CN111028504A - Urban expressway intelligent traffic control method and system - Google Patents
Urban expressway intelligent traffic control method and system Download PDFInfo
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- CN111028504A CN111028504A CN201911182935.5A CN201911182935A CN111028504A CN 111028504 A CN111028504 A CN 111028504A CN 201911182935 A CN201911182935 A CN 201911182935A CN 111028504 A CN111028504 A CN 111028504A
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- G—PHYSICS
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- G—PHYSICS
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- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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Abstract
The invention discloses an intelligent traffic control method and system for an urban expressway, which are used for observing the traffic states of an expressway main road and an expressway auxiliary road, carrying out self-adaptive control on the traffic states by adopting an artificial intelligence algorithm, and simultaneously carrying out cooperative control on a plurality of ramps of the expressway auxiliary road so as to control the traffic flow entering the expressway main road through the ramps. A method and a system for managing and controlling intelligent traffic of an urban expressway are provided, which improve the operation efficiency of a traffic system and solve the rapid and unpleasant traffic trouble by controlling the traffic flow of a ramp entering an expressway main road.
Description
Technical Field
The invention relates to the technical field of expressway traffic control, in particular to a traffic control method and system for expressway ramps.
Background
The express way is an important road which has higher speed and serves long-distance traffic in cities, and mainly connects each main area, main suburbs and main external roads in urban areas. At present, a perfect expressway system only allowing vehicles to pass is built in many big cities in the world, expressway traffic gradually becomes one of the main types of urban traffic, and the expressway system plays an important role in relieving urban road traffic pressure. Along with the continuous improvement of the living standard of the masses in cities and the acceleration of the motorized process of traveling, more and more automobiles enter common families, the urban road construction and management and control lag behind the development of surrounding areas, and the daily life requirements of people cannot be met. Particularly, the traffic safety accidents and congestion of the conventional express way often occur, the traffic efficiency of the express way is seriously influenced, and the traffic distress is caused by fast and unhappy traffic. Due to the convenience of expressway traffic, the control of an area road network on traffic signals is unreasonable, and more residents can select expressways to go out, so that traffic along the express way is not heavy. Therefore, based on the current situation of the current urban road, the operation efficiency of a traffic system is improved for breaking the traffic distress of 'quick and not quick', and the control of the ramp of the expressway is imperative.
Disclosure of Invention
The invention mainly solves the problems and provides a method and a system for urban expressway traffic control.
In order to solve the technical problems, the invention adopts the technical scheme that: a city expressway intelligent traffic control method comprises the following steps: and observing the traffic states of the main road and the auxiliary road of the express way, carrying out self-adaptive control on the traffic states by adopting an artificial intelligence algorithm, and simultaneously carrying out cooperative control on a plurality of ramps of the auxiliary road of the express way so as to control the traffic flow entering the main road of the express way through the ramps.
Further, vehicles on the ramp can only enter the main road of the express way from the lane closest to the main road of the express way, virtual and real dividing lines are arranged between lanes of the ramp within a certain distance range from the stop line, and vehicles can change lanes from the dotted line side to the straight line side and cannot change lanes from the straight line side to the dotted line side.
Further, the traffic state comprises flow f, speed v and density, the flow f can be obtained through microwave, geomagnetism, radar and electric police, data time space multi-dimensional fusion processing is carried out, the speed v can be obtained through microwave, geomagnetism, radar and the internet, and data time space multi-dimensional fusion processing is carried out.
Further, the artificial intelligence algorithm is a reinforcement learning algorithm, and the specific steps of performing adaptive control on the traffic state by adopting the artificial intelligence algorithm comprise:
step one, calculating the traffic capacity S of the express way according to the acquired flow f and speed v, wherein a calculation formula is as follows:
S＝F(f,v) (1)
the express way traffic capacity S is a traffic light state action;
step two, calculating a reward and punishment return value r executed by the control strategy according to the expressway traffic capacity S, wherein the calculation formula is as follows:
r＝G(S) (2)；
step three, total return value R in a certain time periodkFor each reward and punishment return value expectation, the calculation formula is as follows:
the total reported value RkThe current congestion level is obtained, and the traffic state, the express way traffic capacity S and the total return value R are usedkStoring the data into a memory library;
fourthly, the self-adaptive controller according to the traffic state and the total return value RkAnd (5) learning and adjusting.
