A method for evaluating the capacity of urban underground road interweaving areas

Abstract

The present invention discloses a method for evaluating the capacity of an urban underground road interweaving area. The traffic behavior simulation model is improved based on the measured vehicle trajectory data of urban underground roads, and is transplanted to the SUMO platform for simulation experiments and effectiveness verification. Subsequently, based on the simulation experiments of the improved model, a method for calculating the capacity of an interweaving area applicable to various scenarios is proposed; by conducting relevant simulation experiments, an application model for calculating the capacity of an urban underground road interweaving area under typical working conditions is constructed, which is applicable to the rapid calculation and application of capacity in a variety of scenarios. The method for calculating the capacity of an urban underground road interweaving area based on SUMO simulation and the application model for calculating the capacity of an urban underground road interweaving area constructed by the present invention can be used to quickly determine the capacity under different working conditions. At the same time, the influence of various influencing factors on the capacity is analyzed, providing a theoretical basis for the traffic organization design and control management of underground road interweaving areas under a variety of working conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of urban underground road traffic organization design and optimization, in particular to a method for evaluating the traffic capacity of an urban underground road interweaving area. Background Art

[0002] With the increasing population and number of motor vehicles in my country's cities, surface traffic congestion is becoming increasingly severe. To alleviate this traffic pressure, underground road construction has begun to emerge in major cities. The environmental conditions of underground roads differ from those of surface roads. Underground roads are closed and oppressive, with monotonous visuals, poor lighting conditions, high noise levels, and increased anxiety and distraction for drivers. These factors lead to significant differences in the behavioral characteristics of drivers underground compared to those on surface roads.

[0003] The entrance and exit sections of underground roads are important traffic nodes connecting to surface roads. Their design should ensure smooth traffic flow and improve road capacity while ensuring driving safety. For first-in, last-out ramp combinations, vehicles weave between the entrance and exit ramps, creating complex traffic flow. Factors such as weave length, weave flow ratio, and number of lanes all have a significant impact on the weaving area's capacity.

[0004] Current research on calculating weaving area capacity primarily relies on field-measured data estimation and traffic simulation analysis. The field-measured data estimation method is the most widely used. It typically collects a large amount of field-measured data, analyzes the traffic operating characteristics and influencing factors of weaving areas, and constructs a weaving area capacity calculation application model through methods such as regression analysis. Traffic simulation analysis typically uses traffic simulation software to construct weaving areas in different scenarios and simulate vehicle operating conditions. Analysis and calculation application models are then constructed based on simulation experimental data. However, this research primarily focuses on surface road scenarios, with limited basic data and research on urban underground road scenarios. Furthermore, currently, there is a lack of experimental scenarios or relevant data available in China.

[0005] Therefore, considering the significant differences between ground and underground scenarios, and addressing the current lack of technology for calculating the capacity of urban underground road weaving areas, an innovative method for calculating the capacity of urban underground road weaving areas based on measured vehicle trajectory data and an open source simulation platform was proposed, providing effective support for the traffic organization design, control and management of urban underground road weaving areas. Summary of the Invention

[0006] The purpose of the present invention is to provide a method for evaluating the traffic capacity of urban underground road weaving areas, taking into account the impact of the underground road environment on driving behavior characteristics and various working conditions, and providing calculation tools and theoretical basis for the traffic organization design and control management of underground road weaving areas in various scenarios.

[0007] The present invention is achieved through the following technical solutions:

[0008] A method for evaluating the traffic capacity of an urban underground road interweaving area comprises the following steps:

[0009] Step S1: Improvement, transplantation, and verification of the urban underground road scenario simulation model; using measured vehicle trajectory data on urban underground roads, sample car-following and lane-changing behaviors are screened, and parameters of a typical underground road traffic behavior model are calibrated based on the sample data; the typical underground road traffic behavior model mainly includes a car-following model and a lane-changing model, and a genetic algorithm is used to calibrate the model parameters; the calibrated car-following and lane-changing behavior models are transplanted based on the SUMO simulation platform interface, and the simulation operation effect of the improved model is verified for effectiveness;

[0010] Step S2, a method for calculating the traffic capacity of an urban underground road weaving area; by regulating the four influencing factors of the weaving area length (L), the number of lanes (N), the weaving flow ratio (QR), and the outflow flow ratio (DR), a traffic simulation of an underground road weaving area is performed using the SUMO simulation platform after transplanting the improved model, a gradually increasing input traffic volume is set, and detectors are arranged on the road section to collect the simulation output results; the detectors used are divided into E1 single-point detectors and E2 area detectors, the E1 detector is used to collect the number of vehicles passing through the weaving area per unit time, and further obtain the traffic flow; the E2 detector is used to collect the average number of vehicles distributed on the weaving area within a certain period of time, and further obtain the vehicle density; during the simulation process, the input traffic volume gradually increases, and the simulated traffic operation state also changes accordingly, the traffic operation characteristics of the road section are collected by the arranged detectors, the traffic flow and vehicle density are calculated, and a flow-density scatter plot is drawn; and the data are fitted using the least squares method in the form of a parabolic function, and the maximum value of the fitting result function is the traffic capacity of the weaving area under the traffic conditions; wherein:

[0011] Weaving Length (L): Within the weaving area, weaving vehicles must complete lane changes within a certain length. The weaving length reflects the available longitudinal distance for vehicles to change lanes, limiting the driver's freedom of movement.

