A VISSIM microcosmic traffic flow simulation method and system

Abstract

The application provides a VISSIM microcosmic traffic flow simulation method and system, which firstly introduces the road use characteristics (flatness, skid resistance) of solid waste pavement into the VISSIM simulation parameter system; the length optimization is realized by hierarchical constraint + dynamic iteration instead of static single simulation; the quantitative correlation of'material characteristics-traffic parameters-operation zone length' is established by using the traffic capacity attenuation coefficient, so that the decision is changed from 'empirical type' to 'quantitative type', and specifically, through the process, the most economical operation zone length can be found under the premise that the service level of the operation zone is greater than or equal to three, which not only avoids the problems of 'cost waste caused by too long' or 'traffic congestion caused by too short' in the traditional method, but also adapts to the special pavement conditions of the solid waste utilization reconstruction and expansion project.

Description

Technical Field

[0001] This invention belongs to the field of traffic organization technology for reconstruction and expansion projects, and specifically relates to a VISSIM-based microscopic traffic flow simulation method and system. Background Technology

[0002] In highway reconstruction and expansion projects utilizing solid waste, traffic organization in the work area directly impacts the road network's capacity and service level. Existing technologies mostly rely on VISSIM simulations based on standard pavement characteristics, neglecting the influence of solid waste material road performance (such as smoothness, skid resistance, and structural strength) on vehicle behavior. This leads to significant discrepancies between the simulation model and actual traffic flow. Furthermore, the determination of work area length often relies on static single-simulation or empirical values, failing to establish a dynamic relationship between "material properties, traffic parameters, and work area constraints." This makes it difficult to minimize work area length while ensuring service level, resulting in wasted engineering costs or insufficient traffic efficiency. Summary of the Invention

[0003] Based on this, the present invention provides a VISSIM-based micro-traffic flow simulation method and system, which aims to accurately determine the minimum length of the work area while taking into account traffic safety, service level and engineering economy.

[0004] A first aspect of this invention provides a VISSIM-based microscopic traffic flow simulation method, the method comprising:

[0005] By conducting road performance tests on solid waste materials, classifying and measuring the proportion of sensitive vehicle types, and testing real vehicle driving behavior, the parameters of VISSIM's car-following and lane-changing models are corrected in order to construct a basic VISSIM simulation model.

[0006] After collecting and calibrating road network geometric and traffic parameters, traffic demand is configured in the basic VISSIM simulation model.

[0007] Set up parameterized scenarios for the work area, conduct orthogonal experiments, and generate experimental plans;

[0008] Set graded constraint thresholds and run the configured VISSIM simulation model through dynamic feedback iterative logic. After extreme working condition verification and reliability check, determine the preliminary value of the minimum working area length.

[0009] Based on the VISSIM simulation model results corresponding to the initial value of the minimum work area length, the traffic capacity attenuation coefficient is calculated, and the final value of the minimum work area length is determined based on the traffic capacity attenuation coefficient.

[0010] Furthermore, in the step of correcting the parameters of the VISSIM car-following model and lane-changing model through solid waste material road performance testing, sensitive vehicle type classification and proportion measurement, and real vehicle driving behavior testing to construct a basic VISSIM simulation model, a road performance index dataset is obtained through solid waste material road performance testing. The road performance index dataset includes road anti-skid performance, road smoothness, and road structural strength.

[0011] By classifying and measuring the proportion of sensitive vehicle types, we obtained a dataset of the proportion of sensitive vehicle types and the distribution characteristics of vehicle types. Among them, based on the bearing characteristics of solid waste road surfaces, sensitive vehicle types and non-sensitive vehicle types were divided according to axle load.

[0012] Through real-vehicle driving behavior tests, we obtained micro-behavioral datasets of two types of vehicles under different road surfaces and speeds, including following distance, lane change time, and lateral sway amplitude.

[0013] Furthermore, in the step of correcting the parameters of the VISSIM car-following and lane-changing models through solid waste material road performance testing, sensitive vehicle type classification and proportion testing, and real vehicle driving behavior testing to construct the basic VISSIM simulation model, the correction of the car-following model involves establishing a linear regression equation between road performance indicators and model parameters based on real vehicle data:

[0014] For sensitive vehicle models: a = 0.8 + 0.05 × (IRI / IRI0); t1 = 1.2 + 0.3 × (deflection value / deflection value 0);

[0015] For non-sensitive vehicle models: b = 0.9 - 0.08 × (BPN / BPN0); t2 = 0.8 + 0.2 × (IRI / IRI0);

[0016] a is the safety distance coefficient, b is the expected speed deviation coefficient, IRI is the smoothness of the solid waste pavement, IRI0 is the reference smoothness of the standard pavement, t1 and t2 are the reaction time, BPN is the skid resistance of the solid waste pavement, and BPN0 is the skid resistance of the standard pavement.

[0017] For the correction of the lane-changing model, the correction equation is established as follows:

[0018] Critical lane-changing speed for sensitive vehicle types: v_c1 = 40 + 10 × (IRI / IRI0); Critical lane-changing speed for non-sensitive vehicle types: v_c2 = 50 + 8 × (IRI / IRI0);

[0019] Lane changing frequency: f = 0.3 - 0.1 × (P / P0);

[0020] v_c1 is the critical speed for lane changing of sensitive vehicle types, v_c2 is the critical speed for lane changing of non-sensitive vehicle types, IRI is the smoothness of the solid waste road surface, IRI0 is the baseline smoothness of the standard road surface, f is the lane changing frequency, P is the proportion of sensitive vehicle types on the solid waste road surface, and P0 is the proportion of sensitive vehicle types on the standard road surface.