Further, the multiple ramps of the expressway auxiliary road can be cooperatively controlled through a cooperative control device, the cooperative control device comprises a signal lamp and an induction screen, the signal lamp is arranged in front of an entrance of the ramp, and the induction screen is arranged at a position away from the entrance of the ramp by a certain distance.
The utility model provides a city expressway intelligent traffic management and control system, includes: the data acquisition system is used for observing traffic states of the main expressway and the auxiliary expressway, the traffic states are conveyed to the adaptive controller, the adaptive controller adopts a reward and punishment return value executed by an artificial intelligence algorithm calculation control strategy to be used for adaptive control, and the cooperative control device can realize cooperative control on a plurality of ramps of the auxiliary expressway according to the reward and punishment return value so as to control the traffic flow entering the main expressway through the ramps.
Further, the traffic state comprises flow f, speed v and density, and the flow f, the speed v and the density are respectively obtained through the butt joint of the flow access module, the speed access module, the density access module and the data acquisition system.
Further, the data acquisition system includes flow f observation device, speed v observation device and density observation device, flow f observation device includes microwave, earth's magnetism, radar, electric police, speed v observation device includes microwave, earth's magnetism, radar, internet.
Further, the artificial intelligence algorithm is a reinforcement learning algorithm, the self-adaptive controller inputs the traffic state, and the traffic capacity S and the total return value R of the express way are calculated through the reinforcement learning algorithmkThe traffic capacity S of the express way is the traffic light state action, and the total return value RkThe self-adaptive controller is used for judging the current congestion level and sending the traffic state, the express way traffic capacity S and the total return value RkAnd storing the data into a memory bank.
Furthermore, the cooperative control device comprises a signal lamp and an induction screen, the signal lamp is arranged in front of the ramp entrance, the induction screen is arranged at a certain distance from the ramp entrance, and meanwhile, an electronic police snapshot system is further arranged on the ramp and is used for snapshotting the driving behavior not according to the traffic rules.
The invention has the advantages and positive effects that: a method and a system for managing and controlling intelligent traffic of an urban expressway are provided, which improve the operation efficiency of a traffic system and solve the rapid and unpleasant traffic trouble by controlling the traffic flow of a ramp entering an expressway main road. According to the invention, the traffic capacity of the expressway is maximized as a control target, an artificial intelligence reinforcement learning model is applied to the field of expressway traffic control, and a control scheme suitable for the current road condition is output through self-learning and self-optimization of programs. In addition, the induction screen and the signal lamp in the cooperative control device cooperatively change and release contents, so that a vehicle owner can know the road condition ahead and the signal control condition in advance, and the long-time queuing on the ramp is avoided. The virtual and real lane lines of the ramp in the system are designed, so that traffic jam caused by unreasonable lane change of vehicles can be effectively reduced, and the express way traffic efficiency is improved.
Drawings
FIG. 1 is a flow chart of a method for managing and controlling intelligent traffic on urban expressways;
fig. 2 is a schematic diagram of a ramp entrance.
1. A main road of the express way; an expressway auxiliary road; 3, ramp entrance;
4. an induction screen; 5, an electronic police snapshot system; 6, stopping the line;
7. a signal lamp; and 8, a virtual-real boundary.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
Specific embodiments of the present invention will be described in detail below with reference to fig. 1-2.
A city expressway intelligent traffic control method comprises the following steps: and observing the traffic states of the main expressway 1 and the auxiliary expressway 2, carrying out self-adaptive control on the traffic states by adopting an artificial intelligence algorithm, and simultaneously carrying out cooperative control on a plurality of ramps of the auxiliary expressway 2 so as to control the traffic flow entering the main expressway 1 through the ramps.
Vehicles on the ramp can only enter the main expressway 1 from the lane closest to the main expressway 1, virtual and real dividing lines 8 are arranged among lanes of the ramp within a certain distance range from the stop line, and vehicles can change lanes from the dotted line side to the straight line side and cannot change lanes from the straight line side to the dotted line side.
The traffic state comprises flow f, speed v and density, the flow f can be obtained through microwaves, geomagnetism, radars and electric alarms, data time space multi-dimensional fusion processing is carried out, and the speed v can be obtained through microwaves, geomagnetism, radars and the Internet, and data time space multi-dimensional fusion processing is carried out.
