CN112116822A - Expressway traffic capacity cooperative regulation and control method based on CAVs mixed traffic flow lane dynamic allocation - Google Patents

Expressway traffic capacity cooperative regulation and control method based on CAVs mixed traffic flow lane dynamic allocation Download PDF

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CN112116822A
CN112116822A CN202010994023.4A CN202010994023A CN112116822A CN 112116822 A CN112116822 A CN 112116822A CN 202010994023 A CN202010994023 A CN 202010994023A CN 112116822 A CN112116822 A CN 112116822A
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cavs
lane
traffic
traffic capacity
highway
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CN112116822B (en
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郝威
张兆磊
刘理
田大新
王正武
胡林
龙科军
吴伟
李焱
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Changsha University of Science and Technology
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
    • G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
    • 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
    • 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
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Abstract

The invention discloses a coordinated regulation and control method for highway traffic capacity based on dynamic lane allocation of CAVs (vehicular Ad hoc on-demand vehicles) mixed traffic flow, which is characterized in that under the condition that CAVs (vehicular Ad hoc on-demand vehicles) have certain market permeability, a special lane for the CAVs is arranged on a highway, and an integral nonlinear dynamic lane allocation model is established through multi-factor constraint; the method comprises the steps of dynamically adjusting the distribution of lane right-of-way and the driving data of vehicles, outputting a change curve of the traffic capacity of the highway, selecting an optimal solution, feeding the optimal solution back to a traffic control platform, dynamically adjusting the distribution scheme of the lanes of the highway and the control of the driving data of the vehicles in time, and realizing the cooperative regulation and control of the traffic capacity of the highway. According to the invention, the CAVs special lanes are reasonably arranged to separate the mixed traffic flow, and the influence of the road service level and the confluence area on the road is considered, so that the designed traffic capacity of the basic road section of the expressway meets the actual traffic demand, and the purposes of improving traffic efficiency and traffic safety are achieved.

Description

Expressway traffic capacity cooperative regulation and control method based on CAVs mixed traffic flow lane dynamic allocation
Technical Field
The invention belongs to the technical field of intelligent traffic, and relates to a coordinated regulation and control method for highway traffic capacity based on CAVs mixed traffic flow lane dynamic allocation.
Background
In recent years, Connected and Automated Vehicles (CAVs) have been driven into actual roads from test sites, and it is common knowledge that a single CAVs traffic flow can improve the traffic efficiency, safety and fuel utilization rate of expressways, but the popularization of CAVs is not all the same, and a mixed traffic flow (HMVs and CAVs) stage in which CAVs and HMVs are mixed for a long time is required to be passed.
The characteristics and safety of the mixed traffic flow are not clear, most of the current researches on the characteristics of the mixed traffic flow are expressed based on simulation experiments or numerical models, and the defects that the randomness of vehicle driving is not easy to represent and the result is often too optimistic exist. Due to the lack of real data, it is not clear how CAVs in mixed traffic flow affect highway capacity, especially at merge area segments.
Inspired by classical successful traditional lane management, such as a high-occupancy lane, a bus lane, a high-occupancy toll lane, a truck lane and the like, through separating different types of vehicles to improve road traffic efficiency and safety, in recent years, research progress of a CAVs lane management method based on lane management is provided, and research and analysis are mainly carried out on the deployment of the CAVs lane from two levels of road networks and lanes.
On the road network level, most researches establish an analytic model based on the traffic flow theory, only the ideal maximum traffic capacity flow of a main line is usually considered, the confluence influence is also ignored, the method is too ideal, the traffic condition and the environment are ignored, and the obtained result is often too optimistic.
At the lane level, the influence of the CAVs permeability, the road traffic capacity and the CAVs technology (headway, formation strength and the like) on the arrangement of the special lanes is mainly considered. Currently, the CAVs lane management method based on lane Management (ML) mainly analyzes the influence of CAVs on the layout of dedicated lanes by establishing an analysis model or simulation. The analysis model is mainly developed and researched on a special lane based on the CAVs technology, but complex relations among the MPRs, the road network and the lane are split. The influence of CAVs special lane deployment on traffic flow can be obtained by a simulation analysis method, but a reasonable theoretical explanation and a systematic lane deployment scheme are lacked, and theoretical support cannot be provided for traffic planning. The permeability (MPRs) of CAVs and the profit and the disadvantage of traffic demand on setting special lanes are revealed by establishing a mixed traffic flow basic diagram, and the optimal number of lanes is determined through a numerical model to obtain the optimal traffic efficiency. In addition, ramp traffic flow is ignored in most previous researches, a main line traffic flow is directly used as a research object, and in an actual situation, the ramp traffic flow is an indispensable part of an expressway, so that the operation efficiency of the expressway is directly influenced, and the service level of the expressway can be seriously influenced by ramp delay and congestion.
In summary, current research on managing lanes for highway CAVs is relatively rare and in the first exploration phase. In the prior art, a coordinated regulation and control method for highway traffic capacity of lane dynamic allocation of CAVs mixed traffic flow, which is established by integrating complex conditions such as permeability (MPRs) of CAVs, lane management (road network and lane) of CAVs, randomness of traffic conditions and the like, does not exist.
