CN111932909A - Real-time variable lane dynamic allocation method under intelligent vehicle-road cooperative environment - Google Patents

Real-time variable lane dynamic allocation method under intelligent vehicle-road cooperative environment Download PDF

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CN111932909A
CN111932909A CN202010516490.6A CN202010516490A CN111932909A CN 111932909 A CN111932909 A CN 111932909A CN 202010516490 A CN202010516490 A CN 202010516490A CN 111932909 A CN111932909 A CN 111932909A
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delay
traffic
lanes
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CN111932909B (en
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毛丽娜
周桂良
曹惠敏
李文权
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Huaiyin Institute of Technology
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/07—Controlling traffic signals
    • 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
    • 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
    • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/07—Controlling traffic signals
    • G08G1/08—Controlling traffic signals according to detected number or speed of vehicles
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
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Abstract

The invention discloses a real-time variable lane dynamic allocation method under an intelligent vehicle-road cooperative environment, which is characterized in that a road section dynamic lane allocation model is constructed on the basis of a BPR function, in order to embody real-time dynamic conversion, 24 hours in a whole day is divided into a plurality of stages, the traffic volume and the driving impedance of each stage are obtained, the product of the traffic volume and the driving impedance is integrated in time, and finally, a lane combination with the minimum delay in each stage is selected as an optimal scheme. The intersection dynamic lane allocation model is constructed based on an HCM2000 delay model, the minimum delay of vehicles at the intersection is taken as an objective function, the vehicle delay of different lane combinations under different flow states is analyzed, the optimal lane combination is found, and the model is verified through example analysis and MATLAB calculation. The real-time variable lane dynamic allocation method can effectively allocate road resources and reduce driving delay.

Description

Real-time variable lane dynamic allocation method under intelligent vehicle-road cooperative environment
Technical Field
The invention relates to a real-time variable lane allocation method, in particular to a real-time variable lane dynamic allocation method under an intelligent vehicle-road cooperative environment.
Background
With the continuous change of urban layout, a pattern that working units are concentrated in the central area of an urban and residential areas are concentrated on the periphery of the urban is gradually formed, and the pattern causes the generation of tidal traffic flow, and is expressed as phenomena of unbalanced bidirectional road traffic flow, road congestion and the like. The intersection is the bottleneck of an urban traffic system, the fixed lane function division scheme is difficult to adapt to the characteristic of unbalanced traffic flow turning, and the specific expression is that resource redundancy of a certain guide lane is realized, the queuing of the other guide lane is serious, and the utilization rate of lane resources is low. At present, a control technology of timing and fixing road sections is adopted for the variable lanes, and the lane evacuation traffic flow cannot be adjusted in time in the face of real-time traffic conditions; the existing lane division technology has different judgment bases, the conversion between stages lacks safety control, and running risks exist.
Disclosure of Invention
The purpose of the invention is as follows: the invention provides a method for dynamically allocating variable lanes under an intelligent vehicle-road cooperative environment, which is a lane combination method for minimizing vehicle delay based on real-time traffic guide flow quick identification through the internal relation between guide traffic flow conditions and vehicle delay.
The technical scheme is as follows: the invention discloses a real-time variable lane dynamic allocation method under an intelligent vehicle-road cooperative environment, which comprises the following steps:
step 1: collecting various basic traffic data under the intelligent vehicle-road cooperative environment, sending the obtained data to a data processing center, and judging whether a variable lane starting condition is met;
step 2: constructing a road section lane distribution model, inputting the determined lane combination into the road section lane distribution model, calculating the vehicle average delay of different lane combinations according to the real-time traffic volume, and selecting the lane combination with the minimum delay as an optimal lane combination scheme;
and step 3: setting delay optimization difference, interval time and lane number constraint conditions for safety control in order to avoid safety accidents caused by frequent lane change;
and 4, step 4: the optimal lane combination scheme is transmitted to a driver through a variable information board or in-vehicle navigation, and lane changing drainage is performed in advance;
and 5: establishing an intersection variable lane dynamic allocation method based on HCM2000, and performing constraint control on conditions of minimum delay construction objective function, saturation, minimum green light time and signal cycle duration;
step 6: analyzing the delay condition of the change of the unidirectional traffic flow and the change of the straight left-turn flow under the MATLAB environment;
and 7: and 6, providing a basis for selecting the optimal lane combination under the real-time guiding flow condition according to the result obtained in the step 6.
