A method for predicting urban electric vehicle demand under the background of low-carbon travel

Abstract

The present invention discloses a method for predicting the demand for urban electric vehicles under the background of low-carbon travel. The method first combines the scale effect of the urban transportation network to establish operating cost functions that conform to the vehicle operating characteristics for fuel vehicles and electric vehicles respectively. Then, based on the randomness of travelers' subjective perception of travel time, a traffic allocation problem based on the principle of random user equilibrium is constructed; the path flow and travel time in the urban transportation network, as well as the operating costs of fuel vehicles and electric vehicles are obtained. Finally, the present invention combines the Logit behavior selection model to divide the transportation mode, and realizes the effective prediction of the travel demand of urban electric vehicles. The present invention can more accurately predict the travel demand of electric vehicles in the urban transportation network, estimate the environmental costs related to urban transportation, and thus take timely and effective management measures to provide solid support for the construction of energy-saving and low-carbon modern urban transportation.

Description

Technical Field

[0001] The present invention belongs to the field of urban transportation, and in particular relates to a method for predicting urban electric vehicle demand under the background of low-carbon travel. Background Art

[0002] Electric vehicles (EVs), with their low emissions, noiselessness, and high efficiency, are considered to be one of the most promising modes of transportation to reshape the future urban transportation market and beautify the urban environment. They have received widespread attention and strong support from domestic and foreign transportation managers. Data show that by the end of 2021, my country's electric vehicle ownership reached 6.4 million, accounting for about 50% of the world's total electric vehicles, surpassing the United States and Europe to become the world's largest electric vehicle market. The rapid development of electric vehicles has promoted the transformation of modern urban transportation planning, design, and management, such as the layout of charging facilities, the transformation of existing parking spaces, and even the paving of wireless charging roads. The accurate prediction of urban electric vehicle travel demand has become the primary basis for the realization of the above-mentioned transportation planning, design, and management. In addition, analyzing the demand for electric vehicles is of great significance for the management of the automobile sales market and the estimation of environmental costs related to urban transportation.

[0003] A planning method for electric vehicle charging facilities (application number CN202111418526.8) is formed based on regional population size, per capita car ownership level, electric vehicle penetration rate, different travel characteristics and energy consumption characteristics of various types of electric vehicles, charging facility configuration structure system, annual utilization hours of charging facilities, tortuosity coefficient, charging facility service radius and other indicators to form a set of electric vehicle charging facility planning methods, and evaluate the rationality and economic efficiency of electric vehicle charging facility configuration for a certain scene or a certain area in a certain time section, or predict its development scale, propose layout configuration plans, and perform plan rationality analysis, sensitivity analysis and economic evaluation. An electric vehicle charging station site selection method based on traffic distribution results (application number CN202110249480.5) is used to equate each electric vehicle charging station site selection plan to a user equilibrium model, and use the objective function of each equivalent user equilibrium model to solve the path traffic volume, obtain the traffic distribution result, and thus calculate the quantitative judgment index of the electric vehicle charging station site selection plan; and the data envelopment analysis method is used to quantitatively screen the charging facility site selection plan in a special mixed traffic network composed of fuel vehicles and electric vehicles. Both of these existing inventions focus on the site selection and layout planning of electric vehicle charging facilities, but neither takes into account the prediction of electric vehicle travel demand that is necessary before planning. Summary of the invention

[0004] In order to solve the technical problems mentioned in the above background technology, the present invention proposes a method for predicting urban electric vehicle demand under the background of low-carbon travel.

[0005] In order to achieve the above technical objectives, the technical solution of the present invention is:

[0006] A method for predicting urban electric vehicle demand under the background of low-carbon travel includes the following steps:

[0007] S1. Based on the topological structure of the urban traffic network and the randomness of the travel time of travelers, a traffic assignment problem based on the principle of random user equilibrium is constructed and solved;

[0008] S2. Based on the vehicle operation characteristics of fuel vehicles and electric vehicles, formulate an operation cost function that takes into account the scale effect of the urban transportation network;

[0009] S3. Based on the Logit behavior choice model, the traveler’s choice of fuel vehicles and electric vehicles is simulated to predict the total demand for electric vehicle travel in the urban transportation network.

