CN109765801A - The implementation method of car networking desin speed adjustment based on VISSIM emulation - Google Patents
The implementation method of car networking desin speed adjustment based on VISSIM emulation Download PDFInfo
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Abstract
The invention discloses a kind of implementation methods of car networking desin speed adjustment based on VISSIM emulation, urban traffic network model is constructed using VISSIM, optimal velocity model is established in MATLAB with the speed-optimization decision under simulating vehicle networking situation, result is imported in VISSIM again and carries out real-time control, realize the visual simulating of two kinds of road models of vehicle failed cluster and connected state, finally output is assessed data and is compared, to be adjusted to highway layout speed.Example analysis results of the invention show: by the optimal velocity model established in MATLAB, the driving condition under simultaneously simulating vehicle networking can be controlled in VISSIM, obtains the data such as running speed, travel time, the delay time at stop of vehicle.The present invention controls the road model in VISSIM by the car speed Optimal Decision-making established in MATLAB, provides the implementation method of vehicle networked lower desin speed adjustment.
Description
Technical field
The present invention relates to a kind of implementation methods of car networking desin speed adjustment based on VISSIM emulation, belong to nobody and drive
The technical field of road design driven off.
Background technique
Urban traffic demand constantly increases, and how more efficient convenient and fast trip becomes traffic engineer and urban planner urgently
The appearance of problem to be solved, automatic Pilot technology brings new dawn to urban transportation, it can not only liberate driver's
Both hands, moreover it is possible to make accurate path decision with communication system by positioning in real time, keep traffic trip safer quick, greatly
The current rate of traffic and road utilization rate are improved greatly.
Due to the limitation of various aspects factor, automatic Pilot technology also fails to widely available on urban road, and reason is main
Have at following 3 points.Firstly, there are also the very big rising spaces for AI artificial intelligence technology relevant to automatic Pilot, in many of foreign countries
In actual road test, there is also such as sensor accuracies not high enough, path decision not enough rapidly, the inadequate section of emergency processing scheme
Learn the problems such as effective；Secondly, this technology of automatic Pilot will be popularized, it is necessary to have road infrastructure adaptable therewith, such as
The improvement of public traffic station, road confluence, the improvement of vertical and horizontal section, the improvement of highway layout speed etc.；Third is being worked as
Under social background under, people also hold the attitude of suspection to the security performance of automatic driving vehicle, this is also that this technology is wide
General universal one of hindering factor.
Automatic driving vehicle can exchange information in real time under connected state, such as the speed, acceleration of neighbouring vehicle, position
It sets, lane change trend, direction, destination etc., with the trend of this predicting traffic flow, completes the traveling decision of vehicle and real-time
Control.Under the double action of vehicle real-time interconnection and the accurate decision of computer, vehicle can be with most safe and efficient and comfortable side
Formula traveling also improves the average overall travel speed of vehicle to a certain extent while improving traffic passage rate.Herein with road
Roadbed Infrastructure is improved to point of penetration, studies the vehicle networked technology pair under the overall background that automatic Pilot technology will be popularized
The influence of Vehicle Speed, to advise to highway layout speed.
In conclusion the information real-time exchange under vehicle networked state is to influence automatic Pilot in automatic Pilot state
The principal element of vehicle operation action studies the running speed under car networking state, has weight to the determination of highway layout speed
The meaning wanted.
Summary of the invention
Technical problem: the technical issues of present invention faces include:
(1) in the first stage the operation comparative analysis of car networking state and normal condition when, should be arranged which control variable,
What kind of strategy is formulated in order to carry out carry out comprehensive assessment to driving states using emulation platform, and obtains the change of parameters
Change trend and influence factor；
(2) how using VISSIM come the trip information of the vehicle in capture region, and set it is some constraint with more
Truly transport need of the simulating vehicle in different sections of highway；
(3) the optimal design speed of every kind of setting situation, the selection of assessment factor how are found out using the tool box MATLAB
And the problems such as weight.
