CN106898143A - A kind of magnitude of traffic flow modeling method of pilotless automobile - Google Patents
A kind of magnitude of traffic flow modeling method of pilotless automobile Download PDFInfo
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- CN106898143A CN106898143A CN201710228167.7A CN201710228167A CN106898143A CN 106898143 A CN106898143 A CN 106898143A CN 201710228167 A CN201710228167 A CN 201710228167A CN 106898143 A CN106898143 A CN 106898143A
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
Abstract
The invention discloses a kind of magnitude of traffic flow modeling method of pilotless automobile, belong to unmanned field.A kind of magnitude of traffic flow modeling method of pilotless automobile, on the basis of the factor of analyzing influence freeway traffic, set up the classical traffic model based on the differential equation, track quantity to influenceing the per day magnitude of traffic flow has made graph of a relation in Average Life and peak period, introduce the Gipps safe distances rule of pilotless automobile hypothesis and classics, NaSch models are improved, propose the automatic Pilot cellular automaton traffic flow based on safe distance, then by building the cooperation between the new many cars of analysis of the control method based on spring-damper, sensitivity test is carried out finally by by 0.1s are brought up into 1s the reaction time of pilotless automobile.It can realize introducing method improvement traffic safety and congestion that pilotless automobile sets up model.
Description
Technical field
The present invention relates to unmanned field, more specifically to a kind of magnitude of traffic flow modeling of pilotless automobile
Method.
Background technology
At present, the research of the mixed traffic flow on unmanned and pilot steering is also less, and Sunan Huang propose one
Plant and the technology so that highway automates cost and infrastructure requirements reduction coexist on unmanned and pilot steering,
Arnab Bose were travelled to traffic stream characteristics and environment together in 1999 to unmanned and pilot steering in identical track
Influence is analyzed, and Arnab Bose propose unmanned model in 2003, and to automatic and pilot steering in proportion
The close figure of stream analyzed and researched, while also analyzing shock wave；Consider the individual driving performance difference and ACC cars of driver
Operation logic, construct respective operation rule, it is proposed that a new cellular Automation Model, to simulate mixing hand over
Through-flow variation characteristic.
The main tool that cellular Automation Model is studied as Microscopic traffic simulation, after being introduced in field of traffic, obtains
Rapid development, Nagel and Schrenberg proposes the NaSch models of classics within 1992, although model form is simple, but
Analog result that model is obtained and actual traffic phenomenon and traffic behavior are closely similar, and many scholars advise to NaSch models afterwards
Then it is improved, successively proposes TT model cruise controls model, FI models, sensitive driving model, safe driving model etc., this
A little improvement is greatly enriched Cellular Automata Model of Traffic Flow.
The content of the invention
1. the technical problem to be solved
For there is traffic safety and congestion in the prior art, it is an object of the invention to provide one kind, nobody drives
The magnitude of traffic flow modeling method of automobile is sailed, it can realize introducing the method improvement traffic safety that pilotless automobile sets up model
And congestion.
2. technical scheme
To solve the above problems, the present invention is adopted the following technical scheme that.
A kind of magnitude of traffic flow modeling method of pilotless automobile, its step is as follows：
(1) data of the binding ground day magnitude of traffic flow, carry out case study it is assumed that analysis hypothesis includes：
First, there is the ratio in peak hourage per daily traffic volume, based on people's daily schedule of one day, it is assumed that peak
The time of phase and the time of non-peak period,
2nd, many new hand drivers and some drivers not observed traffic rules and regulations are directed to, are investigated according to correlation, compared
Example and the reaction time it is assumed that
3rd, when crossing traffic flow is calculated, the vehicle number in each track of proposition is averaged,
4th, the vehicle considered in the present invention is all kept to the right, and meets the traffic of most of country in the world
Rule,
5th, the vehicle chosen is calculated with standard vehicle equivalents；
(2) conventional traffic model is set up,
First, by data acquisition, traffic congestion empirical distribution function is calculated, then draws traffic congestion empirical distribution function
Figure,
2nd, the current factor of analyzing influence road traffic,
3rd, by data acquisition and calculating, Average Life traffic flow rate and volume of traffic graph of a relation and peak period track are drawn
Number is with average per lane capacity figure；
(3) pilotless automobile intervention traffic model is set up,
First, with reference to classical Gipps model thoughts, introduce safe distance and NaSch models are improved, set up based on peace
The automatic Pilot cellular automaton traffic flow of full distance,
2nd, unmanned Cellular Automata is carried out,
3rd, data analysis, the data analysis includes impact analysis and mixing ratio of the drive manner mixed proportion to traffic
Impact analysis of the example to traffic congestion；
(4) many car cooperations,
Fleet is decomposed into a series of subsystems being made up of three cars using the method for overlapping structure decomposition, is reused point
The optimal solution of system after formula LQ control methods are expanded is dissipated, extension system is finally shunk back into original system, obtain original system
Suboptimal control rate；
(5) sensitivity test,
By 0.1s are brought up into 1s the reaction time of pilotless automobile carries out sensitivity test.
