Traffic demand prediction method for newly-built gas station project

Abstract

The invention discloses a traffic demand prediction method for a new gas station project, and the method comprises the following steps: obtaining primarily-selected gas stations in a target region according to a traffic location condition, calculating the Mahalanobis distance between the primarily-selected gas stations and between the primarily-selected gas stations and a new gas station based on the multi-dimensional feature data of each primarily-selected gas station, and carrying out the calculation of the Mahalanobis distance between the primarily-selected gas stations and the new gas station; n analogy gas stations are screened according to the mahalanobis distance; obtaining the annual average daily sales volume of each oil product, the peak hour sales volume of each oil product and the vehicle type-based traffic generation volume of each oil product in peak hours of N analogy gas stations, and calculating the peak hour sales coefficient of each oil product in the analogy gas stations and the vehicle type-based traffic generation rate of each oil product in peak hours, and on the basis of the peak hour sales coefficient of each oil product of the analogy gas station and the peak hour vehicle type-based traffic generation rate of each oil product, predicting the peak hour vehicle type-based traffic generation rate of the newly-built gas station. The technical problem of how to accurately predict the traffic demand of the new gas station project is solved.

Description

Technical Field

[0001] This invention relates to the field of intelligent transportation planning technology, and in particular to a method for predicting traffic demand for new gas station projects. Background Technology

[0002] As a crucial component of urban transportation infrastructure, the planning, layout, and traffic organization of urban gas stations directly impact the efficiency and safety of regional traffic operations. With accelerating urbanization and the continuous growth of motor vehicle ownership, gas stations, as key nodes for vehicle refueling, have a significant impact on the capacity, traffic order, and safety of the surrounding road network due to their location, scale, and internal traffic flow design. Improper planning can lead to traffic congestion, even causing traffic accidents, and reducing the overall service level of the urban transportation system. Conducting scientific and systematic traffic impact assessments during the site selection and detailed construction planning stages of gas station projects is essential to ensure their coordinated development with urban transportation. According to the "Technical Standards for Traffic Impact Assessment of Construction Projects," traffic demand forecasting is one of the core components of traffic impact assessment. It provides data support for planning decisions by quantitatively analyzing the impact of increased traffic volume after project completion on the surrounding road network. However, current technologies for traffic demand forecasting at gas stations largely rely on traditional models, failing to adequately consider the differences in surrounding road network structure, land use, parking spaces, and traffic flow. This leads to significant discrepancies between predicted and actual traffic demand, directly impacting the reliability of Traffic Demand Assessment (TIA) conclusions and consequently misleading planning decisions. Consequently, gas stations themselves or their surrounding road networks may become new traffic bottlenecks or safety hazards. Therefore, there is an urgent need to propose a new method for traffic demand forecasting of new gas station projects to address the technical challenge of accurately predicting traffic demand for such projects. Summary of the Invention

[0003] The main objective of this invention is to propose a method for predicting traffic demand for new gas station projects, aiming to solve the technical problem of how to accurately predict traffic demand for new gas station projects.

[0004] To achieve the above objectives, the present invention provides a method for predicting traffic demand for new gas station projects, wherein the method includes the following steps:

[0005] S1. Based on the traffic location conditions, obtain the preliminary gas stations in the target area. Based on the multi-dimensional feature data of each preliminary gas station, calculate the Mahalanobis distance between the preliminary gas stations and between the preliminary gas stations and the newly built gas stations. Then, select N analog gas stations based on the Mahalanobis distance.

[0006] S2. Obtain the annual average daily sales volume of each oil product, the peak hour sales volume of each oil product, and the peak hour traffic generation volume of each oil product by vehicle type for N analog gas stations. Calculate the peak hour sales coefficient and peak hour traffic generation rate of each oil product by vehicle type for the analog gas stations. Based on the peak hour sales coefficient and peak hour traffic generation rate of each oil product by vehicle type for the analog gas stations, predict the peak hour traffic generation volume of the newly built gas station by vehicle type.

[0007] One preferred embodiment is that step S1 obtains preliminary gas stations within the target area based on traffic location conditions, specifically as follows:

[0008] Based on the transportation location conditions, obtain the target area There are three initial gas stations, all of which sell the same type of fuel; and multidimensional feature data of the initial gas stations are obtained to obtain a first feature dataset and a second feature dataset.