The intelligent vehicle-road cooperative environment comprises the step 1 of collecting traffic data through a wireless communication technology, a GIS (geographic information system) and a sensor technology, wherein the traffic data comprises a series of traffic parameters such as traffic volume, vehicle speed and vehicle type.
The road section lane distribution model in the step 2 is constructed by adopting a BPR function, and the concrete construction process is as follows:
let the up-going lane direction be r and the down-going lane direction be r'. The traffic capacity of each lane is c, and the traffic capacity of the unidirectional road section is nc if each lane is bidirectional. And taking i as the number of lanes with convertible directions, and increasing or decreasing the traffic capacity of the lanes for ascending and descending by +/-ic when the lanes for ascending and descending are adjusted. In short, the number of lanes in the variable lane does not affect the traffic capacity of the original lane, and the total traffic capacity is still 2 nc. The invention analyzes the road impedance BPR function and recalibrates the parameters in the formula to adapt to the urban road condition. The road impedance in the up-lane direction is as shown in equation (1):
Figure BDA0002530320060000021
in the formula: alpha and beta are undetermined parameters and need to be determined by a large amount of traffic investigation; t is t0Time of travel, x, for free flow on a road sectionrThe unit is pcu/h for the traffic flow in the up-going lane r direction. c. CrThe unit is pcu/h for the traffic capacity in the direction of the up lane r.
The dynamic lane allocation method is to multiply the road impedance and the traffic volume and integrate the road impedance and the traffic volume in time, and aims to minimize the total impedance of all vehicles passing in the directions of an ascending lane and a descending lane. When the direction r of the upper lane is the heavy traffic flow direction in the model, i is positive, the traffic capacity in the heavy traffic flow direction is increased, and the traffic capacity in the light traffic flow direction is correspondingly decreased; and when the direction r of the upper lane is the light traffic flow direction, i is negative.
In a certain time period, under different lane allocation combinations, the impedances of the uplink lane and the downlink lane are added to obtain the total impedance of the whole road section, and the objective function of the model is to minimize the total impedance of the road, as shown in formula (2):
Figure BDA0002530320060000031
in the formula: t is t1Variable lane on time; t is t2Variable lane closing time; t isiThe time is switched for each stage.
In order to realize that the number of variable lanes can be converted according to the real-time traffic volume, a specific period of time is divided into k stages, each stage lasts for h hours, and the optimal lane allocation scheme of each stage under different lane allocation combinations is solved. Transition time T between stagesiThere are many factors to consider. When one stage is finished to the opening of the next stage, enough emptying time is reserved to ensure the driving safety. The calculation of the clearing time comprises the time of driving the driver away and the time of color conversion of the spike lightTime and safe time.
Investigating the traffic volume in each stage, calculating the driving impedance in each stage, multiplying the two parts by time to integrate them by PkExpressed by formula (3):
Figure BDA0002530320060000032
in the above formula, tkDenotes the kth phase start time, ikIndicating the number of reversible lanes, P, in the k-th phasekState variables representing the k-th stage, and decision variables of the model are represented by Wk(Pk) It is represented as the accumulation of state variables over the previous stage. Decision variable Wk(Pk) Is represented by formula (4):
Figure BDA0002530320060000033
knowing the state variables and decision variables of the previous stage, the state variables and decision variables of the next stage are added, and can be expressed by equation (5):
Ek(Wk,Pk)＝Pk+Wk(Pk) (5)
in order to avoid frequent lane changing, the safety control constraint conditions set in step 3 are as follows:
(3.1) delay optimization Difference Δ M
Δ M refers to the difference in average vehicle delays for the road segment between the original lane combination plan and the adjusted lane combination plan. In order to avoid that the optimization effect of switching from the lane combination of the previous stage to the next stage is not obvious, the vehicle has potential safety hazard due to frequent lane change, and therefore, a delay optimization difference value is set as a constraint condition of limitation. The delay optimization difference value delta M is at least larger than the delay time cost generated by the vehicle in the phase switching process, when the delay difference of two phases exceeds the time cost generated in the switching process, the next phase switching is carried out, and if the delay difference of the two phases does not exceed the time cost, the current lane combination scheme is continuously executed. The delay optimization difference expression is as follows:
P(ik)-P(ik+1)＞ΔM (6)
in the formula: p (i)k) -representing the average delay of the vehicles in the lane scenario carried out at stage k in the heavy traffic flow direction, in units of s;
P(ik+1) -mean delay of vehicles in lane plans carried out at stage k +1, representing the heavy traffic flow direction, in units of s.
