Optimal carbon quota pricing method suitable for mixed traffic flow

Abstract

The invention discloses an optimal carbon quota pricing method suitable for mixed traffic flow, and particularly relates to the technical field of traffic planning and low-carbon management. The method comprises the following steps: constructing a mixed traffic circulation service behavior model through a bottleneck model, and designing a travel cost function and a carbon transaction cost function in combination with the loss avoidance psychology of a commuter; secondly, according to a government carbon quota distribution scene, typical travel modes are divided, and an optimization model taking the total carbon emission cost and the total carbon transaction cost as targets is constructed; and finally, through algorithm iteration, optimal carbon quota prices under different weights are determined. The method gives consideration to the traffic system operation efficiency and the carbon emission reduction target, has good adaptability and accuracy, and has an important application prospect in a traffic environment in which multiple travel and a carbon transaction mechanism are fused and developed in the future.

Description

Technical Field

[0001] This invention relates to the field of traffic planning and low-carbon management technology, and more specifically, to an optimal carbon allocation pricing method applicable to mixed traffic flows. Background Technology

[0002] With the development of urbanization and intelligent transportation, urban transportation demand has increased dramatically, and the number of cars has continued to rise, leading to increasingly severe traffic congestion and environmental pollution problems.

[0003] Carbon trading mechanisms, as a market-based economic stimulus, involve trading government-issued carbon allowances and have been widely adopted in many countries and regions, playing a significant role in alleviating traffic congestion and reducing transportation carbon emissions. In recent years, autonomous driving technology has received considerable attention; however, achieving full automation of road traffic still faces limitations and challenges in terms of technology, cost, and regulations. Future traffic flow will likely remain a hybrid of manually driven and autonomous vehicles for a long time to come. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide an optimal carbon allocation pricing method suitable for mixed traffic flows to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] An optimal carbon allocation pricing method applicable to mixed traffic flows includes the following steps:

[0007] S1: Based on the bottleneck commuting model, a traffic scenario in which manually driven cars and autonomous vehicles coexist is set up to simulate the commuter's choice between manually driven cars and autonomous vehicles and departure time based on the expectation of arriving on time.

[0008] S2: Distribute carbon allowances to commuters proportionally, and convert each commuter's carbon emissions at different departure times into corresponding carbon allowance consumption. Construct a carbon trading cost function that takes into account loss aversion psychology, and combine the selling gains and buying losses.

[0009] S3: Combining carbon trading costs and time costs, establish individual travel cost functions for commuters in manually driven cars and autonomous vehicles, respectively;

[0010] S4: Based on the principle of minimizing personal costs for commuters, construct a traffic behavior equilibrium model under different carbon allowance issuance conditions to determine travel behavior and carbon trading behavior under different carbon allowance issuance amounts;

[0011] S5: Taking total carbon emission cost and total carbon trading cost as objectives, and combining traffic behavior models under user equilibrium conditions, establish a multi-objective optimization model with adjustable weights.

[0012] S6: Set the carbon quota price as the optimization variable, and obtain the optimal carbon quota price under different management objectives based on the iterative algorithm and the principle of minimizing the objective function, and output the Pareto solution set.

[0013] In a preferred embodiment, S1 specifically refers to:

[0014] Based on the bottleneck commuting model, a hybrid transportation commuting scenario in which manually driven cars and autonomous vehicles coexist is established.

[0015] Define a group of commuters who have a consistent expectation of arriving at their workplace on time during peak commuting hours;

[0016] Commuters are divided into those who choose manually driven cars and those who choose autonomous vehicles based on their mode of transportation.

[0017] Commuters choose between human-driven and self-driving cars based on their expectation of arriving at their destination on time and on the cost of travel.

[0018] In a preferred embodiment, S2 specifically refers to:

[0019] According to a predetermined ratio, a certain amount of carbon allowances will be distributed equally to all commuters in mixed transportation commuting scenarios.

[0020] Determine the carbon emission intensity per unit time for manually driven and autonomous vehicles;

[0021] Calculate the carbon emissions generated by each commuter's trip at different departure times, and convert the carbon emissions of commuters at different departure times into corresponding carbon allowance consumption based on vehicle type and travel duration;

[0022] Set the unit price for carbon allowance trading in the carbon allowance market, and establish a carbon trading cost function based on the average carbon allowances allocated to commuters.

