Method and system for calculating capacity of rented bicycles in regional internet

Abstract

The invention relates to a method and a system for calculating the capacity of rented bicycles on the regional internet. The method comprises the following steps of: calculating a direct riding travel demand quantity and a connection riding travel demand quantity; calculating a road traffic reachable index and a public traffic reachable index; optimizing the road traffic reachable index and the public traffic reachable index to obtain a direct riding travel demand adjustment coefficient and a connection riding travel demand adjustment coefficient; and according to the riding direct travel demand, the riding connection travel demand, the riding direct travel demand adjustment coefficient, the riding connection travel demand adjustment coefficient and the Internet rental bicycle turnover rate, calculating the Internet rental bicycle carrying capacity. According to the method, a scientific and quantitative decision basis can be provided for urban traffic and city appearance treatment, the urban traffic environment order can be effectively improved, the traffic operation efficiency is improved, and the urban traffic environment is improved for accurately reflecting the regional riding demand and the Internet rental bicycle demand.

Description

Technical Field

[0001] The present invention relates to the field of urban traffic governance, and particularly to a method and system for calculating the accommodation capacity of Internet rental bicycles in a region. Background Art

[0002] By calculating the accommodation capacity of Internet rental bicycles, a solid foundation can be provided for urban traffic and city appearance governance. It can not only improve the order of the urban traffic environment, enhance the city appearance, but also optimize the utilization of road resources, improve traffic efficiency, thereby promoting the orderly and efficient development of the urban public transport system.

[0003] The traditional models of the current Internet rental bicycle capacity measurement schemes overly rely on historical order data and static population statistics, and fail to integrate multi-source heterogeneous data such as the dynamic population distribution of mobile phone signaling and the transfer characteristics of public transport card-swipe data, resulting in insufficient spatio-temporal resolution of demand prediction. At the same time, the existing mainstream schemes have not yet constructed a dual-modal accessibility index system for road networks and public transport services, ignoring road network factors such as non-motorized lane density and intersection complexity, as well as the non-linear effects of public service facilities such as bus stops and rail stations on cycling demand, and also lacking a dynamic response mechanism for infrastructure changes such as road network renovation and bus stop addition, weakening the matching accuracy between the placement of Internet rental bicycles and the urban construction spatial structure. Summary of the Invention

[0004] The present invention provides a method and system for calculating the accommodation capacity of Internet rental bicycles in a region to solve at least one of the above technical problems.

[0005] The technical solution of the present invention for solving the above technical problems is as follows: A method for calculating the accommodation capacity of Internet rental bicycles in a region includes: S1, collecting a mobile phone signaling data set, a travel characteristic data set, a public transport passenger flow data set, a road network data set, and an Internet rental bicycle order data set in the research region, and processing the collected data sets to obtain the population size, per capita travel rate, Internet rental bicycle travel sharing rate, public transport and Internet rental bicycle transfer rate, public transport station passenger flow, public transport average departure interval, road density, non-motorized lane density, intersection node density, public transport station density, and Internet rental bicycle order turnover rate in the research region; S2, calculating the direct cycling travel demand and the cycling transfer travel demand in the research region according to the population size, the per capita travel rate, the Internet rental bicycle travel sharing rate, the public transport and Internet rental bicycle transfer rate, and the public transport station passenger flow; S3. Normalize the road density, non-motorized lane density, intersection node density, public transport stop density, and average headway of public transport respectively, and use the expert scoring method to weight the normalized road density, non-motorized lane density, intersection node density, public transport stop density, and average headway of public transport to obtain the road traffic accessibility index and public transport accessibility index in the research area; S4. Optimize the road traffic accessibility index and the public transport accessibility index using the Sigmoid function model to obtain the adjustment coefficient of the direct cycling travel demand and the adjustment coefficient of the cycling and transfer travel demand in the research area; S5. Calculate the accommodation capacity of internet rental bicycles in the research area according to the direct cycling travel demand volume, cycling and transfer travel demand volume, adjustment coefficient of the direct cycling travel demand, adjustment coefficient of the cycling and transfer travel demand, and the turnover rate of internet rental bicycles.