The artificial intelligence algorithm is a reinforcement learning algorithm, and the specific steps of carrying out self-adaptive control on the traffic state by adopting the artificial intelligence algorithm comprise:
step one, calculating the traffic capacity S of the express way according to the acquired flow f and speed v, wherein a calculation formula is as follows:
S＝F(f,v) (1)
the express way traffic capacity S is a traffic light state action;
step two, calculating a reward and punishment return value r executed by the control strategy according to the expressway traffic capacity S, wherein the calculation formula is as follows:
r＝G(S) (2)；
step three, total return value R in a certain time periodkFor each reward and punishment return value expectation, the calculation formula is as follows:
the total reported value RkThe current congestion level is obtained, and the traffic state, the express way traffic capacity S and the total return value R are usedkStoring the data into a memory library;
fourthly, the self-adaptive controller according to the traffic state and the total return value RkAnd (5) learning and adjusting.
The multiple ramps of the expressway auxiliary road can be cooperatively controlled through the cooperative control device, the cooperative control device comprises a signal lamp 7 and an induction screen 4, the signal lamp 7 is arranged in front of the ramp entrance 3, and the induction screen 4 is arranged at a certain distance from the ramp entrance 3.
The utility model provides a city expressway intelligent traffic management and control system, includes: data acquisition system, adaptive controller and cooperative control device, data acquisition system is used for observing the traffic state of expressway main road 1, expressway auxiliary road 2, the traffic state carry extremely adaptive controller, adaptive controller adopts artificial intelligence algorithm calculation control strategy to carry out reward punishment return value and is used for adaptive control, cooperative control device can be according to reward punishment return value realizes carrying out cooperative control to a plurality of ramps of expressway auxiliary road 2 to the control gets into the vehicle flow of expressway main road 1 through the ramp.
The traffic state comprises flow f, speed v and density, and the flow f, the speed v and the density are respectively obtained by butt joint of a flow access module, a speed access module, a density access module and a data acquisition system.
The data acquisition system comprises a flow f observation device, a speed v observation device and a density observation device, wherein the flow f observation device comprises microwaves, geomagnetism, radars and electric alarms, and the speed v observation device comprises microwaves, geomagnetism, radars and the Internet.
The artificial intelligence algorithm is a reinforcement learning algorithm, the self-adaptive controller inputs the traffic state, and the traffic capacity S and the total return value R of the express way are calculated through the reinforcement learning algorithmkThe traffic capacity S of the express way is in a traffic light state, and the total return value R iskThe self-adaptive controller is used for judging the current congestion level and sending the traffic state, the express way traffic capacity S and the total return value RkAnd storing the data into a memory bank.
The cooperative control device comprises a signal lamp 7 and an induction screen 4, wherein the signal lamp 7 is arranged in front of a ramp entrance 3, the induction screen 4 is arranged at a certain distance from the ramp entrance 3, an electronic police snapshot system 5 is also arranged on the ramp, and the electronic police snapshot system 5 takes a snapshot of driving behaviors which are not according to traffic rules.
The best implementation mode of the intelligent traffic control method and system for the urban expressway can control the traffic flow entering an expressway main road 1 through ramps, observe the traffic state of the expressway, adopt an artificial intelligence algorithm to carry out self-adaptive control, and simultaneously carry out cooperative control on a plurality of entrance ramps of the expressway, thereby realizing maximization of the expressway traffic capacity and reducing the congestion of an expressway network.
The artificial intelligence algorithm adopts a reinforcement learning algorithm. The observed environmental data is input to the neural network as an input to the adaptive controller (neural network), which outputs a set of current actions (traffic light states). Meanwhile, the current reward and punishment level (congestion level) is calculated according to the environmental data and the professional formula of the traffic field. The reward and punishment grade, action and environment data of each cycle can be stored in a memory bank; when the congestion dissipates, the memory database data of the training period is transmitted to the neural network to calculate the loss value of the loss function of the neural network; the neural network updates its weight parameters (learning), and the flow chart is shown in fig. 1.
Taking a rapid road network with 10 entrance ramps as an example, the 10 entrance ramp controlled traffic lights are clustered according to the distance between each road section and each traffic light, and all the road sections are clustered into 10 classes. The average reward for each class of road segment is attributed to the traffic lights closest to that class. I.e. there will be 10 local reward values. The signal control scheme is output every 5 minute cycle.
The first step is as follows: and acquiring and storing the flow, the speed and the density of the expressway network at the starting moment of the K time interval.
The second step is that: calculating the traffic capacity at the beginning of the K time interval, wherein the traffic capacity is represented by a traffic operation index CkFor example.
