Carbon emission quota distribution method based on MaaS environment

Abstract

The invention provides a combined carbon emission quota method based on a MaaS environment. The method comprises the following steps: constructing a user travel scene based on multi-source data, and obtaining user travel parameters; establishing a user utility balance model based on the user travel scene; each route is regarded as a transportation service provider boring, the Nash equilibrium state of the system is calculated based on user travel parameters and the user utility equilibrium model according to route combination pricing and a competitive game model, and an initial quota limit of an unimplemented carbon emission quota method is obtained; and obtaining a system carbon quota scheme based on a set system carbon quota constraint and the initial quota, and obtaining an enterprise carbon quota scheme based on the system carbon quota scheme and a set enterprise carbon quota constraint. According to the method, from the perspective of combining carbon emission quota implementation objects, a method for implementing carbon emission quota on the whole travel system is put forward, management and control of travel demands of a non-green travel mode in a MaaS environment are achieved, and meanwhile, green travel demands are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission quota allocation, and in particular to a carbon emission quota allocation method based on a MaaS environment. Background Art

[0002] The transportation industry accounts for a high proportion of energy consumption and has the fastest growth in greenhouse gas emissions compared to other industries. Among them, the utilization rate of private cars is very low, and its emissions account for 75% of the total global transportation emissions. Reducing the proportion of non-green travel modes and increasing the attractiveness of public transportation are effective ways to reduce carbon emissions from urban transportation. However, the inconvenience and uncertainty of the transfer stage hinder the promotion of public transportation.

[0003] By integrating different modes of transportation, MaaS (Mobility as a service) can provide flexible and convenient combined travel solutions, improve the efficiency of transfers, and reduce users' travel costs. MaaS improves accessibility to destinations and the ability to utilize transportation modes based on customized "door-to-door" services, thereby attracting more private car users to switch to MaaS travel. Therefore, MaaS is an effective way to solve the "last mile" problem, thereby increasing the proportion of urban public transportation travel and reducing urban traffic congestion and air pollution. MaaS includes a variety of travel service modes, and non-green travel service modes such as taxis, online car-hailing and shared cars are usually used as transfer services in travel plans. The increase in demand for public transportation has also led to an increase in the demand for transfers.

[0004] The increase in the demand for connecting with non-green travel services is not conducive to reducing carbon emissions from urban transportation. A survey shows that MaaS increases the frequency of combined use of taxis and public transportation by three times. More connecting travel demand may increase the use of non-green travel modes such as taxis, which runs counter to the goal of promoting MaaS to achieve more sustainable cities. Therefore, it is necessary to consider implementing a carbon quota restriction strategy in the MaaS environment to ensure green travel demand while limiting non-green travel services.

[0005] At present, the allocation of quotas is mainly free allocation, that is, government departments set it, supplemented by auction allocation, that is, purchasing quotas. Quotas are usually set based on historical emissions or emission intensity, and then the quota limit is reduced by a linear reduction method or the proportion of free quotas is reduced to increase the proportion of auction allocations to achieve emission reduction constraints. The implementation object of the carbon emission quota method in the prior art is usually a single high-emission enterprise or an emission reduction target is set for a certain macro-region (province, city, district level). When it comes to the transportation field, it is considered to set quotas from the perspective of transportation service providers. However, the method of setting carbon quotas for a single enterprise is suitable for industries with little impact on the same type of enterprises. However, the connection between different service modes in the urban transportation industry is deep, especially in the MaaS environment, more attention is paid to the connecting and connecting services between different service modes. Unreasonable quota setting may expand the impact between them and weaken the implementation effect of MaaS. Summary of the invention

[0006] The embodiments of the present invention provide a combined carbon emission quota method based on a MaaS environment to effectively reduce urban transportation carbon emissions.

[0007] In order to achieve the above object, the present invention adopts the following technical scheme.

[0008] A combined carbon emission quota method based on the MaaS environment, including:

[0009] Build user travel scenarios based on multi-source data and obtain user travel parameters;

[0010] Establish a user utility equilibrium model based on user travel scenarios;

[0011] Considering each route as an oligopoly of transport service providers, calculating the system Nash equilibrium state according to the competitive game model based on route combination pricing based on user travel parameters and the user utility equilibrium model, and obtaining the initial quota amount without implementing the carbon emission quota method;

[0012] A system carbon quota scheme is obtained based on the set system carbon quota constraint and the initial quota amount, and an enterprise carbon quota scheme is obtained based on the system carbon quota scheme and the set enterprise carbon quota constraint.

[0013] Preferably, the step of constructing a user travel scenario based on multi-source data and obtaining user travel parameters includes:

[0014] The basic information of the city, including the regional economic level, urban traffic operation conditions, and historical travel data, is used as input data. The number of travelers is estimated with reference to historical data. The user travel parameters are calculated based on the basic information of urban traffic and the travel mode adopted. The user travel parameters include the travel starting point set by the user, the travel cost, and the travel plan selected by the user.

[0015] Preferably, the user utility equilibrium model is established based on the user travel scenario, including:

[0016] A user utility equilibrium model is established based on user travel scenarios. It is assumed that all passengers spend a combined travel price P to complete their travel purpose and obtain travel utility U. The travel cost spent by users and travel demand obey a functional relationship with λ as the coefficient. In the user utility equilibrium state, the total travel cost of online car-hailing and shared bicycle routes is the same. Passengers cannot reduce travel costs by changing travel choices. Passengers pay the travel price, spend the travel cost, complete the entire travel purpose and obtain travel utility. The utility transfer function is shown in formula (1):

[0017] u=U―P―λQ (1)

[0018] Where u represents consumer surplus, U represents the total utility obtained by completing the entire trip, P represents the price paid by the user to the transportation service provider, λ represents the travel cost coefficient, Q represents the number of people traveling on the route, and the user's remaining utility is equal to the total utility obtained by the entire trip minus the travel time cost spent in the travel process and the travel price paid to the transportation service provider;

