CN111489039B - Method and system for predicting total quantity of shared bicycle - Google Patents
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Abstract
The invention provides a method and a system for predicting total quantity of a shared bicycle, wherein the method comprises the following steps: based on the travel amount of each traffic mode in the planning year, obtaining the travel demand of the shared single vehicle in the planning year by using a transfer rate method, and calculating the total demand of the shared single vehicle in the planning year according to the travel demand of the shared single vehicle and the turnover rate of the shared single vehicle; based on city characteristics of similar cities and shared bicycle usage characteristics, constructing an inference model based on machine learning, bringing the city characteristics of a target city, and measuring and calculating the reference scale of the shared bicycle of the target city; and comparing the total quantity of the shared bicycle demand with the reference scale of the shared bicycle with similar cities, and determining the reasonable total quantity of the shared bicycle of the target city in the planning year. The method and the system of the invention calculate the total quantity of the vehicle demand through a transfer rate method, and calculate the upper limit of the total quantity of the shared bicycle in the target city through constructing a machine learning reasoning model. Finally, the scientificity and the effectiveness of the measuring and calculating result are ensured by comparing the measuring and calculating result with similar cities.
Description
Technical Field
The invention relates to public transportation technology, in particular to a method and a system for predicting total quantity of shared bicycles in a planned year in a city without putting the shared bicycles.
Background
In 2015, with the development of mobile internet technology, shared bicycles with "borrowing and returning" as main features are gradually spread in large cities. In the initial stage of vehicle release, in order to occupy the market to the greatest extent, each enterprise has appeared "excessive release" phenomenon, and the vehicle is excessive in the city, seriously influences the city view on the one hand, occupies the slow-going space, has caused huge wasting of resources on the other hand.
Since 2017, government departments in each place have standardized the development of "shared bicycles" from the aspects of "passive management" to "active management" in terms of delivery, operation, management, etc.
For the city in which the shared bicycle is put, the upper limit of the scale of the shared bicycle can be accurately estimated through the data such as the rate of riding passengers, the use data of the bicycle, the urban travel structure, the urban environment bearing capacity and the like. However, for cities without "sharing bicycle", how to determine the upper limit of the "sharing bicycle" scale by a scientific and effective method is lacking in a more effective means at present.
Therefore, in order to accurately and effectively predict the reasonable scale of the shared bicycle in the city without putting the shared bicycle, a new method is needed to be designed, and the influence of the vehicles on urban space, order and landscape is reduced while the putting quantity of the vehicles can meet the travel demands of citizens.
Disclosure of Invention
The invention aims to provide a method for predicting total quantity of shared bicycles in a city of which the shared bicycles are not put in planning years.
Another object of the present invention is to provide a system for implementing the method for predicting total amount of shared bicycle.
The following presents a simplified summary of one or more aspects in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements of all aspects nor delineate the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed description that is presented later.
According to an aspect of the present invention, there is provided a method for predicting a total amount of a shared bicycle, including: based on the travel amount of each traffic mode in the planning year, obtaining the travel demand of the shared single vehicle in the planning year by using a transfer rate method, and calculating the total demand of the shared single vehicle in the planning year according to the travel demand of the shared single vehicle and the turnover rate of the shared single vehicle; based on city characteristics of similar cities and shared bicycle usage characteristics, constructing an inference model based on machine learning, bringing the city characteristics of a target city, and measuring and calculating the reference scale of the shared bicycle of the target city; and comparing the total quantity of the shared bicycle demand with the reference scale of the shared bicycle with corresponding parameters of similar cities, and determining the reasonable total quantity of the shared bicycle of the target city in the planning year.
In an embodiment, the method for obtaining the travel demand of the shared bicycle in the planning year by using the transfer rate method based on the travel quantity of each traffic mode in the planning year comprises the following steps: according to urban development and urban land conditions, predicting and planning the travel amounts of the residents of each traffic mode in the year by combining the travel amounts of the residents of each traffic mode in the current stage; investigation is carried out to obtain the probability that residents using each traffic mode replace the current travel mode through the shared bicycle after the shared bicycle is introduced into the city, so as to obtain the probability that each traffic mode is transferred to the shared bicycle; and predicting the travel demand of the shared single car in the planning year according to the probability of transferring each traffic mode to the shared single car and the travel amount of residents in each traffic mode in the planning year.
