CN106355907B - Signalized crossing queue length real-time estimation method based on track of vehicle - Google Patents
Signalized crossing queue length real-time estimation method based on track of vehicle Download PDFInfo
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Abstract
The present invention relates to a kind of signalized crossing queue length real-time estimation method based on track of vehicle, comprising the following steps: 1) according to real-time vehicle trace information, carry out vehicle traffic condition differentiation；2) it according to real-time vehicle trace information, vehicle traffic condition and kinematical equation, carries out vehicle and queuing key point is added and leaves the crucial point estimation of queuing；3) it is added according to the vehicle estimated and is lined up key point, carry out fleet's identification of upstream arrival；4) it is added according to the vehicle estimated and is lined up key point, leaves queuing key point and identified fleet, carry out assembling wave and evanescent wave estimation；5) according to the assembly wave and evanescent wave estimated, intersection signal parameter and queue length estimation are carried out.Compared with prior art, the present invention can adapt to the data environment of low sample frequency, low sampling rate, have many advantages, such as that strong robustness, real-time are high, accuracy is good.
Description
Technical field
The present invention relates to field of traffic control, are lined up more particularly, to a kind of signalized crossing based on track of vehicle
Length real-time estimation method.
Background technique
Chief component of the signalized intersections as city road network can be sent out often since the periodicity of traffic lights replaces
Raw traffic congestion, largely constrains the overall operation efficiency of City road traffic system.Queue length is handed over as evaluation
On the one hand one important indicator of prong operation can be used for estimating indirectly that vehicle delay, stop frequency and journey time etc. refer to
Mark, on the other hand can directly feed back for signal timing optimization.
The existing queue length estimation analogous technical based on track of vehicle mainly has: (1) Cheng et al. is based on High-precision vehicle
Track data, extracts the crucial tracing point for representing vehicle-state variation, so using traffic wave method simulating vehicle be lined up with
Evanishment realizes queue length estimation；(2) Ban et al. differentiates that vehicle is delayed mode using sampling journey time, and based on not
Vehicle queue length estimation is carried out with delay mode；(3) Comert establishes statistical model to analyze Floating Car sample rate and row
The relationship of team's length accuracy, and calculated under the premise of assuming that upstream vehicle reaches distribution and Floating Car sample rate and be lined up queue number
Term hopes；(4) Xiong Yingge et al. considers intersection Floating Car track dot density and acceleration profile feature, true using clustering method
The maximum position of fixed track point averag density or the maximum position of sudden change of acceleration, and as the estimation of average queue length
Value.
The existing queue length Estimation Study based on track of vehicle is summarized, following problems are primarily present: (1) assuming that vehicle arrives
Up to (uniformly or poisson arrival), this hypothesis has ignored the influence that upstream intersection reaches vehicle；(2) assume Floating Car sampling
Rate, actually Floating Car sample rate are not a definite values, can be changed with the variation of when and where, to influence estimation essence
Degree；(3) assume signal parameter it is known that being actually in most cases that can not obtain signal parameter information, particularly with adaptive
Induction signal control system.
Summary of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide one kind to be based on track of vehicle
Signalized crossing queue length real-time estimation method, can adapt to the data environment of low sample frequency, low sampling rate, have
There is the advantages that strong robustness, real-time are high, and accuracy is good.
The purpose of the present invention can be achieved through the following technical solutions:
A kind of signalized crossing queue length real-time estimation method based on track of vehicle the following steps are included:
1) according to real-time vehicle trace information, vehicle traffic condition differentiation is carried out；
2) it according to real-time vehicle trace information, vehicle traffic condition and kinematical equation, carries out vehicle and queuing key is added
Put and leave the crucial point estimation of queuing；
3) it is added according to the vehicle estimated and is lined up key point, carry out fleet's identification of upstream arrival；
4) it is added according to the vehicle estimated and is lined up key point, leaves queuing key point and identified fleet, collected
Tie wave and evanescent wave estimation；
5) according to the assembly wave and evanescent wave estimated, intersection signal parameter and queue length estimation are carried out.
