An optimization method for variable lane system at intersections considering the influence of lane changing

Abstract

The present invention discloses an optimization method for a variable lane system at intersections considering the influence of lane changes, including: S1, initializing each traffic parameter of the variable lane control system; S2, completing sub-period division according to the optimization time step; S3, exhausting all combinations of the number of lane changes, change timing, lane functions, and signal timing to form a lane change sub-period matrix and a lane function signal timing matrix; S4, for each combination scheme, further dividing the sub-period into a lane change sub-period and a signal change sub-period, and respectively calculating the delays of the two types of sub-periods; S5, for each combination scheme, accumulating the delays of all sub-periods to obtain the total delay, and selecting the minimum value of the total delay as the optimal scheme for the entire period. According to the present invention, it overcomes the drawbacks of frequent lane changes in variable lanes without considering switching costs, and at the same time collaboratively optimizes the number of changes, change timing, and signal timing throughout the optimization period, reducing the safety risks brought by frequent changes and improving the overall traffic efficiency of intersections throughout the period.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent transportation systems, and particularly relates to an optimization method for a variable lane system at intersections considering the impact of lane changes. Background Art

[0002] In recent years, with the rapid development of cities and the economy, many cities have the spatial characteristics of job-housing separation. The frequent occurrence of traffic flow distribution imbalance and changes at intersections during the morning and evening commuting peaks. The above phenomena exacerbate the imbalance problem between traffic supply and demand at intersections, seriously affecting the smooth operation of urban traffic. In order to make full use of the time and space resources at intersections to improve the operation efficiency of intersections, in recent years, many large cities in China have built variable guide lane systems at intersections. Variable guide lanes can change the lanes indicating directions according to the magnitudes of different turning traffic flows.

[0003] Although the current variable lane technology alleviates traffic pressure to a certain extent, most still use relatively traditional manual means to control variable lanes, and its benefits are limited. In terms of the current status of variable lane technology, the following deficiencies specifically exist:

[0004] 1. In reality, due to reasons such as lane clearing and driving characteristics, the variable lane switching process will have a certain negative impact on traffic flow. However, in the existing variable lane optimization control algorithms, most of them ignore this point, and may generate non-optimal solutions;

[0005] 2. In traditional scheme optimization methods, the selection of variable lane schemes and switching times mostly focus on adjacent sub-periods, mainly based on threshold judgment. The function of frequently switching lanes may bring potential safety hazards. At the same time, a traffic system model cannot be established for the full sample conditions of the entire time and space scale, and the optimal sub-period variable lane scheme of the system cannot be optimized. Summary of the Invention

[0006] Aiming at the deficiencies existing in the prior art, the purpose of the present invention is to provide an optimization method for a variable lane system at intersections considering the impact of lane changes, to overcome the drawback of frequent switching of variable lanes without considering switching costs, and at the same time to jointly optimize the switching times, switching times and signal timing during the entire optimization period, reduce the safety risks brought by frequent switching, and improve the traffic efficiency of intersections during the entire period. To achieve the above object and other advantages of the present invention, an optimization method for a variable lane system at intersections considering the impact of lane changes is provided, including:

[0007] S1. Initialize each traffic parameter of the variable lane control system;

[0008] S2. Complete sub-period division according to the optimization time step;

[0009] S3. Exhaustively list all combinations of lane change times, change opportunities, lane functions, and signal timings to form a lane change sub-period matrix and a lane function signal timing matrix;

[0010] S4. For each combination scheme, further divide the sub-periods into lane change sub-periods and signal change sub-periods, and calculate the delays of the two types of sub-periods respectively;

[0011] S5. For each combination scheme, accumulate the delays of all sub-periods to obtain the total delay, and select the minimum value of the total delay as the optimal scheme for the entire period.

[0012] Preferably, the traffic parameters in step S1 include intersection geometric conditions, optimization period interval, optimization time step, traffic flow, signal cycle, minimum green time, lane base saturation flow rate, clearance time, lane utilization rate, and variable lane saturated headway.