[0012] Number of lanes (N): The number of lanes reflects the lateral lane-changing space available for weaving vehicles and the distance required for lateral movement;

[0013] Weaving Flow Ratio (QR): Weaving flow ratio refers to the ratio of weaving flow to the total traffic flow in the weaving area. The weaving flow ratio is a direct reflection of the degree of weaving in the weaving area, and also reflects the degree of traffic disorder in the weaving area.

[0014] Outgoing Traffic Ratio (DR): The outgoing traffic ratio refers to the proportion of outgoing traffic to interweaving traffic. The outgoing traffic ratio reflects the distribution of incoming and outgoing traffic in the interweaving traffic.

[0015] Step S3: Calculating and applying the capacity model for the interwoven area of ​​urban underground roads. Based on the calculation method of step S2, the capacity data of the interwoven area under the changes of various influencing factors can be obtained. The influencing mechanisms of the four factors are analyzed respectively, a basic model of the interwoven area capacity is constructed, and the model parameters to be calibrated are determined.

[0016] Considering that there are four factors influencing the capacity of weaving areas, each with multiple value levels (including seven levels for weaving length (L), three levels for number of lanes (N), four levels for weaving flow ratio (QR), and five levels for outflow flow ratio (DR), a pseudo-level method was used to set virtual levels based on common values. An orthogonal experimental design was then used to obtain simulation data for various scenarios. Based on the simulation experimental results, multiple linear regression was used to calibrate the parameters of the basic model of underground road weaving area capacity. After multiple hypothesis tests, a calculation and application model for the urban underground road weaving area capacity under several typical working conditions was finally obtained. This model is suitable for the rapid calculation of capacity in various scenarios.

[0017] As a further improvement to the technical solution of the present invention, in step S1, the car-following model parameters are calibrated;

[0018] The IDM model is a typical car-following model based on speed, headway, and speed difference between front and rear vehicles. It takes into account the influence of human factors on the model, and its control of the vehicle is closer to the actual vehicle driving state. It currently has great advantages in the field of traffic simulation. Its expression is as follows:

[0019]

[0020] in:

[0021] a n (t)——acceleration of the following vehicle at time t;

[0022] V n (t)——speed of the following vehicle at time t;

[0023] δ——acceleration index;

[0024] τ——safe headway;

[0025] s0——the minimum safe distance between vehicles when they are stopped;

[0026] a0——maximum acceleration / deceleration of the following vehicle;

[0027] b0——comfortable deceleration;

[0028] v0——desired vehicle speed;

[0029] The acceleration of the following vehicle is given by the free flow acceleration a0[1-(v n (t) / v0) δ ] and braking deceleration It consists of two parts, and the braking deceleration depends on the expected following distance. and the actual following distance s n , the formula for the expected following distance is as follows:

[0030]

[0031] As a further improvement to the technical solution of the present invention, in step S1, the lane change model parameters are calibrated; the Gipps lane change model presupposes that the driver uses braking and deceleration during the lane change process; similar to the Gipps safety distance following model, the mathematical expression of the model is to calculate the vehicle speed at the next time step under the constraint of the safety distance, as shown below:

[0032]

[0033] in:

[0034] V n (t+T), V n (t)——the speed of the vehicle at time t and time (t+T) (m / s);

[0035] V n-1 (t)——the speed of the preceding vehicle at time t (m / s);

[0036] b n ——Expected deceleration of the vehicle (m / s 2 );

[0037] ——Expected deceleration of the vehicle ahead in the current lane (m / s 2 );

[0038] x n (t), x n-1 (t)——lane position of the vehicle and the preceding vehicle at time t (m);

[0039] S n-1 ——The distance between the front of the vehicle and the vehicle in front (m);

[0040] The parameter that needs to be calibrated is b n 、 and S n-1 .

[0041] As a further improvement to the technical solution of the present invention, in step S1, the simulation model is transplanted and verified; the Python-based Traci interface is used to communicate with SUMO to obtain SUMO simulated vehicle speed data under improved parameters; and a vehicle average speed distribution diagram of the measured data and the simulated data under the default parameters and the improved parameters is plotted. Compared with the simulation results under the SUMO default model, the average speed distribution of vehicles under the improved model is closer to the measured data, proving that the improved model is more suitable for urban underground road scenarios.

