CN112580842A - Shared bicycle supply decision early warning method and system - Google Patents
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Abstract
The invention relates to a shared bicycle supply decision early warning method and a system, wherein the method comprises the following steps: acquiring characteristic data of the required behaviors of the shared bicycle users in the management area; dividing the management area into a plurality of shared bicycle unit management areas according to the characteristic data; acquiring shared single-vehicle use data at t-1 moment in each unit management area, and extracting a prediction independent variable x; inputting an independent variable x into a prediction model for prediction, and predicting and obtaining the predicted parking space occupation rate y of the shared vehicle in each unit management area at the time t; and judging whether the predicted parking space occupancy of the shared bicycle in each unit management area exceeds a set decision threshold, and if so, sending an early warning signal to the dispatching platform. Compared with the prior art, the shared bicycle management area is divided, and the unit management area parking space occupancy prediction model is established, so that supply quantity decision early warning is sent to the shared bicycle operation platform, and the problems of difficulty in borrowing and returning vehicles of users are solved.
Description
Technical Field
The invention relates to the technical field of shared bicycle management, in particular to a shared bicycle supply decision early warning method and system
Background
The sharing bicycle is rapidly raised, the service blank of the last kilometer of urban residents in traveling is filled, and convenience is brought to traveling for people. However, in the process of rapid development, the shared bicycle industry itself also exposes a lot of problems, which causes that a plurality of operating platforms face the phenomena of difficult operation and even closing, and meanwhile, the disordered development of the shared bicycles also brings some problems for urban economy and society, for example, a 'Bailey graveyard' formed by the discarded shared bicycles in some cities causes waste of social resources; the sharing bicycle is parked at the street in disorder, which affects the normal traffic order and the city image. The reason is that the market competition promoted by the risk investment is related to the imbalance of market supply and demand caused by inaccurate prediction of the shared single-vehicle enterprises on user demands and unreasonable resource allocation. Therefore, how to use the existing operation data to scientifically and reasonably allocate the bicycle resources for the shared bicycle enterprises is beneficial to the operation of the enterprises, social resources are fully and reasonably used, and a large number of zombie vehicles are avoided.
The allocation mode of the current shared bicycle is mainly through manual inspection or video monitoring, and has the following defects:
(1) the labor cost is high, the efficiency is low and the real-time performance is poor;
(2) the cost of the consumed equipment is high, and due to the characteristic that the shared bicycle is stopped along with walking, the video monitoring network only can know the parking condition of part of the shared bicycle and cannot check the parking condition of an unconventional network;
(3) the scheduling process is basically determined by the experience of a scheduler, and the prediction accuracy is lacked.
In addition, at present, many researches on demand prediction of shared bicycles include developing a reservation and returning module of a user and putting vehicles in advance, the methods require active cooperation of the user and are difficult to achieve balance of supply and demand of vehicle putting in a short time, in addition, the researches are to classify sites by using methods such as cluster analysis and fuzzy comprehensive evaluation and then predict putting amount of similar sites, and the like.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a shared bicycle supply decision early warning method and system.
The purpose of the invention can be realized by the following technical scheme:
a shared bicycle supply decision early warning method specifically comprises the following steps:
s1, acquiring characteristic data of the shared bicycle user demand behaviors in the management area;
s2, dividing the management area into a plurality of shared bicycle unit management areas according to the characteristic data;
s3, obtaining shared bicycle use data at the t-1 moment in each unit management area, and extracting a prediction independent variable x;
s4, inputting an independent variable x in the prediction model for prediction, and predicting and obtaining the predicted parking space occupancy y of the shared vehicle in each unit management area at the time t;
and S5, judging whether the predicted parking space occupation rate of the shared bicycle in each unit management area exceeds a set decision threshold, and if so, sending an early warning signal to the dispatching platform.
Further, in step S1, the feature data includes a standing population number, a regional rail transit stop number, a regional regular bus stop number, and a plot land type.
Further, in step S2, the partitioning rule of the cell management area is preset, and the following steps are performed:
step S21, judging whether the number of the vehicles locked by switches in unit time in the area exceeds a set value, if so, judging that the vehicles are the active areas of the shared bicycle, and dividing the active areas according to a rule I; and if not, judging the shared bicycle inactive area, and dividing according to a rule II.