The step 1) specifically: obtain track of vehicle information in real time, enable i-th vehicle in tkThe tracing point information at moment
It is expressed as I-th vehicle is respectively indicated in tkSpace coordinate, the velocity and acceleration at moment,
Then i-th vehicle is in tkThe traffic behavior at momentAre as follows:
Wherein, vth1And vth2For the preset threshold speed differentiated for state of motion of vehicle, vth1< vth2, M, C and S points
It Biao Shi not move, three states of low speed stop-go and parking.
The step 2) specifically:
21) estimation vehicle leaves queuing key point:
When i-th vehicle is in tkAnd tk+1The traffic status identification at moment isWithWhen, it obtains corresponding
Tracing point informationWith
IfvfFor free stream velocity, then i-th vehicle is in tkMoment leaves queuing key pointAre as follows:
IfThen i-th vehicle is in tkMoment leaves queuing key pointAre as follows:
Wherein, aaccIt is i-th vehicle in tkAnd tk+1Acceleration between moment, is approximately equal tod3For i-th vehicle
In tkAnd tk+1The distance between moment is equal to
22) estimation vehicle, which is added, is lined up key point:
When i-th vehicle is in tk-1And tkThe traffic status identification at moment isWithWhen, it obtains corresponding
Tracing point informationWith
IfThen i-th vehicle is in tkThe addition at moment is lined up key pointAre as follows:
IfThen i-th vehicle is in tkThe addition at moment is lined up key pointAre as follows:
When i-th vehicle is in tk-1And tkThe traffic status identification at moment isWithWhen, it obtains corresponding
Tracing point informationWith
IfThen i-th vehicle is in tkThe addition at moment is lined up key pointAre as follows:
IfThen i-th vehicle is in tkThe addition at moment is lined up key pointAre as follows:
Wherein, adecIt is i-th vehicle in tk-1And tkAcceleration between moment, is approximately equal tod2For i-th vehicle
In tk-1The distance between starting point, d under the tracing point and low speed stop-go state at moment1For under low speed stop-go state
The distance between starting point and terminating point.
I-th vehicle is in tk-1The distance between starting point d under the tracing point and low speed stop-go state at moment2
Meet following formula:
The distance between starting point and terminating point d under the low speed stop-go state1Meet following formula:
The step 3) specifically:
31) vehicle estimated is added and is lined up key pointI=1 ..., n, n are the sum of vehicle, carry out ascending order row
Sequence；
32) time difference for being lined up key point and range difference is added in Adjacent vehicles after obtaining ascending sort, meets following formula:
Wherein,Indicate the time difference of the addition queuing key point of i-th vehicle and the latter Adjacent vehicles,ΔxiIndicate the range difference of the addition queuing key point of i-th vehicle and the latter Adjacent vehicles；
33) fleet's identification of upstream arrival is carried out, specifically:
Ifθ is fleet's judgment threshold, then i-th vehicle and the latter Adjacent vehicles belong to a fleet, instead
It, does not belong to a fleet.
The step 4) specifically:
41) it is added according to the vehicle estimated in time interval J and is lined up estimation point and identified fleet, to assembly wave
Piecewise linear model fitting is carried out, following formula is met:
s.t.
Wherein,The set for being lined up estimation point, set are added for vehicles all in time interval JMiddle basis has identified
Fleet be divided intoA fleet, m indicate the number of fleet,Respectively indicate m, m+1 fleet's headstocks
The addition of vehicle is lined up key point, xmIt indicates to correspond toVehicle space coordinate, In respectively time interval J
The m sections of slopes and intercept to be estimated for assembling wave,Bright moment, l are opened for corresponding entrance driveway red lightstop-barIndicate stop line institute
In position, ε is positive adjusting parameter, for adjusting constraint hardness；
Then assemble wave expression formula in time interval JAre as follows:
Wherein, t indicates the moment；
42) queuing estimation point is left according to the vehicle estimated in time interval J, least square method is carried out to evanescent wave
Linear fit meets following formula:
s.t.