[0019] The individual passenger consumer surplus utility u is the difference between the utility obtained from completing the journey and the total cost spent. The actual travel demand and the possible maximum travel volume follow formula (2):

[0020]

[0021] Let s represent subway service, e represent online car-hailing service, b represent bicycle service, se represent the combined route of online car-hailing and subway, sb represent the combined route of bicycle and subway, then P se represents the combined travel price of online car-hailing and subway, λ se represents the cost coefficient of online car-hailing service under the MaaS platform, Q e represents the combined travel volume of online ride-hailing and subway, and the same is true for the combined routes of bicycles and subways. max represents the possible maximum travel volume, δ represents the coefficient of residents' travel willingness being controlled by the average travel cost of the network;

[0022] When the user utility equilibrium is reached, the total travel cost of the online car-hailing route and the shared bicycle route is the same, satisfying P se +λ s Q s +λ se Q e =P sb +λ s Q s +λ sb Q b, simplify to get Q s =Q max (1―δ(P se +λ s Q s +λ se Q e ))=Q max (1―δ(P sb +λ s Q s +λ sb Q b ));

[0023] In the MaaS environment, the price of combined service travel is set by the MaaS operator, and the transportation service provider only provides travel services. The MaaS operator will collect an ε-proportion of the revenue, and the rest will be distributed between the two transportation service providers. The profits of each participant are expressed as follows:

[0024] PC MaaS =ε*P sb *Q b +ε*P se *Q e (3)

[0025] PC sb =(1-ε)*P sb *Q b (4)

[0026] PC se =(1-ε)*P se *Q e (5)

[0027] Where PC represents profit, PC MaaS Indicates the profit of MaaS platform, PC sb represents the revenue of the combined route of shared bicycles and subways, PC se represents the revenue of the combined route of online ride-hailing and subway, ε represents the proportion of total revenue collected by the MaaS platform;

[0028] The social welfare under the integrator model is expressed as the total consumer surplus and the total profit of the two routes, which includes the profits of the respective transport service providers and the profit of the MaaS operator taking its own proportion;

[0029] W=uQ s +PC MaaS +PC se +PC sb (6)

[0030] Where W represents the total social welfare, which is used to analyze the overall benefits of the program, uQ s represents the sum of consumer surplus of all users.

[0031] Preferably, the method of treating each route as an oligopoly of transport service providers, calculating the Nash equilibrium state of the system according to the competitive game model based on the route combination pricing based on the user travel parameters and the user utility equilibrium model, and obtaining the initial quota amount without implementing the carbon emission quota method includes:

[0032] Considering each route as an oligopoly of transportation service providers, travelers will always choose the path with the lowest travel cost. Based on the user travel parameters and the user utility equilibrium model, route combination pricing is used to calculate the system Nash equilibrium state according to the competitive game model. Combination pricing is adopted in the MaaS environment. The two combined routes of online car-hailing and shared bicycle routes are regarded as competitive oligopolies respectively, maximizing the profits of each route to achieve the Nash equilibrium state as shown in Table 1;

[0033] Table 1 Nash equilibrium state under the carbon-free quota method

[0034]

[0035] When the carbon quota method is not implemented, the price, demand, and profit in the equilibrium mode are represented by various parameters, λ se represents the cost coefficient of online car-hailing service under the MaaS platform, Q e represents the combined travel volume of online ride-hailing and subway, and the same is true for the combined routes of bicycles and subways. max represents the possible maximum travel volume, δ represents the coefficient of residents' travel willingness being controlled by the average travel cost of the network;

[0036] The Nash equilibrium state is an equilibrium state without the implementation of the carbon emission quota method. In this equilibrium state, when no traveler can reduce his travel expenses by changing his route, the total travel cost of passengers in the system is the same, the profit of each route reaches the maximum, the flow state of the system reaches a stable state, and the user's initial quota amount refers to the carbon emission quota amount value that just meets the travel demand in the stable state, which is also the maximum quota amount of the entire system.

[0037] Preferably, the system carbon quota constraint includes: setting an upper limit value τ of carbon emissions for the entire system sum , the system carbon quota method under the integrator model satisfies the constraint of formula (7);

[0038] θ s *Q s +θ e *Q e ≤τ sum (7)

[0039] Among them, θ s With θ eThey represent the carbon emissions per capita of subway and online car-hailing services in their respective sections. The carbon emissions of shared bicycle services are 0. The travel demand of subway and online car-hailing services is determined by Q s and Q e express.

[0040] Preferably, the enterprise carbon quota constraint strategy includes:

[0041] The enterprise carbon quota method splits the total quota value of the system carbon quota method into the emission cap values ​​τ of subway transportation service providers and online car-hailing transportation service providers respectively. s +τ e =τ sum , the enterprise carbon emission quota method transportation service providers each meet the quota constraints, the enterprise quota constraints are shown in formula (8) and formula (9):

[0042] θ s *Q s ≤τ s (8)

[0043] θ e *Q e ≤τ e (9).