[0023] In a preferred embodiment, S3 specifically refers to:

[0024] In the absence of a carbon trading mechanism, individual travel cost functions are established for commuters using manually driven cars and those using autonomous vehicles. These individual travel cost functions include travel time costs and penalty costs caused by the difference between the commuter's arrival time and the expected on-time arrival time.

[0025] Under the carbon trading mechanism, the carbon trading cost function is incorporated into the individual travel cost function of commuters using manually driven cars and the individual travel cost function of commuters using autonomous vehicles to obtain the individual travel cost function under the carbon trading mechanism.

[0026] In a preferred embodiment, S4 specifically refers to:

[0027] Taking the minimization of individual commuter travel costs as the basic principle for commuters to choose travel time and vehicle type, we establish traffic behavior equilibrium conditions where individual commuter travel costs are equal.

[0028] The equilibrium conditions for the carbon trading market are set based on the total amount of carbon allowances issued and the total amount of carbon allowances consumed by all commuters in a mixed transportation commuting scenario under equilibrium conditions.

[0029] Different traffic behavior scenarios are set based on the number of carbon allowances issued;

[0030] Based on different traffic behavior scenarios, we can determine the commuter travel behavior patterns and obtain the commuter's travel time, vehicle type selection, and carbon quota trading status in equilibrium under each traffic behavior scenario.

[0031] In a preferred embodiment, S5 specifically refers to:

[0032] The optimization objectives are the total carbon emission cost generated by all commuters in a mixed transportation commuting scenario and the total carbon trading cost generated by all commuters trading carbon allowances.

[0033] Establish a multi-objective optimization model with total carbon emission cost and total carbon trading cost as optimization objectives;

[0034] By assigning corresponding weight coefficients to the total carbon emission cost and the total carbon trading cost, and then performing linear weighting, the multi-objective optimization model is transformed into a single-objective optimization model.

[0035] Adjust the weighting coefficients corresponding to total carbon emission cost and total carbon trading cost to form a multi-objective optimization model with adjustable weights.

[0036] In a preferred embodiment, S6 specifically refers to:

[0037] By setting the carbon allowance price as the optimization variable, an optimization objective function is established for a mixed traffic commuting scenario.

[0038] Initialize the carbon quota price and the range of values ​​for the objective function, and determine the iteration step size and convergence criterion in the optimization process;

[0039] An iterative calculation method is used to adjust the carbon allowance price within a range of values ​​and calculate the optimization objective function value under different carbon allowance prices.

[0040] The values ​​of the objective function are sorted and filtered to determine the minimum objective function value and the corresponding carbon quota price under a given combination of weight coefficients, which is taken as the optimal carbon quota price.

[0041] By adjusting the weighting coefficients and repeating the optimization process, a Pareto solution set consisting of a series of optimal carbon quota prices is obtained.

[0042] The technical effects and advantages of the optimal carbon allocation pricing method applicable to mixed traffic flows proposed in this invention are as follows:

[0043] By introducing a carbon trading mechanism into peak-hour commuting bottleneck scenarios where both manually driven and autonomous vehicles coexist, an integrated pricing framework is established, encompassing carbon allowance allocation, individual travel cost calculation, traffic behavior equilibrium, and multi-objective optimization. This framework allows for dynamic adjustment of carbon allowance prices under different allowance issuance volumes, achieving coordinated optimization of the dual objectives of traffic congestion mitigation and carbon emission reduction. By considering commuters' loss aversion psychology and the principle of minimizing travel costs, it guides commuters to make cost- and time-saving travel choices between manual and autonomous driving modes. Through an adjustable-weight multi-objective optimization model, total carbon emission costs and total carbon trading costs are uniformly incorporated into pricing decisions, enabling flexible adjustments to policy priorities based on management needs. The Pareto solution set obtained by the iterative algorithm not only provides multiple carbon allowance pricing schemes but also considers environmental efficiency and market fairness at different stages, making it suitable for future traffic management that deeply integrates diverse travel modes and carbon market mechanisms. This has significant application value for improving urban traffic efficiency and reducing carbon emission intensity. Attached Figure Description

