CN118313179A

CN118313179A - Vehicle load effect simulation method

Info

Publication number: CN118313179A
Application number: CN202410750831.4A
Authority: CN
Inventors: 应国刚; 王康迪; 胡洁亮; 应柳琪
Original assignee: Ningbo Langda Technology Co ltd
Current assignee: Ningbo Langda Technology Co ltd
Priority date: 2024-06-12
Filing date: 2024-06-12
Publication date: 2024-07-09
Anticipated expiration: 2044-06-12
Also published as: CN118313179B

Abstract

The application discloses a vehicle load effect simulation method; the method comprises the following steps: constructing a cellular model of the vehicle, and configuring road network traffic conditions and traffic simulation parameters; formulating an operation rule of the vehicle, carrying out traffic simulation based on the formulated rule, and carrying out measurement statistics on actual traffic data of a simulation road section; under the condition that the simulation is matched with the measured data, respectively calculating the load effect of the cell model under the conditions of vehicle weight loading and axle weight loading; and under the condition that the simulation is not matched with the measured data, updating the operation parameters in real time. The application has the beneficial effects that: the method can more accurately simulate the behavior and interaction of vehicles under various traffic conditions by introducing detailed vehicle and road parameters, and improves the detail and accuracy of the simulation; meanwhile, by using a machine vision technology and statistical data to dynamically update simulation parameters, the change of road traffic conditions can be reflected in real time, and timeliness and relativity of simulation results are ensured.

Description

Vehicle load effect simulation method

Technical Field

The application relates to the technical field of traffic engineering, in particular to a vehicle load effect simulation method.

Background

The vehicle load effect simulation is an important content of bridge bearing capacity assessment and fatigue analysis, and an accurate vehicle load effect simulation result not only can improve the confidence coefficient of the assessment result, but also can provide a basis for the implementation of targeted bridge management and maintenance measures.

At present, the simulation mode of the vehicle load effect mainly comprises two modes, wherein one mode is to describe the vehicle load and the load effect by using probability distribution functions such as generalized pareto distribution, extremum distribution and the like from the statistical perspective. And the other is to generate a simulated fleet by using a cellular automaton and other modes from the viewpoint of traffic simulation to directly load so as to obtain a vehicle load effect. The former has obvious periodic rule and convenient extrapolation, but has unobvious microscopic features, so the method is widely used in the trend analysis tasks such as bridge reliability analysis and residual life prediction. The load microscopic feature is obvious, the structural response feature is convenient to extract, and the method is widely applied to axle coupling analysis, fatigue evaluation and other scenes.

The prior art provides a continuous cellular automaton model based on fuzzy decision, and provides a more accurate and fine modeling method for traffic flow simulation. The continuous cellular automaton simulation process is used for generating stable random traffic flow according to historical data by using factors such as driver behavior characteristics, following theory, driver vision and the like in the form of operation rules and probability density functions successively. The stable random traffic flow can meet the traffic simulation and vehicle load effect simulation requirements of design and evaluation work, but under the scenes of real-time traffic analysis prediction, long-term traffic running state analysis and the like, the random traffic flow generated only by the historical data inevitably has larger difference with the actual measurement data, so that simulation results are distorted. In order to overcome the defect, the invention improves the prior art to realize high-precision vehicle load effect refined simulation.

Disclosure of Invention

One of the objects of the present application is to provide a vehicle load effect simulation method that can solve at least one of the above-mentioned drawbacks of the related art.

In order to achieve at least one of the above objects, the present application adopts the following technical scheme: a vehicle load effect simulation method comprises the following steps:

S100: constructing a cellular model a _n = { P, L, M, W }; and configuring traffic conditions and traffic simulation parameters of the road network;

s200: formulating running rules of the vehicle, including a state updating rule, a lane changing rule and a following rule;

s300: carrying out traffic simulation based on the formulated rules and carrying out measurement statistics on actual traffic data of the simulation road section;

s400: under the condition that the simulation data are matched with the measured data, respectively calculating the load effect of the cell model under the conditions of vehicle weight loading and axle weight loading;

S500: under the condition that the simulation data is not matched with the measured data, taking the initial value of the cell model as a priori condition, taking the measured data as observation data, and adopting a single-variable Bayesian dynamic linear model to update and re-simulate the cell model in real time.

Wherein n represents a vehicle unique number, P represents a cellular operation parameter space-time matrix, L represents a vehicle position matrix, M represents a vehicle motion state matrix, and W represents a vehicle weight parameter matrix.

Preferably, the cell operating parameter space-time matrix; Wherein, type represents a vehicle type number,Indicating the maximum speed limit of the current lane,Represents the maximum speed of the current vehicle model,Represents the maximum acceleration of the current vehicle model,Representing the maximum braking acceleration of the current vehicle type, and l _n represents the length of the vehicle;

Vehicle position matrix l= { L _begin,l_end,lane_last,lane_begin,lane_end,lane_next }; wherein l _begin and l _end represent the vehicle position at the beginning and end of a time step, lane _last represents the number of the lane in which the vehicle was last located, lane _begin and lane _end represent the number of the lane in which the vehicle was in the beginning and end of a time step, and lane _next represents the number of the lane in which the vehicle was next located, respectively;

Vehicle motion state matrix m= { v _n,a_n }; wherein v _n represents the vehicle speed at the start of the time step, and a _n represents the vehicle acceleration at the start of the time step;

The weight parameter matrix w= { G, k, o _f,o_r,d,……,d_k-1,g,……,g_k }; where G represents the vehicle weight, k represents the number of axles, o _f and o _r represent the length of the front-rear suspension of the vehicle, d represents the wheelbase, and G represents the axle weight, respectively.

Preferably, the road network comprises a plurality of different road sections, and the traffic conditions and the traffic simulation parameters of the different road sections are represented by an environment variable matrix E _m; e _m={length,lane_num,v^max,p_rsd is a sequence of steps,P _{changelane_m},EC₁,……,EC_i, mode }; wherein m represents a road segment number, length represents a [1×m ] road segment length matrix, lane_num represents a [1×m ] road segment lane number matrix, v ^max represents a [1×m ] road segment maximum speed limit matrix, P _rsd represents a m road segment different lane random slowing probability,The braking response time of different vehicle types is represented, p _{changelane_m} represents the lane change probability of an m-section, EC _i represents the effect calculation section of an i-th section, and mode represents the vehicle load application mode.

Preferably, the establishment of the lane change rule comprises the determination of a lane change machine, lane change conditions and lane change tracks; the scenario for generating a lane change motivation is as follows: firstly, under the condition that the running speed does not exceed the current road section speed limit and the vehicle speed limit, generating a lane changing machine when the distance between the vehicle and the front vehicle is not satisfied with the running of maintaining the current vehicle speed; secondly, a road changing machine is generated when the position of the split joint opening or the maintenance construction of the bridge road causes the local road to be closed; judging the lane changing conditions of the vehicle according to the number of lanes and the traffic conditions of all lanes based on the scenes generated by the lane changing machine, and triggering lane changing, decelerating or following rules of the vehicle according to the lane changing conditions; and for the vehicle needing lane changing, performing lane changing running according to the set lane changing track.