Technical solution: to achieve the above object: the invention adopts the following technical scheme:
A kind of implementation method of the car networking desin speed adjustment based on VISSIM emulation, comprising the following steps:
1) the wagon flow situation for combining road under normal driving state, establishes standard in VISSIM traffic simulation modeling software
Road model；
2) the standard road model based on foundation establishes the maximized speed-optimization mould of vehicle general speed using MATLAB
Type, vehicle real-time exchange information and the automatic Pilot behavior travelled with optimizing decision under simulating vehicle connected state；
3) vehicle data that VISSIM is detected and exported using C++ regulates and controls MATLAB using Excel as primary control program
Data between VISSIM are output and input；
4) vehicle networked and unconnected state acquisition point data is compared, obtains vehicle networked lower desin speed
Adjustment.
Further, in the step 1), the section of road is divided into data field, redirection zone and shunting zone, default drives
Using Wiedemann99 car-following model, the road model of standard is established in VISSIM traffic simulation modeling software, and vehicle is set
Parameters, the simulation run such as input and path decision simultaneously export evaluation file.
Further, in the step 2), when using the maximized optimal velocity model of general speed as vehicle networked shape
Speed decision model is realized that speed decision model is specific as follows using the fmincon algorithm of MATLAB:
In above formula, formula (1) is objective function, and formula (2) is the constraint function to car speed, and formula (3) indicates same
The minimum range of each car cannot be greater than specified value in one time, and minimum range is arranged to 10 meters, i.e. G heremin=10m；It is public
Formula (4) is the constraint function to acceleration, and formula (5) describes the functional relation between speed, acceleration and distance, i in formula
For the car number on lane；N is the vehicle fleet on lane；T is each time step, indicates which second；vi,tFor a certain second
The speed of a certain vehicle；V_max is speed maximum value；GminFor the distance between adjacent two cars minimum value, xi,tFor certain two vehicle
The position of a certain second；A_min is acceleration minimum value；A_max is acceleration maximum value；ai,tFor adding for a certain vehicle of a certain second
Speed.
Further, in the step 3), on the basis of the standard road model of building, for the speed-optimization mould of foundation
Type reads the traffic data just in simulation run by com interface, with it is per second be a time step, read data field vehicle
The information such as speed, acceleration, position, through a C++ program storage into Excel, using Excel as interlude, as
The terminal of VISSIM and MATLAB data transmission, by Excel link interface Calling MATLAB, by the emulation in VISSIM
Data are passed in MATLAB.MATLAB optimizes the speed received, and returns and export decision value per second, that is, accelerates
Degree, is stored into Excel by Excel link, reads the Optimal Decision-making in Excel by COM excuse, and utilize com interface
It is docked with Excel, is controlled using the interior verification vehicle of C++ intervention model.
Further, in the step 4), according to step 3) output as a result, drawing each under car networking and unconnected state
The average speed of collection point, acceleration relational graph, obtain it is vehicle networked under desin speed adjustment result.
The utility model has the advantages that the present invention is compared with prior art: the present invention is adjusted to unmanned lower desin speed and is provided
One platform and thinking are able to solve existing design velocity gauge and are not fully appropriate for unmanned problem.It is of the invention public
The implementation method for the desin speed adjustment opened can quantitatively be adjusted the desin speed under unmanned, more scientific
And practicability.
Detailed description of the invention
Fig. 1 is method flow diagram provided by the invention；
Fig. 2 is road model schematic diagram；
Fig. 3 is the velocity contrast of connected state and unconnected state collection point；
Fig. 4 is the acceleration comparison of connected state and unconnected state collection point；
Fig. 5 be under connected state and unconnected state each period be averaged travel time；
Fig. 6 is each period mean delay time under connected state and unconnected state.
Specific embodiment
Further explanation is done to the present invention with reference to the accompanying drawing.
As shown in Figure 1, a kind of implementation method of the car networking desin speed adjustment based on VISSIM emulation of the present invention, mainly
The following steps are included:
(1) road simulation models
For the wagon flow situation of road under simulation normal driving state, more pervasive and typical urban road trunk roads are chosen
It is simulated, the road model of standard is established in VISSIM traffic simulation modeling software, setting vehicle inputs and path decision
Etc. parameters, simulation run simultaneously export evaluation file, the information such as output speed.