[0044] Figure 1 This is a schematic diagram of an optimal carbon allocation pricing method applicable to mixed traffic flows according to the present invention. Detailed Implementation

[0045] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0046] Example

[0047] Figure 1 The present invention provides an optimal carbon allocation pricing method applicable to mixed traffic flows, which includes the following steps:

[0048] S1: Based on the bottleneck commuting model, a traffic scenario in which manually driven cars and autonomous vehicles coexist is set up to simulate the commuter's choice between manually driven cars and autonomous vehicles and departure time based on the expectation of arriving on time.

[0049] S2: Distribute carbon allowances to commuters proportionally, and convert each commuter's carbon emissions at different departure times into corresponding carbon allowance consumption. Construct a carbon trading cost function that takes into account loss aversion psychology, and combine the selling gains and buying losses.

[0050] S3: Combining carbon trading costs and time costs, establish individual travel cost functions for commuters in manually driven cars and autonomous vehicles, respectively;

[0051] S4: Based on the principle of minimizing personal costs for commuters, construct a traffic behavior equilibrium model under different carbon allowance issuance conditions to determine travel behavior and carbon trading behavior under different carbon allowance issuance amounts;

[0052] S5: Taking total carbon emission cost and total carbon trading cost as objectives, and combining traffic behavior models under user equilibrium conditions, establish a multi-objective optimization model with adjustable weights.

[0053] S6: Set the carbon quota price as the optimization variable, and obtain the optimal carbon quota price under different management objectives based on the iterative algorithm and the principle of minimizing the objective function, and output the Pareto solution set.

[0054] S1: Based on a bottleneck commuting model, a traffic scenario is set up where manually driven cars and autonomous vehicles coexist. The simulation of commuters' choices between manually driven and autonomous vehicles, and between departure times, based on their expectation of arriving on time, includes:

[0055] Based on the bottleneck commuting model, a hybrid transportation commuting scenario in which manually driven cars and autonomous vehicles coexist is established.

[0056] Define a group of commuters who have a consistent expectation of arriving at their workplace on time during peak commuting hours;

[0057] Commuters are divided into those who choose manually driven cars and those who choose autonomous vehicles based on their mode of transportation.

[0058] Commuters choose between human-driven and self-driving cars based on their expectation of arriving at their destination on time and on the cost of travel.

[0059] Specifically, a mixed traffic flow model is constructed: First, based on a bottleneck economics model, a queuing system for commuters (HV and AV) under mixed traffic flow is built. This system includes a traffic corridor connecting a residential area and workplaces in the same direction, with a bottleneck on the corridor having a variable capacity of 's', and all commuters having the same expected on-time arrival time 't'. * N commuters choose either a manually driven vehicle (HV) or an autonomous vehicle (AV) to commute to their workplace. The number of commuters choosing HV is N. H The number of commuters who chose AV is N. A And satisfy N H +N A =N. Under carbon trading, commuters driving different types of vehicles need to pay corresponding carbon trading costs φ based on the carbon emissions generated by their vehicles during their trips, according to the relevant carbon trading rules. i (t), where i = H, A, where H and A represent manually driven cars and autonomous vehicles, respectively. Travel costs include the travel time cost corresponding to the mode of transportation chosen by commuters, the early arrival penalty cost due to arrival time earlier than the expected on-time arrival time, the late arrival penalty cost due to arrival time later than the expected on-time arrival time, and the carbon trading cost after the implementation of the carbon trading mechanism; where the sum of the number of commuters choosing manually driven cars and the number of commuters choosing autonomous vehicles is the total number of commuters in the mixed traffic commuting scenario.

[0060] S2: Distribute carbon allowances proportionally to commuters and convert each commuter's carbon emissions at different departure times into corresponding carbon allowance consumption. Construct a carbon trading cost function that considers loss aversion, integrating selling gains and buying losses, including:

[0061] According to a predetermined ratio, a certain amount of carbon allowances will be distributed equally to all commuters in mixed transportation commuting scenarios.