[0013] Preferably, set an optimization interval, where the optimization interval is greater than the signal cycle, and select a time step Δt to divide the optimization interval into several sub-periods. Input the traffic demand data for the sub-periods, and use the model to obtain the optimal spatio-temporal resource scheme for each sub-period.

[0014] Preferably, the lane base saturation flow rate is the maximum number of vehicles that can pass through a certain lane section per unit time; the lane utilization rate is the distribution of traffic flow in different lanes in a certain direction, which measures the balance degree of traffic flow distribution; the lane utilization rate is used to correct the theoretical traffic capacity calculated based on the lane base saturation flow rate.

[0015] Preferably, discretize the entire optimization period [T s , T e . With Δt as the time step, there are a total of K sub-periods, and the sub-period division set T0 is:

[0016] T0 = {T s , 1, 2…t, t + Δt…T e}, t ∈ {1, 2, …K} #(1).

[0017] Preferably, for the number of changes and change opportunities in step S3: Enumerate the number of lane function changes M and the lane change sub-periods within the optimization period to form a lane change sub-period matrix as: T = [t1, t2…t M ;

[0018] Lane function and signal timing:

[0019] Enumerate the lane function and signal timing scheme matrix adopted by the i-th approach road at the intersection in the t sub-period including the number of vehicles using lane function p and the green ratio of different phases

[0020]

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] (1) The negative impact of the lane switching process on traffic efficiency is precisely characterized. The saturated headway of variable lanes and lane utilization are used to quantitatively calculate the switching impact. Considering this in the optimization model, the optimization period is further divided into lane switching sub-periods and signal switching sub-periods to generate a variable lane solution that is more in line with engineering practice.

[0023] (2) The proposed method realizes the coordinated optimization of variable lane functions, signal timing, switching times and switching opportunities under traffic fluctuations. Comprehensive decisions are made during the entire optimization period, and solutions suitable for different time periods can be proposed to adapt to changing traffic needs.

[0024] (3) Through the design of algorithmic processes, we can get rid of the limitations of human capabilities and efficiently output the optimal variable lane setting plan. Compared with previous technologies, this can reduce the safety hazards caused by frequent variable lane switching and improve the efficiency of intersection traffic.

[0025] (4) Compared with the fixed lane (FIX) in the same direction, the average saturated headway and lane utilization rate of the variable lane during the switching period are significantly different. Secondly, a coordinated optimization method of lane function and signal timing is proposed to address the differences. Considering the negative utility of lane switching, the number of switches and the timing of switching are comprehensively decided in the optimization interval to minimize the total delay of the entrance lane. Finally, different traffic demand scenarios are set based on real data. The optimization method considering the impact of switching and the threshold judgment method are tested in the same scenario. The traffic efficiency and traffic safety indicators of the two schemes are compared to verify the effectiveness of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A schematic scenario diagram of a study on an intersection variable lane system optimization method considering the influence of lane switching according to the present invention;

[0027] Figure 2 It is an intersection scene and system interface diagram of the intersection variable lane system optimization method considering the influence of lane switching according to the present invention;

[0028] Figure 3 It is a graph of intersection statistics of the intersection variable lane system optimization method considering the influence of lane switching according to the present invention;

[0029] Figure 4 A variable lane switching flow chart of a variable lane system optimization method for an intersection considering the influence of lane switching according to the present invention;

[0030] Figure 5 is the flowchart of the variable lane system optimization considering switching impact for the intersection variable lane system optimization method considering lane switching impact according to the present invention;

[0031] Figure 6 is the intersection geometric condition and traffic flow diagram of the intersection variable lane system optimization method considering lane switching impact according to the present invention;

[0032] Figure 7 is the optimization result diagram of the intersection variable lane system optimization method considering lane switching impact according to the present invention;

[0033] Figure 8 is the numerical analysis result diagram of Example 1 of the intersection variable lane system optimization method considering lane switching impact according to the present invention. Detailed implementation manners

[0034] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0035] Referring to Figure 1-8 , a method for optimizing an intersection variable lane system considering lane switching impact includes: S1. Initialize each traffic parameter of the variable lane control system;

[0036] S2. Complete the sub-period division according to the optimization time step;

[0037] S3. Exhaust all combinations of the number of lane switches, switching times, lane functions, and signal timings, and form a lane switching sub-period matrix and a lane function signal timing matrix;

[0038] S4. For each combination scheme, further divide the sub-period into a lane switching sub-period and a signal switching sub-period, and calculate the delays of the two types of sub-periods respectively;

[0039] S5. For each combination scheme, accumulate the delays of all sub-periods to obtain the total delay, and select the minimum value of the total delay as the optimal scheme for the entire period.

