CN113033883A - Optimization control and profit allocation method in mixed traffic flow environment - Google Patents
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Abstract
The invention provides an optimization control and income distribution method under a mixed traffic flow environment. The method comprises the following steps: receiving vehicle operation information and personal attribute information in real time; predicting the original running track of the vehicle, finding out potential traffic conflicts and a vehicle organization mode capable of improving traffic transport efficiency; determining an optimal control strategy; calculating the income generated by the vehicle running according to the optimal control strategy; making a profit distribution scheme; sending the optimal control strategy and the prepared income distribution scheme to the cooperative vehicle; if the cooperative vehicle is satisfied with the prepared income distribution scheme, determining the cooperative vehicle as a final income distribution scheme, otherwise, re-preparing the income distribution scheme; controlling or guiding the cooperative vehicle to run according to the optimal control strategy; and carrying out revenue distribution according to the final revenue distribution scheme. The method can realize the optimal control and the income distribution of the vehicles, and has good promotion effects on improving the transportation efficiency and reducing the pollution emission.
Description
Technical Field
The invention relates to a cooperative decision control method in a mixed traffic flow environment, in particular to an optimization control and profit allocation method in the mixed traffic flow environment, and belongs to the field of intelligent traffic engineering.
Background
Before all Vehicles running on a road are intelligent networked Vehicles, there is a long-term traffic flow in a mixed traffic flow mode, namely, the traffic flow of the intelligent networked Vehicles (Connected automatic Vehicles) running in a mixed mode with Human-driven Vehicles (traditional driving Vehicles). The intelligent networked vehicle is a vehicle which is provided with an advanced sensing device and a control device, can sense and process information around the driving environment of the vehicle, has the capability of information interaction between the vehicle and people, vehicles, roads and road side facilities, and can run (partially or completely) without the operation of a driver. In the case of a mixed traffic flow, because of differences in driving behaviors and vehicle performances between an intelligent internet vehicle and a human-driven vehicle, the mixed traffic flow needs a more scientific and definite method to solve a traffic conflict problem in various scenes such as a highway ramp junction area, an urban traffic intersection and the like than in an environment where the vehicle is completely driven by a human. In practical situations, the intelligent networked vehicle may be represented as uncoordinated (e.g., will not actively yield) in many cases because of the need to achieve its own purpose, and the human driving the vehicle is not necessarily uncoordinated. Compared with the conventional mixed traffic flow mentioned above, the mixed traffic flow composed of cooperative vehicles and non-cooperative vehicles is more worthy of study.
In the existing scientific research on the intelligent networked vehicles, the intelligent networked vehicles are mostly defaulted to be controllable and cooperative. Depending on the application scenario, a great deal of research effort has been published on optimizing control algorithms and good results have been achieved in experiments. However, the academic world has few studies on cooperative vehicles and non-cooperative vehicles as mixed traffic flows, and the studies on mixed traffic flows at intersections, t-intersections and expressway ramp junction areas are very little.
Therefore, it is very necessary to provide a cooperative decision control method under a mixed traffic flow composed of cooperative vehicles and non-cooperative vehicles.
Disclosure of Invention
The invention aims to: under the mixed traffic flow composed of cooperative vehicles and non-cooperative vehicles, a method capable of reducing and eliminating traffic conflicts and optimizing vehicle tracks is provided, so that the overall benefit of the whole traffic system is improved.