(3.2) time interval constraint
The traffic flow shows dynamic regular change in the early and late peak periods, the determination of the switching interval depends on the dynamic change rule of the traffic flow, and the short interval not only can not see the effect, but also can bring disorder to the running of vehicles on the road surface; the long interval time cannot solve the trouble of short and urgent peak congestion time period, and on the contrary, the significance of real-time dynamic control is lost. The determination of the interval time must be greater than the average travel time of the vehicle through the variable lane section in the steady flow state.
T＞Tmin (7)
In the formula: t-duration of the current lane combination scheme, with unit of min;
Tmin-minimum time interval for lane combination scheme switching in min.
(3.3) number of lanes constraint
-n＜i＜n (8)
And n is the set number of the variable lane one-way lanes. The invention researches the variable lane control on the basis of bidirectional 3 lanes, so that the value of i is limited, i is more than or equal to-3 and less than or equal to 3, but when i is 3, one direction road has to lose right of way, and the situation is not considered, so the value range of the method is more than-3 and less than i and less than 3.
The intersection lane allocation model in the step 5 is constructed by adopting HCM2000, and the specific construction process is as follows:
the most common problem at the intersection is that the queuing time of the vehicles at the entrance lane is too long, so that the vehicle delay is generated, and the delay of all vehicles passing through the intersection should be minimized. The intersection dynamic lane allocation model uses a delay estimation formula of HCM2000 edition to construct an object function, does not consider the initial queuing condition, only uses two parts of uniform delay and incremental delay, and the constructed function is as follows:
Figure BDA0002530320060000051
in the formula: n isijI number of lanes corresponding to j traffic flows on the entrance lane;
qij-the actual arrival traffic volume (pcu/h) for the j lane group on the i entry lane;
dij-all vehicles of the j lane group on the i entrance lane are delayed(s).
Wherein, the constraint conditions in the step 2 are as follows:
at the intersection, the realization of the steering function of each entrance road depends on the time-space configuration of the intersection, the time comprises the period, the signal timing and the minimum green time, and the space comprises the number of lanes and the road saturation, and the factors become the constraint conditions of relevant variables in the model and form a complete nonlinear objective function together with the objective function.
(3.4) saturation constraint
The intersection saturation refers to the ratio of the actual traffic volume of each lane to the saturated traffic capacity of the lane, is one of important indexes reflecting the intersection service level, and is also a key parameter applied by a variable lane changing model.
Firstly, the congestion condition of each steering inlet lane needs to be clarified, so that lanes can be switched, the saturation of the steering lanes is usually adopted to determine the congestion degree of an intersection, and the specific determination conditions are as follows:
TABLE 4 determination of congestion intensity at turning lanes at intersections
Figure BDA0002530320060000052
As can be seen from table 4 above, when the saturation is greater than 0.9, the turning lane starts to be congested, but the traffic flow at the intersection will fluctuate greatly with time, the traffic volume on the secondary road will decrease relative to the traffic volume on the main road, and the saturation of the road will also decrease, and at this time, the traffic volume on the main road needs to be decreased in time to protect the traffic right of the main road.