[0006] Based on the above calculation method for the accommodation capacity of regional internet rental bicycles, the present invention also provides a calculation system for the accommodation capacity of regional internet rental bicycles.

[0007] A calculation system for the accommodation capacity of regional internet rental bicycles, comprising: An index acquisition module, which is used to collect the mobile phone signaling dataset, travel characteristic dataset, public transport passenger flow dataset, road network dataset, and internet rental bicycle order dataset in the research area, and process the collected datasets to obtain the population size, per capita travel rate, travel sharing rate of internet rental bicycles, connection rate between public transport and internet rental bicycles, passenger flow volume of public transport stops, average headway of public transport, road density, non-motorized lane density, intersection node density, public transport stop density, and turnover rate of internet rental bicycle orders in the research area; A travel demand calculation module, which is used to calculate the direct cycling travel demand volume and cycling and transfer travel demand volume in the research area according to the population size, per capita travel rate, travel sharing rate of internet rental bicycles, connection rate between public transport and internet rental bicycles, and passenger flow volume of public transport stops; An accessibility index calculation module, which is used to normalize the road density, the non-motorized lane density, the intersection node density, the public transport stop density, and the average headway of public transport respectively, and use the expert scoring method to weight the road density, the non-motorized lane density, the intersection node density, the public transport stop density, and the average headway of public transport after normalization to obtain the road traffic accessibility index and the public transport accessibility index in the research area; An optimization and adjustment module, which is used to optimize the road traffic accessibility index and the public transport accessibility index by using the Sigmoid function model to obtain the adjustment coefficient of the direct cycling travel demand and the adjustment coefficient of the cycling transfer travel demand in the research area; A capacity calculation module, which is used to calculate the accommodation capacity of internet rental bicycles in the research area according to the direct cycling travel demand, the cycling transfer travel demand, the adjustment coefficient of the direct cycling travel demand, the adjustment coefficient of the cycling transfer travel demand, and the turnover rate of internet rental bicycles.

[0008] The beneficial effects of the present invention are as follows: A calculation method and system for the accommodation capacity of regional internet rental bicycles according to the present invention integrate multi-source heterogeneous data such as mobile phone signaling, public transport passenger flow, internet rental bicycle orders, and comprehensive traffic surveys, and comprehensively calculate the direct cycling travel demand and transfer travel demand in the research area. On this basis, combined with the traffic network structure and supply characteristics, the road traffic accessibility index and the public transport accessibility index are calculated, and further through the Sigmoid model, a non-linear mapping from the index to the adjustment coefficient is realized, so as to accurately adjust the total cycling demand, and finally obtain the accommodation capacity of internet rental bicycles in the research area; The accommodation capacity of internet rental bicycles calculated by the present invention can provide a scientific and quantitative decision-making basis for urban traffic and city appearance governance, can effectively improve the urban traffic environment order, enhance the city appearance, at the same time optimize the utilization of road resources, improve the traffic operation efficiency, and has important practical application value for accurately reflecting the regional cycling demand and the demand for internet rental bicycles, improving the urban traffic environment, and promoting the orderly and efficient development of urban public transport; In addition, the present invention fully considers the characteristics of various types of data in the calculation process, has high adaptability and operability, and is easy to implement in engineering practice. Description of the Drawings

[0009] Figure 1 It is a flowchart of a calculation method for the accommodation capacity of regional internet rental bicycles according to the present invention; Figure 2 It is an example diagram of population distribution calculated based on the mobile phone signaling data set; Figure 3An example diagram of the passenger flow in and out of rail stations calculated based on rail transit card - swiping data; Figure 4 An example diagram of the curve of the Sigmoid function model; Figure 5 A structural block diagram of a calculation system for the accommodation capacity of shared bicycles on the regional Internet in the present invention. Detailed implementation manners

[0010] The principles and features of the present invention will be described below with reference to the accompanying drawings. The examples given are only used to explain the present invention and are not intended to limit the scope of the present invention.