[0009] One preferred option is that the first feature dataset includes the operating land area of ​​the gas station, the number of fuel nozzles, annual sales, the number of parking spaces for motor vehicles within the service area of ​​the gas station, and the traffic flow of the road network within the service area.

[0010] The second feature dataset includes the annual average daily sales volume of each type of oil at gas stations, the peak hour sales volume of each type of oil, and the traffic generation volume of each type of oil by vehicle type during peak hours.

[0011] One preferred embodiment is that step S1 calculates the Mahalanobis distances between the initially selected gas stations and between the initially selected gas stations and the newly built gas stations, specifically as follows:

[0012] The feature covariance matrix is ​​calculated based on the first feature dataset. The feature covariance matrix is ​​then regularized and inverted. The Mahalanobis distances between the initially selected gas stations and between the initially selected gas stations and the newly built gas stations are calculated and standardized.

[0013] One preferred embodiment is that the feature covariance matrix is:

[0014]

[0015] in, for * The characteristic covariance matrix, For the first gas station to be selected The first feature and the second The covariance of each feature;

[0016] The first selected gas station The first feature and the second The covariance of each feature is:

[0017]

[0018] in, This represents the total number of gas stations selected in the initial screening. For the first The first gas station selected in the preliminary round Each characteristic observation value For the first gas station to be selected The mean of the characteristic observations, For the first gas station to be selected The mean of the feature observations.

[0019] One preferred embodiment is that the Mahalanobis distance between the initially selected gas stations is:

[0020]

[0021] in, For the first The Mahalanobis distance between the initial gas stations , , For the first Feature vectors of the initially selected gas stations For the first Feature vectors of the initially selected gas stations The regularization matrix is ​​the eigencovariance matrix. The inverse of the regularization matrix; , For the first The first gas station selected in the preliminary round Each characteristic observation value , For the first The first gas station selected in the preliminary round Each characteristic observation value;

[0022] The Mahalanobis distance between the initially selected gas station and the newly built gas station is:

[0023]

[0024] in, For the first The Mahalanobis distance between the initial selected gas stations and the newly built gas stations The feature vector of the newly built gas station. , For the first newly built gas station Each characteristic observation value For the first Feature vectors of the initially selected gas stations , For the first The first gas station selected in the preliminary round Each characteristic observation value .

[0025] One preferred embodiment is that the Mahalanobis distance is standardized, specifically as follows:

[0026] Calculate the mean and standard deviation of the Mahalanobis distances between the initially selected gas stations; the mean is:

[0027]

[0028] in, for The mean of the Mahalanobis distances between the initial selected gas stations; the standard deviation is:

[0029]

[0030] in, for Standard deviation of Mahalanobis distance between the initial selected gas stations;

[0031] Based on the mean and standard deviation of the Mahalanobis distances between the initially selected gas stations, the standardized Mahalanobis distance between the initially selected gas stations and the newly built gas stations is calculated; the standardized Mahalanobis distance is:

[0032]

[0033] in, The standardized Mahalanobis distance between the initially selected gas stations and the newly built gas stations.

[0034] One preferred embodiment is that the peak hour sales coefficients for each type of fuel at the analog gas station are:

[0035]

[0036] in, To draw an analogy with gas stations oil products Peak hour sales coefficient To draw an analogy with gas stations oil products Peak hourly sales volume To compare with gas stations oil products The average daily sales volume per year;

[0037] The peak hour traffic generation rates for each fuel type and vehicle type are as follows:

[0038]

[0039] in, To draw an analogy with gas stations oil products Model Traffic generation rate To draw an analogy with gas stations Peak hour oil prices Model Traffic generation volume.

[0040] One preferred embodiment is that step S2 predicts the peak-hour traffic generation volume of the newly built gas station based on the peak-hour sales coefficient of each fuel type at the gas station and the peak-hour traffic generation rate of each fuel type by vehicle type, including:

[0041] Based on the peak hour sales coefficients and peak hour traffic generation rates of each fuel type at comparable gas stations, combined with the Mahalanobis distance between comparable gas stations and newly built gas stations, the peak hour sales coefficients and peak hour traffic generation rates of each fuel type at newly built gas stations are predicted.