Wherein,The set for being lined up estimation point is left for all vehicles in time interval J,It is respectively to be estimated
The slope and intercept of evanescent wave；
Then assemble wave expression formula in time interval JAre as follows:
The step 5) specifically:
51) red light, green light open the bright moment in the J of acquisition time intervalWithMeet following formula:
Wherein, lstop-barFor stop line position,Assemble the slope of wave for first segment in time interval J
And intercept；
52) intersection signal parameter: the signal period T in time interval J is obtainedJWith respective inlets road green time PTJ,
Meet following formula:
Or
Wherein,The bright moment is opened for the green light in time interval J-1,When opening bright for the red light in time interval J-1
It carves；
53) maximum queue length in current cycle estimator is obtainedAnd its corresponding momentMeet following formula:
Wherein,The respectively slope and intercept of time interval J final stage assembly wave；
54) secondary queue length is obtainedAnd the corresponding momentMeet following formula:
Wherein,Assemble wave slope for first segment in time interval J+1,When opening bright for red light in time interval J+1
It carves.
Compared with the prior art, the advantages of the present invention are as follows:
1) it discharges known vehicle in the prior art and reaches analysis, known Floating Car sample rate and known signal parameter etc.
It is assumed that more practicability；
2) strong real-time can be realized the queue length detection rolled based on the period, and accuracy is high, can not only realize most
Big queue length estimation, additionally it is possible to realize secondary queuing estimation；
3) method is advanced, strong robustness, can adapt to the data environment that China adapts to low sample frequency, low sampling rate.
4) propose to move, low speed stop-go and parking divide for three traffic behaviors, with utilizing three traffic shapes
State obtains addition queuing key point and queuing key point is left in queuing, can preferably react queue clearance wave and assemble the shape of wave
State obtains data processed result with high accuracy.
5) it for assembly wave is lined up, proposes that upstream reaches fleet and sentences method for distinguishing, is obtained after piecewise fitting and assemble wave formula
The fragmentation state for being lined up and assembling wave can be preferably reacted, the data precision is provided.
Detailed description of the invention
Fig. 1 is flow chart of the method for the present invention；
Fig. 2 is intersection macroscopic view parent map；
Fig. 3 is intersection traffic wave schematic diagram；
Fig. 4 is intersection vehicles velocity profile；
Fig. 5 is that fleet identifies schematic diagram；
Fig. 6 is that bright phase estimate box-shaped figure is opened based on green light；
Fig. 7 is that bright phase estimate box-shaped figure is opened based on red light；
Fig. 8 is queue length error analysis comparison diagram.
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.The present embodiment is with technical solution of the present invention
Premised on implemented, the detailed implementation method and specific operation process are given, but protection scope of the present invention is not limited to
Following embodiments.
The present invention is obtained based on LWR traffic shock wave model foundation, and LWR traffic wave pattern can be good at disclosing intersection
Vehicle queue is formed and evanishment.As shown in Fig. 2, intersection traffic stream meets triangle macroscopic view parent map characteristic, q in Fig. 2: table
Show flow (veh/h), K: indicating density (veh/km), q1,1: for indicating a kind of flow status in intersection, q1,2: for indicating
Intersection another kind flow status, qm: intersection maximum stream flow, k1,1: q1,1Corresponding intersection density, k1,2: with q1,2Phase
Corresponding intersection density, km: density corresponding to the maximum stream flow of intersection, kj: intersection jam density, v1,1,: in q1,1With
k1,1The vehicle first segment of corresponding states assembles wave velocity of wave, v1,2: in q1,2And k1,2The vehicle second segment of corresponding states assembles wave wave
Speed, v2: evanescent wave velocity of wave, v3: free stream velocity.
