CN115909768A

CN115909768A - Intelligent network-connected hybrid traffic flow intersection signal collaborative optimization method and system

Info

Publication number: CN115909768A
Application number: CN202211349527.6A
Authority: CN
Inventors: 王涛; 赵晓寅; 程瑞; 徐奇; 廉冠; 赵红专
Original assignee: Guilin University of Electronic Technology
Current assignee: Guilin University of Electronic Technology
Priority date: 2022-10-31
Filing date: 2022-10-31
Publication date: 2023-04-04

Abstract

The invention discloses a method and a system for collaborative optimization of intelligent network-connected mixed traffic flow intersection signals, wherein the method comprises the steps of carrying out high-precision acquisition on traffic state information; optimizing the intelligent vehicle track based on initial information control; and optimizing based on the optimized signal control of the intelligent vehicle track. Therefore, the defects that the permeability of the intelligent vehicle is low, a large number of intelligent vehicles follow the intelligent vehicle and only control the single intelligent vehicle under the condition of hybrid traffic mode, the multi-vehicle following state is not considered, and the optimization effect is limited are overcome. Meanwhile, the operation efficiency of the intersection is further improved by adopting a track and signal collaborative optimization mode, the data source is ensured to be more accurate and microscopic, and the control strategy is more reasonable and effective.

Description

Intelligent network-connected hybrid traffic flow intersection signal collaborative optimization method and system

Technical Field

The invention relates to the technical field of intelligent traffic control, in particular to a method and a system for collaborative optimization of intersection signals of intelligent network-connected mixed traffic flow.

Background

In recent years, china proposes a double-carbon target, accelerates the formation of a green and low-carbon transportation mode, promotes new energy, intelligent and digital transportation equipment, encourages and guides green travel, and makes traffic more environment-friendly and travel more low-carbon. In the face of many challenges, the intelligent networked automobile integrates the advantages of intellectualization and networking, provides unprecedented opportunities for realizing energy conservation and emission reduction of traffic travel and improving traffic efficiency, obtains many breakthrough achievements in the technical field of intelligent traffic automatic driving, can remarkably relieve energy and environmental crises faced by China, and effectively relieves increasingly serious traffic jam and road safety problems to a certain extent. The breakthrough in technology allows more and more prototype intelligent vehicles to leave the laboratory, test in real road environment, and gradually advance to practical application. The problems of urban traffic jam, traffic accidents and the like are increasingly serious, the intersection is used as a node of an urban traffic network, the operation and control effect of road network traffic is directly influenced, and the solution of the traffic problem at the intersection has great significance for the solution of the whole urban traffic problem.

With the development of communication technology, sensing and computer technology, intelligent networking technology becomes a key technology for solving traffic problems. Although the intelligent internet connection vehicle has made great progress so far, relatively long time is needed to achieve full automation and high market penetration rate of the intelligent internet connection vehicle, and the road has mixed traffic flow of the intelligent internet connection vehicle and human driving vehicles for a long time before the intelligent internet connection vehicle completely replaces the human driving vehicles. Under the environment of intelligent networking, the intelligent networking vehicle and human-driven vehicles have faster information detection capability and shorter reaction time, and the road end sensing can also transmit the detected road and vehicle running conditions in the intersection range to the vehicle end in real time. How to exert the technical advantages of the intelligent networking system in the intersection control problem and realize safe, effective and scientific control is an important direction for the research in the field of traffic control at present.

However, in the existing intelligent vehicle track optimization research based on the hybrid traffic condition, the permeability of the intelligent vehicle is often low, and the detector is difficult to obtain the dynamic running conditions of all vehicles in the research range in the human-computer hybrid driving traffic mode, so that the track planning of the intelligent vehicle is restricted; the existing research is unilateral optimization, and less tracks and signals are optimized cooperatively. In addition, a multi-vehicle following model is less used for speed control, and only a single vehicle is considered in many cases. Aiming at the situation, the invention provides an intelligent network-connected mixed traffic flow intersection signal collaborative optimization method and system, which can effectively improve the prior art and overcome the defects thereof.

Disclosure of Invention

Aiming at the defects of the prior art, the invention provides an intelligent network connection mixed traffic flow intersection signal collaborative optimization method and system, aiming at solving the problems in the prior art, and the specific scheme is as follows:

in a first aspect, the invention provides an intelligent internet-connected hybrid traffic flow intersection signal collaborative optimization method, which comprises the following steps:

step 1: carrying out high-precision acquisition on traffic state information, including target vehicle running state data and an initial signal control scheme;

step 2: optimizing an intelligent vehicle track based on initial information control, wherein the optimization comprises intersection partition definition, intelligent vehicle fleet grouping, speed guide area boundary calculation, following model establishment and vehicle speed guide strategy application;

and step 3: and optimizing based on the optimized signal control of the intelligent vehicle track, wherein the optimizing comprises the steps of optimizing standard car equivalent parameters, determining road traffic capacity occupation coefficients and determining a signal control scheme.