The invention adopts the technical scheme that the invention aims to realize the following steps: a method of optimizing control and revenue sharing in a mixed traffic flow environment, the method comprising the steps of:
s1, receiving vehicle running information of cooperative vehicles and non-cooperative vehicles running in a communication area in real time through the internet of vehicles technology, and personal attribute information reported by the cooperative vehicles;
dividing the vehicle into the cooperative vehicle and the uncooperative vehicle;
the cooperative vehicle refers to a vehicle which receives the optimization control and the profit distribution of the method (namely, the cooperative vehicle refers to an intelligent networked vehicle which receives the optimization control and the profit distribution of the method and a traditional driving vehicle which receives the optimization control and the profit distribution of the method);
the non-cooperative vehicle refers to a vehicle which does not receive the optimization control and the income distribution of the method (namely the non-cooperative vehicle refers to an intelligent networked vehicle which does not receive the optimization control and the income distribution of the method and a traditional driving vehicle which does not receive the optimization control and the income distribution of the method);
s2, integrating the received vehicle running information, predicting to obtain the original running track of the vehicle, and finding out potential traffic conflicts and vehicle organization modes capable of improving traffic transport efficiency;
s3, according to the found potential traffic conflicts and the vehicle organization mode capable of improving the traffic and transportation efficiency, taking the optimal transportation efficiency of the traffic system as an optimization criterion, and obtaining an optimal control strategy by using a dynamic programming algorithm;
s4, analyzing the personal attribute information reported by the cooperative vehicles running in the communication area to obtain the running cost change of the cooperative vehicles running in the communication area according to the original running track of the vehicles and the running according to the optimal control strategy, and respectively calculating the income generated by the running of each vehicle in the cooperative vehicles running in the communication area according to the optimal control strategy;
s5, selecting a profit distribution mode by comprehensively analyzing personal attribute information reported by the cooperative vehicles running in the communication area, and formulating a profit distribution scheme;
s6, sending the optimal control strategy and the prepared income distribution scheme to the cooperative vehicles running in the communication area;
s7, if the cooperative vehicle running in the communication area is satisfied with the formulated revenue distribution scheme, determining the formulated revenue distribution scheme as a final revenue distribution scheme; if the cooperative vehicle traveling in the communication area is not satisfied with the established profit sharing scheme, returning to step S5 to repeat execution until a final profit sharing scheme is established;
s8, performing optimization control on the operation of the intelligent networked vehicles in the cooperative vehicles running in the communication area according to the optimal control strategy; meanwhile, a driver of a conventionally driven vehicle among the cooperative vehicles traveling in the communication area controls the vehicle operation thereof according to the optimal control strategy;
and S9, carrying out revenue distribution according to the final revenue distribution scheme, and enabling the cooperative vehicle with reduced operation cost to provide economic compensation for the cooperative vehicle which gives way actively. (thereby ensuring positive revenue for the traffic system and each cooperating vehicle.)
Further, the income distribution mode comprises an average distribution mode, a dynamic bargaining distribution mode and a bilateral auction distribution mode.
The average distribution mode is suitable for both sides of transaction, which are mild, not greedy and not belonging to emergency trip, and the personal attribute information values of both sides are considered to be equal or close to each other, so that the personal rational condition can be met by utilizing the average distribution mode, no person can cause jealousy, and people consider that the income of the people is larger than that of the other side.
The dynamic bargaining distribution mode reasonably simulates the process of bargaining and counter-selling by people in daily life, people can put forward shares required by themselves, a process of continuous mutual compromise is carried out before the intention is not agreed, the transaction price which is satisfied by both parties is finally reached, and the phenomenon of bargaining with an extreme price can be effectively avoided by utilizing the dynamic bargaining distribution mode to carry out income distribution.
The bilateral auction allocation mode can simultaneously meet the demands of buyers and sellers to a certain extent, the buyers and the sellers simultaneously report a transaction price expected by the buyers and the sellers respectively in the transaction process, the transaction price which can be executed finally is a linear combination of the transaction prices expected by the buyers and the sellers, if the final transaction price enables the income of any participant to be non-positive, the transaction will not be carried out, so in order to obtain the income through the transaction, the participant needs to report a personal attribute information value approaching to the personal attribute true value, and the incentive compatibility condition is met.
Further, the personal attribute information includes a time value and a fuel price.
Further, the operation cost comprises travel time cost and fuel consumption cost.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a cooperative decision control method under a mixed traffic flow composed of cooperative vehicles and non-cooperative vehicles based on an optimization control method under a mixed traffic flow environment of intelligent networked vehicles and traditional driving vehicles, which can realize optimization control and profit distribution of the vehicles under the condition of ensuring transaction fairness and has good promotion effects on improving traffic transportation efficiency, reducing pollution emission and the like.
And secondly, the method ensures that the vehicles which are yielded pay or compensate certain money for the vehicles which are yielded through profit redistribution, so that both sides have positive profit in the cooperative control process to achieve the win-win goal, thus stimulating the non-cooperative vehicles to be cooperative and actively making behaviors of yielding, decelerating and the like, improving the benefit of the whole traffic system and realizing the achievement of ideal goal of traffic management control optimization.