According to LWR traffic shock wave model mechanism, since traffic lights are periodically replaced, intersection will form multiply traffic shock wave, such as
Shown in Fig. 3.When red light opens bright, vehicle is forced to stop, and is formed after assembling wave direction and is transmitted, and assembles wave velocity of wave and reach with upstream vehicle
Change and changes；Green light opens bright moment, and vehicle is sailed out of with saturation volume rate starting, to evanescent wave after formation.Due to backward evanescent wave
Velocity of wave be greater than after to wave is assembled, two traffic shock waves can intersects in c point, reach maximum queue length at this time, while generating preceding to disappearing
Wave is dissipated, queuing dribbles.X in Fig. 3: indicating distance, and wherein intersection parking line position is at 70m a-g: Jin Jinbiao
Indicating is shown a little, A-D: indicates four kinds of different traffic behaviors, i.e., 4 kinds different flows and density.
The basic applicable elements of the method for the present invention are as follows:
1) number is lined up in same intersection vehicles not exceeding twice；
2) without oversize vehicle, such as lorry and bus etc. in queuing vehicle.
As shown in Figure 1, a kind of signalized crossing queue length real-time estimation method based on track of vehicle include with
Lower step:
1) according to real-time vehicle trace information, vehicle traffic condition differentiation is carried out；
2) it according to real-time vehicle trace information, vehicle traffic condition and kinematical equation, carries out vehicle and queuing key is added
Put and leave the crucial point estimation of queuing；
3) it is added according to the vehicle estimated and is lined up key point, carry out fleet's identification of upstream arrival；
4) it is added according to the vehicle estimated and is lined up key point, leaves queuing key point and identified fleet, to dissipation
Wave velocity of wave segmentation/queuing is assembled wave and is estimated, is obtained according to the assembly wave of acquisition and evanescent wave velocity of wave and assembles wave and evanescent wave；
5) according to the assembly wave and evanescent wave estimated, intersection signal parameter (periodic signal etc.) and queue length are carried out
Estimation.
Step 1) specifically: i-th vehicle intersection velocity profile as shown in figure 4, if i-th vehicle is in tkWhen
The tracing point information at quarter is I-th vehicle is respectively indicated in tkSpace coordinate, the speed at moment
And acceleration, space coordinate are obtained by the latitude and longitude information of vehicle, then i-th vehicle is in tkThe traffic behavior at momentAre as follows:
Wherein, vth1And vth2For the preset threshold speed differentiated for state of motion of vehicle, vth1< vth2, M, C and S points
It Biao Shi not move, three states of low speed stop-go and parking.
Work asVehicle belongs to motion state, once car speedThen enter low speed to stop walking state, when
When vehicle is in low speed stop-go state, speed is in [vth2,vth1) range；Work as car speedLower than vth2When, vehicle
Into dead ship condition.In Fig. 4, x1-x4: indicate the space coordinate of 4 track samples；d1-d3Expression is two-by-two between the sample point of track
Traveling.