[0011] As Figure 1 shown, a method for calculating the accommodation capacity of shared bicycles on the regional Internet includes: S1. Collect the mobile signaling data set, travel characteristic data set, public transportation passenger flow data set, road network data set, and shared bicycle order data set in the research area, and process the collected data sets to obtain the population size, per capita travel rate, shared bicycle travel share rate, public transportation and shared bicycle connection rate, public transportation station passenger flow, average headway of public transportation, road density, non - motorized lane density, intersection node density, public transportation station density, and shared bicycle order turnover rate in the research area; S2. Calculate the direct cycling travel demand and cycling connection travel demand in the research area according to the population size, per capita travel rate, shared bicycle travel share rate, public transportation and shared bicycle connection rate, and public transportation station passenger flow; S3. Normalize the road density, non - motorized lane density, intersection node density, public transportation station density, and average headway of public transportation respectively, and use the expert scoring method to weight the normalized road density, non - motorized lane density, intersection node density, public transportation station density, and average headway of public transportation to obtain the road traffic accessibility index and public transportation accessibility index in the research area; S4. Use the Sigmoid function model to optimize the road traffic accessibility index and public transportation accessibility index to obtain the direct cycling travel demand adjustment coefficient and cycling connection travel demand adjustment coefficient in the research area; S5. Calculate the accommodation capacity of shared bicycles on the regional Internet in the research area according to the direct cycling travel demand, cycling connection travel demand, direct cycling travel demand adjustment coefficient, cycling connection travel demand adjustment coefficient, and shared bicycle turnover rate.

[0012] A calculation method for the accommodation capacity of Internet rental bicycles in a region of the present invention combines the characteristics of population travel modes and public transportation travel characteristics in the research area, calculates the direct travel demand and feeder travel demand of Internet rental bicycles, adjusts the total demand according to the traffic network structure and supply, and combines the turnover rate and intact rate of Internet rental bicycles, so as to obtain the accommodation capacity of Internet rental bicycles in the research area. The method of the present invention integrates multi-source heterogeneous data such as the dynamic population distribution of mobile phone signaling and the feeder characteristics of public transportation card swiping data in the research area, improving the spatio-temporal resolution of demand prediction; at the same time, the present invention also constructs a bimodal accessibility index system for road networks and public transportation services, considering road network factors such as non-motorized lane density and intersection complexity, as well as the non-linear effects of public service facilities such as bus stops and rail stations on cycling demand, and also considering the dynamic response mechanism to infrastructure changes such as road network reconstruction and bus stop addition, improving the matching accuracy between the placement of Internet rental bicycles and the urban construction spatial structure.

[0013] In some embodiments, S1 is specifically as follows: Collect the mobile phone signaling data set in the research area, process the mobile phone signaling data set to obtain the population size; Collect the travel characteristic data set in the research area through the method of comprehensive traffic survey, and obtain the per capita travel rate, the travel sharing rate of Internet rental bicycles, and the feeder rate of public transportation and Internet rental bicycles from the travel characteristic data set; Collect the public transportation passenger flow data set in the research area, where the public transportation passenger flow data set includes public transportation card swiping data and public transportation departure time data, process the public transportation card swiping data and the public transportation departure time data to obtain the passenger flow volume at public transportation stops and the average headway time of public transportation; Collect the road network data set of the research area, process the road network data set to obtain the road density, the non-motorized lane density, the intersection node density, and the public transportation stop density; Collect the Internet rental bicycle order data set in the research area, process the Internet rental bicycle order data set to obtain the order turnover rate of Internet rental bicycles.

[0014] Specifically, the connection rate between public transportation and Internet rental bicycles includes the connection rate between rail transit and Internet rental bicycles and the connection rate between buses and Internet rental bicycles; the passenger flow of public transportation stations includes the inbound and outbound passenger flow of rail transit stations and the boarding and alighting passenger flow of bus stations; the density of public transportation stations includes the density of rail transit stations and the density of bus stations; the average departure interval of public transportation includes the average departure interval of rail transit and the average departure interval of buses.

[0015] Figure 2 It is an example diagram of the population distribution result calculated based on the mobile phone signaling dataset. The population size within the research area can be obtained from the population distribution result.

