Urban traffic carbon emission control method and system based on resident personal carbon transaction

Abstract

The invention relates to the technical field of energy conservation and emission reduction, and discloses an urban traffic carbon emission control method and system based on resident personal carbon transactions, and the method comprises the steps: setting a personal carbon transaction pricing mode of an urban traffic system; constructing a network balance model considering heterogeneous users in combination with the personal carbon transaction pricing mode; and solving the network balance model to obtain a carbon quota balance price considering the user heterogeneity in the personal carbon transaction pricing mode, further finding out a relationship between the carbon quota price and the low-carbon travel of the heterogeneous users, and further regulating and controlling the carbon quota to control the travel mode of the heterogeneous users. Therefore, the carbon emission generated by transportation is controlled. According to the invention, travel selection regulation and control of heterogeneous users can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of energy conservation and emission reduction, and in particular to a method and system for controlling carbon emissions from urban transportation based on individual carbon trading of residents. Background Art

[0002] Carbon emissions are an environmental issue that cannot be ignored by every country on a global scale. With the implementation of carbon emission markets such as emission trading plans, total volume control and personal carbon trading mechanisms for downstream consumers have gradually developed and have been introduced into all walks of life. But in general, personal carbon trading in the field of road traffic is still in its early stages, and a unified and complete program system has not yet been formed. Theory, technology, and policy are full of challenges. Faced with the increasingly severe problem of urban transportation carbon emissions, city managers have adopted a series of response strategies, covering policy tools such as transportation technology innovation, public transportation promotion, license plate restrictions, land use planning optimization, shared travel promotion, and congestion charges. However, these measures exposed the following problems during implementation:

[0003] 1) It triggers discussions on social fairness issues and causes great controversy among users;

[0004] 2) Existing carbon emission control measures only have limited effects on a single influencing factor or specific subject, making it difficult to control the total amount of carbon emissions from an overall level;

[0005] 3) The lack of a method to quantitatively calculate the price charged for carbon emissions has resulted in poor implementation of actual carbon emission control measures. Summary of the invention

[0006] The present invention aims to start from the pricing flexibility and wide application scope of the carbon trading system. The present invention provides a method and system for controlling urban transportation carbon emissions based on personal carbon trading of residents, and designs a new personal carbon trading operation model and carbon quota pricing method applicable to all urban travelers, as an important measure for controlling the total amount of urban carbon emissions and regulating personal income.

[0007] The present invention is achieved through the following technical solutions:

[0008] A method for controlling urban transportation carbon emissions based on individual carbon trading of residents, comprising:

[0009] Setting up individual carbon trading pricing models for urban transport systems;

[0010] Combining the individual carbon trading pricing model, a network equilibrium model considering heterogeneous users is constructed;

[0011] Solve the network equilibrium model to obtain the equilibrium price of carbon quota considering user heterogeneity under the personal carbon trading pricing model, and then find out the relationship between the carbon quota price and the low-carbon travel of the heterogeneous users, and then adjust the carbon quota to control the travel mode of the heterogeneous users, thereby controlling the carbon emissions generated by transportation.

[0012] As an optimization, the personal carbon trading pricing model includes the traditional personal carbon trading model, the differential transaction fee rate model and the tiered transaction fee rate model, wherein the traditional personal carbon trading model is expressed as:

[0013] α s =α b =0

[0014] Among them, α b represents the transaction fee rate for buying a unit of carbon quota, α s It represents the transaction fee rate for selling a unit of carbon quota;

[0015] The transaction fee rate model is expressed as:

[0016]

[0017] Among them, η1 and η2 represent the transaction costs of buying a unit carbon quota and selling a unit carbon quota respectively;

[0018] The tiered transaction fee rate model is expressed as:

[0019]

[0020] Among them, k is the cumulative amount of carbon quota consumed by the user, It represents the number of carbon quotas initially allocated, hc represents the minimum value of the second-tier score segment, fd1 represents the transaction fee for buying a unit carbon quota in the first-tier score segment, and fd2 represents the transaction fee for buying a unit carbon quota in the second-tier score segment.

[0021] As an optimization, the specific process of constructing a network equilibrium model considering heterogeneous users in combination with the personal carbon trading pricing model is as follows:

[0022] Constructing a traffic network flow pattern set Φ based on the personal carbon trading pricing model;

[0023]

[0024] represents the traffic flow of path r between the origin and destination pair w, v a represents the traffic flow on road section a, q w represents the travel demand between the origin and destination pair w; R wrepresents the set of all valid paths between the origin and destination pair w; W represents the set of origin and destination pairs; k a It represents the amount of carbon quota collected on road section a; K represents the total amount of carbon quota issued by the government to travelers in the urban transportation system;

[0025] Between OD pairs w∈W, for the mth type of travelers, the perceived cost function perceived by the travelers when choosing path r is It is expressed as:

[0026]

[0027] in represents the fixed cost term, i.e., the cost function for the mth user to choose path r between the destination pair w; is a parameter term; i∈[t,y,w,in,e,pe],t a (v a ) is the travel time of road section a; y a The monetary cost of traveling on the road segment; represents the carbon quota cost of the path; income is the income level; edu is the education level; pre is the preference for public transportation; is the error term; represents the correlation coefficient between the path and the road segment, Represents a road segment a∈A on path r∈R w superior, Indicates that the road segment a∈A is not on the path r∈R w superior; They are the time perception coefficient and the money perception coefficient respectively; The parameters represent the carbon quota cost of the route, the user's preference for public transportation, and the user's education level;

[0028] Design a quantitative indicator of carbon quota cost for heterogeneous users based on carbon quota collection, the quantitative indicator of carbon quota cost for heterogeneous users

[0029]

[0030] Among them, α b Represents the transaction fee rate for buying a unit of carbon quota; α s represents the transaction cost rate of selling a unit carbon quota; p represents the market price of a unit carbon quota; represents the amount of carbon quota collected on the path r between the origin and destination pair w in the traffic network carrying the traffic network flow; W represents the set of all origin and destination pairs OD in all the traffic network flows; R w represents the set of all valid paths between the origin and destination pair w;

[0031] The network equilibrium model considering heterogeneous users under certain conditions of the total amount of urban carbon emissions is constructed, and the network equilibrium model includes a traffic allocation model and an equilibrium model of the carbon trading market, wherein the traffic allocation model is expressed as:

[0032]

[0033] The equilibrium model of the carbon trading market is expressed as:

[0034]

[0035] Wherein, m represents the category of heterogeneous users, r represents the travel path, i.e., the path of the origin-destination pairs in the traffic network carrying the traffic network flow, represents the probability that the mth heterogeneous user chooses path r between the origin and destination pair w, represents the travel demand of the mth heterogeneous user for w at the starting and ending points; K represents the total amount of carbon quotas issued by the government to travelers in the urban transportation system, Ω represents the number of people, k a Then it represents the amount of carbon quota collected on road section a, a∈A, A represents the edge set of the traffic network carrying the traffic network flow, v a represents the traffic flow on road section a, It represents the traffic of the mth heterogeneous user choosing path r between the origin and destination pair w.