Step 2) specifically:
21) estimation vehicle leaves queuing key point:
When i-th vehicle is in tkAnd tk+1The traffic status identification at moment isWithWhen, it obtains corresponding
Tracing point informationWith
IfvfFor free stream velocity, i.e., vehicle is in tk+1Moment, then i-th vehicle was in t still in acceleration modek
Moment leaves queuing key pointAre as follows:
IfI.e. vehicle is in tk+1Moment, then i-th vehicle was in t still in uniform speed motion statekMoment is left
It is lined up key pointAre as follows:
Wherein, aaccIt is i-th vehicle in tkAnd tk+1Acceleration between moment, is approximately equal toIt can value i-th
Vehicle is in tk+1Moment accelerationd3It is i-th vehicle in tkAnd tk+1The distance between moment is equal to
22) estimation vehicle, which is added, is lined up key point:
When i-th vehicle is in tk-1And tkThe traffic status identification at moment isWithWhen, it obtains corresponding
Tracing point informationWith
IfI.e. vehicle is in tk-1Moment, then i-th vehicle was in t still in uniform speed motion statekThe addition at moment
It is lined up key pointAre as follows:
IfI.e. vehicle is in tk-1Moment, then i-th vehicle was in t still in retarded motion statekThe addition at moment
It is lined up key pointAre as follows:
When i-th vehicle is in tk-1And tkThe traffic status identification at moment isWithWhen, it obtains corresponding
Tracing point informationWith
IfI.e. vehicle is in tk-1Moment, then i-th vehicle was in t still in uniform speed motion statekThe addition at moment
It is lined up key pointAre as follows:
IfI.e. vehicle is in tk-1Moment, then i-th vehicle was in t still in retarded motion statekThe addition at moment
It is lined up key pointAre as follows:
Wherein, adecIt is i-th vehicle in tk-1And tkAcceleration between moment, is approximately equal tod2For i-th vehicle
In tk-1The distance between starting point, d under the tracing point and low speed stop-go state at moment1For under low speed stop-go state
The distance between starting point and terminating point.
I-th vehicle is in tk-1The distance between starting point d under the tracing point and low speed stop-go state at moment2Meet
Following formula:
The distance between starting point and terminating point d under low speed stop-go state1Meet following formula:
Step 3) specifically:
31) vehicle estimated is added and is lined up key pointI=1 ..., n, n are the sum of vehicle, carry out ascending order row
(because this is single-lane road, whether be ranked up processing will not influence result to sequence, that is to say, that the addition of previous vehicle
It is lined up certain small one addition later of key point and is lined up key point, i.e.,)；
32) time difference for being lined up key point and range difference is added in Adjacent vehicles after obtaining ascending sort, meets following formula:
Wherein,Indicate the time difference of the addition queuing key point of i-th vehicle and the latter Adjacent vehicles, Δ xiIt indicates
The addition of i-th vehicle and the latter Adjacent vehicles is lined up the range difference of key point；
33) fleet's identification of upstream arrival is carried out, specifically:
Ifθ is fleet's judgment threshold, then i-th vehicle and the latter Adjacent vehicles belong to a fleet,
IfIt does not belong to a fleet then and (it is believed that vehicle i is the head vehicle of another fleet, is also believed to upstream vehicle
Arrival rate discovery variation), recognition effect is as shown in Figure 5.
Step 4) specifically:
41) it is added according to the vehicle estimated in time interval J and is lined up estimation point and identified fleet's (time interval
A vehicle only has a vehicle addition queuing estimation point in J and a vehicle leaves and is lined up estimation point), it is segmented to wave is assembled
Linear model fitting, meets following formula:
s.t.
Wherein,The set for being lined up estimation point, set are added for vehicles all in time interval JMiddle basis has identified
Fleet be divided intoA fleet, m indicate the number of fleet,Respectively indicate m, m+1 fleet's headstocks
The addition of vehicle is lined up key point, xmIt indicates to correspond toVehicle space coordinate, In respectively time interval J
The m sections of slopes and intercept to be estimated for assembling wave,Bright moment, l are opened for corresponding entrance driveway red lightstop-barIndicate stop line institute
In position, ε is positive adjusting parameter, for adjusting constraint hardness；
Wave is then assembled according to the Subsection Queue Disposal after piecewise fitting, obtains assembling wave complete expression in time interval JAre as follows:
Wherein, t indicates the moment,Segmentation depend onSize has identified that upstream reaches fleet's number；
42) queuing estimation point is left according to the vehicle estimated in time interval J, least square method is carried out to evanescent wave
Linear fit meets following formula:
s.t.