Preferably, based on the first scenario of the lane change machine, the vehicle will trigger a deceleration rule when the lane change machine is generated when there is only one lane.

Preferably, based on the first scene of the lane changing machine, when two lanes exist, if the vehicle meets the lane changing condition, lane changing is carried out, otherwise, a speed reduction or following rule is triggered according to the situation; the lane change conditions of the vehicle are as follows:

gap_{n,lane_end}(t)＞gap_{n,lane_begin}(t)；

gap_{n+1,lane_end}(t)＞v_{n+1,lane_end}(t)+a_{n+1,lane_end}(t+1)+gap_n+1,safe(t)；

；

rand(0，1)≤p_{changelane_m}；

Wherein gap _{n,lane_begin} (t) and gap _{n,lane_end} (t) represent the distance between the vehicle with the n time at the beginning and the end of the time at the time of the step and the front vehicle of the same lane respectively, gap _n+1,safe (t) represents the safe distance between the vehicle with the n+1 time at the time of the step, v _{n+1,lane_end} (t) represents the speed of the vehicle with the n+1 time at the time of the step on the lane where the time of the step ends, a _{n+1,lane_end} (t+1) represents the acceleration of the vehicle with the n+1 time of the t+1 time on the lane where the time of the step ends, Represents the vehicle braking response time numbered n +1,Representing the maximum braking acceleration of the model to which the vehicle numbered n belongs, rand (0, 1) represents a random function.

Preferably, based on the first scene of the lane changing machine, when more than two lanes exist, if the vehicle meets the lane changing condition, lane changing is performed, otherwise, a speed reduction or following rule is triggered according to the situation; the lane change judgment formula of the vehicle is as follows:

gap_n,right(t)＞gap_n,left(t)（1）；

gap_n,right(t)＜gap_n,left(t)（2）；

gap_n,left(t)＞gap_n,mid(t)&gap_n,right(t)＜gap_n,mid(t)（3）；

gap_n,left(t)＜gap_n,mid(t)&gap_n,right(t)＞gap_n,mid(t)（4）；

gap_n,left(t)＞gap_n,mid(t)&gap_n,right(t)＞gap_n,mid(t)（5）；

gap_n+1,right(t)＞v_n+1,right(t)+a_n+1,right(t+1)+gap_n+1,safe(t)（7）；

gap_n+1,left(t)＞v_n+1,left(t)+a_n+1,left(t+1)+gap_n+1,safe(t)（8）；

rand(0，1)≤p_{changelane_m}（9）；

rand(0，1)≤p_{changelane_left}（10）；

rand(0，1)≤p_{changelane_right}（11）；

p_{changelane_left}＜rand(0,1)＜p_{changelane_right}（12）；

rand(0，1)≥p_{changelane_right}（13）；

If the formulas (1) and (7) are established, judging that the vehicle is positioned on the left lane and the right lane has overtaking conditions, and then switching lanes to the right when the formula (9) is established; if the formulas (2) and (8) are established, judging that the vehicle is positioned on the right lane and the left lane has overtaking conditions, and then switching lanes to the left when the formula (9) is established; if the formulas (3) and (8) are established, judging that the vehicle is positioned in the middle lane and the left lane has an overtaking condition, and then switching lanes to the left when the formula (10) is established; if the formulas (4) and (7) are established, judging that the vehicle is positioned in the middle lane and the right lane has overtaking conditions, and then switching lanes to the right when the formula (11) is established; if the formulas (5), (7) and (8) are met, judging that the vehicle is positioned in the middle lane and the left lane and the right lane are provided with overtaking adjustment, and then respectively carrying out left lane changing, deceleration following and right lane changing on the vehicle according to the conditions of the formulas (10), (12) and (13);

wherein gap _n,left (t) and gap _n,right (t) represent the following distances of vehicles with the time n on left and right lanes, respectively, gap _n,mid (t) represents the following distances of vehicles with the time n on the middle lane, v _n+1,left (t) and v _n+1,right (t) represent the speeds of vehicles with the time n+1 on left and right lanes, a _n+1,left (t+1) and a _n+1,right (t+1) represent the speeds of vehicles with the time n+1 on left and right lanes, respectively, gap _n+1,safe (t) represents the safe distances of vehicles with the time n+1, and rand (0, 1) represents a random function, and p _{changelane_left} and p _{changelane_right} represent the probabilities of lane change to the left and right, respectively.

Preferably, based on a second scene of the lane changing machine, judging whether the adjacent lane has the lane changing condition of the double lanes in the first scene of the lane changing machine, if so, immediately changing lanes, otherwise, decelerating and stopping until the following lane changing condition is met;

；

when the vehicles change lanes, if the yielding conditions are met, the vehicles entering the lanes will decelerate in advance to yield the parking space, otherwise, the vehicles normally pass through, and the next vehicles are decelerated to yield the parking space; the determination of the yield condition and the formula of the early deceleration are as follows:

v_n(t)+0.5a_n(t+1)-[v_{n+1,lane_end}(t)+ 0.5a_{n+1,lane_end}(t)]≥gap_n+1,safe(t)；

；

v_{n+1,lane_end}(t+1)= v_{n+1,lane_end}(t)+ a_{n+1,lane_end}(t+1)；

Wherein v _{n+1,lane_end} (t) and a _{n+1,lane_end} (t) respectively represent the speed and acceleration of the vehicle with the time n+1 at the end of the time step, gap _n+1,safe (t) represents the safe distance between the vehicle with the time n+1 at the time t, gap _{n+1,lane_end} (t) represents the distance between the vehicle with the time n+1 at the time t and the same lane front vehicle at the end of the time step, Indicating the maximum braking acceleration of the model to which the vehicle numbered n belongs, gap _limit (t) indicates the distance that the vehicle starts decelerating at time t.

Preferably, when the vehicle changes lanes, the real-time track of the vehicle is simulated by adopting a fifth-order polynomial, and the specific real-time track [ x (t), y (t) ] expression is as follows:

x(t)=A₅t⁵+A₄t⁴+A₃t³+A₂t²+A₁t+A₀;

y(t)= B₅t⁵+B₄t⁴+B₃t³+B₂t²+B₁t+B₀;

A₀=x_sp;A₁=v_sp·cosθ_sp;A₂=0.5a_sp·cosθ_sp；

；

B₀=y_sp;B₁=v_sp·sinθ_sp;B₂=0.5a_sp·sinθ_sp；

；

Where (x _sp,y_sp) represents the initial coordinate position of the vehicle, a and B represent constant terms of each term in the polynomial, v _sp and a _sp represent the initial speed and acceleration of the vehicle, respectively, θ _sp represents the initial body heading angle of the vehicle, and (x _ep,y_ep) represents the coordinate position at the end of the lane change of the vehicle, v _ep and a _ep represent the speed and acceleration at the end of the lane change of the vehicle, respectively, θ _ep represents the body heading angle at the end of the lane change of the vehicle, and t _f represents the lane change duration.