[0021] Furthermore, in the step of setting a parameterized scenario for the work area and conducting orthogonal experiments to generate an experimental plan, the work area structure is defined, dividing the work area into a warning area L1, a buffer area L2, an actual work area L3, and a termination area L4. The total length of the work area L = L1 + L2 + L3 + L4, where the total length of the work area is the target variable, with a value range of 300m to 1500m and a step size of 50m. The influencing variables are the lane reduction ratio n = 1 / 3, 1 / 2, 2 / 3, and the proportion of sensitive vehicle types P = 20% to 60% with a step size of 10%. The fixed parameters are: warning area L1 = 200m, termination area L4 = 50m, and work area lane width 3.5m.

[0022] The test factors are determined, and the test factors are divided into levels. An orthogonal array is used to generate a test matrix to obtain the test plan. The test factors include the total length of the work area, the lane reduction ratio, and the proportion of sensitive vehicle types. The total length of the work area is divided into 6 levels, including 300m, 500m, 700m, 900m, 1100m, and 1300m. The lane reduction ratio is divided into 3 levels, including 1 / 3, 1 / 2, and 2 / 3. The proportion of sensitive vehicle types is divided into 5 levels, including 20%, 30%, 40%, 50%, and 60%.

[0023] Furthermore, the steps of setting graded constraint thresholds and running the configured VISSIM simulation model through dynamic feedback iterative logic, verifying the test plan under extreme conditions and checking reliability, and determining the preliminary value of the minimum working area length include:

[0024] Set primary constraints that meet the core indicators of Level 3 Service Level and secondary constraints that meet the safety indicators of solid waste pavement. Run the test schemes in batches according to the test matrix to obtain the test results.

[0025] Obtain initial values, compare the experimental results with the first-level constraints and the second-level constraints respectively, perform iterations, and finally output preliminary candidate values ​​for the minimum working area length;

[0026] Define extreme working conditions, including traffic conditions, vehicle type conditions, and road surface conditions. Substitute the preliminary candidate value of the minimum working area length into the extreme working conditions, run the VISSIM simulation model, and determine whether the running results still satisfy the first-level and second-level constraints.

[0027] If the result of the operation still satisfies the first-level and second-level constraints, then determine the preliminary value of the minimum working area length;

[0028] If the result of the operation does not meet the first-level and second-level constraints, the candidate value of the initial minimum working area length is increased according to the first preset step size, and then verified again;

[0029] Based on the scenario corresponding to the initial value of the minimum work area length, repeat the simulation, calculate the coefficient of variation of the output index, and determine whether the coefficient of variation of the output index is less than the first threshold.

[0030] If the coefficient of variation of the output index is less than the first threshold, then the preliminary value of the minimum working area length is output.

[0031] Furthermore, the step of calculating the capacity attenuation coefficient based on the VISSIM simulation model results corresponding to the initial value of the minimum working area length, and determining the final value of the minimum working area length based on the capacity attenuation coefficient, includes:

[0032] Based on the VISSIM simulation model results corresponding to the preliminary value of the minimum working area length, the capacity attenuation coefficient is calculated.

[0033] Determine whether the traffic capacity attenuation coefficient is less than the second threshold;

[0034] If it is determined that the traffic capacity attenuation coefficient is less than the second threshold, then according to the second preset step size, the length of the initial value of the minimum working area length is increased, and the simulation is repeated.

[0035] If it is determined that the traffic capacity attenuation coefficient is not less than the second threshold, then the preliminary value of the minimum working area length corresponding to the traffic capacity attenuation coefficient is determined as the final value of the minimum working area length.

[0036] Furthermore, in the step of calculating the capacity attenuation coefficient based on the VISSIM simulation model results corresponding to the preliminary value of the minimum working area length, the formula for calculating the capacity attenuation coefficient is as follows:

[0037] ;

[0038] γ(L) = 0.6 + 0.0005 × L;

[0039] Where K is the traffic capacity attenuation coefficient, α is the smoothness influence coefficient, and β is the vehicle type ratio influence coefficient. , where L is the total length of the work area, IRI is the smoothness of the solid waste road surface, IRI0 is the baseline smoothness of the standard road surface, P is the proportion of sensitive vehicle types on the solid waste road surface, and P0 is the proportion of sensitive vehicle types on the standard road surface.

[0040] A second aspect of this invention provides a VISSIM-based micro-traffic flow simulation system for implementing the VISSIM-based micro-traffic flow simulation method described in the first aspect, the system comprising:

[0041] The correction module is used to correct the parameters of VISSIM's car-following and lane-changing models through solid waste material road performance testing, sensitive vehicle classification and proportion testing, and real vehicle driving behavior testing, so as to build a basic VISSIM simulation model.

[0042] The data acquisition module is used to collect road network geometric parameters and traffic parameters, and after calibration, configure traffic demand in the basic VISSIM simulation model.

[0043] The configuration module is used to set the parameterized scenario of the work area, conduct orthogonal experiments, and generate test plans;

[0044] The running module is used to set the hierarchical constraint thresholds and run the configured VISSIM simulation model through dynamic feedback iteration logic. After extreme working condition verification and reliability check, the preliminary value of the minimum working area length is determined.

[0045] The calculation module is used to calculate the traffic capacity attenuation coefficient based on the VISSIM simulation model running results corresponding to the preliminary value of the minimum work area length, and to determine the final value of the minimum work area length based on the traffic capacity attenuation coefficient.