[0036] As an optimization, The specific expression is:

[0037]

[0038] in, |R w | represents the set R w The dimension of , Pr represents probability; and are the utilities of selecting path r and path l for the mth heterogeneous user, is a matrix of fixed costs of all valid paths between the m-th user and the destination pair w; represents the number of users of the mth category between the starting and ending points w |R w The set of fixed costs of | valid paths is a row vector;

[0039] The probability that the mth type of traveler chooses a specific path r can be expressed as:

[0040]

[0041] in, is the cost function for the mth user to select path r between the origin and destination pair w;

[0042]

[0043] Among them, θ>0 represents the discrete parameter of the traveler's perception error.

[0044] As an optimization, the specific process of solving the network balancing model is:

[0045] Quantify the network equilibrium model to obtain a random user equilibrium state constraint model

[0046] In a given carbon quota scheme (K, k a ) under the condition of the quantized random user equilibrium state constraint model Perform equivalent transformation to achieve the random user equilibrium state of the traffic network flow after the introduction of the carbon trading mechanism, and derive the equilibrium price model for carbon quotas;

[0047] Solve the carbon quota equilibrium price model, and finally obtain the relationship between the equilibrium flow and the equilibrium carbon quota price, so as to find out the relationship between the carbon quota price and the low-carbon travel of the heterogeneous users.

[0048] As an optimization, the network equilibrium model is quantified to obtain a random user equilibrium state constraint model The specific process is:

[0049] Construct the perception function τ of the travel time and cost of the road segment a,m (x), let the perception function be the first perception function:

[0050]

[0051] in, is the time perception coefficient, t a (x) is the Federal Highway Administration function of road section a under any traffic mode, that is, the BPR function, which is used to calculate the travel time of users on road section a. is the currency perception coefficient, y a For monetary expenses;

[0052] Constructing perception functions for travel time and cost of road segments in a “nominal” road network Let this perception function be the second perception function:

[0053]

[0054] in, represents the travel time cost and monetary cost of road section a in a non-congested state, and v a represents the traffic flow on road section a; express The first derivative of ;

[0055] Establishing a random user equilibrium state constraint model

[0056]

[0057] Where r represents the travel path, w represents the origin-destination pair, and W represents the set of all origin-destination pairs OD in the traffic network flow; R w represents the set of all valid paths between the origin and destination pair w, represents a continuously differentiable vector function defined based on path flow, and represents the number of |R between the starting and ending points w w |Flow-based vector function of valid paths;

[0058]

[0059] in, It represents the time and monetary cost of this "nominal" road network. represents the correlation coefficient between the path and the road segment, Represents a road segment a∈A on path r∈R w superior, Indicates that the road segment a∈A is not on the path r∈R w superior.

[0060] As an optimization, given the carbon quota scheme (K, k a ) under the condition of the quantized random user equilibrium state constraint model The equivalent transformation is performed so that the traffic network flow after the introduction of the carbon trading mechanism reaches a random user equilibrium state, and the specific process of deriving the equilibrium price model of carbon quotas is as follows:

[0061] Establish an optimization objective function that minimizes the travel cost of the mth type of travel user under the carbon trading model: minZ(f m );

[0062] Construct a Lagrangian function of the mathematical model for minimizing the travel cost of the mth type of travel user under the carbon trading model:

[0063]

[0064] in, represent the Lagrange multiplier variables respectively;

[0065] Set the quantitative index of the transaction process of the travel user on different paths between the starting and ending points w, so as to characterize the carbon trading process of the travel user on different paths between the starting and ending points w. for:

[0066]

[0067] make Then there is and

[0068] It follows from this that:

[0069]

[0070] represents the amount of carbon quota collected on path r between OD pair w, p is the market price of unit carbon quota; r∈[1,2,···,R w ], The mth user chooses the first |R between OD and W. w |The carbon trading process of the function when there are effective paths; It means that the mth user chooses the |Rth w | The Lagrange multiplier variable of the Lagrange function of the mathematical model for minimizing the travel cost when there are | effective paths;

[0071] The Lagrangian function The Carlo Demand-Kuhn-Tucker condition is brought into the above formula, so that the traffic network flow after the introduction of the carbon trading mechanism reaches a random user equilibrium state;

[0072] According to the principle of random network equilibrium and the traffic network flow after the introduction of the carbon trading mechanism and reaching the random user equilibrium state, the carbon quota equilibrium price model is derived. The specific expression of the carbon quota equilibrium price model is:

[0073]

[0074] are the time perception coefficient and the money perception coefficient, respectively, * is the carbon quota price.

[0075] As an optimization, the specific process of solving the carbon quota equilibrium price model is:

[0076] T1. Determine the transaction mode in the carbon trading pricing model, and search for the effective path for each type of user in combination with the constraints of the effective path;

[0077] T2, initialize the parameters of the outer loop, and set the initial carbon quota price p 0>0, outer loop iteration number n=1, outer loop iteration coefficient First tolerance error n is a positive number;

[0078] T3, call the MSA algorithm to calculate the current carbon quota price p n Stochastic user equilibrium section flow solution under transaction mode a (p n );

[0079] T4. Test the convergence of the carbon quota equilibrium price model. If Then output the current carbon quota price p * =p n And the current equilibrium flow solution v a (p n ), otherwise, jump to T5;

[0080] T5. Update the current carbon quota price according to p n+1 =max{0, p n +α n (∑ a∈A v a (p n ) a -K)} updates the carbon quota price, then sets n=n+1, and returns to T3.

[0081] As an optimization, in T3, the MSA algorithm is called to calculate the current carbon quota price p n Stochastic user equilibrium section flow solution under transaction mode a (p n ) is as follows:

[0082] T3.1, initialize the parameters of the inner loop, let the initial section flow v a =0, the number of outer loop iterations h=1, the inner loop iteration coefficient Second tolerance error n is a positive number, and then according to the current trading model and the current carbon quota price p n Calculate the generalized travel cost of each road section under zero flow state, and perform different flow loading based on the needs of different types of heterogeneous users to obtain the allocated flow of each road section

[0083] T3.2. Allocation of traffic according to the current road section Call the cost function and the perceived cost function to update the generalized travel cost of the road segment;

[0084] T3.3, based on the latest generalized travel costs of each road section updated in T3.2, the flow allocation process is performed again according to the travel needs of different types of heterogeneous users to obtain the additional flow solution of each road section.

[0085] T3.4, update the flow through iterative weighted method, the specific formula is:

[0086] T3.5. Allocation of traffic to each road section The convergence of is tested. If Then output the latest distribution flow solution for each road section As the random user equilibrium road flow solution under the current carbon quota price and trading mode, v a (p n ), otherwise, set h=h+1 and jump to T3.2.