Preferably, the following rule formulation includes two scenarios, scenario one: no front vehicle or a far distance from the front vehicle; scene II: the front vehicles are closer in distance and do not change lanes; the judgment formula of the scene is as follows:

；

If the judgment formula is true, the vehicle is in a first scene; at this time, the vehicle tends to travel at a high speed, and the travel simulation rule thereof is as follows:

；

If the random slowing-down situation of the vehicle is considered, the driving simulation rule is as follows:

；

If the judging formula is not established, the vehicle is in a second scene; the driving simulation rules of the vehicle at this time are as follows:

；

When the front inter-vehicle distance is larger than the safety inter-vehicle distance, the rule that the acceleration changes along with the front inter-vehicle distance is as follows:

；

When the front vehicle distance is smaller than or equal to the safety vehicle distance, the rule that the acceleration changes along with the front vehicle distance is as follows:

；

Wherein gap _n (t+1) represents the distance between the vehicle with the number n at the time of t+1 and the front vehicle on the same lane, r represents the ratio of the distance between the front vehicle and the safe distance during acceleration, gap _n,safe (t) represents the safe distance between the vehicle with the number n at the time of t, and rand (-0.2, 0), rand (0, 1) and rand (-1/2, 1/2) represent random functions.

Preferably, under the condition of loading the vehicle weight, the calculation formula of the load effect E _i of the effect calculation road section EC _i is as follows: e _i=ζG_i (t) IL; wherein ζ is a correction parameter matrix, G _i (t) is a vehicle weight matrix, representing the total weight of all vehicles within the EC _i range at time t, and IL represents an influence line matrix;

Based on the axle load loading condition, the calculation formula of the load effect E _i of the effect calculation road section EC _i is as follows: e _i=g_i (t) IL; wherein g _i is an axle weight matrix, representing the total weight of all axles within the range of EC _i at time t.

Compared with the prior art, the application has the beneficial effects that:

(1) The method can more accurately simulate the behavior and interaction of the vehicles under various traffic conditions by introducing detailed vehicle and road parameters, and improves the detail and accuracy of the simulation.

(2) According to the method, the simulation parameters are dynamically updated by using the machine vision technology and the statistical data, so that the change of road traffic conditions can be reflected in real time, and the timeliness and the relevance of simulation results are ensured.

(3) The method is not only suitable for the bearing capacity evaluation of bridges and roads, but also suitable for the fields of urban traffic planning, road design, traffic management and the like, and has wide application prospect. The accurate load effect simulation can help engineers and decision makers to better understand the bearing states of bridges and roads, so that more reasonable maintenance and reinforcement decisions are made, and public safety is improved.

Drawings

Fig. 1 is a schematic overall workflow diagram of the application.

Detailed Description

The present application will be further described with reference to the following specific embodiments, and it should be noted that, on the premise of no conflict, new embodiments may be formed by any combination of the embodiments or technical features described below.

In the description of the present application, it should be noted that, for the azimuth words such as terms "center", "lateral", "longitudinal", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc., the azimuth and positional relationships are based on the azimuth or positional relationships shown in the drawings, it is merely for convenience of describing the present application and simplifying the description, and it is not to be construed as limiting the specific scope of protection of the present application that the device or element referred to must have a specific azimuth configuration and operation.

It should be noted that the terms "first," "second," and the like in the description and in the claims are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order.

The terms "comprises" and "comprising," along with any variations thereof, in the description and claims, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

In one preferred embodiment of the present application, as shown in fig. 1, a vehicle load effect simulation method includes the following steps:

s100: constructing a cellular model a _n = { P, L, M, W }; and configuring traffic conditions and traffic simulation parameters of the road network.

S200: and formulating running rules of the vehicle, including a state updating rule, a lane changing rule and a following rule.

S300: and carrying out traffic simulation based on the established rules and carrying out measurement statistics on actual traffic data of the simulated road section.

S400: and under the condition that the simulation data are matched with the measured data, respectively calculating the load effect of the cell model under the conditions of vehicle weight loading and axle weight loading.

It should be understood that the loading effect refers to the general term for internal forces (such as axial forces, shear forces, bending moments, etc.), deformations, cracks, etc. generated within the structural member due to loading in the design of the building structure. In the construction of bridges, roads and the like, the load effect generated by the running of vehicles is mainly generated; therefore, in order to ensure the safety of bridges and roads, the load effect caused by the vehicles needs to be analyzed and calculated.

In an actual road network, the load effect of a specific road section cannot be calculated or is difficult to directly obtain due to the complexity of road conditions and the complexity of vehicle traffic, so that the load effect is calculated in a simulation mode. The method comprises the steps of constructing a vehicle and a road network model in simulation software, then providing limited conditions to simulate the running state of the vehicle in the road network, and further obtaining corresponding load effects in a simulation mode according to running parameters of the vehicle and structural parameters of the road network. Of course, in order to ensure the accuracy of the simulation result, the simulated traffic flow data and the actual traffic flow data of the corresponding road network can be compared in the simulation process, and the load effect is calculated again under the condition that the simulated traffic flow data and the actual traffic flow data are matched with each other. The acquisition of the road network actual traffic flow data can be obtained through a plurality of modes such as a vehicle counter, machine vision identification, road monitoring video and the like. For convenience of understanding, a detailed description of the specific process of the present embodiment will be given below.

Specifically, as shown in fig. 1, a cellular model of the vehicle is constructed by given initial parameters; and then, carrying out condition constraint on the follow-up simulation process of the cell model, wherein the condition constraint comprises constraint on the traffic state of the simulated road network and the road condition scene constraint faced by the vehicle in the actual driving process. And (3) carrying out simulation of the cell model under the given condition constraint, and obtaining the section traffic flow data of the cell model based on the simulation road section. After the simulation data are obtained, the simulation data can be matched with the actual traffic flow statistical data obtained by visual analysis or statistics of the simulation road section; if the simulation result is matched with the measured data, the simulation result is accurate, and the corresponding load effect value can be obtained by loading the corresponding mode; if the simulation result is not matched with the measured data, the simulation result is inaccurate, that is, the parameter value of the cell model cannot meet the current simulation requirement, the initial value of the cell model can be used as a priori condition, the measured data obtained through visual analysis or statistics can be used as observation data, the parameter of the cell model is optimized by using a single-variable Bayesian Dynamic Linear Model (BDLM), and the optimized parameter is endowed to the cell model again for real-time updating of the parameter. And after the cell model finishes parameter updating, carrying out simulation again, and repeating the process until a corresponding load effect value is obtained.

Compared with the traditional mode, the method can realize automatic updating of simulation parameters; in particular, even in the same road section, the real-time traffic state and the traffic flow change trend are different at different moments, and the method can automatically update the parameters of the current road section traffic flow simulation model according to the traffic flow information of different road sections and different moments recognized by a machine so as to realize accurate simulation of traffic load information at different moments. By dynamically updating simulation parameters by using a machine vision technology and statistical data, the change of road traffic conditions can be reflected in real time, and the timeliness and the relativity of simulation results are ensured.

Meanwhile, the method can more accurately simulate the vehicle behaviors and interactions under various traffic conditions by introducing detailed vehicle and road parameters, and can effectively improve the details and accuracy of the simulation.

It should also be appreciated that the method is not only suitable for the bearing capacity evaluation of bridges and roads, but also suitable for the fields of urban traffic planning, road design, traffic management and the like, and has wide application prospects. Through accurate load effect simulation, engineers and decision makers can be helped to better understand the bearing states of bridges and roads, so that more reasonable maintenance and reinforcement decisions are made, and public safety is improved.

