CN108444494B - Stackelberg game-based path selection method - Google Patents
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Abstract
The invention discloses a fuzzy Stackelberg game-based path selection method, which comprises the steps of obtaining road information data and vehicle information data of all current roads; calculating decision parameters of all current roads and vehicles; obtaining the traffic regulating quantity of each current road by adopting a fuzzy control rule; adopting a Stackelberg game model to recommend a path to each vehicle, and acquiring a path result finally selected by each vehicle; and repeating the steps until the current traffic system reaches a Nash equilibrium state, thereby finishing the final path selection. The invention combines the whole traffic network and vehicles, takes the impedance and the road congestion degree as the gains of the game, leads the traffic network and the vehicles to reach the respective optimal state as much as possible, reduces the congestion possibility of the traffic network, and takes the road congestion degree and the road congestion change rate as input quantities by applying a fuzzy theory to obtain the congestion control quantity, thereby improving the rationality and the accuracy of path selection.
Description
Technical Field
The invention particularly relates to a path selection method based on a Stackelberg game.
Background
The information-Physical fusion System CPS (Cyber-Physical System) is a multi-dimensional complex System integrating Computing, network and Physical environments, and realizes real-time sensing, information services and dynamic Control through organic fusion and deep cooperation of 3C technologies (Computing, Communication and Control). In recent years, the application of CPS in the fields of electricity, medical treatment, transportation, etc. has been studied, and some very valuable results have been obtained. With the construction of urban intelligent transportation systems and the improvement of requirements of people on automobile performance, automobile CPS (Vehicular Cyber-Physical System, VCPS) is also concerned by many scholars. In the automobile CPS, a sensor is introduced in real time to collect real-time information of the automobile or information of other vehicles, information interaction and calculation are completed through a uniform network, and control over the automobile is completed according to feedback information, so that the automobile is easier to drive, faster in response, safer and more intelligent. In most applications of the automobile CPS, such as intelligent path planning, anti-collision systems, and vehicle networking. In recent years, the problem of urban traffic jam caused by rapid expansion of automobile holding capacity is continuously generated, and the national quality of life and the development of national economy are greatly influenced. Research shows that most of traffic jam is not insufficient in road traffic capacity, but because the vehicles do not obtain good flow guidance, the path selection technology is a concern in the automobile CPS.
At present, the common path selection method is based on the shortest path and the shortest time, but under the condition that the current urban traffic is complex and changeable, the shortest path is the road section with the highest traffic jam, the waiting time is longer, a great amount of time and energy are consumed, and the shortest path means that the possible path is longer, and the consumed time and energy are more. The result is relatively single no matter the route is shortest or the time is shortest, and the requirements of urban traffic and travel people cannot be met.
Disclosure of Invention
The invention aims to provide a fuzzy Stackelberg game-based path selection method which can efficiently and comprehensively realize the overall regulation of road traffic and provide scientific and reasonable path selection for car owners.
The invention provides a fuzzy Stackelberg game-based path selection method, which comprises the following steps:
s1, acquiring road information data and vehicle information data of all current roads;
s2, calculating decision parameters of all current roads and vehicles;
s3, obtaining the traffic regulating quantity of each current road by adopting a fuzzy control rule according to the decision parameters obtained in the step S2;
s4, adopting a Stackelberg game model to recommend a path to each vehicle, and obtaining a path result finally selected by each vehicle;
s5, repeating the steps S1-S4 until the current traffic system reaches a Nash equilibrium state, thereby finishing the final path selection.
The road information data and the vehicle information data of all the current roads in step S1 specifically include the running cost (expressed by fuel consumption consumed when the vehicle runs on the road), the number of all the current roads, the average time of the vehicle running on the road without obstacle, the traffic volume of the current road, and the traffic capacity of the road (expressed by the maximum bearable traffic volume of the road).
