CN102505644B - Method for confirming set positions of speed limit signs and size of speed limit during road construction - Google Patents
Method for confirming set positions of speed limit signs and size of speed limit during road construction Download PDFInfo
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Abstract
The invention belongs to the technical field of road design, and relates to a method for confirming set positions of speed limit signs and size of speed limit during road construction, and the method comprises the following steps of: (1) designing a working memory structure of a driver SOAR intellectual body; (2) designing an initial long-term memory rule, and building a long-term memory rule base of the driver SOAR intellectual body; (3) building a decision period of the driver SOAR intellectual body; and (4) setting the set positions of the different speed limit signs, the size of speed limit, and different road loading coefficients so as to perform simulation, judging traffic conflict and severity degree according to overlapping degree of road cells which are occupied by a current vehicle and adjoining vehicles, selecting a simulation condition with the lower traffic conflict and severity degree, and obtaining the set positions of the speed limit signs and the size of the speed limit within a proper construction operation area under different traffic conditions. The method can be used for more accurately reflecting the influence of the set positions of the speed limit signs and the size of the speed limit on the traffic capacity during road construction, thus providing the method for setting the speed limit signs during road construction.
Description
Technical field
The invention belongs to road design technical field, relate to a kind of road construction speed(-)limit sign setting position and the definite method of speed limit size.
Background technology
Road construction operation is requisite road maintenance maintenance work between the urban road infrastructure construction operating period.Owing to changing traffic running environment between road construction operational period, reduce road efficiency, affect resident trip and life etc., therefore, urban road construction operation enjoys each metropolis to pay close attention to, and work out corresponding tecnical regulations, also more for road construction operation research document, mainly based on traffic safety and road efficiency, use cognitive psychology, ergonomics, traffic flow theory, with speeding theory, the theory of overtaking other vehicles, safe sight distance, the traffic capacity of the technology such as microscopic traffic simulation to road construction operation area, the speed of a motor vehicle, works area length and traffic sign arrange further investigation.Nan Zheng etc. use the mathematical model prediction highway construction operation area traffic capacity at < < VariableAnalysis for Freeway Work Zone Capacity Prediction > >; ANXIJIA gathers road construction operation area related data at its Master's thesis < < COMPREHENSIVE EVALUATION OF CONSTRUCTION WORK ZONECAPACITY ANDASSOCIATED ROAD USER COST > > by the emulation of VISSIM traffic simulation software, thereby determines the work zone traffic capacity and operation area length; Chen Yu uses traffic flow theory and driver information processing speed scale-model investigation construction section capacity in paper < < expressway work zone safety analysis and traffic management method research > >, uses VISSIM traffic simulation software to determine construction operation section length and traffic sign setting; Meaningful will just uses cognitive psychology to know and recognize traffic sign process and carry out qualitative analysis driver in the Study on Driver Traffic Signs Comprehension > > based on cognitive psychology at < <, thereby provide theoretical foundation to work zone Traffic Signs Design and installation; Li Yongyi uses Dynamic Comprehensive Evaluation method and VISSIM traffic simulation software to carry out evaluation study to work zone Traffic Organization in the design of < < highway construction road section traffic volume organization scheme and evaluation study > >.
From existing pertinent literature reading analysis, for the prior art of road construction operation area research, there is following characteristics:
(1) prior art supposition vehicle monomer is that indifference is driven monomer, i.e. each car driver driving behavior when running into work zone is consistent, and this does not conform to real traveling state of vehicle.Driver is different to contents such as condition of road surface familiarity, the driving behavior difference of processing burst traffic accident, driving habitses, and the driving behavior of each vehicle monomer is naturally also different.For improving the required precision of road construction operation area related content research, be necessary to analyze as research object using single unit vehicle monomer.
(2) to arrange according to VISSIM traffic simulation data and cognitive psychology qualitative analysis be basis to road construction operation area traffic sign.Prior art cannot characterize traffic sign and arrange and multi-form driver is affected to mechanism, and quantitative study traffic sign arranges form to driver's influence degree, therefore, present situation road construction operation area traffic sign arranges and has subjective factor impact significantly, cannot formulate reasonably and accurately the traffic sign plan of establishment.