Preferably, the step 2 includes:

s21, intersection partition defining: the detection range L of the collector is divided into a plurality of regions, mainly a fleet decision region L _D Speed guide areaL _V To form a mixture; the motorcade marshalling area has the main function of marshalling the intelligent internet vehicles and the manually driven vehicles; the main function of the speed guide area is to apply a speed control strategy to realize the real-time control of the intelligent motorcade, and to ensure the traffic safety, L _V The length of the inlet duct is less than the solid line area L of the inlet duct _limit (ii) a The composition and constraints are as follows:

L＝L _D +L _V

L _V ＜L _limit ；

s22, intelligent fleet grouping: due to the man-machine mixed driving environment, the intelligent networked vehicles exist in the intersection according to a certain permeability; in a fleet decision zone L _D With intelligent networked vehicles (CAV) as the head vehicle of the grouped fleet

(wherein m is the serial number of the fleet where the head vehicle is located, and l is the serial number of the lane where the head vehicle is located); several manually driven vehicles whose heels are in contact>

(where m is the number of the motorcade in which j is the serial number of the manually-driven vehicle in the motorcade) and the head vehicle->

Form intelligent networking motorcade M together _n ；

S23, calculating the boundary of the speed guide area: determining that the vehicle is initially brought into the influence range of the speed control strategy by the centralized controller, different from the detection boundary of the detector; the judgment condition is an acceleration extreme value a acceptable to the driver _c And according to the acceleration extreme value a _c With road speed limit V _L Calculating comfortable braking distance S _c (ii) a The calculation formula is as follows:

according to the calculated comfortable braking distance S _c Based on the stop line of the intersectionDetermining a speed guidance zone L _V A boundary; if S _c ＞S _limit Taking the boundary distance as S _limit ；

S24, establishing a following model: intelligent networked vehicle team M due to partial manual driving of vehicles in vehicle team _n The driving characteristics of the leading vehicle will induce the following manually driven vehicle to pass through the intersection or stop at the same target speed; because the traffic acquisition system based on the radar all-in-one machine can obtain the running state information of all vehicles including manually driven vehicles, an improved FVD (full speed difference) following model based on the vehicle team can be applied to obtain the head vehicle of the next vehicle team

The last vehicle of the previous team->

The following acceleration is calculated as follows:

a _m+1 ＝α(V′ _m -V _m (t))+β(V _m+1 (t)-V _m (t))

in the formula: a is a _m+1 -head vehicle following acceleration in the m +1 fleet;

V′ _m -m fleet head vehicle target speed;

V _m (t) -fleet tail speed at time m;

V _m+1 (t) -fleet head speed at time t, m + 1;

α — driver sensitivity coefficient; beta-driver response coefficient;

s25, vehicle speed guiding strategy application: intelligent internet motorcade M _n Arrival speed guide L _V When the boundary is detected, the phase judgment is carried out, and the traveling direction signal lamp is in a red-yellow lamp phase P _r With the remaining time set to T _r (ii) a Or green phase P _g With the remaining time set to T _g (ii) a And different vehicle speed guiding strategies are adopted according to different phase states, and t is set _r Driver reaction time;

wherein, the centralized controller only carries out the trajectory optimization under the condition that no downstream vehicle in line or vehicle in line is not interfered:

1) When in use

Or->

Then, a target vehicle speed V 'is taken' _m ＝V _max (ii) a The following acceleration of the rear motorcade is as follows: a is _m+1 ＝α(V _max -V _m (t))+β(V _m+1 (t)-V _m (t))；

2) When in use

In time, the vehicle speed V should be reduced _m To target vehicle speed V' _m The time when the vehicle fleet reaches the stop line is set to V 'at the green light on time' _m The solving method is as follows:

in the formula: v _min -road minimum speed limit; v _max -road top speed limit.

Preferably, the step 3 comprises:

s31, standard car equivalent parameter optimization: based on the accuracy of the radar all-in-one machine for obtaining the running state of the vehicle, the equivalent conversion coefficient of the car can be improved by utilizing the vehicle type, the vehicle size, the occupied space and the running speed of the running vehicle in the intersection:

wherein:

to convert the total traffic volume of k lanes, G _s For all cars, G _r For all medium-sized vehicles, G _l Is a stand forThere are large-sized vehicles, G _b The counting of all buses is directly given by the radar and video integrated machine acquisition system;

λ _s 、λ _r 、λ _l 、λ _b the space occupation coefficients of the cars, the medium-sized cars, the large-sized cars and the buses respectively reflect the occupation degrees of various vehicles on the road area; and determines the bus priority, i.e. lambda, based on the traffic flow rate _b Taking values;

δ _s 、δ _r 、δ _l 、δ _b the road traffic capacity occupation coefficients of the cars, the medium-sized cars, the large-sized cars and the buses are respectively;

s32, determining a road traffic capacity occupation coefficient: according to the average speed of the lane

Normalizing with the average speed of the target vehicle, and the influence of the vehicle on the traffic capacity is smaller as the vehicle is closer to the average speed of the lane; based on the average speed value, the closer to the speed limit value, the lower the occupancy coefficient is, and the closer to 0km/h, the higher the occupancy coefficient is; in order to avoid the excessive disturbance of the coefficient, the value of the coefficient is restricted; therefore, the following coefficient calculation formula is provided:

s33, determining a signal control scheme: according to the calculation formula in step S32, the accurate traffic flow q of each key lane can be obtained _i Introducing a classical British TRRL method to carry out signal timing calculation to generate a phase scheme;

in one period, the integrated controller reads the running states of all vehicles at the intersection after the track optimization in real time, and the intersection signal control is carried out by using a webster method based on the equivalent weight of the improved standard car.