The saturation constraints are as follows:
Figure BDA0002530320060000061
in the formula: x is the number ofijI saturation of the j lane group on the entrance lane;
qij-the actual arrival traffic volume (pcu/h) for the j lane group on the i entry lane;
CAPij-i traffic capacity of the j lane group on the entrance lane (pcu/h);
Sij-the saturation flow rate (pcu/h) for the j lane groups on the i inlet lane;
λij-the split of the signal phase belonging to the j lane group on the i entrance lane.
(3.5) minimum Green time constraint
Each phase has a minimum green time which represents the minimum transit time given at the beginning of the phase to ensure that the vehicles in line from the stop line to the vehicle detector can all pass the stop line.
Gmink≤gk (11)
In the formula: k ═ 1,2,3,4 — representing four different phases, respectively;
Gminkthe minimum green time value of the signal phase k is generally 10 s;
gk-the effective green time of signal phase k.
(3.6) Signal cycle duration constraint
The signal period is the time required for the signal light color to be displayed for one week in the set phase sequence, and is represented by C in seconds(s). The cycle time is limited, and increasing the cycle time is helpful for improving the traffic efficiency of the whole intersection when necessary, but sometimes the cycle time is only temporary, the traffic capacity is reduced along with the increase of time, the vehicle delay is also improved, and the maximum cycle time needs to be adjusted at this moment. However, the shorter the cycle time, the better, the shorter the cycle time, the higher the total loss time, and thus the lower the traffic capacity, and the higher the vehicle delay.
Figure BDA0002530320060000062
In the formula: gk-effective green time of signal phase k;
c is the signal period length, which is 120s in the text;
l-total lost time in one signal cycle.
(3.7) number of lanes constraint
The original entrance turning lane is assumed to be 3 lanes, when the function of the variable lane on the road section is started, the number of turning lanes of the intersection entrance is changed, but the entrance turning lane occupies at most 2 lanes in opposite direction, so that the intersection turning lane also opens several lanes when the variable lane on the road section is opened, and when the variable lane on the road section is not opened, the intersection turning lane is suitable for the intersection turning traffic condition to determine whether the intersection is independently opened to turn to the variable lane.
Figure BDA0002530320060000071
In the formula: n isijI number of lanes corresponding to j traffic flows on the entrance lane;
Nii total number of lanes of the entrance lane.
Has the advantages that: according to the invention, lane division is carried out through a dynamic lane allocation model, a variable lane dynamic allocation method is adopted, and intelligent spike lamps installed on the road surface are used as lane markings for whole-course guidance, so that road traffic information can be transmitted in real time, lane allocation decisions can be made quickly, real-time dynamic variable lane control is realized, traffic flow is guided safely, the road resource utilization efficiency is improved, traffic jam pressure is relieved, and a low-delay and high-efficiency variable lane control mode is really realized.
Drawings
FIG. 1: the invention relates to a road section variable lane distribution model operation flow chart;
FIG. 2: is a 3-lane function combination;
FIG. 3: 4 lane function combination;
FIG. 4: is a 5-lane function combination;
FIG. 5: 3, combining different lanes on an entrance lane and changing a delay in a straight-going direction;
FIG. 6: 4, a straight-ahead direction delay change diagram is formed by combining different lanes on an entrance lane;
FIG. 7: a delay variation diagram in the straight-ahead direction under different lane combinations on the 5 entrance lane;
FIG. 8:3, a bidirectional delay variation graph under different lane combinations on an entrance way;
FIG. 9: is a bidirectional delay variation graph under different lane combinations on 4 entrance lanes;
FIG. 10: is a bidirectional delay variation graph under different lane combinations on 5 entrance lanes.
Detailed Description
In this embodiment, first, a road section variable lane analysis is performed:
the combination of road sections and lanes adopts an enumeration method, as shown in table 1, and all lane allocation schemes are taken as a solution space of the model to be substituted into operation. As can be seen from table 1, for bidirectional 3 lanes, there are 3 lane assignment schemes, i.e. 3-3, 4-2, 5-1. When the road vehicle runs smoothly, i is 0, the two directions are 3 lanes, and the number of lanes does not need to be changed; when obvious bidirectional traffic flow imbalance occurs on the road, changing the direction of one lane inside the light traffic flow direction into the heavy traffic flow direction, wherein i is 1; when the bidirectional traffic flow on the road is unbalanced and extremely congested, i is 2, and 2 lanes on the inner side of the heavy traffic flow direction are converted into the heavy traffic flow direction; in order to protect the right of way for the oncoming traffic, the case where i is 3 is not considered herein.
