A method for measuring the capacity of internet rental bicycles based on travel demand

Abstract

The application discloses a kind of internet rental bicycle capacity measurement method based on travel demand, by calculating the daily travel demand of internet rental bicycle whole journey, and the daily travel demand of internet rental bicycle connection public transport is calculated to obtain the total daily travel demand of internet rental bicycle travel;Then based on the characteristics of similar city, construct the city internet rental bicycle turnover rate measurement model based on geographic weighted regression, based on the model, the internet rental bicycle turnover rate of target city is measured. The total amount of internet rental bicycle scale required for urban travel is calculated by the total daily travel demand of internet rental bicycle and the internet rental bicycle turnover rate, that is, the internet rental bicycle capacity. The application is used for the city traffic management department to measure the number of internet rental bicycle, and can provide basis for the put amount control and operation management of city internet rental bicycle.

Description

Technical Field

[0001] This invention relates to the field of bicycle rental technology, specifically a method for calculating the capacity of internet-based rental bicycles based on travel demand. Background Technology

[0002] Since 2016, with the advent of the internet and the sharing economy, the "internet and bicycle" travel model has emerged. Internet-based bicycle rental services, represented by Ofo and Mobike, have developed rapidly as a new type of green transportation tool under the support of relevant policies in my country. The emergence of internet-based bicycle rental has played a positive role in better meeting public travel needs, effectively solving the "last mile" problem in urban transportation, alleviating urban traffic congestion, and building a green travel system, playing a crucial role in the urban public transportation system. However, the rapid development of the industry has also caused and exposed many problems.

[0003] The rapid development of internet-based bicycle rental has attracted numerous companies to the market, often resorting to aggressive deployment to increase market share. However, this excessive deployment not only disrupts urban traffic order and causes congestion, but also negatively impacts the user experience. Furthermore, it has led to numerous problems such as haphazard parking, untimely vehicle dispatch, and insufficient parking space, deviating from the original intention of developing the sharing economy. Therefore, addressing the problems arising from the development of internet-based bicycle rental while fully leveraging the advantages of integrating the sharing economy with green and low-carbon practices to promote green urban travel is a pressing issue that needs to be resolved.

[0004] To address these issues, it's necessary to tackle the root causes and control the number of internet-based rental bicycles in cities within a reasonable range. This means meeting residents' daily travel needs while preventing excessive deployment from straining urban road space. Therefore, a scientific and reasonable calculation of the deployment scale of internet-based rental bicycles is needed. Based on this calculation, resource allocation for internet-based rental bicycle companies should be optimized, and industry regulation should be strengthened to prevent competition from creating factors that could affect social stability. Effective control must be exercised before excessive deployment of internet-based rental bicycles impacts traffic, thus promoting the further development and improvement of the urban transportation system. Therefore, a method for calculating the capacity of internet-based rental bicycles based on travel demand is required. Summary of the Invention

[0005] The purpose of this invention is to provide a method for calculating the capacity of internet-based rental bicycles based on travel demand. The demand for internet-based rental bicycles mainly includes two categories: full-journey trips and connecting trips. Full-journey trips refer to residents using internet-based rental bicycles to complete a single trip from the origin to the destination. Connecting trips refer to residents using internet-based rental bicycles to complete the "last mile" before and after their trip; this connecting trip mainly involves connecting to public transportation such as buses and subways.

[0006] After the deployment of internet-based rental bicycles, some residents who previously traveled by walking or owning their own bicycles, as well as those who used public transportation such as buses, subways, or other means for short-distance travel, will switch to using internet-based rental bicycles for their entire journey. Some residents who previously connected to public transportation such as buses or subways by walking or owning their own bicycles will also switch to using internet-based rental bicycles for these connections.

[0007] This method starts with the travel structure of the target city, calculating the demand for internet-rented bicycles for the entire journey by shifting from other modes of transportation to internet-rented bicycles, and the travel demand for connecting internet-rented bicycles with public transportation such as buses and subways. This yields the total travel demand for internet-rented bicycles that matches the city's overall travel needs. Combined with the turnover rate of internet-rented bicycles, the total scale of internet-rented bicycles that matches the city's travel demands is then determined.