Preferably, the step S33 includes:

s41, calculating the flow ratio sum of the key lanes: and (3) calculating the sum of the flow ratio according to the traffic volume of each lane under the improvement given in the step (3.2), wherein the calculation method comprises the following steps:

wherein i is an index of a phase and needs to be specifically set according to the actual running condition of the intersection;

the flow ratio of the k lanes in the i phase is calculated as follows:

wherein

The saturation flow rate of the k lanes in the i phase is determined according to relevant traffic regulation standards;

s42, the optimal period duration: the period duration of TRRL method signal control should be calculated according to the following formula:

40s≤C ₀ ≤180s

wherein L is defined as the total loss time of the signal, and is calculated as follows:

wherein L is _s Defined as the startup lost time; AR is the clearing time of the all-red intersection of the i phase;

s43, calculating the effective green light time: allocating optimal period duration C according to accurate flow ratio of each phase ₀ The calculation method is as follows:

generation of phase combination G = [ G ] from effective green time ₁ ，g ₂ ，g ₃ ，...]The system is used for controlling the intersection signal of the next period; and when the next period starts, returning to the step 2 to continue to perform the intelligent vehicle track optimization based on the signaling control scheme given in the step 3.

In a second aspect, the invention provides an intelligent network-connected hybrid traffic flow intersection signal collaborative optimization system, which comprises:

the acquisition module is used for carrying out high-precision acquisition on traffic state information and comprises target vehicle running state data and an initial signal control scheme;

the processing module is used for optimizing an intelligent vehicle track controlled based on initial information, and comprises intersection partition definition, intelligent vehicle fleet grouping, speed guide area boundary calculation, following model establishment and vehicle speed guide strategy application;

and the optimization module is used for optimizing based on the optimized signal control of the intelligent vehicle track, and comprises standard car equivalent parameter optimization, road traffic capacity occupancy coefficient determination and signal control scheme determination.

In a third aspect, the invention provides an intelligent network-connected hybrid traffic flow intersection signal collaborative optimization device, which comprises:

the communication bus is used for realizing the connection communication between the processor and the memory;

a memory for storing a computer program;

a processor for executing the computer program to implement the steps of:

carrying out high-precision acquisition on traffic state information, including target vehicle running state data and an initial signal control scheme;

optimizing an intelligent vehicle track based on initial information control, wherein the optimization comprises intersection partition definition, intelligent vehicle fleet grouping, speed guide area boundary calculation, following model establishment and vehicle speed guide strategy application;

and optimizing based on the optimized signal control of the intelligent vehicle track, wherein the optimizing comprises the steps of optimizing standard car equivalent parameters, determining road traffic capacity occupancy coefficients and determining a signal control scheme.

In a fourth aspect, the invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the method according to the first aspect.

Has the beneficial effects that: the intelligent network-connected mixed traffic flow intersection signal collaborative optimization method and system provided by the invention have the advantages that the traffic state information is collected at high precision, and the traffic state information comprises target vehicle running state data and an initial signal control scheme; optimizing an intelligent vehicle track based on initial information control, wherein the optimization comprises intersection partition definition, intelligent vehicle fleet grouping, speed guide area boundary calculation, following model establishment and vehicle speed guide strategy application; and optimizing based on the optimized signal control of the intelligent vehicle track, wherein the optimizing comprises the steps of optimizing standard car equivalent parameters, determining road traffic capacity occupancy coefficients and determining a signal control scheme. Therefore, the intelligent vehicle and the artificial vehicle are grouped in a vehicle grouping mode, and a vehicle team is used for following; by utilizing the acquisition characteristic of the radar and vision all-in-one machine, the motorcade leading intelligent vehicle can obtain the speed of manually driving the vehicle at the tail of the front motorcade, so that the following is performed, and the problems that the permeability of the intelligent vehicle is low and a large amount of manually and intelligently driven following intelligent vehicles exist in a hybrid traffic mode are solved; and the intelligent vehicle is only controlled to drive a single vehicle, the multi-vehicle following state is not considered, and the optimization effect is limited. Meanwhile, by adopting a track and signal collaborative optimization mode, the defects that the existing mixed traffic condition only adopting unilateral optimization only often only aims at the running state of the vehicle to carry out signal optimization or track optimization and speed control are carried out under a fixed timing scheme are avoided, the running efficiency of the intersection is further improved, the data source is more accurate and microscopic, and the control strategy is more reasonable and effective.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and the embodiments in the drawings do not constitute any limitation to the present invention, and for those skilled in the art, other drawings may be obtained according to the drawings without creative efforts.

Fig. 1 is a flow diagram of an embodiment of an intelligent network-connected hybrid traffic flow intersection signal collaborative optimization method of the invention.

Fig. 2 is a schematic structural diagram of an embodiment of the intelligent network-connected hybrid traffic flow intersection signal collaborative optimization system.

Fig. 3 is a schematic structural diagram of an embodiment of the intelligent network-connected hybrid traffic flow intersection signal collaborative optimization device of the invention.

Detailed Description

The technical solution of the present invention will be further described in detail with reference to the accompanying drawings and embodiments, which are preferred embodiments of the present invention. It is to be understood that the described embodiments are merely some, and not all embodiments of the invention; it should be noted that the embodiments and features of the embodiments may be combined with each other without conflict. 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.