[0044] Preferably, the step of obtaining a system carbon quota scheme based on the set system carbon quota constraint and the initial quota, and obtaining an enterprise carbon quota scheme based on the system carbon quota scheme and the set enterprise carbon quota constraint includes: 1

[0045] When carbon emission constraints are imposed on the entire MaaS system, θ s *Q s +θ e *Q e ≤τ sum =τ s +τ e , the entire system is constrained as a whole; assuming the current state: the quota constraint condition is just satisfied, that is, θ s *Q s +θ e *Q e =τ s +τ e , the shared bicycle route maximizes profit, and the online car-hailing route, as a condition for satisfying the quota limit, reaches a critical state. At this time, the price and demand of each route are as follows:

[0046] For subway(s)+online car-hailing(e)

[0047] The price is:

[0048]

[0049] The requirements are:

[0050]

[0051] For subway(s)+shared bike(b)

[0052] The price is:

[0053] The requirements are:

[0054] As a carbon quota constraint imposed on the entire system, the two routes will also obey the Cournot game model to reach the Nash equilibrium point under the constraint condition, implement the enterprise quota restriction strategy in the MaaS environment, consider imposing carbon emission restrictions on online car-hailing and subway transportation service providers, and the subway service section meets the carbon emission constraint Q s ≤τ s / θ s , the travel demand that satisfies the Nash equilibrium state is outside the constraint range, that is:

[0055]

[0056] When carbon emission constraints are imposed on online car-hailing routes, e ≤τ e / θ e , while satisfying the Nash equilibrium state greater than the constraint state:

[0057]

[0058] When the online ride-hailing transportation service provider imposes an emission quota cap on the service segment τ e When , the equilibrium results of the critical state are shown in Table 3. Let the total travel demand in Table 3 be A. When the constraint τ is satisfied s ≥(θ s *A), the equilibrium result does not change. When the subway transportation service provider's service section does not meet the emission constraints, τ s ≤(θ s *A), in this state, the maximum travel demand of the system as a whole is τ s / θ s , while not satisfying the profit maximization requirement of the combined subway and online ride-hailing routes, it is easy to prove that the maximization demand always fails to meet the quota cap of online ride-hailing service providers;

[0059] Table 3 Critical point status of corporate carbon quota strategy

[0060]

[0061] The above table obtains the equilibrium critical points under the system carbon quota plan and the enterprise carbon quota plan. Through the above critical analysis results, the travel price, travel demand, profits of each transportation service provider, consumer surplus and social welfare of the system carbon quota plan and the enterprise carbon quota plan when setting different quota amounts are obtained.

[0062] It can be seen from the technical solutions provided by the above-mentioned embodiments of the present invention that the method of the present invention proposes to consider implementing a carbon emission quota method for the overall travel system from the perspective of the implementation object of the combined carbon emission quota, so as to achieve the control of travel demand for non-green travel modes in the MaaS environment while ensuring green travel demand.

[0063] Additional aspects and advantages of the present invention will be given in part in the following description, which will become obvious from the following description, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0065] Figure 1 A processing flow chart of a combined carbon emission quota method based on a MaaS environment provided by an embodiment of the present invention;

[0066] Figure 2 A schematic diagram of a travel connection scenario provided by an embodiment of the present invention;

[0067] Figure 3 A schematic diagram of the impact of a change in the carbon quota of an online car-hailing supplier on a comparison between a system carbon quota strategy and an enterprise carbon quota strategy provided by an embodiment of the present invention;

[0068] Figure 4 A schematic diagram of the effect of changes in the quota amount τ_s of a subway transportation service provider on travel demand provided by an embodiment of the present invention;

[0069] Figure 5 A schematic diagram of the impact of changes in the carbon quota amount τ_e of an online car-hailing service on the profits of a MaaS operator resulting from the implementation of two carbon quota strategies provided in an embodiment of the present invention;

[0070] Figure 6 A schematic diagram of the impact of the combined effect of τ_e and δ on the travel demand and social welfare comparison between the system carbon quota strategy and the enterprise carbon quota strategy provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0071] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0072] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "one", "said", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, the "connection" or "coupling" used herein may include wireless connection or coupling. The term "and / or" used herein includes any unit and all combinations of one or more associated listed items.

[0073] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless defined as herein.

[0074] To facilitate understanding of the embodiments of the present invention, several specific embodiments will be further explained below with reference to the accompanying drawings, and each embodiment does not constitute a limitation on the embodiments of the present invention.

[0075] MaaS is the concept of integrated travel services. All kinds of urban travel services and even inter-city travel services can realize connecting and connecting services in this environment. Users set the starting point and departure time on the platform. The platform provides a one-stop combined travel plan of "point to point" through its travel services and sells it in the form of packages. The various service modes realize the exchange of passenger travel information, and the service status of the front journey and the back journey service are closely controlled, which greatly improves the efficiency of connecting and changing and the competitiveness of public transportation, and reduces the proportion of private travel. At the same time, the MaaS platform is dominated by the government and public sectors, with public transportation as the main body. The combined travel fare is set by the platform, which is lower than the direct addition of the single fare. The platform collects part of the profit to ensure operation, and the remaining profit is distributed by each transportation service provider under its umbrella in a certain proportion (such as adding the prices when not participating in the MaaS platform, calculating the proportion of each price to the total travel price, and using this as the proportion of profit distribution).

[0076] The setting of combined service prices in the MaaS environment and the closer connection between transportation service providers will help expand the implementation methods of the carbon emission quota method and achieve better implementation results. The higher the integration of MaaS, the more conducive it is to the implementation of the system carbon quota method. MaaS operators combine pricing, travel costs are lower than the payment costs of individual services, and travel demand is higher. The unified reservation payment platform supports the constraint of the number of combined tickets, adjusts the number of reservation tickets according to a certain time window, and realizes the quota constraint function. In the MaaS system, each route is regarded as an oligopoly of transportation service providers based on route combination pricing, and the Nash equilibrium state of the system is calculated according to the competitive game model, which is used as the initial quota for quota setting. The quota of the system carbon quota method is set as the overall quota, and the enterprise carbon quota method constrains a single transportation service provider. The system carbon quota method restricts the travel mode of the main transportation function to preserve the advantages of green shuttle transportation demand. When the restrictions on the main transportation service providers under the enterprise carbon quota method are high, the demand for shuttle routes is also restricted. A user utility equilibrium model is established based on travel scenarios, and various indicators are set to compare the implementation effects of the system carbon emission quota method and the enterprise carbon quota method.

[0077] The present invention considers introducing the carbon emission quota method into the transportation field, and proposes a processing flow of a combined carbon emission quota method based on the MaaS environment as follows: Figure 1 As shown, the processing steps include the following:

[0078] Step S1: Construct a user travel scenario based on multi-source data and obtain user travel parameters.