[0042] The peak-hour sales coefficients for each type of fuel at the newly built gas station are as follows:

[0043]

[0044] in, For the new gas station fuel Peak hour sales coefficient To draw an analogy with gas stations Mahal distance from the newly built gas station;

[0045] The peak hour traffic generation rates for each fuel type and vehicle type are as follows:

[0046]

[0047] in, For peak hour oil products Model Traffic generation rate.

[0048] One preferred embodiment is that step S2 predicts the peak-hour traffic generation volume of the newly built gas station based on the peak-hour sales coefficient of each fuel type at the gas station and the peak-hour traffic generation rate of each fuel type by vehicle type, including:

[0049] Based on the peak-hour sales coefficients of each fuel type at the newly built gas station and the peak-hour traffic generation rates of each fuel type by vehicle type, the peak-hour traffic generation volume of the newly built gas station by vehicle type is predicted as follows:

[0050]

[0051] in, For peak-hour vehicle types at newly built gas stations Traffic generation volume, For the peak hour fuel quality of newly built gas stations Model Traffic generation volume;

[0052] The newly built gas station's peak-hour fuel quality Model Traffic generation volume is:

[0053]

[0054] in, For the new gas station fuel Annual sales volume This refers to the number of operating days per year for newly built gas stations.

[0055] The above-described technical solution of this invention provides a method for predicting traffic demand for a newly constructed gas station project, comprising the following steps: obtaining preliminary gas stations within a target area based on traffic location conditions; calculating Mahalanobis distances between the preliminary gas stations and between the preliminary gas stations and the newly constructed gas station based on multi-dimensional feature data of each preliminary gas station; selecting N analog gas stations based on these Mahalanobis distances; obtaining the average daily annual sales volume of each fuel type, peak hour sales volume of each fuel type, and peak hour traffic generation volume of each fuel type by vehicle type for each of the N analog gas stations; calculating the peak hour sales coefficient and peak hour traffic generation rate of each fuel type by vehicle type for each of the analog gas stations; and predicting the peak hour traffic generation volume of the newly constructed gas station by vehicle type based on the peak hour sales coefficient and peak hour traffic generation rate of each fuel type by vehicle type for each of the analog gas stations. This invention solves the technical problem of how to accurately predict traffic demand for a newly constructed gas station project. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0057] Figure 1 This is a schematic diagram of a traffic demand forecasting method for a newly built gas station project according to an embodiment of the present invention.

[0058] The realization of the objective, functional characteristics and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] The technical solutions of the various embodiments of the present invention can be combined with each other, but this must be based on the ability of a person skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0061] See Figure 1 According to one aspect of the present invention, the present invention provides a method for predicting traffic demand for a newly constructed gas station project, wherein the method for predicting traffic demand for a newly constructed gas station project includes the following steps:

[0062] S1. Based on the traffic location conditions, obtain the preliminary gas stations in the target area. Based on the multi-dimensional feature data of each preliminary gas station, calculate the Mahalanobis distance between the preliminary gas stations and between the preliminary gas stations and the newly built gas stations. Then, select N analog gas stations based on the Mahalanobis distance.

[0063] S2. Obtain the annual average daily sales volume of each oil product, the peak hour sales volume of each oil product, and the peak hour traffic generation volume of each oil product by vehicle type for N analog gas stations. Calculate the peak hour sales coefficient and peak hour traffic generation rate of each oil product by vehicle type for the analog gas stations. Based on the peak hour sales coefficient and peak hour traffic generation rate of each oil product by vehicle type for the analog gas stations, predict the peak hour traffic generation volume of the newly built gas station by vehicle type.

[0064] Specifically, in this embodiment, step S1, which involves obtaining preliminary gas stations within the target area based on traffic location conditions, specifically comprises: obtaining gas stations within the target area based on traffic location conditions. A preliminary selection of gas stations, all of which sell the same type of fuel; and acquisition of multi-dimensional feature data of the preliminary selection of gas stations to obtain a first feature dataset and a second feature dataset; in this invention... =7, This invention does not impose specific limitations; the specific settings can be configured as needed. The following text will refer to... =7 For example, the first feature dataset includes the operating land area of ​​the gas station, the number of fuel nozzles, annual sales, the number of parking spaces for motor vehicles within the service area of ​​the gas station, and the traffic flow of the road network within the service area; the number of feature categories in the first feature dataset is 5, and the second feature dataset includes the annual average daily sales volume of each oil product at the gas station, the peak hour sales volume of each oil product, and the traffic generation volume of each oil product by vehicle type during peak hours.