[0042] As a further improvement of the technical solution of the present invention, in step S3, the urban underground roads are interwoven

[0043] The basic model of the district is as follows:

[0044] C w =C0+aL-bQR+c(DR-0.5) 2 -dN (4)

[0045] in:

[0046] C w - Single lane capacity of underground road interweaving areas;

[0047] C0 - Single lane capacity of underground road basic section at level 3 service level;

[0048] L——interweaving length;

[0049] QR – interleaved flow ratio;

[0050] DR – outbound traffic ratio;

[0051] N——Number of lanes in the weaving area;

[0052] a, bc, d——parameters to be calibrated, a, b, c, d>0.

[0053] As a further improvement to the technical solution of the present invention, in step S2, by changing the combination conditions of the influencing factors, the traffic capacity values ​​of the weaving area under various scenarios can be obtained.

[0054] The present invention has the following beneficial effects:

[0055] 1) This paper takes into account the differences in the environment and traffic conditions between underground roads and surface roads. It calibrates the parameters of the underground road driving behavior model based on measured vehicle trajectory data and transplants it into the SUMO simulation model, improving its applicability to underground road scenarios.

[0056] 2) The proposed method for calculating the capacity of urban underground road weaving zones based on SUMO simulation addresses the current lack of data on urban underground road weaving zones in China. It also constructs an application model for calculating the capacity of urban underground road weaving zones, enabling rapid determination of capacity under various operating conditions. It also analyzes the impact of various factors on capacity, providing a theoretical basis for traffic organization design, control, and management in underground road weaving zones under various operating conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 To improve the model, the average vehicle speed distribution diagram is simulated. Compared with the simulation results under the platform's default model, the average vehicle speed distribution under the improved parameters is closer to the measured data and has a smaller deviation.

[0058] Figure 2 This is a basic relationship diagram between traffic flow and traffic density in the weaving section. Each scattered point in the figure represents the traffic flow and density of the weaving section within a 5-minute time node, which is used to fit and calculate the traffic capacity of the weaving area under this scenario.

[0059] Figure 3 The figure shows the scatter plot relationship and regression diagram of the single lane capacity in the weaving area and the weaving length, reflecting the impact of the weaving length on the capacity of the weaving area.

[0060] Figure 4 The figure shows the scatter plot relationship and regression diagram of the single lane capacity and the number of lanes in the weaving area, reflecting the impact of the number of lanes on the capacity of the weaving area.

[0061] Figure 5 The figure shows the scatter plot relationship and regression diagram of the single lane capacity in the weaving area and the weaving flow ratio, reflecting the impact of the weaving flow ratio on the capacity of the weaving area.

[0062] Figure 6 The figure shows the scatter plot relationship and regression diagram of the single lane capacity in the weaving area and the outflow flow ratio, reflecting the impact of the outflow flow ratio on the capacity of the weaving area.

[0063] Figure 7 The present invention is a flowchart of a method for evaluating the traffic capacity of an urban underground road interweaving area. DETAILED DESCRIPTION

[0064] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The exemplary embodiments and descriptions of the present invention are used to explain the present invention but are not intended to limit the present invention.

[0065] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, front, back, upper end, lower end, top, bottom...) are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0066] In the present invention, unless otherwise specified or limited, the term "connection" should be understood in a broad sense. For example, "connection" can mean fixed connection, detachable connection, or integration; mechanical connection, electrical connection; direct connection, or indirect connection through an intermediate medium; internal communication between two elements, or interaction between two elements, unless otherwise specified. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0067] In addition, the terms "first," "second," and so on, used in this disclosure are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly indicating the number of the technical features indicated. Therefore, features defined as "first" or "second" may explicitly or implicitly include at least one such feature. Furthermore, the technical solutions of various embodiments may be combined with each other, but only on the basis that they can be implemented by a person of ordinary skill in the art. If the combination of technical solutions contradicts or cannot be implemented, it shall be deemed that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this disclosure.

[0068] The following combination Figures 1 to 7 The present invention is described in further detail.

[0069] A method for assessing the capacity of urban underground road interweaving zones is implemented using the SUMO traffic simulation platform. SUMO is open-source microscopic traffic simulation software, whose source code can be modified and extended as needed. Using an open road network data format, it can simulate the behavior of individual vehicles in a traffic system, helping to understand traffic operations under different traffic conditions. The main contents of the invention include:

[0070] (1) Improvement, transplantation and verification of urban underground road scene simulation models;

[0071] (2) Calculation method of traffic capacity in urban underground road interweaving areas;

[0072] (3) Application model for calculating traffic capacity of urban underground road interweaving areas;

[0073] First, based on the measured vehicle trajectory data of domestic urban underground roads, typical traffic behavior samples of underground roads were screened, and the genetic algorithm was used to calibrate the parameters of the vehicle following behavior model and lane changing behavior model. The improved model was transplanted into the SUMO simulation platform, and the effectiveness of the improved model was verified by speed distribution comparison, thus realizing the construction of an urban underground road scene simulation platform.