Step S2, calculating the decision parameters of all the current roads and vehicles, specifically calculating the following decision parameters:
A. the impedance T of each road is calculated by the following formulaa(t)：
In the formula, t0The average time of barrier-free running of the vehicles on the road a is taken as q, the traffic volume of the road at the current moment is taken as c, the traffic capacity of the current road is taken as c, and alpha and beta are preset coefficients;
B. the income function Z of the traveler is calculated by the following formulat：
Wherein x is road impedance or driving cost, sigma is the variance of all parameters x of the current road, and mu is the average value of all parameters x of the current road;
C. the following formula is used to calculate the revenue function Z of the manager (regarding the entire traffic network as the manager)c：
Where n is the number of prime paths in the traffic network, ZtRevenue letter for travelers (vehicles considered as travelers)The number of the first and second groups is,
the impedance of each path at the current time is averaged.
D. Calculating the road congestion degrees d of all the current roads by adopting the following formula:
wherein q is the traffic volume of the road at the current moment, and c is the traffic capacity of the current road;
F. calculating the congestion change rate r of all the current roads by adopting the following formula:
where d is the road congestion degree of the current road.
The step S3 of obtaining the traffic regulation amount of each current road by using the fuzzy control rule is to obtain the traffic regulation amount of each current road by using the madarny inference method according to a pre-specified fuzzy rule after the road congestion degree and the congestion change rate are fuzzified.
The fuzzy rule is specifically established by adopting the following steps:
(1) on the domain of road congestion degree d, 5 fuzzy subsets are defined, which are respectively: very small, moderate, large, and very large;
(2) on the domain of the congestion change rate r, 5 fuzzy subsets are defined, which are respectively: very small, moderate, large, and very large;
(3) on the domain of the traffic regulation Q, 5 fuzzy self are defined, which are respectively: very small, moderate, large, and very large;
(4) the following rules are adopted as fuzzy rules:
if d is very small and r is very small, the value of Q is very large;
if d is very small and r is small, the value of Q is very large;
if d is very small and r is moderate, the value of Q is larger;
if d is very small and r is relatively large, the value of Q is moderate;
if d is very small and r is very large, the value of Q is small;
if d is smaller and r is smaller, the value of Q is larger;
if d is smaller and r is smaller, the value of Q is larger;
if d is smaller and r is moderate, Q is moderate;
if d is smaller and r is larger, the value of Q is smaller;
if d is smaller and r is larger, the value of Q is smaller;
if d is moderate and r is very small, the value of Q is larger;
if d is moderate and r is small, Q is moderate;
if d is moderate and r is moderate, the value of Q is small;
if d is moderate and r is large, the value of Q is small;
if d is moderate and r is large, the value of Q is small;
if d is larger and r is smaller, the value of Q is moderate;
if d is larger and r is smaller, the value of Q is smaller;
if d is larger and r is moderate, the value of Q is smaller;
if d is larger and r is larger, the value of Q is very small;
if d is large and r is large, the value of Q is small;
if d is very large and r is very small, the value of Q is small;
if d is very large and r is small, the value of Q is small;
if d is very large and r is moderate, the value of Q is very small;
if d is very large and r is large, the value of Q is very small;
if d is large and r is large, Q will be small.
The invention provides a path selection method based on a fuzzy Stackelberg game, which combines a whole traffic network (manager) and vehicles (travelers) by adopting the Stackelberg game method, takes impedance and road congestion degree as the profits of the travelers and the managers of two parties of the game respectively, ensures that the travelers and the managers reach the respective optimal state as much as possible, reduces the congestion possibility of the traffic network, and uses the road congestion degree and the road congestion change rate as input quantities by applying a fuzzy theory to obtain congestion control quantity and improve the rationality and the accuracy of path selection.
Drawings
FIG. 1 is a process flow diagram of the process of the present invention.