[0062] Determine the carbon emission intensity per unit time for manually driven and autonomous vehicles;

[0063] Calculate the carbon emissions generated by each commuter's trip at different departure times, and convert the carbon emissions of commuters at different departure times into corresponding carbon allowance consumption based on vehicle type and travel duration;

[0064] Set the unit price for carbon allowance trading in the carbon allowance market, and establish a carbon trading cost function based on the average carbon allowances allocated to commuters;

[0065] Specifically, the carbon emission intensity per unit time of a manually driven car is higher than that of an autonomous vehicle. Calculate the carbon trading cost considering loss aversion effects:

[0066] Carbon allowance allocation: The government issues a total of K carbon allowances, which are distributed equally among N commuters. Each commuter initially receives a carbon allowance from the government. As shown in the formula:

[0067]

[0068] Next, the carbon allowance k paid by commuters departing at time t will be... i (t) is defined as a direct proportional function of travel time T(t), as shown in formula (2):

[0069] k i (t)=λ i T(t),i=H,A (2)

[0070] Where, λ i The carbon emission intensity per unit time represents different vehicle types. Due to the advantages of autonomous driving technology in terms of behavioral efficiency, vehicle maintenance, integration technology, and route optimization, the carbon emission intensity of HV (Hardware Vehicle) is greater than that of AV (Autonomous Vehicle), i.e., λ. H >λ A .

[0071] Calculation of carbon trading costs: The loss aversion psychology of commuters is considered in the carbon trading process to measure the psychological perceived cost of commuters under the implementation of carbon trading policies. The magnitude of this cost reflects the degree of acceptance of commuters under the implementation of carbon trading policies. The smaller the value, the higher the degree of acceptance of commuters and the higher the efficiency of carbon trading implementation. Then, based on the carbon quotas in (1)... Using t as a reference point, a carbon trading cost function φ is constructed for commuters departing at time t. i (t):

[0072]

[0073] Where P represents the price per unit of carbon allowance when the carbon trading market reaches equilibrium (P>0). When there is a surplus of carbon allowances issued, commuters need to sell their carbon allowances, therefore φ i (t) represents the profit; when At times, if the issued carbon allowances are insufficient, commuters need to purchase carbon allowances. In this case, φ i (t) represents the loss. η represents the loss aversion sensitivity coefficient, and η>1 indicates that commuters are more sensitive to losses when buying carbon allowances than to gains when selling them. The larger the value of η, the higher the commuter's loss sensitivity.

[0074] S3: Combining carbon trading costs and time costs, establish individual travel cost functions for commuters in both manually driven and autonomous vehicles, including:

[0075] In the absence of a carbon trading mechanism, individual travel cost functions are established for commuters using manually driven cars and those using autonomous vehicles. These individual travel cost functions include travel time costs and penalty costs caused by the difference between the commuter's arrival time and the expected on-time arrival time.

[0076] Under the carbon trading mechanism, the carbon trading cost function is incorporated into the individual travel cost function of commuters using manually driven cars and the individual travel cost function of commuters using autonomous vehicles to obtain the individual travel cost function under the carbon trading mechanism.

[0077] Specifically, the travel cost function is designed as follows: For HV commuters, travel costs mainly include travel time costs and penalty costs for arriving early or late. For AV commuters, in addition to travel time costs and penalty costs for arriving early or late, a fixed cost Δ is also considered. This is the inconvenience cost that AV brings to commuters. Due to the current immaturity of autonomous driving systems, AVs are more cautious than HVs when encountering complex situations, such as time delays. Therefore, the travel costs for commuters are as follows:

[0078] The travel cost function for HV commuters without a carbon trading mechanism is:

[0079]

[0080] The travel cost function for AV commuters is:

[0081]

[0082] Where, α H α represents the unit travel time cost for HV commuters. A This represents the unit travel time cost for AV commuters, and α A =φα H φ<1 means that AV allows commuters to engage in other activities while driving, compared to HV, thus saving driving time. β(γ) is the unit penalty cost for commuters arriving early (late).

[0083] When implementing a carbon trading mechanism, the carbon trading cost function φ A (t) is incorporated into formulas (4) and (5):

[0084] HV Commuter's travel cost function C H (t) is:

[0085]

[0086] The travel cost function C for AV commuters A (t) is:

[0087]

[0088] The individual travel cost function for manually driven car commuters is obtained by adding the travel time cost and penalty cost of the manually driven car commuter.