[0040] Further, the traffic parameters in step S1 include intersection geometric conditions, optimization time period interval, optimization time step, traffic flow, signal cycle, minimum green time, basic saturation flow rate of lanes, clearance time, lane utilization rate, and saturated headway of variable lanes. The basic saturation flow rate of a lane is the maximum number of vehicles that can pass through a lane section per unit time; the lane utilization rate is the distribution of traffic flow in different lanes in a certain direction, which measures the balance degree of traffic flow distribution; the lane utilization rate is used to correct the theoretical traffic capacity calculated based on the basic saturation flow rate of lanes. Each time the research team conducts a plan transition at the intersection, they count the data of 7 signal cycles. The statistical results of the headway between vehicles and the number of vehicles entering the variable lane and adjacent lanes in the same direction are shown in Figure 2 .

[0041] 1) Headway

[0042] Figure 3 In d, the sample means of the headways of the variable lane and the fixed lane are 3.09 s and 2.53 s respectively. Generally, the headway of the variable lane is greater than that of the ordinary lane. A reasonable explanation is that drivers have a certain reaction time to the variable lane sign and usually have a lower speed, resulting in a larger headway.

[0043] The saturated headway is usually statistically analyzed starting from the 4th vehicle. Since the saturated headway data fails the normality test, Table 1 gives the independent sample non-parametric test results of the saturated headways of the two lanes. The means of the saturated headways of the variable and fixed lanes are 2.64 s and 2.23 s respectively, an increase of 18.4%. p < 0.001 indicates that there is a significant difference in the measured values of the saturated headway between the variable lane and the fixed lane, which is consistent with the Figure 3 observation law in d.

[0044] 2) Lateral distribution of vehicles

[0045] Figure 3 In e and g, the vehicle distribution statistics are for the straight-ahead to left-turn switch. The difference in the number of vehicles using the variable lane and the fixed lane is small. Table 2 gives the independent sample non-parametric test results of the number of variable and fixed vehicles under different switching sequences:

[0046] When switching from straight-ahead to left-turn, p > 0.05 indicates that there is no obvious difference in the number of vehicles entering the variable lane and the fixed lane. A possible explanation is that the intersection adopts the phase sequence of straight-ahead first and then left-turn. Observing the straight-ahead traffic flow is beneficial to identifying the function of the variable lane, and left-turn drivers are more willing to enter the variable lane;

[0047] When turning left and switching to going straight, the average number of vehicles entering the two lanes is 7 and 10.88 respectively, and the vehicles in the variable lane only account for 39.1% of the total traffic flow. p<0.05 indicates that there is a significant difference in the number of vehicles entering the variable lane and the fixed lane. The possible explanation is that after the lane switching function, drivers who are not familiar with the variable lane tend to choose the fixed lane, resulting in a lower lane utilization rate of the variable lane.

[0048] From the analysis of on-site data, when the DLG controls the lane switching function, both the saturated headway and the number of entering vehicles in the variable lane are inferior to those in the same-direction fixed lane. Among them, the average saturated headway increases by 18.4%, and the average number of entering vehicles only accounts for 39.1% of the total traffic flow and shows dynamic changes over time.

[0049] Table 1 Results of non-parametric test of samples

[0050]

[0051] Regarding the measurement of the impact during the lane switching process, the setting of the variable lane not only has a positive impact on traffic efficiency, but sometimes also has a negative impact. Analyzing the changes in traffic flow during the switching process is particularly important for the accuracy of the optimization plan. The collection locations are the intersection of Binsheng Road and Airport City Avenue in Hangzhou, Binkang Road and Huoju Avenue in Hangzhou, and the intersection of Tianyueqiao Road and South Zhongshan Second Road in Shanghai. One or more import lanes are equipped with variable lane facilities, and the phase sequence is a symmetric four-phase control with straight-ahead first and then left-turn. The data is from video detectors, and the time is May and July 2020. Systematic tests are carried out for several signal cycles for the left-turn to straight-ahead and straight-ahead to left-turn of the variable lane respectively. The intersection geometric conditions and system interface are as Figure 2 .