[0074] Next, based on the improved simulation model, traffic simulation experiments were conducted in underground weaving zones. Factors influencing the weaving zone's capacity were analyzed. A gradually increasing input traffic volume was set, and detectors were deployed to collect traffic operation characteristics on the road section. Traffic flow and vehicle density were calculated, and a basic flow-density diagram was plotted. The data was fitted using a parabolic function using the least squares method. The maximum value of the fitted function was the weaving zone's capacity under those traffic conditions. This platform and method can be applied to the design or management of traffic organization in underground weaving zones, conducting simulation experiments in real-world scenarios to calculate the weaving zone's capacity and inform decision-making.

[0075] Furthermore, to facilitate faster decision-making during actual design or management, based on the aforementioned calculation method, a controlled variable method was used to experimentally obtain capacity data under the influence of typical factors. The influencing mechanisms were analyzed to construct a basic capacity model for underground road weaving zones. Subsequently, an orthogonal experimental method was used to obtain capacity data for weaving zones under typical operating conditions. The basic capacity model was calibrated using multivariate linear regression analysis, resulting in an application model for calculating the capacity of urban underground road weaving zones. This model can be used to rapidly calculate the capacity of weaving zones under typical operating conditions.

[0076] The present invention fully considers the impact of the particularity of the underground road environment on driving behavior, proposes an improved traffic behavior simulation model based on the measured vehicle trajectory data of urban underground roads, and transplants it to the SUMO platform for simulation experiments and effectiveness verification. Subsequently, based on the simulation experiments of the improved model, a method for calculating the traffic capacity of interweaving areas suitable for various scenarios was proposed. In addition, by carrying out relevant simulation experiments, an application model for calculating the traffic capacity of urban underground road interweaving areas under typical working conditions was constructed, which is suitable for rapid calculation of traffic capacity in various scenarios. The invention provides a new calculation method for evaluating the traffic capacity of urban underground road interweaving areas, and provides a decision support tool for traffic organization design and control management of interweaving areas.

[0077] Example:

[0078] A method for evaluating the traffic capacity of an urban underground road interweaving area comprises the following steps:

[0079] 1. Using measured vehicle trajectory data from urban underground roads, we screened samples of car-following and lane-changing behaviors and calibrated the parameters of a typical underground traffic behavior model based on this data. The typical underground traffic behavior model primarily includes a car-following model and a lane-changing model, and a genetic algorithm was used to calibrate the model parameters.

[0080] (1) Car-following model parameter calibration

[0081] The IDM model is a typical vehicle following model based on speed, headway, and speed difference between front and rear vehicles. It takes into account the influence of human factors (reaction time, estimation error) on the model, and its control of the vehicle is closer to the actual vehicle driving state. It currently has great advantages in the field of traffic simulation. Its expression is as follows:

[0082]

[0083] in:

[0084] a n (t)——acceleration of the following vehicle at time t;

[0085] V n (t)——speed of the following vehicle at time t;

[0086] δ——acceleration index;

[0087] τ——safe headway;

[0088] s0——the minimum safe distance between vehicles when they are stopped;

[0089] a0——maximum acceleration / deceleration of the following vehicle;

[0090] b0——comfortable deceleration;

[0091] v0——desired vehicle speed.

[0092] The acceleration of the following vehicle is given by the free flow acceleration a0[1-(v n (t( / v0) δ ] and braking deceleration It consists of two parts, and the braking deceleration depends on the expected following distance. and the actual following distance s n , the formula for the expected following distance is as follows:

[0093]

[0094] A genetic algorithm is a method that searches for optimal solutions by simulating the natural evolutionary process. Through computer simulation, the problem-solving process is converted into a process similar to the crossover and mutation of chromosomes in biological evolution. The typical computational process of a genetic algorithm is: encoding—population initialization—evaluation of the fitness of individuals within the population—selection—crossover—mutation; the fitness of individuals in the new generation of populations is evaluated, followed by a cycle of selection—crossover—mutation until the optimal solution is obtained. A genetic algorithm was selected to calibrate the parameters of a vehicle-following model for urban underground roads.

[0095] 1,679 urban underground vehicle following sample segments were divided into training and test sets in a 7:3 ratio, resulting in 1,175 training samples and 504 validation samples. Parameters of the IDM car-following model based on the genetic algorithm were calibrated eight times, resulting in the following stable parameter results.

[0096]

[0097] (2) Lane-changing model parameter calibration

[0098] The Gipps lane-changing model assumes that the driver brakes to slow down during the lane change process. Similar to the Gipps safety distance following model, the mathematical expression for the model calculates the vehicle speed at the next time step while taking into account the safety distance constraint, as shown below:

[0099]

[0100] in:

[0101] V n (t+T), V n (t)——the speed of the vehicle at time (t+T) and time t (m / s);

[0102] V n-1 (t)——the speed of the preceding vehicle at time t (m / s);

[0103] b n ——Expected deceleration of the vehicle (m / s 2 );

[0104] ——Expected deceleration of the vehicle ahead in the current lane (m / s 2 );

[0105] x n (t), x n-1 (t)——lane position of the vehicle and the preceding vehicle at time t (m);

[0106] S n-1 ——The distance between the front of this vehicle and the vehicle in front (m).