In an embodiment, the turnover rate of the shared bicycle is determined according to the measurement and calculation of the balance condition of the enterprise of the shared bicycle.
In an embodiment, the building the machine learning based reasoning model based on the city characteristics and the shared bicycle usage characteristics of the similar cities comprises: determining similar cities of the target city, collecting city characteristic data of the target city and the similar cities, and collecting shared bicycle use characteristic data of the similar cities; the method comprises the steps that urban characteristic data of a target city and urban characteristic data of similar cities are spatially fused on a geographic information system; dividing a target city and a similar city into grids in a geographic information system respectively, and carrying out space set counting processing and multi-element association analysis on the fused data; and constructing an inference model based on machine learning by taking the shared bicycle use characteristics of the similar cities as dependent variables and taking the other city characteristics of the similar cities except the shared bicycle use characteristics as independent variables.
In one embodiment, the city characteristic data of the method includes POI data, bus network data, rail network data, and demographic data.
In one embodiment, the comparing the total amount of shared bicycle demand and the reference size of the shared bicycle to similar cities comprises: the sharing bicycle ten-thousand-person congestion rate of the target city is measured and calculated based on the total sharing bicycle demand and the sharing bicycle reference scale respectively, and the sharing bicycle ten-thousand-person congestion rate of the target city is compared with the sharing bicycle ten-thousand-person congestion rate of the similar city; and comparing the total quantity of the shared bicycle requirements and the reference scale of the shared bicycle with the total quantity of the shared bicycle in the similar city.
According to another aspect of the present invention, there is also provided a system for predicting total amount of a shared bicycle, including: the shared bicycle demand total amount measuring and calculating module is used for obtaining the shared bicycle travel demand of the planning year by using a transfer rate method based on the travel amount of each traffic mode of the planning year, and calculating the shared bicycle demand total amount of the planning year according to the shared bicycle travel demand and the shared bicycle transfer rate; the shared bicycle reference scale measuring and calculating module is used for constructing an inference model based on machine learning based on urban features of similar cities and shared bicycle use features, bringing the urban features of the target cities into the inference model, and measuring and calculating the shared bicycle reference scale of the target cities; and the comparison module is used for comparing the total quantity of the shared bicycle demand and the reference scale of the shared bicycle with similar cities and determining the reasonable total quantity of the shared bicycle of the target city in the planning year.
In one embodiment, the total shared bicycle demand measurement module of the system includes: the system comprises a planning year resident travel quantity prediction module, a planning year resident travel quantity prediction module and a planning year management module, wherein the planning year resident travel quantity prediction module is used for predicting planning year resident travel quantity according to city development and city land conditions and combining the current period resident travel quantity of each transportation resident; the probability obtaining module is used for obtaining the probability of transferring each traffic mode to the shared bicycle by using residents of each traffic mode to replace the current travel mode through the shared bicycle according to the probability of obtaining the city of the shared bicycle after the shared bicycle is introduced into the city through investigation; and the shared bicycle travel demand calculation module is used for predicting the travel demand of the shared bicycle in the planning year according to the probability of transferring each traffic mode to the shared bicycle and the travel amount of residents in each traffic mode in the planning year.
In one embodiment, the shared bicycle reference size measurement module of the system comprises: the feature data collection module is used for determining similar cities of the target cities, collecting city feature data of the target cities and the similar cities, and collecting shared bicycle use feature data of the similar cities; the data fusion module is used for fusing the city characteristic data of the target city and the city characteristic data of the similar city and the shared bicycle use characteristic data of the similar city in space on the geographic information system; the rasterization processing module is used for dividing the target city and the similar city into grids in the geographic information system respectively, and carrying out space set counting processing and multi-element association analysis on the fused data; the inference model construction module is used for constructing an inference model based on machine learning by taking the shared bicycle use characteristics of similar cities as dependent variables and taking other city characteristics of similar cities except the shared bicycle use characteristics as independent variables.