Therefore, in order to expand the mixed traffic flow lane management strategy and make up for the defect of an optimistic analysis model result, a highway traffic capacity cooperative regulation and control method for dynamically allocating lanes of a CAVs (vehicular ad hoc systems) mixed traffic flow is needed, the CAVs special lanes are reasonably arranged to separate the mixed traffic flow, and the influence of road service level and a confluence area on a road is considered, so that the designed traffic capacity of a basic highway section meets the actual traffic demand, the purpose of improving traffic efficiency and traffic safety is achieved, important technical guidance is provided for future highway mixed traffic flow management, and a theoretical basis can be provided for highway theoretical traffic capacity calculation.
Disclosure of Invention
In order to achieve the purpose, the invention provides a coordinated regulation and control method for highway traffic capacity based on CAVs mixed traffic flow lane dynamic allocation, which separates mixed traffic flows by reasonably arranging CAVs special lanes, simultaneously considers the influence of road service level and confluence area on roads, enables the designed traffic capacity of basic road sections of a highway to meet the actual traffic demand, achieves the purpose of improving traffic efficiency and traffic safety, provides important technical guidance for future highway mixed traffic flow management, also provides theoretical basis for highway theoretical traffic capacity calculation, and solves the problems in the prior art.
The technical scheme adopted by the invention is that the highway traffic capacity cooperative regulation and control method based on the lane dynamic allocation of the CAVs mixed traffic flow sets a special lane for the network connection automatic driving vehicle on the highway under the condition that the CAVs have certain market penetration rate, and comprises the following steps:
step 1: collecting data: acquiring lane information of a main line and a ramp of a highway and vehicle running information on a lane based on a vehicle networking system; the lane information includes: the type, number and basic traffic capacity of the lanes; the types of lanes include CAVs special lanes, general lanes;
step 2: establishing an integral nonlinear lane dynamic allocation model based on the data acquired in the step 1 and the constraints of the service level and the lane saturation of the expressway;
and step 3: the highway traffic capacity is cooperatively regulated and controlled:
step 31: according to the lane information of the current main line and the ramp and the vehicle running information on the lane, inputting an integral nonlinear lane dynamic allocation model and outputting the total traffic capacity of the expressway;
step 32: monitoring the change of the traffic capacity of the expressway, dynamically adjusting the distribution of lane right of way according to lane information and vehicle running information on the lane, adjusting the running data of vehicles based on an internet of vehicles system, outputting the change curve of the traffic capacity of the expressway, selecting an optimized solution, feeding the optimized solution back to a traffic control platform, dynamically adjusting the distribution scheme of the lane of the expressway and the management and control of the vehicle running data in time, and realizing the cooperative regulation and control of the traffic capacity of the expressway;
in step 2, the integer nonlinear lane dynamic allocation model is:
Figure BDA0002691884140000031
s.t：lA+lh＝L (2)；
Qramp≥dr (3)；
QA≤(V/CA)B·CA (4)；
QH≤(V/CH)B·CH (5)；
lA、lh∈N* (6)；
in the formula, ML represents lane management; q denotes the total traffic capacity of the highway; max represents the maximum value; lARepresenting the total number of lanes dedicated to CAVs; qATraffic volume representing lanes dedicated to CAVs; lhRepresenting a total number of general lanes; qHRepresenting the traffic volume of a general lane; qrampRepresenting the traffic capacity of the ramp; l represents the total number of lanes of the highway; drRepresenting ramp traffic demand; (V/C)A)BRepresenting the saturation of the lanes dedicated to the CAVs when the service level is class B; (V/C)H)BWhen the service level is represented as B level, the method is generalThe saturation of the lane; cARepresenting the basic traffic capacity of CAVs special lanes; cHRepresenting the basic traffic capacity of the general lane; n denotes a positive integer.
Further, in step 1, the vehicle driving information includes the CAVs vehicles, HMVs vehicle types and numbers and their operation data, and includes: current time velocity v of vehicle, minimum parking space s0Safe headway T, free flow velocity v0Length of vehicle l, minimum gap t that vehicles on ramp can pass throughcDistance t between following vehicles on rampg。
Further, an integer nonlinear lane dynamic allocation model adopts a road right allocation method as follows: the CAVs preferentially drive in the CAVs lane, when the saturation of the CAVs special lane reaches SBWhen, the CAVs are assigned to the general lane, SBIndicating CAVs-specific lane saturation when the service level is class B.
Further, traffic volume Q of CAVs exclusive laneAComprises the following steps:
QA(lA，p，d，SB)＝min(pd，SBlACA)；
wherein p represents the total permeability of CAVs, d represents the demand of main line mixed traffic flow, and SBIndicating CAVs-specific lane saturation when the service level is class B.
Further, the traffic volume Q of the general laneHComprises the following steps:
QH＝min(d-QA，(L-lA)Cmix)：
in the formula, CmixFor the basic traffic capacity of the general lane, d represents the main mixed traffic flow demand.
Further, the general lane basic traffic capacity CmixComprises the following steps:
Figure BDA0002691884140000041
；
in the formula, v is the current time speed of the vehicle; p is a radical ofhRepresenting the proportion of a human-driven vehicle; s0Is the minimum parking distance; t isSafe headway; v. of0Is the free flow velocity; l is the vehicle length; p is a radical ofaRepresents the scale of an ACC vehicle; t is taA constant headway desired to be maintained for an ACC vehicle;
Figure BDA0002691884140000042
permeability of CAVs in the general lane.