[0016] Figure 3 It is an example diagram of the inbound and outbound passenger flow of rail transit stations calculated based on the rail transit card-swipe data.

[0017] In some embodiments, S2 is specifically: Calculate the direct cycling travel demand according to the population size, the per capita travel rate, and the sharing rate of Internet rental bicycle trips; Calculate the cycling connection travel demand according to the connection rate between public transportation and Internet rental bicycles and the passenger flow of public transportation stations; Among them, the formula for calculating the direct cycling travel demand is: ; In the formula, represents the direct cycling travel demand, represents the population size, represents the per capita travel rate, represents the sharing rate of Internet rental bicycle trips.

[0018] Among them, the formula for calculating the cycling connection travel demand is: ; In the formula, represents the cycling connection travel demand, represents the inbound and outbound passenger flow of rail transit stations, represents the boarding and alighting passenger flow of bus stations, represents the connection rate between rail transit and Internet rental bicycles, represents the connection rate between buses and Internet rental bicycles.

[0019] In some embodiments, S3 is specifically: The forward normalization model is used to perform forward normalization processing on the road density, the non-motor vehicle lane density, the intersection node density, and the public transportation station density respectively, and the road density score, the non-motor vehicle lane density score, the intersection node density score, and the public transportation station density score are obtained correspondingly; The reverse normalization model is used to perform reverse normalization processing on the average departure interval time of public transportation, and the average departure interval time score of public transportation is obtained; The expert scoring method is adopted to perform weighted processing on the road density score, the non-motor vehicle lane density score, and the intersection node density score, and the road traffic accessibility index is obtained; The expert scoring method is adopted to perform weighted processing on the public transportation station density score and the average departure interval time score of public transportation, and the public transportation accessibility index is obtained.

[0020] Furthermore, the forward normalization model is: ; Among them, represents the forward index score, represents the forward index, represents the minimum value of the forward index in the same type of research area, represents the maximum value of the forward index in the same type of research area; the forward indexes include the road density, the non-motor vehicle lane density, the intersection node density, and the public transportation station density; The reverse normalization model is: ; Among them, represents the reverse index score, represents the reverse index, represents the minimum value of the reverse index in the same type of research area, represents the maximum value of the reverse index in the same type of research area; the reverse index includes the average departure interval time of public transportation.

[0021] Specifically, the minimum value of the forward index , the maximum value of the forward index , the minimum value of the reverse index , and the maximum value of the reverse index in the same type of research area are all the minimum and maximum values of the corresponding indexes in multiple research areas of the same type. For example, if there are 10 research areas in the whole city, for the road density in the forward index, the minimum value of the road density is the minimum value of the road density in these 10 research areas, and the maximum value of the road density is the maximum value of the road density in these 10 research areas.

[0022] Furthermore, the public transportation station density includes rail transit station density and bus station density. Correspondingly, the public transportation station density score includes rail transit station density score and bus station density score; the average headway of public transportation includes the average headway of rail transit and the average headway of buses. Correspondingly, the average headway score of public transportation includes the average headway score of rail transit and the average headway score of buses. The formula for calculating the road traffic accessibility index is: ; In the formula, represents the road traffic accessibility index, and respectively represent the road density score, the non-motorized lane density score, and the intersection node density score, , and are all weights of the corresponding scores obtained by the expert scoring method, and ; The formula for calculating the public transportation accessibility index is: ; In the formula, represents the public transportation accessibility index, and respectively represent the rail transit station density score, the bus station density score, the average headway score of rail transit, and the average headway score of buses, and are all weights of the corresponding scores obtained by the expert scoring method, and .

[0023] Specifically, and are obtained through the expert scoring method and are empirical values.