[0089] The individual travel cost function for autonomous vehicle commuters is obtained by adding the travel time cost and penalty cost of autonomous vehicle commuters.

[0090] S4: Based on the principle of minimizing personal costs for commuters, construct a traffic behavior equilibrium model under different carbon allowance issuance conditions to determine travel and carbon trading behaviors under different carbon allowance issuance amounts, including:

[0091] Taking the minimization of individual commuter travel costs as the basic principle for commuters to choose travel time and vehicle type, we establish traffic behavior equilibrium conditions where individual commuter travel costs are equal.

[0092] The equilibrium conditions for the carbon trading market are set based on the total amount of carbon allowances issued and the total amount of carbon allowances consumed by all commuters in a mixed transportation commuting scenario under equilibrium conditions.

[0093] Different traffic behavior scenarios are set based on the number of carbon allowances issued;

[0094] Based on different traffic behavior scenarios, we can determine the commuter travel behavior patterns and obtain the commuter's travel time, vehicle type selection, and carbon quota trading status in equilibrium under each traffic behavior scenario.

[0095] Specifically, the solution for travel behavior under carbon trading is as follows: Based on the principle of carbon trading market equilibrium, the travel behavior under user equilibrium is modeled and solved according to the total amount of carbon allowances and user travel costs, and three travel mode scenarios are divided according to the amount of carbon allowances issued by the government.

[0096] User equilibrium travel behavior modeling: User equilibrium specifically refers to the state where the transportation network reaches equilibrium, and no commuter can reduce their travel cost by unilaterally changing their departure time decision; that is, the travel cost C of a manually driven commuter. H (t) and the travel cost C for autonomous driving commuters A (t) are all equal, as shown in formula (8):

[0097] C H (t)=C A (t) (8)

[0098] The principle of equilibrium in the carbon trading market means that in the carbon trading market, the total amount of carbon allowances paid by all commuters must equal the initial total amount of carbon allowances issued by the government, where... The total amount of carbon allowances paid by all commuters during the entire morning rush hour [t1,t5]. The total amount of carbon allowances issued by the government is given, and the carbon trading equilibrium is shown in formula (9):

[0099]

[0100] Therefore, the commuter travel behavior model is shown in Equation (10):

[0101] C H (t)=C A (t) (10)

[0102]

[0103] In the constraints, formula (10) represents the constraints of the user balance principle, where represents the carbon allowances issued. Both the carbon allowance price P and the number of commuters N choosing HV travel are positive. H The number N of commuters choosing AV travel A The sum of these numbers is the total number of people N, and all of them are positive integers.

[0104] Travel behavior analysis and scenario classification: based on carbon allowances issued by the government. The amount of travel is calculated using formula (10) to obtain the three travel mode scenarios under equilibrium conditions.

[0105] Table 2. Classification of Travel Mode Scenarios for Different Carbon Quota Ranges

[0106]

[0107] In Scenario 1, when a large number of carbon allowances are issued, HV commuters can both sell and buy carbon allowances, while AV commuters can only buy carbon allowances. In Scenario 3, HV commuters can only sell carbon allowances, while AV commuters can both sell and buy carbon allowances. In Scenario 2, both HV and AV commuters can freely buy and sell carbon allowances.

[0108] S5: Using total carbon emission costs and total carbon trading costs as objectives, and combining traffic behavior models under user equilibrium conditions, establish a multi-objective optimization model with adjustable weights, including:

[0109] The optimization objectives are the total carbon emission cost generated by all commuters in a mixed transportation commuting scenario and the total carbon trading cost generated by all commuters trading carbon allowances.

[0110] Establish a multi-objective optimization model with total carbon emission cost and total carbon trading cost as optimization objectives;

[0111] By assigning corresponding weight coefficients to the total carbon emission cost and the total carbon trading cost, and then performing linear weighting, the multi-objective optimization model is transformed into a single-objective optimization model.

[0112] Adjust the weighting coefficients corresponding to total carbon emission cost and total carbon trading cost to form a multi-objective optimization model with adjustable weights.