[0087] The present invention also discloses an urban traffic carbon emission control system based on individual carbon trading for residents, which is used to execute the aforementioned urban traffic carbon emission control method based on individual carbon trading for residents, comprising:

[0088] Carbon trading model setting module, used to set the individual carbon trading pricing model for the urban transportation system;

[0089] A modeling module, used to construct a network equilibrium model considering heterogeneous users in combination with the individual carbon trading pricing model;

[0090] A solution module is used to solve the network equilibrium model to obtain the carbon quota equilibrium price considering user heterogeneity under the personal carbon trading pricing model, and then find out the relationship between the carbon quota price and the low-carbon travel of the heterogeneous users, and then adjust the carbon quota to control the travel mode of the heterogeneous users, thereby controlling the carbon emissions generated by transportation.

[0091] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0092] The present invention can enrich the trading model of personal carbon trading in the field of transportation. Based on the premise that personal carbon quotas are tradable, the present invention proposes three personal carbon trading models: the traditional personal carbon trading model with equal carbon quota buying and selling rates, the differential transaction fee rate model with a buying rate greater than the selling rate, and the step transaction fee rate model with a progressively increasing transaction fee rate for carbon quota buyers only.

[0093] The present invention innovatively introduces the personal carbon trading model into the transportation field to achieve urban carbon emission control. Based on the fixed total carbon quota of the entire transportation system, the present invention sets a quantitative indicator of the carbon quota cost of heterogeneous users based on the transaction rate, thereby constructing a new transportation network based on carbon trading.

[0094] The present invention proposes a new method for solving the equilibrium state of heterogeneous user networks under the carbon trading model and the corresponding carbon quota equilibrium price. The present invention first creates a carbon trading process characterization function for users on different paths to achieve the equilibrium state of the transportation network under the given carbon quota scheme, so as to further calculate the carbon quota equilibrium price under the equilibrium state.

[0095] Based on the above trading mode settings, the transportation network construction method based on carbon trading, and the carbon quota equilibrium price solution method, the present invention will achieve the travel choice regulation of heterogeneous users, that is, guide users to choose transportation methods with lower carbon emissions, thereby achieving total control of urban carbon emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0097] Figure 1 This is a schematic diagram of the framework of the personal carbon trading system;

[0098] Figure 2 Schematic diagram of the algorithm flow for solving the problem;

[0099] Figure 3 It is the node diagram of the urban transportation network;

[0100] Figure 4 It is a multi-modal transportation network topology diagram;

[0101] Figure 5 The distribution diagram of the transport mode share rate under four scenarios, where (a) indicates that personal carbon trading is not implemented; (b) indicates traditional personal carbon trading; (c) indicates differential transaction cost rate; and (d) indicates tiered transaction cost rate.

[0102] Figure 6 The equilibrium price distribution diagram of carbon quotas, (a) represents the differential transaction cost rate model, (b) represents the ladder transaction cost rate model;

[0103] Figure 7 It is a distribution map of low-carbon travel structure of the transportation system;

[0104] Figure 8 Schematic diagram of the transportation network and independent network. DETAILED DESCRIPTION

[0105] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0106] This embodiment 1 provides a method for controlling urban transportation carbon emissions based on individual carbon trading of residents, including:

[0107] S1. Set up a personal carbon trading pricing model for the urban transportation system.

[0108] In some embodiments, the personal carbon trading pricing model includes a traditional personal carbon trading model, a differential transaction fee rate model, and a tiered transaction fee rate model.

[0109] 1) Traditional individual carbon trading model

[0110] It refers to the mode in which users buy or sell personal carbon quotas on the personal carbon trading management platform according to the carbon quota price given by the market. In essence, the carbon emission right fee α per unit for all users in this mode is exactly the same. The transaction fee rate under this mode is in the form of:

[0111] α s =α b =0

[0112] Among them, α b represents the transaction fee rate for buying a unit of carbon quota, α s Represents the transaction fee rate for selling a unit of carbon quota.

[0113] 2) Pricing based on different transaction fee rates

[0114] By setting different buying and selling transaction fee rates, users’ transaction fees are charged according to different proportions. Generally, let α b >α s That is, the cost of buying the same amount of carbon quota is higher than the cost of selling the same quota. In order to avoid losses, users will carefully choose carbon quota transactions.

[0115]

[0116] Among them, η1 and η2 represent the transaction costs of buying a unit carbon quota and selling a unit carbon quota respectively;

[0117] 3) Pricing based on the tiered transaction fee rate model

[0118] Set a stage - increasing transaction fee rate based on the additional purchase amount exceeding the initial carbon quota. This model is only applicable to carbon quota buyers. As the quantity of additional carbon quota purchased by users increases, the price per unit of carbon quota increases accordingly. It is intended to levy additional transaction fees in this way to subsidize the operating system costs, while imposing greater economic constraints on high - carbon emitters, thereby forming an indirect penalty mechanism to guide high - carbon emitters to consciously control and reduce their carbon emission behaviors.

[0119] In the first stage, when an individual's cumulative carbon quota consumption does not exceed the per - capita allocation benchmark, no transaction fees are levied. To ensure that the first and second stages can cover most travelers, the coverage ratio is usually set between 70% and 90%. In this study, 70% is selected as the reference value. Regarding the specific number of segments in the second stage, it is as follows:

[0120] For the entire travel group, consider the historical carbon emission level of an individual as a random variable. Based on the proportion of the number of individuals with a historical carbon emission level lower than the threshold of the number of segments in the second stage to the total number of people, use this probability distribution to describe and quantify the distribution of residents with different carbon emission levels. The probability distribution function of the historical travel carbon emissions of residents is:

[0121] F(hc) = p{HC < hc};

[0122] In the formula, HC represents the random variable of the historical carbon emission level of individual travel in the group, hc is the number of segments in the second stage of the ladder - type charging, and the above - mentioned probability distribution function can be determined through regression analysis of sample data. Then, let p{HC < hc} = 70%, and the number of segments in the second stage can be obtained.

[0123] Through the above analysis, finally, the ladder - type transaction fee rate setting function can be obtained as follows:

[0124]

[0125] Among them, k is the quantity of carbon quota consumed by the user cumulatively, represents the initially allocated carbon quota quantity, hc represents the minimum value of the second - stage score segment, fd1 represents the transaction fee for purchasing a unit of carbon quota in the first - stage score segment, and fd2 represents the transaction fee for purchasing a unit of carbon quota in the second - stage score segment.

[0126] S2. Combine the above - mentioned individual carbon trading pricing model to construct a network equilibrium model considering heterogeneous users.