[0008] Demand for internet-based bicycle rental for the entire journey. Based on the city's resident population and average daily trip frequency, the total daily trips of the city's resident population are obtained. Based on the revealed preference (RP) survey, travel information from respondents is collected, and the modal share of different modes of transportation (walking, cycling, public transport, subway, etc.) in the target city is obtained through statistical data. Based on the total daily trips of the city's resident population and the modal share of each mode of transportation, the average daily trip volume of residents for each mode of transportation is obtained. Based on the stated preference (SP) survey, the proportion of residents switching from other modes of transportation, such as walking, public transport, and private bicycles, to internet-based bicycle rental for the entire journey after the introduction of internet-based bicycle rental is obtained, yielding the transfer rate of each mode of transportation to internet-based bicycle rental for the entire journey. Based on the average daily trip volume of residents for each mode of transportation and the transfer rate of each mode of transportation to internet-based bicycle rental for the entire journey, the average daily demand for internet-based bicycle rental for the entire journey can be obtained.

[0009] Demand for connecting public transportation using internet-based rental bicycles. The daily average number of trips per resident in the city is calculated based on the city's permanent resident population and the average number of trips per day. The modal share of public transportation (PPP) and subway is obtained based on the revealed preference (RP) survey, yielding the average daily trip volume for each. The proportion of residents using internet-based rental bicycles to connect with public transportation (PPP) and subway is obtained based on the stated preference (SP) survey, yielding the connection rate of internet-based rental bicycles to PPP. Based on the average daily trip volume of public transportation (PPP) and subway and the connection rate of internet-based rental bicycles to PPP, the demand for residents to use internet-based rental bicycles to connect with public transportation (PPP) and subway is calculated.

[0010] This invention is implemented as follows:

[0011] A method for calculating the capacity of internet-based rental bicycles based on travel demand, specifically comprising the following steps: calculating the average daily travel demand of internet-based rental bicycles for the entire journey and the average daily travel demand of internet-based rental bicycles for connecting journeys, to obtain the total average daily travel demand of internet-based rental bicycles.

[0012] Then, based on the characteristics of similar cities, a city-wide internet-based rental bicycle turnover rate calculation model based on geographical weighted regression is constructed. Based on this model, the turnover rate of internet-based rental bicycles in the target city is calculated. By dividing the daily average total demand for internet-based rental bicycles by the bicycle turnover rate, the total scale of internet-based rental bicycles required for urban travel, i.e., the internet-based rental bicycle capacity, is calculated.

[0013] S1: Calculate the average daily travel demand for all trips using internet-based rental bicycles;

[0014] S 1.1 The total daily number of trips by the city's permanent residents is obtained based on the city's permanent resident population and the average number of trips per day.

[0015] S 1.2 Based on RP surveys, we collect travel information from respondents, obtain the travel modal share of different modes of transportation such as walking, cycling, public transportation and subway in the target city through statistical data, and obtain the average daily travel volume of residents for each mode of transportation based on the average daily travel volume of the city's permanent residents and the travel modal share of each mode of transportation.

[0016] S 1.3 Based on the SP survey, the proportion of residents who switched from walking, public transportation, driving, or owning bicycles to using internet-rented bicycles for their entire journey was obtained, thus obtaining the conversion rate of each mode of transportation to using internet-rented bicycles for their entire journey.

[0017] S1.4 Based on the average daily travel volume of residents in each mode of transportation and the conversion rate of each mode of transportation to internet-rented bicycles for the entire journey, the average daily travel demand for internet-rented bicycles for the entire journey is calculated.

[0018] Furthermore, S2: Calculate the average daily demand for internet-based rental bicycle shuttle services in the target city;

[0019] S 2.1 The total daily number of trips by the city's permanent residents is obtained based on the city's permanent resident population and the average number of trips per day.

[0020] S 2.2 Based on the RP survey, the respective travel modal share of public transport and subway was obtained, and the average daily travel volume of residents by public transport and subway was obtained;

[0021] S 2.3 Based on SP surveys, the proportion of residents using internet-rented bicycles to connect with buses and subways after the introduction of internet-rented bicycles into cities was obtained, thus obtaining the connection rate of internet-rented bicycles to buses and subways.

[0022] S 2.4 Based on the average daily travel volume of residents using public transportation and subways and the connection rate of internet-rented bicycles to public transportation and subways, the travel demand of residents using internet-rented bicycles to connect to public transportation and subways can be obtained.