[0010] Preferably, the specific contents of step S1 are as follows: according to the specific road network connection and traffic zone division in the city, a corresponding network topology structure G=(N, A) is constructed, any node n∈N, N represents a set of road nodes, any road section a∈A, A represents a set of directed road sections, travel demand occurs at the starting point and ends at the destination, the starting and ending points with travel demand form an OD pair in the network, any OD pair w∈W, W represents a set of OD pairs, and the travel demand between OD pairs is q w ; The path set between OD pairs w is P w , any path p∈P w ;

[0011] On the basis of the network topology, a traffic assignment problem based on the principle of random user equilibrium is established, and the formula is generated and solved to obtain the path flow and travel time in the urban traffic network. The formula is as follows:

[0012]

[0013]

[0014] In the formula, represents the flow of path p between OD pairs; represents the path travel time function of path p between OD pairs; θ represents the discrete parameter of travelers' perception of travel time in the network; ω represents the integral variable.

[0015] Preferably, the specific content of S2 is as follows: obtaining the fuel consumption of the fuel vehicle and the power consumption of the electric vehicle, and the calculation formula is as follows:

[0016] ε g=(0.00001v 2 -0.00182v+0.13408)×d

[0017] ε e =(0.0096v 3 +84.775v+1000)×t

[0018] In the formula, ε g represents the average fuel consumption of fuel vehicles, ε e represents the average power consumption of electric vehicles; d represents the travel distance, t represents the travel time; v represents the average running speed of the vehicle, Based on the urban traffic network, the automobile operation cost function is established, and the formula is as follows:

[0019]

[0020]

[0021] In the formula, c g and c e Represent the travel costs of fuel vehicles and electric vehicles respectively; r f represents the gasoline price, r e represents the price of electricity;

[0022] The travel distance is calculated by the floating car method, that is, the investigator drives through the measurement section at the free flow speed v0 within the time t0, and the above formula is transformed into:

[0023]

[0024]

[0025] According to the operating cost function in any road section, the path operating cost of fuel vehicles and electric vehicles in the entire urban transportation network is calculated. The formula is as follows:

[0026]

[0027]

[0028] In the formula, and They represent the operating costs of fuel vehicles and electric vehicles on the OD pair path p respectively; It represents the free flow time of path p between OD pairs, and its calculation formula is:

[0029]

[0030] In the formula, t a，0 represents the free flow time of section a; represents the link-path association coefficient. If path p passes through link a, then otherwise

[0031] Preferably, the specific content of step S3 is as follows: based on the Logit behavior selection model, combined with the operating cost functions of fuel vehicles and electric vehicles formulated in S2, the travel utility of the two types of travel modes is calculated, and the formula is expressed as follows:

[0032]

[0033] In the formula, and They represent the travel utility of choosing a fuel vehicle and an electric vehicle on the OD pair path p; θV represents the travel mode selection parameter, and They represent the operating costs of fuel vehicles and electric vehicles on the OD path p, respectively. Respectively express the utility The unobservable items in express All follow the Gombel type extreme value distribution of independent and identically distributed random variables; there are OD pairs in the network formed by the origin and destination of travel demand. For any OD pair w∈W, W represents the set of OD pairs, the travel demand between OD pairs is q w ; The path set between OD pairs w is P w , any path p∈P w ;

[0034] Then the fuel vehicle flow rate of path p between OD pairs is and electric vehicle traffic The expression is as follows:

[0035]

[0036]

[0037] in, represents the flow rate of path p between OD pairs, exp represents the exponential function with the natural constant e as the base;

[0038] Fuel vehicle flow according to OD pair path p and electric vehicle traffic Derive the total demand for electric vehicles in the entire urban transportation network and total demand for fuel vehicles The formula is as follows:

[0039]

[0040]

[0041] Where w is any OD pair in the network, and p is any path in the network.

[0042] The beneficial effects brought by adopting the above technical solution are:

[0043] The present invention establishes a method for predicting the demand for urban electric vehicles under the background of low-carbon travel, which is used to predict the demand for electric vehicles in a specific urban transportation network under the background of low-carbon travel. The present invention takes into account the scale effect of the urban transportation network, and establishes operating cost functions that conform to the vehicle operation characteristics for fuel vehicles and electric vehicles respectively. According to the randomness of travelers' subjective perception of travel time, the present invention constructs and solves the traffic allocation problem based on the principle of random user equilibrium, thereby obtaining the path flow and travel time in the urban transportation network, as well as the operating costs of fuel vehicles and electric vehicles. Finally, the present invention combines the Logit behavior selection model to divide the travel mode and realize the effective prediction of the travel demand of urban electric vehicles. The present invention can help relevant enterprises and institutions to more accurately predict the travel demand of electric vehicles in a special mixed transportation network composed of fuel vehicles and electric vehicles, estimate the environmental costs related to urban transportation, and thus take timely and effective management measures to provide solid support for the construction of modern urban transportation that meets the "dual carbon" goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a flow chart of the method of the present invention;

[0045] Figure 2 This is the network topology of the present invention. DETAILED DESCRIPTION

[0046] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings.