Preferably, the foundation is based on carrying out specification system during the automatic Pilot cellular automaton traffic flow of safe distance
It is fixed：
First, according to safe distance principle, n-th driver of car estimates to the maximum deceleration of its front vehicles
Meter, and then determine the safe distance Gap for avoiding ensureing needed for being knocked into the back with its frontsafe,n, and the safe speed for travelling
vsafe,n, τnIt is n-th reaction time of car, bnIt is n-th maximum deceleration of car, vnT () is n-th speed of car；
Second, accelerate rule
Vehicle is required when the following distance between n-th car and its front vehicles is travelled more than the car in the middle of traveling
Safe spacing when, i.e. Gapn＞ Gapsafe,nWhen, GapnBe the spacing of n-th car and above car, in order to meet driver for
The traveling of desired speed higher, the car is then given it the gun according to following rule；
vn(t)→min(vn(t)+an,Vmax,vsafe,n(t),Gapn)
3rd, at the uniform velocity rule
When the following distance between n-th car and its front vehicles is travelled with the car, required safe spacing is equal,
That is Gapn=Gapsafe,n, in the case where vehicle safe driving is ensured, then the vehicle will not take any acceleration and deceleration measure, protect
Hold former speed traveling；
vn(t)→min(vn(t),Gapn)
4th, rule of slowing down
When the following distance between n-th car and its front vehicles is travelled less than the car during required safe spacing, i.e.,
Gapn＜ Gapsafe,nWhen, in order to ensure safe driving is then slowed down.If front vehicles are static, i.e. vn(t)=0, based on safety
Property consider, then take safety deceleration rule slowed down, that is, ensure that the car and the following distance of front vehicles cannot be less than 0.5m；
If front vehicles nonstatic, i.e. vnT () ≠ 0, then take certainty deceleration rule to be slowed down, corresponding specific rules are as follows：
Safety is slowed down
vn(t)→max{min(vsafe,n(t),Gapn-1),0)
Certainty is slowed down
vn(t)→max{min(vsafe,n(t),Gapn),0)
5th, random slowing down probability
The uncertainty of the driving behavior existed in the process of moving in view of driver, introduced in evolution rule with
Machine slowing down probability Rp, the vehicle in traveling carries out the slowing down in speed according to random slowing down probability, and velocity variations observe formula, then press
More solito deceleration is slowed down；
vn(t)→max(vn(t)-bn,0)
6th, location updating
On the basis of speed develops and updates rule, the renewal of vehicle location is carried out；
xn(t)→xn(t)+vn(t)
In formula：GapnIt is n-th car and the following distance of front truck n+1, i.e. Gapn=xn+1(t)-xn(t)-ln+1。
Preferably, the current factor of influence road traffic includes adverse weather factor, static bottleneck road and dynamic bottle
Neck section, the adverse weather factor refers to rain, snow, mist, high wind etc.；The static bottleneck road refers to tunnel, bridge, up to descending
Section etc.；The bottleneck section refers to large car, " ghost blocking ", traffic accident and vehicle trouble.