Detailed Description
FIG. 1 shows a flow chart of the method of the present invention: the invention provides a fuzzy Stackelberg game-based path selection method, which comprises the following steps:
s1, acquiring road information data and vehicle information data of all current roads; the method specifically includes, but is not limited to, the following data information: the driving cost of all roads at present (expressed by the oil consumption consumed when the vehicle drives the road), the number of all roads at present, the mean time of barrier-free driving of the vehicle on the road, the traffic volume of the road at present and the traffic capacity of the road (expressed by the maximum bearable traffic volume of the road) and the like;
s2, calculating decision parameters of all current roads and vehicles; the method specifically comprises the following decision parameters:
A. the impedance T of each road is calculated by the following formulaa(t)：
In the formula, t0The average time of barrier-free running of the vehicles on the road a is taken as q, the traffic volume of the road at the current moment is taken as c, the traffic capacity of the current road is taken as c, and alpha and beta are preset coefficients;
B. the income function Z of the traveler is calculated by the following formulat：
Wherein x is road impedance or driving cost, sigma is the variance of all parameters x of the current road, and mu is the average value of all parameters x of the current road;
C. the following formula is used to calculate the revenue function Z of the manager (regarding the entire traffic network as the manager)c：
Where n is the number of prime paths in the traffic network, ZtAs a function of the profitability of the traveler (considering the vehicle as the traveler),
the impedance of each path at the current time is averaged.
D. Calculating the road congestion degrees d of all the current roads by adopting the following formula:
wherein q is the traffic volume of the road at the current moment, and c is the traffic capacity of the current road;
F. calculating the congestion change rate r of all the current roads by adopting the following formula:
wherein d is the road congestion degree of the current road;
s3, obtaining the traffic regulating quantity of each current road by adopting a fuzzy control rule according to the decision parameters obtained in the step S2; after the road congestion degree and the congestion change rate are fuzzified, the traffic regulating quantity of each current road is obtained by adopting a Ma-Darni reasoning method according to a preset fuzzy rule; specifically, the fuzzy rule is established by adopting the following steps:
(1) on the domain of road congestion degree d, 5 fuzzy subsets are defined, which are respectively: very small, moderate, large, and very large;
(2) on the domain of the congestion change rate r, 5 fuzzy subsets are defined, which are respectively: very small, moderate, large, and very large;
(3) on the domain of the traffic regulation Q, 5 fuzzy self are defined, which are respectively: very small, moderate, large, and very large;
(4) the following rules are adopted as fuzzy rules:
if d is very small and r is very small, the value of Q is very large;
if d is very small and r is small, the value of Q is very large;
if d is very small and r is moderate, the value of Q is larger;
if d is very small and r is relatively large, the value of Q is moderate;
if d is very small and r is very large, the value of Q is small;
if d is smaller and r is smaller, the value of Q is larger;
if d is smaller and r is smaller, the value of Q is larger;
if d is smaller and r is moderate, Q is moderate;
if d is smaller and r is larger, the value of Q is smaller;
if d is smaller and r is larger, the value of Q is smaller;
if d is moderate and r is very small, the value of Q is larger;
if d is moderate and r is small, Q is moderate;
if d is moderate and r is moderate, the value of Q is small;
if d is moderate and r is large, the value of Q is small;
if d is moderate and r is large, the value of Q is small;
if d is larger and r is smaller, the value of Q is moderate;
if d is larger and r is smaller, the value of Q is smaller;
if d is larger and r is moderate, the value of Q is smaller;
if d is larger and r is larger, the value of Q is very small;
if d is large and r is large, the value of Q is small;
if d is very large and r is very small, the value of Q is small;
if d is very large and r is small, the value of Q is small;
if d is very large and r is moderate, the value of Q is very small;
if d is very large and r is large, the value of Q is very small;
if d is very large and r is very large, the value of Q is very small;
s4, adopting a Stackelberg game model to recommend a path to each vehicle, and obtaining a path result finally selected by each vehicle;
when a traveler of each vehicle selects a specific road, the road can be selected according to the specific conditions of the traveler: for example, when the traveler is unfamiliar with the current road, the route recommended by the current manager can be directly selected, and if the traveler is familiar with the current road, the road can be selected according to the experience of the traveler, and only the road recommended by the manager is taken as a reference;
s5, repeating the steps S1-S4 until the current traffic system reaches a Nash equilibrium state, thereby finishing the final path selection.
