current intersection running speed reduction and the intersection running speed reduction value V. k , combined with formula (1) to calculate the intersection turning radius R under the influence of this factor e , the formula is as follows:

6. The lane safety allocation method for improving left-turn efficiency of large vehicles according to claim 1, characterized in that: The safe turning radius R of the vehicle under the influence of road factors r The calculation method is as follows: Driving risk can be calculated using the following formula: P=w1×S1+w2×S2+w3×S3+w4×S4 (18); Among them, w1-w4 are weight coefficients, which are determined by expert scoring method. S1-S4 are vehicle safety score, road form score, traffic flow score, and environment score respectively. The specific scores are scored by experts.

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