[0046] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the VISSIM-based micro-traffic flow simulation method provided in the first aspect.

[0047] A fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the VISSIM-based micro-traffic flow simulation method provided in the first aspect.

[0048] This invention provides a VISSIM-based microscopic traffic flow simulation method and system. Through solid waste material road performance testing, sensitive vehicle type classification and proportional measurement, and real vehicle driving behavior testing, the parameters of the VISSIM car-following and lane-changing models are corrected to construct a basic VISSIM simulation model. Road network geometric parameters and traffic parameters are collected, calibrated, and then traffic demand is configured in the basic VISSIM simulation model. A parameterized scenario for the work area is set, and orthogonal experiments are conducted to generate experimental plans. Hierarchical constraint thresholds are set, and the experimental plans are run through dynamic feedback iterative logic. The VISSIM simulation model, once set up, is verified under extreme conditions and its reliability is checked to determine the initial value of the minimum working area length. Based on the VISSIM simulation model running results corresponding to the initial value of the minimum working area length, the traffic capacity attenuation coefficient is calculated. Based on the traffic capacity attenuation coefficient, the final value of the minimum working area length is determined. Specifically, through this process, the most economical working area length can be found while ensuring that the service level of the working area is ≥ Level 3. This avoids the traditional problems of "too long leading to cost waste" or "too short leading to traffic congestion" and adapts to the special road conditions of solid waste utilization and reconstruction projects. Attached Figure Description

[0049] Figure 1 The flowchart illustrates the implementation of a VISSIM-based microscopic traffic flow simulation method according to Embodiment 1 of the present invention.

[0050] Figure 2 This is a structural block diagram of a VISSIM-based microscopic traffic flow simulation system provided in Embodiment 2 of the present invention;

[0051] Figure 3 This is a structural block diagram of an electronic device provided in Embodiment 3 of the present invention. Detailed Implementation

[0052] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0053] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0054] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0055] Example 1

[0056] According to an embodiment of the present invention, a method for simulating microscopic traffic flow based on VISSIM is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0057] This embodiment provides a VISSIM-based microscopic traffic flow simulation method that can be used in electronic devices, such as computers. VISSIM (VISSIM Traffic Simulation) is a microscopic, discrete, multimodal traffic flow simulation software.

[0058] Please see Figure 1 , Figure 1 The flowchart of the implementation of a VISSIM-based micro-traffic flow simulation method provided in Embodiment 1 of the present invention is shown, specifically including steps S01 to S05.

[0059] Step S01 involves refining the parameters of the VISSIM car-following and lane-changing models by conducting road performance tests on solid waste materials, classifying and measuring the proportion of sensitive vehicle types, and testing actual vehicle driving behavior, in order to construct a basic VISSIM simulation model.

[0060] Specifically, a pavement performance index dataset is obtained through road performance testing of solid waste materials. The pavement performance index dataset includes pavement skid resistance, pavement smoothness, and pavement structural strength. Among them, pavement skid resistance is the skid resistance coefficient (BPN), pavement smoothness is the International Roughness Index (IRI), and pavement structural strength is the deflection value.

[0061] By classifying and measuring the proportion of sensitive vehicle types, we obtained a dataset of the proportion of sensitive vehicle types and the distribution characteristics of vehicle types. Based on the load-bearing characteristics of solid waste road surfaces, sensitive and non-sensitive vehicle types were divided according to axle load. For example, sensitive vehicle types: axle load > 10t (heavy trucks, semi-trailers), proportion P; non-sensitive vehicle types: axle load ≤ 10t (small passenger cars, light trucks), proportion 1-P.

[0062] In this embodiment of the invention, the specific proportion of sensitive vehicle types is determined as follows: three observation points are set up at highway entrances / exits and service areas, and video surveillance combined with manual counting is used to continuously observe three peak periods (7:00-9:00 AM, 11:00-1:00 PM, and 5:00-7:00 PM), with each period requiring an observation duration of ≥120 minutes and a vehicle type recognition accuracy rate of ≥95%.

[0063] Through real-world driving behavior tests, we obtained micro-behavioral datasets of two types of vehicles under different road surfaces and speeds, including following distance, lane change time, and lateral sway amplitude. The specific test scenario was to drive at a constant speed of 30km / h, 40km / h, 50km / h, 60km / h, and 70km / h on a solid waste road test section, and record the following distance, lane change time, and lateral sway amplitude.

[0064] It should be noted that the default Wiedemann99 model of VISSIM is used, and its core parameters include: safety distance coefficient (a), expected speed deviation coefficient (b), and reaction time (t). For the correction of the car-following model, a linear regression equation between road performance indicators and model parameters is established based on real vehicle data.

[0065] For sensitive vehicle models: a = 0.8 + 0.05 × (IRI / IRI0); t1 = 1.2 + 0.3 × (deflection value / deflection value 0);

[0066] For non-sensitive vehicle models: b = 0.9 - 0.08 × (BPN / BPN0); t2 = 0.8 + 0.2 × (IRI / IRI0);

[0067] a is the safety distance coefficient, b is the expected speed deviation coefficient, IRI is the smoothness of the solid waste pavement, IRI0 is the reference smoothness of the standard pavement, t1 and t2 are the reaction time, BPN is the skid resistance of the solid waste pavement, and BPN0 is the skid resistance of the standard pavement.