According to above-mentioned, to existing investigative technique, analyze known, for the cognitive psychology of road construction operation area, ergonomics, traffic flow theory, with speeding, the technology such as theory, the theory of overtaking other vehicles, safe sight distance, microscopic traffic simulation are unidirectional and static, cannot accurately characterize dependency relation and the driving behavior of different steering vehicle monomers, particularly for road construction operation area traffic sign, arrange.It between steering vehicle monomer and traffic sign, is two-way, dynamic influence process, monomer can be according to traffic sign facilities, road traffic condition, in conjunction with former driving experience, make corresponding driving behavior, this behavior is extremely complicated, but the Rationality Assessment that traffic sign is arranged is most important really.
Summary of the invention
The object of the invention is to overcome the above-mentioned deficiency of prior art, a kind of road construction speed(-)limit sign setting position and the definite method of speed limit size are proposed, to reducing the impact of construction operation process on urban traffic, for the traffic sign setting of road construction operation area provides more scientific and effective foundation.Technical scheme of the present invention is as follows:
A kind of road construction speed(-)limit sign setting position and speed limit size are determined method, comprise the following steps:
(1) design driver SOAR intelligent body working memory structure
Consider type of vehicle, car speed, driver's type, input attributes and output attribute, design driver SOAR intelligent body working memory structure, input attributes is wherein subdivided into perception block up degree, adjacent position travel condition of vehicle, traffic signal sign, and output attribute comprises operator action type.
(2) design initial long-term memory rule, set up the long-term memory rule base of driver SOAR intelligent body
Every rule in long-term memory rule base comprises that matching condition and matching condition meet the lower operator that can advise, and under this condition, advise the numerical value preference value of this operator, operator action type is divided into Four types: Class1 turns to operator for determining, comprises that A selects forward, A selects left, A selects three kinds to the right; Type 2 is the road conditions operator that blocks up for a change, each downstream road section that driver thinks according to extraneous transport information and the driver that self summarizes the experience out congestion at that time; Type 3 is driven object operator for driver changes, and driving object is divided into the most economical and saves time two kinds most, and the most economical driver of requirement selects shortest path to arrive destination, saves time most and requires driver to select the shortest path of driving time; Type 4 is driver's mood change operator, and driver's mood is summarised as to two kinds, i.e. happy mood and rashness, and driver is in the state of happy mood, and the matching precision in decision process is high, and in rashness, matching precision declines.
(3) set up the decision-making period of driver SOAR intelligent body
1) input phase: create the element that the extraneous traffic of reflection changes by perception in working memory, complete the assignment to each perception information, set up perception vector;
2) state is set forth the stage: the content of perception vector in working memory is mated with the condition part of rule in long-term memory;
3) the suggestion operator stage: the production rule of all Satisfying Matching Conditions in trigger memory, produces operator and the corresponding preference index of suggestion;
4) select the operator stage, according to suggestion operator and preference index, select the optimum operator under current state, if unmatch rule or suggestion operator can not compare, knowledge is not enough to support decision-making, produces predicament, enter chunk learning phase, concrete operations are as follows:
With step-length λ, change matching precision, from all long-term memories, search coupling operator makes problem move to dbjective state, if continuous η
gin inferior decision-making driver's actual travel time all meet the expectation requirement, i.e. T (s
e)-T
e(s
e)≤η
ztime, corresponding operator is added to dbjective state s in decision process
e, chunk learning success, in formula, T (s
e) represent that driver is at dbjective state s
eunder actual driving time, T
e(s
e) represent that driver is at dbjective state s
eunder expectation driving time, T
e(s
e)=T
d(t, l)+T
v[v (l)], wherein T
d(t, l) is the average driving time of driver on moment t section l;
represent the impact of traffic sign on driving time,
represent the average running time of section l when traffic sign state is v, T
r(l) represent section l with reference to running time, η
zfor regular chunk upgrades threshold value;
5) the application operator stage, if the operator type of selecting is for turning to operator, output action, otherwise, application changes the road conditions operator that blocks up, change and drive object operator and mood change operator, a part of component that changes current state obtains an intermediateness, and the intermediateness that application operator is obtained is as current state;
6) intensified learning
Utilize formula p
r(s
e)=[T (s
e)-T
e(s
e)]
αrepresent end-state s
eunder total preference value of feedback, in formula, α is less than 1 constant, driver is carried out to preference distribution by the total preference of operator relating in the SOAR decision process of work zone, the Range-based of its preference allocated size and each state and dbjective state, intermediateness s
kwith dbjective state s
edistance d (s
k, s
e) attribute that changes by SOAR operator calculates, design formulas is
Wherein, d
s(s
e), d
e(s
e), m
o(s
e) represent respectively dbjective state s
eunder the perception intensity grade that blocks up, target area Position Number and driver's degrees of emotion, d
s(s
k), d
e(s
k), m
o(s
k) the rest may be inferred, f
1, f
2, f
3, f
4for constant, decision-making period t state transition path in k the operator o (s that state is corresponding
k) feedback preference value λ [d (s
k, s
e)] p
r(s
e), λ [d (s
k, s
e), r (s
k)] be p
r(s
e) be assigned to o (s
k) on weight, it is d (s
k, s
e) and s
kstate transition path r (the s at place
k) function,
Wherein | r (s
k) | be s
kthe number of states that path, place comprises.