3. beneficial effect
Compared to prior art, the advantage of the invention is that：
(1) this programme is simulated with cellular automata to road traffic, more truly reflects road
Actual traffic, and maximal rate, cellular quantity, vehicle fleet size and interval time can change, program
It is very flexible, and can clearly find out running each time.
(2) considering reality and has carried out detailed sensitivity analysis, careful accordingly to have modified model.
(3) when the traffic conditions of road are considered, road conditions peak period has been divided into and non-peak period has been modeled, made
Result is more effective.
(4) adverse weather factor, static bottleneck road and bottleneck section, the reality with other scientists are also contemplated
Test result of study be shown to be it is consistent.
Brief description of the drawings
Fig. 1 is module map of the invention.
Specific embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention；Technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation is described；Obviously；Described embodiment is only a part of embodiment of the invention；Rather than whole embodiments.It is based on
Embodiment in the present invention；It is every other that those of ordinary skill in the art are obtained under the premise of creative work is not made
Embodiment；Belong to the scope of protection of the invention.
Embodiment 1：
Fig. 1 is referred to, a kind of magnitude of traffic flow modeling method of pilotless automobile, its step is as follows：
(1) data of the binding ground State of Washington highway day magnitude of traffic flow, carry out case study it is assumed that analysis is false
If including：
First, 8% per daily traffic volume occurred in peak hourage, based on people's daily schedule of one day, by traffic feelings
The peak period for being divided into 3 hours for one day for 24 hours of condition and the non-peak period of 21 hours,
2nd, many new hand drivers and some drivers not observed traffic rules and regulations are directed to, are investigated according to correlation, it is assumed that its
Account for 12% and 5% respectively, other 83% is belonging to normal driver, reaction time of this three is approximately respectively 2S, 3S, 1S,
3rd, when crossing traffic flow is calculated, the vehicle number in each track of proposition is averaged,
4th, the vehicle considered in the present invention is all kept to the right, and meets the traffic of most of country in the world
Rule,
5th, the vehicle chosen is calculated with standard vehicle equivalents,
(2) conventional traffic model is set up,
First, by data acquisition, traffic congestion empirical distribution function is calculated, then draws traffic congestion empirical distribution function
Figure,
2nd, the current factor of analyzing influence road traffic, the current factor of influence road traffic includes adverse weather factor, static state
Bottleneck road and bottleneck section, adverse weather factor refer to rain, snow, mist, high wind etc.；Static bottleneck road refer to tunnel, bridge,
Up to descending section etc.；Bottleneck section refers to large car, " ghost blocking ", traffic accident and vehicle trouble.
3rd, by data acquisition and calculating, Average Life traffic flow rate and volume of traffic graph of a relation and peak period track are drawn
Number is with average per lane capacity figure；
(3) pilotless automobile intervention traffic model is set up,
First, with reference to classical Gipps model thoughts, introduce safe distance and NaSch models are improved, set up based on peace
The automatic Pilot cellular automaton traffic flow of full distance,
2nd, unmanned Cellular Automata is carried out,
3rd, data analysis, data analysis includes impact analysis and mixed proportion pair of the drive manner mixed proportion to traffic
The impact analysis of traffic congestion；
(4) many car cooperations,
Fleet is decomposed into a series of subsystems being made up of three cars using the method for overlapping structure decomposition, is reused point
The optimal solution of system after formula LQ control methods are expanded is dissipated, extension system is finally shunk back into original system, obtain original system
Suboptimal control rate；
(5) sensitivity test,
By 0.1s are brought up into 1s the reaction time of pilotless automobile carries out sensitivity test.