[0065] Specifically, in this embodiment, the newly built gas station is numbered 0, and the initially selected gas station is numbered... , The road network traffic within the service area of ​​the gas station refers to the total mileage traveled by vehicles in the road network during peak hours; the first feature dataset and the second feature dataset are shown in Table 1 and Table 2.

[0066] Table 1 First Feature Dataset

[0067] Gas station (number) Operating area of ​​a gas station (㎡) Number of fuel nozzles at gas stations (pieces) Annual sales revenue of gas stations (in ten thousand yuan) Number of parking spaces (in ten thousand) within the service area of ​​the gas station Traffic flow within the service area of ​​the gas station (10,000 vehicle-kilometers / hour) 0 1110 16 12997 1.5 3.2 1 954 13 8262 1.4 3.9 2 1456 14 8270 1.4 3.5 3 1060 14 10217 1.8 3 4 1057 9 11781 1.5 3.8 5 1010 12 9671 2 3.1 6 1438 10 10526 1.8 3.3 7 951 14 11301 1.8 3.7

[0068] Table 2 Second Feature Dataset

[0069]

[0070] Specifically, in this embodiment, the oil types include 92-octane gasoline, 95-octane gasoline, 98-octane gasoline, and 0-grade diesel, and the number of oil types is as follows: The vehicle types include motorcycles, small passenger cars, large passenger cars, small trucks, medium-sized trucks, and large trucks, with a total number of vehicle types. The fuel type codes at gas stations are shown in Table 3, and the vehicle type codes are shown in Table 4. These can be adjusted appropriately based on actual conditions during use.

[0071] Table 3. Gas Station Fuel Type - Code Reference Table

[0072]

[0073] Table 4 Vehicle Model Number Comparison Table

[0074]

[0075] Specifically, in this embodiment, step S1, calculating the Mahalanobis distance between the initially selected gas stations and between the initially selected gas stations and the newly built gas stations, specifically involves:

[0076] The feature covariance matrix is ​​calculated based on the first feature dataset. The feature covariance matrix is ​​then regularized and inverted. The Mahalanobis distances between the initially selected gas stations and between the initially selected gas stations and the newly built gas stations are calculated and standardized.

[0077] Specifically, in this embodiment, the feature covariance matrix is:

[0078]

[0079] in, for * The characteristic covariance matrix, For the first gas station to be selected The first feature and the second The covariance of each feature;

[0080] The first selected gas station The first feature and the second The covariance of each feature is:

[0081]

[0082] in, This represents the total number of gas stations selected in the initial screening. For the first The first gas station selected in the preliminary round Each characteristic observation value For the first gas station to be selected The mean of the characteristic observations, For the first gas station to be selected The mean of the characteristic observations;

[0083] The first selected gas station The mean of the feature observations is:

[0084]

[0085] The regularized covariance matrix is:

[0086]

[0087] in, The regularized covariance matrix is... As a parameter, in this invention, This invention does not impose specific limitations; the specific details can be set according to needs. It is a 5x5 identity matrix.

[0088] Specifically, in this embodiment, the Mahalanobis distance between the initially selected gas stations is:

[0089]

[0090] in, For the first The Mahalanobis distance between the initial gas stations , , For the first Feature vectors of the initially selected gas stations For the first Feature vectors of the initially selected gas stations The regularization matrix is ​​the eigencovariance matrix. The inverse of the regularization matrix; , For the first The first gas station selected in the preliminary round Each characteristic observation value , For the first The first gas station selected in the preliminary round Each characteristic observation value;

[0091] The Mahalanobis distance between the initially selected gas station and the newly built gas station is:

[0092]

[0093] in, For the first The Mahalanobis distance between the initial selected gas stations and the newly built gas stations The feature vector of the newly built gas station. , For the first newly built gas station Each characteristic observation value For the first Feature vectors of the initially selected gas stations , For the first The first gas station selected in the preliminary round Each characteristic observation value .