By taking the unidirectional two lanes trunk roads in city as an example, main stem overall length is 520 meters, and lane width is 3.75 meters, does not set centre
Band is equipped with the bilateral shunting in north and south, and desin speed 50km/h, Maximum speed limit 70km/h are about 20m/s after conversion, therefore speed
Optimized model is using 20m/s as most high speed angle value.Road model is as shown in Figure 2.
0 to 200 meter of road model is data acquisition control area for region 1, and the speed of all vehicles in this region accelerates
The information such as degree, position will be collected and be input in MATLAB, and MATLAB is calculated and found out optimal vehicle control strategy, then is passed back
VISSIM carries out real-time control to vehicle.Since within a data area, the number that vehicle lane change will lead to vehicle changes, unfavorable
In data acquisition and return control, therefore assume vehicle during by way of region 1 can not lane change, with solid line table in Fig. 2
Show.
200 to 400 meters are diversion of traffic area for region 2, since Some vehicles will be shunted in C point, are on the one hand considered
To influence of the acceleration to shunting of data field, lane change area is unsuitable too long；It is shunted from the aspect of two and is possible to bring traffic congestion
And delay, lane change area are unsuitable too short.Therefore by lane change area be set as with data field equal length, to neutralize two above factor
It influences.
400 to 520 meters are shunting zone, and vehicle will be shunted herein with the ratio of 6:2:2, this region allows vehicle with one
Cut rational method normally travel.
(2) optimal velocity model is established
Based on built road model, it is assumed that vehicle data field be automatic driving mode, and can not lane change, gone out data field
It is switched to the driving mode traveling of VISSIM default afterwards.The vehicle of data field in the process of moving, position, speed, acceleration
Etc. information be output in MATLAB, MATLAB carries out decision to the acceleration of vehicle according to following optimal velocity model, and
It returns to VISSIM and carries out real-time control, by this method vehicle real-time exchange information and with optimizing decision under simulating vehicle connected state
The automatic Pilot behavior of traveling.Optimal control policy is formulated to nonlinear optimal problem, formula (1) strategy of speed control thus
Objective function so that the speed total value of all vehicles is maximum on lane.
This model also uses restraint to parameters such as the speed, acceleration of vehicle, minimum ranges, and formula (2) is to vehicle speed
The constraint of degree, the speed of each car is no more than maximum value.Formula (3) is that the minimum range of each car in the same time cannot be big
In specified value, minimum range is arranged to 10 meters, i.e. G heremin=10m.Formula (4) is the constraint to acceleration, and acceleration is
The decision variable of model, and return to the output valve of control, here a_min=-2m/s2, a_max=2m/s2.Formula (5) description
Relationship between speed, acceleration and distance, acceleration is derivative of the speed relative to the time, and speed is operating range phase
For the derivative of time.
I is the car number on lane in formula；N is the vehicle fleet on lane；T is each time step, which is indicated
Second；vi,tFor the speed of a certain vehicle of a certain second；V_max is speed maximum value；GminFor the distance between adjacent two cars minimum
Value, xi,tFor the position of certain two vehicles a certain second；A_min is acceleration minimum value；A_max is acceleration maximum value；ai,tIt is a certain
The acceleration of a certain vehicle of second.
This model, for a judgement interval, was further divided into 10 1 second decision step-lengths, in each 1 second step with 10 seconds
When long beginning, the speed for each car collected, location information are passed in MATLAB by VISSIM, and seismic responses calculated is optimal out
It returns to VISSIM after control strategy to be controlled, this decision parameters is the acceleration a of each cari,t, by optimizing these acceleration
Degree, reaches the maximized target of general speed of all vehicles in region.
(3) realization of the optimal velocity model in MATLAB
Using fmincon local optimum function, major function is to seek multivariable Constrained Nonlinear functional minimum value,
Using the first derivative information of objective function and constraint function, since the initial point given, under conditions of meeting constraint, edge
Objective function decline direction iteration, finally converge to locally optimal solution.
Grammer used in this model are as follows:
[x, fval, exitflag, output]=fmincon (fun, x0, A, b, Aeq, beq, lb, ub, nonlcon)
That is given initial value x0, the constraint condition for solving fun functional minimum value x, fun function is Aeqx=beq, Ax≤b,
The lower bound lb and upper bound ub for defining design variable x, so that always there is lb≤x≤ub.On the basis of above, join in nonlcon
Nonlinear inequalities c (x)≤0 or equation ceq (x)=0 are provided in number.Fval is the target function value of XiexChu, and exitflag is
The condition of iteration is terminated, output is the output function for optimizing information.