[0113] Specifically, the total carbon emission cost is the sum of the products of the total queuing time for both human-driven and autonomous vehicle commuters under equilibrium conditions, the corresponding carbon emission intensity per unit time, and the carbon emission cost per unit.

[0114] The total queuing time for manually driven commuters is the area enclosed by the cumulative departure time curve and the cumulative arrival time curve for manually driven commuters under equilibrium conditions.

[0115] The total queuing time for autonomous vehicle commuters is the area enclosed by the cumulative departure time curve and the cumulative arrival time curve of autonomous vehicle commuters under equilibrium conditions. An optimal carbon allocation pricing model is established under mixed traffic flow: total carbon emission cost and total carbon trading cost are selected as system performance indicators. Based on the obtained user travel costs, the total carbon trading cost can be calculated. Then, combined with the total carbon emission cost under equilibrium conditions, the evaluation function method is adopted, and a linear weighting method is selected to transform the bi-objective optimization problem into a single evaluation function. The weight ω reflects the government's relative emphasis on different objectives. Thus, the bi-objective optimization model can be obtained as shown in formula (11):

[0116] minF(P)=min(ω·TCEC(P)+(1-ω)·TCTC(P)) (11)

[0117]

[0118] Depending on the carbon allowances issued, the penetration rate of autonomous vehicles, and the carbon trading scheme, there will be different carbon emission costs and carbon trading costs. Taking the most complex and more general scenario 2 as an example, the total carbon emission cost (TCEC) expression is as follows:

[0119]

[0120] ξ represents the unit carbon emission cost, TQT H TQT represents the total queuing time for HV commuters, which is the area between the departure and arrival curves of HV commuters in the travel pattern diagram. AThis represents the total queuing time for AV commuters, which is the area between the departure curve and the arrival curve of AV commuters.

[0121] The total carbon trading cost (TCTC) is expressed as follows:

[0122]

[0123] The first part represents the cost incurred by all commuters who buy carbon allowances, and the second part represents the revenue gained by all commuters who sell carbon allowances.

[0124] S6: Set the carbon quota price as the optimization variable. Based on the iterative algorithm and the principle of minimizing the objective function, obtain the optimal carbon quota price under different management objectives, and output the Pareto solution set, including:

[0125] By setting the carbon allowance price as the optimization variable, an optimization objective function is established for a mixed traffic commuting scenario.

[0126] Initialize the carbon quota price and the range of values ​​for the objective function, and determine the iteration step size and convergence criterion in the optimization process;

[0127] An iterative calculation method is used to adjust the carbon allowance price within a range of values ​​and calculate the optimization objective function value under different carbon allowance prices.

[0128] The values ​​of the objective function are sorted and filtered to determine the minimum objective function value and the corresponding carbon quota price under a given combination of weight coefficients, which is taken as the optimal carbon quota price.

[0129] By adjusting the weighting coefficients and repeating the optimization process, a Pareto solution set consisting of a series of optimal carbon quota prices is obtained.

[0130] Specifically, the optimal carbon allowance pricing model under mixed traffic flow is solved: the variable is the carbon allowance price, when F(P) * When F(P) ≤ F(P), the optimal carbon quota price is P. * The specific scheme of the algorithm that optimizes the objective is as follows:

[0131] Let arrays ρ∈(0,maxP] and F(P) represent the carbon quota price and the objective function value, respectively. Given a sufficiently small positive number ε as a fixed step size and convergence criterion, and set i as the total number of iterations, let i0=0, ρ(0)=0, and ε=0.001.

[0132] For a given ρ(i), the personal travel cost C of a driver-driven car commuter can be obtained. H Personal travel costs C for commuters using autonomous vehicles A If |C H -C AIf |<ε, proceed to step 3; otherwise, consider the number N of commuters using autonomous vehicles. A When N A ≤NN H When N is reached, let i = i0 + 1, and continue repeating step 2. A >NN H If the road network cannot achieve equilibrium, the loop exits. At this point, all solutions that bring the system to equilibrium can be obtained.

[0133] By sorting the objective function values ​​F(P), we can obtain the minimum objective function value F(P). * ) and the optimal carbon quota price P * .