[0068] For the correction of the lane-changing model, the correction equation is established:

[0069] Critical lane-changing speed for sensitive vehicle types: v_c1 = 40 + 10 × (IRI / IRI0); Critical lane-changing speed for non-sensitive vehicle types: v_c2 = 50 + 8 × (IRI / IRI0);

[0070] Lane changing frequency: f = 0.3 - 0.1 × (P / P0);

[0071] v_c1 is the critical speed for lane changing of sensitive vehicle types, v_c2 is the critical speed for lane changing of non-sensitive vehicle types, IRI is the smoothness of the solid waste road surface, IRI0 is the baseline smoothness of the standard road surface, f is the lane changing frequency, P is the proportion of sensitive vehicle types on the solid waste road surface, and P0 is the proportion of sensitive vehicle types on the standard road surface.

[0072] Step S02: Collect road network geometric parameters and traffic parameters, perform calibration, and then configure traffic demand in the basic VISSIM simulation model.

[0073] In this embodiment of the invention, the road network geometric parameters include lane width (3.5m~3.75m), roadbed slope (0%~3%), radius of curvature (≥1000m), and interchange location (error ≤10m). The traffic parameters include current peak hour traffic volume (PCU / h), road segment speed distribution (20km / h~80km / h), and entrance / exit traffic flow ratio.

[0074] Furthermore, using a 1:1 scale, the location was calibrated by importing satellite maps (Google Earth), and then the input was set in the "Traffic Demand" module according to the measured vehicle type ratio and traffic flow distribution during different time periods.

[0075] Step S03: Set the parameterized scenario of the work area and conduct orthogonal experiments to generate an experimental plan.

[0076] Specifically, firstly, the work area structure is defined, dividing the work area into a warning zone L1, a buffer zone L2, an actual work area L3, and a termination zone L4. The total length of the work area L = L1 + L2 + L3 + L4, where the total length of the work area is the target variable, ranging from 300m to 1500m with a step size of 50m. The influencing variables are the lane reduction ratio n = 1 / 3, 1 / 2, and 2 / 3, and the proportion of sensitive vehicle types P = 20% to 60% with a step size of 10%. The fixed parameters are: warning zone L1 = 200m, termination zone L4 = 50m, and work area lane width 3.5m. Understandably, through VISSIM's "Scene Management" module, variable relationships are established to achieve rapid switching between different parameter combinations (without repeatedly drawing the work area).

[0077] The test factors are determined, and the test factors are divided into levels. An orthogonal array is used to generate the test matrix, resulting in the test plan. The test factors include the total length of the work area, the lane reduction ratio, and the proportion of sensitive vehicle types. The total length of the work area is divided into 6 levels: 300m, 500m, 700m, 900m, 1100m, and 1300m. The lane reduction ratio is divided into 3 levels: 1 / 3, 1 / 2, and 2 / 3. The proportion of sensitive vehicle types is divided into 5 levels: 20%, 30%, 40%, 50%, and 60%. Specifically, L... 90Using a (6×3×5) orthogonal array, invalid combinations were eliminated (e.g., when n=2 / 3 and L=300m, the work area could not be arranged), and finally 60 valid test schemes were determined.

[0078] Step S04: Set the hierarchical constraint threshold, and run the configured VISSIM simulation model through dynamic feedback iterative logic. After extreme working condition verification and reliability check, determine the preliminary value of the minimum working area length.

[0079] Specifically, primary constraints meeting the core indicators of Level 3 Service Level (LSL) and secondary constraints meeting the safety indicators of solid waste road surfaces are set. The test schemes are run in batches according to the test matrix sequence to obtain test results. In this embodiment of the invention, the primary constraints (mandatory thresholds, LSL core indicators) include average vehicle speed ≥ 40 km / h (operational area section), queue length ≤ 200 m (end of upstream buffer zone), and vehicle delay ≤ 120 s / vehicle. The secondary constraints (optimization thresholds, solid waste road surface safety indicators) include lateral sway amplitude of sensitive vehicle types ≤ 0.5 m and vehicle conflict count ≤ 2 times / hour (operational area boundary). Furthermore, the simulation parameters are set as follows: simulation duration: 1800 s (first 300 s for warm-up, last 1500 s for valid data), time step: 0.1 s, output indicators: average vehicle speed, traffic capacity, queue length, vehicle delay, lane changing frequency, and conflict count.

[0080] Initial values ​​are obtained, and the experimental results are compared with the first-level constraints and the second-level constraints respectively. Iteration is performed to finally output preliminary candidate values ​​for the minimum working area length. Specifically, the initial value is set to the recommended minimum working area length L0 = 500m, corresponding to n = 1 / 2 and P = 30%. The iteration rules are as follows: If the first-level constraint is not met (e.g., vehicle speed = 38km / h < 40km / h): L = L + 100m, n is reduced by one level (e.g., n = 1 / 2 → 1 / 3), and the simulation is repeated; if the first-level constraint is met but the second-level constraint is not met (e.g., lateral sway = 0.6m > 0.5m): L = L + 50m, n remains unchanged, and the simulation is repeated; if both levels of constraints are met and the indicators are redundant (e.g., vehicle speed = 55km / h > 40km / h): L = L - 50m, P is increased by 10%, and the simulation is repeated; the iteration termination condition is that both levels of constraints are met, and L is the minimum value under the current parameter combination (reducing it by another 50m would result in the constraint not being met).