Initial value information is inputted first, i.e. input x_1, tri- vectors of v_1, a_1, and read the length deposit of x_1 vector
Car_max determines the number of output circulation with this.The limits value of speed, acceleration is set, and structural matrix is come when storing each
Between the speed, acceleration of step-length, location information.Then call majorized function fmincon, successive iteration and return output it is optimal
Solution, input results chart.
(4) car networking simulation frame is realized
By the VISSIM road model of foundation, executes simulation model and obtain result.Then it reads and is simulated by com interface
Data on flows.Specifically, the speed that vehicle in data acquisition and control region is read using 1 second time step, is added
Speed, position and other information.Then, it is stored data in Excel by C++ program.Excel be used as realize VISSIM and
The interlude of MATLAB data transmission.By Excel link interface Calling MATLAB, and by the emulation data in VISSIM
It is transferred to MATLAB.And MATLAB optimal velocity model using fmincon algorithm optimization speed and returns to output valve per second, i.e., plus
Speed, and stored it in Excel by Excel link.Then Optimal Decision-making is read in Excel by com interface,
It is connect using com interface with Excel.At this time by C++ program come the vehicle in Controlling model kernel.Up to the present, it is based on
Optimal Decision-making, VISSIM exchange information by decision-making simulation vehicle immediately under connected state and determine the driving of its own
Behavior.Then VISSIM output simulation assessment data, complete simulation.
(5) vehicle networked speed-optimization result
Data collection point is separately positioned on two lanes of B point and C point.It is ordered the collection point of No. 1 and No. 2 lane B point
The collection point of entitled collection point 1 and 2, No. 1 and No. 2 lane C point is named as collection point 3 and 4.The average speed of each acquisition vehicle
Degree and acceleration are as shown in table 1.
The average speed and acceleration of the vehicle networked state of table 1 and failed cluster situation state
As it can be seen from table 1 the average speed of vehicle increases, but their acceleration when vehicle is in connected state
Degree does not change much.Under connected state, the vehicle acceleration of B point is lower than unconnected state.Therefore, in the speed of MATLAB
Under Optimal Decision-making, when the distance between all vehicles reach minimum value, vehicle is close to constant speed.In other words, networking vehicle
Acceleration close to zero.
Equally, the vehicle acceleration of C point is apparently higher than B point, and most of vehicles are all accelerating.Since vehicle is not become by lane
The control of area's optimal velocity model is changed, they still will comply with the Wiedemann99 vehicle of VISSIM.When vehicle driving to C point simultaneously
When waiting in line, in order to ensure the uniformity of traffic flow, the vehicle passed through will accelerate to advance.
Velocity and acceleration of 2 vehicle of table in collection point 2,4
From table 2 it can be seen that the velocity deviation of collection point 4 be greater than collection point 2 velocity deviation, collection point 2 most greatly
Speed is 2.00m/s2, is less than the acceleration limiting of a_max=2m/s2.Moreover, the minimum value of acceleration and the maximum of speed
Value is not above limitation.This shows under optimal velocity model, can satisfy speed, the limitation of acceleration and minimum range.
In order to compare the velocity and acceleration of collection point under networking and unconnected state, by average speed under two states and
The analog result of acceleration is placed in line chart, as shown in Figure 3 and Figure 4.
The average vehicle speed of Multi simulation running result under connected state it can be seen from velocity contrast Fig. 3 is not about than
The high 4km/h of average speed under connected state.
As seen from Figure 4, the variation tendency of acceleration has points of resemblance under two states.Under unconnected state, vehicle
There is more apparent deceleration behavior in data field (collection point 1,2), this is because initial value is higher when vehicle has just entered network,
After into network due to interacting speed, the spacing etc. between Adjacent vehicles, vehicle is intended to Reduced Speed Now.And
After entering lane change area from data field, due to the increase of number of track-lines, the variability of vehicle driving decision increases, and vehicle is in driving process
In just have obvious acceleration behavior, therefore acceleration of the vehicle in data field (collection point 3,4) is positive value.