7) continue next decision-making period, problem is moved towards dbjective state direction;
(4) different speed(-)limit sign setting positions and speed limit size and different road load coefficients are set and carry out emulation, according to the overlapping degree that takies road cellular when vehicle in front and adjacent vehicle, traffic conflict and the order of severity are differentiated, select traffic conflict and the lower simulated conditions of the order of severity, obtain work zone speed(-)limit sign setting position and speed limit size suitable under different transportation conditions.
The present invention uses the careful driver's of the portraying traffic behavior of SOAR cognitive techniques, the complicated driving behavior can accurate description steering vehicle monomer under different road construction environment, traffic sign being shown, for the traffic sign setting of road construction operation area provides more scientific and effective foundation.Particularly the invention has the advantages that:
(1) conventional art supposition vehicle monomer is indifference steering vehicle monomer, and driving behavior is identical.The present invention uses SOAR cognitive frame to build road construction operation area driver's agent model, carefully portray intelligent body working memory, long-term memory, procedural operator system of selection and study mechanism, thus the complicated driving behavior that accurate description steering vehicle monomer shows traffic sign under different road construction environment.
(2) in the past road construction operation area traffic sign to arrange according to VISSIM traffic simulation data and cognitive psychology qualitative analysis be basis, cannot characterize traffic sign and arrange and multi-form driver is affected to mechanism.Road construction operation area driver's agent model that the present invention builds can be to various traffic behavior study, memory and judgement, and the energy quantitative analysis evaluation traffic sign plan of establishment, thereby, for the traffic sign setting of road construction operation area provides more scientific and effective foundation.
Accompanying drawing explanation
Fig. 1-a is the working memory hierarchy of a driver SOAR intelligent body.
Fig. 1-b is some by the graph structure of road construction operation area driver's intelligent body working memory.
Fig. 2 flow chart decision-making period.
Fig. 3 emulation road network.
Fig. 4-a emulation work zone schematic diagram.
Fig. 4-b emulation experiment scene.
The impact of Fig. 5 speed(-)limit sign position difference on general conflict.
The impact of Fig. 6 speed(-)limit sign position difference on medium conflict.
The impact of Fig. 7 speed(-)limit sign position difference on Serious conflicts.
Fig. 8 speed limit 40km/h, traffic flow space-time diagram during L=10m.
Fig. 9 speed limit 40km/h, traffic flow space-time diagram during L=30m.
Figure 10 speed limit 40km/h, traffic flow space-time diagram during L=70m.
The specific embodiment
The present invention adopts SOAR cognitive frame to carry out modeling, road construction operation area driver's intelligent body is designed, comprise that intelligent body working memory, long-term memory, procedural operator system of selection and study mechanism are described in detail, and under experiment condition, carried out Multi simulation running, verify the validity that it arranges speed(-)limit sign optimization.
The present invention adopts the careful traffic behavior of portraying work zone driving vehicle driver of SOAR cognitive frame, the complicated traffic behavior of steering vehicle monomer in work zone described, and calmodulin binding domain CaM road grid traffic situation and construction operation distribution, the satisfy the need impact of the net traffic capacity of assessment work zone arrangement, and then the reasonable value of proposition lengths of upstream transition regions of construction operation area, to reducing the impact of construction operation process on urban traffic.Below the present invention is elaborated.
(1) SOAR cognitive frame
The full name of SOAR is state, operator and target (State, Operator and Result), in a kind of framework that is called " universal intelligent " of nineteen eighty-three exploitation by people such as AllenNewell, main discussion knowledge, thinking, intelligence and memory are cognitive structures that range of application is very wide.Wherein, state is that current situation about will deal with problems characterizes, and operator is the operation that can change state, produce new state, and target is the desired result that need to deal with problems.The operation of SOAR is exactly to apply constantly operator and select next operator until the process of this problem realization of goal at problem space.