Further, the division rule of the unit management area is as follows:
rule one is as follows: firstly, judging whether the unit management area is a street or a town area, and if the unit management area is a street, dividing the unit management area into three types, namely an area within a radius range of 600m from a track station, an area within a radius range of 600-1000 m and an area above 1000 m; if the area is a town area, the area is divided into three types, namely an area within a radius range of 600m from a track station, an area within a radius range of 600-1500 m and an area above 1500 m;
rule two: partitioning according to the land type of the area, wherein the land type is a first type if the land type is a commercial land; if the land is a residential land, the land is a second type; if it is used as another place, it is classified as the third place.
Further, the method for establishing the prediction model comprises the following steps:
s31, initially selecting independent variable x of the prediction modeln；
S32, selecting the initial independent variable xnAnd dependent variable ytDependent variable ytThe number of occupied berths in a unit area at time t is represented, and a dependent variable y is selectedtIndependent variable x with obvious correlationi，i≤n；
S33, selecting the independent variable xiPerforming correlation analysis again, removing independent variables with high correlation according to the correlation analysis result, and determining a prediction independent variable x of the model;
s34, calibrating model parameters, and predicting independent variable x and dependent variable ytA linear fit is performed.
Further, determining a berthage occupancy decision threshold of each unit management area according to the accessibility of the distance track station and the type of the area land, wherein the decision threshold is divided into a high limit threshold and a low limit threshold.
Further, still include:
s51, judging whether the predicted parking space occupancy of the shared bicycle in each unit management area exceeds a set high-limit threshold, and if so, sending a first-class early warning signal; if not, go to step S52;
s52, judging whether the predicted parking space occupancy rate of the shared bicycle in each unit management area exceeds a set low limit threshold value, if so, executing a step S3; if not, a second type of early warning signal is sent out.
A shared bicycle offer decision early warning system, comprising:
the first acquisition module is used for acquiring the characteristic data of the shared bicycle user demand behavior;
the processing module is used for dividing the shared bicycle unit management area according to the characteristic data;
the second acquisition module is used for acquiring shared bicycle use data at the t-1 moment in each unit management area and extracting a prediction independent variable x;
the prediction module is used for inputting the independent variable x in the prediction model for prediction, and predicting and obtaining the predicted parking space occupancy rate y of the shared vehicle in each unit management area at the time t;
and the judging module is used for judging whether the predicted parking space occupancy of the shared bicycle in each unit management area exceeds a set decision threshold, and if so, sending an early warning signal to the dispatching platform.
Compared with the prior art, the invention has the following advantages:
according to the invention, the shared single-vehicle unit management areas are divided according to the type of the street areas, the distance from the rail transit station and the like, a parking occupancy prediction model is established according to real-time operation data of each unit management area, and the parking occupancy at the next moment is predicted, so that supply quantity decision early warning is sent to the shared single-vehicle operation platform, the problems of difficulty in borrowing and returning vehicles of users are relieved, and the satisfaction degree of the users is improved.
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FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
As shown in fig. 1, the embodiment provides a shared bicycle supply decision early warning method, which specifically includes the following steps:
step S1, acquiring characteristic data of the shared bicycle user demand behavior in the management area;
step S2, dividing the management area into a plurality of shared bicycle unit management areas according to the characteristic data;
s3, obtaining shared bicycle use data at the t-1 moment in each unit management area, and extracting a prediction independent variable x;
step S4, inputting an independent variable x in a prediction model for prediction, and predicting and obtaining the predicted parking space occupancy rate y of the shared vehicle in each unit management area at the time t;
step S5, judging whether the predicted parking space occupancy of the shared bicycle in each unit management area exceeds a set decision threshold, if so, sending an early warning signal to a dispatching platform, specifically:
step S51, judging whether the predicted parking space occupancy of the shared bicycle in each unit management area exceeds a set high-limit threshold, and if so, sending a first-class early warning signal; if not, go to step S52;
step S52, judging whether the predicted parking space occupancy of the shared bicycle in each unit management area exceeds a set low-limit threshold, if so, executing step S3; if not, a second type of early warning signal is sent out.