[0079] In this step, the basic information of the city, including the regional economic level, urban traffic operation, historical travel data, etc., is used as input data. The number of travelers is estimated with reference to historical data. The user travel parameters are calculated based on the basic information of urban traffic and the travel mode adopted. The user travel parameters include the travel starting point set by the user and the travel plan selected by the user. The user time value is estimated in combination with the regional economy.

[0080] Step S2: Establish a user utility equilibrium model based on user travel scenarios.

[0081] Users travel for a certain purpose, and they can get a certain sense of satisfaction by spending a certain amount of travel costs to reach their destination. This sense of satisfaction is defined as travel utility, where the difference between travel utility and travel cost is consumer surplus. User travel costs mainly include two parts: travel expenses and travel time costs. It is assumed that travel time costs are related to free flow travel time and the number of travelers. Completing this trip means that utility has been transferred, and a utility transfer model is established by describing this process. If it is assumed that all users are homogeneous, the utility of the entire journey is the same for everyone.

[0082] Step S3: Consider each route as an oligopoly of transport service providers, and calculate the Nash equilibrium state of the system according to the competitive game model based on the route combination pricing based on the user travel parameters and the user utility equilibrium model, and obtain the initial quota amount without implementing the carbon emission quota method. The quota change can be set in stages according to linear decrease or reference to emission reduction targets.

[0083] The user's travel cost is related to the number of travelers who choose the route. Travelers will always choose the path with the lowest travel cost, and the travel cost on the unselected path is greater than or equal to the minimum path cost. Only when no travelers can reduce their travel costs by changing their paths, the system's traffic state reaches a stable state. The user's initial quota refers to the carbon emission quota value that just meets the travel demand in the stable state, which is also the maximum quota of the entire system. The quota is subsequently reduced to constrain travel demand and reduce carbon emissions.

[0084] Step S4: obtaining a system carbon quota plan based on the set system carbon quota constraint and the above initial quota amount, and obtaining an enterprise carbon quota plan based on the system carbon quota plan and the set enterprise carbon quota constraint.

[0085] System carbon quota scheme: implement a total quota limit for the entire travel system, and the transportation service providers serving the entire travel system meet the quota limit as a whole. The quotas of suppliers in the entire MaaS system can be transferred to each other and complement each other. The MaaS system has a high degree of integration. Enterprise carbon quota scheme: implement a single quota limit for each transportation service provider, similar to the carbon emission cap for high-emission enterprises. The target is the transportation service provider, and the quota cannot be transferred.

[0086] Specifically, the above step S1 includes: selecting a daily combined travel route in the city, especially a non-direct travel corridor including public transportation. Figure 2 A schematic diagram of a travel connection scenario provided by an embodiment of the present invention, Figure 2 In the figure, sections AB are served by the subway, and sections BC are served by online car-hailing and shared bicycles. Based on the regional population and past traffic data, the maximum travel volume Q in a certain period of time in the area is estimated. max . Determine the travel distances of the two ends of the journey as d AB , d BC , usually d AB Much larger than d BC , while d BC Greater than the maximum distance residents can tolerate walking.

[0087] Determine the travel time T of public transportation for segment AB s , the number of trips in a certain period of time n, the carrying area of ​​a single vehicle S, and the time multiplier under congestion conditions selected according to the degree of congestion in reference to previous literature research The passenger's time value coefficient α is set according to actual conditions. The subway travel cost coefficient is

[0088] In the MaaS environment, assuming that the shuttle vehicle is guaranteed to wait for the user during the scheduled time period during the transfer phase, ignoring the transfer time cost, the travel cost coefficient of the BC segment of the online car-hailing travel mode The passenger's time value coefficient is α, and the travel time at the average speed of this route is T e , the basic traffic capacity is C e .

[0089] The cost coefficient of the shared bicycle travel mode service BC segment is similar to that of online car-hailing. The travel time at the average speed of this route is T b , the basic traffic capacity of a bicycle is C b .

[0090] When passengers choose a travel route, they compare the travel costs of various combined travel modes and choose the one with the lowest total cost. When the travel demand in the travel system continues to grow, the travel cost continues to increase. When it grows to a certain level, the passengers' willingness to achieve their travel goals through the system's services decreases, and they then exit the system. The coefficient of residents' travel willingness is δ, which is set according to the income level of regional passengers. Based on the travel data of different time periods in the past, a day is divided into multiple travel periods, or several key travel periods are intercepted from them, and quotas are set for each period.

[0091] Specifically, the above step S2 includes: establishing a user utility equilibrium model based on the user travel scenario, in which it is assumed that all passengers spend a combined travel price P to complete the travel purpose and obtain the travel utility U, and at the same time, the process travel cost and travel demand obey the functional relationship with λ as the coefficient. In the equilibrium state, the total travel cost of online car-hailing and shared bicycle routes is the same, and passengers cannot reduce the travel cost by changing their travel options. Passengers pay the travel price, spend the travel cost, complete the entire travel purpose and obtain the travel utility. The utility transfer function is shown in formula (1).

[0092] u=U―P―λQ (1)

[0093] Where u represents consumer surplus, u represents the total utility obtained from completing the entire trip, P represents the price paid by the user to the transportation service provider, λ represents the travel cost coefficient, which is related to the service capacity of the service mode, Q represents the number of travelers on the route, and the user's remaining utility is equal to the total utility obtained from the entire trip minus the travel time cost spent in the travel process and the travel price paid to the transportation service provider.

[0094] The individual passenger consumer surplus utility u is the difference between the utility gained from completing the journey and the total cost spent. The actual travel demand and the possible maximum travel volume follow formula (2).