In one embodiment, the comparison module of the system comprises: the ten-thousand-person congestion rate comparison module is used for measuring and calculating the ten-thousand-person congestion rate of the shared single vehicle in the target city based on the total demand of the shared single vehicle and the reference scale of the shared single vehicle respectively, and comparing the ten-thousand-person congestion rate of the shared single vehicle in the similar city; and the shared bicycle total quantity comparison module is used for comparing the shared bicycle required total quantity and the shared bicycle reference scale with the shared bicycle total quantity of the similar city.
The method and the system have the beneficial effects that: on one hand, the total vehicle demand is accurately calculated by a transfer rate method, and the travel demands of residents are met. On the other hand, by constructing a machine learning reasoning model and adopting a city feature analysis method, the upper limit of the total quantity of the shared single vehicles in the target city is calculated, so that the influence of the vehicles on the aspects of city space, order and landscape is reduced when the shared single vehicles are put in the target city. Finally, judging whether the measuring and calculating result accords with the reality or not by comparing the measuring and calculating result with similar cities, and ensuring the scientificity and effectiveness of the final measuring and calculating result.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
The above features and advantages of the present invention will be better understood after reading the detailed description of embodiments of the present disclosure in conjunction with the following drawings. In the drawings, the components are not necessarily to scale and components having similar related features or characteristics may have the same or similar reference numerals.
FIG. 1 is a schematic flow chart of an embodiment of the method of the present invention;
FIG. 2 is a block diagram of an embodiment of the system of the present invention.
Detailed Description
The invention is described in detail below with reference to the drawings and the specific embodiments. It is noted that the aspects described below in connection with the drawings and the specific embodiments are merely exemplary and should not be construed as limiting the scope of the invention in any way.
As shown in fig. 1, the embodiment of the invention discloses a method for predicting total quantity of a shared bicycle, which mainly comprises the following steps:
s1, based on the travel amount of each traffic mode in the planning year, obtaining the travel demand of the shared bicycle in the planning year by using a transfer rate method, and calculating the total demand of the shared bicycle in the planning year according to the travel demand of the shared bicycle and the turnover rate of the shared bicycle;
in this step, the travel amounts of each traffic mode in the planned year can be predicted by combining the travel amounts of each traffic mode in the current year with urban development and urban land conditions, and can also be predicted according to annual congruent growth rates of the occurrence amounts of each traffic mode.
The transfer rate method refers to obtaining the travel demand of the shared bicycle by multiplying the travel quantity of each traffic mode by the transfer probability of the travel mode of the shared bicycle, for example: the travel amount of the bus is 500 ten thousand people/year, the probability that residents riding the bus change to use the shared bicycle is 10%, and the shared bicycle use requirement of 50 ten thousand people/year can be generated.
The total amount of shared bicycle demand is related to the amount of shared bicycle demand and the turnover rate, and the higher the turnover rate is, the smaller the number of shared bicycles is required under the same shared bicycle demand. At present, the number of times of daily turnover is about 1.1-1.5 times/vehicle due to excessive throwing or unreasonable throwing positions of partial city sharing bicycles. Therefore, the daily turnover rate of the shared bicycle can be set to be about 3-5 times per bicycle during planning.
S2, constructing an inference model based on machine learning based on urban features of similar cities and shared bicycle usage features, bringing the inference model into urban features of a target city, and measuring and calculating a shared bicycle reference scale of the target city;
it should be noted that, similar cities refer to cities which have been put in shared bicycles and have reasonable sizes and the same population level or traffic scale, and preferably, cities with similar characteristics such as rail traffic and subways are selected. For example, the city of su state, which has not been put in shared bicycle in 2019, may be the south Beijing city, the Hangzhou city and the Shenzhen city as similar cities. The upper limit of the size of the shared bicycle is calculated and the vehicle cleaning work is carried out in Nanjing city, hangzhou city and Shenzhen city, and the total amount of the shared bicycle in the three cities is in a reasonable scale in 2019.