SOAR framework mainly comprises I/O interface, long-term memory district, working memory district three parts, also has some other potential mechanism as decision-making period, learning process etc.SOAR must alternately, be mapped to the external world in working memory by perception by perception/action interface and extraneous generation, by moving the sign returning to external environment of working memory inside and producing action.SOAR inside has working memory district and the long-term memory district of different forms of characterization, is used for respectively describing current state and the long-term memory of problem solving.Working memory represents result, moving target and the movable operator etc. of the perception data relevant to current state, middle reasoning with the state/object diagram with hierarchical organization.Long-term memory comprises procedural memory, Semantic memory and episodic memory.SOAR is by a fixing treatment mechanism---decision-making period, completes the function such as choice and application operator of SOAR.Being accompanied by decision-making period SOAR has four kinds of different study mechanisms, is respectively intensified learning, chunk, episodic study and Semantic study (the present invention has adopted first two study mechanism).
(2) driver's intelligent body working memory design
Working memory district is the place that short-term memory unit is deposited, be used for reflecting the knowledge that the situation of presence is relevant, as current state and operator etc., it is comprised of the object that comprises a series of attribute and property value, the present invention adopts hierarchy to represent driver's intelligent body working memory, mainly considers type of vehicle, car speed, driver's type, input and output attribute.Driver's type comprises each driver's character type, the familiarity to road network, matching precision and current location.Input attributes represents the information relevant to section and surrounding enviroment that intelligent body perceives from the external world, can be subdivided into again perception block up degree, adjacent position travel condition of vehicle, traffic signal sign; Output attribute represents operator and the affect result of application operator on working memory and surrounding enviroment that output selection will be applied, and is mainly operator action type.Except output attribute, in all working memory, variable attribute is state, comprises car speed, perception block up degree, adjacent position travel condition of vehicle, traffic signal sign.
Fig. 1-a is the working memory hierarchy of a driver SOAR intelligent body, has represented that driver is by the scene example of work zone, the original state S1 of intelligent body " driver-car-unit ", when original state, intelligent body is in 2 tracks, and target area is zone3, and sub-state is S0; Io attribute is the input and output of S1, respectively by input-link and output-link Interface realization, wherein the identifier I3 of input-link has an attribute road, represent intelligent body from the external world, perceive with section relevant information, comprise attribute sign and density, C-sign represents each section, the downstream of traffic light system jam situation, density represents the perception congestion status in each track, current road, neighbor-pos represents the vehicle-state of current intelligent body adjacent position, to meet, changes rule and represents.The identifier I2 of output interface output-link represents output possible under this state, for a change destination of the operator of selecting under current state.
Fig. 1-b is some by the graph structure of road construction operation area driver's intelligent body working memory.State S1 in working memory comprises five attributes, and wherein the value of vehicle, driver, input and output attribute is object V1, D1 and IO1, and all the other two attributes represent that respectively the name of this state is called s1, and it does not have father's state.The relevant information of the V1 attribute representation intelligent body component units vehicle of state S1, comprises that expression vehicle is of a size of little, and the purposes of vehicle is private car, and present speed is very fast.The D1 attribute representation driver's of S1 relevant information, comprises that driver's sex is man, and the age is 45, and the driving age is 20 years, and monthly income is 8000, and personality is steady type, very familiar to road network, and the matching precision of current driver's is 1, and current location is initial position.IO attribute is the input and output of S1, by input/output interface, realize respectively, wherein the identifier I2 of input interface has four attributes, represent respectively the information relevant to section and surrounding enviroment that intelligent body perceives from the external world, comprise attribute speed(-)limit sign, warning mark, current section block up degree and the prediction downstream degree of blocking up, in upper figure, corresponding property value represents that respectively the speed(-)limit sign signal on each section is to be 10-20km lower than current vehicle speed gap, caution sign type is guidance type, the perception jam situation 2 (blocking up very much) of current road, the jam situation of the downstream road section predicting according to perception is 220 (left sides, section, front all blocks up, right-hand rotation section is unobstructed).The identifier O3 of output interface represents the output under this state, such as operator and application operator that selection will be applied are exported by the attribute under O3 the result that affects of working memory and surrounding enviroment.
(3) long-term memory
Long-term memory is achievement memory district, and it comprises all achievements, and user can complete concrete function by creative resultant, and driver of the present invention is mainly procedural memory by work zone driving behavior SOAR intelligent body, adopts production representation.