When the shared bicycle management platform receives the first type of early warning signals, the management platform calls the bicycle to a basic balance area or a tension input area. When the shared bicycle management platform receives the second type of early warning signals, the management platform calls vehicles from an excessive early warning area or a basic balance area nearby. The management platform can reasonably share the bicycle resource allocation, the user experience level is improved, and the phenomenon of a large number of zombie vehicles is avoided.
In step S1, the feature data includes the number of standing population, the number of regional rail transit stations, the number of regional regular bus stations, the type of plot land, and the like. The shared bicycle running conditions in the region obtained from the platform comprise the number of occupied berths in the region, the unlocking number in unit hour and the locking number in unit hour. And performing space-time characteristic analysis on the user requirements by using the obtained survey data. Analysis results show that the influence of the number of regional rail transit stations and the type of the plot area on the number of the parked vehicles in the region is the largest in all the characteristic data.
In step S2, different ways of division are made for the shared-bicycle active region and inactive region by preset cell management zone division rules according to the analysis result of step S1.
The preset unit management area division rule is shown as the following table:
the specific execution method comprises the following steps:
step S21, determining whether the number of the vehicle locked and unlocked in the area per unit time exceeds a set value, in this embodiment, the set value is 100, if yes, determining that the area is a shared bicycle active area, and executing step S22; if not, determining that the shared bicycle is in the inactive area, and executing the step S23;
step S22, dividing the active area into three categories according to a rule II;
step S23, dividing the inactive area into three categories according to the first rule;
the prediction model establishment method comprises the following steps:
step S31, according to the variable selection principle, primarily selecting the primary independent variable x of the prediction modelnThe variable selection principle is specifically as follows:
principle one: selecting quantifiable indexes as much as possible;
principle two: the method is simple and easy to understand as much as possible and has strong availability;
principle three: is related to or has certain influence and reaction on the judgment index y.
Thus, the initial selection argument x in this embodimentnThe following were used:
x 1: the number of unlocked vehicles in a certain unit management area at the time of t-1;
x 2: the number of vehicles locked in a certain unit management area at the time of t-1;
x 3: and a number of occupied berths in a unit management area at the time of t-1.
Step S32, selecting the initial independent variable xnAnd dependent variable ytPerforming correlation analysis to select the dependent variable ytIndependent variables xi, i ≦ n with significant correlation (correlation coefficient greater than 0.7) including:
x 2: the number of vehicles locked in a certain unit management area at the time of t-1;
x 3: and a number of occupied berths in a unit management area at the time of t-1.
Step S33, if the independent variables have strong correlation, the independent variables will generate collinearity to influence the fitting effect of the model, and the selected independent variable x is subjected toiAnd performing correlation analysis again, removing one of independent variables with high correlation according to the correlation analysis result, and finally determining the number of the occupied berths in the unit management area when the predicted independent variable x is t-1.
And step S34, calibrating parameters of the model according to 7 days a week, for example, calibrating the parameters of Monday by adopting the number occupied by the berths of the first 10 Monday weeks, and repeating the steps from Tuesday to weekend. The expression of the prediction model is:
yt＝axt-1+m
wherein a and m are model parameters, xt-1The number of occupied parking spaces at time t-1, ytThe number of occupied berths is t.
And step S35, analyzing the model fitting effect and evaluating the model. If goodness of fit R2<0.8 repeat all the processes of step S3 until the goodness of fit R2>0.8。
In step S5, a berth occupancy decision threshold of each cell management area is determined according to the reachability from the track site and the type of the area right, and the decision threshold is divided into a high-limit threshold and a low-limit threshold.
The first type unit management area is over-high early warning threshold value 1 and over-low early warning threshold value 0.4;
the second type unit management area is over-high early warning threshold value of 0.9 and over-low early warning threshold value of 0.5;
and in the third type unit management area, the over-high early warning threshold value is 0.8, and the over-low early warning threshold value is 0.2.