[0127] The specific process is as follows:

[0128] 1) Construct a set Φ of traffic network flow patterns based on the above - mentioned individual carbon trading pricing model. This process is mainly a method for constructing traffic network flow based on carbon trading;

[0129]

[0130] represents the traffic flow of path r between the origin and destination pair w, v a represents the traffic flow on road section a, q w represents the travel demand between the origin and destination pair w; R w represents the set of all valid paths between the origin and destination pair w; W represents the set of origin and destination pairs; k a Then it represents the amount of carbon quota collected on road section a; K represents the total amount of carbon quota issued by the government to travelers in the urban transportation system; specifically, a directed graph G = (N, A) is used to represent the urban road traffic network, N, A represent the node set and edge set of the traffic network respectively, W represents the set of all origin-destination pairs (OD) (the origin-destination pair refers to the starting point and end point of a process or activity. In the urban traffic survey, the origin-destination survey (OD survey) is a survey of the entire process of urban residents' travel and cargo flow from the beginning to the end. Through the origin-destination survey, we can grasp the distribution law of urban traffic in time and space, find the main traffic problems, and provide decision-making basis for traffic planning and urban development), and use q w Quantify the travel demand between OD pairs (origin and destination pairs) w∈W. R=(R w ,w∈W)(or expressed as R=(R w , w∈W), that is, R w Equivalent to R w ) is a set of effective paths. An effective path refers to the path that travelers will consider in the actual travel decision-making process. Each effective path represents a complete travel plan that includes specific transportation modes, driving time, transfer time, travel cost, and carbon quota cost. w (R w ) represents the set of all valid paths between OD pairs w∈W. The valid path set is the subset of all valid paths.

[0131] In the urban transportation network system that implements the personal carbon trading system, the decision-making behavior of travelers (users) is affected by factors such as travel time, monetary cost, and carbon quota cost. According to the random utility theory, for a specific OD pair w∈W, for the mth type of travelers, the perceived cost function when choosing path r is It can be expressed as:

[0132]

[0133] in represents a fixed cost item; is a parameter term; i∈[t,y,w,in,e,pe],t a (v a) is the travel time of the road section; y a The monetary cost of traveling on the road segment; represents the carbon quota cost of the path; The superscript in stands for income, income is the income level; edu is the education level; pre is the preference for public transportation; is the error term; represents the correlation coefficient between the path and the road segment, Represents a road segment a∈A on path r∈R w superior, Indicates that the road segment a∈A is not on the path r∈R w superior; They are the time perception coefficient and the money perception coefficient respectively; The parameters represent the carbon quota cost of the route, the user's preference for public transportation, and the user's education level;

[0134] (K,k a ) is used to represent the total amount of carbon quotas and collection schemes in the personal carbon trading system, where K represents the total amount of carbon quotas issued by the government to travelers in the urban transportation system Ω represents the number of people; k a It represents the amount of carbon quota collected on the road section a∈A, and the amount of carbon quota collected is only related to the emission. When different transportation modes run in independent sub-network layers, Figure 8 As shown, once the factors such as the transportation mode and length of a certain road section in the directed graph are determined, the amount of carbon quota collected corresponding to the road section can be determined.

[0135] use Represents a path r∈R w Traffic flow on the a and t a They represent the traffic flow and travel time on road section a∈A respectively.

[0136] Therefore, we can design a feasible network flow morphology set Φ (assuming it is non-empty) under a given carbon quota scheme:

[0137]

[0138] 2) Design a quantitative indicator for the carbon quota cost of heterogeneous users based on transaction rates. This process is mainly the design process of a quantitative indicator for the carbon quota cost of heterogeneous users based on transaction rates.

[0139] Quantify the carbon quota cost of heterogeneous users The carbon quota cost considering transaction costs can be calculated as:

[0140]

[0141] in, It represents the amount of carbon quota collected on the path r between OD pair w, and p is the market price of a unit carbon quota.

[0142] definition Then the quantitative index of carbon quota cost for heterogeneous users can be obtained:

[0143]

[0144] in:

[0145]

[0146] Among them, α b Represents the transaction fee rate for buying a unit of carbon quota; α s represents the transaction cost rate of selling a unit carbon quota; p represents the market price of a unit carbon quota; represents the amount of carbon quota collected on the path r between the origin and destination pair w in the traffic network carrying the traffic network flow; W represents the set of all origin and destination pairs OD in all the traffic network flows; R w represents the subset of all valid paths between the origin and destination pair w.

[0147] 3) Constructing the network equilibrium model considering heterogeneous users under certain conditions of total urban carbon emissions. This process mainly constructs a network equilibrium state based on heterogeneous users under certain conditions of total urban carbon emissions.

[0148] The network equilibrium model includes a flow distribution model and a carbon trading market equilibrium model, wherein the flow distribution model is expressed as:

[0149]

[0150] The equilibrium model of the carbon trading market is expressed as:

[0151]

[0152] Wherein, m represents the category of heterogeneous users, r represents the travel path, i.e., the path of the origin-destination pairs in the traffic network carrying the traffic network flow, represents the probability that the mth heterogeneous user between the origin and destination pair w chooses path r, represents the travel demand of the mth heterogeneous user for w at the starting and ending points; K represents the total amount of carbon quotas issued by the government to travelers in the urban transportation system, Ω represents the number of people, k a Then it represents the amount of carbon quota collected on road section a, a∈A, A represents the edge set of the traffic network carrying the traffic network flow, v arepresents the traffic flow on road section a, It represents the traffic of the mth heterogeneous user choosing path r between the origin and destination pair w.

[0153] p=p{HC <hc}

[0154] Where HC represents the random number of historical carbon emission levels of individual travel of residents in the group. The above probability distribution function can be determined through sample data regression analysis.

[0155] In the urban transportation network system that implements the personal carbon trading plan, the decision-making behavior of travelers is affected by factors such as travel time, monetary cost, and carbon quota cost. Different factors have different effects on the travel choices of different travelers. The survey method of behavior and intention survey (RP+SP) based on the background of personal carbon trading can classify travelers into potential categories and divide travelers into m groups. Based on the random utility maximization theory, the probability of OD for the mth type of travelers to choose path r between w can be calculated:

[0156]

[0157] in, |R w | is the set R w Dimension; Pr is probability; and The utility of choosing path r and path l for the mth type of traveler respectively. is a matrix of fixed costs of all valid paths between point pairs w; represents the number of |R between the starting and ending points w w |The set of fixed costs of valid paths, a row vector.

[0158] Assuming that the random perception error term of travelers follows the Gumbel distribution, the probability that the mth type of travelers choose a specific path r (travel plan r) can be expressed as:

[0159]

[0160] in, is the cost function for the mth user to select path r between the origin and destination pair w:

[0161]

[0162] Where θ>0 represents the discrete parameter of the traveler's perception error. Here, r′ is r.

[0163] In a given carbon quota scheme (K, k a) condition, when the traffic network reaches equilibrium, the internal traffic distribution of the network must follow the traffic distribution rule of random user equilibrium:

[0164]

[0165] In a given carbon quota scheme (K, k a ) condition, when the transportation network reaches equilibrium, its internal flow distribution and carbon quota price should meet the following network equilibrium conditions:

[0166] Traffic distribution conditions:

[0167] Equilibrium conditions of the carbon trading market:

[0168]

[0169] The traffic allocation condition means that when the traffic network reaches equilibrium, the traffic distribution within the network must follow the traffic allocation rule of random user equilibrium. The equilibrium condition of the carbon trading market means that only when all carbon quotas in the carbon trading market are completely consumed, that is, when the supply and demand in the market are balanced, the equilibrium price of carbon quotas will be positive, and the carbon trading mechanism can effectively play a role in regulating travel demand.

