Method for measuring and calculating putting scale of urban shared electric bicycles

Abstract

The invention discloses a method for measuring and calculating the delivery scale of urban shared electric bicycles, and the method specifically comprises the following steps: (1) collecting the original travel mode of a user through questionnaire survey, and building a multi-term Logistic model to calculate the proportion of the user transferred to the shared electric bicycles; and (2) calculating the travel volume transferred to the shared electric bicycle by combining the daily average travel volume and the transfer proportion of each travel mode in a city measurement and calculation year, and calculating the demand for transferring to the shared electric bicycle according to the utilization rate, the turnover rate and the actual load rate of the electric bicycle. And (3) determining the main connection mode and proportion of the shared electric bicycle user based on questionnaire survey, calculating the connection travel amount of the shared electric bicycle through the daily average travel amount, the transfer proportion and the transfer coefficient, and calculating the shared electric bicycle demand of the connection travel based on the connection travel amount. And (4) integrating the shared electric bicycle demands of the transfer trip and the connection trip to obtain the total demand of the shared electric bicycle in the city in the measured year. And (5) calculating the total demand of the shared electric bicycles in the target year through the average growth rate coefficient, and distributing the release demand of the shared electric bicycles in each administrative region in the target year according to the permanent resident population and the attraction degree of each region. The operation process is clear, the calculation method is easy to implement, and the prediction result is closer to the actual demand of the shared electric bicycle in each region.

Description

Technical Field

[0001] The present invention relates to the field of shared transportation operation management and proposes a method for calculating the scale of deployment of shared electric motorcycles in a city. Background Art

[0002] Shared electric motorcycles provide residents with convenient and economical short-distance travel options, effectively reduce carbon emissions and ease traffic congestion, and help improve users' travel experience and health levels. However, with the continuous expansion of the shared electric motorcycle industry, some cities have seen a phenomenon of oversupply. Too many electric motorcycles have led to underutilization of resources and additional management and maintenance costs. In some cities, due to the small number of electric motorcycles deployed, users may face the situation of not being able to find electric motorcycles during peak riding demand, reducing travel convenience. Therefore, a reasonable deployment scale is particularly important to better meet the travel needs of urban residents.

[0003] The existing methods for calculating the scale of shared electric motorcycle deployment mainly include the following: (1) Demand forecasting method: forecasting the demand for shared electric motorcycles through data such as population density, residents' travel frequency and peak-hour usage rate. (2) Regional analysis method: evaluating the differences in demand for electric motorcycles in different regions based on traffic flow and public transportation connections in different regions. (3) Pilot operation: conducting pilots in specific areas and adjusting the number of deployments based on actual usage. (4) User feedback method: collecting user opinions, understanding changes in demand and usage experience, so as to dynamically adjust the scale of deployment. However, the above-mentioned methods for calculating the scale of shared electric motorcycle deployment often fail to accurately reflect the rapid changes in the market or the uncertainty of regional differences and user behavior due to incomplete data, resulting in uneven resource allocation, resulting in a significant difference between the final measurement results and the actual demand for shared electric motorcycles. Summary of the invention

[0004] In response to the above-mentioned problems existing in the deployment of shared electric motorcycles in cities, the present invention proposes a method for calculating the scale of deployment of shared electric motorcycles in cities, which can solve the above-mentioned problems existing in the prior art.

[0005] The technical solution adopted by the present invention is a method for calculating the scale of deployment of shared electric motorcycles in a city, which specifically includes the following steps:

[0006] 1. A method for calculating the scale of urban shared electric motorcycle deployment, the method comprising the following steps:

[0007] (1) Use questionnaire surveys to determine the travel modes (original travel modes) that shared electric motorcycle users have previously used, such as walking, bicycles, public transportation, taxis or online ride-hailing, and private cars. Establish a multi-logistic model to determine the proportion of each original travel mode in the city that has been transferred to shared electric motorcycles.

[0008] (2) Based on the average daily travel volume and transfer ratio of each travel mode, calculate the travel volume transferred to shared electric motorcycles. Based on the average utilization rate, turnover rate and actual load rate of shared electric motorcycles, calculate the demand for shared electric motorcycles for transferred travel.

[0009] (3) Determine the proportion of users who transfer from each connection mode (public transportation, private car) to shared electric motorcycles through questionnaire surveys. Calculate the number of shared electric motorcycles for connecting trips based on the average daily trip volume, transfer ratio, and transfer coefficient of the connecting mode. Calculate the demand for shared electric motorcycles for connecting trips based on indicators such as the utilization rate and turnover rate of connecting trips.