[0052] Furthermore, an optimization interval is set. The optimization interval is greater than the signal cycle, and the time step Δt is selected to divide the optimization interval into several sub-periods. Input the traffic demand data of the sub-periods, and use the model to obtain the optimal spatio-temporal resource plan for each sub-period.

[0053] Furthermore, discretize the entire optimization period [T s , T e . Δt is the time step, and there are a total of K sub-periods. The set T0 of sub-period division is:

[0054] T0 = {T s , 1, 2…t, t + Δt…T e}, t ∈ {1, 2, …K} #(1).

[0055] Furthermore, in step S3, the number of switches and the switching timing: enumerate the number of lane function switches M and the lane switching sub-periods within the optimization period, and form a lane switching sub-period matrix as: T = [t1, t2…t M ;

[0056] Lane functions and signal timing:

[0057] Enumerate the matrix of lane functions and signal timing schemes adopted by the approach of intersection i during sub-period t including the number of vehicles using lane function p and the green signal ratios of different phases

[0058]

[0059] For the variable lane optimization model considering the switching impact, first make basic assumptions. During the two-phase signal control intersection and the whole optimization period under saturated or oversaturated conditions, the traffic demand is assumed to be known and not considered temporarily. The main purpose of setting variable lanes is to alleviate the supply-demand contradiction in different directions at intersections and improve the operation efficiency. Delay can fully reflect the operation efficiency of intersections and is widely used as a standard to measure the performance of DLG, and can be directly used as an optimization goal. The model aims to minimize the sum of cumulative delays in all sub-periods.

[0060]

[0061]

[0062] For the signal switching sub-period, assume that there is no initial queuing delay from the previous cycle, and only uniform delay and incremental delay need to be considered. The delay of the approach sub-period Formula:

[0063]

[0064] For the lane switching sub-period, the switching process affects the capacity of variable lanes to fluctuate. For the convenience of optimization modeling in the present invention, its influencing factors are divided into two aspects: clearance time impact and driver characteristics.

[0065] Lane clearance impact

[0066] During the lane function transition period, the variable lane is closed in advance to clear the original-direction traffic flow, and then the traffic flow steering conversion is carried out. Lane clearance ensures traffic safety, but at the same time leads to a loss of capacity. Under different variable lane switching sequences, the capacity loss can be quantitatively analyzed with the help of the cumulative vehicle curve graph.

[0067] The present invention considers two variable lane switching methods: straight-ahead to left-turn and left-turn to straight-ahead, and the switching processes are as shown in Figure 4 a and b. Taking the straight-ahead to left-turn as an example, in the early stage when the straight-ahead green light is on, the straight-ahead vehicles in the variable lane keep being released; when entering the clearance stage, the straight-ahead vehicles are prohibited from entering the variable lane, and when the left-turn green light is on, the left-turn traffic flow is allowed to enter the variable lane to complete the switching.

[0068] When the variable lane changes from going straight to turning left, Figure 4 c and d) are the cumulative vehicle curves for going straight and turning left respectively. During the clearing phase, upstream straight vehicles are prohibited from entering the variable lane, and the straight vehicles already in the lane still leave smoothly. The impact of the switch on the passing capacity of straight vehicles can be ignored. When the left-turn phase starts, there is a loss of passing capacity due to the filling time of the variable lane. Assume that the filling time is equal to the clearing time t c is equal, Figure 4 The area of polygon OAD in c is the straight delay d1, Figure 4 The area of triangle AODE in d is the left-turn delay d2; the delay d during the lane-switching sub-period t is calculated as follows:

[0069]

[0070]

[0071] In the formula, r1 and r2 are the red-light durations for going straight and turning left respectively, g1 and g2 are the effective green-light durations, λ t 、λ l are the vehicle arrival rates, s1 and s2 are the saturation flow rates, s′1 represents the saturation flow rate after switching, and t c is the clearing time.