Wherein,The set for being lined up estimation point is left for all vehicles in time interval J,It is respectively to be estimated
The slope and intercept of evanescent wave, ε are positive adjusting parameter, for adjusting constraint hardness；
Then according to the queue clearance wave after least square linear fit, obtain assembling wave complete expression in time interval JAre as follows:
Step 5) specifically:
51) red light, green light open the bright moment in the J of acquisition time intervalWithMeet following formula:
Wherein, lstop-barFor stop line position,Assemble the slope of wave for first segment in time interval J
And intercept,The respectively slope and intercept of evanescent wave；
52) intersection signal parameter: the signal period T in time interval J is obtainedJWith respective inlets road green time PTJ,
Meet following formula:
Or
Wherein,The bright moment is opened for the green light in time interval J-1,When opening bright for the red light in time interval J-1
It carves；
53) maximum queue length in current cycle estimator is obtainedAnd its corresponding momentMeet following formula:
Wherein,The respectively slope of time interval J final stage assembly wave (intersecting with evanescent wave)
And intercept；
54) in supersaturated situation, except the maximum queue length in step 53), it there is also secondary queuing phenomena, then two
Secondary queue lengthAnd the corresponding momentMeet following formula:
Wherein,Assemble wave slope for first segment in time interval J+1,When opening bright for red light in time interval J+1
It carves, vfFor free stream velocity,The moment is corresponded to for the maximum queue length that estimation obtains in step 54).
The method for building up of intersection signal parameter and queue length the estimation model based on real-time vehicle track includes such as
Lower step:
1) simulation model is built
For the estimation effect for verifying inventive algorithm, established using the VISSIM5.4 micro-simulation simulator of German PTV company
Simulation model.Really to reflect influence that upstream intersection reaches downstream vehicle, this simulation model includes 3 intersections, away from
From respectively 200 meters, 350 meters, signal period 100s, when thing phase a length of 50s, lane flow is 1000veh/hr.
Simultaneously to keep simulation model more general, urban intersection can be represented, by intersection speed limit in simulation model
Facility is 45Km/h, and desired speed distribution normal distribution emulates in the section of 40Km/h-50Km/h in 10 different random kinds
8400s is separately operable under son, preceding 600s is preheating time.Using third intersection as research object, extract 600s to 8400s it
Between track of vehicle, partial traces signal is as shown in table 1.
1 part acquisition trajectories database schematic table of table
In order to examine estimation effect of the inventive algorithm under different vehicle track sample rate and sample frequency, setting 5s,
3 sample frequencys of 15s, 25s and 5% to 100%20 sample rate form 60 test scenes altogether.It is simultaneously further verifying
The validity of inventive algorithm is also compared with the Ramezani and Geroliminis algorithm proposed, needed for two kinds of algorithms
Parameter setting is as shown in table 2:
Parameter setting table needed for 2 two kinds of algorithms of table
2) simulation result
(1) cycle length
Under different tracks sample rate and sample frequency, 20 box-shaped figures for opening the bright moment based on green light or red light of statistics are such as schemed
Shown in 6 and Fig. 7, while counting the maximum absolute error (MAE) and average absolute percentage of the phase estimate under same sample frequency
Error (MAPE) is as shown in table 3.
The different sample frequency lower period estimated result comparisons (unit: s) of table 3
(2) queue length
Fig. 8 is the present invention and Ramezani and queue length of the Geroliminis algorithm under above-mentioned 60 test scenes
Illustrate with success rate.
3) analysis of simulation result
Available such as draw a conclusion from phase estimate box-shaped figure and Statistical Comparison average period:
(1) the phase estimate result for opening the bright moment based on green light, which is better than, opens bright phase estimate result based on red light；
(2) for two kinds of estimation methods, with the increase in sampling interval, evaluated error and fluctuation also increase therewith
Greatly.