[0010] (4) The demand for shared electric motorcycles for transfer travel and connecting travel is combined to obtain the total demand for the city in the measurement year. The total demand for shared electric motorcycles in the target year is further estimated using the average growth coefficient method.

[0011] (5) Allocate the demand for shared electric motorcycles to each administrative region in the target year based on the permanent population and attractiveness indicators.

[0012] Furthermore, the specific steps in step (1) are as follows:

[0013] Considering individual attributes, travel attributes and attitude perception attributes, a questionnaire was designed to investigate whether users have switched to shared electric motorcycles from various original travel modes after the emergence of shared electric motorcycles.

[0014] Based on the data collected from the questionnaire, users whose original travel modes are walking, bicycle, public transportation, taxi (online car-hailing) and private cars are reclassified. SPSS is used to establish multi-logistic models for each original travel mode for the newly created data set. For example, for users whose original travel mode is walking, a multi-logistic model is established to explore the probability of users transferring to walking, bicycle, public transportation, taxi (online car-hailing) and private cars after the emergence of shared electric motorcycles.

[0015] The model used is a multinomial logistic model, and the basic form of the model can be described as:

[0016]

[0017] P in represents the probability that user n chooses travel mode i, C n It is a collection of travel modes, including walking, cycling, public transportation, taxi or online car-hailing, private car, etc. is the kth influencing factor affecting the selection of the mode, β k is the regression coefficient of the kth influencing factor affecting mode selection.

[0018] Furthermore, the specific steps in step (2) are as follows:

[0019] Based on the city's calculation year, the average daily travel volume of each mode i is T i and the transfer ratio p i , calculate the travel volume transferred to shared electric motorcycles (transferred volume). Based on the above transferred volume, as well as the average utilization rate α, average turnover rate β, and actual load rate γ of shared bicycles, calculate the shared electric motorcycle demand D for the city in the measured year 转移 .

[0020]

[0021] Furthermore, the specific steps in step (3) are as follows:

[0022] Based on the city's average daily travel volume of each connection mode in the year, T j , transfer ratio t j and the conversion coefficient δ 公共交通 , calculate the shuttle volume of shared electric motorcycles (shutdown volume). Based on the above shuttle volume, as well as the average utilization rate α, average turnover rate β, and actual load rate γ of shared bicycles, calculate the demand D of shared electric motorcycles for shuttle travel in the city in the measurement year 接驳 .

[0023]

[0024] Furthermore, the specific steps in step (4) are as follows:

[0025] The total demand for shared electric motorcycles in the city in the year of calculation is D, which is the demand for shared electric motorcycles for transfer travel. 转移 and the demand for shared electric bikes for shuttle travel 接驳 The total demand for shared electric motorcycles in the city in the target year is D o It is equal to the total demand D for shared electric motorcycles in the city in the measurement year multiplied by the average growth coefficient r of GDP.

[0026] D=D 转移 +D 接驳

[0027] D o =D·(1+r)^n

[0028] Furthermore, the specific steps in step (5) are as follows:

[0029] The total demand for shared electric motorcycles in each administrative district of the city in the target year Based on the number of permanent residents in each district P k And the attractiveness of each area A k Proportional calculations are performed.

[0030]

[0031] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0032] The present invention starts from the actual operational problems of the enterprise and, through data analysis and user demand investigation, can help shared bicycle enterprises to rationally plan the scale of deployment, optimize resource allocation, and improve operational efficiency.

[0033] For urban development, the present invention analyzes the transfer ratio and connecting travel ratio of various travel modes, and takes into account the population and attractiveness of each administrative area to calculate demand, so as to ensure that the deployment of shared electric motorcycles is more accurate and meet the travel needs of different regions. The prediction results are closer to the actual needs of each region, thereby reducing the gap between the calculated number of shared bicycles deployed and the actual needs of the city. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is an implementation example of the present invention: a flow chart of a method for calculating the scale of deployment of shared electric motorcycles in a city; DETAILED DESCRIPTION

[0035] In order to more fully illustrate the objectives, technical solutions and advantages of the embodiments of the present invention, the technical solutions are described below. The described embodiments represent only a part of the present invention, not all of it. According to the embodiments of the present invention, all other implementation methods obtained by technicians without creative work are deemed to be within the scope of protection of the present invention.