[0024] In some embodiments, S4 is specifically: Using the Sigmoid function model to optimize the road traffic accessibility index to obtain the adjustment coefficient of the direct cycling travel demand; Using the Sigmoid function model to optimize the public transportation accessibility index to obtain the adjustment coefficient of the cycling transfer travel demand; Among them, the formula for optimizing the road traffic accessibility index using the Sigmoid function model is: ; The formula for optimizing the public transport accessibility index using the Sigmoid function model is as follows: ; In the formula, represents the adjustment coefficient of the direct cycling travel demand, represents the road traffic accessibility index, represents the average value of the road traffic accessibility index in the same type of research areas; represents the adjustment coefficient of the cycling transfer travel demand, represents the public transport accessibility index, represents the average value of the public transport accessibility index in the same type of research areas; and are both preset curvature coefficients, represents the natural constant.

[0025] Specifically, the average value of the road traffic accessibility index in the same type of research areas is the average value of the road traffic accessibility indices in multiple research areas of the same type. For example, if there are 10 research areas in the whole city, for the average value of the road traffic accessibility index in the same type of research areas, first calculate the road traffic accessibility indices of each research area, and then calculate the average value of the road traffic accessibility indices of these 10 research areas to obtain the average value of the road traffic accessibility index in the same type of research areas. The calculation of the average value of the public transport accessibility index in the same type of research areas is the same as that of the average value of the road traffic accessibility index in the same type of research areas and will not be elaborated here. In addition, and are preset curvature coefficients that control the steepness of the function and directly affect the sensitivity of the adjustment coefficient to the accessibility index. It is recommended that the value range be within the interval of [0.5, 2].

[0026] Figure 4 is an example graph of the curve of the Sigmoid function model. The Sigmoid function model has the characteristic of non-linear mapping and can accurately depict the marginal diminishing effect of traffic accessibility on cycling demand. By this model, the correction coefficient is restricted within the interval of [0.8, 1.2] to avoid the step risk of traditional linear correction. In addition, this model is also flexible and can adjust the model sensitivity through the curvature coefficient based on the differences between different cities or regions, so as to adapt to diverse prediction scenarios.

[0027] In some embodiments, the S5 is specifically: Calculate the total demand for cycling trips based on the direct demand for cycling trips, the demand for cycling feeder trips, the adjustment coefficient for direct cycling trip demand, and the adjustment coefficient for cycling feeder trip demand; Calculate the capacity of internet rental bicycles based on the total demand for cycling trips and the turnover rate of internet rental bicycles; Among them, the formula for calculating the total demand for cycling trips is: ; In the formula, represents the total demand for cycling trips, represents the direct demand for cycling trips, represents the demand for cycling feeder trips, represents the adjustment coefficient for direct cycling trip demand, represents the adjustment coefficient for cycling feeder trip demand; The formula for calculating the capacity of internet rental bicycles is: ; In the formula, represents the capacity of internet rental bicycles, represents the turnover rate of internet rental bicycles, represents the intact rate of internet rental bicycles, and the intact rate of internet rental bicycles is set according to experience, and the recommended value is 95%.

[0028] Based on the above method for calculating the capacity of internet rental bicycles in a region, the present invention also provides a system for calculating the capacity of internet rental bicycles in a region.

[0029] As Figure 5 shown, a system for calculating the capacity of internet rental bicycles in a region includes: An index acquisition module, which is used to collect mobile phone signaling datasets, travel characteristic datasets, public transportation passenger flow datasets, road network datasets, and internet rental bicycle order datasets in the research area, and process the collected datasets to obtain the population size, per capita travel rate, internet rental bicycle travel share rate, public transportation and internet rental bicycle connection rate, public transportation station passenger flow, public transportation average departure interval time, road density, non-motor vehicle lane density, intersection node density, public transportation station density, and internet rental bicycle order turnover rate in the research area; A travel demand calculation module, which is used to calculate the direct cycling travel demand and the cycling transfer travel demand in the research area according to the population size, the per capita travel rate, the sharing rate of Internet rental bicycles for travel, the connection rate between public transportation and Internet rental bicycles, and the passenger flow of public transportation stations; An accessibility index calculation module, which is used to normalize the road density, the non-motorized lane density, the intersection node density, the public transportation station density, and the average headway of public transportation respectively, and use the expert scoring method to weight the road density, the non-motorized lane density, the intersection node density, the public transportation station density, and the average headway of public transportation after normalization to obtain the road traffic accessibility index and the public transportation accessibility index in the research area; An optimization and adjustment module, which is used to optimize the road traffic accessibility index and the public transportation accessibility index by using the Sigmoid function model to obtain the adjustment coefficient of the direct cycling travel demand and the adjustment coefficient of the cycling transfer travel demand in the research area; A capacity calculation module, which is used to calculate the accommodation capacity of Internet rental bicycles in the research area according to the direct cycling travel demand, the cycling transfer travel demand, the adjustment coefficient of the direct cycling travel demand, the adjustment coefficient of the cycling transfer travel demand, and the turnover rate of Internet rental bicycles.