And calculating the traffic operation index by adopting a method and a system based on the travel time ratio in GB/T33171-2016.
Calculating the travel time ratio TTI of each evaluation cellular section in the k-1 time intervali,k-1
TTIi,k-1Representing the travel time ratio of the evaluation unit cell i in the k-1 time interval;
ti，k-1representing the free stream journey time of the evaluation unit cell i in the k-1 time interval;
vi，k-1representing the free prevalence speed of the evaluation cell i in the k-1 time interval;
setting TTI when the average travel time of the road section is less than the free stream travel timei-1＝1；
Calculating the weight gamma of each evaluation celli,k-1
Calculating the weight of each evaluation unit cell by adopting the algorithm of GB/T29107-2012
Firstly, calculating VKT of each evaluation cellular road section within k-1 time intervali
VKTi,k-1＝fi,k-1*Li(5)
VKTi,k-1The number of kilometers of the evaluation cell i in the k-1 time interval is shown;
fi,k-1indicating that the traffic flow of the evaluation cell i in the k-1 time interval;
Lidenotes the evaluation cell i length;
then, calculating to obtain the vehicle kilometer number VKT of all the evaluation cellular express waysk-1
VKTk-1＝∑VKTi,k-1(6)
Finally, the weight gamma of each evaluation cell is calculatedi,k-1
Calculating and evaluating total travel time ratio TTI of cellular road sectionk-1
TTIk-1＝∑γi,k-1*TTIi,k-1(8)
Evaluation of cellular road section total traffic operation index C based on conversionk
Conversion to C according to Table B.1kTable b.1 gives the recommended conversion relationship between road network travel time ratio and city traffic operation index:
TABLE B.1 recommended conversion relationship between road network travel time ratio and urban traffic operation index
The third step: calculating a reward and punishment return value R for the execution of the control strategy in the K-1 time intervalkAnd store RkThe value is obtained.
RkIs a piecewise function with total intersection of cellular road segmentsGeneral running index CkIn order to be an input, the user can select,
the fourth step: and judging the traffic state at the starting moment of the K time interval, and storing the control strategy generated in the step.
The fifth step: and (4) learning and adjusting the weight parameters of the neural network of the adaptive controller.
R according to which the system is required to be based on each congestion formation to congestion relief slow-blocking periodkValue, environmental observation and measurement, control strategy, and guiding the learning and adjustment of the weight parameters of the neural network.
A method and a system for managing and controlling intelligent traffic of urban expressways are provided, which improve the operation efficiency of a traffic system and solve the rapid and unpleasant traffic trouble by controlling the traffic flow of a ramp entering an expressway main road. The method takes the maximum traffic capacity of the expressway as a control target, applies the artificial intelligence reinforcement learning model to the field of expressway traffic control, self-optimizes through program self-learning, and outputs a control scheme suitable for the current road condition. In addition, the induction screen and the signal lamp in the cooperative control device cooperatively change and release contents, so that a vehicle owner can know the road condition ahead and the signal control condition in advance, and the long-time queuing on the ramp is avoided. The virtual and real lane lines of the ramp in the system are designed, so that traffic jam caused by unreasonable lane change of vehicles can be effectively reduced, and the express way traffic efficiency is improved.
The embodiments of the present invention have been described in detail, but the description is only for the purpose of simple exemplary embodiments of the present invention and should not be construed as limiting the scope of the present invention. All equivalent changes and modifications made within the scope of the present invention should be considered to fall within the scope of the present patent.
Claims (10)
1. A city expressway intelligent traffic control method is characterized by comprising the following steps: the method comprises the steps of observing traffic states of a main road and an auxiliary road of the express way, carrying out self-adaptive control on the traffic states by adopting an artificial intelligence algorithm, and simultaneously carrying out cooperative control on a plurality of ramps of the auxiliary road of the express way so as to control the traffic flow entering the main road of the express way through the ramps.
2. The intelligent traffic control method for urban expressways according to claim 1, characterized in that: vehicles on the ramp can only enter the main road of the express way from the lane closest to the main road of the express way, virtual and real dividing lines are arranged between lanes of the ramp within a certain distance range from the stop line, and the vehicles can change lanes from the dotted line side to the straight line side and cannot change lanes from the straight line side to the dotted line side.
3. The intelligent traffic control method for urban expressways according to claim 2, characterized in that: the traffic state comprises flow f, speed v and density, the flow f can be acquired through microwaves, geomagnetism, radar and electric police, data time space multi-dimensional fusion processing is carried out, the speed v can be acquired through microwaves, geomagnetism, radar and the Internet, and data time space multi-dimensional fusion processing is carried out.