Urban features refer to POI (point of interest) data, public transportation network data, rail network data, population data and the like. The shared bicycle usage feature may employ shared bicycle operational data such as shared bicycle total, daily turnover rate, etc.
The method comprises the steps of constructing an inference model based on machine learning, namely constructing an inference model of shared bicycle usage characteristics and other city characteristics by using a machine learning method, so that the shared bicycle usage characteristics of a target city can be obtained after the inference model is brought into the city characteristics of the target city, and further the reference scale of the shared bicycle is obtained. The machine learning can adopt algorithms such as random forest, artificial neural network, bayesian learning, deep learning, association rules and the like, and is not limited herein, so long as the constructed reasoning model can accurately reflect the association relation between the shared bicycle use characteristics and other city characteristics.
S3, comparing the total quantity of the shared bicycle demand and the reference scale of the shared bicycle with corresponding parameters of similar cities, and determining the reasonable total quantity of the shared bicycle of the target city in the planning year.
It should be noted that, it is not very accurate to determine the reasonable total amount of the shared bicycle only by the total amount of the shared bicycle demand, because more variables are involved in the travel amount or the transfer rate of each traffic mode in the planning year, which may result in larger result deviation. In addition, the transfer rate may gradually increase as the sharing bicycle is put in place. The reference scale of the shared bicycle cannot consider the differences of traffic, resident trip habits and the like among cities, so that the total demand of the shared bicycle and the reference scale of the shared bicycle are considered together, and a reasonable and accurate result can be obtained by comparing the total demand of the shared bicycle with the corresponding parameters of the similar cities.
In a possible embodiment, step S1 specifically includes:
s1-1, calculating the current total travel amount of residents in each traffic mode by utilizing public bicycle card swiping data, bus card swiping data, rail traffic card swiping data and taxi boarding and alighting data and combining the urban resident travel structure. And predicting the total travel amount of residents in each traffic mode in the planning year according to urban development, urban land use and other conditions.
S1-2, acquiring the probability that residents using each traffic mode replace the current travel mode through the sharing bicycle after the sharing bicycle is introduced into the city through a questionnaire survey mode and the like, and acquiring the probability that each traffic mode is transferred to the sharing bicycle.
S1-3, predicting the travel demand of the shared bicycle in the planning year according to the probability of transferring each traffic mode to the shared bicycle and the travel amount of residents in each traffic mode in the planning year.
Preferably, the daily turnover rate of the shared bicycle can be set according to the condition that an operation enterprise planning to year the shared bicycle can make full use of balance, so that the travel demands of residents can be met while the healthy development of the enterprise can be ensured.
In a possible embodiment, step S2 specifically includes:
s2-1, determining similar cities of a target city, collecting city characteristic data of the target city and the similar cities, including POI data, bus network data, track network data and population data, and collecting shared bicycle use characteristic data of the similar cities;
s2-2, spatially fusing city characteristic data of a target city and city characteristic data of similar cities and shared bicycle use characteristic data of the similar cities on a Geographic Information System (GIS);
s2-3, dividing a target city and a similar city into grids in a geographic information system, for example, 500m multiplied by 500m grids, and performing space set counting processing and multi-element association analysis on the fused data, wherein the analysis comprises the steps of analyzing the using characteristics of a shared bicycle in each grid of the similar city and the characteristics of other cities except the using characteristics of the shared bicycle of the target city and the similar city;
s2-4, constructing an inference model based on machine learning by taking the shared bicycle use characteristics of the similar cities as dependent variables and taking the other city characteristics of the similar cities except the shared bicycle use characteristics as independent variables.
In a possible embodiment, step S3 specifically includes:
s3-1, measuring and calculating the rate of the ten thousand people of the shared bicycle in the target city based on the total demand of the shared bicycle and the reference scale of the shared bicycle, and comparing the rate with the rate of the ten thousand people of the shared bicycle in the similar city;
s3-2, comparing the total quantity of the shared bicycle demand and the reference scale of the shared bicycle with the total quantity of the shared bicycle in the similar city.