[0094] Table 5. Mahalanobis distances between each of the eight gas stations

[0095]

[0096] Specifically, in this embodiment, the Mahalanobis distance is standardized as follows:

[0097] Calculate the mean and standard deviation of the Mahalanobis distances between the initially selected gas stations; the mean is:

[0098]

[0099] in, for The mean of the Mahalanobis distances between the initial selected gas stations; the standard deviation is:

[0100]

[0101] in, for Standard deviation of Mahalanobis distance between the initial selected gas stations;

[0102] Based on the mean and standard deviation of the Mahalanobis distances between the initially selected gas stations, the standardized Mahalanobis distance between the initially selected gas stations and the newly built gas stations is calculated; the standardized Mahalanobis distance is:

[0103]

[0104] in, The standardized Mahalanobis distance between the initially selected gas stations and the newly built gas stations.

[0105] Table 6 Standardized Mahalanobis distance between the initially selected gas stations and the newly built gas stations

[0106]

[0107] Specifically, in this embodiment, step S1, which selects N analog gas stations based on Mahalanobis distance, specifically involves:

[0108] Set threshold ,pass Standardized Mahalanobis distance between the initially selected gas stations and the newly built gas stations Filtering is performed, i.e., those that meet the conditions are selected. ≤ The initial selection of gas stations is designated as analogous gas stations, and their corresponding number is denoted as M. If M ≥ 3, these M gas stations are determined as analogous gas stations. If M < 3, additional gas stations need to be selected, and the above steps are repeated until M ≥ 3. The final number of analogous gas stations is then determined to be N. After screening the initial gas stations, N analog gas stations were obtained. The Mahalanobis distance between these N analog gas stations and the newly built gas stations is: .

[0109] Specifically, in this embodiment, when =1, specifically meaning that the Mahalanobis distance between the analogous gas stations and the newly built gas stations, and the mean of the Mahalanobis distance between them and the initially selected gas stations, does not exceed 1 standard deviation. See Table 6 for the standardized Mahalanobis distances of the initially selected gas stations and the newly built gas stations numbered 1, 2, 3, 4, 5, 6, and 7. Therefore, these six initial gas stations were identified as analogous gas stations. For ease of subsequent description, the six analogous gas stations were renumbered sequentially. The Mahalanobis distance between it and the newly built gas station is denoted as . , .

[0110] Specifically, in this embodiment, the peak hour sales coefficients for each type of oil at the analog gas station are as follows:

[0111]

[0112] in, To draw an analogy with gas stations oil products Peak hour sales coefficient To draw an analogy with gas stations oil products Peak hourly sales volume To draw an analogy with gas stations oil products The average daily sales volume per year; =1 indicates that it is 92-octane gasoline. =2, which indicates 95-octane gasoline. =3, which indicates 98-octane gasoline. =4, which indicates that it is No. 0 diesel fuel; =1 indicates a motorcycle. =2 indicates a small passenger vehicle. =3 indicates a large passenger bus. =4 indicates a small truck. =5 indicates a medium-sized truck. =6 indicates a large truck;

[0113] The peak hour traffic generation rates for each fuel type and vehicle type are as follows:

[0114]

[0115] in, To draw an analogy with gas stations oil products Model Traffic generation rate To draw an analogy with gas stations Peak hour oil prices Model Traffic generation volume;

[0116] For the peak hour sales coefficients of various oil products at gas stations and the traffic generation rates of various oil products by vehicle type during peak hours, please refer to Tables 7 and 8.

[0117] Table 7. Peak Hour Sales Coefficients of Various Oil Products at Analogous Gas Stations

[0118]

[0119] Table 8 Traffic Generation Rates by Fuel Type and Vehicle Type During Peak Hours

[0120]

[0121] Specifically, in this embodiment, step S2 predicts the peak-hour traffic generation volume of the newly built gas station based on the peak-hour sales coefficient of each oil product at the gas station and the peak-hour traffic generation rate of each oil product by vehicle type, including:

[0122] Based on the peak hour sales coefficients and peak hour traffic generation rates of each fuel type at comparable gas stations, combined with the Mahalanobis distance between comparable gas stations and newly built gas stations, the peak hour sales coefficients and peak hour traffic generation rates of each fuel type at newly built gas stations are predicted.

[0123] The peak-hour sales coefficients for each type of fuel at the newly built gas station are as follows:

[0124]

[0125] in, For the new gas station fuel Peak hour sales coefficient To draw an analogy with gas stations Mahal distance from the newly built gas station;

[0126] The peak hour traffic generation rates for each fuel type and vehicle type are as follows:

[0127]

[0128] in, For peak hour oil products Model Traffic generation rate.