In step S5, the predicted parking space occupancy is the predicted parking space occupancy number ytA ratio to the total number of berths, wherein the total number of berths is known data.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (8)
1. A shared bicycle supply decision early warning method is characterized by comprising the following steps:
s1, acquiring characteristic data of the shared bicycle user demand behaviors in the management area;
s2, dividing the management area into a plurality of shared bicycle unit management areas according to the characteristic data;
s3, obtaining shared bicycle use data at the t-1 moment in each unit management area, and extracting a prediction independent variable x;
s4, inputting an independent variable x in the prediction model for prediction, and predicting and obtaining the predicted parking space occupancy y of the shared vehicle in each unit management area at the time t;
and S5, judging whether the predicted parking space occupation rate of the shared bicycle in each unit management area exceeds a set decision threshold, and if so, sending an early warning signal to the dispatching platform.
2. The shared bicycle supply decision pre-warning method as claimed in claim 1, wherein in step S1, the characteristic data includes a standing population number, a regional rail transit stop number, a regional regular bus stop number and a plot land type.
3. The shared bicycle supply decision warning method as claimed in claim 1, wherein in step S2, a partition rule of the unit management area is preset, and the following steps are performed: judging whether the number of the vehicles locked in the area in unit time exceeds a set value or not, if so, judging that the vehicles are shared bicycle active areas, and dividing according to a rule I; and if not, judging the shared bicycle inactive area, and dividing according to a rule II.
4. The shared bicycle supply decision early warning method according to claim 1, wherein the division rule of the unit management area is as follows:
rule one is as follows: firstly, judging whether the unit management area is a street or a town area, and if the unit management area is a street, dividing the unit management area into three types, namely an area within a radius range of 600m from a track station, an area within a radius range of 600-1000 m and an area above 1000 m; if the area is a town area, the area is divided into three types, namely an area within a radius range of 600m from a track station, an area within a radius range of 600-1500 m and an area above 1500 m;
rule two: partitioning according to the land type of the area, wherein the land type is a first type if the land type is a commercial land; if the land is a residential land, the land is a second type; if it is used as another place, it is classified as the third place.
5. The shared bicycle supply decision pre-warning method as claimed in claim 1, wherein the method for establishing the predictive model comprises:
s31, initially selecting independent variable x of the prediction modeln；
S32, selecting the initial independent variable xnAnd dependent variable ytDependent variable ytThe number of occupied berths in a unit area at time t is represented, and a dependent variable y is selectedtIndependent variable x with obvious correlationi，i≤n；
S33, selecting the independent variable xiPerforming correlation analysis again, removing independent variables with high correlation according to the correlation analysis result, and determining a prediction independent variable x of the model;
s34, calibrating model parameters, and predicting independent variable x and dependent variable ytAdvancing lineAnd (5) performing sexual fitting.
6. The shared bicycle supply decision early warning method as claimed in claim 1, wherein the berth occupancy decision threshold of each unit management area is determined according to the accessibility of a distance track station and the type of land used for the area, and the decision threshold is divided into a high-limit threshold and a low-limit threshold.
7. The shared bicycle supply decision warning method of claim 6, further comprising:
s51, judging whether the predicted parking space occupancy of the shared bicycle in each unit management area exceeds a set high-limit threshold, and if so, sending a first-class early warning signal; if not, go to step S52;
s52, judging whether the predicted parking space occupancy rate of the shared bicycle in each unit management area exceeds a set low limit threshold value, if so, executing a step S3; if not, a second type of early warning signal is sent out.
8. A shared bicycle supply decision-making early warning system, comprising:
the first acquisition module is used for acquiring the characteristic data of the shared bicycle user demand behavior;
the processing module is used for dividing the shared bicycle unit management area according to the characteristic data;
the second acquisition module is used for acquiring shared bicycle use data at the t-1 moment in each unit management area and extracting a prediction independent variable x;
the prediction module is used for inputting the independent variable x in the prediction model for prediction, and predicting and obtaining the predicted parking space occupancy rate y of the shared vehicle in each unit management area at the time t;
and the judging module is used for judging whether the predicted parking space occupancy of the shared bicycle in each unit management area exceeds a set decision threshold, and if so, sending an early warning signal to the dispatching platform.
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| 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 |
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