[0023] S3: Calculate the total demand for internet-based bicycle rentals, divide the bicycle turnover rate, and then calculate the total number of bicycles.

[0024] Furthermore, the modal share of each mode of transportation is calculated as shown in equation (1):

[0025]

[0026] Where: C i : The modal share of the i-th mode of transportation;

[0027] m i : The number of trips using the i-th mode of transportation within the statistical range;

[0028] M: Total number of trips by residents within the statistical scope.

[0029] Furthermore, the average number of trips per day for citizens is calculated as shown in equation (2):

[0030]

[0031] in: Average number of trips per day for residents;

[0032] n: Sample size in the sampling survey;

[0033] M: Total number of trips by residents within the statistical scope.

[0034] Furthermore, the proportion of residents switching from other modes of transportation to internet-based rental bicycles for their entire journey, α i As shown in equation (3);

[0035]

[0036] Where: α i : The proportion of people who switched from the i-th mode of transportation to internet-rented bicycles for their entire journey;

[0037] b i : The number of trips within the statistical scope that switch from the i-th mode of transportation to internet-rented bicycles for the entire journey;

[0038] m i The number of trips using the i-th mode of transportation within the statistical range.

[0039] The proportion of residents using internet-based rental bicycles to connect with buses and subways (β) i As shown in equation (4);

[0040]

[0041] Where: β i The proportion of people using internet-rented bicycles to connect with buses and subways; i : The number of trips within the statistical scope using internet-rented bicycles to connect with public transportation and subways; m i The number of trips using public transportation such as buses and subways within the statistical scope.

[0042] Furthermore, based on the characteristics of similar cities, a geographically weighted regression model for calculating the turnover rate of internet-rented bicycles is constructed. The process is as follows: Similar cities to the city being measured are identified, and characteristic data of the city being measured and similar cities are collected, including resident population, built-up area, average daily travel volume of residents, number of bus stops, number of subway stations, 500-meter coverage rate of public transportation stops, bicycle travel modal share, and internet-rented bicycle turnover rate. Using the internet-rented bicycle turnover rate of similar cities as the dependent variable and other characteristics as independent variables, a geographically weighted regression model for calculating the turnover rate of urban internet-rented bicycles is constructed, as shown in Equation (5).

[0043]

[0044] Where, λ i Let x be the turnover rate of internet-rented bicycles in the i-th sample city. ij Let β be the value of the j-th independent variable in the i-th sample city.j ε is the regression coefficient. i This is the random error term. (u) i ,v i Let ) be the coordinates of the i-th sample city, and β be the coordinates of the i-th sample city. j (u i ,v i Let be the j-th regression parameter for the i-th sample city. This step can be achieved using the geographic weighted regression tool in ArcGIS software.

[0045] Based on this model, by inputting the characteristic data of the target city, the turnover rate of internet-based rental bicycles in the target city can be calculated.

[0046] The capacity calculation for internet-based rental bicycles is shown in equations (6)-(7):

[0047]

[0048] Where: P: resident population of the target city;

[0049] Average daily number of trips by residents of the target city;

[0050] C i : The modal share of the i-th mode of transportation;

[0051] α i The proportion of people who switched from the i-th mode of transportation to internet-rented bicycles for their entire journey;

[0052] C b Public transport modal share;

[0053] β b The percentage of people using internet-rented bicycles to connect with public transportation;

[0054] C s Subway travel modal share;

[0055] β s The percentage of people using internet-rented bicycles to connect to the subway.

[0056] The daily total number of trips using internet-based rental bicycles is calculated as shown in formula (7).

[0057]

[0058] Where: R: Urban internet-based bicycle rental capacity; m B λ: Daily total number of trips made by internet-based rental bicycles; λ: Turnover rate of internet-based rental bicycles;

[0059] Compared with the prior art, the beneficial effects of the present invention are:

[0060] This information is provided to urban traffic management departments and operating companies to estimate the number of internet-based rental bicycles that a city can accommodate. It can provide a basis for controlling the deployment and operation management of internet-based rental bicycles in cities, solve the problem of blind deployment of internet-based rental bicycles, and promote the healthy and orderly development of the internet-based rental bicycle industry. Attached Figure Description

[0061] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained from these drawings without creative effort.

[0062] Figure 1 This is a flowchart of a method for calculating the capacity of internet-based rental bicycles based on travel demand, according to the present invention. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, 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. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to represent selected embodiments of the invention. 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.