In this embodiment, for the cell operation parameter space-time matrix P in the cell model a _n = { P, L, M, W }, the calibration is mainly performed on performance data of different vehicle types. Generally, performance parameters corresponding to different vehicle types are different, and thus states of running in the road network are also different. For example, under the same vehicle distance, the vehicle model with better performance can smoothly change the road due to faster acceleration; however, the vehicle with poor performance may not be able to complete lane change due to slow acceleration; therefore, the vehicle type has influence on road network traffic.

Specifically, parameters of the space-time matrix P for the cell operation parameters are set as follows: . Wherein, type represents a vehicle type number, Indicating the maximum speed limit of the current lane,Represents the maximum speed of the current vehicle model,Represents the maximum acceleration of the current vehicle model,Indicating the maximum braking acceleration of the current vehicle model, and l _n indicating the vehicle length.

In this embodiment, for the vehicle position matrix L in the cellular model a _n = { P, L, M, W }, it is mainly to calibrate the position parameters of the vehicle running in the road network; such as the lane position where the vehicle is traveling at different moments in time, etc.

Specifically, the parameters for the vehicle position matrix L are set as: l= { L _begin,l_end,lane_last,lane_begin,lane_end,lane_next }. Wherein l _begin and l _end represent the vehicle position in meters at the beginning and end of the time step, respectively; lane _last represents the number of the lane where the vehicle was last located, and may be numbered with 1,2,3, … …; lane _begin and Lane _end respectively represent the number of lanes in which the vehicle is positioned at the beginning and the end of a time step, and can be represented by 0,1,2,3 and … …, wherein 0 represents that the vehicle is in a lane change state, 1,2,3 and … … are used for numbering the lanes, and the larger the number value is, the closer the corresponding lane is to the outer side of the road, and the lower the corresponding vehicle speed is; lane _next represents the number of the lane in which the vehicle is next located, and may be numbered with 1,2,3, … ….

It should be noted that time step and time step are a concept used in numerical simulation to represent discretization of the simulation system in time; in numerical modeling, the time range of a problem is divided into successive small time intervals, each time interval being referred to as a time step; can be regarded as a unit time.

In this embodiment, for the vehicle motion state matrix M in the cellular model a _n = { P, L, M, W }, it mainly calibrates the state of the vehicle during running, and mainly includes speed and acceleration. The parameters of the vehicle motion state matrix M are set as: m= { v _n,a_n }; where v _n denotes the vehicle speed at the start of a time step and a _n denotes the vehicle acceleration at the start of a time step.

In this embodiment, for the vehicle weight parameter matrix W in the cellular model a _n = { P, L, M, W }, it is mainly to calibrate the structural parameters of the vehicle, such as weight and length. The parameters of the vehicle weight parameter matrix W are set as follows: w= { G, k, o _f,o_r,d,……,d_k-1,g,……,g_k }; where G represents the vehicle weight, k represents the number of axles, o _f and o _r represent the lengths of front and rear suspensions of the vehicle, respectively, d represents the wheelbase, d _k-1 represents the wheelbase between the kth axle and the k-1 th axle, G represents the axle weight, and G _k represents the weight of the kth axle.

It can be understood that compared with the traditional mode, the method is based on the load effect simulation of the axle load, establishes a cell model by taking the vehicle as a unit, and adds the axle number, the axle base and the axle load parameters on the basis of the vehicle size and the traffic state parameters. The method and the device innovate the format and the content of the cell model, further realize load effect simulation based on axle load, and solve the problem of large direct calculation result of loading of the vehicle weight influence line. By introducing detailed vehicle and road parameters such as axle number, axle base, axle load and the like, the vehicle behavior and interaction under various traffic conditions can be more accurately simulated, and the details and accuracy of the simulation are improved.

It should be noted that a road network generally includes a plurality of different road segments, and the calculation of the load effect is generally based on a certain road segment; therefore, the road network traffic conditions and the configuration of the traffic simulation parameters need to be specific to road sections. In this embodiment, traffic conditions and traffic simulation parameters for different road segments may be represented by an environment variable matrix E _m; e _m={length,lane_num,v^max,p_rsd is a sequence of steps,，p_{changelane_m},EC₁,……,EC_i，mode}。

Where m represents a link number.

Length represents a [1×m ] link length matrix, length= [ length ₁,……,length_m ].

Lane_num represents a [1×m ] link lane number matrix, lane_num= [ Lane_num ₁,……,lane_num_m ].

V ^max denotes a [1 x m ] road section maximum speed limit matrix,。

P _rsd is a [1×Lane_num _m ] matrix, which represents the random slowing probability of different lanes of m road sections, and the initial values can be determined according to actual needs, and in the embodiment, the initial values are all 0.2.

The matrix is [1×type ] and represents the braking response time of different vehicle types, and the initial value can be determined by self according to actual needs, and in the embodiment, the initial value is 0.2s.

P _{changelane_m} is a [1×Lane_num _m ] matrix, which represents the lane change probability of the m road section, and the initial value of each element can be determined according to the number of lanes.

EC _i represents the effect calculation road segment of the i-th segment, which is a [1×2] matrix, including the road segment start point and end point.

Mode represents the current vehicle load application mode, and only has two states of 0 and 1, which correspond to the vehicle weight loading and the axle weight loading respectively; mode=0 represents axle load, and mode=1 represents vehicle load.

It can be understood that the driving behavior of the vehicle is affected due to the differences of conditions such as road surface conditions, the number of lanes, and the speed limit of the road in different road sections. According to the method, the simulation parameters of different road sections are set differently, so that the problem that the traditional cellular automaton model cannot adapt to the traffic simulation task of the complex road section can be solved, and the accuracy and reliability of the simulation result are further ensured.

In this embodiment, when step S200 is performed, the state update rule for the vehicle mainly includes a position update and a speed update; and the location update includes an own location update of the vehicle and a change update of the inter-vehicle distance. The specific state update formula is as follows:

The speed update formula: v _n(t+1)=v_n(t)+a_n (t+1). DELTA.t; wherein Δt represents a time step, and the value of Δt is an interval time between adjacent time instants, i.e., Δt=1, so the speed update formula may be: v _n(t+1)=v_n(t)+a_n(t+1);v_n (t+1) represents the speed of the vehicle with the number n at time t+1, v _n (t) represents the speed of the vehicle with the number n at time t, and a _n (t+1) represents the acceleration of the vehicle with the number n at time t+1. The values of Δt involved in the subsequent calculation formulas are all 1 and omitted.

The location update formula: x _n(t+1)= x_n(t)+v_n(t) ·Δt +0.5a_n(t+1)·Δt²;x_n (t+1) represents the position of the vehicle with the number n at time t+1, and x _n (t) represents the position of the vehicle with the number n at time t; when Δt=1, x _n(t+1)= x_n(t)+v_n(t) ·Δt +0.5a_n(t+1)·Δt².

The relative position update formula: x _n-1(t)-x_n(t)=gap_n(t)+l_n-1;x_n-1 (t) represents the position of the vehicle with the number n-1 at the time t, namely, the front vehicle of the same lane, gap _n (t) represents the distance between the vehicle with the number n at the time t and the front vehicle of the same lane, and l _n-1 represents the length of the vehicle with the number n-1, namely, the front vehicle of the same lane.