[0107] The parameter that needs to be calibrated is b n 、 and s n-1 .

[0108] Because the calibration of the Gipps lane-changing model requires a balance between vehicle speed prediction accuracy and the lane-changing success rate identified by the model, we employed the built-in MATLAB function gamultiobj to solve a multi-objective optimization problem based on 1544 extracted vehicle trajectory data from forced lane changes and 1506 extracted vehicle trajectory data from free lane changes. This function uses a genetic algorithm-based approach to find a set of solutions that balance multiple conflicting objectives. This set of solutions is called the Pareto front. The optimal solution calibration results are shown below.

[0109]

[0110] (3) Simulation model transplantation and verification

[0111] Based on the SUMO simulation platform interface, the calibrated car-following behavior model and lane-changing behavior model were transplanted, and the simulation operation effect of the improved model was verified.

[0112] The Python-based Traci interface is used to communicate with SUMO to obtain the SUMO simulated vehicle speed data under improved parameters. The average vehicle speed distribution of the measured data and the simulated data under default parameters and improved parameters is plotted as follows: Figure 1 As shown in the figure, compared with the simulation results under the SUMO default model, the average speed distribution of vehicles under the improved model is closer to the measured data, proving that the improved model is more suitable for urban underground road scenarios.

[0113] 2. Calculation method for traffic capacity of urban underground road interweaving areas

[0114] Traffic capacity refers to the maximum number of vehicles that can pass through a section of a road per unit time under certain road and traffic conditions. It is an important parameter for evaluating the traffic operation status of weaving areas. The factors affecting weaving areas are complex, as follows:

[0115] Weaving length (L): In the weaving area, weaving vehicles need to complete lane changes within a certain length range. The weaving length reflects the available longitudinal distance for vehicles to change lanes and limits the driver's freedom of movement. In theory, the longer the weaving length, the more conducive it is for weaving vehicles to complete lane changes, and thus the traffic capacity of the weaving area will also increase accordingly.

[0116] Number of lanes (N): The number of lanes reflects the lateral space available for weaving vehicles to change lanes and the distance required for lateral movement. More lanes increase the cross-sectional capacity of the weaving area, but the capacity of each lane decreases.

[0117] Weaving Flow Ratio (QR): The weaving flow ratio refers to the proportion of weaving flow to the total traffic flow in the weaving area. The weaving flow ratio provides a direct reflection of the degree of weaving in the weaving area and also reflects the degree of traffic disorder within the weaving area. When the weaving flow is relatively high, weaving vehicles will change lanes more frequently, resulting in increased operational disorder in the weaving area and reduced traffic capacity in the weaving area.

[0118] Outgoing Traffic Ratio (DR): The outgoing traffic ratio refers to the proportion of outgoing traffic to weaving traffic. The outgoing traffic ratio reflects the distribution of incoming and outgoing traffic in the weaving traffic. Different incoming and outgoing traffic conditions will also have different impacts on vehicle operation in the weaving area, thereby affecting the traffic capacity of the weaving area.

[0119] By controlling the four influencing factors of the weaving area length (L), the number of lanes (N), the weaving flow ratio (QR), and the outflow flow ratio (DR), the traffic simulation of the underground road weaving area is carried out using the SUMO simulation platform after transplanting the improved model. The input traffic volume is set to gradually increase, and detectors are arranged on the road section to collect the simulation output results. The detectors used are divided into E1 single-point detectors and E2 area detectors. The E1 detector is used to collect the number of vehicles passing through the weaving area per unit time to further obtain the traffic flow; the E2 detector is used to collect the average number of vehicles distributed on the weaving area within a certain period of time to further obtain the vehicle density. During the simulation process, the input traffic volume gradually increases, and the simulated traffic operation status also changes accordingly. The traffic operation characteristics of the road section are collected through the arranged detectors, and the traffic flow and vehicle density are calculated. The flow-density scatter plot is drawn as shown below. Figure 2 As shown, the data is fitted using the least squares method in the form of a parabola function. The maximum value of the fitting function is the traffic capacity of the weaving area under the traffic conditions. In actual application, by changing the combination of influencing factors, the traffic capacity value of the weaving area under various scenarios can be obtained. 3. Application model for calculating the traffic capacity of urban underground road weaving areas

[0120] The capacity of the weaving area is calculated according to the above method, and the control variable method is used to obtain the capacity data under the influence of various factors. The results are further analyzed to understand the mechanism of the influence of various factors on the capacity of the weaving area.

[0121] (1) Interweaving length (L)

[0122] When exploring the impact of weaving length on the capacity of the weaving area, the number of controlled lanes was 2, the weaving flow ratio QR was 0.2, the outflow flow ratio DR was 0.5, the weaving length was set to 100-700m, the step length was 100m, and a total of 7 groups of experiments were set up. Each group of experiments was repeated three times to determine the capacity value of the weaving area under each condition.