The present invention will be described in further detail with reference to the following detailed description and the accompanying drawings, which are not intended to limit the scope of the invention.
Drawings
Fig. 1 is a schematic diagram of an optimization control and profit sharing method in a mixed traffic flow environment according to an embodiment of the present invention.
Fig. 2 is a schematic view of a highway ramp junction scenario according to an embodiment of the present invention.
FIG. 3 is a profit comparison graph of three profit sharing modes of the main road vehicle under the method of the present embodiment of the present invention.
Fig. 4 is a revenue comparison graph of three revenue distribution modes of the ramp vehicle in the embodiment of the invention under the method.
FIG. 5 is a graph of the travel time of a vehicle before and after optimal control and revenue distribution for the method of the present example.
FIG. 6 is a graph of fuel consumption before and after optimization control and revenue sharing in accordance with an embodiment of the present invention.
Fig. 7 is a travel cost variation diagram before and after the embodiment of the present invention participates in the optimization control and revenue allocation of the method of this embodiment.
Fig. 8 is a comparison graph of system travel time saving amounts under different flow rates of the main road and the ramp according to the embodiment of the invention.
Fig. 9 is a comparison graph of oil consumption and energy saving of the system under the condition of different flow ratios of the main road and the ramp according to the embodiment of the invention.
Detailed Description
Examples
This example provides a method for optimizing control and revenue allocation in a mixed traffic flow environment, the method comprising the steps of:
s1, receiving vehicle running information of cooperative vehicles and non-cooperative vehicles running in a communication area in real time through the internet of vehicles technology, and personal attribute information reported by the cooperative vehicles;
dividing the vehicle into the cooperative vehicle and the uncooperative vehicle;
the cooperative vehicle refers to a vehicle which receives the optimization control and the profit distribution of the method (namely, the cooperative vehicle refers to an intelligent networked vehicle which receives the optimization control and the profit distribution of the method and a traditional driving vehicle which receives the optimization control and the profit distribution of the method);
the non-cooperative vehicle refers to a vehicle which does not receive the optimization control and the income distribution of the method (namely the non-cooperative vehicle refers to an intelligent networked vehicle which does not receive the optimization control and the income distribution of the method and a traditional driving vehicle which does not receive the optimization control and the income distribution of the method);
s2, integrating the received vehicle running information, predicting to obtain the original running track of the vehicle, and finding out potential traffic conflicts and vehicle organization modes capable of improving traffic transport efficiency;
s3, according to the found potential traffic conflicts and the vehicle organization mode capable of improving the traffic and transportation efficiency, taking the optimal transportation efficiency of the traffic system as an optimization criterion, and obtaining an optimal control strategy by using a dynamic programming algorithm;
s4, analyzing the personal attribute information reported by the cooperative vehicles running in the communication area to obtain the running cost change of the cooperative vehicles running in the communication area according to the original running track of the vehicles and the running according to the optimal control strategy, and respectively calculating the income generated by the running of each vehicle in the cooperative vehicles running in the communication area according to the optimal control strategy;
s5, selecting a profit distribution mode by comprehensively analyzing personal attribute information reported by the cooperative vehicles running in the communication area, and formulating a profit distribution scheme;
s6, sending the optimal control strategy and the prepared income distribution scheme to the cooperative vehicles running in the communication area;
s7, if the cooperative vehicle running in the communication area is satisfied with the formulated revenue distribution scheme, determining the formulated revenue distribution scheme as a final revenue distribution scheme; if the cooperative vehicle traveling in the communication area is not satisfied with the established profit sharing scheme, returning to step S5 to repeat execution until a final profit sharing scheme is established;
s8, performing optimization control on the operation of the intelligent networked vehicles in the cooperative vehicles running in the communication area according to the optimal control strategy; meanwhile, a driver of a conventionally driven vehicle among the cooperative vehicles traveling in the communication area controls the vehicle operation thereof according to the optimal control strategy;
and S9, carrying out revenue distribution according to the final revenue distribution scheme, and enabling the cooperative vehicle with reduced operation cost to provide economic compensation for the cooperative vehicle which gives way actively. (thereby ensuring positive revenue for the traffic system and each vehicle.)