[0134] Based on the management objectives at different stages, set the weights ω in step 5, repeat the above operations, minimize the evaluation function F, and determine the optimal carbon quota price P. * .

[0135] The corresponding carbon allowance price is solved using a numerical example. The parameters are set as follows: Assume there are N = 5000 car commuters traveling from their residential areas to their workplaces via a bottleneck during the morning rush hour, and the commuters' expected arrival time t... * =9:00. The unit time value of early commuters β = 4.3 (yuan / hour), the unit time value of late commuters γ = 14.7 (yuan / hour), the unit carbon emission cost ξ = 0.25 (yuan / kg), and the loss aversion sensitivity coefficient η = 2.3.

[0136] For HV commuters, the value of a unit of travel time (Yuan / hour), carbon emission intensity per unit time is (kg / hour); For AV commuters, the proportional parameters related to autonomous driving are: Carbon emission intensity per unit time is (kg / hour), additional fixed costs (kg / hour).

[0137] Carbon allowance price P and the amount of carbon allowances issued under different carbon allowance scenarios Relationship

[0138] In each scenario, the carbon allowances issued Both decrease as the carbon allowance price P increases. The range of values ​​for the carbon allowance price and the issued carbon allowances is detailed in Table 2.

[0139] Table 2. Range of carbon allowance prices and issued carbon allowances under different travel mode scenarios.

[0140]

[0141] In scenario 1, the carbon allowance price P increases from 0 to 0.40 yuan, and the issued carbon allowances... The price of carbon allowances decreases from 2.82 to 1.77; in scenario 2, the carbon allowance price P increases from 0.06 to 5.48 yuan, and the issued carbon allowances... The carbon allowance price gradually decreases from 2.56 to 0.43; in scenario 3, the carbon allowance price P increases indefinitely from 0.21 yuan, and the issued carbon allowances... It then gradually decreases from 2.28, eventually approaching 0.

[0142] Optimal carbon sizing price under different weights ω

[0143] As the carbon allowance price P increases, the total carbon emission cost (TCEC) gradually decreases, while the total carbon trading cost (TCTC) gradually increases. Therefore, to minimize the total carbon trading cost (0.147 × 10⁻⁶), 3 To improve the efficiency of carbon trading implementation, a lower carbon allowance price (0.06 yuan) should be set; however, to minimize the total carbon emission cost (0.476 × 10^6 yuan), a lower price should be set. 3 To improve the environmental benefits of carbon emission reduction, a higher carbon allowance price (5.48 yuan) should be set. Discussions should focus on how to price carbon allowances in a way that balances the total carbon trading cost and the total carbon emission cost of the system.

[0144] Table 3 Optimal carbon blending price under different weights ω

[0145]

[0146] When government officials place greater emphasis on the environmental benefits of carbon emission reduction, they should reasonably increase the price of carbon allowances. Conversely, when government officials focus more on the efficiency of carbon trading implementation, they should decrease the price of carbon allowances.

[0147] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0148] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0149] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0150] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0151] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0152] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0153] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0154] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0155] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0156] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An optimal carbon allocation pricing method applicable to mixed traffic flows, characterized in that, Includes the following steps: S1: Based on the bottleneck commuting model, a traffic scenario in which manually driven cars and autonomous vehicles coexist is set up to simulate the commuter's choice between manually driven cars and autonomous vehicles and departure time based on the expectation of arriving on time. S2: Distribute carbon allowances to commuters proportionally, and convert each commuter's carbon emissions at different departure times into corresponding carbon allowance consumption. Construct a carbon trading cost function that takes into account loss aversion psychology, and combine the selling gains and buying losses. S3: Combining carbon trading costs and time costs, establish individual travel cost functions for commuters in manually driven cars and autonomous vehicles, respectively; S4: Based on the principle of minimizing personal costs for commuters, construct a traffic behavior equilibrium model under different carbon allowance issuance conditions to determine travel behavior and carbon trading behavior under different carbon allowance issuance amounts; S5: Taking total carbon emission cost and total carbon trading cost as objectives, and combining traffic behavior models under user equilibrium conditions, establish a multi-objective optimization model with adjustable weights. S6: Set the carbon quota price as the optimization variable, and obtain the optimal carbon quota price under different management objectives based on the iterative algorithm and the principle of minimizing the objective function, and output the Pareto solution set.