Production use of the present invention " if-so " Rule Expression " condition-action ".As fruit part has indicated the condition of regular motion, part has illustrated and has caused individual action or behavior so.Following formula is the initial long-term memory rule that the present invention arranges, and adopts Soar to stylize and represents with accurate rules for writing.
Above-mentioned production rule name is called r1, if represent that driver personality is for conservative type, control signal is red, green, green, the blocking up as seriously blocking up of perception, and satisfied right-hand rotation condition, and driver destination is zone3, selects right-hand rotation section, downstream.It should be noted that SOAR intelligent body is only provided with the initial long-term memory rule of a part, it can learn in decision and feedback process, and rule is upgraded gradually and increased.In fact, what no matter initial rules arranged is how perfect, also can not comprise or correctly comprise all policy-making thought and preference, therefore to make intelligent physical efficiency preferably Simulation of Driver by work zone driving behavior, intelligent body need to, through training after a while, make to remember rule more perfect, the more approaching reality of preference, in this process, the impact of study mechanism is most important.
(4) decision-making period
The decision-making period of SOAR intelligent body is actual is exactly generation, comparison, choice and application operator, so that the process that current state shifts towards dbjective state.Every rule in long-term memory rule base comprises that matching condition and matching condition meet the lower operator that can advise, and under this condition, advises the numerical value preference value of this operator.Operator of the present invention is divided into Four types: Class1 turns to operator for determining, comprises that A selects forward, A selects left, A selects three kinds to the right; Type 2 is the road conditions operator that blocks up for a change, each downstream road section that driver thinks according to extraneous transport information and the driver that self summarizes the experience out congestion at that time; Type 3 is driven object operator for driver changes, and driving object is divided into the most economical and saves time two kinds most, and the most economical driver of requirement selects shortest path to arrive destination, saves time most and requires driver to select the shortest path of driving time; Type 4 is driver's mood change operator, and the present invention is summarised as two kinds driver's mood, i.e. happy mood and rashness, and driver is in the state of happy mood, and the matching precision in decision process is high, and in rashness, matching precision declines.Operator is transitioned into intermediateness by original state, and finally arrives dbjective state through the transfer of multiple intermediatenesses.The SOAR Decision-making of Agent cycle of the present invention as shown in Figure 2.
In Fig. 2, input phase is created the element that the extraneous traffic of reflection changes in working memory by perception, complete the assignment to each perception information; The state stage of setting forth mates the content of perception vector in working memory with the condition part of rule in long-term memory; The suggestion operator stage is triggered the production rule (in procedural memory) of all Satisfying Matching Conditions, produces operator and the corresponding preference index of suggestion; Select the operator stage, according to suggestion operator and preference index, select the optimum operator under current state.If knowledge is not enough to support decision-making (can not compare etc. as unmatched rule or suggestion operator), produce predicament, enter chunk learning phase; The application operator stage, if the operator type of selecting is for turning to operator, output action, otherwise changing a part of component of current state obtains an intermediateness (application changes the road conditions operator that blocks up, change and drive object operator and mood change operator etc.), the intermediateness in this case application operator being obtained, as current state, continues next decision-making period, and problem is moved towards dbjective state direction.