[0064] Please see Figure 1 A method for calculating the capacity of internet-based rental bicycles based on travel demand, specifically following these steps:

[0065] In this embodiment, an implementation example is given below to describe the specific implementation of the present invention. Taking Yinchuan City as an example, the capacity of internet-based rental bicycles in Yinchuan City is calculated using the method described in this invention.

[0066] In this embodiment, at the end of 2019, Yinchuan City conducted a questionnaire survey on the travel structure of its residents to comprehensively understand the distribution of residents' travel modes. The questionnaire collected respondents' revealed preference (RP) and declarative preference (SP) data. RP data is revealed preference data observed from respondents' actual travel, which statistically analyzes the number of trips, travel purposes, and travel modes of users in the most recent day. SP data is declarative preference data obtained through a hypothesis-choice experiment, which statistically analyzes respondents' travel choices under a hypothetical scenario (after the deployment of internet-based rental bicycles), i.e., residents' willingness to use internet-based rental bicycles. The survey mainly adopted the method of street interception to conduct a quantitative questionnaire survey of Yinchuan residents, with survey locations concentrated in business districts, schools, enterprises and institutions, large residential communities, and bus stops.

[0067] In this embodiment, the survey covered the central urban area of ​​Yinchuan City (Xingqing District, Jinfeng District, and Xixia District). The sample was allocated according to the proportion of each district's population to the city's total population. A total of 4,796 valid questionnaires were collected, involving 9,586 trips.

[0068] In this embodiment, the modal share (%) of each mode of transportation is calculated as shown in equation (1):

[0069]

[0070] C i : The modal share of the i-th mode of transportation;

[0071] m i : The number of trips using the i-th mode of transportation within the statistical range;

[0072] M: Total number of trips by residents within the statistical scope;

[0073] Based on the survey respondents' data, the modal share of all modes of travel for urban residents in Yinchuan City is shown in Table 1.

[0074] Table 1. Share of all modes of transportation among residents of Yinchuan City

[0075]

[0076]

[0077] In this embodiment, the average number of trips per day for residents of this city is calculated as shown in equation (2):

[0078]

[0079] Average number of trips per day for residents;

[0080] n: Sample size in the sampling survey;

[0081] M: Total number of trips by residents within the statistical scope;

[0082] Based on the survey respondents' data, the average number of trips per day for urban residents in Yinchuan City is 2.01.

[0083] In this embodiment, the SP survey investigates the travel choices of residents under a hypothetical scenario (after the deployment of internet-rented bicycles) to obtain the proportion α of residents who switched from the i-th mode of transportation (such as walking, public transportation, or their own bicycles) to internet-rented bicycles for their entire journey after internet-rented bicycles entered the city. i As shown in equation (3);

[0084]

[0085] α i The percentage of people who switched from the i-th mode of transportation to internet-rented bicycles for their entire journey;

[0086] b i : The number of trips within the statistical scope that switch from the i-th mode of transportation to internet-rented bicycles for the entire journey;

[0087] m i : The number of trips using the i-th mode of transportation within the statistical range;

[0088] Based on the survey respondents' data, the proportion of people who switched from other modes of transportation to internet-based bicycle rental for their entire journey is shown in Table 2.

[0089] Table 2. Percentage of trips made using internet-rented bicycles

[0090] Original mode of transportation Transfer ratio (%) public buses and trolleybuses 2.76 bike 27.21 walk 10.78 taxi 1.06 private cars 0.03 other 0.07

[0091] In this embodiment, the SP survey of surveyed residents revealed their travel choices under a hypothetical scenario (after the deployment of internet-rented bicycles) to obtain the proportion β of residents using internet-rented bicycles to connect with public transportation after their introduction to the city. i (i includes buses and subways). Since the user group for this type of connecting travel is passengers using public transportation, this question in the survey also only targets public transportation users. Yinchuan City does not yet have a subway system, therefore there is no subway service. In this SP survey, the proportion of residents using internet-rented bicycles to connect with public transportation was 15.37%.