Finally, comprehensively considering comparison results of two dimensions of the ten thousand people's congestion rate and the total scale, and determining the reasonable upper limit of the vehicle scale of the target city.
As shown in fig. 2, the present invention further provides a system for predicting total amount of a shared bicycle, including:
the shared bicycle demand total amount measuring and calculating module 101 is used for obtaining the shared bicycle travel demand of the planning year by using a transfer rate method based on the travel amount of each traffic mode of the planning year, and calculating the shared bicycle demand total amount of the planning year according to the shared bicycle travel demand and the shared bicycle transfer rate;
the shared bicycle reference scale measuring and calculating module 102 is used for constructing an inference model based on machine learning based on urban features of similar cities and shared bicycle use features, bringing the city features of a target city, and measuring and calculating the shared bicycle reference scale of the target city;
and the comparison module 103 is used for comparing the total quantity of the shared bicycle demand and the reference scale of the shared bicycle with similar cities and determining the reasonable total quantity of the shared bicycle of the target city in the planning year.
Preferably, the shared bicycle demand total amount calculation module 101 includes:
the system comprises a planning year resident travel quantity prediction module, a planning year resident travel quantity prediction module and a planning year management module, wherein the planning year resident travel quantity prediction module is used for predicting planning year resident travel quantity according to city development and city land conditions and combining the current period resident travel quantity of each transportation resident;
the probability obtaining module is used for obtaining the probability of transferring each traffic mode to the shared bicycle by using residents of each traffic mode to replace the current travel mode through the shared bicycle according to the probability of obtaining the city of the shared bicycle after the shared bicycle is introduced into the city through investigation;
and the shared bicycle travel demand calculation module is used for predicting the travel demand of the shared bicycle in the planning year according to the probability of transferring each traffic mode to the shared bicycle and the travel amount of residents in each traffic mode in the planning year.
Preferably, the shared bicycle reference scale calculation module 102 includes:
the feature data collection module is used for determining similar cities of the target cities, collecting city feature data of the target cities and the similar cities, and collecting shared bicycle use feature data of the similar cities;
the data fusion module is used for fusing the city characteristic data of the target city and the city characteristic data of the similar city and the shared bicycle use characteristic data of the similar city in space on the geographic information system;
the rasterization processing module is used for dividing the target city and the similar city into grids in the geographic information system respectively, and carrying out space set counting processing and multi-element association analysis on the fused data;
the inference model construction module is used for constructing an inference model based on machine learning by taking the shared bicycle use characteristics of similar cities as dependent variables and taking other city characteristics of similar cities except the shared bicycle use characteristics as independent variables.
Preferably, the comparison module 103 comprises:
the ten-thousand-person congestion rate comparison module is used for measuring and calculating the ten-thousand-person congestion rate of the shared single vehicle in the target city based on the total demand of the shared single vehicle and the reference scale of the shared single vehicle respectively, and comparing the ten-thousand-person congestion rate of the shared single vehicle in the similar city;
and the shared bicycle total quantity comparison module is used for comparing the shared bicycle required total quantity and the shared bicycle reference scale with the shared bicycle total quantity of the similar city.
In summary, the method and the system of the embodiment of the invention can scientifically and effectively predict the upper limit of the size of the shared bicycle in the city without putting the shared bicycle. On the one hand, the total vehicle demand is accurately calculated through the actual operation data of urban traffic and the questionnaire survey data of residents, and meanwhile, the profit and loss balance of enterprises is considered. On the other hand, by constructing a machine learning reasoning model and a city feature analysis method, the upper limit of the total quantity of the shared single vehicles in the target city is calculated, so that the influence of the vehicles on the aspects of city space, order and landscape is reduced when the shared single vehicles are put in the target city. Finally, judging whether the measuring and calculating result accords with the reality or not by a method of comparing the vehicle congestion rate of ten thousands of people and the total amount of vehicles in similar cities, and ensuring the scientificity and the effectiveness of the final measuring and calculating result.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other.