(5) study mechanism
1) chunk study
Chunk study is the basic means that makes to expand soar intelligent body rule, in the time of can not supporting that intelligent body makes decisions under current rule system, this state of intelligent body is called to " predicament ", when predicament produces, meaning in the long-term memory of current system does not have utilizable operator that problem solving process is moved forward in problem space, need automatically to create a new rule and solve current predicament, the foundation of chunking rule need to be analyzed production rule relevant to reaching result in long-term memory
The chunk study mechanism that the present invention adopts mainly comprises that predicament produces condition, and predicament solution and chunking rule form mechanism.In operator set feature, if operator set be empty or set in preference optimum operator be less than with the difference of suboptimum operator the operator that state s is corresponding and directly select threshold tau (s), so meet predicament generation condition.When predicament produces, the step of predicament solution is: first substate attribute in search condition, the regular creation operator that utilizes sub-state to trigger makes current state (father's state of sub-state) mobile, as fruit state also produces predicament or substate property value is nil (sky), with step-length λ, change s matching precision, from all long-term memories, search coupling operator makes problem move to dbjective state.If η continuously
gin the chunking rule of inferior employing, driver's actual travel time all meet the expectation requirement, i.e. T (s
e)-T
e(s
e)≤η
ztime, corresponding operator being added in decision process and run into predicament state, chunk is successful.Wherein T (s
e) represent that driver is at dbjective state s
eunder actual driving time, T
e(s
e) represent that driver is at dbjective state s
eunder expectation driving time, T
e(s
e)=T
d(t, l)+T
v[v (l)], wherein T
dfor the average driving time (expression driving experience) of driver on moment t section l;
2) intensified learning
The source of intensified learning knowledge is the feedback of external environment condition, and it can adjust the expection that rewarded future, and then these awards are used to select to obtain greatest hope in future and reward in the action.In SOAR intelligent body, driving time and the total value of feedback of operator are connected.Adopt formula p
r(s
e)=[T (s
e)-T
e(s
e)]
αrepresent end-state s
eunder total preference value of feedback, α is less than 1 constant, in the embodiment of the present invention, parameter alpha gets 0.5.Driver relates to multiple states and operator by work zone behavior SOAR decision process, total operator preference need to be carried out to preference distribution, the Range-based of its preference allocated size and each state and dbjective state.Intermediateness s
kwith dbjective state s
edistance d (s
k, s
e) attribute that changes by SOAR operator calculates,
Wherein, d
s(s
e), d
e(s
e), m
o(s
e) represent respectively dbjective state s
eunder the perception intensity grade that blocks up, target area Position Number and driver's degrees of emotion, d
s(s
k), d
e(s
k), m
o(s
k) the rest may be inferred, f
1, f
2, f
3, f
4for constant.Decision-making period t state transition path in k the operator o (s that state is corresponding
k) feedback preference value λ [d (s
k, s
e)] p
r(s
e), λ [d (s
k, s
e), r (s
k)] be p
r(s
e) be assigned to o (s
k) on weight, it is d (s
k, s
e) and s
kstate transition path r (the s at place
k) function, the present invention
(6) emulation experiment
1) simulated conditions and speed(-)limit sign setting position and speed limit optimised evaluation method
Adopt road as shown in Figure 3 to carry out emulation, the long 2km in two-way six-lane through street, design speed is 80km/ hour, traffic capacity 1500pcu/h/ln, lane width is 3.75 meters.From 800m place, crossing, carrying out construction operation, sealing to temporary parking band and two tracks, and becomes two-way Four-Lane Road by other four lane-rebuildings of road from east orientation west, and the free stream velocity of car is 80km/h.The traffic sign of laying in construction advance notice section is as shown below, and wherein speed(-)limit sign is L apart from the distance of position, boundary, upstream transition district, recognizes and reads starting position for apart from speed(-)limit sign [60,100] scope.
Speed(-)limit sign setting position and speed limit optimised evaluation method are: when vehicle approaching work zone, need to change, and exist and interweave at the corresponding track moving traffic of changing.
The present invention differentiates traffic conflict and the order of severity according to the overlapping degree that takies road cellular when vehicle in front and adjacent vehicle.The conflict order of severity is divided into three grades: generally conflict, medium conflict, Serious conflicts, its corresponding overlapping cellular number is respectively 1,2,3 and more than.Wherein the length and width of two-dimentional cellular are 1.25 meters.
2) emulation experiment and result
Road construction maintenance work is to carry out in the construction operation section of partial closure, and near construction operation section, vehicle flowrate is large, traffic environment is poor.Construction section operated by rotary motion sign, graticule, canalization facility, obstruction, and be equipped with construction operation vehicle.As shown in Fig. 4-a, typical two-way six-lane highway is when maintenance construction, and general construction section specifically can be divided into following components:
(1) construction advance notice section: the construction of prompting road ahead, makes driver note traffic situation of change, to take measures in time; In front, construction and blockade construction operation section, should set up construction caution sign, for noticing the Traffic interruption of road, the situation that detours, driver is asked sometimes and adjust its road speed.
(2) upstream transition district: play guide functions, guided vehicle changes travel direction, causes vehicle changing Lane to meet at fast by runway.Also can image be referred to as the transition section that confluxes.
(3) buffer area, upstream: for driving person and constructor provide buffer protection, buffering is forbidden to park utensil, vehicle, material in section, forbids that staff stops, with the accident of avoiding occurring due to lose control of one's vehicle.
(4) construction working district: the place that is maintenance construction personnel activity and work, between track and operation section, spacer assembly must be set, operation section also should be engineering truck safe import and export is provided, and construction operation section whole process is used with blocking or taper traffic sign and through lane are isolated on the road of direction.Sailing the vehicle in this section into can not overtake other vehicles, and can only sail with speeding on.