In this embodiment, the rule of changing the road needs to be formulated by considering why the vehicle needs to change the road, whether the current road section can change the road, and how the vehicle changes the road; therefore, the establishment of the channel changing rule mainly comprises the determination of a channel changing machine, channel changing conditions and channel changing tracks.

For the generation of a lane-change engine, there are mainly two common scenarios as follows.

Scene one: under the condition that the running speed does not exceed the current road section speed limit and the vehicle speed limit, the road changing machine is generated when the distance between the vehicle and the front vehicle is not satisfied to maintain the current vehicle speed. For easy understanding, a judgment formula for whether the lane change machine generated by the vehicle accords with the first scene is as follows; and when the judging formula is established, the road changing machine generated by the vehicle is in accordance with the first scene.

。

Wherein gap _{n,lane_begin} (t) represents the distance between the vehicle with the number n at the time point t and the front vehicle of the same lane at the beginning of the time step.

Scene II: under the conditions of the position of a split junction or the local road closure caused by bridge road maintenance construction, the current vehicles cannot pass through and are forced to generate a road changing machine. For convenience of understanding, a judgment formula for whether the lane changing machine generated by the vehicle accords with the second scene is as follows; and when the judging formula is established, the road changing machine generated by the vehicle is in accordance with the second scene.

gap_{n,lane_begin}(t)≤gap_limit(t)。

。

The gap _limit (t) represents the distance of the vehicle to start decelerating at the time t, and a specific value can be set according to actual needs, for example, an initial value can be set to be 100m; length _m represents road segment mileage with reduced number of available tracks; The reaction time for braking the vehicle numbered n is indicated, Is equal to the value of；The maximum braking acceleration of the vehicle model to which the vehicle numbered n belongs is indicated.

In this embodiment, after the vehicle has completed judging the lane changing machine, it is necessary to judge the lane changing condition of the vehicle according to the number of lanes of the current road section and the traffic conditions of all lanes, and then trigger the lane changing, deceleration or following rule of the vehicle according to the judged lane changing condition, for whether the vehicle can change lanes. And for the vehicle needing lane changing, performing lane changing running according to the set lane changing track. For ease of understanding, the following describes in detail the lane change situation of the vehicle according to different lane change motivation generation scenarios.

1. Based on the first scenario of the lane changing machine, the vehicle mainly comprises three lane changing conditions as shown below.

Case one: when the corresponding road section has only one lane, no lane change condition exists, so that the vehicle triggers the speed reduction rule when the lane change machine is generated.

And a second case: when two lanes exist in the corresponding road section, if the vehicle meets the lane changing condition, lane changing is carried out, otherwise, a speed reduction or following rule is triggered according to the situation. The following judgment formulas for the lane change conditions of the vehicle are shown, and the vehicle performs lane change when the following judgment formulas are all established.

gap_{n,lane_end}(t)＞gap_{n,lane_begin}(t)。

gap_{n+1,lane_end}(t)＞v_{n+1,lane_end}(t)+a_{n+1,lane_end}(t+1)+gap_n+1,safe(t)。

。

rand(0，1)≤p_{changelane_m}。

Wherein gap _{n,lane_begin} (t) and gap _{n,lane_end} (t) respectively represent the distance between the vehicle with the number n at the moment t and the front vehicle of the same lane when the time step starts and ends; gap _{n+1,lane_end} (t) represents the distance between the vehicle numbered n+1 at time t, i.e., the vehicle in the adjacent lane that is in close proximity to the preceding vehicle in the same lane at the end of the time step. v _{n+1,lane_end} (t) represents the speed of the vehicle with the number n+1 at the time t on the lane where the time step is finished; a _{n+1,lane_end} (t+1) represents acceleration of the vehicle numbered n+1 at time t+1 on the lane where the time step ends; gap _n+1,safe (t) represents the safe distance between vehicles numbered n+1 at time t; represents the vehicle braking response time numbered n +1, AndRepresenting the maximum braking acceleration of the vehicle model of the vehicles with the numbers of n and n+1; rand (0, 1) represents a random function, and in particular, may represent a probability of generating a random number within a range of (0, 1), and the working principle of the rand random function is well known to those skilled in the art, and thus is not described in detail herein.

It should be noted that, the specific value of the lane change probability p _{changelane_m} in the above-mentioned judgment formula may be determined according to the lane change direction required by the vehicle. In general, the vehicle traveling speed in the right lane is low, and the vehicle is biased to the left lane to make a lane change and overtake when traveling on the actual road. Therefore, in the present embodiment, if the vehicle is located on the right lane to change lanes leftwards, the initial value of the lane change probability p _{changelane_m} may be set to 0.7; if the vehicle is on the left lane for a lane change to the right, the initial value of the lane change probability p _{changelane_m} may be set to 0.3.

And a third case: when more than two lanes exist in the corresponding road section, if the vehicle meets the lane changing condition, lane changing is carried out, otherwise, a speed reduction or following rule is triggered according to the situation. It should be noted that, based on the current situation three, there are three situations of the lane position where the vehicle is located: firstly, the vehicle is positioned on a left lane, namely the leftmost side of a road section, and the vehicle can only change lanes to the right if the lane change is required; secondly, the vehicle is positioned on a right lane, namely the rightmost side of the road section, and the vehicle can only change lanes leftwards if the vehicle needs to change lanes; thirdly, the vehicle is positioned in the middle lane, and the vehicle can change lanes on the left side and the right side if the lane change is required. For the convenience of understanding, the lane changing process of the vehicle will be described in detail below according to the position of the vehicle.

(1) The vehicle is located in the left lane, and the judgment formula for the lane change condition of the vehicle is as follows.

gap_n,right(t)＞gap_n,left(t)。

gap_n+1,right(t)＞v_n+1,right(t)+a_n+1,right(t+1)+gap_n+1,safe(t)。

rand(0，1)≤p_{changelane_m}。

When the above-mentioned judging formulas are all established, it can be judged that the right lane of the vehicle has overtaking condition, and then the vehicle can make right lane change. Wherein gap _n,left (t) and gap _n,right (t) respectively represent the following distances of vehicles with the number n at the time t on left and right lanes; gap _n+1,right (t) represents the following distance of the vehicle numbered n+1 on the right lane; v _n+1,right (t) represents the speed of the vehicle numbered n+1 at time t on the right lane; a _n+1,right (t+1) represents acceleration of the vehicle numbered n+1 at time t+1 on the right lane; gap _n+1,safe (t) represents the safe distance between vehicles numbered n+1 at time t; the initial value of the lane change probability p _{changelane_m} in the current scenario is 0.3.