In addition, the acceleration of collection point 1 changes less in both cases, and the acceleration of collection point 2 is by -0.4m/s2
Increasing degree is larger, close to zero, it is seen that optimal velocity model seeks optimal value during speed-optimization, works as Adjacent vehicles
When reaching minimum spacing, and reaching after successive ignition general speed maximum, vehicle tends at the uniform velocity to go close to zero acceleration
It sails.
From table 3 it can be seen that the average travel time of online vehicles and delay time at stop are respectively less than unconnected state.Two kinds of shapes
The line chart difference of average travel time and delay time at stop under state is as shown in Figure 5,6.
The traveling of the vehicle networked state of table 3 and failed cluster situation state condition and delay time at stop
It can be seen from Fig. 5,6 under connected state, the average travel time in the data field of each period and lane change area and
The mean delay time is reduced.As it can be seen that the average speed of whole wagon flow can be improved to a certain extent when vehicle networked
Degree, average travel time reduce vehicle delay, therefore thus can propose to adjust to highway layout speed.In conclusion solution of the present invention
Existing vehicle networked technology of having determined lacks the defect that highway layout parameter is supported, while method disclosed by the invention is simply, conveniently,
The result of acquisition is more accurate, so as to use optimal desin speed according to the result of calculating, mentions to a certain extent
Road passage capability under height is unmanned.
Claims (5)
1. a kind of implementation method of the car networking desin speed adjustment based on VISSIM emulation, it is characterised in that: including following step
It is rapid:
1) the wagon flow situation for combining road under normal driving state, establishes the road of standard in VISSIM traffic simulation modeling software
Road model；
2) the standard road model based on foundation establishes the maximized optimal velocity model of vehicle general speed, mould using MATLAB
Intend vehicle real-time exchange information and the automatic Pilot behavior travelled with optimizing decision under vehicle networked state；
3) vehicle data that VISSIM is detected and exported using C++, using Excel as primary control program regulation MATLAB and
Data between VISSIM are output and input；
4) vehicle networked and unconnected state acquisition point data is compared, obtains the tune of vehicle networked lower desin speed
It is whole.
2. the implementation method of the car networking desin speed adjustment according to claim 1 based on VISSIM emulation, feature
It is, in the step 1), the section of road is divided into data field, redirection zone and shunting zone, default, which drives, to be used
Wiedemann99 car-following model, establishes the road model of standard in VISSIM traffic simulation modeling software, and setting vehicle inputs
Simultaneously evaluation file is exported with parameters, simulation runs such as path decisions.
3. the implementation method of the car networking desin speed adjustment according to claim 1 based on VISSIM emulation, feature
It is, in the step 2), speed decision model when using the maximized optimal velocity model of general speed as vehicle networked shape,
Realized that speed decision model is specific as follows using the fmincon algorithm of MATLAB:
In above formula, formula (1) is objective function, and formula (2) is the constraint function to car speed, and formula (3) is indicated with for the moment
The minimum range of interior each car cannot be greater than specified value, and minimum range is arranged to 10 meters, i.e. G heremin=10m；Formula
It (4) is the constraint function to acceleration, formula (5) describes the functional relation between speed, acceleration and distance, and i is in formula
Car number on lane；N is the vehicle fleet on lane；T is each time step, indicates which second；vi,tFor a certain second
The speed of one vehicle；V_max is speed maximum value；GminFor the distance between adjacent two cars minimum value, xi,tFor certain two vehicle
One second position；A_min is acceleration minimum value；A_max is acceleration maximum value；ai,tFor the acceleration of a certain vehicle of a certain second
Degree.
4. the implementation method of the car networking desin speed adjustment according to claim 1 based on VISSIM emulation, feature
It is, in the step 3), on the basis of the standard road model of building, for the optimal velocity model of foundation, is connect by COM
Mouth reads traffic data just in simulation run, with it is per second be a time step, read the speed of data field vehicle, accelerate
The information such as degree, position, through a C++ program storage into Excel, using Excel as interlude, as VISSIM and
Emulation data in VISSIM are passed to by the terminal of MATLAB data transmission by Excellink interface Calling MATLAB
In MATLAB.MATLAB optimizes the speed received, and returns and export decision value per second, i.e. acceleration, passes through
Excel link is stored into Excel, reads the Optimal Decision-making in Excel by COM excuse, and utilize com interface and Excel
It is docked, is controlled using the interior verification vehicle of C++ intervention model.