[0072] After the variable lane changes from turning left to going straight, the other direction is released, and the filling time coincides with the red-light time. The negative impact of lane filling can be ignored. The minimum duration of the clearing time t c needs to satisfy that all the vehicles in the original direction on the variable guide lane leave the intersection during the green-light period in the original direction:

[0073]

[0074] Affected by driving characteristics, when the variable lane switches lanes, drivers who are not familiar with the variable lane facilities may feel confused, resulting in a larger saturated headway, and tend to drive into the fixed lane. During the optimization interval, the saturated headway of the variable lane in the t sub-period is denoted as h(t), and Equation 11 represents the saturated headway matrix H of the variable lane in the optimization interval. The utilization rate of the variable lane in the t sub-period is denoted as f(t), and Equation 12 represents the utilization rate matrix F of the variable lane in the optimization interval:

[0075] H = [h(1), h(2)…h(t)], t ∈ {1, 2, …K} #(11)

[0076] F = [f(1), f(2)…f(t)], t ∈ {1, 2, …K} #(12)

[0077] The calculation of the saturated flow rate s(t) of the variable lane in the t sub-period is as shown in Equation 13, and the saturated flow rate of the optimized interval variable lane is denoted as matrix S:

[0078]

[0079] S = [s(1), s(2)…s(t)], t ∈ {1, 2, …K} #(14)

[0080] s0 is the basic saturated flow rate, and the lane utilization rate f(t) is the correction factor for the saturated headway in the t sub-period, and the calculation is as follows:

[0081] In the formula, Q t is the total flow of the lane group where the variable lane is located in the t sub-period, is the maximum single-lane flow of the lane group where the variable lane is located in the t sub-period, and N t is the number of lanes in the lane group in the t sub-period.

[0082] The constraint condition is: As a relatively stable part of traffic control, the lane function should limit its change frequency to ensure the safe operation of the intersection. Here, the shortest switching duration is set to 10 minutes, Δt ≥ 10 #(16). The number of lanes of various lane functions at each import of the intersection conforms to the formula, and the sum of all lane numbers conforms to the formula:

[0083]

[0084]

[0085] If the flow of a certain direction is greater than 0, there must be a lane function for that direction. Similarly, if the flow of a certain direction is equal to 0, the lane function for that direction is prohibited, which is expressed as Equation (19) indicates that the green signal ratio of each phase should be obtained between the minimum green light duration and the cycle duration. Equation (20) indicates that the sum of the green signal ratios of each phase should not exceed the cycle:

[0086]

[0087]

[0088] Example 1

[0089] In the example, the intersection of Airport Avenue and Binsheng Road in Hangzhou is selected as the case intersection, and the geometric layout is as Figure 6 , and two lanes at the west import are set as variable lanes. Only the optimization analysis of the west import of the intersection is carried out in the analysis. The reason is that the left-turn traffic flow at the west import is relatively large during the peak period, and the problem of unbalanced turning ratios at the import lane is obvious. The sub-direction traffic demands of 30 cycles at the west import are shown in Figure 6b. Protected left turn phase, no overlapping phase. In the analysis, the signal cycle is 150 s, and the minimum green time is taken as 10 s.

[0090] The lane functions and signal timings at the approach are mainly affected by factors such as traffic flow and flow direction ratios. To analyze the output results of the model under different traffic demand conditions, based on the total traffic flow magnitude and the trend of each turning ratio at the case approach, multiple scenarios are adjusted Figure 6 for medium traffic demand. There are mainly two parameters for adjustment:

[0091] Flow direction imbalance coefficient α: Adjust the ratio between different turning flows at the approach, α ∈ [3, 5], with an interval of 0.2

[0092] Traffic flow coefficient β: Amplify the total traffic flow at the approach, β ∈ [1.5, 2.5], with an interval of 0.1.