Further, permeability of CAVs in general purpose lanes
Figure BDA0002691884140000043
Comprises the following steps:
Figure BDA0002691884140000044
。
further, ramp traffic capacity QrampComprises the following steps:
Figure BDA0002691884140000045
；
in the formula, CrampRepresenting the basic traffic capacity of the ramp; i represents the number of vehicles; qmRepresenting a virtual traffic volume; piRepresenting the probability of a gap theoretically occurring that allows i vehicles to cross.
Further, the probability P of a gap allowing i vehicles to pass through theoretically occurringiComprises the following steps:
Figure BDA0002691884140000046
；
in the formula, λ2Virtual vehicle arrival rates of different MPRs mixed traffic flows of a highway main line are obtained; t is tcThe vehicles on the ramp can pass through the minimum clearance; t is tgThe following time interval of the ramp vehicle; p (k) represents a mixed traffic flow headway probability distribution function; k represents the number of vehicles forming the fleet; qPRepresenting the total number of CAVs forming the fleet; n is a radical ofPRepresenting the number of CAVs fleets.
Further, the total number of CAVs Q forming the fleet of vehiclesPComprises the following steps:
Figure BDA0002691884140000047
；
in the formula, Q is the traffic volume of a main line of the highway;
number N of said CAVs fleetPComprises the following steps:
Figure BDA0002691884140000051
。
the invention has the beneficial effects that:
(1) the invention provides a highway traffic capacity cooperative regulation and control method for dynamic lane allocation based on mixed traffic flow characteristics of CAVs and HMVs, establishes a traffic capacity theoretical calculation model simultaneously considering a main line lane and a ramp of a mixed traffic flow, considers the influence of traffic demands, service levels, MPRs and merging areas on the highway traffic capacity at the same time, provides an integer nonlinear programming model with the maximum total traffic capacity, can adapt to the calculation of the road traffic capacity in various environments, enables the designed traffic capacity of a basic highway section to meet the actual traffic demands, achieves the purposes of improving traffic efficiency and traffic safety, helps a planner to quickly determine whether to deploy automatic driving traffic flows in various environments, deploy and lay at any time and any place, and provide important technical guidance for the future mixed traffic flow management of the highway, and a theoretical basis can be provided for calculating the theoretical traffic capacity of the expressway.
(2) The invention provides a dynamic road traffic capacity calculation method, which is consistent with the reality and conforms to the large environment of mixed running of future automatic driving vehicles and traditional vehicles; the invention provides a method for enabling a common lane to be used by CAVs and traditional vehicles, and establishes a road right distribution model, thereby greatly saving road resources.
(3) The invention designs a highway confluence area scene, and respectively carries out numerical case analysis and simulation, the result shows that the traffic demand and the permeability jointly influence the deployment of the special lanes, the effect of setting the special lanes is optimal when the CAVs permeability is 50-70%, and the CAVs special lanes under low permeability and high permeability can not improve the road traffic capacity.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a theoretical throughput result graph for lane management using the integer nonlinear lane dynamic assignment model of the present invention.
FIG. 2 is a graph showing the result of the simulated traffic capacity for lane management using the integral nonlinear lane dynamic assignment model of the present invention.
FIG. 3 is a schematic diagram of distribution characteristics of vehicles on a main line of an expressway under different permeability of CAVs according to the invention.
FIG. 4 is a schematic diagram illustrating the probability that the headway of mixed traffic flow is greater than 8s for different CAVs permeability and different traffic volumes.
Fig. 5 is a ramp traffic capacity gain diagram of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
A first part: calculating a heterogeneous traffic flow density-speed basic diagram under a mixed traffic flow (intelligent network vehicle and manual driving vehicle are mixed) environment, wherein the specific contents are as follows:
1. firstly, based on a manual driving vehicle (HV) following model and an intelligent networked automobile following model (a collaborative adaptive cruise control following model and an adaptive cruise control following model), the specific contents are as follows:
1.1, HV car following model:
Figure BDA0002691884140000061
in the formula (7), the reaction mixture is,
Figure BDA0002691884140000062
is the target acceleration in m/s2(ii) a v is the speed at the current moment, and the unit is m/s; a is the maximum acceleration in m/s2；v0Is the free stream velocity in m/s; s0Is the minimum parking space in m; t is a safe headway, and the unit is m; Δ v is the speed difference term of the front vehicle and the rear vehicle, and the unit is m/s; b is comfort deceleration in m/s2(ii) a h is the distance between the car heads and the unit is m; l is the vehicle length in m.
1.2, cooperative adaptive cruise control following model (CACC):
ek＝xk-1-xk-thwvk (8)；
Figure BDA0002691884140000063
in formula (8): e.g. of the typekThe error between the actual distance of the main vehicle at the current moment and the expected distance of the main vehicle is s; x is the number ofk-1The unit is m, which is the position of the front vehicle at the current moment; x is the number ofkThe unit is m, which is the position of the current time of the host vehicle; t is thwThe desired headway in units of s; v. ofkThe unit is m/s which is the speed of the current moment of the host vehicle; t is thwThe calibration result of the PATH real vehicle experiment is 0.6 s.