4. The intelligent traffic control method for urban expressways according to claim 3, characterized in that: the artificial intelligence algorithm is a reinforcement learning algorithm, and the specific steps of carrying out self-adaptive control on the traffic state by adopting the artificial intelligence algorithm comprise:
step one, calculating the traffic capacity S of the express way according to the acquired flow f and speed v, wherein a calculation formula is as follows:
S＝F(f,v) (1)
the express way traffic capacity S is a traffic light state action;
step two, calculating a reward and punishment return value r executed by the control strategy according to the expressway traffic capacity S, wherein the calculation formula is as follows:
r＝G(S)(2)；
step three, total return value R in a certain time periodkFor each reward and punishment return value expectation, the calculation formula is as follows:
the total reported value RkThe current congestion level is obtained, and the traffic state, the express way traffic capacity S and the total return value R are usedkStoring the data into a memory library;
fourthly, the self-adaptive controller according to the traffic state and the total return value RkAnd (5) learning and adjusting.
5. The intelligent traffic control method for urban expressways according to claim 4, wherein: the multiple ramps of the expressway auxiliary road can be cooperatively controlled through the cooperative control device, the cooperative control device comprises a signal lamp and an induction screen, the signal lamp is arranged in front of an entrance of the ramp, and the induction screen is arranged at a position away from the entrance of the ramp by a certain distance.
6. The utility model provides a city expressway wisdom traffic management and control system which characterized in that: including data acquisition system, adaptive controller and cooperative control device, data acquisition system is used for observing the traffic state of expressway main road, expressway auxiliary road, the traffic state carry extremely adaptive controller, adaptive controller adopts artificial intelligence algorithm calculation control strategy to carry out reward punishment return value and is used for adaptive control, cooperative control device can be according to reward punishment return value realizes carrying out cooperative control to a plurality of ramps of expressway auxiliary road to the control gets into the vehicle flow of expressway main road through the ramp.
7. The urban expressway intelligent traffic control system according to claim 6, wherein: the traffic state comprises flow f, speed v and density, and the flow f, the speed v and the density are respectively obtained through the butt joint of a flow access module, a speed access module, a density access module and a data acquisition system.
8. The urban expressway intelligent traffic control system according to claim 7, wherein: the data acquisition system comprises a flow f observation device, a speed v observation device and a density observation device, wherein the flow f observation device comprises microwaves, geomagnetism, radars and electric alarms, and the speed v observation device comprises microwaves, geomagnetism, radars and the Internet.
9. The intelligent traffic control system for urban expressways according to claim 8, wherein: the artificial intelligence algorithm is a reinforcement learning algorithm, the self-adaptive controller inputs the traffic state, and the traffic capacity S and the total return value R of the express way are calculated through the reinforcement learning algorithmkThe traffic capacity S of the express way is the traffic light state action, and the total return value RkThe self-adaptive controller is used for judging the current congestion level and sending the traffic state, the express way traffic capacity S and the total return value R to the adaptive controllerkAnd storing the data into a memory bank.
10. The urban expressway intelligent traffic control system according to claim 9, wherein: the cooperative control device comprises a signal lamp and an induction screen, the signal lamp is arranged in front of an entrance of a ramp, the induction screen is arranged at a position away from the entrance of the ramp by a certain distance, and meanwhile, an electronic police snapshot system is further arranged on the ramp and is used for snapshotting driving behaviors which do not follow traffic rules.
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| CN111783396A (en) * | 2020-06-01 | 2020-10-16 | 宁波工程学院 | Space positioning code for urban expressway |
| CN112150809A (en) * | 2020-09-25 | 2020-12-29 | 哈尔滨工业大学 | Urban expressway short-distance travel control method |
| CN112802350A (en) * | 2021-01-29 | 2021-05-14 | 安徽达尔智能控制系统股份有限公司 | Multi-dimensional intelligent high-speed control method and system |
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| CN114743386A (en) * | 2022-04-15 | 2022-07-12 | 广西盖德科技有限公司 | Self-coordination method and system for dynamically distributing public resources based on flow rate |
| CN114743386B (en) * | 2022-04-15 | 2024-07-02 | 广西盖德科技有限公司 | Self-coordination method and system for dynamically distributing public resources based on flow velocity |
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