[0047] The present invention provides a method for predicting urban electric vehicle demand under the background of low-carbon travel. Figure 1 As shown, the following process is included:

[0048] The embodiments are only for illustrating the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the present invention.

[0049] 1) Based on the topological structure of the urban traffic network and the randomness of travelers' subjective perception of travel costs, a traffic assignment problem based on the principle of random user equilibrium is constructed and solved.

[0050] Consult the map data to obtain the specific road network connections and traffic zone divisions within the target city, and construct Figure 2 The network topology shown in Figure 1 has 24 nodes, 76 road segments and 528 OD pairs. Let G = (N, A) represent the network topology. N represents the set of road nodes, any node n∈N; A represents the set of directed road segments, any road segment a∈A. Let W represent the set of OD pairs, any OD pair w∈W. The travel demand between OD pair w is q w , the path set is P w , any path p∈P w In the target city transportation network, the travel demand q between each OD pair is w The values ​​of are shown in Table 1:

[0051] Table 1 Travel demand of target city transportation network

[0052]

[0053]

[0054]

[0055]

[0056]

[0057]

[0058] Path travel time function of the target city for:

[0059]

[0060] In the formula, represents the flow of path p between OD pairs w; t a，0 represents the free flow time of section a; represents the link-path association coefficient. If path p passes through link a, then otherwise c a Represents the traffic capacity of section a; ρ and τ are both empirical parameters, and the recommended values ​​are ρ=0.15 and τ=4.

[0061] In the target city traffic network, the free flow time t of each road section a，0 and capacity c a The values ​​of are shown in Table 2:

[0062] Table 2. Parameter values ​​of road sections in the target city traffic network

[0063]

[0064]

[0065] Based on the network topology, a traffic assignment problem based on the principle of random user equilibrium is established, and its mathematical form is as follows:

[0066]

[0067]

[0068] Where θ represents the discrete parameter of travelers’ perception of travel time in the network, and its value is θ = 0.1.

[0069] It is easy to prove that the mathematical form of the traffic assignment problem is a strictly convex mathematical programming model. The model is solved by using algorithms such as the Frank-Wolfe algorithm or the successive average method to obtain the path flow and travel time in the target city's traffic network.

[0070] 2) Based on the vehicle operating characteristics of fuel vehicles and electric vehicles, formulate an operating cost function that takes into account the scale effect of the urban transportation network.

[0071] The path operation costs of fuel vehicles and electric vehicles in the target city’s transportation network are:

[0072]

[0073]

[0074] In the formula, and They represent the operating costs of fuel vehicles and electric vehicles on path p between OD pairs w. f represents the gasoline price ($ / L), with a value of r f =1.5; r e represents the electricity price ($ / kW·h), and takes the value r e = 0.10932. v0 represents the free flow speed (km / h) in the floating car method, and the value is v0 = 60. represents the free flow time of path p between OD pairs w, which is calculated as:

[0075]

[0076] 3) Based on the Logit behavioral choice model, the traveler’s choice of fuel vehicles and electric vehicles is simulated, and the total demand for electric vehicle travel in the urban transportation network is predicted.

[0077] Based on the Logit behavior choice model, combined with the operating cost functions of fuel vehicles and electric vehicles, the travel utility of the two types of travel modes is calculated:

[0078]

[0079] In the formula, and They represent the travel utility of choosing a fuel vehicle and an electric vehicle on path p between OD pairs w; θ V Represents the travel mode selection parameter, with a value of θ V =0.1; Positive They represent the unobservable terms in utility, and both follow the extreme value distribution of independent and identically distributed random variables.

[0080] Then the fuel vehicle flow rate of path p between OD and w is Yang Electric Vehicle Traffic They are:

[0081]

[0082]

[0083] Total electric vehicle demand across the entire urban transportation network for:

[0084]

[0085] Total demand for fuel vehicles for:

[0086]

[0087] After calculation, the total demand for electric vehicles in the target city example network is 184,086, and the total demand for fuel vehicles is 177,214.