Formula (A), (B) and9) The method comprises the following steps: v. ofprevThe unit is m/s which is the speed of the current moment of the host vehicle; k is a radical ofpAs a pitch error control coefficient, kdIs a control coefficient of a differential term of the vehicle distance error,
Figure BDA0002691884140000064
is ekThe derivative term over time t. k is a radical ofp、kdThe calibration results of the PATH real vehicle experiment are 0.45 and 0.25 respectively.
1.3, self-adaptation cruise control follows car model (ACC), does not have the vehicle in front of the automatic driving vehicle promptly, can't follow the car:
Figure BDA0002691884140000071
in formula (10): k is a radical of1，k2To control the coefficient, k1Is in the unit of s-2，k2Is in the unit of s-1；taA constant headway that is desired to be maintained for the ACC, i.e., the travel time desired for the ACC vehicle to travel at an instantaneous speed to the end displacement of the preceding vehicle, is in units of s. k is a radical of1，k2The calibration results of the PATH real vehicle experiment are respectively 0.23s-2And 0.07s-1，taThe calibration result of the PATH real vehicle experiment is 1 s.
2. Then, the acceleration and the speed difference in the formulas (7) to (10) are made to be zero, namely the traffic flow reaches a steady state, and the distance (h) between the heads of the single traditional manual driving vehicle in the steady state can be respectively calculatedr) Pure ACC vehicle headway distance (h)a) And pure CACC vehicle headway distance (h)c)。
Figure BDA0002691884140000072
ha＝vta+l+s0 (12)；
hc＝vtc+l+s0 (13)；
According to a traffic flow density calculation formula:
Figure BDA0002691884140000073
wherein k represents density in veh/km.
3. Therefore, various vehicle type basic diagrams can be obtained according to the formulas (11) to (14):
conventional manually driven vehicles (HV):
Figure BDA0002691884140000074
in the formula (15), q is a flow rate value in veh/h.
ACC vehicle
Figure BDA0002691884140000075
CACC vehicle
Figure BDA0002691884140000081
4. And (4) integrating formulas (15) to (17), and representing the locomotive spacing of the mixed traffic flow by the average value of the locomotive spacing of three driving modes to obtain a heterogeneous traffic flow density-speed basic diagram:
the CACC permeability is p, the actual CACC vehicle ratio is pcThe ratio of ACC vehicles is paRatio of manually driven vehicles is ph：pc＝p2，pa＝p(1-p)，ph＝1-p； (18)；
Wherein p isc+pa+ph＝1，pc+pa＝p，pa≤ph，pa≤p；
Figure BDA0002691884140000082
A second part: and determining the right of the CAVs special lane, and further determining the total traffic capacity of the expressway. The highway lane management strategy of the invention is as follows: the traditional vehicles can only run on general lanes, the CAVs vehicles preferentially run on the CAVs lanes, and when the saturation of the CAVs special lanes reachesSBIn time, the CAVs will be assigned to a common lane, with priority to ensure the comfort of the CAVs. Establishing the following traffic flow distribution models based on the highway lane management strategy:
1. total traffic volume of CAVs dedicated lanes:
QA(lA，p，d，SB)＝min(pd，SBlACA) (20)；
q in formula (20)ATraffic volume representing lanes dedicated to CAVs; lARepresenting the total number of lanes dedicated to CAVs, p representing the total permeability of CAVs, d being the mixed traffic flow demand (mixed traffic demand), SBRepresenting the lane saturation when the service level is class B; cALane-only basic capacity for CAVs.
2. Traffic volume of a general lane:
2.1 basic traffic capacity C of general lanemix：
Permeability of CAVs in the general lane is:
Figure BDA0002691884140000083
in the formula (21), the compound represented by the formula,
Figure BDA0002691884140000084
permeability of CAVs in the general lane, max (0, pd-S)BlACA) Representing the number of the remaining vehicles distributed outside the special lane by the total vehicle demand of the CAVs, and representing the number of the remaining vehicles by zero when no vehicle is left; max (1, d-Q)A) Representing general lane traffic, using max function to avoid d-QA。
The basic traffic capacity of the general lane is as follows:
Figure BDA0002691884140000091
2.2, the traffic volume of the general lane is as follows:
QH＝min(d-QA，(L-lA)Cmix) (23)；
in the equation (23), L represents the total number of lanes of the highway.
3. Traffic capacity of ramp
Ramp capacity (Q)ramp) Traffic volume (Q) that can be passed through to the main line by the ramp vehiclec) And basic traffic capacity of ramp (C)ramp) Jointly, the minimum value between the two is the actual traffic capacity of the ramp, i.e. the capacity of the ramp
Qramp＝min(Qc，Cramp) (24)；
In the usual case Cramp＞QcTherefore the ramp traffic capacity QrampFrom QcAnd (4) determining.
Traffic Q of ramp vehicle capable of passing through main linecClosely related to the time interval when the main line of the highway can cross the vehicle head: when the vehicles on the main line of the expressway are dense, the head time distance is small, the head time distance which can pass through is small, and at the moment, the traffic flow on the main line of the expressway is large, and the ramp traffic capacity is small.
The headway distribution of the traditional traffic flow main line is mainly related to traffic volume and vehicle reaching distribution, but the headway under the mixed traffic flow is also closely related to MPRs.