(5) downstream transition region: be to remove section compression, the transition section that recovery is normally travelled, plays guide functions, and guided vehicle changes travel direction, and changing Lane, enters normal traveling lane.Also can image be referred to as to shunt transition section.
(6) construction terminator: represent the end of construction section and the releasing of speed-limited, be positioned at the end of construction section.Traffic flow recovers normal operating condition gradually.
Emulation experiment scene of the present invention as shown in Figure 5.Change the speed restriction upper limit, be set to respectively 30km/h, 35km/h, 40km/h, 45
Km/h, 50km/h, 55km/h, 60km/h, and speed(-)limit sign setting position L length is made as respectively to 10m, 20m, 30m, 40m, 50m, 60m, 70m, departure frequency is made as 1050pcu/h/ln, be that road load coefficient is 0.7,7 kinds of friction speeds are arranged to the upper limit and mark position and intersect emulation experiment, in emulation 120 minutes, obtain traffic conflict index under different condition as shown in table 1-3 and Fig. 5-10, Figure 11-14 are the traffic flow space-time diagram under different condition.
General conflict number of times under table 1 speed(-)limit sign diverse location and speed limit size
Medium conflict number of times under table 2 speed(-)limit sign diverse location and speed limit size
Serious conflicts number of times under table 3 speed(-)limit sign diverse location and speed limit size
3) analysis of simulation result
(1), as table 1-3, shown in Figure 57, for the different setting position of speed(-)limit sign and speed limit size, general conflict, medium conflict and Serious conflicts intensity of variation are different.When speed limit size is constant, while changing speed(-)limit sign desired location, L increases to 70m process from 10m, and it is comparatively obvious that the number of times that generally conflicts reduces trend, and this shows possibility that minor accident occurs along with the reach of speed(-)limit sign desired location and reduces; Medium conflict number of times is along with the reach of speed(-)limit sign desired location changes and not obvious, this show possibility that medium accident occurs along with the reach of speed(-)limit sign desired location without significant change; Along with the reach of speed(-)limit sign setting position, Serious conflicts number of times has different variation tendencies to different speed limit sizes, when speed limit size is larger, substantially present minimizing trend, and when speed limit size less (30-35km), change curve afterbody upwarps, and when setting position increases to more than 50 meters, Serious conflicts degree has the trend of rising on the contrary.
(2) if Fig. 8-10 are speed limit 40km/h, traffic flow space-time diagram when L gets diverse location.During L=10m, in the occurrence of large-area in construction area front, block up, and along with the growth of time, congestion regions expands gradually; During L=30 rice, front, works area block up area reduce very fast, congestion regions expand trend controlled; During L=70 rice, front, works area blocks up, and area is further to be reduced but still existence, and during with L=30 rice, traffic flow modes difference is little.
(3) consider traffic conflict and traffic congestion situation, be recommended under emulation experiment condition, the traffic sign setting position of speed limit 40km/h is left and right, 30 meters, front, construction transition district.
Claims (1)
1. road construction speed(-)limit sign setting position and speed limit size are determined a method, comprise the following steps:
(1) design driver SOAR intelligent body working memory structure
Consider type of vehicle, car speed, driver's type, input attributes and output attribute, design driver SOAR intelligent body working memory structure, input attributes is wherein subdivided into perception block up degree, adjacent position travel condition of vehicle, traffic signal sign, and output attribute comprises operator action type;
(2) design initial long-term memory rule, set up the long-term memory rule base of driver SOAR intelligent body
Every rule in long-term memory rule base comprises that matching condition and matching condition meet the lower operator that can advise, and under this condition, advise the numerical value preference value of this operator, operator action type is divided into Four types: Class1 turns to operator for determining, comprises that A selects forward, A selects left, A selects three kinds to the right; Type 2 is the road conditions operator that blocks up for a change, each downstream road section that driver thinks according to extraneous transport information and the driver that self summarizes the experience out congestion at that time; Type 3 is driven object operator for driver changes, and driving object is divided into the most economical and saves time two kinds most, and the most economical driver of requirement selects shortest path to arrive destination, saves time most and requires driver to select the shortest path of driving time; Type 4 is driver's mood change operator, and driver's mood is summarised as to two kinds, i.e. happy mood and rashness, and driver is in the state of happy mood, and the matching precision in decision process is high, and in rashness, matching precision declines;