TABLE 1 road segment variable lane assignment scheme
Figure BDA0002530320060000081
A specific road section variable lane assignment model operation flowchart is shown in fig. 1.
In the aspect of calibration of the road resistance function, which is determined based on a large number of traffic surveys, α is 1.5, β is 3, and the vehicle travel time in the initial free flow state is assumed to be 60 seconds, i.e., t060 s. In the aspect of road section traffic volume data, a bidirectional 3 lane is selected, namely n is 3, and the traffic capacity of a single lane is 1200pcu/h, so that the traffic capacity of the whole road section is 7200pcu/h no matter how the lane is adjusted. Setting the delta M value of the case as 60 seconds for demonstration and analysis, if the delay difference value of the two stages is within 1 minute, not considering lane change, and if the delay difference value of the two stages is more than 1 minute, selecting a lane change scheme. The traffic data of 7:00-19:00 on the No-Sn lake great tide section are known, the traffic is counted every 15 minutes, and 48 groups of data are counted, as shown in the table 2.
TABLE 2 No-Sn Lihu great tide road section 7:00-19:00 traffic volume
Figure BDA0002530320060000091
As mentioned above, after calibrating the unknown variables and parameters, 15 minutes are selected as the switching interval time, and the objective function becomes:
Figure BDA0002530320060000092
the variable lane allocation model is constructed by using an integral function with a road resistance function as a main body, compiling codes for integral calculation by using MATLAB, carrying out code conversion on the variables, and obtaining data in a table 3 through calculation.
TABLE 3 delay analysis for different lane combinations
Figure BDA0002530320060000093
Figure BDA0002530320060000101
Figure BDA0002530320060000111
As can be seen from table 3, the delays between different lane combinations are significantly different, and lane control and optimal decision are also stable, so that a lane scheme is often presented within a period of time, and traffic accidents caused by frequent lane changes within a short time are avoided. The 1:5 lane combination delay of the lane combination of 18:15-18:30 is found to be the minimum compared with other lane combinations of the same section, but the delay difference of the lane combination of the lane.
The method for dynamically allocating the lanes on the road section can allocate the lanes according to the traffic flow in real time, takes the delay of the minimum vehicles as the basis, and enables the total delay of all vehicles on the road section to be minimum.
And (3) intersection variable lane analysis:
the intersection dynamic lane distribution model is used for searching the relation between intersection flow conditions and vehicle delays, starting from two directions of one-way traffic flow change and left-turn straight traffic flow change for analysis, comparing and analyzing the vehicle delays of different lane combinations of each entrance lane, and calculating which lane combination is more optimal and has less delays under the same traffic conditions.
The intersection approach is divided into 3 cases, 3 lanes, 4 lanes and 5 lanes. The lane assignment of the different entrance lanes is as follows in fig. 2, fig. 3, fig. 4. The layout of the intersection follows the design specification of the intersection, and various lane combinations are also distributed according to the traffic volume condition.
In view of the fact that the current vehicle-road cooperative system is still incomplete and effective data are difficult to count, in the embodiment, the situation of simulating the traffic flow of the entrance lane under different lane combinations is taken as an experimental basis for data analysis, and the right turn flow situation is omitted for simplifying the research.
For the intersection with the 3 lanes at the entrance, assuming that the left-turn traffic volume 250pcu/h is determined as a fixed value, the value of the straight traffic volume is changed, and the change of the vehicle delay along with the straight traffic volume is observed. It can be seen from fig. 5 that when the straight-ahead flow rate is 400pcu/h, the vehicle delay of the two lane combinations is very small, but with the increase of the straight-ahead flow rate, the vehicle delay of the two lane combinations is also increased. Overall, the delay for the left 1 straight 2 lane combination is minimal compared to the left 2 straight 1.