3.1, determining the head time distance of the main line of the mixed traffic flow highway:
3.1.1, determining the time headway of the main line of the traditional single traffic flow expressway:
according to the conventional highway traffic flow operation characteristic research, vehicles on a main line enter a road to reach the road and obey Poisson distribution:
Figure BDA0002691884140000092
in the formula (25), p (m) is the probability of reaching m vehicles within t time; λ is the average arrival rate of the vehicles in a unit time interval (vehicles/s); t is the duration(s) of the counting time interval; and m is the number of arriving vehicles in the time t.
Let m be 0 in equation (25), i.e., the probability that no vehicle arrives at time t is:
Figure BDA0002691884140000093
therefore, a probability distribution function with the headway longer than t can be obtained:
P(h≥t)＝P(0)＝e-λt (27)；
from equation (27), a density probability function p (t) of headway can be obtained:
Figure BDA0002691884140000101
wherein h represents the headway of the highway traffic stream.
Equation (28) represents a density probability function for which the headway is less than t, indicating that the vehicles at that time form a fleet.
3.1.2, calculating the density probability function of the expressway main line headway of the mixed traffic flow according to the density probability function of the single traffic flow headway:
after the mixed traffic flows down the vehicles and enters the highway to reach equilibrium, a fleet of vehicles may be formed between adjacent CAVs on the same lane. The headway between vehicles in a fleet is significantly smaller than the traditional vehicle-to-vehicle headway, which makes the headway distribution of heterogeneous traffic flows different from that of traditional single traffic flows.
Market penetration of CAVs is p; the traffic volume of the main line of the expressway is Q; CAVs formation fleet two conditions need to be met:
1) continuous appearance of CAVs;
2) forming a car following.
To simplify the modeling process, the headway between CAVs is made less than tfThe vehicles are followed to form a CAVs fleet.
The probability of k consecutive vehicles appearing is:
Pk＝pk(1-p)k＝1，2……N* (29)；
in equation (29), p represents the market penetration of CAVs, k represents the length of the vehicle fleet, and the length of the vehicle fleet cannot exceed N vehicles.
The probability of forming k CAVs car-following is:
Pg＝[P(h＜tf)]kP(h≥tf) (30)；
the probability of the fleet forming the length of k vehicles from equations (29) and (30) is:
P(k)＝pk[P(h＜tf)]k∩[(1-p)∪P(h≥tf)] (31)；
in formula (31), p represents the market penetration of CAVs; p (h < t)f) Indicating that the headway is less than tfThe probability of (d); p (h is more than or equal to t)f) The headway is longer than tfThe probability of (c).
From equation (31), the total number Q of CAVs forming the fleet can be calculatedpAnd number of CAVs fleets Np。
Figure BDA0002691884140000102
In the formula (32), Q is the traffic volume of the main line of the expressway, and Q.P (k) represents that k vehicles form a vehicle group;
Figure BDA0002691884140000111
when the traffic flow is stable, the vehicles are not newly formed, and the vehicle following distance between CAVs is small (0.6s), so that a vehicle team can be regarded as a 'big vehicle', and new traffic volume (Q) can be obtainedm) Herein defined as virtual traffic. The virtual traffic volume is obtained from equations (32) to (33) as follows:
Qm＝3600*λ-Qp+NP (34)；
in the formula (34), λ is an average arrival rate of the vehicle in the unit time interval; virtual traffic volume QmThe unit of (d) is veh/h.
According to the formula (28) and the formula (34), the probability density of the headway time distribution of the mixed traffic flow of different MPRs on the main line of the expressway can be obtained as follows:
Figure BDA0002691884140000112
wherein λ is2The average arrival rate of the virtual vehicles in the unit time interval is calculated as follows:
Figure BDA0002691884140000113
synthesis of virtual traffic volumes Q available in (25) to (36)mComprises the following steps:
Figure BDA0002691884140000114
the probability distribution function of the mixed traffic flow headway:
Figure BDA0002691884140000115
3.2 Single Lane ramp traffic Capacity
The vehicles are uniformly distributed on the ramp, and the following time interval of the vehicles is tg(s)，tcIs traversable through a minimum gap; the ramp vehicle is only influenced by the rightmost lane of the main line, and the converged target lane is the rightmost lane of the main line. When t isc≤h＜tc+tgAllowing a vehicle to enter; when t isc+(i-1)tg≤h＜tc+itgWhen the vehicle enters the main line, i vehicles are allowed to enter the main line. The probability P of the gap allowing i vehicles to pass through theoretically occurringiComprises the following steps:
Figure BDA0002691884140000116
in formula (38), tf-cThe following time distance in CAVs motorcade.
Therefore, the traffic capacity of the single-lane ramp can be obtained.
Figure BDA0002691884140000117
In the formula (39), QmRepresenting virtual traffic volume, CrampThe method is the basic traffic capacity of the single-lane ramp.