(3) set up the decision-making period of driver SOAR intelligent body
1) input phase: create the element that the extraneous traffic of reflection changes by perception in working memory, complete the assignment to each perception information, set up perception vector;
2) state is set forth the stage: the content of perception vector in working memory is mated with the condition part of rule in long-term memory;
3) the suggestion operator stage: the production rule of all Satisfying Matching Conditions in trigger memory, produces operator and the corresponding preference index of suggestion;
4) select the operator stage, according to suggestion operator and preference index, select the optimum operator under current state, if unmatch rule or suggestion operator can not compare, knowledge is not enough to support decision-making, produces predicament, enter chunk learning phase, concrete operations are as follows:
With step-length λ, change matching precision, from all long-term memories, search coupling operator makes problem move to dbjective state, if continuous η
gin inferior decision-making driver's actual travel time all meet the expectation requirement, i.e. T (s
e)-T
e(s
e)≤η
ztime, corresponding operator is added to dbjective state s in decision process
e, chunk learning success, in formula, T (s
e) represent that driver is at dbjective state s
eunder actual driving time, T
e(s
e) represent that driver is at dbjective state s
eunder expectation driving time, T
e(s
e)=T
d(t, l)+T
v[v (l)], wherein T
d(t, l) is the average driving time of driver on moment t section l;
represent the impact of traffic sign on driving time,
represent the average running time of section l when traffic sign state is v, T
r(l) represent section l with reference to running time, η
zfor regular chunk upgrades threshold value;
5) the application operator stage, if the operator type of selecting is for turning to operator, output action, otherwise, application changes the road conditions operator that blocks up, change and drive object operator and mood change operator, a part of component that changes current state obtains an intermediateness, and the intermediateness that application operator is obtained is as current state;
6) intensified learning
Utilize formula p
r(s
e)=[T (s
e)-T
e(s
e)]
αrepresent end-state s
eunder total preference value of feedback, in formula, α is less than 1 constant, driver is carried out to preference distribution by the total preference of operator relating in the SOAR decision process of work zone, the Range-based of its preference allocated size and each state and dbjective state, intermediateness s
kwith dbjective state s
edistance d (s
k, s
e) attribute that changes by SOAR operator calculates, design formulas is
Wherein, d
s(s
e), d
e(s
e), m
o(s
e) represent respectively dbjective state s
eunder the perception intensity grade that blocks up, target area Position Number and driver's degrees of emotion, d
s(s
k), d
e(s
k), m
o(s
k) the rest may be inferred, f
1, f
2, f
3, f
4for constant, decision-making period t state transition path in k the operator o (s that state is corresponding
k) feedback preference value λ [d (s
k, s
e)] p
r(s
e), λ [d (s
k, s
e), r (s
k)] be p
r(s
e) be assigned to o (s
k) on weight, it is d (s
k, s
e) and s
kstate transition path r (the s at place
k) function,
wherein | r (s
k) | be s
kthe number of states that path, place comprises;
7) continue next decision-making period, problem is moved towards dbjective state direction;
(4) different speed(-)limit sign setting positions and speed limit size and different road load coefficients are set and carry out emulation, according to the overlapping degree that takies road cellular when vehicle in front and adjacent vehicle, traffic conflict and the order of severity are differentiated, select traffic conflict and the lower simulated conditions of the order of severity, obtain work zone speed(-)limit sign setting position and speed limit size suitable under different transportation conditions.
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| CN103114529B (en) * | 2013-01-21 | 2015-07-01 | 东南大学 | Comprehensive setting method for speed control facilities of road-accident-prone road section |
| CN107780346B (en) * | 2016-08-31 | 2020-12-29 | 深圳市富友昌科技股份有限公司 | Traffic sign control method and device |
| CN108389391B (en) * | 2018-02-27 | 2021-08-27 | 智慧城市(保定)企业管理服务有限公司 | Mobile internet road condition computing system |
| CN111159832B (en) * | 2018-10-19 | 2024-04-02 | 百度在线网络技术（北京）有限公司 | Traffic information stream construction method and device |
| CN111275922A (en) * | 2020-01-10 | 2020-06-12 | 深圳市麦谷科技有限公司 | Administrative region warning method and system |
| CN112991726B (en) * | 2021-02-08 | 2022-01-18 | 东南大学 | Method for setting road marking in urban expressway interweaving area |
| CN116311940B (en) * | 2023-03-23 | 2024-03-26 | 东南大学 | Dynamic traffic guidance system and method for expressway reconstruction and expansion operation area |
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