Specification institution is carried out when setting up the automatic Pilot cellular automaton traffic flow based on safe distance：
First, according to safe distance principle, n-th driver of car estimates to the maximum deceleration of its front vehicles
Meter, and then determine the safe distance Gap for avoiding ensureing needed for being knocked into the back with its frontsafe,n, and the safe speed for travelling
vsafe,n；
Second, accelerate rule
Vehicle is required when the following distance between n-th car and its front vehicles is travelled more than the car in the middle of traveling
Safe spacing when, i.e. Gapn＞ Gapsafe,nWhen, in order to meet traveling of the driver for desired speed higher, the car is then pressed
Given it the gun according to following rule；
vn(t)→min(vn(t)+an,Vmax,vsafe,n(t),Gapn)
3rd, at the uniform velocity rule
When the following distance between n-th car and its front vehicles is travelled with the car, required safe spacing is equal,
That is Gapn=Gapsafe,n, in the case where vehicle safe driving is ensured, then the vehicle will not take any acceleration and deceleration measure, protect
Hold former speed traveling；
vn(t)→min(vn(t),Gapn)
4th, rule of slowing down
When the following distance between n-th car and its front vehicles is travelled less than the car during required safe spacing, i.e.,
Gapn＜ Gapsafe,nWhen, in order to ensure safe driving is then slowed down.If front vehicles are static, i.e. vn(t)=0, based on safety
Property consider, then take safety deceleration rule slowed down, that is, ensure that the car and the following distance of front vehicles cannot be less than 0.5m；
If front vehicles nonstatic, i.e. vnT () ≠ 0, then take certainty deceleration rule to be slowed down, corresponding specific rules are as follows：
Safety is slowed down:
vn(t)→max{min(vsafe,n(t),Gapn-1),0)
Certainty is slowed down:
vn(t)→max{min(vsafe,n(t),Gapn),0)
5th, random slowing down probability
The uncertainty of the driving behavior existed in the process of moving in view of driver, introduced in evolution rule with
Machine slowing down probability Rp, the vehicle in traveling carries out the slowing down in speed according to random slowing down probability, and velocity variations observe formula, then press
More solito deceleration is slowed down；
vn(t)→max(vn(t)-bn,0)
5th, location updating
On the basis of speed develops and updates rule, the renewal of vehicle location is carried out；
xn(t)→xn(t)+vn(t)
In formula：GapnIt is n-th car and the following distance of front truck n+1, i.e. Gapn=xn+1(t)-xn(t)-ln+1。
The above；Preferably specific embodiment only of the invention；But protection scope of the present invention is not limited thereto；
Any one skilled in the art the invention discloses technical scope in；Technology according to the present invention scheme and its
Improve design and be subject to equivalent or change；Should all cover within the scope of the present invention.
Claims (3)
1. the magnitude of traffic flow modeling method of a kind of pilotless automobile, it is characterised in that：Its step is as follows：
(1) data of the binding ground day magnitude of traffic flow, carry out case study it is assumed that analysis hypothesis includes：
First, there is the ratio in peak hourage per daily traffic volume, based on people's daily schedule of one day, it is assumed that peak period
Time and the time of non-peak period,
2nd, many new hand drivers and some drivers not observed traffic rules and regulations are directed to, according to correlation investigate, carry out ratio and
Reaction time it is assumed that
3rd, when crossing traffic flow is calculated, the vehicle number in each track of proposition is averaged,
4th, the vehicle considered in the present invention is all kept to the right, and meets the traffic rules of most of country in the world,
5th, the vehicle chosen is calculated with standard vehicle equivalents；
(2) conventional traffic model is set up,
First, by data acquisition, traffic congestion empirical distribution function is calculated, then draws traffic congestion empirical distribution function figure,
2nd, the current factor of analyzing influence road traffic,
3rd, by data acquisition and calculating, draw Average Life traffic flow rate and volume of traffic graph of a relation and peak period number of track-lines with
Average every lane capacity figure；
(3) pilotless automobile intervention traffic model is set up,
First, with reference to classical Gipps model thoughts, introduce safe distance and NaSch models be improved, set up based on safety away from
From automatic Pilot cellular automaton traffic flow,
2nd, unmanned Cellular Automata is carried out,
3rd, data analysis, the data analysis includes impact analysis and mixed proportion pair of the drive manner mixed proportion to traffic
The impact analysis of traffic congestion；
(4) many car cooperations,
Fleet is decomposed into a series of subsystems being made up of three cars using the method for overlapping structure decomposition, distributing is reused
LQ control methods be expanded after system optimal solution, extension system is finally shunk back into original system, obtain the suboptimum of original system
Control rate；
(5) sensitivity test,
By 0.1s are brought up into 1s the reaction time of pilotless automobile carries out sensitivity test.