While, for purposes of simplicity of explanation, the methodologies are shown and described as a series of acts, it is to be understood and appreciated that the methodologies are not limited by the order of acts, as some acts may, in accordance with one or more embodiments, occur in different orders and/or concurrently with other acts from that shown and described herein or not shown and described herein, as would be understood and appreciated by those skilled in the art.
The previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Thus, the disclosure is not intended to be limited to the examples and designs described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description is of the preferred embodiment of the present application and is not intended to limit the invention to the particular form disclosed, but on the contrary, the intention is to cover all modifications, equivalents, alternatives, and alternatives falling within the spirit and scope of the invention.
Claims (4)
1. A method for predicting total amount of a shared bicycle, comprising:
s1, based on the travel amount of each traffic mode in the planning year, obtaining the travel demand of the shared bicycle in the planning year by using a transfer rate method, and calculating the total demand of the shared bicycle in the planning year according to the travel demand of the shared bicycle and the turnover rate of the shared bicycle;
the method for obtaining the travel demand of the shared bicycle in the planning year by using the transfer rate method based on the travel quantity of each traffic mode in the planning year specifically comprises the following steps:
according to urban development and urban land conditions, predicting and planning the travel amounts of the residents of each traffic mode in the year by combining the travel amounts of the residents of each traffic mode in the current stage;
investigation is carried out to obtain the probability that residents using each traffic mode replace the current travel mode through the shared bicycle after the shared bicycle is introduced into the city, so as to obtain the probability that each traffic mode is transferred to the shared bicycle;
predicting the travel demand of the shared bicycle in the planning year according to the probability of transferring each traffic mode to the shared bicycle and the travel quantity of residents in each traffic mode in the planning year;
s2, constructing an inference model based on machine learning based on urban features of similar cities and shared bicycle usage features, bringing the inference model into urban features of a target city, and measuring and calculating a shared bicycle reference scale of the target city;
the method for constructing the inference model based on the machine learning is based on city characteristics of similar cities and shared bicycle use characteristics, and specifically comprises the following steps:
determining similar cities of the target city, collecting city characteristic data of the target city and the similar cities, and collecting shared bicycle use characteristic data of the similar cities;
the method comprises the steps that urban characteristic data of a target city and urban characteristic data of similar cities are spatially fused on a geographic information system;
dividing a target city and a similar city into grids in a geographic information system respectively, and carrying out space set counting processing and multi-element association analysis on the fused data;
taking the shared bicycle use characteristics of similar cities as dependent variables, and taking other city characteristics of similar cities except the shared bicycle use characteristics as independent variables to construct an inference model based on machine learning;
s3, comparing the total quantity of the shared bicycle demand and the reference scale of the shared bicycle with corresponding parameters of similar cities, and determining the reasonable total quantity of the shared bicycle of the target city in the planning year;
the method for comparing the total quantity of the shared bicycle demand and the reference scale of the shared bicycle with the corresponding parameters of the similar city specifically comprises the following steps:
the sharing bicycle ten-thousand-person congestion rate of the target city is measured and calculated based on the total sharing bicycle demand and the sharing bicycle reference scale respectively, and the sharing bicycle ten-thousand-person congestion rate of the target city is compared with the sharing bicycle ten-thousand-person congestion rate of the similar city;
and comparing the total quantity of the shared bicycle requirements and the reference scale of the shared bicycle with the total quantity of the shared bicycle in the similar city.
2. The method for predicting total quantity of shared bicycle according to claim 1, wherein the turnover rate of the shared bicycle is determined according to the calculation of the balance condition of the enterprise of the shared bicycle.
3. The method of claim 1, wherein the city feature data comprises POI data, bus network data, rail network data, and demographic data.