Steps S1, S2, S3 are computation parts in the CPS for computing various required data parameters, and step S4 is a communication and decision part of the CPS, the decision part including a decision recommended to the manager for the path and a result of the final selection of the path by the traveler, the communication part being a decision broadcast of the manager to the traveler, and a path feedback process selected by the traveler.
Claims (2)
1. A path selection method based on a Stackelberg game comprises the following steps:
s1, acquiring road information data and vehicle information data of all current roads;
s2, calculating decision parameters of all current roads and vehicles; specifically, the following decision parameters are calculated:
A. the impedance T of each road is calculated by the following formulaa(t)：
In the formula, t0The average time of barrier-free running of the vehicles on the road a is taken as q, the traffic volume of the road at the current moment is taken as c, the traffic capacity of the current road is taken as c, and alpha and beta are preset coefficients;
B. the income function Z of the traveler is calculated by the following formulat：
Wherein x is road impedance or driving cost, sigma is the variance of all parameters x of the current road, and mu is the average value of all parameters x of the current road;
C. the yield function Z of the manager is calculated by adopting the following formulac：
Where n is the number of prime paths in the traffic network, ZtAs a function of the profitability of the traveler,
the average of the impedance of each path at the current time;
D. calculating the road congestion degrees d of all the current roads by adopting the following formula:
wherein q is the traffic volume of the road at the current moment, and c is the traffic capacity of the current road;
F. calculating the congestion change rate r of all the current roads by adopting the following formula:
wherein d is the road congestion degree of the current road;
s3, obtaining the traffic regulating quantity of each current road by adopting a fuzzy control rule according to the decision parameters obtained in the step S2; after the road congestion degree and the congestion change rate are fuzzified, the traffic regulating quantity of each current road is obtained by adopting a Ma-Darni reasoning method according to a preset fuzzy rule;
the fuzzy rule is established by adopting the following steps:
(1) on the domain of road congestion degree d, 5 fuzzy subsets are defined, which are respectively: very small, moderate, large, and very large;
(2) on the domain of the congestion change rate r, 5 fuzzy subsets are defined, which are respectively: very small, moderate, large, and very large;
(3) on the domain of the traffic regulation Q, 5 fuzzy self are defined, which are respectively: very small, moderate, large, and very large;
(4) the following rules are adopted as fuzzy rules:
if d is very small and r is very small, the value of Q is very large;
if d is very small and r is small, the value of Q is very large;
if d is very small and r is moderate, the value of Q is larger;
if d is very small and r is relatively large, the value of Q is moderate;
if d is very small and r is very large, the value of Q is small;
if d is smaller and r is smaller, the value of Q is larger;
if d is smaller and r is smaller, the value of Q is larger;
if d is smaller and r is moderate, Q is moderate;
if d is smaller and r is larger, the value of Q is smaller;
if d is smaller and r is larger, the value of Q is smaller;
if d is moderate and r is very small, the value of Q is larger;
if d is moderate and r is small, Q is moderate;
if d is moderate and r is moderate, the value of Q is small;
if d is moderate and r is large, the value of Q is small;
if d is moderate and r is large, the value of Q is small;
if d is larger and r is smaller, the value of Q is moderate;
if d is larger and r is smaller, the value of Q is smaller;
if d is larger and r is moderate, the value of Q is smaller;
if d is larger and r is larger, the value of Q is very small;
if d is large and r is large, the value of Q is small;
if d is very large and r is very small, the value of Q is small;
if d is very large and r is small, the value of Q is small;
if d is very large and r is moderate, the value of Q is very small;
if d is very large and r is large, the value of Q is very small;
if d is very large and r is very large, the value of Q is very small;
s4, adopting a Stackelberg game model to recommend a path to each vehicle, and obtaining a path result finally selected by each vehicle;
s5, repeating the steps S1-S4 until the current traffic system reaches a Nash equilibrium state, thereby finishing the final path selection.
2. The method for selecting a route according to claim 1, wherein the road information data and the vehicle information data of all roads in step S1 include running cost of all roads, number of all roads, average time of vehicle driving without obstacle, traffic volume of the road and traffic capacity of the road.
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