[0123] According to the simulation results, a scatter plot of the single lane capacity and the weaving length in the weaving area is drawn as follows: Figure 3 As shown in the figure, it can be seen that the traffic capacity of the single-lane weaving area increases with the increase of the weaving length. The increase of the weaving length is conducive to the weaving vehicles completing the lane changing behavior in the weaving area, so it can improve the traffic capacity of the single-lane weaving area.

[0124] (2) Number of lanes (N)

[0125] When exploring the impact of the number of lanes on the capacity of the weaving area, the weaving length was controlled to 500, the weaving flow ratio QR was 0.2, the outflow flow ratio DR was 0.5, and the number of lanes was set to 2 lanes, 3 lanes and 4 lanes respectively. A total of 3 groups of experiments were set up, and each group of experiments was repeated three times to determine the capacity value of the weaving area under each condition.

[0126] According to the simulation results, a scatter plot of the single lane capacity and the number of lanes in the weaving area is drawn as follows: Figure 4 As shown in the figure, it can be seen that the traffic capacity of a single-lane weaving area decreases with the increase of the number of lanes. As the number of lanes increases, the lateral displacement required for some weaving vehicles to merge in and out increases, and the weaving behavior has a greater impact on the traffic in the weaving area, thereby reducing the traffic capacity of a single-lane weaving area.

[0127] (3) Interleaved flow ratio (QR)

[0128] When exploring the impact of the weaving flow ratio QR on the weaving area capacity, the weaving length was controlled to 500, the number of lanes was 2, the outflow flow ratio DR was 0.5, and the weaving flow ratio QR was set to 0.1-0.4 respectively. A total of 4 groups of experiments were set up, and each group of experiments was repeated three times to determine the weaving area capacity value under each condition.

[0129] According to the simulation results, a scatter plot of the single lane capacity and the weaving flow ratio QR in the weaving area is drawn as follows: Figure 5 As shown in the figure, it can be seen that the traffic capacity of the single-lane weaving area decreases with the increase of the weaving flow ratio QR. Under the condition of the same total traffic flow, the weaving flow ratio QR increases, that is, the weaving flow increases, and the weaving phenomenon in the weaving area becomes more intense, thereby reducing the traffic capacity of the single-lane weaving area.

[0130] (4) Outbound Traffic Ratio (DR)

[0131] When exploring the impact of the outflow flow ratio DR on the weaving area's capacity, the weaving length was controlled to 500, the number of lanes was 2, the weaving flow ratio QR was 0.2, and the outflow flow ratio DR was set to 0.1-0.9 respectively. A total of 5 groups of experiments were set up, and each group of experiments was repeated three times to determine the weaving area's capacity value under each condition.

[0132] According to the simulation results, a scatter plot of the single lane capacity and outflow flow ratio DR in the weaving area is drawn as follows: Figure 6 As shown in the figure, it can be seen that the capacity of the single-lane weaving area first decreases and then increases with the increase of the outgoing flow ratio DR. Therefore, it can be concluded that when the incoming and outgoing flows are similar, that is, when the outgoing flow ratio DR is 0.5, vehicles weave most frequently, which has the greatest impact on the traffic operation in the weaving area. At this time, the capacity of the single-lane weaving area is the lowest.

[0133] Based on the analysis results of the influencing factors in the previous step, the basic model of the urban underground road interweaving area is proposed as follows:

[0134] C W =C0+aL-bQR+c(DR-0.5) 2 -dN (4)

[0135] in:

[0136] C w - Single lane capacity of underground road interweaving areas;

[0137] C0 - Single lane capacity of underground road basic section at level 3 service level;

[0138] L——interweaving length;

[0139] QR – interleaved flow ratio;

[0140] DR – outbound traffic ratio;

[0141] N——Number of lanes in the weaving area;

[0142] a, b, c, d——parameters to be calibrated, a, b, c, d>0.

[0143] Orthogonal experiments are used to obtain simulation data and calibrate the application model for calculating the capacity of underground road weaving areas. Orthogonal experiments are an experimental design method that aims to systematically study the effects of multiple factors on one or more response variables through a limited number of experiments. By selecting appropriate experimental points, as much information as possible can be obtained within a limited number of experiments. There are four influencing factors for the capacity of weaving areas: weaving length (L), number of lanes (N), weaving flow ratio QR, and outflow flow ratio DR. There are 7 levels for weaving length, 3 levels for number of lanes, 4 levels for weaving flow ratio, and 5 levels for outflow flow ratio. Under these mixed level conditions, it is difficult to design an orthogonal experiment that fully conforms to the properties of the orthogonal table. Therefore, this study adopts the pseudo-level method, sets virtual levels for the number of lanes, weaving flow ratio, and outflow flow ratio according to common values, and conducts orthogonal table design to carry out simulation experiments.

[0144] At the beginning of the simulation, the total traffic volume in the weaving area was set at 500 veh / (h·ln) and then gradually increased to 3000 veh / (h·ln). Each stage lasted 30 minutes. During the simulation, the Python-based Traci interface was used to control vehicles to perform strategic lane changes only within the weaving area, simulating the phenomenon of vehicles weaving in the weaving area.