[0095]

[0096] Let s represent subway service, e represent online car-hailing service, b represent bicycle service, se represent the combined route of online car-hailing and subway, sb represent the combined route of bicycle and subway, then P se represents the combined travel price of online car-hailing and subway, λ se represents the cost coefficient of online car-hailing service under the MaaS platform, Q e It represents the combined travel volume of online car-hailing and subway, and the same is true for the combined routes of bicycles and subways. max represents the possible maximum travel volume, and δ represents the control coefficient of residents' travel willingness affected by the average travel cost of the network.

[0097] When the equilibrium state is reached, the total travel costs of the online car-hailing route and the shared bicycle route are the same, satisfying P se +λ s Q s +λ se Q e =P sb +λ s Q s +λ sb Q b , simplify to get Q s =Q max (1―δ(P se +λ s Q s +λ se Q e ))=Q max (1―δ(P sb +λ s Q s +λ sb Q b )).

[0098] In the MaaS environment, the price of combined service travel is set by the MaaS operator, and the transportation service provider only provides travel services. The MaaS operator will collect an ε-proportion of the revenue, and the rest will be distributed between the two transportation service providers. The profits of each participant are expressed as follows:

[0099] PC MaaS =ε*P sb *Q b +ε*P se *Q e (3)

[0100] PC sb =(1-ε)*P sb *Q b (4)

[0101] PC se =(1-ε)*P se *Q e (5)

[0102] Where PC represents profit, PC MaaS Indicates the profit of MaaS platform, PC sb represents the revenue of the combined route of shared bicycles and subways, PC se represents the revenue of the combined route of online car-hailing and subway, and ε represents the proportion of total revenue collected by the MaaS platform.

[0103] The social welfare under the integrator model is expressed as the total consumer surplus and the total profit of the two routes, which includes the profits of the respective transport service providers and the profit of the MaaS operator taking its own proportion.

[0104] W=uQ s +PC MaaS +PC se +PC sb (6)

[0105] Where W represents the total social welfare, which is used to analyze the overall benefits of the program, uQ s represents the sum of consumer surplus of all users.

[0106] (1) System carbon quota constraint strategy

[0107] The system carbon quota method is transformed into setting an upper limit value τ for carbon emissions for the entire system sum The change of MaaS business model creates the possibility of implementing a combined carbon quota method. Closer information interaction and connection between different travel modes enable unified management of the entire platform, which can clearly obtain important travel information such as travel demand and service distance of each transportation service provider, facilitate the calculation of travel carbon emissions, and also provide convenience for the calculation of total emissions. The system carbon quota method under the integrator model satisfies the constraint of formula (7).

[0108] θ s *Q s +θ e *Q e ≤τ sum (7)

[0109] Among them, θ s With θ e They represent the carbon emissions per capita of subway and online car-hailing services in their respective sections. Shared bicycle services do not produce carbon emissions, so they are 0. The travel demand for subway and online car-hailing services is determined by Q s and Q e In order to facilitate the subsequent analysis of the difference from the corporate carbon emission quota method, the total quota value of the system carbon quota method can be split into the emission upper limit values ​​τ of the subway transportation service providers and the online car-hailing transportation service providers under the corporate carbon quota method. s +τ e =τ sum .

[0110] (2) Enterprise carbon quota constraint strategy

[0111] Enterprise carbon emission quota method Transportation service providers each meet quota constraints, similar to high-emission enterprises setting emission caps. Due to the difference between main transportation services and connecting transportation services, when the main quota constraint is strong, the overall travel demand is reduced, and the quota of the connecting service route is not fully used, resulting in a decrease in overall social welfare. Enterprise quota constraints are shown in formulas (8) and (9).

[0112] θ s *Q s ≤τ s (8)

[0113] θ e *Q e ≤τ e (9)

[0114] The Nash equilibrium state under the carbon-free quota method is used as the initial amount of quota setting, and the different implementation effects of the two quota methods are focused on through the differences in the same state under the two strategies.

[0115] Specifically, the above step S3 includes setting the initial quota amount.

[0116] In the MaaS environment, combined pricing is adopted, and the two combined routes of online car-hailing and shared bicycle routes are regarded as competitive oligopolies, maximizing the profits of each route to achieve the Nash equilibrium state as shown in Table 1. This state is the equilibrium state without the implementation of the carbon emission quota method. The total travel cost of passengers in the system is the same, and the profit of each route is maximized.

[0117] Table 1 Nash equilibrium state under the carbon-free quota method

[0118]

[0119]

[0120] When the carbon quota method is not implemented, the price, demand, and profit in the equilibrium mode can be represented by various parameters. se represents the cost coefficient of online car-hailing service under the MaaS platform, Q e represents the combined travel volume of online ride-hailing and subway, and the same is true for the combined routes of bicycles and subways. max represents the maximum possible travel volume, and δ represents the coefficient of residents' travel willingness being controlled by the average travel cost of the network. This equilibrium state is also the basis for setting the initial value of the quota.

[0121] The above state can assist in determining the initial amount of carbon quota setting, and can also be considered as the upper limit of carbon emission quota within this time period. The quota reflects the number of packages, and the reduction of the quota and the constraint of the number of tickets can guide users to change their travel habits. Finally, the impact of quota changes on the implementation of the two strategies is analyzed to study the advantages and disadvantages of the system carbon quota method.

[0122] Step 4: Implementation of the system carbon quota approach

[0123] It is easy to constrain the carbon emissions of the entire system in a MaaS environment, and the transfer of quotas enables the mutual allocation of green travel service quotas and non-green travel services. In the early stage of the implementation of the system carbon quota method, the quota of non-green travel services guarantees the quota demand of green travel services. In the later stage of the implementation of the quota method, the quota of green travel services is transferred to non-green travel services, easing the constraints and reducing conflicts.