2. the magnitude of traffic flow modeling method of a kind of pilotless automobile according to claim 1, it is characterised in that：It is described to build
Be based on safe distance automatic Pilot cellular automaton traffic flow when carry out specification institution：
First, according to safe distance principle, n-th driver of car estimates the maximum deceleration of its front vehicles, enters
And determine the safe distance Gap for avoiding ensureing needed for being knocked into the back with its frontsafe,n, and the safe speed v for travellingsafe,n；
Second, accelerate rule
Vehicle is central in traveling, the required peace when the following distance between n-th car and its front vehicles is travelled more than the car
During full spacing, i.e. Gapn＞ Gapsafe,nWhen, in order to meet traveling of the driver for desired speed higher, the car is then according to such as
Lower rule is given it the gun；
vn(t)→min(vn(t)+an,Vmax,vsafe,n(t),Gapn)
3rd, at the uniform velocity rule
When the following distance between n-th car and its front vehicles is travelled with the car, required safe spacing is equal, i.e. Gapn
=Gapsafe,n, in the case where vehicle safe driving is ensured, then the vehicle will not take any acceleration and deceleration measure, keep former speed
Degree traveling；
vn(t)→min(vn(t),Gapn)
4th, rule of slowing down
When the following distance between n-th car and its front vehicles is travelled less than the car during required safe spacing, i.e. Gapn＜
Gapsafe,nWhen, in order to ensure safe driving is then slowed down.If front vehicles are static, i.e. vnT ()=0, is examined based on security
Consider, then take the rule of safety deceleration to be slowed down, that is, ensure that the car and the following distance of front vehicles cannot be less than 0.5m；If preceding
Square vehicle nonstatic, i.e. vnT () ≠ 0, then take certainty deceleration rule to be slowed down, corresponding specific rules are as follows：
Safety is slowed down
vn(t)→max{min(vsafe,n(t),Gapn-1),0)
Certainty is slowed down
vn(t)→max{min(vsafe,n(t),Gapn),0)
5th, random slowing down probability
The uncertainty of the driving behavior existed in the process of moving in view of driver, introduces random slow in evolution rule
Change probability Rp, the vehicle in traveling carries out the slowing down in speed according to random slowing down probability, and velocity variations observe formula, then according to normal
Rule deceleration is slowed down；
vn(t)→max(vn(t)-bn,0)
6th, location updating
On the basis of speed develops and updates rule, the renewal of vehicle location is carried out；
xn(t)→xn(t)+vn(t)
In formula：GapnIt is n-th car and the following distance of front truck n+1, i.e. Gapn=xn+1(t)-xn(t)-ln+1。
3. the magnitude of traffic flow modeling method of a kind of pilotless automobile according to claim 1, it is characterised in that：The shadow
Ringing the current factor of road traffic includes adverse weather factor, static bottleneck road and bottleneck section, the adverse weather because
Element refers to rain, snow, mist, high wind etc.；The static bottleneck road refers to tunnel, bridge, up to descending section etc.；The bottleneck road
Section refers to large car, " ghost blocking ", traffic accident and vehicle trouble.