4. A shared bicycle total amount prediction system, comprising:
the shared bicycle demand total amount measuring and calculating module is used for obtaining the shared bicycle travel demand of the planning year by using a transfer rate method based on the travel amount of each traffic mode of the planning year, and calculating the shared bicycle demand total amount of the planning year according to the shared bicycle travel demand and the shared bicycle transfer rate;
the shared bicycle reference scale measuring and calculating module is used for constructing an inference model based on machine learning based on urban features of similar cities and shared bicycle use features, bringing the urban features of the target cities into the inference model, and measuring and calculating the shared bicycle reference scale of the target cities;
the comparison module is used for comparing the total quantity of the shared bicycle demand and the reference scale of the shared bicycle with similar cities and determining the reasonable total quantity of the shared bicycle of the target city in the planning year;
the shared bicycle demand total amount measuring and calculating module specifically comprises:
the system comprises a planning year resident travel quantity prediction module, a planning year resident travel quantity prediction module and a planning year management module, wherein the planning year resident travel quantity prediction module is used for predicting planning year resident travel quantity according to city development and city land conditions and combining the current period resident travel quantity of each transportation resident;
the probability obtaining module is used for obtaining the probability of transferring each traffic mode to the shared bicycle by using residents of each traffic mode to replace the current travel mode through the shared bicycle according to the probability of obtaining the city of the shared bicycle after the shared bicycle is introduced into the city through investigation;
the shared bicycle travel demand calculation module is used for predicting the travel demand of the shared bicycle in the planning year according to the probability of transferring each traffic mode to the shared bicycle and the travel amount of residents in each traffic mode in the planning year;
the shared bicycle reference scale measuring and calculating module specifically comprises:
the feature data collection module is used for determining similar cities of the target cities, collecting city feature data of the target cities and the similar cities, and collecting shared bicycle use feature data of the similar cities;
the data fusion module is used for fusing the city characteristic data of the target city and the city characteristic data of the similar city and the shared bicycle use characteristic data of the similar city in space on the geographic information system;
the rasterization processing module is used for dividing the target city and the similar city into grids in the geographic information system respectively, and carrying out space set counting processing and multi-element association analysis on the fused data;
the inference model construction module is used for constructing an inference model based on machine learning by taking the shared bicycle use characteristics of similar cities as dependent variables and taking other city characteristics of the similar cities except the shared bicycle use characteristics as independent variables;
the comparison module specifically comprises:
the ten-thousand-person congestion rate comparison module is used for measuring and calculating the ten-thousand-person congestion rate of the shared single vehicle in the target city based on the total demand of the shared single vehicle and the reference scale of the shared single vehicle respectively, and comparing the ten-thousand-person congestion rate of the shared single vehicle in the similar city;
and the shared bicycle total quantity comparison module is used for comparing the shared bicycle required total quantity and the shared bicycle reference scale with the shared bicycle total quantity of the similar city.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107909731A (en) * | 2017-10-23 | 2018-04-13 | 东南大学 | Share bicycle Delivery Management System in a kind of city based on big data |
| CN108133302A (en) * | 2016-12-01 | 2018-06-08 | 上海浦东建筑设计研究院有限公司 | A kind of public bicycles potential demand Forecasting Methodology based on big data |
| CN109582664A (en) * | 2018-11-23 | 2019-04-05 | 温州职业技术学院 | A kind of shared bicycle trip characteristics analysis method |
| CN110009046A (en) * | 2019-04-09 | 2019-07-12 | 中通服公众信息产业股份有限公司 | A kind of community in urban areas safety predicting method based on big data |
| CN110163660A (en) * | 2019-04-19 | 2019-08-23 | 深圳大学 | A kind of shared bicycle dispensing scale calculation method and system |
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108133302A (en) * | 2016-12-01 | 2018-06-08 | 上海浦东建筑设计研究院有限公司 | A kind of public bicycles potential demand Forecasting Methodology based on big data |
| CN107909731A (en) * | 2017-10-23 | 2018-04-13 | 东南大学 | Share bicycle Delivery Management System in a kind of city based on big data |
| CN109582664A (en) * | 2018-11-23 | 2019-04-05 | 温州职业技术学院 | A kind of shared bicycle trip characteristics analysis method |
| CN110009046A (en) * | 2019-04-09 | 2019-07-12 | 中通服公众信息产业股份有限公司 | A kind of community in urban areas safety predicting method based on big data |
| CN110163660A (en) * | 2019-04-19 | 2019-08-23 | 深圳大学 | A kind of shared bicycle dispensing scale calculation method and system |
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