[0129] Table 9. Peak-hour sales coefficients and peak-hour traffic generation rates of various fuel types for newly built gas stations.

[0130]

[0131] Specifically, in this embodiment, step S2 predicts the peak-hour traffic generation volume of the newly built gas station based on the peak-hour sales coefficient of each oil product at the gas station and the peak-hour traffic generation rate of each oil product by vehicle type, including:

[0132] Based on the peak-hour sales coefficients of each fuel type at the newly built gas station and the peak-hour traffic generation rates of each fuel type by vehicle type, the peak-hour traffic generation volume of the newly built gas station by vehicle type is predicted as follows:

[0133]

[0134] in, For peak-hour vehicle types at newly built gas stations Traffic generation volume, For the peak hour fuel quality of newly built gas stations Model Traffic generation volume;

[0135] The newly built gas station's peak-hour fuel quality Model Traffic generation volume is:

[0136]

[0137] in, For the new gas station fuel Annual sales volume This refers to the number of operating days per year for newly built gas stations.

[0138] Specifically, in this embodiment, based on the projected annual sales volume of each oil product in the feasibility study report of the new gas station (the annual sales volumes of 92-octane gasoline, 95-octane gasoline, 98-octane gasoline and 0# diesel are 4457.89, 3984.21, 3615.79 and 3042.11 tons respectively) and the number of operating days per year (365 days), the traffic generation volume of each oil product by vehicle type during the peak hours of the new gas station is obtained.

[0139] Table 10 Traffic Generation by Fuel Type and Vehicle Type During Peak Hours at Newly Built Refueling Stations

[0140]

[0141] The above are merely preferred embodiments of the present invention and do not limit the patent scope of the present invention. All equivalent structural transformations made using the contents of the present invention's specification and drawings under the inventive concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A method for predicting traffic demand for a newly constructed gas station project, characterized in that, Includes the following steps: S1. Based on the traffic location conditions, obtain the preliminary gas stations in the target area. Based on the multi-dimensional feature data of each preliminary gas station, calculate the Mahalanobis distance between the preliminary gas stations and between the preliminary gas stations and the newly built gas stations. Then, select N analog gas stations based on the Mahalanobis distance. S2. Obtain the annual average daily sales volume of each oil product, the peak hour sales volume of each oil product, and the peak hour traffic generation volume of each oil product by vehicle type for N analog gas stations. Calculate the peak hour sales coefficient and peak hour traffic generation rate of each oil product by vehicle type for the analog gas stations. Based on the peak hour sales coefficient and peak hour traffic generation rate of each oil product by vehicle type for the analog gas stations, predict the peak hour traffic generation volume of the newly built gas station by vehicle type. Based on the analogy of peak-hour sales coefficients for various fuel types at gas stations and peak-hour traffic generation rates for different fuel types by vehicle type, the peak-hour traffic generation volume for newly built gas stations by vehicle type is predicted as follows: Based on the peak hour sales coefficients and peak hour traffic generation rates of each fuel type at comparable gas stations, combined with the Mahalanobis distance between comparable gas stations and newly built gas stations, the peak hour sales coefficients and peak hour traffic generation rates of each fuel type at newly built gas stations are predicted. Based on the peak hour sales coefficients of each oil product at the newly built gas station and the peak hour traffic generation rate of each oil product by vehicle type, the peak hour traffic generation volume of the newly built gas station by vehicle type is predicted.

2. The method for predicting traffic demand for a newly constructed gas station project according to claim 1, characterized in that, Step S1, which involves obtaining preliminary gas stations within the target area based on traffic location conditions, specifically includes: Based on the transportation location conditions, obtain the target area There are three initial gas stations, all of which sell the same type of fuel; and multidimensional feature data of the initial gas stations are obtained to obtain a first feature dataset and a second feature dataset.

3. The method for predicting traffic demand for a newly constructed gas station project according to claim 2, characterized in that, The first feature dataset includes the operating land area of ​​the gas station, the number of fuel nozzles, annual sales, the number of parking spaces for motor vehicles within the service area of ​​the gas station, and the traffic flow of the road network within the service area; The second feature dataset includes the annual average daily sales volume of each type of oil at gas stations, the peak hour sales volume of each type of oil, and the traffic generation volume of each type of oil by vehicle type during peak hours.