5. the implementation method of the car networking desin speed adjustment according to claim 1 based on VISSIM emulation, feature
It is, in the step 4), according to step 3) output as a result, drawing being averaged for each collection point under car networking and unconnected state
Speed, acceleration relational graph, obtain it is vehicle networked under desin speed adjustment result.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111695241A (en) * | 2020-05-18 | 2020-09-22 | 东南大学 | Method for determining length of accelerating lane of left-turn ramp confluence area based on VISSIM simulation |
| CN111753425A (en) * | 2020-06-26 | 2020-10-09 | 北京百度网讯科技有限公司 | Simulation method, simulation device, electronic equipment and storage medium |
| CN111856968A (en) * | 2020-07-31 | 2020-10-30 | 成都信息工程大学 | Large-scale traffic simulation system and method based on parallel computing |
| CN112991713A (en) * | 2019-12-13 | 2021-06-18 | 百度在线网络技术（北京）有限公司 | Data processing method, device, equipment and storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170243485A1 (en) * | 2012-04-24 | 2017-08-24 | Zetta Research and Development LLC, ForC series | V2v safety system using learned signal timing |
| US20170309092A1 (en) * | 2016-04-26 | 2017-10-26 | Walter Steven Rosenbaum | Method for determining driving characteristics of a vehicle and vehicle analyzing system |
| CN108961753A (en) * | 2018-07-17 | 2018-12-07 | 重庆大学 | A kind of speed bootstrap technique based on bus or train route communication |
-
2019
- 2019-01-04 CN CN201910006280.XA patent/CN109765801A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170243485A1 (en) * | 2012-04-24 | 2017-08-24 | Zetta Research and Development LLC, ForC series | V2v safety system using learned signal timing |
| US20170309092A1 (en) * | 2016-04-26 | 2017-10-26 | Walter Steven Rosenbaum | Method for determining driving characteristics of a vehicle and vehicle analyzing system |
| CN108961753A (en) * | 2018-07-17 | 2018-12-07 | 重庆大学 | A kind of speed bootstrap technique based on bus or train route communication |
Non-Patent Citations (4)
| Title |
|---|
| DANIEL GÖHRING 等: "Semi-autonomous Car Control Using Brain Computer Interfaces", 《INTELLIGENT AUTONOMOUS SYSTEMS 12》 * |
| SINA DABIRI 等: "Arterial traffic signal optimization using Particle Swarm Optimization in an integrated VISSIM-MATLAB simulation environment", 《2016 IEEE 19TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC)》 * |
| YUANCHANG XIE 等: "Collaborative merging strategy for freeway ramp operations in a connected and autonomous vehicles environment", 《JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS》 * |
| 林培群 等: "车联网环境下交叉口交通流微观控制模型及其求解与仿真", 《中国公路学报》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112991713A (en) * | 2019-12-13 | 2021-06-18 | 百度在线网络技术（北京）有限公司 | Data processing method, device, equipment and storage medium |
| CN112991713B (en) * | 2019-12-13 | 2022-11-22 | 百度在线网络技术（北京）有限公司 | Data processing method, device, equipment and storage medium |
| CN111695241A (en) * | 2020-05-18 | 2020-09-22 | 东南大学 | Method for determining length of accelerating lane of left-turn ramp confluence area based on VISSIM simulation |
| CN111753425A (en) * | 2020-06-26 | 2020-10-09 | 北京百度网讯科技有限公司 | Simulation method, simulation device, electronic equipment and storage medium |
| CN111753425B (en) * | 2020-06-26 | 2023-07-25 | 北京百度网讯科技有限公司 | Simulation method, simulation device, electronic equipment and storage medium |
| CN111856968A (en) * | 2020-07-31 | 2020-10-30 | 成都信息工程大学 | Large-scale traffic simulation system and method based on parallel computing |
| CN111856968B (en) * | 2020-07-31 | 2023-07-21 | 成都信息工程大学 | Large-scale traffic simulation system and method based on parallel computing |
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