For the intersection with the 4 lanes at the entrance, assuming that the left-turn traffic volume 300pcu/h is determined as a fixed value, the value of the straight traffic volume is changed, and the change of the vehicle delay along with the straight traffic volume is observed. It can be seen from fig. 6 that the delay variation of all vehicles of the 3 lane combinations is not large until the straight traffic volume is 600pcu/h, but as the straight traffic volume continues to increase, the delay of all vehicles of the left 3 straight 1 is linearly increased, and the delay increases of all vehicles of the left 2 straight 2 and the left 1 straight 3 are gentle. Overall, the vehicle delay for the left 2 straight 2 lane combination is minimal before the straight traffic volume is 800pcu/h, and the vehicle delay for the left 1 straight 3 lane combination is minimal after 800 pcu/h.
For the intersection with the 5 lanes at the entrance, assuming that the left-turn traffic volume 300pcu/h is determined as a fixed value, the value of the straight traffic volume is changed, and the change of the vehicle delay along with the straight traffic volume is observed. As can be seen from fig. 7, the vehicle delay of the left 4 straight 1 lane combination is much larger than that of the other 3 lane combinations, and continuously rises along with the increase of the straight traffic; before the straight traffic volume is 1000pcu/h, the vehicle delay of the left 1 straight 4 lane combination, the vehicle delay of the left 2 straight 3 lane combination and the vehicle delay of the left 3 straight 2 lane combination are basically equal and have small changes, after 1000pcu/h, the vehicle delay of the left 1 straight 4 lane combination is minimum, and the vehicle delay change of the left 2 straight 3 lane combination and the vehicle delay change of the left 3 straight 2 lane combination show a slow rising trend.
Through analyzing the lane combination analysis of the above 3 different entrance lanes, two conclusions can be drawn, firstly, no matter which change of left turn and straight traffic, as long as the traffic flow at the intersection is increased, the delay of the vehicle is increased, and the delay change of different lane combinations is different; secondly, although the number of the lanes of each entrance way is different and the lane combination is different, the optimal lane combination of each entrance way is matched with the different traffic combinations, so that the delay of the vehicles is minimum.
Unlike the above, if the traffic volume in one direction is changed, if the straight traffic volume and the left-turn traffic volume are changed, the presenting method of the vehicle delay at the intersection is as follows, specifically, the following fig. 8, 9 and 10 present the vehicle delay condition of different lane combinations of the 3 lane entrance lane, the 4 lane entrance lane and the 5 lane entrance lane under different traffic flow conditions.
The delay differences of different lane combinations are more intuitively represented by drawing a three-dimensional graph by Matlab, and the three-dimensional graph shows that:
(1) the left-turn and straight-going flow thresholds are certain, so that the delays of vehicles with different lane combinations are the same;
(2) for different inlet lane numbers and different steering flows, a lane combination is always matched with the inlet lane numbers and the steering flows, so that the delay of all vehicles at the intersection is minimum, and the lane combination is the optimal lane combination;
(3) when the saturation of each turning lane of the entrance lane is higher, delay of different lane combination vehicles is more obvious, the delay of the vehicle in which the lane combination is the smallest can be obviously seen, otherwise delay of various lane combinations is not obvious, and the necessity of changing lane conversion is avoided, so that the condition that the variable lane is arranged at the intersection is that the traffic flow of each entrance lane is larger, and the lane is in an oversaturated state.

Claims (5)

1. A real-time variable lane dynamic allocation method under an intelligent vehicle-road cooperative environment is characterized in that: the method comprises the following steps:
step (1): collecting various basic traffic data under the intelligent vehicle-road cooperative environment, sending the obtained data to a data processing center, and judging whether a variable lane starting condition is met;
step (2): constructing a road section lane distribution model, inputting the determined lane combination into the road section lane distribution model, calculating the vehicle average delay of different lane combinations according to the real-time traffic volume, and selecting the lane combination with the minimum delay as an optimal lane combination scheme;
and (3): setting delay optimization difference, interval time and lane number constraint conditions for safety control in order to avoid safety accidents caused by frequent lane change;
and (4): and the optimal lane combination scheme is transmitted to a driver through a variable information board or in-vehicle navigation, and lane changing drainage is performed in advance.