4. The method for predicting traffic demand for a newly constructed gas station project according to claim 2, characterized in that, Step S1 calculates the Mahalanobis distances between the initially selected gas stations and between the initially selected gas stations and the newly built gas stations, specifically as follows: The feature covariance matrix is ​​calculated based on the first feature dataset. The feature covariance matrix is ​​then regularized and inverted. The Mahalanobis distances between the initially selected gas stations and between the initially selected gas stations and the newly built gas stations are calculated and standardized.

5. The method for predicting traffic demand for a newly constructed gas station project according to claim 4, characterized in that, The feature covariance matrix is: ； in, for * The characteristic covariance matrix, For the first gas station to be selected The first feature and the second The covariance of each feature; The first selected gas station The first feature and the second The covariance of each feature is: ； in, This represents the total number of gas stations selected in the initial screening. For the first The first gas station selected in the preliminary round Each characteristic observation value For the first gas station to be selected The mean of the characteristic observations, For the first gas station to be selected The mean of the feature observations.

6. The method for predicting traffic demand for a newly constructed gas station project according to claim 5, characterized in that, The Mahalanobis distance between the initially selected gas stations is: ； in, For the first The Mahalanobis distance between the initial gas stations , , For the first Feature vectors of the initially selected gas stations For the first Feature vectors of the initially selected gas stations The regularization matrix is ​​the eigencovariance matrix. The inverse of the regularization matrix; , For the first The first gas station selected in the preliminary round Each characteristic observation value , For the first The first gas station selected in the preliminary round Each characteristic observation value; The Mahalanobis distance between the initially selected gas station and the newly built gas station is: ； in, For the first The Mahalanobis distance between the initial selected gas stations and the newly built gas stations The feature vector of the newly built gas station. , For the first newly built gas station Each characteristic observation value For the first Feature vectors of the initially selected gas stations , For the first The first gas station selected in the preliminary round Each characteristic observation value .

7. The method for predicting traffic demand for a newly constructed gas station project according to claim 6, characterized in that, The Mahalanobis distance is standardized as follows: Calculate the mean and standard deviation of the Mahalanobis distances between the initially selected gas stations; the mean is: ； in, for The mean of the Mahalanobis distances between the initial selected gas stations; the standard deviation is: ； in, for Standard deviation of Mahalanobis distance between the initial selected gas stations; Based on the mean and standard deviation of the Mahalanobis distances between the initially selected gas stations, the standardized Mahalanobis distance between the initially selected gas stations and the newly built gas stations is calculated; the standardized Mahalanobis distance is: ； in, The standardized Mahalanobis distance between the initially selected gas stations and the newly built gas stations.

8. A method for predicting traffic demand for a newly constructed gas station project according to any one of claims 1-7, characterized in that, The peak hour sales coefficients for each type of fuel at the analogous gas stations are as follows: ； in, To draw an analogy with gas stations oil products Peak hour sales coefficient To draw an analogy with gas stations oil products Peak hourly sales volume To draw an analogy with gas stations oil products The average daily sales volume per year; The peak hour traffic generation rates for each fuel type and vehicle type are as follows: ； in, To draw an analogy with gas stations oil products Model Traffic generation rate To draw an analogy with gas stations Peak hour oil prices Model Traffic generation volume.

9. The method for predicting traffic demand for a newly constructed gas station project according to claim 8, characterized in that, The peak-hour sales coefficients for each type of fuel at the newly built gas station are as follows: ； in, For the new gas station fuel Peak hour sales coefficient To draw an analogy with gas stations Mahal distance from the newly built gas station; The peak hour traffic generation rates for each fuel type and vehicle type are as follows: ； in, For peak hour oil products Model Traffic generation rate.

10. The method for predicting traffic demand for a newly constructed gas station project according to claim 9, characterized in that, The peak hour traffic volume generated by the newly built gas station, categorized by vehicle type, is as follows: ； in, For peak-hour vehicle types at newly built gas stations Traffic generation volume, For the peak hour fuel quality of newly built gas stations Model Traffic generation volume; The newly built gas station's peak-hour fuel quality Model Traffic generation volume is: ； in, For the new gas station fuel Annual sales volume This refers to the number of operating days per year for newly built gas stations.

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