The reason of two above conclusion occur is analyzed as follows:
(1) conclusion 1 illustrates that the phase estimate method for opening the bright moment based on green light is more practical, this is because green light opens bright wink
Between, queuing vehicle to sail out of close to saturation volume rate, stablize by queue clearance process, and evanescent wave is close to definite value.Opposite, it is hot
Lamp opens bright, and vehicle deceleration slowly stops, and it is unstable to be lined up assembling process, and assembles wave and reach variation with upstream vehicle and change.
(2) conclusion 2 illustrates the increase with the sampling interval, and phase estimate method accuracy and robustness of the present invention are under
Drop.This is because the sampling interval is bigger, the vehicle estimated joins and departs queuing key point precision and declines therewith.
Such as draw a conclusion from queue length error comparison diagram is available:
The queuing evaluated error of (1) 2 kind of algorithm is the increase with sample rate and declines；
(2) under low sampling rate, evaluated error of the invention is less than Ramezani and Geroliminis algorithm.But
It is the increase with sample rate, there are a critical values, so that Ramezani and Geroliminis algorithm is better than this hair
Bright algorithm, and with the increase in sampling interval, threshold sampling rate is smaller, and (critical under 5s, 15s and 25s sampling interval is adopted
Sample rate is respectively 47%, 40% and 20%).
The reason of two above conclusion occur is analyzed as follows:
(1) conclusion 1 illustrates that two kinds of algorithms can make a response to the variation of sample rate, under three kinds of sampling intervals, sampling
The increase of rate is capable of providing the estimated accuracy of queue length；
(2) conclusion 2 illustrates that inventive algorithm is suitable for the data environment of current low sample frequency, low sampling rate.
Claims (5)
1. a kind of signalized crossing queue length real-time estimation method based on track of vehicle, which is characterized in that including with
Lower step:
1) according to real-time vehicle trace information, vehicle traffic condition differentiation is carried out；
2) according to real-time vehicle trace information, vehicle traffic condition and kinematical equation, carry out vehicle be added be lined up key point and
Leave the crucial point estimation of queuing；
3) it is added according to the vehicle estimated and is lined up key point, carry out fleet's identification of upstream arrival；
4) it is added according to the vehicle estimated and is lined up key point, leaves queuing key point and identified fleet, carry out assembly wave
Estimate with evanescent wave；
5) according to the assembly wave and evanescent wave estimated, intersection signal parameter and queue length estimation are carried out；
The step 1) specifically: obtain track of vehicle information in real time, enable i-th vehicle in tkThe tracing point information at moment indicates
For I-th vehicle is respectively indicated in tkSpace coordinate, the velocity and acceleration at moment, then i-th
Vehicle is in tkThe traffic behavior at momentAre as follows:
Wherein, vth1And vth2For the preset threshold speed differentiated for state of motion of vehicle, vth2< vth1, M, C and S distinguish table
Show three movement, low speed stop-go and parking states；
The step 2) specifically:
21) estimation vehicle leaves queuing key point:
When i-th vehicle is in tkAnd tk+1The traffic status identification at moment isWithWhen, obtain corresponding track
Point informationWith
IfvfFor free stream velocity, then i-th vehicle is in tkMoment leaves queuing key pointAre as follows:
IfThen i-th vehicle is in tkMoment leaves queuing key pointAre as follows:
Wherein, aaccIt is i-th vehicle in tkAnd tk+1Acceleration between moment, is approximately equal tod3It is i-th vehicle in tk
And tk+1The distance between moment is equal to
22) estimation vehicle, which is added, is lined up key point:
When i-th vehicle is in tk-1And tkThe traffic status identification at moment isWithWhen, obtain corresponding track
Point informationWith
IfThen i-th vehicle is in tkThe addition at moment is lined up key pointAre as follows:
IfThen i-th vehicle is in tkThe addition at moment is lined up key pointAre as follows:
When i-th vehicle is in tk-1And tkThe traffic status identification at moment isWithWhen, obtain corresponding track
Point informationWith
IfThen i-th vehicle is in tkThe addition at moment is lined up key pointAre as follows:
IfThen i-th vehicle is in tkThe addition at moment is lined up key pointAre as follows:
Wherein, adecIt is i-th vehicle in tk-1And tkAcceleration between moment, is approximately equal tod2Exist for i-th vehicle
tk-1The distance between starting point, d under the tracing point and low speed stop-go state at moment1To be risen under low speed stop-go state
The distance between initial point and terminating point.