Therefore, the available total traffic volume of the highway:
Q＝QA+min(d-QA，(L-lA)Cmix)+Qramp (40)；
and a third part: based on the dynamic highway traffic capacity under different environments, the constraint of ramps and service levels is considered, and a special lane distribution model for the automatic driving vehicle is provided, so that the optimal configuration of lanes and the cooperative regulation and control of the highway traffic capacity are realized. The specific contents are as follows:
let CAVs Special Lane (DL) traffic capacity be QAThe traffic capacity of the General Lane (GL) is QHThe traffic capacity of the ramp is QrampThen, the total throughput (total throughput) Q ═ QA+QH+Qramp. In the past, a great deal of CAVs lane research is developed around the maximum traffic capacity of roads, and the influence of actual traffic conditions on the traffic capacity is ignored. The actual traffic capacity of a road can be affected by speed and confluence. The present invention therefore takes into account the constraint of highway service levels on the main traffic flow, i.e. the traffic volume allocated per lane has a threshold value, exceeding which results in a reduction in vehicle speed and a reduction in passenger comfort. According to the above objectives and constraints, an integer nonlinear programming model is constructed:
Figure BDA0002691884140000121
s.t：lA+lh＝L (2)；
Qramp≥dr (3)；
QA≤(V/CA)B·CA (4)；
QH≤(V/CH)B·CH (5)；
lA、lh∈N* (6)。
the formula (1) is an objective function and represents the maximum total traffic capacity of the expressway; in the formula (1), ML is lane management; q is the total traffic capacity of the expressway, and the unit is veh/h;
Figure BDA0002691884140000122
for mathematical notation, the objective function is represented byADetermining; lATotal number of lanes dedicated to CAVs; qARepresenting the traffic volume of CAVs special lanes, and the unit is veh/h; lhNumber of lanes of general traffic lane, QHThe traffic volume of the general lane is veh/h; qrampThe ramp traffic capacity; n denotes a positive integer.
Expression (2) represents the total number l of lanes dedicated to CAVs in expression (1)AAnd total number of general lanes lhThe condition that the sum of the two is equal to the total number L of lanes is satisfied.
Formula (3) represents the traffic capacity Q of the medium-grade ramp in formula (1)rampTo meet traffic demand d of more than or equal to ramprThe conditions of (1).
Expression (4) represents the traffic Q of the CAVs-dedicated lane in expression (1)AThe saturation (V/C) of CAVs special lane is satisfied when the service level is B gradeA)BBasic traffic capacity C of lanes dedicated to CAVsAThe product of (a).
Expression (5) represents the traffic volume Q of the general-purpose lane in expression (1)HThe saturation (V/C) of the general lane is satisfied when the service level is less than or equal to the B levelH)BBasic traffic capacity C of general laneHThe product of (a).
Equations (4) to (5) respectively indicate that the service level of the main lane is higher than the level B (HCM).
Formula (6) N represents a positive integer.
Numerical and simulation analysis
The invention designs an expressway scene, which comprises four main lanes and single lane ramps.
Main line analysis:
and carrying out simulation analysis by using the integral nonlinear lane dynamic allocation model, wherein the simulated parameters set the service level and the vehicle saturation. Under different traffic volumes, lane management is performed on mixed traffic flows under different CAVs permeability, and corresponding highway traffic capacity results are analyzed, wherein the results are shown in fig. 1 and fig. 2, fig. 1 shows the theoretical traffic capacity of the highway, and fig. 2 shows the simulated traffic capacity of the highway.
As shown in a in fig. 1, when the traffic volume is small, the traffic demand is 1600veh/h, the improvement of the permeability of the CAVs has little influence on the improvement of the traffic efficiency, the theoretical traffic capacity of different CAVs permeabilities is very small under the premise of setting one CAVs-dedicated lane, when two CAVs-dedicated lanes are set, the theoretical traffic capacity is improved, and along with the improvement of the permeability of the CAVs, the theoretical traffic capacity is improved, and when more than two CAVs-dedicated lanes are set, the theoretical traffic capacity is reduced compared with the theoretical traffic capacity when two CAVs-dedicated lanes are set. As shown in a in fig. 2, at the time of low traffic volume, the simulation result and the variation law regarding the influence of the rules of the CAVs vehicle permeability and the lane management on the theoretical traffic capacity are the same as a in fig. 1.
As shown in b in fig. 1 and c in fig. 1, under the condition that the road traffic capacity meets the traffic demand, the total traffic capacity may be reduced by arranging a plurality of dedicated lanes for CAVs, so that the dedicated lanes cannot be arranged blindly. With the continuous increase of traffic demands, when the traffic capacity of a main line cannot meet the traffic demands, the improvement of the permeability of the CAVs and the arrangement of the special lanes can improve the traffic capacity and relieve traffic jam. The simulation results and rules of b in fig. 2 and c in fig. 2 are consistent with those of b in fig. 1 and c in fig. 1.
As shown in d in fig. 1, when the permeability of the CAVs is 10%, setting a professional lane may not alleviate the traffic jam and may cause a more serious jam, and the method for solving the traffic jam may increase the permeability of the CAVs and increase the lanes. The simulation results and rules of d in fig. 2 are consistent with d in fig. 1.
As shown in f of fig. 1, when D is 4800veh/h, the traffic capacity can be improved by providing one CAVs exclusive lane when the permeability is 30%, and no exclusive lane needs to be provided when the permeability is more than 50%. The simulation results for f in fig. 2 are consistent with f in fig. 1.
As shown in g in fig. 1, when the traffic volume is very large, for example, D is 5600veh/h, the permeability is 30% to 50%, two special lanes are provided to meet the requirement, and the permeability is 70% to 90%, and one special lane may be provided. The simulation results and rules of g in fig. 2 are consistent with g in fig. 1.
The results show that the number of CAVs-dedicated lanes is determined by both CAVs permeability and traffic volume, and a high permeability of CAVs does not represent a need for more CAVs-dedicated lanes.