[0124] When carbon emission constraints are imposed on the entire MaaS system, θ s *Q s +θ e *Q e ≤τ sum =τ s +τ e The entire system is constrained as a whole, but for the subsequent study of the mutual influence of quota setting among transportation service providers, it is temporarily divided into τ s and τ e In this case, the pricing problem is involved. For the transportation service providers of the two routes, the optimal state cannot be defined analytically, and the optimal equilibrium state is not easy to express. We use the special state points of the process for analysis. Assume the current state: the quota restriction condition is just satisfied, that is, θ s *Q s +θ e *Q e =τ s +τ e , the shared bicycle route maximizes profit, and the online car-hailing route, as a condition for satisfying the quota limit, reaches a critical state. At this time, the price and demand of each route are as follows:

[0125] For subway(s)+online car-hailing(e)

[0126] The price is:

[0127]

[0128] The requirements are:

[0129]

[0130] For subway(s)+shared bike(b)

[0131] The price is:

[0132] The requirements are:

[0133] The imposed constraint point must be smaller than the carbon emission sum at the optimal point when no constraint is imposed, otherwise the imposed constraint is invalid. As a carbon quota constraint imposed on the entire system, the two routes will still obey the Cournot game model to reach the Nash equilibrium point under the constraint condition, but this point does not mean the critical point of the quota restriction. The difference between the two strategies can be compared with the critical point state analysis, and the solution method at the quota restriction point of the free competition model can still be referred to, and can be compared under the same conditions.

[0134] Implement enterprise quota restriction strategy in MaaS environment, and consider imposing carbon emission restrictions on online car-hailing and subway transportation service providers. Subway service section meets carbon emission constraint Q s ≤τ s / θ s , where the travel demand that satisfies the Nash equilibrium state is outside the constraint range, that is:

[0135]

[0136] When carbon emission constraints are imposed on online car-hailing routes, e ≤τ e / θ e , while satisfying the Nash equilibrium state greater than the constraint state:

[0137]

[0138] When the online ride-hailing transportation service provider imposes an emission quota cap on the service segment τ e When , the equilibrium result of the critical state is shown in Table 3. At this time, it is necessary to identify whether the service section of the subway transportation service provider meets the emission constraints. Let the total travel demand in Table 3 be A. When the constraint is met, τ s ≥(θ s *A), the equilibrium result does not change. When the subway transportation service provider's service section does not meet the emission constraints, τ s ≤(θ s *A), in this state, the maximum travel demand of the system as a whole is τ s / θ s , the main transport restriction effect is stronger, and the overall cost is lower. At the same time, it does not meet the profit maximization requirement of the combined route of subway and online car-hailing, because the maximization demand always does not meet the quota cap of the online car-hailing service provider.

[0139] Table 3 Critical point status of corporate carbon quota strategy

[0140]

[0141] The above table obtains the equilibrium critical points under two different schemes. The combined prices set by the MaaS platform when satisfying the quota constraints under different schemes are different, that is, the different response strategies to the schemes lead to different travel demands and profits of each participant. Through the above critical analysis results, the travel prices, travel demands, profits of each transportation service provider, consumer surplus and social welfare when setting different quotas can be obtained to analyze the advantages and disadvantages of the two different schemes.

[0142] In the later stage of the implementation of the carbon quota method, the overall system constraints are more favorable to transportation service providers of non-green travel modes. First of all, the total limit imposed must be higher than the constraint limit imposed on a single service mode. In the later stage of strict quota constraints, the limit of low-emission green travel modes often fails to meet the standards, thus transferring the remaining limit to non-green travel modes.

[0143] In the shuttle travel scenario, in the travel mode where there is a gathering of people, if a certain carbon quota is set for the main road, it will not only affect the travel demand of one route, but also have a huge impact on the shuttle transportation that adopts green travel mode. It is very likely that the use of green travel services will be reduced due to the increase in total travel costs. In the MaaS environment, it is easier to achieve the integration of the overall travel system and the aggregation of carbon emission quotas. At this time, it will reduce the impact on green travel modes while restricting non-green travel modes.

[0144] Figure 3 A schematic diagram of the impact of changes in the carbon quota of an online car-hailing supplier on the system carbon quota strategy and the enterprise carbon quota strategy provided in an embodiment of the present invention; the system carbon quota strategy makes travel prices lower, the travel demand higher, and the consumer surplus and social welfare higher. When the carbon quota of online car-hailing is high, the system carbon quota strategy is more conducive to the profits of shared bicycle transportation service providers for green travel. When the carbon quota of online car-hailing is low, the quota restriction effect is stronger. At this time, the system carbon quota strategy is more conducive to the profits of online car-hailing transportation service providers. While playing a restrictive role, it also guarantees the profits of certain non-green travel service providers. The system carbon quota strategy in the MaaS environment can play a role in attracting transportation service providers to join the MaaS system. Under the condition of ensuring that the main subway quota is high (in line with the actual situation), the system carbon quota strategy can alleviate certain conflicts of interest.

[0145] Figure 4 A subway transportation service provider quota τ provided in an embodiment of the present invention sSchematic diagram of the impact of changes on travel demand. When the subway carbon emission quota is weak and the quota is high, the system carbon quota strategy makes the total travel demand higher than the total demand under the enterprise carbon quota strategy. As the subway carbon quota increases, the increase in total demand is also increasing. As the carbon emission quota of online car-hailing decreases, the advantage of the system carbon quota strategy will weaken. This is mainly because the increase in restrictions on online car-hailing has caused a large amount of travel demand to shift to shared bicycle travel, increasing the service pressure on shared bicycle travel routes and increasing travel costs. The more demand increases, the higher the cost, which in turn limits the travel demand on this route and reduces the increase in total travel demand.

[0146] Figure 5 A carbon quota for online car-hailing provided in an embodiment of the present invention e Schematic diagram of the impact of changes on the profits of MaaS operators resulting from the implementation of two carbon quota strategies; the subway carbon quota is set at 2.235. When the quota of online ride-hailing transportation service providers is higher, the profits of MaaS operators under the system carbon quota strategy are lower than those under the corporate carbon quota strategy. When the online ride-hailing quota is reduced and the restrictive effect is strengthened, the profits of the system carbon quota strategy decline more slowly and gradually exceed those of the corporate carbon quota strategy.