[0145] Experiments were conducted according to different mainline design speeds. Condition 1 was a mainline design speed of 40 km / h and a ramp design speed of 20 km / h; Condition 2 was a mainline design speed of 60 km / h and a ramp design speed of 40 km / h; Condition 3 was a mainline design speed of 80 km / h and a ramp design speed of 50 km / h. The design of the remaining roads referred to the relevant provisions of the "Code for Design of Urban Underground Road Engineering" (CJJ221-2015), such as acceleration and deceleration lanes, lane isolation sections, etc. Three repeated experiments were conducted under each experimental condition, and a total of 297 experimental data points were obtained.

[0146] Multiple linear regression was used to calibrate the parameters of the basic model of underground road weaving area capacity. According to the previous research, the capacity of a single-lane weaving area is quadratic in relationship to the outflow flow ratio. When the outflow flow ratio DR is 0.5, the capacity of a single-lane weaving area is the smallest. Therefore, the outflow flow ratio DR needs to be transformed during the parameter calibration process, converting DR to (DR-0.5). 2 Finally, the application model for calculating the traffic capacity of urban underground road interweaving areas under various working conditions is obtained:

[0147] For the main line design speed of 40km / h:

[0148] C w =1300+0.24L-376.59QR+491.98(DR-05) 2 -45.89NR2 =0.603 (5)

[0149] For the main line design speed of 60km / h:

[0150] C w =1400+0.19L-403.01QR+638.33(DR-0.5) 2 -47.88NR 2 =0.638(6)

[0151] For the main line design speed of 80km / h:

[0152] C w =1800++0.21L-708.75QR+483.00 (DR-0.5) 2 -60.48NR 2 =0.783 (7)

[0153] Finally, the significance of the parameter calibration results was verified by T test, and the calculation application model was tested to meet the multivariate linear regression assumptions, confirming that it can be applied to the rapid calculation of the traffic capacity of the weaving area under typical working conditions.

[0154] Compared with the existing technology, the present invention has the following beneficial effects:

[0155] 1) This paper takes into account the differences in the environment and traffic conditions between underground roads and surface roads. It calibrates the parameters of the underground road driving behavior model based on measured vehicle trajectory data and transplants it into the SUMO simulation model, improving its applicability to underground road scenarios.

[0156] 2) The proposed method for calculating the capacity of urban underground road weaving zones based on SUMO simulation addresses the current lack of data on urban underground road weaving zones in China. It also constructs an application model for calculating the capacity of urban underground road weaving zones, enabling rapid determination of capacity under various operating conditions. It also analyzes the impact of various factors on capacity, providing a theoretical basis for traffic organization design, control, and management in underground road weaving zones under various operating conditions.

[0157] The technical solutions provided by the embodiments of the present invention are introduced in detail above. Specific examples are used herein to illustrate the principles and implementation methods of the embodiments of the present invention. The description of the above embodiments is only applicable to help understand the principles of the embodiments of the present invention. At the same time, for those skilled in the art, according to the embodiments of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. A method for evaluating the traffic capacity of an urban underground road interweaving area, characterized in that: The following steps are involved: Step S1, improvement, transplantation and verification of the urban underground road scene simulation model; By using the measured vehicle trajectory data on urban underground roads, we screened samples of vehicle following and lane-changing behaviors, and calibrated the parameters of the typical underground road traffic behavior model based on the sample data. underground The typical traffic behavior model on the road mainly includes the car-following model and the lane-changing model, and the genetic algorithm is used to calibrate the model parameters; The calibrated car-following and lane-changing behavior models were transplanted based on the SUMO simulation platform interface, and the simulation results of the improved models were verified. Step S2: Calculation method for traffic capacity of urban underground road interweaving area; By controlling the four influencing factors of the weaving area length (L), the number of lanes (N), the weaving flow ratio (QR), and the outflow flow ratio (DR), the SUMO simulation platform with the transplanted improved model was used to simulate the traffic in the weaving area of ​​underground roads. The input traffic volume was set to gradually increase, and detectors were arranged on the road section to collect the simulation output results. The detectors used were divided into E1 single-point detectors and E2 area detectors. The E1 detector was used to collect the number of vehicles passing through the weaving area per unit time to further obtain the traffic flow; the E2 detector was used to collect the average number of vehicles distributed in the weaving area within a certain period of time to further obtain the vehicle density. During the simulation process, the input traffic volume gradually increased, and the simulated traffic operation state also changed accordingly. The traffic operation characteristics of the road section were collected through the arranged detectors, and the traffic flow and vehicle density were calculated. The flow-density scatter plot was drawn. The data was fitted using the least squares method using a parabolic function. The maximum value of the fitting result function was the traffic capacity of the weaving area under the traffic conditions. Weaving Length (L): Within the weaving area, weaving vehicles must complete lane changes within a certain length. The weaving length reflects the available longitudinal distance for vehicles to change lanes, limiting the driver's freedom of movement. Number of lanes (N): The number of lanes reflects the lateral lane-changing space available for weaving vehicles and the distance required for lateral movement; Weaving Flow Ratio (QR): Weaving flow ratio refers to the ratio of weaving flow to the total traffic flow in the weaving area. The weaving flow ratio is a direct reflection of the degree of weaving in the weaving area, and also reflects the degree of traffic disorder in the weaving area. Outgoing Traffic Ratio (DR): The outgoing traffic ratio refers to the proportion of outgoing traffic to interweaving traffic. The outgoing traffic ratio reflects the distribution of incoming and outgoing traffic in the interweaving traffic. Step S3: Calculating and applying the capacity model for the interwoven area of ​​urban underground roads. Based on the calculation method of step S2, the capacity data of the interwoven area under the changes of various influencing factors can be obtained. The influencing mechanisms of the four factors are analyzed respectively, a basic model of the interwoven area capacity is constructed, and the model parameters to be calibrated are determined. Considering that there are four factors influencing the capacity of weaving areas, each with multiple value levels (including seven levels for weaving length (L), three levels for number of lanes (N), four levels for weaving flow ratio (QR), and five levels for outflow flow ratio (DR), a pseudo-level method was used to set virtual levels based on common values. An orthogonal experimental design was then used to obtain simulation data for various scenarios. Based on the simulation experimental results, multiple linear regression was used to calibrate the parameters of the basic model of underground road weaving area capacity. After multiple hypothesis tests, a calculation and application model for the urban underground road weaving area capacity under several typical working conditions was finally obtained. This model is suitable for the rapid calculation of capacity in various scenarios.