[0081] Extreme operating conditions are defined, including traffic conditions, vehicle type conditions, and road surface conditions. The preliminary candidate values ​​for the minimum working area length are substituted into the extreme operating conditions, and the VISSIM simulation model is run to determine whether the running results still satisfy the first-level and second-level constraints. Here, traffic conditions refer to peak hourly flow × 1.2 (oversaturated flow), vehicle type conditions refer to the proportion of sensitive vehicle types P = 60% (peak value), and road surface conditions refer to IRI = 3.5 m / km (worst roughness) and BPN = 45 (worst skid resistance).

[0082] If the result of the operation still satisfies the first-level and second-level constraints, then determine the preliminary value of the minimum working area length;

[0083] If the result of the operation does not meet the first-level and second-level constraints, the candidate value of the initial minimum working area length is increased according to the first preset step size, which is 50m~100m, and then verified again.

[0084] Based on the scenario corresponding to the initial value of the minimum working area length, repeat the simulation, calculate the coefficient of variation of the output index, and determine whether the coefficient of variation of the output index is less than the first threshold. It can be understood that the coefficient of variation of the output index is the ratio of the standard deviation to the average value of the repeated simulation results of the same VISSIM simulation output index (such as the average speed of the road segment, vehicle delay, queue length, etc.), multiplied by 100% to get the coefficient of variation of the index.

[0085] If the coefficient of variation of the output index is less than the first threshold, then the preliminary value of the minimum working area length is output.

[0086] Step S05: Calculate the traffic capacity attenuation coefficient based on the VISSIM simulation model running results corresponding to the preliminary value of the minimum working area length, and determine the final value of the minimum working area length based on the traffic capacity attenuation coefficient.

[0087] Specifically, based on the VISSIM simulation model results corresponding to the preliminary value of the minimum working area length, the capacity attenuation coefficient is calculated. The formula for calculating the capacity attenuation coefficient is as follows:

[0088] ;

[0089] γ(L) = 0.6 + 0.0005 × L;

[0090] Wherein, K is the traffic capacity attenuation coefficient, α is the smoothness influence coefficient (fitted from real vehicle data, α=0.3~0.5), and β is the vehicle type proportion influence coefficient (β=0.2~0.4 (the higher the proportion of sensitive vehicle types, the larger β is)). The influence coefficient of work area length (L unit: m, fitted R) 2≥0.9), L is the total length of the work area, IRI is the smoothness of the solid waste road surface, IRI0 is the benchmark smoothness of the standard road surface, P is the proportion of sensitive vehicle types on the solid waste road surface, and P0 is the proportion of sensitive vehicle types on the standard road surface.

[0091] Determine whether the traffic capacity attenuation coefficient is less than the second threshold;

[0092] If it is determined that the traffic capacity attenuation coefficient is less than the second threshold, then according to the second preset step size, the length of the initial value of the minimum working area length is increased, and the simulation is repeated, wherein the second preset step size is less than the first preset step size;

[0093] If it is determined that the traffic capacity attenuation coefficient is not less than the second threshold, then the preliminary value of the minimum work area length corresponding to the traffic capacity attenuation coefficient is determined as the final value of the minimum work area length.

[0094] In summary, the VISSIM-based microscopic traffic flow simulation method in the above embodiments of the present invention corrects the parameters of the VISSIM car-following and lane-changing models by conducting road performance tests on solid waste materials, classifying and measuring the proportion of sensitive vehicle types, and testing real vehicle driving behavior, in order to construct a basic VISSIM simulation model; it collects road network geometric parameters and traffic parameters, calibrates them, and then configures traffic demand in the basic VISSIM simulation model; it sets parameterized scenarios for the work area and conducts orthogonal experiments to generate test plans; it sets hierarchical constraint thresholds and runs the test plans through dynamic feedback iterative logic. The VISSIM simulation model, once set up, is verified under extreme conditions and its reliability is checked to determine the initial value of the minimum working area length. Based on the VISSIM simulation model running results corresponding to the initial value of the minimum working area length, the traffic capacity attenuation coefficient is calculated. Based on the traffic capacity attenuation coefficient, the final value of the minimum working area length is determined. Specifically, through this process, the most economical working area length can be found while ensuring that the service level of the working area is ≥ Level 3. This avoids the traditional problems of "too long leading to cost waste" or "too short leading to traffic congestion" and adapts to the special road conditions of solid waste utilization and reconstruction projects.

[0095] Example 2

[0096] Please see Figure 2 , Figure 2 This is a structural block diagram of a VISSIM-based micro-traffic flow simulation system provided in Embodiment 2 of the present invention. This VISSIM-based micro-traffic flow simulation system 200 is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0097] Specifically, the VISSIM-based microscopic traffic flow simulation system 200 includes: a correction module 21, a data acquisition module 22, a setting module 23, a running module 24, and a calculation module 25, wherein:

[0098] The correction module 21 is used to correct the parameters of the VISSIM car-following model and lane-changing model through solid waste material road performance testing, sensitive vehicle classification and proportion testing, and real vehicle driving behavior testing, so as to construct a basic VISSIM simulation model. Through solid waste material road performance testing, a road performance index dataset is obtained, which includes road anti-skid performance, road smoothness and road structural strength.

[0099] By classifying and measuring the proportion of sensitive vehicle types, we obtained a dataset of the proportion of sensitive vehicle types and the distribution characteristics of vehicle types. Among them, based on the bearing characteristics of solid waste road surfaces, sensitive vehicle types and non-sensitive vehicle types were divided according to axle load.

[0100] Through real-vehicle driving behavior tests, we obtained micro-behavioral datasets of two types of vehicles under different road surfaces and vehicle speeds, including following distance, lane change time, and lateral sway amplitude.