[0147] Figure 6 A method for providing a method for e Schematic diagram of the impact of the combined effect of δ on travel demand and social welfare under the system carbon quota strategy and the enterprise carbon quota strategy. As the travel cost sensitivity coefficient increases, the difference between travel demand and social welfare under the system carbon quota strategy and the enterprise carbon quota strategy accounts for a higher proportion, and the system carbon quota strategy has a greater advantage. On the other hand, in developed regions with lower sensitivity coefficients, demand is sufficient, and the system carbon quota strategy is not much different from the enterprise carbon quota strategy. As the quota of online car-hailing transportation service providers decreases, travel demand and social welfare are generally reduced, but the system carbon quota strategy has a smaller decline, which is more conducive to the overall interests.

[0148] In summary, the method of the present invention proposes to consider implementing a carbon emission quota method for the overall travel system from the perspective of the combined carbon emission quota implementation object, so as to achieve the control of non-green travel demand under the MaaS environment while ensuring green travel demand.

[0149] Compared with the implementation of a single enterprise carbon quota strategy, the combined service price between transportation service providers under the overall system carbon quota strategy is always lower, and as the quota decreases and the restriction effect increases, the price gap of green travel combined services gradually increases with the reduction of quotas. The price of green travel routes under the system carbon quota strategy is lower, making green travel more attractive and the demand for green travel higher. The system carbon quota strategy has the function of preserving the demand for green shuttle transportation when the travel mode of the main transportation role is restricted. At the same time, when the restriction effect is strong, the profit of transportation service providers under the system carbon quota strategy is compensated to a certain extent, which makes it easier to implement the strategy in the later stage of implementation and reduces contradictions. The individual consumer surplus under the system carbon quota strategy is always higher than that of the enterprise carbon quota strategy. Under high restriction conditions, the system carbon quota strategy can better protect the rights and interests of users. In terms of social welfare, the subway quota remains at a high level to ensure that the social welfare under the system carbon quota strategy is always higher than that of the enterprise carbon quota strategy. At the same time, as the quota of online car-hailing suppliers is reduced, the difference in social welfare will be further increased.

[0150] Those skilled in the art can understand that the accompanying drawings are only schematic diagrams of an embodiment, and the modules or processes in the accompanying drawings are not necessarily required to implement the present invention.

[0151] It can be known from the description of the above implementation methods that those skilled in the art can clearly understand that the present invention can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present invention or certain parts of the embodiments.

[0152] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0153] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A combined carbon emission quota method based on MaaS environment, characterized in that: include: Build user travel scenarios based on multi-source data and obtain user travel parameters; Establish a user utility equilibrium model based on user travel scenarios; Considering each route as an oligopoly of transport service providers, calculating the system Nash equilibrium state according to the competitive game model based on route combination pricing based on user travel parameters and the user utility equilibrium model, and obtaining the initial quota amount without implementing the carbon emission quota method; A system carbon quota scheme is obtained based on the set system carbon quota constraint and the initial quota amount, and an enterprise carbon quota scheme is obtained based on the system carbon quota scheme and the set enterprise carbon quota constraint.

2. The method according to claim 1, characterized in that The construction of user travel scenarios based on multi-source data and obtaining user travel parameters include: The basic conditions of the city, including the regional economic level, urban traffic operation conditions, and historical travel data, are taken as input data. The number of travelers is estimated with reference to historical data, and the user travel parameters are calculated based on the basic conditions of urban traffic and the travel mode adopted. The user travel parameters include the travel starting point set by the user, travel cost, and the travel plan selected by the user.

3. The method according to claim 2, characterized in that The user utility equilibrium model is established based on the user travel scenario, including: A user utility equilibrium model is established based on user travel scenarios. It is assumed that all passengers spend a combined travel price P to complete their travel purpose and obtain travel utility U. The travel cost spent by users and travel demand obey a functional relationship with λ as the coefficient. In the user utility equilibrium state, the total travel cost of online car-hailing and shared bicycle routes is the same. Passengers cannot reduce travel costs by changing travel choices. Passengers pay the travel price, spend the travel cost, complete the entire travel purpose and obtain travel utility. The utility transfer function is shown in formula (1): u=U―P―λQ (1) Where u represents consumer surplus, U represents the total utility obtained by completing the entire trip, P represents the price paid by the user to the transportation service provider, λ represents the travel cost coefficient, Q represents the number of people traveling on the route, and the user's remaining utility is equal to the total utility obtained by the entire trip minus the travel time cost spent in the travel process and the travel price paid to the transportation service provider; The individual passenger consumer surplus utility u is the difference between the utility obtained from completing the journey and the total cost spent. The actual travel demand and the possible maximum travel volume follow formula (2): Let s represent subway service, e represent online car-hailing service, b represent bicycle service, se represent the combined route of online car-hailing and subway, sb represent the combined route of bicycle and subway, then P se represents the combined travel price of online car-hailing and subway, λ se represents the cost coefficient of online car-hailing service under the MaaS platform, Q e represents the combined travel volume of online ride-hailing and subway, and the same is true for the combined routes of bicycles and subways. max represents the possible maximum travel volume, δ represents the coefficient of residents' travel willingness being controlled by the average travel cost of the network; When the user utility equilibrium is reached, the total travel cost of the online car-hailing route and the shared bicycle route is the same, satisfying P se +λ s Q s +λ se Q e =P sb +λ s Q s +λ sb Q b , simplify to get Q s =Q max (1―δ(P se +λ s Q s +λ se Q e ))=Q max (1―δ(P sb +λ s Q s +λ sb Q b )); In the MaaS environment, the price of combined service travel is set by the MaaS operator, and the transportation service provider only provides travel services. The MaaS operator will collect an ε-proportion of the revenue, and the rest will be distributed between the two transportation service providers. The profits of each participant are expressed as follows: PC MaaS =ε*P sb *Q b +ε*P se *Q e (3) PC sb =(1-ε)*P sb *Q b (4) PC se =(1-ε)*P se *Q e (5) Where PC represents profit, PC MaaS Indicates the profit of MaaS platform, PC sb represents the revenue of the combined route of shared bicycles and subways, PC se represents the revenue of the combined route of online ride-hailing and subway, ε represents the proportion of total revenue collected by the MaaS platform; The social welfare under the integrator model is expressed as the total consumer surplus and the total profit of the two routes, which includes the profits of the respective transport service providers and the profit of the MaaS operator taking its own proportion; W=uQ s +PC MaaS +PC se +PC sb (6) Where W represents the total social welfare, which is used to analyze the overall benefits of the program, uQ s represents the sum of consumer surplus of all users.