[0092] In this embodiment, an internet-based bicycle rental turnover rate model based on geographic weighted regression was constructed using ArcGIS software. Input parameters included the target city's resident population, built-up area, average daily travel volume, number of public transportation stops, 500-meter coverage rate of public transportation stops, and bicycle modal share, to obtain the target city's internet-based bicycle rental turnover rate λ. The sample cities selected for constructing the geographic weighted regression model were Hohhot, Baotou, Linyi, and Dongying, cities of similar size. The final calculated value of the internet-based bicycle rental turnover rate λ for Yinchuan City was 2.51.

[0093] In this embodiment, based on the above parameters, the capacity calculation of internet-based rental bicycles in Yinchuan City is shown in equations (6)-(7):

[0094]

[0095] P: The permanent resident population of the target city;

[0096] Average daily number of trips by residents of the target city;

[0097] C i : The modal share of the i-th mode of transportation;

[0098] α i The proportion of people who switched from the i-th mode of transportation to internet-rented bicycles for their entire journey;

[0099] C b Public transport modal share;

[0100] β b The percentage of people using internet-rented bicycles to connect with public transportation;

[0101] C s Subway travel modal share;

[0102] β s The percentage of people using internet-rented bicycles to connect to the subway;

[0103] The daily total number of trips using internet-based rental bicycles is calculated as shown in formula (7).

[0104]

[0105] R: Urban internet-based bicycle rental capacity;

[0106] m B Total daily trips using internet-based bike rental services;

[0107] λ: Turnover rate of internet-based bicycle rentals;

[0108] In this embodiment, the permanent resident population of the central urban area of ​​Yinchuan City (Xingqing District, Jinfeng District, and Xixia District) is 1,387,600, and the average number of trips per day for Yinchuan residents, as determined by the survey, is 2.01. The parameters required for this invention's calculations are as follows:

[0109] P: The permanent resident population of the target city, 1,387,600;

[0110] The average number of trips per day for residents of the target city is 2.01.

[0111] C i The modal share of the i-th mode of transportation is shown in Table 1.

[0112] α i The percentage of travelers who switched from the i-th mode of transportation to internet-based bicycle rental for their entire journey is shown in Table 2.

[0113] C b The public transport modal share was 34.42%.

[0114] β b The percentage of people using internet-rented bicycles to connect with public transportation was 15.37%.

[0115] λ: Turnover rate of internet-based bicycle rentals, 2.51;

[0116]

[0117] According to formula (6), the daily total number of trips using internet-based rental bicycles in Yinchuan City can be obtained in m. B The number is 369,327.

[0118]

[0119] According to formula (7), the capacity of internet-based rental bicycles in Yinchuan City is 147,100.

[0120] The embodiments of the present invention have been described in detail above. It should be understood that any modifications or partial substitutions made by a person with ordinary skills in this field without departing from the scope of the present invention are within the scope of protection of the claims of the present invention.

[0121] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for calculating the capacity of internet-based rental bicycles based on travel demand, characterized in that: Specifically, the following steps are taken: The total daily travel demand for internet-rented bicycles is calculated by calculating the average daily travel demand for full-journey trips and the average daily travel demand for connecting trips with internet-rented bicycles. Then, based on the characteristics of similar cities, a city-based internet-based rental bicycle turnover rate calculation model based on geographical weighted regression is constructed. Based on this model, the turnover rate of internet-based rental bicycles in the target city is calculated. By dividing the daily average demand for internet-based rental bicycles by the bicycle turnover rate, the total number of internet-based rental bicycles required for travel in the target city can be calculated, i.e., the internet-based rental bicycle capacity. The specific steps for obtaining the average daily travel demand for internet-based bike rental in the target city are as follows: S1: Calculate the average daily travel demand for all trips using internet-based rental bicycles; S 1.1 The total daily number of trips by the city's permanent residents is obtained based on the city's permanent resident population and the average number of trips per day. S 1.2 Based on RP surveys, we collect travel information from respondents, obtain the travel modal share of different modes of transportation such as walking, cycling, public transportation and subway in the target city through statistical data, and obtain the average daily travel volume of residents for each mode of transportation based on the average daily travel volume of the city's permanent residents and the travel modal share of each mode of transportation. S 1.3 Based on the SP survey, the proportion of residents who switched from walking, public transportation, driving, or owning bicycles to using internet-rented bicycles for their entire journey was obtained, thus obtaining the conversion rate of each mode of transportation to using internet-rented bicycles for their entire journey. S 1.4 Based on the average daily travel volume of residents in each mode of transportation and the conversion rate of each mode of transportation to internet-rented bicycles for the entire journey, the average daily travel demand for internet-rented bicycles for the entire journey is calculated. S2: Calculate the average daily demand for internet-based bike-sharing services in the target city, following these steps: S 2.1 The total daily number of trips by the city's permanent residents is obtained based on the city's permanent resident population and the average number of trips per day. S 2.2 Based on the RP survey, the respective travel modal share of public transport and subway was obtained, and the average daily travel volume of residents by public transport and subway was obtained. S 2.3 Based on SP surveys, the proportion of residents using internet-rented bicycles to connect with buses and subways after the introduction of internet-rented bicycles into cities was obtained, thus obtaining the connection rate of internet-rented bicycles to buses and subways. S 2.4 Based on the average daily travel volume of residents using public transportation and subways and the connection rate of internet-rented bicycles to public transportation and subways, the travel demand of residents using internet-rented bicycles to connect to public transportation and subways can be obtained. S3: By calculating the average daily travel demand for internet-based rental bicycles and dividing it by the bicycle turnover rate, the total number of bicycles can be calculated.