(2) The vehicle is located in the right lane, and the judgment formula for the lane change condition of the vehicle is as follows.

gap_n,right(t)＜gap_n,left(t)。

gap_n+1,left(t)＞v_n+1,left(t)+a_n+1,left(t+1)+gap_n+1,safe(t)。

rand(0，1)≤p_{changelane_m}。

When the above-mentioned judging formulas are all established, it can be judged that the left lane of the vehicle has overtaking condition, and then the vehicle can make left lane change. Wherein gap _n,left (t) and gap _n,right (t) respectively represent the following distances of vehicles with the number n at the time t on left and right lanes; gap _n+1,left (t) represents the inter-vehicle distance of the vehicle numbered n+1 on the left lane; v _n+1,left (t) represents the speed of the vehicle numbered n+1 at time t on the left lane; a _n+1,left (t+1) represents acceleration of the vehicle numbered n+1 at time t+1 on the left lane; gap _n+1,safe (t) represents the safe distance between vehicles numbered n+1 at time t; the channel change probability p _{changelane_m} is 0.3 at the initial value in the current scenario.

(3) The vehicle is located in the middle lane, and there are three possibilities for lane changing of the vehicle, and the three possibilities will be described in detail below.

(3.1) The left lane of the vehicle has an overtaking condition, and the right lane does not have an overtaking condition, so the vehicle changes lanes to the left when changing lanes, and a specific judgment formula is shown as follows.

gap_n,left(t)＞gap_n,mid(t)&gap_n,right(t)＜gap_n,mid(t)。

gap_n+1,left(t)＞v_n+1,left(t)+a_n+1,left(t+1)+gap_n+1,safe(t)。

rand(0，1)≤p_{changelane_left}。

Wherein gap _n,mid (t) represents the following distance of the vehicle with the number n at the moment t in the middle lane; p _{changelane_left} represents the probability of the vehicle lane change to the left, and a specific value can be determined by self according to actual needs, for example, an initial value can be set to be 0.7.

(3.2) The right lane of the vehicle has an overtaking condition, and the left lane does not have an overtaking condition, so the vehicle changes lanes to the right when changing lanes, and a specific judgment formula is shown as follows.

gap_n,left(t)＜gap_n,mid(t)&gap_n,right(t)＞gap_n,mid(t)。

gap_n+1,right(t)＞v_n+1,right(t)+a_n+1,right(t+1)+gap_n+1,safe(t)。

rand(0，1)≤p_{changelane_right}。

Wherein, p _{changelane_right} represents the probability of the vehicle lane change to the right, and the specific value can be determined by self according to the actual requirement, for example, the initial value can be set to be 0.3.

(3.3) The left and right lanes of the vehicle are provided with overtaking conditions, and a specific judgment formula is shown as follows.

gap_n,left(t)＞gap_n,mid(t)&gap_n,right(t)＞gap_n,mid(t)。

gap_n+1,left(t)＞v_n+1,left(t)+a_n+1,left(t+1)+gap_n+1,safe(t)。

gap_n+1,right(t)＞v_n+1,right(t)+a_n+1,right(t+1)+gap_n+1,safe(t)。

Under the condition that the three formulas are established, if the random function rand (0, 1) meets rand (0, 1) and p _{changelane_left} is less than or equal to, the vehicle changes lanes leftwards; if the random function rand (0, 1) meets rand (0, 1) not less than p _{changelane_right}, the vehicle changes lanes to the right; if the random function rand (0, 1) satisfies p _{changelane_left}＜rand(0,1)＜p_{changelane_right}, the vehicle will be decelerating and following. The specific values of the lane change probabilities p _{changelane_left} and p _{changelane_right} can be determined according to actual needs, for example, the initial value of p _{changelane_left} can be set to be 0.4, and the initial value of p _{changelane_right} can be set to be 0.7.

2. Scene two based on the channel changing machine; at the position of the split and merging openings, vehicles are generally merged leftwards or rightwards; at a local road closing position caused by bridge road maintenance construction, the left side or the right side of a road is generally closed, so that vehicles need to change lanes to the right or the left.

Based on the analysis, the lane changing scene of the vehicle in the second scene is similar to the lane changing scene of the double lanes in the first scene, so that the judging process of whether the adjacent lanes have the lane changing condition in the second scene can refer to the lane changing condition of the double lanes in the first scene; however, the difference is that the judgment of the distance between vehicles of the current lane and the lane change probability of 1 are not needed in the present scene, and the specific judgment formula of the lane change condition is shown as follows.

gap_{n+1,lane_end}(t)＞gap_n+1,safe(t)。

。

If the above-mentioned judging formula is established, the vehicle immediately changes lanes, otherwise, the vehicle is decelerated and stopped until the following lane changing conditions are met, and lane changing is performed.

。

It can be understood that the traffic of the road under the second scene is relatively congested, namely the traffic flow is relatively large, and at the moment, the situation that the rear vehicle changes the road in advance of the front vehicle may exist; therefore, when the vehicle changes lanes, the following yield conditions are provided. At the moment, the vehicle entering the lane can be decelerated in advance so as to give a parking space to provide a lane changing condition for the vehicle in front of the adjacent lane; otherwise, the vehicle normally passes through, and the next vehicle is decelerated to give the parking space. The determination of the yield condition and the formula of early deceleration are as follows.

Judging formula of yield condition:

v_n(t)+0.5a_n(t+1)-[v_{n+1,lane_end}(t)+ 0.5a_{n+1,lane_end}(t)]≥gap_n+1,safe(t)。

。

formula for early deceleration: v _{n+1,lane_end}(t+1)= v_{n+1,lane_end}(t)+ a_{n+1,lane_end} (t+1).

Wherein v _{n+1,lane_end} (t) and a _{n+1,lane_end} (t) respectively represent the speed and acceleration of the vehicle with the time n+1 at the end of the time step, gap _n+1,safe (t) represents the safe distance between the vehicle with the time n+1 at the time t, gap _{n+1,lane_end} (t) represents the distance between the vehicle with the time n+1 at the time t and the same lane front vehicle at the end of the time step,The maximum braking acceleration of the vehicle model to which the vehicle numbered n belongs is indicated.

In this embodiment, when the vehicle changes lanes, an initial position coordinate (x _sp,y_sp) of the lane-changing vehicle may be defined, the initial speed is v _sp, the initial acceleration is a _sp, and the initial heading angle of the vehicle body is θ _sp. After the lane change of the vehicle is finished, the position coordinate of the vehicle which finishes the lane change can be defined as (x _ep,y_ep), the speed at the time of the lane change is v _ep, the acceleration at the time of the lane change is a _ep, and the heading angle of the vehicle body at the time of the lane change is theta _ep. Based on the above parameters, there are various expressions of the track equation of the vehicle during the lane change, and in this embodiment, it is preferable to use a fifth order polynomial to represent the real-time track of the vehicle during the lane change, and the specific expression of the real-time track [ x (t), y (t) ] is as follows.

x(t)=A₅t⁵+A₄t⁴+A₃t³+A₂t²+A₁t+A₀.

y(t)= B₅t⁵+B₄t⁴+B₃t³+B₂t²+B₁t+B₀.