[0101] To refine the car-following model, a linear regression equation between road performance indicators and model parameters is established based on real-vehicle data:

[0102] For sensitive vehicle models: a = 0.8 + 0.05 × (IRI / IRI0); t1 = 1.2 + 0.3 × (deflection value / deflection value 0);

[0103] For non-sensitive vehicle models: b = 0.9 - 0.08 × (BPN / BPN0); t2 = 0.8 + 0.2 × (IRI / IRI0);

[0104] a is the safety distance coefficient, b is the expected speed deviation coefficient, IRI is the smoothness of the solid waste pavement, IRI0 is the reference smoothness of the standard pavement, t1 and t2 are the reaction time, BPN is the skid resistance of the solid waste pavement, and BPN0 is the skid resistance of the standard pavement.

[0105] For the correction of the lane-changing model, the correction equation is established as follows:

[0106] Critical lane-changing speed for sensitive vehicle types: v_c1 = 40 + 10 × (IRI / IRI0); Critical lane-changing speed for non-sensitive vehicle types: v_c2 = 50 + 8 × (IRI / IRI0);

[0107] Lane changing frequency: f = 0.3 - 0.1 × (P / P0);

[0108] v_c1 is the critical speed for lane changing of sensitive vehicle types, v_c2 is the critical speed for lane changing of non-sensitive vehicle types, IRI is the smoothness of solid waste road surface, IRI0 is the baseline smoothness of standard road surface, f is the lane changing frequency, P is the proportion of sensitive vehicle types on solid waste road surface, and P0 is the proportion of sensitive vehicle types on standard road surface.

[0109] The acquisition module 22 is used to acquire road network geometric parameters and traffic parameters, and after calibration, configure traffic demand in the basic VISSIM simulation model.

[0110] Module 23 is used to set the parameterized scenario of the work area, conduct orthogonal experiments, generate test plans, define the work area structure, and divide the work area into a warning area L1, a buffer area L2, an actual work area L3, and a termination area L4. The total length of the work area L = L1 + L2 + L3 + L4 is the target variable, with a value range of 300m to 1500m and a step size of 50m. The influencing variables are the lane reduction ratio n = 1 / 3, 1 / 2, 2 / 3, and the proportion of sensitive vehicle types P = 20% to 60% with a step size of 10%. The fixed parameters are: warning area L1 = 200m, termination area L4 = 50m, and work area lane width 3.5m.

[0111] The test factors are determined, and the test factors are divided into levels. An orthogonal array is used to generate a test matrix to obtain the test plan. The test factors include the total length of the work area, the lane reduction ratio, and the proportion of sensitive vehicle types. The total length of the work area is divided into 6 levels, including 300m, 500m, 700m, 900m, 1100m, and 1300m. The lane reduction ratio is divided into 3 levels, including 1 / 3, 1 / 2, and 2 / 3. The proportion of sensitive vehicle types is divided into 5 levels, including 20%, 30%, 40%, 50%, and 60%.

[0112] The running module 24 is used to set the hierarchical constraint threshold and run the configured VISSIM simulation model through dynamic feedback iterative logic to determine the preliminary value of the minimum working area length after extreme working condition verification and reliability check.

[0113] The calculation module 25 is used to calculate the traffic capacity attenuation coefficient based on the VISSIM simulation model running results corresponding to the preliminary value of the minimum working area length, and to determine the final value of the minimum working area length based on the traffic capacity attenuation coefficient.

[0114] Furthermore, in some optional embodiments of the present invention, the operating module 24 includes:

[0115] The operation unit is used to set the first-level constraints that meet the core indicators of the service level 3 and the second-level constraints that meet the safety indicators of solid waste pavement. It runs the test plans in batches according to the test matrix order to obtain the test results.

[0116] An iterative unit is used to obtain initial values, compare the experimental results with the first-level constraints and the second-level constraints respectively, perform iterations, and finally output preliminary candidate values ​​for the minimum working area length.

[0117] The first judgment unit is used to define extreme working conditions, including traffic conditions, vehicle type conditions, and road conditions. The initial candidate value of the minimum working area length is substituted into the extreme working conditions, the VISSIM simulation model is run, and the result is judged to determine whether the running result still satisfies the first-level constraints and the second-level constraints.

[0118] The first determining unit is used to determine the initial value of the minimum working area length if the result of the operation still satisfies the first-level and second-level constraints.

[0119] The verification unit is used to increase the preliminary minimum work area length candidate value according to the first preset step size and verify again if the running result does not meet the first-level constraint and the second-level constraint.

[0120] The second judgment unit is used to repeatedly simulate the scenario corresponding to the initial value of the minimum work area length, calculate the coefficient of variation of the output index, and determine whether the coefficient of variation of the output index is less than the first threshold.

[0121] The output unit is used to output a preliminary value of the minimum working area length if the coefficient of variation of the output index is less than the first threshold.

[0122] Furthermore, in some optional embodiments of the present invention, the computing module 25 includes:

[0123] The calculation unit is used to calculate the capacity attenuation coefficient based on the VISSIM simulation model results corresponding to the preliminary value of the minimum working area length. The formula for calculating the capacity attenuation coefficient is as follows:

[0124] ;

[0125] γ(L) = 0.6 + 0.0005 × L;

[0126] Where K is the traffic capacity attenuation coefficient, α is the smoothness influence coefficient, and β is the vehicle type ratio influence coefficient. The length of the work area is the influence coefficient, L is the total length of the work area, IRI is the smoothness of the solid waste road surface, IRI0 is the benchmark smoothness of the standard road surface, P is the proportion of sensitive vehicle types on the solid waste road surface, and P0 is the proportion of sensitive vehicle types on the standard road surface.