2. The method for evaluating the traffic capacity of an urban underground road interweaving area according to claim 1, characterized in that: In the step S1, the car-following model parameters are calibrated; The IDM model is a typical car-following model based on speed, headway, and speed difference between front and rear vehicles. It takes into account the influence of human factors on the model, and its control of the vehicle is closer to the actual vehicle driving state. It currently has great advantages in the field of traffic simulation. Its expression is as follows: in: a n (t)——acceleration of the following vehicle at time t; V n (t)——speed of the following vehicle at time t; δ——acceleration index; τ——safe headway; s0——the minimum safe distance between vehicles when they are stopped; a0——maximum acceleration / deceleration of the following vehicle; b0——comfortable deceleration; v0——desired vehicle speed; The acceleration of the following vehicle is given by the free flow acceleration a0[1-(v n (t) / v0) δ ] and braking deceleration It consists of two parts, and the braking deceleration depends on the expected following distance. and the actual following distance s n , the formula for the expected following distance is as follows:

3. The method for evaluating the traffic capacity of an urban underground road interweaving area according to claim 1, characterized in that: In step S1, the lane-changing model parameters are calibrated. The Gipps lane-changing model presupposes that the driver brakes and decelerates during the lane-changing process. Similar to the Gipps safety distance car-following model, the mathematical expression of the model is to calculate the vehicle speed at the next time step under the constraint of the safety distance, as shown below: in: V n (t+T), V n (t)——the speed of the vehicle at time (t+T) and time t (m / s); V n-1 (t)——the speed of the preceding vehicle at time t (m / s); b n ——Expected deceleration of the vehicle (m / s 2 ); ——Expected deceleration of the vehicle ahead in the current lane (m / s 2 ); x n (t), x n-1 (t)——lane position of the vehicle and the preceding vehicle at time t (m); s n-1 ——The distance between the front of the vehicle and the vehicle in front (m); The parameter that needs to be calibrated is b n 、 and S n-1 .

4. The method for evaluating the traffic capacity of an urban underground road interweaving area according to claim 1, characterized in that: In step S1, the simulation model is transplanted and verified; the Python-based Traci interface is used to communicate with SUMO to obtain SUMO simulated vehicle speed data under improved parameters; and a vehicle average speed distribution diagram of the measured data and the simulated data under the default parameters and improved parameters is plotted. Compared with the simulation results under the SUMO default model, the average speed distribution of vehicles under the improved model is closer to the measured data, proving that the improved model is more suitable for urban underground road scenarios.

5. The method for evaluating the traffic capacity of an urban underground road interweaving area according to claim 1 is characterized by: In step S3, the basic model of the urban underground road interweaving area is as follows: C W =C0+aL-bQR+c(DR-0.5) 2 -dN (4) in: C W - Single lane capacity of underground road interweaving areas; C0 - Single lane capacity of underground road basic section at level 3 service level; L——interweaving length; QR – interleaved flow ratio; DR – outbound traffic ratio; N——Number of lanes in the weaving area; a, b, c, d——parameters to be calibrated, a, b, c, d>0.

6. The method for evaluating the traffic capacity of an urban underground road interweaving area according to claim 1, characterized in that: In step S2, by changing the combination conditions of the influencing factors, the traffic capacity values ​​of the weaving area under various scenarios can be obtained.

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