And (5): establishing an intersection variable lane dynamic allocation method based on HCM2000, constructing an objective function with minimum delay, and setting conditions such as saturation, minimum green time, signal cycle duration and the like for constraint control;
and (6): analyzing the delay condition of the change of the unidirectional traffic flow and the change of the straight left-turn flow under the MATLAB environment;
and (7): and (4) providing a basis for selecting the optimal lane combination under the real-time guiding flow condition according to the result obtained in the step (6).
2. The real-time variable lane dynamic allocation method in the intelligent vehicle-road cooperative environment according to claim 1, characterized in that: in the step (2), the road section lane distribution model is constructed by adopting a BPR function, and the direction of an uplink lane is assumed to be r, and the direction of a downlink lane is assumed to be r'; the traffic capacity of each lane is c, and the traffic capacity of the unidirectional road section is nc if each lane is bidirectional and n lanes are provided on the road section; and taking i as the number of lanes with convertible directions, and increasing or decreasing the traffic capacity of the lanes for ascending and descending by +/-ic when the lanes for ascending and descending are adjusted.
3. The real-time variable lane dynamic allocation method in the intelligent vehicle-road cooperative environment according to claim 1, characterized in that: in order to avoid frequent lane changing, the safety control constraint conditions in the step (3) comprise:
(3.1) delaying the optimization difference value delta M;
P(ik)-P(ik+1)＞ΔM (6)
in the formula: p (i)k) -representing the average delay of the vehicles in the lane scenario carried out at stage k in the heavy traffic flow direction, in units of s;
P(ik+1) -mean delay of vehicles in lane plans carried out at stage k +1, representing the heavy traffic flow direction, in units of s.
(3.2) time interval constraints;
T＞Tmin (7)
in the formula: t-duration of the current lane combination scheme, with unit of min;
Tmin-minimum time interval for lane combination scheme switching in min.
(3.3) lane number constraint;
-n＜i＜n (8)
in the formula: i-number of convertible direction lanes;
n is the number of one-way lanes.
4. The real-time variable lane dynamic allocation method in the intelligent vehicle-road cooperative environment according to claim 1, characterized in that: the objective function constructed in step (5) is as follows:
Figure FDA0002530320050000021
in the formula: n isijI number of lanes corresponding to j traffic flows on the entrance lane;
qij-the actual arrival traffic volume (pcu/h) for the j lane group on the i entry lane;
dij-j lane groups on the i entrance laneAll vehicles in (a) are delayed(s).
5. The real-time variable lane dynamic allocation method in the intelligent vehicle-road cooperative environment according to claim 1, characterized in that: the constraint conditions in the step (5) are as follows:
(5.1) saturation constraint;
Figure FDA0002530320050000022
in the formula: x is the number ofijI saturation of the j lane group on the entrance lane;
qij-the actual arrival traffic volume (pcu/h) for the j lane group on the i entry lane;
CAPij-i traffic capacity of the j lane group on the entrance lane (pcu/h);
Sij-the saturation flow rate (pcu/h) for the j lane groups on the i inlet lane;
λij-the split of the signal phase belonging to the j lane group on the i entrance lane.
(5.2) a minimum green time constraint;
Gmink≤gk (11)
in the formula: k ═ 1,2,3,4 — representing four different phases, respectively;
Gminkthe minimum green time value of the signal phase k is generally 10 s;
gk-the effective green time of signal phase k.
(5.3) signal period duration constraint;
Figure FDA0002530320050000031
in the formula: gk-effective green time of signal phase k;
c is the signal period length, which is 120s in the text;
l-total lost time in one signal cycle.
(5.4) lane number constraint;
Figure FDA0002530320050000032
in the formula: n isijI number of lanes corresponding to j traffic flows on the entrance lane;
Nii total number of lanes of the entrance lane.
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