[0088] The above embodiments are only preferred implementation modes of the present invention. It should be pointed out that ordinary technicians in this technical field can make several improvements and equivalent substitutions without departing from the principles of the present invention. These technical solutions after improvements and equivalent substitutions to the claims of the present invention all fall within the protection scope of the present invention.

Claims

1. A method for predicting urban electric vehicle demand under the background of low-carbon travel, characterized in that: The following steps are involved: S1. Based on the topological structure of the urban traffic network and the randomness of the travel time of travelers, a traffic assignment problem based on the principle of random user equilibrium is constructed and solved; S2. Based on the vehicle operation characteristics of fuel vehicles and electric vehicles, formulate an operation cost function that takes into account the scale effect of the urban transportation network; S3. Based on the Logit behavior choice model, simulate the traveler's choice of fuel vehicles and electric vehicles, and predict the total demand for electric vehicle travel in the urban transportation network; The specific content of S2 is as follows: obtain the fuel consumption of the fuel vehicle and the power consumption of the electric vehicle, and the calculation formula is as follows: e g =(0.00001v 2 -0.00182v+0.13408)×d e e =(0.0096v 3 +84.775v+1000)×t In the formula, ε g represents the average fuel consumption of fuel vehicles, ε e represents the average power consumption of electric vehicles; d represents the travel distance, t represents the travel time; v represents the average running speed of the vehicle, Based on the urban traffic network, the automobile operation cost function is established, and the formula is as follows: In the formula, c g and c e Represent the travel costs of fuel vehicles and electric vehicles respectively; r f represents the gasoline price, r e represents the price of electricity; The travel distance is calculated by the floating car method, that is, the investigator drives through the measurement section at the free flow speed v0 within the time t0, and the above formula is transformed into: According to the operating cost function in any road section, the path operating cost of fuel vehicles and electric vehicles in the entire urban transportation network is calculated. The formula is as follows: In the formula, and They represent the operating costs of fuel vehicles and electric vehicles on the OD pair path p respectively; It represents the free flow time of path p between OD pairs, and its calculation formula is: Where, t a,0 represents the free flow time of section a; represents the link-path association coefficient. If path p passes through link a, then otherwise The specific content of step S3 is as follows: Based on the Logit behavior selection model, combined with the operating cost functions of fuel vehicles and electric vehicles formulated in S2, the travel utility of the two types of travel modes is calculated, and the formula is expressed as follows: In the formula, and They represent the travel utility of choosing a fuel vehicle and an electric vehicle on the OD pair path p; θ V represents the travel mode selection parameters, and They represent the operating costs of fuel vehicles and electric vehicles on the OD path p, respectively. Respectively express the utility The unobservable items in express All follow the Gombel type extreme value distribution of independent and identically distributed random variables; there are OD pairs in the network formed by the origin and destination of travel demand. For any OD pair w∈W, W represents the set of OD pairs, the travel demand between OD pairs is q w ; The path set between OD pairs w is P w , any path p∈P w ; Then the fuel vehicle flow rate of path p between OD pairs is and electric vehicle traffic The expression is as follows: in, represents the flow rate of path p between OD pairs, exp represents the exponential function with the natural constant e as the base; Fuel vehicle flow according to OD pair path p and electric vehicle traffic Derive the total demand for electric vehicles in the entire urban transportation network and total demand for fuel vehicles The formula is as follows: Where w is any OD pair in the network, and p is any path in the network.

2. According to the method for predicting urban electric vehicle demand under the background of low-carbon travel in claim 1, it is characterized in that: The specific contents of step S1 are as follows: according to the specific road network connection and traffic zone division in the city, a corresponding network topology structure G=(N,A) is constructed, any node n∈N, N represents the set of road nodes, any road section a∈A, A represents the set of directed road sections, travel demand occurs at the starting point and ends at the destination, the starting and ending points with travel demand form OD pairs in the network, any OD pair w∈W, W represents the set of OD pairs, and the travel demand between OD pairs is q w ; The path set between OD pairs w is P w , any path p∈P w ; On the basis of the network topology, a traffic assignment problem based on the principle of random user equilibrium is established, and the formula is generated and solved to obtain the path flow and travel time in the urban traffic network. The formula is as follows: In the formula, represents the flow of path p between OD pairs; represents the path travel time function of path p between OD pairs; θ represents the discrete parameter of travelers' perception of travel time in the network; ω represents the integral variable.

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