The technical scheme of the embodiment of the invention has the following main ideas: carrying out high-precision acquisition on traffic state information, including target vehicle running state data and an initial signal control scheme; optimizing an intelligent vehicle track based on initial information control, wherein the optimization comprises intersection partition definition, intelligent vehicle fleet grouping, speed guide area boundary calculation, following model establishment and vehicle speed guide strategy application; and optimizing based on the optimized signal control of the intelligent vehicle track, wherein the optimizing comprises the steps of optimizing standard car equivalent parameters, determining road traffic capacity occupancy coefficients and determining a signal control scheme.

In order to better understand the technical solutions, the technical solutions will be described in detail below with reference to the drawings and specific embodiments.

Example one

An embodiment of the present invention provides an intelligent network-connected hybrid traffic flow intersection signal collaborative optimization method, and as shown in fig. 1, the data processing method may specifically include the following steps:

step S101, carrying out high-precision acquisition on traffic state information, wherein the traffic state information comprises target vehicle running state data and an initial signal control scheme;

in an embodiment of the present invention, the target vehicle operating state data may specifically include: object type C _s Target longitudinal velocity V _p Target longitudinal acceleration V _ap And the number L of the lane where the target is located _i Target to stopping line distance Y _i (ii) a The initial signal control scheme acquisition comprises green light time, yellow light time, full red time and a phase sequence scheme of each phase. Data are collected through a target detection system of the radar vision all-in-one machine established at the intersection, and information obtained through real-time collection is simultaneously input into the track optimization and signal control scheme optimization method.

Step S102, optimizing an intelligent vehicle track based on initial information control, wherein the optimization comprises intersection partition definition, intelligent vehicle fleet grouping, speed guide area boundary calculation, following model establishment and vehicle speed guide strategy application;

specifically, when a cycle starts, the integrated controller is adopted to read the running states of all vehicles at the intersection and simultaneously control the speed of all intelligent networked vehicles within the control range of the intersection.

In the embodiment of the present invention, the intersection partition definition may specifically include: the detection range L of the collector is divided into a plurality of regions, mainly a fleet decision region L _D A speed guide area L _V The components are mixed; the fleet marshalling area mainly has the function of marshalling the intelligent networked vehicles and the manually driven vehicles; the main function of the speed guide area is to apply a speed control strategy to realize the real-time control of the intelligent motorcade, and L is used for ensuring the traffic safety _V The length of the inlet duct is less than the solid line area L of the inlet duct _limit (ii) a The composition and constraints are as follows:

L＝L _D +L _V

L _V ＜L _limit ；

in an embodiment of the present invention, the intelligent fleet grouping specifically may include: due to the man-machine mixed driving environment, the intelligent networked vehicles exist in the intersection according to a certain permeability; in a fleet decision zone L _D Using intelligent internet vehicles (CAVs) as the head car of the grouped fleet

(where m is the number of the motorcade in which the vehicle is located, and j is the number of the man-operated vehicle in the motorcade) and a head vehicle

Form intelligent networking motorcade M together _n ；

In the embodiment of the present invention, the calculating of the boundary of the speed guidance area may specifically include: determining that the vehicle is initially brought into the influence range of the speed control strategy by the centralized controller, different from the detection boundary of the detector; the judgment condition is an acceleration extreme value a acceptable to the driver _c And according to the acceleration extreme a _c And road limit speed V _L Calculating comfortable braking distance S _c (ii) a The calculation formula is as follows:

according to the calculated comfortable braking distance S _c Determining a speed guidance area L by taking the stop line of the intersection as a reference _V A boundary; if S _c ＞S _limit Taking the boundary distance as S _limit ；

In the embodiment of the present invention, the following model establishment may specifically include: intelligent networked vehicle team M due to partial manual driving of vehicles in vehicle team _n The driving characteristics of the head car will induce the following manual drivingThe driving vehicles pass through the intersection or stop at the same target speed; because the traffic collection system based on the radar-vision all-in-one machine can obtain the running state information of all vehicles including manually driven vehicles, an improved FVD (full speed difference) following model based on a vehicle team can be applied to obtain a head vehicle of a next vehicle team

The last vehicle of the previous team->

The following acceleration is calculated as follows:

a _m+1 ＝α(V′ _m -V _m (t))+β(V _m+1 (t)-V _m (t))

in the formula: a is _m+1 -m +1 head vehicle following acceleration in the fleet;

V′ _m -m fleet head vehicle target speed;

V _m (t) -fleet tail speed at time m;

V _m+1 (t) -fleet head speed at time t, m + 1;

α — driver sensitivity coefficient; beta-driver response coefficient;

in the embodiment of the present invention, the application of the vehicle speed guidance strategy may specifically include: intelligent internet motorcade M _n Arrival speed guide L _V When the boundary is detected, the phase judgment is carried out, and the traveling direction signal lamp is in a red-yellow lamp phase P _r With the remaining time set to T _r (ii) a Or green phase P _g With the remaining time set to T _g (ii) a And different vehicle speed guiding strategies are adopted according to different phase states, and t is set _r Reaction time for the driver;

1) When in use

Or->

Then, the target vehicle speed V 'is taken' _m ＝V _max (ii) a The following acceleration of the rear motorcade is as follows: a is _m+1 ＝α(V _max -V _m (t))+β(V _m+1 (t)-V _m (t))；

2) When in use

When the vehicle speed V is to be reduced _m To target vehicle speed V' _m The time when the vehicle fleet reaches the stop line is set to V 'at the green light on time' _m The solving method is as follows:

in the formula: v _min -road minimum speed limit; v _max -road top speed limit.