2. The method for calculating the capacity of internet-based rental bicycles based on travel demand as described in claim 1, characterized in that, The modal share of each mode of transportation is calculated as shown in equation (1): Equation (1) in: : The modal share of the i-th mode of transportation; : The number of trips using the i-th mode of transportation within the statistical range; Total number of trips taken by residents within the statistical scope.

3. The method for calculating the capacity of internet-based rental bicycles based on travel demand as described in claim 1, characterized in that, The average number of trips per day for citizens is calculated as shown in formula (2): Equation (2) in: : Average number of trips per day for residents; : Sample size in a sampling survey; Total number of trips taken by residents within the statistical scope.

4. The method for calculating the capacity of internet-based rental bicycles based on travel demand as described in claim 1, characterized in that, The proportion of residents switching from other modes of transportation to internet-based bicycle rentals for their entire journey. As shown in equation (3); Equation (3) in: : The proportion of travelers who switched from the i-th mode of transportation to internet-rented bicycles for their entire journey; : The number of trips within the statistical scope that switch from the i-th mode of transportation to internet-rented bicycles for the entire journey; The number of trips using the i-th mode of transportation within the statistical range.

5. The method for calculating the capacity of internet-based rental bicycles based on travel demand as described in claim 1, characterized in that, The proportion of residents using internet-based rental bicycles to connect with buses and subways As shown in equation (4); Equation (4) in: The proportion of people using internet-rented bicycles to connect with buses and subways; The number of trips within the statistical scope using internet-rented bicycles to connect with public transportation and subways; The number of trips using public transportation such as buses and subways within the statistical scope.

6. The method for calculating the capacity of internet-based rental bicycles based on travel demand as described in claim 1, characterized in that, Taking cities where internet-based rental bicycles have been deployed as samples, the permanent population, built-up area, average daily travel volume of residents, number of public transportation stations, 500-meter coverage rate of public transportation stations, and bicycle travel modal share of these sample cities are used as independent variables, and the turnover rate of internet-based rental bicycles in the sample cities is used as the dependent variable. A city internet-based rental bicycle turnover rate calculation model based on geographical weighted regression is constructed, as shown in Equation (5). Equation (5) in, Let be the turnover rate of internet-rented bicycles in the i-th sample city. Let j be the value of the j-th independent variable in the i-th sample city. For regression coefficients, For random error term, Let i be the coordinates of the i-th sample city. Let j be the regression parameter for the i-th sample city. This step is achieved using the geographic weighted regression tool in ArcGIS software. The capacity calculation for internet-based rental bicycles is shown in equations (6) and (7): Equation (6) Where: P: resident population of the target city; The average daily travel demand for internet-based bicycle rental services; The average number of trips per day for residents of the target city; : The modal share of the i-th mode of transportation; The proportion of people who switched from the i-th mode of transportation to internet-rented bicycles for their entire journey; Public transport modal share; The percentage of people using internet-rented bicycles to connect with public transportation; Subway travel modal share; The percentage of people using internet-rented bicycles to connect to the subway; Equation (7) Where: R: Urban internet-based bicycle rental capacity; The average daily travel demand for internet-based bicycle rental services; : Turnover rate of internet-based rental bicycles.

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