A₀=x_sp;A₁=v_sp·cosθ_sp;A₂=0.5a_sp·cosθ_sp。

。

B₀=y_sp;B₁=v_sp·sinθ_sp;B₂=0.5a_sp·sinθ_sp。

。

Wherein x (t) represents the distance that the vehicle moves forward along the driving direction in the lane changing process; y (t) represents the translation distance of the vehicle in the direction of entering the lane in the lane change process; a and B represent constant terms of each of the polynomials; t _f represents the lane change duration of the vehicle, and the specific value can be calculated according to the vehicle speed, for example。

It will be appreciated that in order to ensure that the lane change process of the vehicle proceeds smoothly, it is necessary to define the position and shape changes of the vehicle before and after lane change. The distance of the vehicle in the traveling direction is limited, and the limiting value is related to the traveling speed of the vehicle and can be set. The translation distance of the vehicle in the direction of entering the lane is defined, which is related to the width of the lane, y _ep-y_sp = 3.75 can be set. The vehicles can be restricted to change lanes at a constant speed, and the vehicles before and after changing lanes are in a direction parallel to the driving direction of the lane, and v _sp= v_ep,a_sp= a_ep=0,θ_sp= θ_ep = 0.

In this embodiment, the following rule formulation of the vehicle includes two scenarios; scene one: the lane where the vehicle runs has no front vehicle or the distance between the front vehicles is far, so that the vehicle does not need to change lanes at the moment, namely the vehicle follows; scene II: the distance between vehicles in front of the current driving lane is relatively short and no lane change is performed, and at the moment, the rear vehicles, namely the current vehicles, do not meet the lane change condition, so that the speed reduction and the following are required for safety consideration. The judgment formulas for the above two scenes are as follows.

。

Wherein gap _n (t+1) represents the distance between the vehicle with the number n at the time of t+1 and the front vehicle on the same lane; r represents the ratio of the distance between the front vehicle and the safe vehicle distance during acceleration, and the specific value can be determined by self according to actual needs, for example, the initial value can be set to be 4.

(1) If the judging formula is true, the vehicle is in the following scene I; at this time, the vehicle tends to travel at a high speed, and the driving simulation rules thereof are as follows.

。

It will be appreciated that the following optimization of the driving simulation rules may be performed in view of the random slowing of the vehicle during driving.

。

Wherein, rand (-0.2, 0) and rand (0, 1) each represent a random function, rand (-0.2, 0) specifically represents a probability that a random number is within a range (-0.2, 0), and rand (0, 1) specifically represents a probability that a random number is within a range (0, 1).

(2) If the judging formula is not established, the vehicle is in the following scene II; the driving simulation rules of the vehicle at this time are as follows.

。

When the front inter-vehicle distance is larger than the safe inter-vehicle distance, the vehicle may be appropriately decelerated, and the rule of the acceleration variation with the front inter-vehicle distance is as follows.

。

Wherein gap _n (t) represents the distance between the vehicle with the number n at the time t and the front vehicle on the same lane; gap _n,safe (t) represents the safe distance of the vehicle with the number n at the time t; rand (-1/2, 1/2) represents a random function, and rand (-1/2, 1/2) specifically represents the probability that a random number is within a range (-1/2, 1/2).

When the front inter-vehicle distance is smaller than or equal to the safe inter-vehicle distance, the vehicle needs to be braked urgently, and the rule that the acceleration changes along with the front inter-vehicle distance is as follows.

。

In this embodiment, as can be seen from step S400, there are mainly two loading modes in calculating the load effect of the road section based on the effect of the vehicle, one is the vehicle weight loading and the other is the axle weight loading. It should be appreciated that conventional approaches are generally based on the calculation of the loading effect in the heavy loading mode, but the weight of the vehicle is transferred to the road surface through the wheels, and the number and spacing of the front wheels of the vehicle are represented by the spacing and number of axles. Because the axle distance and the axle number are different, the influence degree of the same vehicle weight on the road is different, so in the embodiment, in order to improve the accuracy of the load effect calculation, the load effect calculation of the effect calculation road section adopts a vehicle weight and axle weight dual loading mode for calculation. For ease of understanding, the specific calculation process for the two loading modes will be described in detail below.

Specifically, under the condition of vehicle weight loading, the calculation formula of the load effect E _i of the effect calculation road section EC _i is as follows: e _i=ζG_i (t) IL. Wherein ζ is a correction parameter matrix, G _i (t) is a vehicle weight matrix, which represents the total weight of all vehicles within the EC _i range at time t, and IL represents an influence line matrix. Based on the axle load loading condition, the calculation formula of the load effect E _i of the effect calculation road section EC _i is as follows: e _i=g_i (t) IL; wherein g _i is an axle weight matrix, representing the total weight of all axles within the range of EC _i at time t.

It is understood that, in performing calculation based on the load effect of the same vehicle, the calculation results of the vehicle weight loading mode and the axle weight loading mode theoretically correspond to each other; in practice, however, there may be vehicles for which the axle weight cannot be obtained accurately, and for such vehicles, only the calculation of the load effect in the vehicle weight mode can be performed and the vehicle load reduction coefficient, i.e. the correction parameter matrix ζ, is introduced. By introducing the vehicle load reduction coefficient, the vehicle load effect can be calculated more accurately; aiming at the situation that the axle load of the vehicle cannot be accurately measured, the load effect is calculated by adopting the mode of vehicle load reduction coefficient multiplied by vehicle weight multiplied by influence line, and a more accurate load effect simulation result can be obtained. The vehicle load reduction coefficient is used for reducing the load effect of the vehicle weight loading, the element value is the ratio of the effect calculated value of the vehicle weight influence line loading to the effect calculated value of the axle weight influence line loading, and the ratio can be obtained through regression of the finite element simulation result.

In the embodiment, the model simulation parameters can reflect the change of traffic flow in real time by establishing the parameter automatic updating mechanism, so that the simulation accuracy and the simulation practicability are improved. The automatic updating mode of the parameters is various, and the method is preferably realized by adopting a single-variable Bayesian Dynamic Linear Model (BDLM) based on real-time traffic data, and the model can dynamically adjust simulation parameters to match actual traffic flow states, so that the difference between simulation and actual conditions is reduced.

It is understood that the manner of parameter updating using a single variable bayesian dynamic linear model is conventional in the art, and the specific operation and principles thereof are well known to those skilled in the art and will not be described in detail herein. Of course, for ease of understanding, the observation equations and state equations of the univariate bayesian dynamic linear model can be described simply.

Observation equation: y _T=μ_T+v_T,v_T~N[0,V_T ].

The equation of state: θ _T=[μ_T,β_T ].

μ_T=μ_T-1+β_T-1+w_T1。

β_T=β_T-1+w_T2。

w_T=(w_T1,w_T2)^T,w_T~N[0,W_T]。

Wherein y _T represents a correction value of a vehicle operation parameter after T times of updating, and the operation parameter mainly comprises random slowing probability, lane changing probability, vehicle starting deceleration distance, lane changing starting end point distance, lane changing time, braking reaction time of different vehicle types and the like of different road sections; θ _T represents an unknown parameter describing the state of the process at T updates; mu _T represents the mean of observations up to T updates; beta _T-1 and beta _T represent normal random variables at T-1 and T updates, respectively, V _T and W _T each represent zero-mean normal random variables at T updates, and V _T and W _T each represent variances at T updates.

The foregoing has outlined the basic principles, features, and advantages of the present application. It will be understood by those skilled in the art that the present application is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present application, and various changes and modifications may be made therein without departing from the spirit and scope of the application, which is defined by the appended claims. The scope of the application is defined by the appended claims and equivalents thereof.