[0170] S3. Solve the network equilibrium model to obtain the equilibrium price of the carbon quota considering user heterogeneity under the personal carbon trading pricing model, and then find out the relationship between the carbon quota price and the low-carbon travel of the heterogeneous users, and then adjust the carbon quota to control the travel mode of the heterogeneous users, thereby controlling the carbon emissions generated by transportation.

[0171] The specific process is as follows:

[0172] S3.1. Quantify the network equilibrium model to obtain a random user equilibrium state constraint model The process is mainly based on the quantification of the network equilibrium state of carbon trading;

[0173] The specific process is:

[0174] a. Constructing the perception function τ of the travel time and cost of the road section a,m (x), let the perception function be the first perception function.

[0175]

[0176] in, is the time perception coefficient, t a(x) is the Federal Highway Administration function of road segment a under any traffic mode, namely the BPR function, which is used to calculate the travel time of users on road segment a and can be regarded as a continuously differentiable and strictly monotonically increasing function of the road segment flow. is the currency perception coefficient, y a For monetary expenses;

[0177] b. Constructing the perception function of travel time and cost of the road segments of the “nominal” road network Let this perceptual function be the second perceptual function.

[0178] The "nominal" road network is an equivalent road network. It is to construct an equivalent / alternative road network that maintains the same topological structure as the original road network. In the equivalent / alternative new road network, except for the adjustment of the time cost function, the other properties are exactly the same as the original road network.

[0179] because and a are all positive constants that are independent of x. x is the independent variable of the BPR function and is the traffic volume passing through the road section at that time. Therefore, τ a,m (x) and t a (x) is a continuously differentiable and strictly monotonically increasing function. Based on the above characteristics, we can construct a perception function of the travel time and cost of a "nominal" road network that maintains the same topological structure as the original road network.

[0180]

[0181] in, represents the travel time cost and monetary cost of road section a in a non-congested state. Given that τ a,m (x) is a continuously differentiable and strictly increasing function, has the same mathematical properties, even in the "nominal" network, except for the time cost function, the other properties are exactly the same as the original network, and v a represents the traffic flow on road section a, express The first derivative of ;

[0182] c. Establish a random user equilibrium state constraint model

[0183] For any non-zero feasible path flow pattern, there must be a continuously differentiable vector function defined based on the path flow It can be regarded as a cost function based on the path. Under the action of this vector function, the path flow can maintain the state of stochastic user equilibrium, that is, the m-th user group follows the following SUE condition (SUE (Stochastic User Equilibrium) condition means that in the traffic network, each traveler chooses the optimal path they think, that is, each traveler believes that the path they choose has the smallest random perceived impedance. SUE condition is an important concept in the traffic assignment model, which is used to describe the path selection behavior of travelers in a random environment. The following formula is used to quantify SUE condition):

[0184]

[0185] Where r represents the travel path, w represents the origin-destination pair, and W represents the set of all origin-destination pairs OD in the traffic network flow; R w represents the set of all valid paths between the starting and ending point pair w, represents a continuously differentiable vector function defined based on path flow, and represents the number of |R between the starting and ending points w w |Flow-based vector function of valid paths;

[0186] According to existing research results, for any non-zero feasible path flow pattern, there must be a continuously differentiable vector function defined based on the path flow It can be regarded as a path-based cost function, under which the path traffic can maintain a random user equilibrium state.

[0187] in,

[0188] in, It represents the time and monetary cost of this "nominal" road network. The "-" with a dash is the parameter of the "nominal" road network. Here a = 1, indicating road section 1; represents the correlation coefficient between the path and the road segment, Represents a road segment a∈A on path r∈R w superior, Indicates that the road segment a∈A is not on the path r∈R w Here, r = 1, which is the correlation coefficient of the first path.

[0189] S3.2, given the carbon quota scheme (K, k a ) under the condition of the quantized random user equilibrium state constraint model Perform equivalent transformation to achieve the random user equilibrium state of the traffic network flow after the introduction of the carbon trading mechanism, and derive the equilibrium price model for carbon quotas;

[0190] The specific process is:

[0191] S3.2.1. Establish an optimization objective function that minimizes the travel cost of the mth type of travel user under the carbon trading model: minZ(f m );

[0192] a. Construct a Lagrangian function of the mathematical model for minimizing the travel cost of the mth type of travel users (heterogeneous users, heterogeneous users refer to the classification of all travel users) under the carbon trading model:

[0193]

[0194] in, represent the Lagrange multiplier variables respectively;

[0195] b. Set the quantitative indicators of the transaction process of the travel users on different paths between the starting and ending points w This is used to characterize the carbon trading process of travel users on different paths between the starting and ending points w.

[0196] Since for a certain type of user, as the carbon quota cost of a path increases, the probability of choosing the path should decrease, the cost perception parameter term for the carbon quota cost is non-negative, that is, At this point, a non-negative function can be constructed to describe the carbon trading process of travelers on different paths between OD pairs w:

[0197]

[0198] At this time, Then there is because Then there is Satisfy the complementary slackness constraint.

[0199] It follows from this that:

[0200]

[0201] represents the amount of carbon quota collected on path r between OD pair w, p is the market price of unit carbon quota; r∈[1,2,···,R w ], The mth user chooses the first |R between OD and W. w |The carbon trading process of the function when there are effective paths;

[0202] Describe the transaction process of travelers on different paths between OD pairs w. When travelers sell carbon quotas, When travelers buy carbon quotas,

[0203] It means that the mth user chooses the |Rth w |The Lagrange multiplier variable of the Lagrange function of the mathematical model for minimizing the travel cost when there are | valid paths.

[0204] These two parameters Related to the foregoing, namely:

[0205] Establish an optimization objective function that minimizes the travel cost of the mth type of travel user under the carbon trading model: minZ(f m );

[0206] Construct a Lagrangian function of the mathematical model for minimizing the travel cost of the mth type of travel user under the carbon trading model:

[0207]

[0208] in, represent the Lagrange multiplier variables respectively;

[0209] That is to say,

[0210]

[0211] when r=|R w |It is Furthermore, the KKT condition of the Lagrangian function is derived:

[0212]

[0213] r′∈R w ,w′∈W

[0214]

[0215] Substitute the KKT condition into the random user equilibrium state constraint model, that is, substitute the KKT condition into the SUE condition formula (substitute the Lagrangian function The Carlo-Kuhn-Tucker condition is substituted into the above formula to construct the carbon trading process characterization function It is possible to achieve a random user equilibrium state for the traffic network flow after the introduction of the carbon trading mechanism, that is, to achieve a random user equilibrium state for the traffic network flow after the introduction of the carbon trading mechanism.