[0036] Taking the calculation of the scale of shared electric motorcycle deployment in a certain city as an example, the specific implementation steps are as follows:

[0037] S1. According to the results of a questionnaire survey on the use of shared electric motorcycles in a certain city in 2024, the main modes of travel for shared electric motorcycle users were walking, bicycles and buses; some used to travel by taxi, online car-hailing or private cars. Based on the data collected from the questionnaire, a multi-logistic model was established to estimate the proportion of people who moved from walking to shared electric motorcycles, which was 9.65%, the proportion of people who moved from public transportation to shared electric motorcycles was 7.22%, the proportion of people who moved from public bicycles to shared electric motorcycles was 78.5%, and the proportion of people who moved from taxis (online car-hailing) and private cars to shared electric motorcycles was 6.08%.

[0038] S2. According to the "Analysis Report on the Public Transport System of a Certain City", the total number of public transport passengers in a certain city in April 2023 is 1,070,700, with an average of 35,700 public transport trips per day, 73,100 pedestrian trips per day, 5,098 public bicycle rentals per day, and 68,900 taxi (online ride-hailing) and private car trips per day. Based on the above transfer ratios of walking, public transport, public bicycles, taxis (online ride-hailing) and private cars to shared electric motorcycles, the travel volume transferred to shared electric motorcycles can be obtained as shown in Table 1:

[0039] Table 1 Proportion and share of transfer to shared electric motorcycles

[0040]

[0041] S3. The results of the questionnaire survey on the use of shared electric motorcycles show that shared electric motorcycles are mainly connected with buses and motor vehicles. Among bus trips, about 4.35% use shared bicycles for connection, and the connection rate of other motor vehicles is 2.21%. According to the current average daily travel volume of buses and other motor vehicles in a certain city, the proportion of shared electric motorcycle connection is calculated. By querying the analysis report of the city's public transportation system, the average daily travel volume of buses is 35,700 people, and the bus transfer coefficient is 1.07; the average daily travel volume of other motor vehicles is 118,000 people. Therefore, the travel volume of public transportation and other motor vehicles transferring to shared electric motorcycles is shown in Tables 2 and 3.

[0042] Table 2 Number of shared electric motorcycles and bus connections (unit: 10,000 people)

[0043]

[0044] Table 3: Connection volume of shared electric motorcycles with other motor vehicles (unit: 10,000 people)

[0045]

[0046] S4. The specific steps for the total amount of shared electric motorcycles used (connection + transfer) are as follows:

[0047] (1) Based on the average daily passenger volume of each travel mode, combined with the travel transfer share rate and connection ratio, the shared electric motorcycle transfer volume and connection volume are calculated, totaling approximately 22,000 passengers per day.

[0048] (2) Once a shared electric motorcycle is put into use, it is inevitable that it will malfunction and require maintenance. At the same time, due to the need for charging and discharging, the average service life of a shared electric motorcycle is 3 years. Therefore, the utilization rate α of a shared electric motorcycle takes an average of 85% during its service life. At the same time, in order to avoid a large number of shared electric motorcycles being idle, promote enterprises to improve service quality and form healthy competition, refer to the daily turnover rate of each electric motorcycle in cities with good riding data nationwide, which is 4-5 times / day / vehicle, and take the turnover rate β as 4 times / day / vehicle. Since shared electric motorcycles do not have seats for passengers and only carry one person at a time, the actual load rate is 1.0.

[0049] (3) Based on the above analysis, by substituting the above parameters into the function expression of the total demand for shared electric motorcycles in a certain city, it can be calculated that the total demand for shared electric motorcycles in a certain city in 2024 is approximately 6,435 vehicles.

[0050] (4) Further, referring to the average growth rate of the city's GDP from 2021 to 2023 (7.78%), the growth rate of the number of shared electric motorcycles is taken as 8%. It can be obtained that the total demand for shared electric motorcycles in the first planning target year (2025) is 7,506, and the total demand for shared electric motorcycles in the second planning target year (2027) is about 8,755. Combined with the questionnaire survey, and based on the standardized and scientific operation of shared bicycle companies, it is recommended that the number of shared electric motorcycles put into use in 2025 be 7,500, and the number of shared electric motorcycles put into use in 2027 be 8,800.