And S103, optimizing based on the optimized signal control of the intelligent vehicle track, wherein the optimizing comprises standard car equivalent parameter optimization, road traffic capacity occupancy coefficient determination and signal control scheme determination.

Specifically, in one period, the integrated controller reads the running states of all vehicles at the intersection after the track optimization in real time, and the intersection signal control is carried out by using a webster method based on the equivalent weight of the improved standard car.

In the embodiment of the present invention, the optimization of the equivalent parameters of the standard car may specifically include: based on the accuracy of the radar all-in-one machine for obtaining the running state of the vehicle, the equivalent conversion coefficient of the car can be improved by utilizing the vehicle type, the vehicle size, the occupied space and the running speed of the running vehicle in the intersection:

wherein:

to convert the total traffic volume of k lanes, G _s For all cars, G _r For all medium-sized vehicles, G _l For all large vehicles, G _b The counting of all buses is directly given by the thunder and vision integrated machine acquisition system;

λ _s 、λ _r 、λ _l 、λ _b the space occupation coefficients of cars, medium-sized vehicles, large-sized vehicles and buses respectively reflect the occupation degree of various vehicles on the road area; and determines the bus priority, i.e. lambda, based on the traffic flow rate _b Taking values;

δ _s 、δ _r 、δ _l 、δ _b the road traffic capacity occupation coefficients of cars, medium-sized vehicles, large-sized vehicles and buses are respectively;

in the embodiment of the present invention, the determining of the road traffic capacity occupancy coefficient may specifically include: according to the average speed of the lane

Normalizing with the average speed of the target vehicle, wherein the closer to the average speed of the lane, the smaller the influence of the vehicle on the traffic capacity is; based on the average speed value, the closer to the speed limit value, the lower the occupancy coefficient is, and the closer to 0km/h, the higher the occupancy coefficient is; in order to avoid the overlarge disturbance of the coefficient, the value of the coefficient is restricted; therefore, the following coefficient calculation formula is provided:

in the embodiment of the present invention, specifically, the determining by the signaling control scheme may specifically include: according to the calculation formula in step S32, the accurate traffic flow q of each key lane can be obtained _i And a classical British TRRL method is introduced to carry out signal timing calculation to generate a phase scheme.

Preferably, the above steps may specifically include:

firstly, the calculation of the sum of the flow ratios of the key lanes is to calculate the sum of the flow ratios of the traffic volumes of all lanes under the improvement given in step 3.2, and the calculation method is as follows:

the flow ratio of k lanes in the i phase is calculated as follows:

wherein

The saturation flow rate of the k lanes in the i phase is determined according to relevant traffic regulation standards; then, the period duration which is not controlled by the TRRL method signal in the optimal period duration should be calculated according to the following formula:

40s≤C ₀ ≤180s

finally, the effective green time calculation allocates the optimal cycle duration C according to the accurate flow rate ratio of each phase ₀ The calculation method is as follows:

in the embodiment of the present application, the phase combination G = [ G ] is generated from the effective green time ₁ ，g ₂ ，g ₃ ，...]The system is used for controlling the intersection signal of the next period; and when the next period starts, returning to the step 2 to continue to perform the intelligent vehicle track optimization based on the signal control scheme given in the step 3.

Example two

An embodiment of the present invention provides an intelligent network-connected hybrid traffic flow intersection signal collaborative optimization system, which may specifically include the following modules as shown in fig. 2:

in an embodiment of the present invention, the target vehicle operating state data may specifically include: object type C _s Target longitudinal velocity V _p Target longitudinal acceleration V _ap And the number L of the lane where the target is located _i Target to stopping line distance Y _i (ii) a The initial signal control scheme acquisition comprises green light time, yellow light time, full red time and a phase sequence scheme of each phase. The data are collected through a radar and vision all-in-one machine target detection system established at the intersection, and the information obtained through real-time collection is simultaneously input into the track optimization and signal control scheme optimization method.

In an embodiment of the present invention, the intersection partition definition may specifically include: the detection range L of the collector is partitioned and is mainly usedTo be determined by the fleet decision zone L _D A speed guide area L _V The components are mixed; the motorcade marshalling area has the main function of marshalling the intelligent internet vehicles and the manually driven vehicles; the main function of the speed guide area is to apply a speed control strategy to realize the real-time control of the intelligent motorcade, and L is used for ensuring the traffic safety _V The length of the inlet duct is less than the solid line area L of the inlet duct _limit (ii) a The composition and constraints are as follows:

L＝L _D +L _V

L _V ＜L _limit ；

in an embodiment of the present invention, the intelligent fleet grouping specifically may include: due to the man-machine mixed driving environment, the intelligent networked vehicles exist in the intersection according to a certain permeability; in a fleet decision zone L _D With intelligent networked vehicles (CAV) as the head vehicle of the grouped fleet

(where m is the number of the fleet where the vehicle is located and j is the number of the manually driven vehicle in the fleet) and the head vehicle

Form intelligent networking motorcade M together _n ；

In the embodiment of the present invention, the calculating of the boundary of the speed guidance area may specifically include: determining that the vehicle is initially brought into the influence range of the speed control strategy by the centralized controller, different from the detection boundary of the detector; the judgment condition is an acceleration extreme value a acceptable to the driver _c And according to the acceleration extreme a _c With road speed limit V _L Calculating comfortable braking distance S _c (ii) a The calculation formula is as follows:

In the embodiment of the present invention, the following model establishment may specifically include: because partial man-driven vehicles exist in the motorcade, the intelligent networked motorcade M _n The driving characteristics of the leading vehicle will induce the following manually driven vehicle to pass through the intersection or stop at the same target speed; because the traffic acquisition system based on the radar all-in-one machine can obtain the running state information of all vehicles including manually driven vehicles, an improved FVD (full speed difference) following model based on the vehicle team can be applied to obtain the head vehicle of the next vehicle team

The last vehicle of the previous team->

The following acceleration is calculated as follows:

a _m+1 ＝α(V′ _m -V _m (t))+β(V _m+1 (t)-V _m (t))

V′ _m -m fleet head vehicle target speed;

V _m (t) -fleet tail speed at time m;

V _m+1 (t) -t time m +1 fleet head speed;

α - -driver sensitivity coefficient; β -driver reaction coefficient;

in the embodiment of the present invention, the application of the vehicle speed guidance strategy may specifically include: intelligent internet motorcade M _n Arrival speed guide L _V At the boundary, the phase judgment is carried out, and the traveling direction signal lamp is in a red and yellow lamp phase P _r With the remaining time set to T _r (ii) a Or green phase P _g With the remaining time set to T _g (ii) a And different vehicle speed guiding strategies are adopted according to different phase states, and t is set _r Reaction time for the driver;

1) When in use

Or->

2) When in use

in the formula: v _min -road minimum speed limit; v _max -road top speed limit.

wherein:

in the embodiment of the present invention, specifically, the determining by the signaling control scheme may specifically include: according to the calculation formula in step S32, the accurate traffic flow q of each key lane can be obtained _i The classical uk TRRL method is used to perform signal timing calculations to generate a phase scheme.

Preferably, the above steps may specifically include:

firstly, the flow ratio sum of the key lanes is calculated according to the traffic volume of each lane under the improvement given in the step 3.2, and the calculation method is as follows:

the flow ratio of the k lanes in the i phase is calculated as follows:

wherein

then, the period duration which is not controlled by the TRRL method signal in the optimal period duration should be calculated according to the following formula:

40s≤C ₀ ≤180s

finally, the effective green time is calculated by allocating the optimal cycle duration C according to the accurate flow ratio of each phase ₀ The calculation method is as follows:

EXAMPLE III

An embodiment of the present invention provides an intelligent network-connected hybrid traffic flow intersection signal collaborative optimization system, which may specifically include the following modules as shown in fig. 3:

a memory for storing a computer program; the memory may comprise high-speed RAM memory and may also comprise non-volatile memory, such as at least one disk memory. The memory may optionally comprise at least one memory device.

A processor for executing the computer program to implement the steps of:

firstly, carrying out high-precision acquisition on traffic state information, including target vehicle running state data and an initial signal control scheme;

in an embodiment of the present invention, the target vehicle operating state data may specifically include: object type C _s Target longitudinal velocityV _p Target longitudinal acceleration V _ap And the number L of the lane where the target is located _i Target to stopping line distance Y _i (ii) a The initial signal control scheme acquisition comprises green light time, yellow light time, full red time and a phase sequence scheme of each phase. Data are collected through a target detection system of the radar vision all-in-one machine established at the intersection, and information obtained through real-time collection is simultaneously input into the track optimization and signal control scheme optimization method.

Then, optimizing an intelligent vehicle track based on initial information control, wherein the optimization comprises intersection partition definition, intelligent vehicle fleet grouping, speed guide area boundary calculation, car following model establishment and vehicle speed guide strategy application;

L＝L _D +L _V

L _V ＜L _limit ；

in an embodiment of the present invention, the intelligent fleet grouping may specifically include: due to the man-machine mixed driving environment, the intelligent networked vehicles exist in the intersection according to a certain permeability; in a fleet decision zone L _D With intelligent networked vehicles (CAV) as the head vehicle of the grouped fleet

(wherein m is the serial number of the fleet where the head vehicle is located, and l is the serial number of the lane where the head vehicle is located); several artificial drives following behind itDriving vehicle>

Form intelligent networking motorcade M together _n ；

In the embodiment of the present invention, the calculating the boundary of the speed guiding area may specifically include: determining that the vehicle is started to be included in the influence range of the speed control strategy by the integrated controller, wherein the detection boundary is different from the detection boundary of the detector; the judgment condition is an acceleration extreme value a acceptable to the driver _c And according to the acceleration extreme value a _c And road limit speed V _L Calculating comfortable braking distance S _c (ii) a The calculation formula is as follows:

In the embodiment of the present invention, the following model establishment may specifically include: intelligent networked vehicle team M due to partial manual driving of vehicles in vehicle team _n The driving characteristics of the leading vehicle will induce the following manually driven vehicle to pass through the intersection or stop at the same target speed; because the traffic acquisition system based on the radar all-in-one machine can obtain the running state information of all vehicles including manually driven vehicles, an improved FVD (full speed difference) following model based on the vehicle team can be applied to obtain the head vehicle of the next vehicle team

The last vehicle of the previous team->

The following acceleration is calculated as follows:

a _m+1 ＝α(V′ _m -V _m (t))+β(V _m+1 (t)-V _m (t))

in the formula: a is a _m+1 -m +1 head vehicle following acceleration in the fleet;