[0216] c. When the carbon quota price is 0, the carbon quota price is naturally unique, and the carbon quota scheme has no regulatory effect on the market. Therefore, only the case where the carbon quota price is greater than 0 is discussed. For the two paths r1, r2∈R between OD and w∈W with different carbon quota collection amounts w , according to the principle of random network equilibrium:

[0217]

[0218] r∈R w ,w∈W

[0219] The expression of carbon quota equilibrium price (carbon quota equilibrium price model) can be further derived as follows:

[0220]

[0221] are the time perception coefficient and the currency perception coefficient, y a is the monetary cost, p * is the carbon quota price.

[0222] because but At this time, the equilibrium price of carbon quota is uniquely determined by the above formula, that is, it is uniquely determined by the path equilibrium flow result.

[0223] S3.3. Solve the carbon quota equilibrium price model, and finally obtain the relationship between the equilibrium flow and the equilibrium carbon quota price, so as to find out the relationship between the carbon quota price and the low-carbon travel of the heterogeneous users.

[0224] The algorithm of the present invention is a double-loop solution algorithm, which includes two parts. The inner loop part uses the continuous average algorithm (MSA algorithm) to perform random user equilibrium traffic flow allocation for heterogeneous users, and the outer loop part is a carbon quota price iteration process based on the Lagrange dual algorithm. Each outer loop process needs to call the MSA algorithm, i.e., the inner loop module. The algorithm flow is as follows: Figure 2 , the steps of the algorithm are as follows:

[0225] T1. Determine the trading model in the carbon trading pricing model, and search for the effective path for each type of user in combination with the constraints of the effective path; the constraints of the effective path refer to the travel cost of the path being within the acceptable range for the user.

[0226] T2, initialize the parameters of the outer loop, and set the initial carbon quota price p 0 >0, outer loop iteration number n=1, outer loop iteration coefficient First tolerance error n is a positive number;

[0227] T3, call the MSA algorithm to calculate the current carbon quota price p n Stochastic user equilibrium section flow solution under transaction mode a (p n );

[0228] Inner loop: Use the MSA algorithm to allocate and update traffic in the multimodal transportation super network.

[0229] T3.1, initialize the parameters of the inner loop, let the initial section flow v a =0, the number of outer loop iterations h=1, the inner loop iteration coefficient Second tolerance error n is a positive number, and then according to the current trading model and the current carbon quota price p n Calculate the generalized travel cost of each road section under zero flow state, and perform different flow loading based on the needs of different types of heterogeneous users to obtain the allocated flow of each road section

[0230] T3.2. Update the generalized travel cost of the road segment: based on the current allocated flow of the road segment The cost function and the perception cost function are called to update the generalized travel cost of the road section; the generalized travel cost includes time cost, perception cost, road maintenance cost and other costs in addition to the ticket price.

[0231] T3.3, determine the descending direction: Based on the latest generalized travel costs of each road section updated in T3.2, perform different flow allocation processes again according to the travel needs of different types of heterogeneous users to obtain the additional flow solution for each road section.

[0232] In this embodiment, since there are three types of transaction modes, heterogeneous users are divided into three categories. Then, T3.3 is the latest generalized travel cost of each road section updated in step ②. The flow allocation process is performed three times again according to the travel needs of the three types of user groups to obtain the additional flow solution for each road section.

[0233] T3.4, Update the traffic flow solution of the road section: Update the traffic flow through the iterative weighted method. The specific formula is:

[0234] T3.5, Convergence test: Allocation of flow to each section The convergence of is tested. If Then output the latest distribution flow solution for each road section As the random user equilibrium road flow solution under the current carbon quota price and trading mode, v a (p n), otherwise, set h=h+1 and jump to T3.2.

[0235] T4. Convergence test: Test the convergence of the carbon quota equilibrium price model. Then output the current carbon quota price p * =p n And the current equilibrium flow solution v a (p n ), otherwise, jump to T5;

[0236] T5. Update the current carbon quota price: according to p n+1 =max{0, p n +α n (∑ a∈A v a (p n ) a -K)} updates the carbon quota price, then sets n=n+1, and returns to T3.

[0237] Refer to existing research using non-negative projection Updates to carbon allowance prices. is the Lagrangian dual function of Model_1, Then there is therefore

[0238] Embodiment 2 further discloses an urban transportation carbon emission control system based on individual carbon trading for residents, which is used to execute an urban transportation carbon emission control method based on individual carbon trading for residents in Embodiment 1, including:

[0239] Carbon trading model setting module, used to set the individual carbon trading pricing model for the urban transportation system;

[0240] A modeling module, used to construct a network equilibrium model considering heterogeneous users in combination with the individual carbon trading pricing model;

[0241] A solution module is used to solve the network equilibrium model to obtain the carbon quota equilibrium price considering user heterogeneity under the personal carbon trading pricing model, and then find out the relationship between the carbon quota price and the low-carbon travel of the heterogeneous users, and then adjust the carbon quota to control the travel mode of the heterogeneous users, thereby controlling the carbon emissions generated by transportation.

[0242] The total carbon quota for one cycle (one week) in the urban transportation sector of Sichuan Province is 44.52 tons, and the free carbon quota per person per cycle is For example, users are divided into three groups (represented by M1, M2, and M3 respectively), and carbon quotas are collected in real time by monitoring and deducting travel activities based on the user's travel mileage and the carbon emission intensity of different transportation tools. The urban transportation network is abstracted into a directed graph G = (N, A) consisting of a node set N and an edge set A, such as Figure 3 By introducing virtual nodes and transfer segments, the multi-modal urban transportation network system is transformed into a transportation super network structure consisting of multiple interrelated transportation sub-network layers, such as Figure 4 .

[0243] (1) Transaction fee setting for different carbon trading models

[0244] Scenario 1: Control scenario, that is, the carbon quota cost is not considered.

[0245] Scenario 2: Using the traditional individual carbon trading model, that is, not considering transaction fees, α s =α b =0.

[0246] Scenario 3: Differential transaction fee rate mode, set α s =0.2, α b =0.6.

[0247] Scenario 4: Tiered transaction fee rate mode. The tiered transaction fee rate setting scheme is as follows:

[0248]

[0249] (2) Stochastic user equilibrium allocation of traffic flow considering heterogeneous users under the carbon trading model

[0250] The network equilibrium model construction method proposed in the present invention is used to perform random user equilibrium allocation of traffic flow for the above four scenarios, and then the share rate of each transportation mode in the urban transportation network is obtained, such as Figure 4 It can be seen that after the implementation of the three personal carbon trading models, the proportion of high-carbon emission car travel in the three user groups has dropped significantly, and then turned to low-carbon emission subway travel.

[0251] (3) Carbon quota balance calculation

[0252] According to the results of random user equilibrium traffic flow allocation for heterogeneous users, the carbon quota equilibrium price solution algorithm proposed in the present invention is adopted, and the carbon quota equilibrium price that meets the convergence conditions is obtained through continuous iteration, as shown in the following table.

[0253]

[0254] (4) Analysis on the effectiveness of transaction costs in regulating the carbon trading market

[0255] For the differential transaction fee rate mode, set the buy rate α between [0,1] with an interval of 0.1 b and selling rate α s For the tiered transaction fee model, set the second tier buy rate α between [0,1] with an interval of 0.1 b2 and the third-tier buy-in rate α b3 Take the value, and α b2 <α b3 The equilibrium price of carbon quotas, total user costs, system travel structure and platform revenue are calculated under different transaction cost rates.