And (3) ramp analysis:
the mixed traffic flow headway probability distribution function describes the headway distribution characteristics of vehicles on the main line of the highway under different CAVs permeability on a macroscopic level, and has great benefits for mixed traffic flow characteristic analysis and traffic flow management. For example, when the main line traffic volume is 800veh/h and the head-crossing time span is 8s, as shown in fig. 3, as the permeability of the CAVs increases, the probability that the head-crossing time span of the traffic flow is more than 8s increases, which means that the traffic capacity of the ramp is improved, which is greatly helpful for the design of the expressway. The cumulative headway distribution function of the mixed traffic flow describes the probability that headway of a main line vehicle is larger than a certain value, and the function can be used for analyzing the efficiency of confluence and providing theoretical guidance for the design and management of a highway confluence area. For example, assuming a traversable headway of 8s, fig. 4 depicts the probability of mixed traffic flow headway greater than 8s at different CAVs permeabilities and different traffic volumes, indicating: when the permeability of the CAVs is large, the distribution of time headway of a main line is greatly influenced, and the function value is mainly determined by traffic under the low permeability of the CAVs.
The results of table 1 were obtained from the mentioned cases and the ramp traffic capacity calculation model. In order to intuitively acquire the influence of the MPRs on the traffic capacity of the ramp, a traffic capacity gain map is drawn (FIG. 5). As shown in fig. 5, at low traffic volume (light traffic), the permeability does not greatly increase the traffic capacity of the ramp; when the traffic volume is moderate, the permeability can obviously improve the traffic capacity; along with the increase of the traffic volume, the vehicle following probability is increased, and the income of the permeability on the traffic capacity is gradually reduced. Overall, at moderate traffic volumes, the increase in permeability can significantly increase ramp capacity.
TABLE 1 influence of different main line traffic volumes and CAVs permeability on the ramp traffic capacity
Figure BDA0002691884140000151
The main line analysis and the ramp analysis are based on the integer nonlinear lane dynamic allocation model of the invention, the lane management strategy of the invention is analyzed by using numerical values and simulation means, the influence of the lane management strategy on traffic and benefits is analyzed, and the following conclusion about the integer nonlinear lane dynamic allocation model of the invention is obtained:
(1) when the permeability of the CAVs is 50% -70%, the benefit of deploying the CAVs special lanes is high, and when the permeability of the CAVs is low, the effect of setting the CAVs special lanes can cause traffic congestion.
(2) When the permeability of the CAVs is high, the CAVs plays a role in controlling the traditional vehicles, so that the speed difference of the vehicles on the whole road can be reduced, the safety of the vehicles is ensured, but the speed cannot be increased, and the reasonable arrangement of the CAVs lanes can not only increase the speed of a general lane, but also ensure that the vehicles can drive freely.
(3) The traffic capacity of the ramp is determined by the traffic volume of the main line and the permeability of CAVs, and the sensitivity of the traffic capacity to the permeability of CAVs is higher under moderate traffic volume.
The highway traffic capacity cooperative regulation and control method based on the CAVs mixed traffic flow dynamic lane distribution dynamically regulates the distribution of lane right of way and regulates the vehicle running data based on the vehicle networking system by monitoring the change of the highway traffic capacity, outputting the change curve of the highway traffic capacity, selecting an optimization solution and feeding the optimization solution back to a traffic control platform, and dynamically regulating a highway lane distribution scheme and vehicle running data management and control in time, thereby realizing the highway traffic capacity cooperative regulation and control.
Compared with the existing lane management method/model, the lane management/integer nonlinear lane dynamic allocation model has the advantages that:
(1) the method considers the interaction influence of the ramp and the main line of the expressway, and deeply analyzes the influence of market penetration rates of different internet automatic driving vehicles on the ramp and the main line of the expressway.
(2) The traffic capacity for road management in the past is an empirical value or a fixed value, and is not in accordance with the reality.
(3) In the prior lane management, CAVs only have the right of way on a special lane, the invention provides a method for using a general lane for CAVs and a traditional vehicle together, and establishes a right of way distribution model, thereby greatly saving road resources.
It is noted that, in the present application, relational terms such as first, second, third, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.

Claims (10)

1. A highway traffic capacity cooperative regulation and control method based on lane dynamic allocation of CAVs (vehicular Ad hoc networks) mixed traffic flow is characterized by comprising the following steps of:
step 1: collecting data: acquiring lane information of a main line and a ramp of a highway and vehicle running information on a lane based on a vehicle networking system; the lane information includes: the type, number and basic traffic capacity of the lanes; the types of lanes include CAVs special lanes, general lanes;
step 2: establishing an integral nonlinear lane dynamic allocation model based on the data acquired in the step 1 and the constraints of the service level and the lane saturation of the expressway;
and step 3: the highway traffic capacity is cooperatively regulated and controlled:
step 31: according to the lane information of the current main line and the ramp and the vehicle running information on the lane, inputting an integral nonlinear lane dynamic allocation model and outputting the total traffic capacity of the expressway;
step 32: monitoring the change of the traffic capacity of the expressway, dynamically adjusting the distribution of lane right of way according to lane information and vehicle running information on the lane, adjusting the running data of vehicles based on an internet of vehicles system, outputting the change curve of the traffic capacity of the expressway, selecting an optimized solution, feeding the optimized solution back to a traffic control platform, dynamically adjusting the distribution scheme of the lane of the expressway and the management and control of the vehicle running data in time, and realizing the cooperative regulation and control of the traffic capacity of the expressway;
in step 2, the integer nonlinear lane dynamic allocation model is:
Figure FDA0002691884130000011
s.t：lA+lh＝L (2)；
Qramp≥dr (3)；
QA≤(V/CA)B·CA (4)；
QH≤(V/CH)B·CH (5)；
lA、lh∈N* (6)；
in the formula, ML represents lane management; q denotes the total traffic capacity of the highway; max represents the maximum value; lARepresenting the total number of lanes dedicated to CAVs; qATraffic volume representing lanes dedicated to CAVs; lhRepresenting a total number of general lanes; qHRepresenting the traffic volume of a general lane; qrampRepresenting the traffic capacity of the ramp; l represents the total number of lanes of the highway; drRepresenting ramp traffic demand; (V/C)A)BRepresenting the saturation of the lanes dedicated to the CAVs when the service level is class B; (V/C)H)BRepresenting the saturation of the general lane when the service level is B level; cARepresenting the basic traffic capacity of CAVs special lanes; cHRepresenting the basic traffic capacity of the general lane; n denotes a positive integer.