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Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108417026A (en) * | 2017-12-01 | 2018-08-17 | 安徽优思天成智能科技有限公司 | A kind of intelligent vehicle ratio acquisition methods for keeping road passage capability optimal |
| CN109711009A (en) * | 2018-12-13 | 2019-05-03 | 北京掌行通信息技术有限公司 | Pilotless automobile method of evaluating performance, device, electronic equipment and medium |
| CN110021164A (en) * | 2019-03-02 | 2019-07-16 | 合肥学院 | Net based on travel time data about bus or train route net occupation rate analysis method |
| CN110021163A (en) * | 2019-03-02 | 2019-07-16 | 合肥学院 | Net based on driving mileage data about bus or train route net occupation rate analysis method |
| CN111243309A (en) * | 2020-01-10 | 2020-06-05 | 北京航空航天大学 | Expressway traffic flow full-sample trajectory reconstruction method based on automatic driving vehicle movement detection |
| CN112330135A (en) * | 2020-11-02 | 2021-02-05 | 苏州工业园区测绘地理信息有限公司 | Urban traffic jam space evolution method based on improved cellular automaton model |
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| CN113158424A (en) * | 2021-03-17 | 2021-07-23 | 北京易控智驾科技有限公司 | Model construction method and device for traffic flow optimization of mine unmanned road network |
| CN113204863A (en) * | 2021-04-09 | 2021-08-03 | 燕山大学 | Manual-CACC automatic driving vehicle mixed flow simulation method based on cellular automata |
| CN113345227A (en) * | 2021-05-31 | 2021-09-03 | 上海涵润汽车电子有限公司 | Random traffic flow generation method and device |
| CN113838287A (en) * | 2021-10-18 | 2021-12-24 | 清华大学深圳国际研究生院 | Method and device for judging mixed traffic flow state in internet automatic driving environment |
| CN114120654A (en) * | 2022-01-26 | 2022-03-01 | 南京原力智能科技有限公司 | General calculation method for influence of number of vehicles running on road network traffic capacity |
| CN114239272A (en) * | 2021-12-16 | 2022-03-25 | 北京工业大学 | Hybrid bicycle flow microscopic modeling method and device based on retrograde behavior |
| CN115035711A (en) * | 2022-04-14 | 2022-09-09 | 福建船政交通职业学院 | Traffic diversion control method for connection section of expressway tunnel and interchange |
| CN116884225A (en) * | 2023-09-05 | 2023-10-13 | 天津大学 | Method and device for evaluating influence of proportion of automatic driving vehicles on traffic efficiency |
| CN117408086A (en) * | 2023-12-14 | 2024-01-16 | 中汽信息科技(天津)有限公司 | Mixed traffic flow simulation method based on driver response time |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7366606B2 (en) * | 2004-04-06 | 2008-04-29 | Honda Motor Co., Ltd. | Method for refining traffic flow data |
| CN105930614A (en) * | 2016-05-13 | 2016-09-07 | 东南大学 | Cell transmission model parameter calibration and verification method specific to variable speed limit control |
| CN106530691A (en) * | 2016-10-25 | 2017-03-22 | 中山大学 | Hybrid vehicle model multilane cellular automaton model considering vehicle occupancy space |
-
2017
- 2017-04-10 CN CN201710228167.7A patent/CN106898143A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7366606B2 (en) * | 2004-04-06 | 2008-04-29 | Honda Motor Co., Ltd. | Method for refining traffic flow data |
| CN105930614A (en) * | 2016-05-13 | 2016-09-07 | 东南大学 | Cell transmission model parameter calibration and verification method specific to variable speed limit control |
| CN106530691A (en) * | 2016-10-25 | 2017-03-22 | 中山大学 | Hybrid vehicle model multilane cellular automaton model considering vehicle occupancy space |
Non-Patent Citations (2)
| Title |
|---|
| 邱小平等: "基于安全距离的元胞自动机交通流模型研究", 《交通运输系统工程与信息》 * |
| 邱小平等: "基于安全距离的手动-自动驾驶混合交通流研究", 《交通运输系统工程与信息》 * |
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| CN113204863B (en) * | 2021-04-09 | 2022-02-11 | 燕山大学 | Manual-CACC automatic driving vehicle mixed flow simulation method based on cellular automata |
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| CN113838287B (en) * | 2021-10-18 | 2022-08-12 | 清华大学深圳国际研究生院 | Method and device for judging mixed traffic flow state in internet automatic driving environment |
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| CN114120654A (en) * | 2022-01-26 | 2022-03-01 | 南京原力智能科技有限公司 | General calculation method for influence of number of vehicles running on road network traffic capacity |
| CN115035711A (en) * | 2022-04-14 | 2022-09-09 | 福建船政交通职业学院 | Traffic diversion control method for connection section of expressway tunnel and interchange |
| CN115035711B (en) * | 2022-04-14 | 2023-11-17 | 福建船政交通职业学院 | Traffic diversion control method for highway tunnel and interchange connection section |
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Application publication date: 20170627 |