[0127] The third judgment unit is used to determine whether the traffic capacity attenuation coefficient is less than the second threshold.

[0128] The simulation unit is used to, if it is determined that the traffic capacity attenuation coefficient is less than the second threshold, increase the length of the initial value of the minimum working area length according to the second preset step size and re-simulate;

[0129] The second determining unit is used to determine the preliminary value of the minimum working area length corresponding to the traffic capacity attenuation coefficient as the final value of the minimum working area length if it is determined that the traffic capacity attenuation coefficient is not less than the second threshold.

[0130] Example 3

[0131] In another aspect, the present invention also proposes an electronic device, please refer to [link to relevant documentation]. Figure 3 The electronic device shown is an embodiment of the present invention, including a memory 20, a processor 10, and a computer program 30 stored in the memory and executable on the processor. When the processor 10 executes the computer program 30, it implements the VISSIM-based micro-traffic flow simulation method as described above.

[0132] In some embodiments, the processor 10 may be a central processing unit (CPU), controller, microcontroller, microprocessor or other data processing chip, used to run program code stored in memory 20 or process data, such as executing access restriction programs.

[0133] The memory 20 includes at least one type of readable storage medium, such as flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 20 can be an internal storage unit of an electronic device, such as the hard disk of the electronic device. In other embodiments, the memory 20 can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. Furthermore, the memory 20 can include both internal and external storage units of the electronic device. The memory 20 can be used not only to store application software and various types of data of the electronic device, but also to temporarily store data that has been output or will be output.

[0134] It should be pointed out that, Figure 3 The structure shown does not constitute a limitation on the electronic device. In other embodiments, the electronic device may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0135] This invention also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the VISSIM-based micro-traffic flow simulation method described above.

[0136] Those skilled in the art will understand that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0137] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0138] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0139] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0140] The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this patent should be determined by the appended claims.

Claims

1. A VISSIM-based microscopic traffic flow simulation method, characterized in that, The method includes: Through road performance testing of solid waste materials, classification and proportional testing of sensitive vehicle types, and actual vehicle driving behavior testing, the parameters of VISSIM's car-following and lane-changing models are corrected to construct a basic VISSIM simulation model. In addition, based on the bearing characteristics of solid waste road surfaces, sensitive and non-sensitive vehicle types are classified according to axle load. After collecting and calibrating road network geometric and traffic parameters, traffic demand is configured in the basic VISSIM simulation model. Set up parameterized scenarios for the work area, conduct orthogonal experiments, and generate experimental plans; A hierarchical constraint is set, and the test plan is run through a configured VISSIM simulation model using dynamic feedback iterative logic. After extreme working conditions verification and reliability check, the preliminary value of the minimum working area length is determined. The extreme working conditions include traffic conditions, vehicle type conditions, and road conditions. The hierarchical constraint consists of a first-level constraint that meets the core indicators of the three service levels and a second-level constraint that meets the solid waste road safety indicators. The first-level constraint includes at least the constraint of the average vehicle speed of the working area road section, and the second-level constraint includes at least the constraint of the lateral sway amplitude of sensitive vehicle types. Based on the VISSIM simulation model results corresponding to the initial value of the minimum working area length, the traffic capacity attenuation coefficient is calculated, and the final value of the minimum working area length is determined based on the traffic capacity attenuation coefficient. In the step of constructing a basic VISSIM simulation model by correcting the parameters of the VISSIM car-following and lane-changing models through solid waste material road performance testing, sensitive vehicle type classification and proportion testing, and real vehicle driving behavior testing, the correction of the car-following model involves establishing a linear regression equation between road performance indicators and model parameters based on real vehicle data. For sensitive vehicle models: a = 0.8 + 0.05 × (IRI / IRI0); t1 = 1.2 + 0.3 × (deflection value / deflection value 0); For non-sensitive vehicle models: b = 0.9 - 0.08 × (BPN / BPN0); t2 = 0.8 + 0.2 × (IRI / IRI0); a is the safety distance coefficient, b is the expected speed deviation coefficient, IRI is the smoothness of the solid waste pavement, IRI0 is the reference smoothness of the standard pavement, t1 and t2 are the reaction time, BPN is the skid resistance of the solid waste pavement, and BPN0 is the skid resistance of the standard pavement. For the correction of the lane-changing model, the correction equation is established: Critical lane-changing speed for sensitive vehicle types: v_c1 = 40 + 10 × (IRI / IRI0); Critical lane-changing speed for non-sensitive vehicle types: v_c2 = 50 + 8 × (IRI / IRI0); Lane changing frequency: f = 0.3 - 0.1 × (P / P0); v_c1 is the critical speed for lane changing of sensitive vehicle types, v_c2 is the critical speed for lane changing of non-sensitive vehicle types, IRI is the smoothness of solid waste road surface, IRI0 is the baseline smoothness of standard road surface, f is the lane changing frequency, P is the proportion of sensitive vehicle types on solid waste road surface, and P0 is the proportion of sensitive vehicle types on standard road surface. The formula for calculating the capacity attenuation coefficient is: ； γ(L) = 0.6 + 0.0005 × L; Where K is the traffic capacity attenuation coefficient, α is the smoothness influence coefficient, and β is the vehicle type ratio influence coefficient. is the influence coefficient of the work area length, and L is the total length of the work area.