[0030] For the specific functions of each module in the system for calculating the accommodation capacity of Internet rental bicycles in a region of the present invention, refer to the steps of the method for calculating the accommodation capacity of Internet rental bicycles in a region of the present invention, which will not be elaborated here.

[0031] A calculation method and system for the accommodation capacity of Internet rental bicycles in a region integrate multi-source heterogeneous data such as mobile phone signaling, public transportation passenger flow, Internet rental bicycle orders, and comprehensive traffic surveys, and comprehensively calculate the direct cycling travel demand and feeder travel demand within the research region. On this basis, combined with the traffic network structure and supply characteristics, calculate the road traffic accessibility index and the public transportation accessibility index, and further realize the non-linear mapping from the index to the adjustment coefficient through the Sigmoid model, so as to accurately adjust the total cycling demand, and finally obtain the accommodation capacity of Internet rental bicycles in the research region; the accommodation capacity of Internet rental bicycles calculated by the present invention can provide a scientific and quantitative decision-making basis for urban traffic and city appearance management, can effectively improve the urban traffic environment order, enhance the city appearance, optimize the utilization of road resources, improve the traffic operation efficiency, and has important practical application value for accurately reflecting the regional cycling demand and the demand for Internet rental bicycles, improving the urban traffic environment, and promoting the orderly and efficient development of urban public transportation; in addition, the present invention fully considers the characteristics of various types of data in the calculation process, has high adaptability and operability, and is easy to implement in engineering practice.

[0032] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A calculation method for the accommodation capacity of regional Internet rental bicycles, characterized in that, Including: S1: Collect the mobile signaling dataset, travel characteristic dataset, public transport passenger flow dataset, road network dataset, and Internet rental bicycle order dataset within the research area, and process the collected datasets to obtain the population size, per capita travel rate, sharing rate of Internet rental bicycle trips, transfer rate between public transport and Internet rental bicycles, passenger flow at public transport stations, average departure headway of public transport, road density, non-motorized lane density, intersection node density, public transport station density, and turnover rate of Internet rental bicycle orders within the research area; S2: Calculate the direct cycling travel demand and transfer cycling travel demand within the research area based on the population size, per capita travel rate, sharing rate of Internet rental bicycle trips, transfer rate between public transport and Internet rental bicycles, and passenger flow at public transport stations; S3: Normalize the road density, non-motorized lane density, intersection node density, public transport station density, and average departure headway of public transport respectively, and use the expert scoring method to weight the normalized road density, non-motorized lane density, intersection node density, public transport station density, and average departure headway of public transport to obtain the road traffic accessibility index and public transport accessibility index within the research area; S4: Optimize the road traffic accessibility index and public transport accessibility index using the Sigmoid function model to obtain the adjustment coefficient of direct cycling travel demand and the adjustment coefficient of transfer cycling travel demand within the research area; S5: Calculate the accommodation capacity of Internet rental bicycles within the research area based on the direct cycling travel demand, transfer cycling travel demand, adjustment coefficient of direct cycling travel demand, adjustment coefficient of transfer cycling travel demand, and turnover rate of Internet rental bicycles; 2. The calculation method for the accommodation capacity of regional Internet rental bicycles according to claim 1, wherein, The specific content of S1 is as follows: Collect the mobile signaling dataset within the research area and process the mobile signaling dataset to obtain the population size; Collect the travel characteristic dataset within the research area through the method of comprehensive traffic survey, and obtain the per capita travel rate, sharing rate of Internet rental bicycle trips, and transfer rate between public transport and Internet rental bicycles from the travel characteristic dataset; Collect the public transport passenger flow dataset within the research area. The public transport passenger flow dataset includes public transport card swipe data and public transport departure time data, and process the public transport card swipe data and public transport departure time data to obtain the passenger flow at public transport stations and the average departure headway of public transport; Collect the road network dataset of the research area and process the road network dataset to obtain the road density, non-motorized lane density, intersection node density, and public transport station density; Collect the Internet rental bicycle order dataset within the research area, and process the Internet rental bicycle order dataset to obtain the turnover rate of the Internet rental bicycle orders.