[0093] The design plan is as follows: To evaluate the benefits of the optimization model, the subsequent analysis will compare the optimization plan (proposed method) proposed in this paper with the traditional rule-based switching plan (rule-based method). Among them,

[0094] Proposed method (PM): Consider the switching impact on the system optimization plan, and simultaneously optimize the switching times, switching timing, lane functions, and signal timings; the duration θ of the switching process impact is set to two cycles. According to the table, the average saturated headway h(t) of the variable lane is fixed at 2.64 s. Substitute the values in the table into formula (), and the utilization rate f(t) of the variable lane is fixed at 0.82;

[0095] Rule-based method (RM): Do not consider the switching impact, optimize the lane functions and signal timings, and the implementation of the plan only considers traffic efficiency: change the lane function when the total delay at the approach is reduced by μ.

[0096] Model benefit analysis: When α and β are taken as 3.8 and 2.1 respectively, it is denoted as point M1(3.8, 2.1). When the switching threshold μ = 10%, a detailed analysis is carried out for two scenarios of M1 and M2(4.6, 2.3). The traffic flow is as shown in the figure, where L and T represent left turn and straight respectively. Figure 7 It represents the optimized results of lane functions and signal timings output by the two plans. Each square represents a cycle. The colors of the L and T squares are the green signal ratios of left turn and straight respectively. The lane arrows represent the driving directions, and the red marks represent the variable lane switching points. The total delay is calculated according to the model optimization plan as Figure 7 shown.

[0097] Analysis shows that in the M1 scenario cycle interval [1, 10], for the sharp increase in straight-through traffic flow, the RM scheme selects to switch lanes 2 times. Due to considering the negative benefits of switching, the PM scheme does not switch lanes and only increases the straight-through green ratio, and the total delay of PM is lower than that of RM; in the cycle interval (10, 30), both schemes switch twice, and the difference lies in the switching timing. In the cycle interval [11, 12], although the total delay of PM is higher than that of RM, greater benefits are achieved in the interval [13, 14]. Generally speaking, compared with the RM scheme, PM reduces the number of lane switches by 50%, and the reduction in total delay is 9.9%.

[0098] In the M2 scenario, due to the further imbalance of turning demands, both schemes switch 4 times, and the switching timing mostly concentrates on the stage of significant changes in traffic demand, while the smaller fluctuations are adjusted by signal timing, which is consistent with the original concept of the model.

[0099] Generally speaking, compared with the RM scheme, when PM maintains the same number of lane switches, by adjusting the switching timing, the reduction in total delay is 4.1%.

[0100] In summary, compared with the switching method based on threshold judgment, the system optimization scheme considering switching impacts proposed in the two scenarios, by reducing the number of lane switches and adjusting the lane switching timing, helps to reduce the total delay level of the approach lane. Especially in the M1 scenario, the significant reduction in the frequency of lane switches means reducing the safety risks of the DLG control method.

[0101] Conduct further analysis through cross-combination experiments on α and β. Figure 8 (a) and (b) represent the statistics of the number of lane switches in different scenarios of the two schemes. The colors represent the number of lane switches in different lanes. Blue, green, and red represent no switch, 2 times, and 4 times respectively. When in the region of β + 0.16α > 2.3, both schemes do not have the lane-switching function; when in the region of β + 0.16α < 2.3, the red area of the proposed method significantly shrinks. Generally speaking, compared with the rule-based method, the method proposed in this paper adopts a strategy of fewer lane function switching times, reducing the safety risks brought by frequent switching.

[0102] Figure 8 (d) shows the comparison results of the objective functions of the proposed method and the rule-based method when the switching threshold μ = 10%. The depth of the grid color represents the percentage reduction in total delay. And to further analyze the influence of the switching threshold on the proposed switching system optimization model considering the system, the figure studies the changes in total delay under different μ values.

[0103] It can be seen from the figure:

[0104] (1) Generally speaking, the proposed method helps to reduce the total delay of the import lane, especially when the traffic demand and turning imbalance of the import lane are relatively large. In the numerical example, compared with the optimization method based on threshold judgment, the maximum reduction in the total delay at the point (4, 2.1) when μ = 5% is up to 12.89%.