[0256] From the perspective of carbon quota equilibrium price:

[0257] from Figure 6 (a) Under the differential transaction fee rate model, it can be seen that the equilibrium price of carbon quotas is affected by changes in buying and selling rates, and the trading plan can be adjusted accordingly to achieve more effective market regulation.

[0258] from Figure 6 (b) It can be seen that under the tiered transaction fee rate model, as the second tier buying fee rate α b2 and the third-tier buy-in rate α b3 The growth of carbon quota market equilibrium price p * Therefore, under the trading model, macro-control of the carbon trading market can also be achieved by adjusting the transaction fee rate.

[0259] From the perspective of system travel structure:

[0260] In α b =α s =0, that is, when the traditional individual carbon trading model is adopted, the low-carbon travel structure of the system reaches the minimum value. This shows that after the introduction of the transaction cost rate, the overall low-carbon travel ratio has been significantly improved. Figure 7 (a), it can be seen that under the differential transaction cost rate model, the low-carbon travel structure changes with the buying rate α b The sales rate α s With the growth of low-carbon travel, the structure of low-carbon travel first rises, then falls, and then rises again.

[0261] like Figure 7 As shown in (b), for the step transaction fee rate model, it can be observed that the low-carbon travel structure changes with the second step buying fee α b2 With the increase of the third-tier buy-in rate α b3 When only α b2When it increases, the difference in rates between the two tiers decreases, resulting in a relatively weaker penalty for users who exceed the second tier emissions, making more users inclined to purchase carbon quotas, thus causing a decrease in the overall low-carbon travel ratio of the system. On the contrary, if α is maintained b2 Unchanged, but increasing α b3 , which means that the economic penalty for high-carbon emitters will be further increased, prompting these users to reduce their purchase of carbon quotas, thereby promoting the improvement of the low-carbon travel structure of the entire system.

[0262] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for controlling urban traffic carbon emissions based on individual carbon trading of residents, characterized in that: include: Setting up individual carbon trading pricing models for urban transport systems; Combining the individual carbon trading pricing model, a network equilibrium model considering heterogeneous users is constructed; Solve the network equilibrium model to obtain the equilibrium price of carbon quota considering user heterogeneity under the personal carbon trading pricing model, and then find out the relationship between the carbon quota price and the low-carbon travel of the heterogeneous users, and then adjust the carbon quota to control the travel mode of the heterogeneous users, thereby controlling the carbon emissions generated by transportation.

2. According to claim 1, a method for controlling urban traffic carbon emissions based on individual carbon trading of residents is characterized in that: The personal carbon trading pricing model includes the traditional personal carbon trading model, the differential transaction cost rate model and the tiered transaction cost rate model, wherein the traditional personal carbon trading model is expressed as: α s ＝α b ＝0 Among them, α b represents the transaction fee rate for buying a unit of carbon quota, α s It represents the transaction fee rate for selling a unit of carbon quota; The differential transaction cost rate model is expressed as: Among them, η1 and η2 represent the transaction costs of buying a unit carbon quota and selling a unit carbon quota respectively; The tiered transaction fee rate model is expressed as: Among them, k is the cumulative amount of carbon quota consumed by the user, It represents the number of carbon quotas initially allocated, hc represents the minimum value of the second-tier score segment, fd1 represents the transaction fee for buying a unit carbon quota in the first-tier score segment, and fd2 represents the transaction fee for buying a unit carbon quota in the second-tier score segment.

3. The urban transportation carbon emission control method based on resident individual carbon trading according to claim 2 is characterized in that: The specific process of constructing a network equilibrium model considering heterogeneous users in combination with the personal carbon trading pricing model is as follows: Constructing a traffic network flow pattern set Φ based on the personal carbon trading pricing model; represents the traffic flow of path r between the origin and destination pair w, v a represents the traffic flow on road section a, q w represents the travel demand between the origin and destination pair w; R w represents the set of all valid paths between the origin and destination pair w; W represents the set of origin and destination pairs; k a It represents the amount of carbon quota collected on road section a; K represents the total amount of carbon quota issued by the government to travelers in the urban transportation system; Between OD pairs w∈W, for the mth type of travelers, the perceived cost function perceived by the travelers when choosing path r is It is expressed as: in represents the fixed cost term, i.e., the cost function for the mth user to choose path r between the destination pair w; is a parameter term; i∈[t,y,c,in,e,pr],t a (v a ) is the travel time of road section a; y a The monetary cost of traveling on the road segment; represents the carbon quota cost of the path; income is the income level; edu is the education level; pre is the preference for public transportation; is the error term; represents the correlation coefficient between the path and the road segment, Represents a road segment a∈A on path r∈R w superior, Indicates that the road segment a∈A is not on the path r∈R w superior; They are the time perception coefficient and the money perception coefficient respectively; The parameters represent the carbon quota cost of the route, the user's preference for public transportation, and the user's education level; Design a quantitative indicator of heterogeneous user carbon quota cost based on transaction costs. The quantitative indicator of heterogeneous user carbon quota cost is: Among them, α b Represents the transaction fee rate for buying a unit of carbon quota; α s represents the transaction cost rate of selling a unit carbon quota; p represents the market price of a unit carbon quota; represents the amount of carbon quota collected on the path r between the origin and destination pair w in the traffic network carrying the traffic network flow; W represents the set of all origin and destination pairs OD in all the traffic network flows; R w represents the set of all valid paths between the origin and destination pair w; The network equilibrium model considering heterogeneous users under certain conditions of the total amount of urban carbon emissions is constructed, and the network equilibrium model includes a traffic allocation model and an equilibrium model of the carbon trading market, wherein the traffic allocation model is expressed as: The equilibrium model of the carbon trading market is expressed as: Wherein, m represents the category of heterogeneous users, r represents the travel path, i.e., the path of the origin-destination pairs in the traffic network carrying the traffic network flow, represents the probability that the mth heterogeneous user chooses path r between the origin and destination pair w, represents the travel demand of the mth heterogeneous user for w at the starting and ending points; K represents the total amount of carbon quotas issued by the government to travelers in the urban transportation system, Ω represents the number of people, k a Then it represents the amount of carbon quota collected on road section a, a∈A, A represents the edge set of the traffic network carrying the traffic network flow, v a represents the traffic flow on road section a, It represents the traffic of the mth heterogeneous user choosing path r between the origin and destination pair w.

4. The urban transportation carbon emission control method based on resident individual carbon trading according to claim 3 is characterized in that: The specific expression is: in, |R w | represents the set R w The dimension of , Pr represents probability; and are the utilities of selecting path r and path l for the mth heterogeneous user, is a matrix of fixed costs of all valid paths between the m-th user and the destination pair w; represents the number of users of the mth category between the starting and ending points w |R w The set of fixed costs of | valid paths is a row vector; The probability that the mth type of traveler chooses a specific path r can be expressed as: in, is the cost function for the mth user to select path r between destination pair w; Among them, θ>0 represents the discrete parameter of the traveler's perception error.