V′ _m -m fleet head vehicle target speed;

V _m (t) -fleet tail speed at time m;

V _m+1 (t) -fleet head speed at time t, m + 1;

α -driver sensitivity coefficient; β -driver reaction coefficient;

in the embodiment of the present invention, the application of the vehicle speed guidance strategy may specifically include: intelligent internet motorcade M _n Arrival speed guidance zone L _V When the boundary is detected, the phase judgment is carried out, and the traveling direction signal lamp is in a red-yellow lamp phase P _r With the remaining time set to T _r (ii) a Or green phase P _g With the remaining time set to T _g (ii) a And different vehicle speed guiding strategies are adopted according to different phase states, and t is set _r Reaction time for the driver;

1) When the temperature is higher than the set temperature

Or->

2) When the temperature is higher than the set temperature

When the vehicle speed V is to be reduced _m To target vehicle speed V' _m To make the vehicleV 'when team reaches stop line at green light starting time' _m The solving method is as follows:

in the formula: v _min -road minimum speed limit; v _max -road top speed limit.

And finally, optimizing based on the optimized signal control of the intelligent vehicle track, wherein the optimizing comprises the steps of optimizing standard car equivalent parameters, determining road traffic capacity occupation coefficients and determining a signal control scheme.

Specifically, in one period, the integrated controller reads the running states of all vehicles at the intersection after the track optimization in real time, and performs intersection signal control by using a webster method based on the equivalent weight of the improved standard car.

wherein:

to convert the total traffic volume of k lanes, G _s For all cars, G _r For all medium-sized vehicles, G _l For all large vehicles, G _b The counting of all buses is directly given by the radar and video integrated machine acquisition system;

λ _s 、λ _r 、λ _l 、λ _b the space occupation coefficients of cars, medium-sized vehicles, large-sized vehicles and buses respectively reflect the occupation degree of various vehicles on the road area; and determining public transport according to traffic flow rate conditionsDegree of priority, i.e. λ _b Taking values;

Preferably, the above steps may specifically include:

flow ratio of k lanes in i phaseThe calculation method is as follows:

wherein

The saturation flow rate of the k lanes in the i phase is determined according to relevant traffic regulation standards; then, the optimal period duration not controlled by the TRRL method signal should be calculated according to the following formula:

40s≤C ₀ ≤180s

in the embodiment of the present application, the phase combination G = [ G ] is generated by the effective green time ₁ ，g ₂ ，g ₃ ，...]The system is used for controlling the intersection signal of the next period; and when the next period starts, returning to the step 2 to continue to perform the intelligent vehicle track optimization based on the signaling control scheme given in the step 3.

The processor in this embodiment may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The processor described above may be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. The processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware processor, or may be implemented by a combination of hardware and software modules in the processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.

Example four

An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the collaborative optimization method.

In summary, the intelligent network-connected hybrid traffic flow intersection signal collaborative optimization method and system provided by the embodiment of the invention can be used for collecting the traffic state information with high precision, wherein the traffic state information comprises the running state data of the target vehicle and an initial signal control scheme; optimizing an intelligent vehicle track based on initial information control, wherein the optimization comprises intersection partition definition, intelligent vehicle fleet grouping, speed guide area boundary calculation, following model establishment and vehicle speed guide strategy application; and optimizing based on the optimized signal control of the intelligent vehicle track, wherein the optimizing comprises the steps of optimizing standard car equivalent parameters, determining road traffic capacity occupancy coefficients and determining a signal control scheme. In this way, the intelligent vehicle and the artificial vehicle are organized into groups by adopting a vehicle organizing mode, and the vehicle fleet is used for following; by utilizing the acquisition characteristic of the radar and vision all-in-one machine, the motorcade leading intelligent vehicle can obtain the speed of manually driving the vehicle at the tail of the front motorcade so as to follow, and the problems that the permeability of the intelligent vehicle is low and a large number of manually and intelligently driven following intelligent vehicles exist in a hybrid traffic mode are solved; and the intelligent vehicle is only controlled to drive a single vehicle, the multi-vehicle following state is not considered, and the optimization effect is limited. Meanwhile, by adopting a track and signal collaborative optimization mode, the defects that the existing mixed traffic condition only adopting unilateral optimization often only aims at the running state of the vehicle to carry out signal optimization or track optimization and speed control are carried out under a fixed timing scheme are avoided, the running efficiency of the intersection is further improved, the data source is more accurate and microscopic, and the control strategy is more reasonable and effective.

It should be noted that for simplicity of description, the above-mentioned method embodiments are shown as a series of combinations of acts, but those skilled in the art will recognize that the present invention is not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the invention. Further, those skilled in the art should also appreciate that the embodiments described in the specification are exemplary embodiments and that the acts and modules illustrated are not necessarily required to practice the invention.

The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. The procedures or functions described in accordance with the embodiments of the present application are generated in whole or in part when the computer program instructions are loaded or executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, data center, etc., that contains one or more collections of available media. The available media may be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor medium may be a solid state disk.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to the related descriptions of other embodiments.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

It is noted that, herein, relational terms such as first and second, 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 phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and these modifications or substitutions do not depart from the spirit of the corresponding technical solutions of the embodiments of the present invention.