2. The coordinated control method for highway traffic capacity based on CAVs mixed traffic flow lane dynamic allocation according to claim 1, wherein in step 1, the vehicle driving information is CAVs vehicles, HMVs vehicle types and quantity and running data thereof, and comprises the following steps: current time velocity v of vehicle, minimum parking space s0Safe headway T, free flow velocity v0Length of vehicle l, minimum gap t that vehicles on ramp can pass throughcDistance t between following vehicles on rampg。
3. The coordinated control method for the traffic capacity of the expressway based on the CAVs mixed traffic flow dynamic allocation according to claim 1, wherein the integral nonlinear lane dynamic allocation model adopts a road right allocation method as follows: CAVs vehicle prefers CAVS lane driving, when the saturation of CAVs special lane reaches SBWhen, the CAVs are assigned to the general lane, SBIndicating CAVs-specific lane saturation when the service level is class B.
4. The coordinated control method for highway traffic capacity based on CAVs mixed traffic flow lane dynamic allocation according to claim 1, wherein the traffic volume Q of the CAVs special laneAComprises the following steps:
QA(lA，p，d，SB)＝min(pdSBlACA)；
wherein p represents the total permeability of CAVs, d represents the demand of main line mixed traffic flow, and SBIndicating CAVs-specific lane saturation when the service level is class B.
5. The coordinated control method for the traffic capacity of the expressway based on the dynamic allocation of the lanes of the CAVs mixed traffic flow according to claim 1, wherein the traffic volume Q of the general laneHComprises the following steps:
QH＝min(d-QA，(L-lA)Cmix)；
in the formula, CmixFor the basic traffic capacity of the general lane, d represents the main mixed traffic flow demand.
6. The coordinated control method for highway traffic capacity based on CAVs (vehicular ad hoc networks) mixed traffic flow lane dynamic allocation according to claim 5, wherein the general lane basic traffic capacity CmixComprises the following steps:
Figure FDA0002691884130000021
；
in the formula, v is the current time speed of the vehicle; p is a radical ofhRepresenting the proportion of a human-driven vehicle; s0Is the minimum parking distance; t is a safe headway; v. of0Is the free flow velocity; l is the vehicle length; p is a radical ofaRepresents the scale of an ACC vehicle; t is taA constant headway desired to be maintained for an ACC vehicle;
Figure FDA0002691884130000022
permeability of CAVs in the general lane.
7. The method of claim 6, wherein the permeability of the CAVs in the general purpose lane is determined by the permeability of the CAVs in the general purpose lane
Figure FDA0002691884130000023
Comprises the following steps:
Figure FDA0002691884130000031
。
8. the coordinated control method for highway traffic capacity based on CAVs (vehicular ad hoc networks) mixed traffic flow lane dynamic allocation according to claim 1, wherein the ramp traffic capacity Q isrampComprises the following steps:
Figure FDA0002691884130000032
；
in the formula, CrampRepresenting the basic traffic capacity of the ramp; i represents the number of vehicles; qmRepresenting a virtual traffic volume; piRepresenting the probability of a gap theoretically occurring that allows i vehicles to cross.
9. The coordinated control method for highway traffic capacity based on CAVs mixed traffic flow lane dynamic allocation according to claim 8, wherein the probability P of theoretically occurring gap allowing i vehicles to pass through is characterized in thatiComprises the following steps:
Figure FDA0002691884130000033
；
in the formula, λ2Virtual vehicle arrival rates of different MPRs mixed traffic flows of a highway main line are obtained; t is tcThe vehicles on the ramp can pass through the minimum clearance; t is tgThe following time interval of the ramp vehicle; p (k) represents a mixed traffic flow headway probability distribution function; k represents the number of vehicles forming the fleet; qPRepresenting the total number of CAVs forming the fleet; n is a radical ofPRepresenting the number of CAVs fleets.
10. The coordinated control method for highway traffic capacity based on CAVs mixed traffic flow lane dynamic allocation according to claim 9, wherein the total number Q of CAVs forming the fleet isPComprises the following steps:
Figure FDA0002691884130000034
；
in the formula, Q is the traffic volume of a main line of the highway;
number N of said CAVs fleetPComprises the following steps:
Figure FDA0002691884130000035
。
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