The profit sharing method described in this embodiment includes an average sharing method, a dynamic bargaining (i.e., dynamic negotiation) sharing method, and a double-sided auction sharing method.
The average distribution mode is suitable for both sides of transaction, which are mild, not greedy and not belonging to emergency trip, and the personal attribute information values of both sides are considered to be equal or close to each other, so that the personal rational condition can be met by utilizing the average distribution mode, no person can cause jealousy, and people consider that the income of the people is larger than that of the other side.
The dynamic bargaining distribution mode reasonably simulates the process of bargaining and counter-selling by people in daily life, people can put forward shares required by themselves, a process of continuous mutual compromise is carried out before the intention is not agreed, the transaction price which is satisfied by both parties is finally reached, and the phenomenon of bargaining with an extreme price can be effectively avoided by utilizing the dynamic bargaining distribution mode to carry out income distribution.
The bilateral auction allocation mode can simultaneously meet the demands of buyers and sellers to a certain extent, the buyers and the sellers simultaneously report a transaction price expected by the buyers and the sellers respectively in the transaction process, the transaction price which can be executed finally is a linear combination of the transaction prices expected by the buyers and the sellers, if the final transaction price enables the income of any participant to be non-positive, the transaction will not be carried out, so in order to obtain the income through the transaction, the participant needs to report a personal attribute information value approaching to the personal attribute true value, and the incentive compatibility condition is met.
The personal attribute information in this example includes time value and fuel price.
The operation cost in the embodiment comprises travel time cost and fuel consumption cost.
The schematic diagram of the optimization control and profit allocation method in the mixed traffic flow environment is shown in fig. 1, and the method is suitable for different mixed traffic flow scenes, including mixed traffic flow scenes of confluence areas of crossroads, T-junctions, expressway ramps and the like.
The following are related results obtained by performing simulation analysis on the optimization control and profit allocation method in the mixed traffic flow environment provided by the present example in combination with a mixed traffic flow scene of a highway ramp junction area (as shown in fig. 2, the method performs information interaction with main road vehicles and ramp vehicles through a road test unit, calculates an optimal control strategy for the vehicles by using the obtained vehicle operation information, and suggests cooperative vehicles to operate according to the strategy, thereby improving traffic transport efficiency):
the profit situations of the three profit sharing modes under the method of the present embodiment are shown in fig. 3 and fig. 4, wherein fig. 3 shows the profit situations of the three profit sharing modes under the method of the present embodiment for the main road vehicle, and fig. 4 shows the profit situations of the three profit sharing modes under the method of the present embodiment for the ramp vehicle. As can be seen from fig. 3 and 4, after the ramp vehicles compensate the main road vehicles, the net profit of each main road vehicle is 0.05 yuan, and the maximum profit of the main road vehicles and the ramp vehicles is 0.22 yuan and 0.27 yuan, respectively.
FIG. 5 illustrates the travel time of the vehicle before and after the optimal control and allocation of revenue in the method of the present example; FIG. 6 shows the fuel consumption of the vehicle before and after the optimization control and the allocation of revenue in the method of the present example; fig. 7 shows the variation of the travel cost (i.e., the running cost) of the vehicle before and after the optimization control and the profit sharing in the method of this example, where the variation of the travel cost of the vehicle before and after the optimization control and the profit sharing in the method of this example is obtained by converting the variation of the travel time and the variation of the fuel consumption of the vehicle before and after the optimization control and the profit sharing in the method of this example into the money cost. As can be seen from fig. 5 and 6, after the optimal control and profit allocation of the method of the present embodiment, the vehicle on the main road will bring about an increase in travel time of 4.13% and an increase in fuel consumption of 26.46% due to the way being yielded, and the corresponding vehicle on the ramp will bring about a decrease in travel time of 4.69% and a decrease in fuel consumption of 34.19% due to the way being yielded. As can be seen from fig. 7, after the optimization control and the revenue allocation of the method of the present embodiment are participated, the average travel cost of each main road vehicle is increased by 0.11 yuan, and the average travel cost of each ramp vehicle is reduced by 0.18 yuan. Therefore, the traffic transportation efficiency can be effectively improved and the energy consumption can be reduced by adopting the optimization control and profit allocation method under the mixed traffic flow environment.