3. The calculation method for the accommodation capacity of regional Internet rental bicycles according to claim 1, characterized in that In step S2, specifically calculate the direct cycling travel demand according to the population size, the per capita travel rate, and the sharing rate of Internet rental bicycles for travel; among them, the formula for calculating the direct cycling travel demand is: ； In the formula, represents the direct demand for cycling trips, represents the population size, represents the per capita travel rate, represents the sharing rate of trips by internet rental bicycles.

4. The calculation method of the accommodation capacity of regional Internet rental bicycles according to claim 1, wherein In step S2, specifically calculate the transfer cycling travel demand according to the transfer rate between public transportation and Internet rental bicycles and the passenger flow at public transportation stations. Among them, the transfer rate between public transportation and Internet rental bicycles includes the transfer rate between rail transit and Internet rental bicycles and the transfer rate between buses and Internet rental bicycles, and the passenger flow at public transportation stations includes the inbound and outbound passenger flow at rail transit stations and the boarding and alighting passenger flow at bus stations; the formula for calculating the transfer cycling travel demand is: ； In the formula, represents the demand for the cycling and feeder trips, represents the passenger flow in and out of the rail transit station, represents the passenger flow getting on and off at the bus stop, represents the connection rate between the rail transit and the internet rental bicycles, represents the connection rate between the bus and the internet rental bicycles.

5. The calculation method for the accommodation capacity of regional Internet rental bicycles according to claim 1, characterized in that Step S3 specifically is: Use the positive index normalization model to perform positive normalization processing on the road density, the non-motorized lane density, the intersection node density, and the public transportation station density respectively, and correspondingly obtain the road density score, the non-motorized lane density score, the intersection node density score, and the public transportation station density score. Use the reverse index normalization model to perform reverse normalization processing on the average headway of public transportation to obtain the average headway score of public transportation. Adopt the expert scoring method to perform weighted processing on the road density score, the non-motorized lane density score, and the intersection node density score to obtain the road traffic accessibility index. Adopt the expert scoring method to perform weighted processing on the public transportation station density score and the average headway score of public transportation to obtain the public transportation accessibility index.

6. The calculation method of the accommodation capacity of regional Internet rental bicycles according to claim 5, characterized in that, The positive index normalization model is: ； Among them, represents the positive index score, represents the positive index, represents the minimum value of the positive index in the same type of research area, represents the maximum value of the positive index in the same type of research area; the positive index includes the road density, the non-motor vehicle lane density, the intersection node density, and the public transport stop density; The reverse index normalization model is: ； Among them, represents the reverse index score, represents the reverse index, represents the minimum value of the reverse index in the same type of research area, represents the maximum value of the reverse index in the same type of research area; the reverse index includes the average departure interval time of public transportation.

7. The calculation method of the accommodation capacity of regional Internet rental bicycles according to claim 5, characterized in that, The public transportation station density includes the rail transit station density and the bus station density. Correspondingly, the public transportation station density score includes the rail transit station density score and the bus station density score; the average headway of public transportation includes the average headway of rail transit and the average headway of buses. Correspondingly, the average headway score of public transportation includes the average headway score of rail transit and the average headway score of buses. The formula for calculating the road traffic accessibility index is: ； In the formula, represents the road traffic accessibility index, and respectively represent the road density fraction, the non-motorized lane density fraction, and the intersection node density fraction, , and are all the weights of the corresponding scores obtained by the expert scoring method, and ; The formula for calculating the public transportation accessibility index is: ； Wherein, represents the public transport accessibility index, and respectively represent the rail station density fraction, the bus station density fraction, the average headway time fraction of rail transit, and the average headway time fraction of bus, and are both weights of the corresponding scores obtained by the expert scoring method, and .