2. the signalized crossing queue length real-time estimation method according to claim 1 based on track of vehicle,
It is characterized in that, i-th vehicle is in tk-1The distance between starting point d under the tracing point and low speed stop-go state at moment2
Meet following formula:
The distance between starting point and terminating point d under the low speed stop-go state1Meet following formula:
3. the signalized crossing queue length real-time estimation method according to claim 1 based on track of vehicle,
It is characterized in that, the step 3) specifically:
31) vehicle estimated is added and is lined up key pointN is the sum of vehicle, carries out ascending sort；
32) time difference for being lined up key point and range difference is added in Adjacent vehicles after obtaining ascending sort, meets following formula:
Wherein,Indicate the time difference of the addition queuing key point of i-th vehicle and the latter Adjacent vehicles,Δ
xiIndicate the range difference of the addition queuing key point of i-th vehicle and the latter Adjacent vehicles；
33) fleet's identification of upstream arrival is carried out, specifically:
Ifθ is fleet's judgment threshold, then i-th vehicle and the latter Adjacent vehicles belong to a fleet, conversely,
A fleet is not belonged to.
4. the signalized crossing queue length real-time estimation method according to claim 3 based on track of vehicle,
It is characterized in that, the step 4) specifically:
41) it is added according to the vehicle estimated in time interval J and is lined up estimation point and identified fleet, carried out to wave is assembled
Piecewise linear model fitting, meets following formula:
s.t.
Wherein,The set for being lined up estimation point, set are added for vehicles all in time interval JIt is middle according to identified vehicle
Team is divided intoA fleet, m indicate the number of fleet,Respectively indicate m, m+1 fleet's headstock vehicles
Addition be lined up key point, xmIt indicates to correspond toVehicle space coordinate, M in respectively time interval J
The section slope and intercept to be estimated for assembling wave,Bright moment, l are opened for corresponding entrance driveway red lightstop-barIndicate that stop line institute is in place
It sets, ε is positive adjusting parameter, for adjusting constraint hardness；
Then assemble wave expression formula in time interval JAre as follows:
Wherein, t indicates the moment；
42) queuing estimation point is left according to the vehicle estimated in time interval J, it is linear to carry out least square method to evanescent wave
Fitting, meets following formula:
s.t.
Wherein,The set for being lined up estimation point is left for all vehicles in time interval J,Dissipation respectively to be estimated
The slope and intercept of wave；
Then assemble wave expression formula in time interval JAre as follows:
5. the signalized crossing queue length real-time estimation method according to claim 4 based on track of vehicle,
It is characterized in that, the step 5) specifically:
51) red light, green light open the bright moment in the J of acquisition time intervalWithMeet following formula:
Wherein, lstop-barFor stop line position,Assemble the slope of wave for first segment in time interval J and cuts
Away from；
52) intersection signal parameter: the signal period T in time interval J is obtainedJWith respective inlets road green time PTJ, meet
Following formula:
Or
Wherein,The bright moment is opened for the green light in time interval J-1,The bright moment is opened for the red light in time interval J-1；
53) maximum queue length in current cycle estimator is obtainedAnd its corresponding momentMeet following formula:
Wherein,The respectively slope and intercept of time interval J final stage assembly wave；
54) secondary queue length is obtainedAnd the corresponding momentMeet following formula:
Wherein,Assemble wave slope for first segment in time interval J+1,The bright moment is opened for red light in time interval J+1.
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