Claims

1. The vehicle load effect simulation method is characterized by comprising the following steps of:

S100: constructing a cellular model A _n = { P, L, M, W }, and configuring road network traffic conditions and traffic simulation parameters;

S500: under the condition that simulation data and measured data are not matched, taking an initial value of a cell model as a priori condition, taking the measured data as observation data, and adopting a single-variable Bayesian dynamic linear model to update and re-simulate the cell model in real time;

2. The vehicle loading effect simulation method of claim 1, wherein the cellular operational parameters are a spatiotemporal matrix; Wherein, type represents a vehicle type number,Indicating the maximum speed limit of the current lane,Represents the maximum speed of the current vehicle model,Represents the maximum acceleration of the current vehicle model,Representing the maximum braking acceleration of the current vehicle type, and l _n represents the length of the vehicle;

3. The vehicle load effect simulation method according to claim 2, wherein the road network comprises a plurality of different road segments, and traffic conditions and traffic simulation parameters for the different road segments are represented by an environment variable matrix E _m;；

Wherein m represents a road segment number, length represents a [1×m ] road segment length matrix, lane_num represents a [1×m ] road segment lane number matrix, v ^max represents a [1×m ] road segment maximum speed limit matrix, P _rsd represents a m road segment different lane random slowing probability, The braking response time of different vehicle types is represented, p _{changelane_m} represents the lane change probability of an m-section, EC _i represents the effect calculation section of an i-th section, and mode represents the vehicle load application mode.

4. A vehicle load effect simulation method according to claim 3, wherein the formulation of the lane change rule includes determination of a lane change machine, a lane change condition and a lane change trajectory; the scenario for generating a lane change motivation is as follows:

firstly, under the condition that the running speed does not exceed the current road section speed limit and the vehicle speed limit, generating a lane changing machine when the distance between the vehicle and the front vehicle is not satisfied with the running of maintaining the current vehicle speed;

secondly, a road changing machine is generated when the position of the split joint opening or the maintenance construction of the bridge road causes the local road to be closed;

Judging the lane changing conditions of the vehicle according to the number of lanes and the traffic conditions of all lanes based on the scenes generated by the lane changing machine, and triggering lane changing, decelerating or following rules of the vehicle according to the lane changing conditions;

And for the vehicle needing lane changing, performing lane changing running according to the set lane changing track.

5. The vehicle load effect simulation method according to claim 4, wherein, based on the scene one of the lane change machine, when there are two lanes, if the vehicle meets the lane change condition, lane change is performed, otherwise a speed reduction or following rule is triggered optionally; the lane change conditions of the vehicle are as follows:

gap_{n,lane_end}(t)＞gap_{n,lane_begin}(t)；

；

rand(0，1)≤p_{changelane_m}；

6. The vehicle load effect simulation method according to claim 4, wherein if more than two lanes exist based on the first scene of the lane changing machine, lane changing is performed if the vehicle meets the lane changing condition, otherwise a speed reduction or following rule is triggered according to the situation; the lane change judgment formula of the vehicle is as follows:

gap_n,right(t)＞gap_n,left(t)（1）；

gap_n,right(t)＜gap_n,left(t)（2）；

gap_n,left(t)＞gap_n,mid(t)&gap_n,right(t)＜gap_n,mid(t)（3）；

gap_n,left(t)＜gap_n,mid(t)&gap_n,right(t)＞gap_n,mid(t)（4）；

gap_n,left(t)＞gap_n,mid(t)&gap_n,right(t)＞gap_n,mid(t)（5）；

gap_n+1,right(t)＞v_n+1,right(t)+a_n+1,right(t+1)+gap_n+1,safe(t)（7）；

gap_n+1,left(t)＞v_n+1,left(t)+a_n+1,left(t+1)+gap_n+1,safe(t)（8）；

rand(0，1)≤p_{changelane_m}（9）；

rand(0，1)≤p_{changelane_left}（10）；

rand(0，1)≤p_{changelane_right}（11）；

p_{changelane_left}＜rand(0,1)＜p_{changelane_right}（12）；

rand(0，1)≥p_{changelane_right}（13）；

if the formulas (1) and (7) are established, judging that the vehicle is positioned on the left lane and the right lane has overtaking conditions, and then switching lanes to the right when the formula (9) is established;

If the formulas (2) and (8) are established, judging that the vehicle is positioned on the right lane and the left lane has overtaking conditions, and then switching lanes to the left when the formula (9) is established;

if the formulas (3) and (8) are established, judging that the vehicle is positioned in the middle lane and the left lane has an overtaking condition, and then switching lanes to the left when the formula (10) is established;

if the formulas (4) and (7) are established, judging that the vehicle is positioned in the middle lane and the right lane has overtaking conditions, and then switching lanes to the right when the formula (11) is established;

If the formulas (5), (7) and (8) are met, judging that the vehicle is positioned in the middle lane and the left lane and the right lane are provided with overtaking adjustment, and then respectively carrying out left lane changing, deceleration following and right lane changing on the vehicle according to the conditions of the formulas (10), (12) and (13);

7. The vehicle load effect simulation method according to claim 5, wherein based on the scene two of the lane change machine, judging whether the adjacent lane has the lane change condition of the double lanes in the scene one of the lane change machine, if yes, immediately changing lanes, otherwise, decelerating and stopping until the following lane change condition is met;

；

When the vehicle changes lanes, if the yielding conditions are met, the vehicle entering the lane will decelerate in advance to yield a parking space; otherwise, the vehicle normally passes through, and the next vehicle is decelerated to give a parking space; the determination of the yield condition and the formula of the early deceleration are as follows:

；

v_{n+1,lane_end}(t+1)= v_{n+1,lane_end}(t)+ a_{n+1,lane_end}(t+1)；

8. The vehicle load effect simulation method according to claim 4, wherein when the vehicle changes lanes, the real-time trajectory of the vehicle is simulated by using a five-degree polynomial, and the specific real-time trajectory [ x (t), y (t) ] is expressed as follows:

x(t)=A₅t⁵+A₄t⁴+A₃t³+A₂t²+A₁t+A₀;

y(t)= B₅t⁵+B₄t⁴+B₃t³+B₂t²+B₁t+B₀;

A₀=x_sp;A₁=v_sp·cosθ_sp;A₂=0.5a_sp·cosθ_sp；

；

B₀=y_sp;B₁=v_sp·sinθ_sp;B₂=0.5a_sp·sinθ_sp；

；

9. The vehicle load effect simulation method according to any one of claims 3 to 8, wherein the following rule formulation includes two scenarios, scenario one: no front vehicle or a far distance from the front vehicle; scene II: the front vehicles are closer in distance and do not change lanes; the judgment formula of the scene is as follows:

；

10. The vehicle load effect simulation method according to claim 1, wherein the calculation formula of the load effect E _i of the effect calculation section EC _i under the vehicle weight loading condition is: e _i=ζG_i (t) IL;

based on the axle load loading condition, the calculation formula of the load effect E _i of the effect calculation road section EC _i is as follows: e _i=g_i (t) IL;

wherein ζ is a correction parameter matrix; g _i (t) is a weight matrix representing the total weight of all vehicles within the range of EC _i at time t; IL represents an influence line matrix; g _i is the axle weight matrix, representing the total weight of all axles in the range of EC _i at time t.