2. The VISSIM-based microscopic traffic flow simulation method according to claim 1, characterized in that, In the step of correcting the parameters of the VISSIM car-following model and lane-changing model through solid waste material road performance testing, sensitive vehicle type classification and proportion test, and real vehicle driving behavior test to construct a basic VISSIM simulation model, the road performance index dataset is obtained through solid waste material road performance testing. The road performance index dataset includes road anti-skid performance, road smoothness, and road structural strength. By classifying and measuring the proportion of sensitive vehicle types, we can obtain the percentage of sensitive vehicle types and the distribution characteristics of vehicle types. Through real-vehicle driving behavior tests, we obtained micro-behavioral datasets of two types of vehicles under different road surfaces and speeds, including following distance, lane change time, and lateral sway amplitude.

3. The VISSIM-based microscopic traffic flow simulation method according to claim 2, characterized in that, In the step of setting up a parameterized scenario for the work area, conducting orthogonal experiments, and generating an experimental plan, the work area structure is defined, dividing the work area into a warning zone L1, a buffer zone L2, an actual work area L3, and a termination zone L4. The total length of the work area L = L1 + L2 + L3 + L4, where the total length of the work area is the target variable, with a value range of 300m to 1500m. The influencing variable is the lane reduction ratio, with the proportion of sensitive vehicle types P = 20% to 60%. The fixed parameters are warning zone L1 = 200m and termination zone L4 = 50m, and the lane width of the work area is 3.5m. The test factors are determined, and the test factors are divided into levels. An orthogonal array is used to generate a test matrix to obtain the test plan. The test factors include the total length of the work area, the lane reduction ratio, and the proportion of sensitive vehicle types. The total length of the work area is divided into 6 levels, including 300m, 500m, 700m, 900m, 1100m, or 1300m. The lane reduction ratio is divided into 3 levels, including 1 / 3, 1 / 2, or 2 / 3. The proportion of sensitive vehicle types is divided into 5 levels, including 20%, 30%, 40%, 50%, or 60%.

4. The VISSIM-based microscopic traffic flow simulation method according to claim 3, characterized in that, The steps of setting hierarchical constraints, running the test plan through dynamic feedback iterative logic on the configured VISSIM simulation model, and determining the preliminary value of the minimum working area length after extreme condition verification and reliability check include: Set primary constraints that meet the core indicators of Level 3 Service Level and secondary constraints that meet the safety indicators of solid waste pavement. Run the test schemes in batches according to the test matrix to obtain the test results. Obtain initial values, compare the experimental results with the first-level constraints and the second-level constraints respectively, perform iterations, and finally output preliminary candidate values ​​for the minimum working area length; Define extreme working conditions, substitute the preliminary candidate values ​​of minimum working area length into the extreme working conditions, run the VISSIM simulation model, and determine whether the running results still satisfy the first-level and second-level constraints. If the result of the operation still satisfies the first-level and second-level constraints, then determine the initial value of the minimum working area length; If the result of the operation does not meet the first-level and second-level constraints, the candidate value of the initial minimum working area length is increased according to the first preset step size, and then verified again; Based on the scenario corresponding to the initial value of the minimum work area length, repeat the simulation, calculate the coefficient of variation of the output index, and determine whether the coefficient of variation of the output index is less than the first threshold. The coefficient of variation is the ratio of the standard deviation to the average value of the repeated simulation results of the same VISSIM simulation output index. If the coefficient of variation of the output index is less than the first threshold, then the preliminary value of the minimum working area length is output.

5. The VISSIM-based microscopic traffic flow simulation method according to claim 4, characterized in that, The steps of calculating the capacity attenuation coefficient based on the VISSIM simulation model results corresponding to the preliminary value of the minimum work area length, and determining the final value of the minimum work area length based on the capacity attenuation coefficient, include: Calculate the capacity attenuation coefficient based on the VISSIM simulation model results corresponding to the preliminary value of the minimum working area length; Determine whether the traffic capacity attenuation coefficient is less than the second threshold; If it is determined that the traffic capacity attenuation coefficient is less than the second threshold, then according to the second preset step size, the length of the initial value of the minimum working area length is increased, and the simulation is repeated. If it is determined that the traffic capacity attenuation coefficient is not less than the second threshold, then the preliminary value of the minimum work area length corresponding to the traffic capacity attenuation coefficient is determined as the final value of the minimum work area length.

6. A VISSIM-based microscopic traffic flow simulation system, characterized in that, The system for implementing the VISSIM-based micro-traffic flow simulation method as described in any one of claims 1-5 includes: The correction module is used to correct the parameters of VISSIM's car-following and lane-changing models through solid waste material road performance testing, sensitive vehicle type classification and proportion testing, and real vehicle driving behavior testing, so as to build a basic VISSIM simulation model. The data acquisition module is used to collect road network geometric parameters and traffic parameters, and after calibration, configure traffic demand in the basic VISSIM simulation model. The configuration module is used to set the parameterized scenario of the work area, conduct orthogonal experiments, and generate test plans; The running module is used to set hierarchical constraints and run the configured VISSIM simulation model through dynamic feedback iterative logic. After extreme working condition verification and reliability check, the preliminary value of the minimum working area length is determined. The calculation module is used to calculate the traffic capacity attenuation coefficient based on the VISSIM simulation model running results corresponding to the preliminary value of the minimum work area length, and to determine the final value of the minimum work area length based on the traffic capacity attenuation coefficient.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the VISSIM-based micro-traffic flow simulation method as described in any one of claims 1-5.

8. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the VISSIM-based micro-traffic flow simulation method as described in any one of claims 1-5.

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