Fig. 8 shows a comparison of the amount of time savings for the system (here "system" means the system made up of all vehicles in the area of the ramp confluence) for the case of this example with different flow ratios between the main road and the ramp. Fig. 9 shows a comparison of the fuel consumption (i.e., fuel consumption) savings of the system in this example (where "system" refers to a system made up of all vehicles in the area of the junction of the ramps) under different flow ratios between the main road and the ramps. In FIGS. 8 and 9, the ratio of the main path flow to the ramp flow is q1:q2The graph shows the comparison of ramp vehicle system travel time savings and fuel consumption savings for main and ramp flow ratios of 900:900, 1000:500, 1100:700, 1300:1000, respectively. As can be seen from fig. 8 and 9, in the case of different flow rates of the main road and the ramp, the optimized control of each successful transaction can save 1.91 seconds of travel time and 34.27g of fuel consumption for the ramp vehicle system, which are converted into about 0.42 dollars of money, for each optimized group of vehicles affecting the cost of the entire ramp vehicle system. Therefore, by adopting the optimization control and income distribution method under the mixed traffic flow environment, each ramp area can save about 80-100 yuan per hour, and a considerable expense can be saved for the country every year by aiming at a huge road network in China.
The above examples only show some specific embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the spirit of the invention, and these are all covered by the scope of the invention.
Claims (4)
1. A method for optimizing control and allocating revenue in a mixed traffic flow environment, the method comprising the steps of:
s1, receiving vehicle running information of cooperative vehicles and non-cooperative vehicles running in a communication area in real time through the internet of vehicles technology, and personal attribute information reported by the cooperative vehicles;
dividing the vehicle into the cooperative vehicle and the uncooperative vehicle;
the cooperative vehicle is a vehicle which receives the optimization control and the income distribution of the method;
the non-cooperative vehicle is a vehicle which does not accept the optimization control and the profit distribution of the method;
s2, integrating the received vehicle running information, predicting to obtain the original running track of the vehicle, and finding out potential traffic conflicts and vehicle organization modes capable of improving traffic transport efficiency;
s3, according to the found potential traffic conflicts and the vehicle organization mode capable of improving the traffic and transportation efficiency, taking the optimal transportation efficiency of the traffic system as an optimization criterion, and obtaining an optimal control strategy by using a dynamic programming algorithm;
s4, analyzing the personal attribute information reported by the cooperative vehicles running in the communication area to obtain the running cost change of the cooperative vehicles running in the communication area according to the original running track line of the vehicles and the running according to the optimal control strategy, and respectively calculating the income generated by running each vehicle in the cooperative vehicles running in the communication area according to the optimal control strategy;
s5, selecting a profit distribution mode by comprehensively analyzing personal attribute information reported by the cooperative vehicles running in the communication area, and formulating a profit distribution scheme;
s6, sending the optimal control strategy and the prepared income distribution scheme to the cooperative vehicles running in the communication area;
s7, if the cooperative vehicle running in the communication area is satisfied with the formulated revenue distribution scheme, determining the formulated revenue distribution scheme as a final revenue distribution scheme; if the cooperative vehicle traveling in the communication area is not satisfied with the benefit allocation plan, returning to step S5 to repeat the execution until a final benefit allocation plan is determined;
s8, performing optimization control on the operation of the intelligent networked vehicles in the cooperative vehicles running in the communication area according to the optimal control strategy; meanwhile, a driver of a conventionally driven vehicle among the cooperative vehicles traveling in the communication area controls the vehicle operation thereof according to the optimal control strategy;
and S9, carrying out revenue distribution according to the final revenue distribution scheme, and enabling the cooperative vehicle with reduced operation cost to provide economic compensation for the cooperative vehicle which gives way actively.
2. The method for optimizing control and allocating revenue under the mixed traffic flow environment according to claim 1, wherein the revenue allocation mode comprises an average allocation mode, a dynamic bargaining allocation mode and a bilateral auction allocation mode.
3. The method of claim 1, wherein the personal attribute information includes time value, fuel price.
4. The method of claim 1, wherein the operational costs include travel time costs and fuel consumption costs.
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