5. The urban transportation carbon emission control method based on resident individual carbon trading according to claim 3 is characterized in that: The specific process of solving the network equilibrium model is: Quantify the network equilibrium model to obtain a random user equilibrium state constraint model In a given carbon quota scheme (K, k a ) under the condition of the quantized random user equilibrium state constraint model Perform equivalent transformation to achieve the random user equilibrium state of the traffic network flow after the introduction of the carbon trading mechanism, and derive the equilibrium price model for carbon quotas; Solve the carbon quota equilibrium price model, and finally obtain the relationship between the equilibrium flow and the equilibrium carbon quota price, so as to find out the relationship between the carbon quota price and the low-carbon travel of the heterogeneous users.

6. The urban transportation carbon emission control method based on individual carbon trading of residents according to claim 5 is characterized in that: Quantify the network equilibrium model to obtain a random user equilibrium state constraint model The specific process is: Construct the perception function τ of the travel time and cost of the road segment a,m (x), let the perception function be the first perception function: in, is the time perception coefficient, t a (x) is the Federal Highway Administration function of road section a under any traffic mode, that is, the BPR function, which is used to calculate the travel time of users on road section a. is the currency perception coefficient, y a For monetary expenses; Constructing the perception function of travel time and cost of road segments in the "nominal" road network Let this perception function be the second perception function: in, represents the travel time cost and monetary cost of road section a in a non-congested state, and v a represents the traffic flow on road section a, express The first derivative of ; Establishing a random user equilibrium state constraint model Where r represents the travel path, w represents the origin-destination pair, and E represents the set of all origin-destination pairs OD in the traffic network flow; R w represents the set of all valid paths between the origin and destination pair w, represents a continuously differentiable vector function defined based on path flow, and represents the number of |R between the starting and ending points w w |Flow-based vector function of valid paths; in, It represents the time and monetary cost of this "nominal" road network. represents the correlation coefficient between the path and the road segment, Represents a road segment a∈A on path r∈R w superior, Indicates that the road segment a∈A is not on the path r∈R w superior.

7. The urban transportation carbon emission control method based on resident individual carbon trading according to claim 6 is characterized in that: In a given carbon quota scheme (K, k a ) under the condition of the quantized random user equilibrium state constraint model The equivalent transformation is performed so that the traffic network flow after the introduction of the carbon trading mechanism reaches a random user equilibrium state, and the specific process of deriving the equilibrium price model of carbon quotas is as follows: Establish an optimization objective function that minimizes the travel cost of the mth type of travel user under the carbon trading model: minZ(f m ); Construct a Lagrangian function of the mathematical model for minimizing the travel cost of the mth type of travel user under the carbon trading model: in, represent the Lagrange multiplier variables respectively; Set the quantitative index of the transaction process of the travel user on different paths between the starting and ending points w, so as to characterize the carbon trading process of the travel user on different paths between the starting and ending points w. for: make Then there is and It follows from this that: represents the amount of carbon quota collected on path r between OD pair w, p is the market price of unit carbon quota; r∈[1,2,···,R w ], The mth user chooses the first |R between OD and W. w |The carbon trading process of the function when there are effective paths; It means that the mth user chooses the |Rth w | The Lagrange multiplier variable of the Lagrange function of the mathematical model for minimizing the travel cost when there are | effective paths; The Lagrangian function The Carlo Demand-Kuhn-Tucker condition is brought into the above formula, so that the traffic network flow after the introduction of the carbon trading mechanism reaches a random user equilibrium state; According to the principle of random network equilibrium and the traffic network flow after the introduction of the carbon trading mechanism and reaching the random user equilibrium state, the carbon quota equilibrium price model is derived. The specific expression of the carbon quota equilibrium price model is: are the time perception coefficient and the currency perception coefficient, y a is the monetary cost, p * is the carbon quota price, Denotes the traffic flow of the mth user on path r2.

8. The urban transportation carbon emission control method based on resident individual carbon trading according to claim 7 is characterized in that: The specific process of solving the carbon quota equilibrium price model is as follows: T1. Determine the transaction mode in the carbon trading pricing model, and search for the effective path for each type of user in combination with the constraints of the effective path; T2, initialize the parameters of the outer loop, and set the initial carbon quota price p 0 >0, outer loop iteration number n=1, outer loop iteration coefficient First tolerance error n is a positive number; T3, call the MSA algorithm to calculate the current carbon quota price p n Stochastic user equilibrium section flow solution under transaction mode a (p n ); T4. Test the convergence of the carbon quota equilibrium price model. If Then output the current carbon quota price p * =p n And the current equilibrium flow solution v a (p n ), otherwise, jump to T5; T5. Update the current carbon quota price according to p n+1 =max{0, p n +α n (∑ a∈A v a (p n ) a -K)} updates the carbon quota price, then sets n=n+1, and returns to T3.

9. The urban transportation carbon emission control method based on resident individual carbon trading according to claim 8 is characterized in that: In T3, the MSA algorithm is called to calculate the current carbon quota price p n Stochastic user equilibrium section flow solution under transaction mode a (p n ) is as follows: T3.1, initialize the parameters of the inner loop, let the initial section flow v a =0, the number of outer loop iterations h=1, the inner loop iteration coefficient Second tolerance error n is a positive number, and then according to the current trading model and the current carbon quota price p n Calculate the generalized travel cost of each road section under zero flow state, and perform different flow loading based on the needs of different types of heterogeneous users to obtain the allocated flow of each road section T3.

2. Allocation of traffic according to the current road section Call the cost function and the perceived cost function to update the generalized travel cost of the road segment; T3.3, based on the latest generalized travel costs of each road section updated in T3.2, the flow allocation process is performed again according to the travel needs of different types of heterogeneous users to obtain the additional flow solution of each road section. T3.4, update the flow through iterative weighted method, the specific formula is: T3.

5. Allocation of traffic to each road section The convergence of is tested. If Then output the latest distribution flow solution for each road section As the random user equilibrium road flow solution under the current carbon quota price and trading mode, v a (p n ), otherwise, set h=h+1 and jump to T3.

2.

10. An urban transportation carbon emission control system based on individual carbon trading for residents, used to implement an urban transportation carbon emission control method based on individual carbon trading for residents as claimed in any one of claims 1 to 9, characterized in that: include: Carbon trading model setting module, used to set the individual carbon trading pricing model for the urban transportation system; A modeling module, used to construct a network equilibrium model considering heterogeneous users in combination with the individual carbon trading pricing model; A solution module is used to solve the network equilibrium model to obtain the carbon quota equilibrium price considering user heterogeneity under the personal carbon trading pricing model, and then find out the relationship between the carbon quota price and the low-carbon travel of the heterogeneous users, and then adjust the carbon quota to control the travel mode of the heterogeneous users, thereby controlling the carbon emissions generated by transportation.

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