2. The optimal carbon allocation pricing method applicable to mixed traffic flow according to claim 1, characterized in that, S1, specifically: Based on the bottleneck commuting model, a hybrid transportation commuting scenario in which manually driven cars and autonomous vehicles coexist is established. Define a group of commuters who have a consistent expectation of arriving at their workplace on time during peak commuting hours; Commuters are divided into those who choose manually driven cars and those who choose autonomous vehicles based on their mode of transportation. Commuters choose between human-driven and self-driving cars based on their expectation of arriving at their destination on time and on the cost of travel.

3. The optimal carbon allocation pricing method applicable to mixed traffic flows according to claim 2, characterized in that, S2, specifically: According to a predetermined ratio, a certain amount of carbon allowances will be distributed equally to all commuters in mixed transportation commuting scenarios. Determine the carbon emission intensity per unit time for manually driven and autonomous vehicles; Calculate the carbon emissions generated by each commuter's trip at different departure times, and convert the carbon emissions of commuters at different departure times into corresponding carbon allowance consumption based on vehicle type and trip duration; Set the unit price for carbon allowance trading in the carbon allowance market, and establish a carbon trading cost function based on the average carbon allowances allocated to commuters.

4. The optimal carbon allocation pricing method applicable to mixed traffic flows according to claim 3, characterized in that, S3, specifically: In the absence of a carbon trading mechanism, individual travel cost functions are established for commuters using manually driven cars and those using autonomous vehicles. These individual travel cost functions include travel time costs and penalty costs resulting from the difference between the commuter's arrival time and the expected on-time arrival time. Under the carbon trading mechanism, the carbon trading cost function is incorporated into the individual travel cost function of commuters using manually driven cars and the individual travel cost function of commuters using autonomous vehicles to obtain the individual travel cost function under the carbon trading mechanism.

5. The optimal carbon allocation pricing method applicable to mixed traffic flows according to claim 4, characterized in that, S4, specifically: Taking the minimization of individual commuter travel costs as the basic principle for commuters to choose travel time and vehicle type, we establish traffic behavior equilibrium conditions where individual commuter travel costs are equal. The equilibrium conditions for the carbon trading market are set based on the total amount of carbon allowances issued and the total amount of carbon allowances consumed by all commuters in a mixed transportation commuting scenario under equilibrium conditions. Different traffic behavior scenarios are set based on the number of carbon allowances issued; Based on different traffic behavior scenarios, we can determine the commuter travel behavior patterns and obtain the commuter's travel time, vehicle type selection, and carbon quota trading status in equilibrium under each traffic behavior scenario.

6. The optimal carbon allocation pricing method applicable to mixed traffic flows according to claim 5, characterized in that, S5, specifically: The optimization objectives are the total carbon emission cost generated by all commuters in a mixed transportation commuting scenario and the total carbon trading cost generated by all commuters trading carbon allowances. Establish a multi-objective optimization model with total carbon emission cost and total carbon trading cost as optimization objectives; By assigning corresponding weight coefficients to the total carbon emission cost and the total carbon trading cost, and then performing linear weighting, the multi-objective optimization model is transformed into a single-objective optimization model. Adjust the weighting coefficients corresponding to total carbon emission cost and total carbon trading cost to form a multi-objective optimization model with adjustable weights.

7. The optimal carbon allocation pricing method applicable to mixed traffic flows according to claim 6, characterized in that, S6, specifically: By setting the carbon allowance price as the optimization variable, an optimization objective function is established for a mixed traffic commuting scenario. Initialize the carbon quota price and the range of values ​​for the objective function, and determine the iteration step size and convergence criterion in the optimization process; An iterative calculation method is used to adjust the carbon allowance price within a range of values ​​and calculate the optimization objective function value under different carbon allowance prices. The values ​​of the objective function are sorted and filtered to determine the minimum objective function value and the corresponding carbon quota price under a given combination of weight coefficients, which is taken as the optimal carbon quota price. By adjusting the weighting coefficients and repeating the optimization process, a Pareto solution set consisting of a series of optimal carbon quota prices is obtained.

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