[0051] S5. Based on the permanent population of each administrative district of a city and its attractiveness to residents, the allocation weights of each administrative district in the target year are calculated, and the recommended values ​​for the scale of shared electric motorcycle deployment in each administrative district in 2025 and 2027 are as follows:

[0052] Table 4 Distribution of shared electric motorcycle deployment in each administrative district of a city in 2025 and 2027

[0053]

[0054]

Claims

1. A method for calculating the scale of urban shared electric motorcycle deployment, the method comprising the following steps: (1) A questionnaire survey was used to determine the travel modes (original travel modes) previously used by shared electric motorcycle users, such as walking, bicycle, public transportation, taxi or online car-hailing, and private car. A multi-logistic model was established to determine the proportion of each original travel mode in the city that was transferred to shared electric motorcycles. (2) Based on the average daily travel volume and transfer ratio of each travel mode, calculate the travel volume transferred to shared electric motorcycles. Based on the average utilization rate, turnover rate and actual load rate of shared electric motorcycles, calculate the demand for shared electric motorcycles for transferred travel. (3) Determine the proportion of users who transfer from each connection mode (public transportation, private car) to shared electric motorcycles through questionnaire surveys. Calculate the number of shared electric motorcycles for connecting trips based on the average daily trip volume, transfer ratio, and transfer coefficient of the connecting mode. Calculate the demand for shared electric motorcycles for connecting trips based on indicators such as the utilization rate and turnover rate of connecting trips. (4) The demand for shared electric motorcycles for transfer travel and connecting travel is combined to obtain the total demand for the city in the measurement year. The total demand for shared electric motorcycles in the target year is further estimated using the average growth coefficient method. (5) Allocate the demand for shared electric motorcycles to each administrative region in the target year based on the permanent population and attractiveness indicators.

2. The method for calculating the scale of deployment of shared electric motorcycles in a city as claimed in claim 1, wherein step (1) specifically comprises the following contents: Considering individual attributes, travel attributes and attitude perception attributes, a questionnaire was designed to investigate whether users have switched to shared electric motorcycles from various original travel modes after the emergence of shared electric motorcycles. Based on the data collected from the questionnaire, users whose original travel modes are walking, bicycle, public transportation, taxi (online car-hailing) and private cars are reclassified. SPSS is used to establish multi-logistic models for each original travel mode for the newly created data set. For example, for users whose original travel mode is walking, a multi-logistic model is established to explore the probability of users transferring to walking, bicycle, public transportation, taxi (online car-hailing) and private cars after the emergence of shared electric motorcycles. The model used is a multinomial logistic model, and the basic form of the model can be described as: P in represents the probability that user n chooses travel mode i, C n It is a collection of travel modes, including walking, cycling, public transportation, taxi or online car-hailing, private car, etc. is the kth influencing factor affecting the selection of the mode, β k is the regression coefficient of the kth influencing factor affecting mode selection.

3. The method for calculating the scale of deployment of shared electric motorcycles in a city as claimed in claim 1, wherein the step (2) specifically comprises the following contents: Based on the city's calculation year, the average daily travel volume of each mode i is T i and the transfer ratio p i , calculate the travel volume transferred to shared electric motorcycles (transferred volume). Based on the above transferred volume, as well as the average utilization rate α, average turnover rate β, and actual load rate γ of shared bicycles, calculate the shared electric motorcycle demand D for the city in the measured year 转移 .

4. The method for calculating the scale of deployment of shared electric motorcycles in a city as claimed in claim 1, wherein the step (3) specifically comprises the following contents: Based on the city's average daily travel volume of each connection mode in the year, T j , transfer ratio t j and the conversion coefficient δ 公共交通 , calculate the shuttle volume of shared electric motorcycles (shutdown volume). Based on the above shuttle volume, as well as the average utilization rate α, average turnover rate β, and actual load rate γ of shared bicycles, calculate the demand D of shared electric motorcycles for shuttle travel in the city in the measurement year 接驳 .

5. The method for calculating the scale of deployment of shared electric motorcycles in a city as claimed in claim 1, wherein the step (4) specifically comprises the following contents: The total demand for shared electric motorcycles in the city in the year of calculation is D, which is the demand for shared electric motorcycles for transfer travel. 转移 and the demand for shared electric bikes for shuttle travel 接驳 The total demand for shared electric motorcycles in the city in the target year is D o It is equal to the total demand D for shared electric motorcycles in the city in the measurement year multiplied by the average growth coefficient r of GDP. D=D 转移 +D 接驳 D o =D·(1+r)^n 6. The method for calculating the scale of deployment of shared electric motorcycles in a city as claimed in claim 1, wherein the step (5) specifically comprises the following contents: The total demand for shared electric motorcycles in each administrative district of the city in the target year Based on the number of permanent residents in each district P k And the attractiveness of each area A k Proportional calculations are performed.

Images