[0105] (2) Reducing the number of switches will significantly reduce the total delay. The advantageous regions of the proposed method are divided into two types: red and orange. The red region is similar to point M1, and mainly reduces the total delay by reducing the number of unnecessary switches; the orange region is similar to point M2, where the number of switches of the two methods is the same, and the difference in the total delay comes from the different switching times.

[0106] (3) As the switching threshold μ increases, the advantageous region of the proposed method gradually shrinks. This is mainly because the increase in the switching threshold μ is equivalent to considering the negative impact brought by the lane function switching. However, when μ = 30%, the rule-based method does not switch lanes, which is equivalent to the fixed-lane method at this time.

[0107] (4) Under different switching thresholds μ, there are significant differences in the optimization effects at point M1, indicating that the rule-based method needs to formulate corresponding switching thresholds in different scenarios to obtain the best benefits, lacking flexibility. And in all scenarios, the benefits of the system optimization method are not inferior to those of the rule-based method and the fixed-lane method, and it always performs better in region D2, with the maximum reduction in the total delay up to 12.22%.

[0108] Taking α = 4 and β = 2 as the boundaries, Figure 8 (f) is divided into four regions: D1 - D4. Take the average value of the percentage reduction in delay of the proposed method compared with the rule-based method under all switching thresholds μ and the number of switches of the two schemes, as shown in Table 2. In terms of the average number of switches, the average number of the proposed method is 1.2 times, less than 1.6 times of the rule-based method. As the switching threshold μ increases, the gap in the number of switches between the two continuously shrinks. In terms of the percentage reduction in delay, it can be found that except for region D2, as the switching threshold μ increases, the difference in the optimized delay between the two schemes continuously shrinks, but in region D2, the proposed method is always better than the rule-based method. Compared with the rule-based method, the proposed method can reduce the average number of switches by 34.3% while reducing the total delay level of the intersection by 6.55%.

[0109] Table 2 Overall analysis results

[0110]

[0111] The number of devices and the processing scale described here are used to simplify the description of the present invention, and the application, modification and variation of the present invention are obvious to those skilled in the art.

[0112] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and the embodiments. It can be fully applied to various fields suitable for the present invention. For those skilled in the art, additional modifications can be easily made. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the examples shown and described herein.

Claims

1. An optimization method for a variable lane system at intersections considering the impact of lane changes, characterized in that It includes the following steps: S1. Initialize each traffic parameter of the variable lane control system; the traffic parameters include intersection geometric conditions, optimization period intervals, optimization time steps, traffic flow, signal cycles, minimum green times, lane basic saturation flow rates, clearance times, lane utilization rates, and variable lane saturated headways; S2. Complete sub-period division according to the optimization time step; S3. Exhaustively list all combinations of lane change times, change opportunities, lane functions, and signal timings to form a lane change sub-period matrix and a lane function signal timing matrix; change times and change opportunities: Enumerate the number of lane function changes within the optimization period and the lane change sub-periods to form the lane change sub-period matrix as follows: ; Lane functions and signal timing: enumerate Sub-period intersection Matrix of lane functions and signal timing schemes used on the entrance lanes , including using the lane function Number of vehicles And different phase green signal ratio ; ； S4. For each combination plan, further divide the sub-period into lane switching sub-periods and signal switching sub-periods, and calculate the delays of the two types of sub-periods respectively; S5. For each combination plan, accumulate the delays of all sub-periods to obtain the total delay, and select the minimum value of the total delay as the optimal plan for the entire period; Set an optimization interval, where the optimization interval is greater than the signal period, and select a time step Divide the optimization interval into several sub-periods, input the traffic demand data of the sub-periods, and use the model to obtain the optimal spatio-temporal resource plans for each sub-period; The lane basic saturation flow rate is the maximum number of vehicles that can pass through a lane section per unit time; the lane utilization rate is the distribution of traffic flow in different lanes in a certain direction, which measures the balance degree of traffic flow distribution; The lane utilization rate is used to correct the theoretical traffic capacity calculated based on the lane basic saturation flow rate.

2. The optimization method for the variable lane system at intersections considering the influence of lane changes as claimed in claim 1, wherein Optimize the full time period Discretize as the time step, with a total of sub-time periods, and the sub-time period division set is as follows: 。

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