Claims

1. An intelligent network-connection mixed traffic flow intersection signal collaborative optimization method is characterized by comprising the following steps:

and 2, step: optimizing an intelligent vehicle track based on initial information control, wherein the optimization comprises intersection partition definition, intelligent vehicle fleet grouping, speed guide area boundary calculation, following model establishment and vehicle speed guide strategy application;

and 3, step 3: and optimizing based on the optimized signal control of the intelligent vehicle track, wherein the optimizing comprises the steps of optimizing standard car equivalent parameters, determining road traffic capacity occupancy coefficients and determining a signal control scheme.

2. The method of claim 1, wherein step 2 comprises:

s21, intersection partition defining: the detection range L of the collector is divided into a plurality of regions, mainly a fleet decision region L _D A speed guide area L _V The components are mixed; the fleet marshalling area mainly has the function of marshalling the intelligent networked vehicles and the manually driven vehicles; the main function of the speed guide area is to apply a speed control strategy to realize the real-time control of the intelligent motorcade, and to ensure the traffic safety, L _V The length of the inlet duct is less than the solid line area L of the inlet duct _limit (ii) a The composition and constraints are as follows:

L＝L _D +L _V

L _V <L _limit ；

s22. IntelligenceThe method can be used for grouping a fleet: due to the man-machine mixed driving environment, the intelligent networked vehicles exist in the intersection according to a certain permeability; in fleet decision zone L _D With intelligent networked vehicles (CAV) as the head vehicle of the grouped fleet

(wherein m is the serial number of the fleet where the head vehicle is located, and l is the serial number of the lane where the head vehicle is located); several manually driven vehicles HV with rear running _j ^m (where m is the number of the motorcade in which j is the serial number of the manually-driven vehicle in the motorcade) and the head vehicle->

Form intelligent networking motorcade M together _n ；

S23, calculating the boundary of the speed guide area: determining that the vehicle is initially brought into the influence range of the speed control strategy by the centralized controller, different from the detection boundary of the detector; the judgment condition is the acceleration extreme value a accepted by the driver _c And according to the acceleration extreme value a _c And road limit speed V _L Calculating comfortable braking distance S _c (ii) a The calculation formula is as follows:

according to the calculated comfortable braking distance S _c Determining a speed guidance area L by taking the stop line of the intersection as a reference _V A boundary; if S _c >S _limit Taking the boundary distance as S _limit ；

S24, establishing a following model: intelligent networked vehicle team M due to partial manual driving of vehicles in vehicle team _n The driving characteristics of the leading vehicle will induce the following manually driven vehicle to pass through the intersection or stop at the same target speed; because the traffic collection system based on the radar-vision all-in-one machine can obtain the running state information of all vehicles including manually driven vehicles, an improved FVD (full speed difference) following model based on a vehicle team can be applied to obtain a head vehicle of a next vehicle team

HV of last vehicle of previous team _j ^m The following acceleration is calculated as follows:

a _m+1 ＝α(V′ _m -V _m (t))+β(V _m+1 (t)-V _m (t))

in the formula: a is _m+1 -the head vehicle following acceleration in the m +1 fleet;

V′ _m -m fleet head vehicle target speed;

V _m (t) -fleet tail speed at time m;

V _m+1 (t) -fleet head speed at time t, m + 1;

α -driver sensitivity coefficient; β -driver reaction coefficient;

wherein, the centralized controller only carries out the orbit optimization under the condition that there is no downstream vehicle in line or the vehicle in line is noiseless:

1) When in use

Or->

2) When in use

When the vehicle speed V is to be reduced _m To target vehicle speed V' _m The time when the vehicle fleet reaches the stop line is set to V 'at the green light on time' _m The solving method of (1) is as follows:

in the formula: v _min -road minimum speed limit; v _max -road top speed limit.

3. The method of claim 1, wherein step 3 comprises:

wherein:

δ _s 、δ _r 、δ _l 、δ _b are respectively provided withThe road traffic capacity occupation coefficient of cars, medium-sized vehicles, large-sized vehicles and buses;

Normalizing with the average speed of the target vehicle, and the influence of the vehicle on the traffic capacity is smaller as the vehicle is closer to the average speed of the lane; based on the average speed value, the closer to the speed limit value, the lower the occupancy coefficient is, and the closer to 0km/h, the higher the occupancy coefficient is; in order to avoid the overlarge disturbance of the coefficient, the value of the coefficient is restricted; therefore, the following coefficient calculation formula is provided:

s33, determining by the signal control scheme: according to the calculation formula in step S32, the accurate traffic flow q of each key lane can be obtained _i Introducing a classical British TRRL method to carry out signal timing calculation to generate a phase scheme;

4. The method according to claim 3, wherein the step S33 comprises:

the flow ratio of the k lanes in the i phase is calculated as follows:

wherein

s42, the optimal period duration: the period duration of TRRL method signal control should be calculated as follows:

40s≤C ₀ ≤180s

generation of phase combinations G = [ G ] from effective green time ₁ ,g ₂ ,g ₃ ,…]The system is used for controlling the intersection signal of the next period; when the next period starts, the step 2 is returned to for continuing the operationAnd (4) giving out intelligent vehicle track optimization under the signal control scheme based on the step 3.

5. An intelligent networked hybrid traffic flow intersection signal collaborative optimization system, the system comprising:

6. The utility model provides an intelligent networking mixed traffic flow intersection signal collaborative optimization equipment which characterized in that, equipment includes:

a memory for storing a computer program;

a processor for executing the computer program to implement the steps of:

7. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-4.