4. The method according to claim 3, characterized in that The method of treating each route as an oligopoly of transportation service providers, calculating the Nash equilibrium state of the system according to the competitive game model based on the route combination pricing based on the user travel parameters and the user utility equilibrium model, and obtaining the initial quota amount without implementing the carbon emission quota method includes: Considering each route as an oligopoly of transportation service providers, travelers will always choose the path with the lowest travel cost. Based on the user travel parameters and the user utility equilibrium model, route combination pricing is used to calculate the system Nash equilibrium state according to the competitive game model. Combination pricing is adopted in the MaaS environment. The two combined routes of online car-hailing and shared bicycle routes are regarded as competitive oligopolies respectively, maximizing the profits of each route to achieve the Nash equilibrium state as shown in Table 1; Table 1 Nash equilibrium state under the carbon-free quota method When the carbon quota method is not implemented, the price, demand, and profit in the equilibrium mode are represented by various parameters, λ se represents the cost coefficient of online car-hailing service under the MaaS platform, Q e represents the combined travel volume of online ride-hailing and subway, and the same is true for the combined routes of bicycles and subways. max represents the possible maximum travel volume, δ represents the coefficient of residents' travel willingness being controlled by the average travel cost of the network; The Nash equilibrium state is an equilibrium state without the implementation of the carbon emission quota method. In this equilibrium state, when no traveler can reduce his travel expenses by changing his route, the total travel cost of passengers in the system is the same, the profit of each route reaches the maximum, the flow state of the system reaches a stable state, and the user's initial quota amount refers to the carbon emission quota amount value that just meets the travel demand in the stable state, which is also the maximum quota amount of the entire system.

5. The method according to claim 4, characterized in that The system carbon quota constraint includes: setting an upper limit value τ of carbon emissions for the entire system sum , the system carbon quota method under the integrator model satisfies the constraint of formula (7); i s *Q s +θ e *Q e ≤τ sum (7) Among them, θ s With θ e They represent the carbon emissions per capita of subway and online car-hailing services in their respective sections. The carbon emissions of shared bicycle services are 0. The travel demand of subway and online car-hailing services is determined by Q s and Q e express.

6. The method according to claim 5, characterized in that The enterprise carbon quota constraint strategy includes: The enterprise carbon quota method splits the total quota value of the system carbon quota method into the emission cap values ​​τ of subway transportation service providers and online car-hailing transportation service providers respectively. s +τ e =τ sum , the enterprise carbon emission quota method transportation service providers each meet the quota constraints, the enterprise quota constraints are shown in formula (8) and formula (9): i s *Q s ≤τ s (8) i e *Q e ≤τ e (9)。 7. The method according to claim 6, characterized in that The obtaining of the system carbon quota scheme based on the set system carbon quota constraint and the initial quota amount, and the obtaining of the enterprise carbon quota scheme based on the system carbon quota scheme and the set enterprise carbon quota constraint include: When carbon emission constraints are imposed on the entire MaaS system, θ s *Q s +θ e *Q e ≤τ sum =τ s +τ e , the entire system is constrained as a whole; assuming the current state: the quota constraint condition is just satisfied, that is, θ s *Q s +θ e *Q e =τ s +τ e , the shared bicycle route maximizes profit, and the online car-hailing route, as a condition for satisfying the quota limit, reaches a critical state. At this time, the price and demand of each route are as follows: For subway(s)+online car-hailing(e) The price is: The requirements are: For subway(s)+shared bike(b) The price is: The requirements are: As a carbon quota constraint imposed on the entire system, the two routes will also obey the Cournot game model to reach the Nash equilibrium point under the constraint condition, implement the enterprise quota restriction strategy in the MaaS environment, consider imposing carbon emission restrictions on online car-hailing and subway transportation service providers, and the subway service section meets the carbon emission constraint Q s ≤τ s / θ s , the travel demand that satisfies the Nash equilibrium state is outside the constraint range, that is: When carbon emission constraints are imposed on online car-hailing routes, e ≤τ e / θ e , while satisfying the Nash equilibrium state greater than the constraint state: When the online ride-hailing transportation service provider imposes an emission quota cap on the service segment τ e When , the equilibrium results of the critical state are shown in Table 3. Let the total travel demand in Table 3 be A. When the constraint τ is satisfied s ≥(θ s *A), the equilibrium result does not change. When the service section of the subway transportation service provider does not meet the emission constraints, τ s ≤(θ s *A), in this state, the maximum travel demand of the system as a whole is τ s / θ s , while not satisfying the profit maximization requirement of the combined subway and online ride-hailing routes, it is easy to prove that the maximization demand always fails to meet the quota cap of online ride-hailing service providers; Table 3 Critical point status of corporate carbon quota strategy The above table obtains the equilibrium critical points under the system carbon quota plan and the enterprise carbon quota plan. Through the above critical analysis results, the travel price, travel demand, profits of each transportation service provider, consumer surplus and social welfare of the system carbon quota plan and the enterprise carbon quota plan when setting different quota amounts are obtained.

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