8. The calculation method for the accommodation capacity of regional Internet rental bicycles according to claim 1, characterized in that, Step S4 specifically is: Use the Sigmoid function model to optimize the road traffic accessibility index to obtain the adjustment coefficient of the direct cycling travel demand. Use the Sigmoid function model to optimize the public transportation accessibility index to obtain the adjustment coefficient of the transfer cycling travel demand. Among them, the formula for optimizing the road traffic accessibility index using the Sigmoid function model is: ； The formula for optimizing the public transportation accessibility index using the Sigmoid function model is: ； Wherein, represents the adjustment coefficient of the direct cycling travel demand, represents the road traffic accessibility index, represents the average value of the road traffic accessibility index in the same type of research area; represents the adjustment coefficient of the cycling feeder travel demand, represents the public transport accessibility index, represents the average value of the public transport accessibility index in the same type of research area; and are both preset curvature coefficients.

9. The calculation method for the accommodation capacity of regional Internet rental bicycles according to claim 1, characterized in that, Step S5 specifically is: Calculate the total demand for cycling trips based on the direct demand for cycling trips, the demand for cycling feeder trips, the adjustment coefficient for direct demand for cycling trips, and the adjustment coefficient for demand for cycling feeder trips; Calculate the capacity of Internet rental bicycles based on the total demand for cycling trips and the turnover rate of Internet rental bicycles; Among them, the formula for calculating the total demand for cycling trips is: ； Wherein, represents the total demand for cycling trips, represents the direct demand for cycling trips, represents the demand for cycling feeder trips, represents the adjustment coefficient of the direct demand for cycling trips, represents the adjustment coefficient of the demand for cycling feeder trips; The formula for calculating the capacity of Internet rental bicycles is: ； In the formula, represents the accommodation capacity of the Internet rental bicycles, represents the turnover rate of the Internet rental bicycles, represents the intact rate of the Internet rental bicycles.

10. A calculation system for the accommodation capacity of regional Internet rental bicycles, characterized in that, Including: An index acquisition module, which is used to collect the mobile signaling data set, travel characteristic data set, public transportation passenger flow data set, road network data set, and Internet rental bicycle order data set in the research area, and process the collected data sets to obtain the population size, per capita travel rate, Internet rental bicycle travel sharing rate, public transportation and Internet rental bicycle connection rate, public transportation station passenger flow, average headway of public transportation, road density, non-motorized lane density, intersection node density, public transportation station density, and Internet rental bicycle order turnover rate in the research area; A travel demand calculation module, which is used to calculate the direct demand for cycling trips and the demand for cycling feeder trips in the research area based on the population size, per capita travel rate, Internet rental bicycle travel sharing rate, public transportation and Internet rental bicycle connection rate, and public transportation station passenger flow; An accessibility index calculation module, which is used to normalize the road density, non-motorized lane density, intersection node density, public transportation station density, and average headway of public transportation respectively, and use the expert scoring method to weight the road density, non-motorized lane density, intersection node density, public transportation station density, and average headway of public transportation after normalization to obtain the road traffic accessibility index and public transportation accessibility index in the research area; An optimization and adjustment module, which is used to optimize the road traffic accessibility index and the public transportation accessibility index by using the Sigmoid function model to obtain the adjustment coefficient for direct demand for cycling trips and the adjustment coefficient for demand for cycling feeder trips in the research area; A capacity calculation module, which is used to calculate the capacity of Internet rental bicycles in the research area based on the direct demand for cycling trips, the demand for cycling feeder trips, the adjustment coefficient for direct demand for cycling trips, the adjustment coefficient for demand for cycling feeder trips, and the turnover rate of Internet rental bicycles.

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